Antivir Ther. 2010;15(5):765-73
de Bruijne J, Bergmann JF, Weegink CJ, van Nieuwkerk CM, de Knegt RJ, Komoda Y, van de Wetering de Rooij JJ, van Vliet A, Jansen PL, Molenkamp R, Schinkel J, Reesink H, Janssen HL.
Department of Gastroenterology and Hepatology, Academic Medical Center, University of Amsterdam, the Netherlands. j.debruijne@amc.nl
Abstract
BACKGROUND: Standard treatment of chronic hepatitis C with pegylated interferon and ribavirin is associated with suboptimal virological response rates and substantial side effects. This study describes the in vitro and in vivo development of JTK-652, a novel pyrrolopyridazin-derived HCV infection inhibitor.
METHODS: JTK-652 was evaluated in multiple cell lines using an in vitro HCV infection model consisting of HCV pseudotype vesicular stomatitis virus bearing HCV E1/E2 envelope proteins. Safety, tolerability, pharmacokinetics and efficacy of JTK-652 were tested in a randomized double-blind and placebo-controlled study in healthy male volunteers (n=36) and chronic hepatitis C patients. A total of 10 HCV genotype-1-infected patients (treatment-naive [n=2] and treatment-experienced [n=8]) with HCV RNA>1x10(5) IU/ml received an oral dose of 100 mg JTK-652 three times daily or placebo (8:2 ratio) for 4 weeks.
RESULTS: JTK-652 showed potent inhibitory activity against HCV genotype 1a and 1b pseudotype viruses bearing HCV E1/E2 envelope proteins in HepG2 cells and in human primary hepatocytes. No significant clinical laboratory, vital sign, ECG or physical examination abnormalities were observed during the Phase I trial. JTK-652 was found to be well tolerated. No significant changes in HCV RNA levels compared with baseline were observed at the end of treatment.
CONCLUSIONS: Although results from the preclinical studies indicated that JTK-652 has well-established antiviral properties and a Phase I clinical trial has showed that JTK-652 was safe and well tolerated at a 100 mg three times daily dose level, plasma HCV RNA levels in chronically HCV-infected patients did not decrease during 28 days of dosing at a 100 mg three times daily dose level.
PMID: 20710058 [PubMed - in process]
Source
August 24, 2010
Health in the future: Hepatitis E to Z
AFP PHOTO/NOEL CELIS
Wednesday, 25 August 2010
Hepatitis has evolved all the way to Z (HZV) and vaccines are in the works for hepatitis E (HEV) and HZV.
The World Health Organization (WHO) defines hepatitis as "an inflammation of the liver most commonly caused by a viral infection," with symptoms that include "jaundice (yellowing of the skin and eyes), dark urine, extreme fatigue, nausea, vomiting and abdominal pain." Although hepatitis types exist from A through Z, the WHO notes there are five main types A, B, C, D and E.
On July 30, the US National Institute working on Hepatitis discovered cases of Hepatitis Z in California amongst cult members that prefer to eat raw liver, according to the journal Annals of Internal Medicine. Epidemiologists believe that eating "yellow sushi," an illegal "pate made from the raw livers of endangered fish garibaldi" led to development of HZV.
To date it is unknown if HZV can be spread via sex like "hepatitis types B, K, O, and M (the love letter hepatitides)," but it is believed that an expensive "prototype HZV vaccine" is in the works by HEP-VAXMAX.
Meanwhile the HEV vaccine (HEV 239) is well underway with promising results showing it is 100 percent effective.
On August 23, Professor Ningshao Xia, MD, the director of the National Institute of Diagnosis and Vaccine Development in infectious diseases at ?Xiamen University in China told Relaxnews, the "license application of this vaccine is now being evaluated by Chinese SFDA [State Food and Drug Administration].
"How long the evaluation will take is uncertain, maybe several months or more than one year. After that the vaccine can be available for use by travelers and in outbreak areas in China," continued Xia.
"The phase 3 clinical trial of the vaccine was funded by scientific grants from Chinese central and local government, and the development of the vaccine was funded by a vaccine company, Xiamen Innovax Biotech," explained Xia.
To avoid getting HVZ, do not eat raw liver and to ward off HEV before HEV 239 is available it is best to stay clear of contaminated food and water especially in areas where HEV outbreaks are prevalent including Central and South-East Asia, North and West Africa, and in Mexico according to the WHO.
For more information on "Hepatitis A through E and Beyond"
Full study, "Efficacy and safety of a recombinant hepatitis E vaccine in healthy adults: a large-scale, randomised, double-blind placebo-controlled, phase 3 trial"
"Alphabet Now Complete: NIH Discovers Hepatitis Z Virus"
Source
Alcoholic liver disease is more aggressive than other chronic liver diseases
Public release date: 24-Aug-2010
Contact: Knut Stokkeland, M.D., Ph.D.
knut.stokkeland@gotland.se
46-498-268139 (Sweden)
Visby Hospital
Johan Franck, M.D., Ph.D.
johan.franck@ki.se
46-739-660736 (Sweden)
Karolinska Institutet
Alcoholism: Clinical & Experimental Research
• While diagnostic and treatment options for chronic liver disease are numerous, their effectiveness is unclear.
• New findings show that patients hospitalized with alcoholic liver disease have an increased mortality risk compared to patients with non-alcoholic liver disease.
• This indicates that alcoholic liver disease is more aggressive than other chronic liver diseases.
Many diagnostic and treatment options have been developed for chronic liver disease during the last 40 years, yet their influence on survival remain unclear. A new study of the prognosis for patients hospitalized for liver diseases between 1969 and 2006, and of differences in mortality and complications between patients with alcoholic and non-alcoholic liver diseases, has found that the general prognosis for patients hospitalized with chronic liver diseases has not improved.
Results will be published in the November 2010 issue of Alcoholism: Clinical & Experimental Research and are currently available at Early View.
"The most effective changes in treatment for chronic liver disease during the last 40 years are, in my opinion, combination treatment for hepatitis C and treatment with prednisolone and azathioprine for autoimmune hepatitis," said Knut Stokkeland, an instructor in the department of medicine at Visby Hospital in Sweden and corresponding author for the study. "In addition, new diagnostic tools such as endoscopic examinations, computed tomography, MRI, and ultrasound have probably increased our possibilities to detect early disease and the development of cirrhosis."
Stokkeland added that the key difference between alcoholic and non-alcoholic liver disease is alcohol dependence (AD), which almost all patients with alcoholic liver disease have. "AD increases the risks of social problems, being a smoker, and severe psychiatric diseases," he said. "It also inhibits staying sober, which may stop disease progression."
Stokkeland and his colleagues used data from the Swedish Hospital Discharge Register and Cause of Death Register between 1969 and 2006 to both identify and follow up with a cohort of 36,462 patients hospitalized with alcoholic liver diseases and 95,842 patients hospitalized with non-alcoholic liver diseases.
"The main finding of Dr. Stokkeland's study is the much increased mortality risk of having an alcohol- versus a non-alcohol-related liver disease," observed Johan Franck, a professor of clinical addiction research at Karolinska Institutet in Sweden. "Thus, patients with alcohol-induced liver diseases should receive more attention, and they should routinely be offered treatment for their alcohol-use disorder. Presumably, the various treatment systems involved – such as hepatology versus substance-abuse care – may not be very well coordinated and this may present an area for improvement."
Stokkeland agreed. "This may be caused by the fact that hospitalized patients with [alcoholic] liver disease have such a severe liver disease that no effort may change their prognosis," he said. "I hope this study will motivate clinicians and scientists in the field of hepatology and gastroenterology to design clinical studies to see if any changes in care-taking of our patients with alcoholic liver disease may change their severe prognosis. We must also focus on treating their AD so that they may stop drinking."
"Given that alcohol doubles the risk of having a serious liver disease," added Franck, "efforts to reduce alcohol drinking will likely have a positive impact on the disease's outcome."
###
Alcoholism: Clinical & Experimental Research (ACER) is the official journal of the Research Society on Alcoholism and the International Society for Biomedical Research on Alcoholism. Co-authors of the ACER paper, "Increased Risk of Esophageal Varices, Liver Cancer and Death in Patients with Alcoholic Liver Disease," were Fereshte Ebrahim of the National Board of Health and Welfare, and Anders Ekbom of the Department of Medicine at the Karolinksa Institutet, both of Stockholm, Sweden. The study was funded by the Bengt Ihre Foundation, the Karolinksa Institutet, and Visby Hospital. This release is supported by the Addiction Technology Transfer Center Network at http://www.attcnetwork.org/.
Source
Also See: Chronic drinking causes more liver injury than acute or binge drinking
Contact: Knut Stokkeland, M.D., Ph.D.
knut.stokkeland@gotland.se
46-498-268139 (Sweden)
Visby Hospital
Johan Franck, M.D., Ph.D.
johan.franck@ki.se
46-739-660736 (Sweden)
Karolinska Institutet
Alcoholism: Clinical & Experimental Research
• While diagnostic and treatment options for chronic liver disease are numerous, their effectiveness is unclear.
• New findings show that patients hospitalized with alcoholic liver disease have an increased mortality risk compared to patients with non-alcoholic liver disease.
• This indicates that alcoholic liver disease is more aggressive than other chronic liver diseases.
Many diagnostic and treatment options have been developed for chronic liver disease during the last 40 years, yet their influence on survival remain unclear. A new study of the prognosis for patients hospitalized for liver diseases between 1969 and 2006, and of differences in mortality and complications between patients with alcoholic and non-alcoholic liver diseases, has found that the general prognosis for patients hospitalized with chronic liver diseases has not improved.
Results will be published in the November 2010 issue of Alcoholism: Clinical & Experimental Research and are currently available at Early View.
"The most effective changes in treatment for chronic liver disease during the last 40 years are, in my opinion, combination treatment for hepatitis C and treatment with prednisolone and azathioprine for autoimmune hepatitis," said Knut Stokkeland, an instructor in the department of medicine at Visby Hospital in Sweden and corresponding author for the study. "In addition, new diagnostic tools such as endoscopic examinations, computed tomography, MRI, and ultrasound have probably increased our possibilities to detect early disease and the development of cirrhosis."
Stokkeland added that the key difference between alcoholic and non-alcoholic liver disease is alcohol dependence (AD), which almost all patients with alcoholic liver disease have. "AD increases the risks of social problems, being a smoker, and severe psychiatric diseases," he said. "It also inhibits staying sober, which may stop disease progression."
Stokkeland and his colleagues used data from the Swedish Hospital Discharge Register and Cause of Death Register between 1969 and 2006 to both identify and follow up with a cohort of 36,462 patients hospitalized with alcoholic liver diseases and 95,842 patients hospitalized with non-alcoholic liver diseases.
"The main finding of Dr. Stokkeland's study is the much increased mortality risk of having an alcohol- versus a non-alcohol-related liver disease," observed Johan Franck, a professor of clinical addiction research at Karolinska Institutet in Sweden. "Thus, patients with alcohol-induced liver diseases should receive more attention, and they should routinely be offered treatment for their alcohol-use disorder. Presumably, the various treatment systems involved – such as hepatology versus substance-abuse care – may not be very well coordinated and this may present an area for improvement."
Stokkeland agreed. "This may be caused by the fact that hospitalized patients with [alcoholic] liver disease have such a severe liver disease that no effort may change their prognosis," he said. "I hope this study will motivate clinicians and scientists in the field of hepatology and gastroenterology to design clinical studies to see if any changes in care-taking of our patients with alcoholic liver disease may change their severe prognosis. We must also focus on treating their AD so that they may stop drinking."
"Given that alcohol doubles the risk of having a serious liver disease," added Franck, "efforts to reduce alcohol drinking will likely have a positive impact on the disease's outcome."
###
Alcoholism: Clinical & Experimental Research (ACER) is the official journal of the Research Society on Alcoholism and the International Society for Biomedical Research on Alcoholism. Co-authors of the ACER paper, "Increased Risk of Esophageal Varices, Liver Cancer and Death in Patients with Alcoholic Liver Disease," were Fereshte Ebrahim of the National Board of Health and Welfare, and Anders Ekbom of the Department of Medicine at the Karolinksa Institutet, both of Stockholm, Sweden. The study was funded by the Bengt Ihre Foundation, the Karolinksa Institutet, and Visby Hospital. This release is supported by the Addiction Technology Transfer Center Network at http://www.attcnetwork.org/.
Source
Also See: Chronic drinking causes more liver injury than acute or binge drinking
Resistance Analysis of the Hepatitis C Virus NS5A Inhibitor BMS-790052 in an In Vitro Replicon System
Antimicrobial Agents and Chemotherapy, September 2010, p. 3641-3650, Vol. 54, No. 9
0066-4804/10/$12.00+0 doi:10.1128/AAC.00556-10
Copyright © 2010, American Society for Microbiology. All Rights Reserved.
Robert A. Fridell,* Dike Qiu, Chunfu Wang, Lourdes Valera, and Min Gao
Department of Virology, Bristol-Myers Squibb Research and Development, Wallingford, Connecticut
Received 23 April 2010/ Returned for modification 27 May 2010/ Accepted 23 June 2010
BMS-790052 is the most potent hepatitis C virus (HCV) inhibitor reported to date, with 50% effective concentrations (EC50s) of 50 pM against genotype 1 replicons. This exceptional potency translated to rapid viral load declines in a phase I clinical study. By targeting NS5A, BMS-790052 is distinct from most HCV inhibitors in clinical evaluation. As an initial step toward correlating in vitro and in vivo resistances, multiple cell lines and selective pressures were used to identify BMS-790052-resistant variants in genotype 1 replicons. Similarities and differences were observed between genotypes 1a and 1b. For genotype 1b, L31F/V, P32L, and Y93H/N were identified as primary resistance mutations. L23F, R30Q, and P58S acted as secondary resistance substitutions, enhancing the resistance of primary mutations but themselves not conferring resistance. For genotype 1a, more sites of resistance were identified, and substitutions at these sites (M28T, Q30E/H/R, L31M/V, P32L, and Y93C/H/N) conferred higher levels of resistance. For both subtypes, combining two resistance mutations markedly decreased inhibitor susceptibility. Selection studies with a 1b/1a hybrid replicon highlighted the importance of the NS5A N-terminal region in determining genotype-specific inhibitor responses. As single mutations, Q30E and Y93N in genotype 1a conferred the highest levels of resistance. For genotype 1b, BMS-790052 retained subnanomolar potency against all variants with single amino acid substitutions, suggesting that multiple mutations will likely be required for significant in vivo resistance in this genetic background. Importantly, BMS-790052-resistant variants remained fully sensitive to alpha interferon and small-molecule inhibitors of HCV protease and polymerase.
* Corresponding author. Mailing address: Department of Virology, Bristol-Myers Squibb Research and Development, 5 Research Parkway, Wallingford, CT 06492. Phone: (203) 677-7034. Fax: (203) 677-6088. E-mail: robert.fridell@bms.com
Published ahead of print on 28 June 2010.
Present address: Discovery Biology, Bristol-Myers Squibb Research and Development, Hopewell, NJ.
Source
0066-4804/10/$12.00+0 doi:10.1128/AAC.00556-10
Copyright © 2010, American Society for Microbiology. All Rights Reserved.
Robert A. Fridell,* Dike Qiu, Chunfu Wang, Lourdes Valera, and Min Gao
Department of Virology, Bristol-Myers Squibb Research and Development, Wallingford, Connecticut
Received 23 April 2010/ Returned for modification 27 May 2010/ Accepted 23 June 2010
BMS-790052 is the most potent hepatitis C virus (HCV) inhibitor reported to date, with 50% effective concentrations (EC50s) of 50 pM against genotype 1 replicons. This exceptional potency translated to rapid viral load declines in a phase I clinical study. By targeting NS5A, BMS-790052 is distinct from most HCV inhibitors in clinical evaluation. As an initial step toward correlating in vitro and in vivo resistances, multiple cell lines and selective pressures were used to identify BMS-790052-resistant variants in genotype 1 replicons. Similarities and differences were observed between genotypes 1a and 1b. For genotype 1b, L31F/V, P32L, and Y93H/N were identified as primary resistance mutations. L23F, R30Q, and P58S acted as secondary resistance substitutions, enhancing the resistance of primary mutations but themselves not conferring resistance. For genotype 1a, more sites of resistance were identified, and substitutions at these sites (M28T, Q30E/H/R, L31M/V, P32L, and Y93C/H/N) conferred higher levels of resistance. For both subtypes, combining two resistance mutations markedly decreased inhibitor susceptibility. Selection studies with a 1b/1a hybrid replicon highlighted the importance of the NS5A N-terminal region in determining genotype-specific inhibitor responses. As single mutations, Q30E and Y93N in genotype 1a conferred the highest levels of resistance. For genotype 1b, BMS-790052 retained subnanomolar potency against all variants with single amino acid substitutions, suggesting that multiple mutations will likely be required for significant in vivo resistance in this genetic background. Importantly, BMS-790052-resistant variants remained fully sensitive to alpha interferon and small-molecule inhibitors of HCV protease and polymerase.
* Corresponding author. Mailing address: Department of Virology, Bristol-Myers Squibb Research and Development, 5 Research Parkway, Wallingford, CT 06492. Phone: (203) 677-7034. Fax: (203) 677-6088. E-mail: robert.fridell@bms.com
Published ahead of print on 28 June 2010.
Present address: Discovery Biology, Bristol-Myers Squibb Research and Development, Hopewell, NJ.
Source
Hepatitis C Virus Infections Among HIV-Infected Men Who Have Sex With Men: An Expanding Epidemic
August 23, 2010
Since 2000, outbreaks of sexually transmitted hepatitis C virus have been reported among HIV-positive men who have sex with men (MSM). In the current study, the authors conclude that the prevalence of HCV in this population is "high and increasing."
The setting for the research was a large STD clinic in Amsterdam, where the authors studied the prevalence and determinants of HCV among MSM.
In 2007 and 2008, an anonymous, bi-annual cross-sectional survey was administered to 3,125 patients, of whom 689 were MSM. Participants were interviewed and screened for HIV and HCV antibodies, and all anti-HCV-positive and HIV-positive persons were tested for HCV RNA. Phylogenetic analysis was used to compare HCV strains of the STD clinic patients with those isolated from MSM with acute HCV in 2000-2007. Logistic regression was used to analyze determinants of HCV infection.
HCV infection was diagnosed in two of 532 HIV-negative MSM (0.4 percent) and 28 of 157 HIV-positive MSM (17.8 percent). Among HIV-positive MSM, HCV prevalence increased from 14.6 percent to 20.9 percent during the study period. Acute HCV infection was noted among seven of 28 co-infected MSM (25 percent). Of the 28 co-infected men, only five reported any history of injection drug use (IDU).
HIV infection, IDU, fisting, and use of gamma hydroxyl butyrate (GHB) were found to be significantly associated with HCV infection. A high degree of MSM-specific clustering was found through phylogenetic testing.
"Though not statistically significant, this trend, and the relatively large population of acute infections suggest ongoing transmission of HCV in HIV-positive MSM," the authors concluded. "Regardless of IDU, rough sexual techniques and use of recreational drugs were associated with HCV infection; phylogenetic analysis supported sexual transmission. Targeted prevention, like raising awareness and routine testing, is needed to stop the further spread among HIV-infected MSM, and to prevent possible spillover to HIV-negative MSM."
Source
Since 2000, outbreaks of sexually transmitted hepatitis C virus have been reported among HIV-positive men who have sex with men (MSM). In the current study, the authors conclude that the prevalence of HCV in this population is "high and increasing."
The setting for the research was a large STD clinic in Amsterdam, where the authors studied the prevalence and determinants of HCV among MSM.
In 2007 and 2008, an anonymous, bi-annual cross-sectional survey was administered to 3,125 patients, of whom 689 were MSM. Participants were interviewed and screened for HIV and HCV antibodies, and all anti-HCV-positive and HIV-positive persons were tested for HCV RNA. Phylogenetic analysis was used to compare HCV strains of the STD clinic patients with those isolated from MSM with acute HCV in 2000-2007. Logistic regression was used to analyze determinants of HCV infection.
HCV infection was diagnosed in two of 532 HIV-negative MSM (0.4 percent) and 28 of 157 HIV-positive MSM (17.8 percent). Among HIV-positive MSM, HCV prevalence increased from 14.6 percent to 20.9 percent during the study period. Acute HCV infection was noted among seven of 28 co-infected MSM (25 percent). Of the 28 co-infected men, only five reported any history of injection drug use (IDU).
HIV infection, IDU, fisting, and use of gamma hydroxyl butyrate (GHB) were found to be significantly associated with HCV infection. A high degree of MSM-specific clustering was found through phylogenetic testing.
"Though not statistically significant, this trend, and the relatively large population of acute infections suggest ongoing transmission of HCV in HIV-positive MSM," the authors concluded. "Regardless of IDU, rough sexual techniques and use of recreational drugs were associated with HCV infection; phylogenetic analysis supported sexual transmission. Targeted prevention, like raising awareness and routine testing, is needed to stop the further spread among HIV-infected MSM, and to prevent possible spillover to HIV-negative MSM."
Source
Chronic drinking causes more liver injury than acute or binge drinking
Posted
August 24th, 2010
Alcohol consumption is known to cause liver damage. Yet the specifics of alcohol-induced liver injury can differ depending on the pattern of drinking. New rodent findings show that chronic drinking causes more injury – as measured by gene-expression changes – to the liver than acute or binge drinking.
Results will be published in the April issue of Alcoholism: Clinical & Experimental Research and are currently available at Early View.
“Different patterns of drinking can] produce a different set or pattern of gene expression by the liver because of adaptation by the liver which occurs when the same level of blood alcohol is repeated over and over again,” explained Samuel W. French, Distinguished Professor of pathology at the UCLA School of Medicine, and chief of anatomic pathology at Harbor-UCLA Medical Center. Basically, the liver “learns” or “remembers” its response to alcohol.
“Unfortunately, these adaptive changes in gene expression are injurious to the liver and are furthermore persistent in the liver even when alcohol drinking has stopped,” French added. “This is why people who develop liver disease after chronic alcohol abuse continue to be sick from liver damage for many months after they have stopped drinking. In fact, they actually get worse when they stop drinking because their liver is programmed epigenetically to work under the influence of alcohol. Think of it as deleterious conditioning and a learning process for the liver.”
“Rodents do differ from humans in some of their responses to alcohol because they are rodents, not humans,” said Terrence M. Donohue, Jr., a research scientist in the Liver Study Unit at the Department of Veterans Affairs Medical Center and the University of Nebraska Medical Center. “However, overall these results could potentially be applicable to humans and it’s likely that they are, as both rodents and humans are mammals.”
French and his colleagues used microarray analysis on livers from rats that had been fed an acute/binge dose of alcohol (6 g/kg body weight), enough to intoxicate the animals, and then sacrificed at three and 12 hours after dosage. The gene microarrays were then compared to those from an earlier study of livers from rats that had been fed alcohol for one month (comprising 36 percent of their calories), the equivalent of chronic drinking.
Results showed that chronic exposure to alcohol leads to more gene-expression changes in the liver than does acute exposure to alcohol.
“The liver damage in the two groups was different,” said French. “For instance, after chronic abuse the liver cells become swollen and filled with fat stores, some liver cells died and cells in the liver that make scars are activated. These changes do not occur in the liver after an acute or binge dose, as demonstrated by gene expression.”
The important lesson that these rodent findings teach us about liver disease in humans, he added, is that daily, excessive drinking can program the liver to become dependent on alcohol. “So when a person stops drinking, their liver will continue to be sick for a while; but if they don’t stop drinking, their liver will become even sicker.”
Source: Alcoholism Clinical and Experimental Research
Source: PhysOrg – Gene Expression
Source
August 24th, 2010
Alcohol consumption is known to cause liver damage. Yet the specifics of alcohol-induced liver injury can differ depending on the pattern of drinking. New rodent findings show that chronic drinking causes more injury – as measured by gene-expression changes – to the liver than acute or binge drinking.
Results will be published in the April issue of Alcoholism: Clinical & Experimental Research and are currently available at Early View.
“Different patterns of drinking can] produce a different set or pattern of gene expression by the liver because of adaptation by the liver which occurs when the same level of blood alcohol is repeated over and over again,” explained Samuel W. French, Distinguished Professor of pathology at the UCLA School of Medicine, and chief of anatomic pathology at Harbor-UCLA Medical Center. Basically, the liver “learns” or “remembers” its response to alcohol.
“Unfortunately, these adaptive changes in gene expression are injurious to the liver and are furthermore persistent in the liver even when alcohol drinking has stopped,” French added. “This is why people who develop liver disease after chronic alcohol abuse continue to be sick from liver damage for many months after they have stopped drinking. In fact, they actually get worse when they stop drinking because their liver is programmed epigenetically to work under the influence of alcohol. Think of it as deleterious conditioning and a learning process for the liver.”
“Rodents do differ from humans in some of their responses to alcohol because they are rodents, not humans,” said Terrence M. Donohue, Jr., a research scientist in the Liver Study Unit at the Department of Veterans Affairs Medical Center and the University of Nebraska Medical Center. “However, overall these results could potentially be applicable to humans and it’s likely that they are, as both rodents and humans are mammals.”
French and his colleagues used microarray analysis on livers from rats that had been fed an acute/binge dose of alcohol (6 g/kg body weight), enough to intoxicate the animals, and then sacrificed at three and 12 hours after dosage. The gene microarrays were then compared to those from an earlier study of livers from rats that had been fed alcohol for one month (comprising 36 percent of their calories), the equivalent of chronic drinking.
Results showed that chronic exposure to alcohol leads to more gene-expression changes in the liver than does acute exposure to alcohol.
“The liver damage in the two groups was different,” said French. “For instance, after chronic abuse the liver cells become swollen and filled with fat stores, some liver cells died and cells in the liver that make scars are activated. These changes do not occur in the liver after an acute or binge dose, as demonstrated by gene expression.”
The important lesson that these rodent findings teach us about liver disease in humans, he added, is that daily, excessive drinking can program the liver to become dependent on alcohol. “So when a person stops drinking, their liver will continue to be sick for a while; but if they don’t stop drinking, their liver will become even sicker.”
Source: Alcoholism Clinical and Experimental Research
Source: PhysOrg – Gene Expression
Source
Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers
Danan Wang , Qinghui Wang , Fengping Shan , Beixing Liu and Changlong Lu
BMC Infectious Diseases 2010, 10:251
doi:10.1186/1471-2334-10-251
Published: 24 August 2010
Abstract (provisional)
Background
Liver fibrosis progression is commonly found in patients with CHB. Liver biopsy is a gold standard for identifying the extent of liver fibrosis, but has many draw-backs. It is essential to construct a noninvasive model to predict the levels of risk for liver fibrosis. It would provide very useful information to help reduce the number of liver biopsies of CHB patients.
Methods
339 chronic hepatitis B patients with HBsAg-positive were investigated retrospectively, and divided at random into 2 subsets with twice as many patients in the training set as in the validation set; 116 additional patients were consequently enrolled in the study as the testing set. A three-layer artificial neural network was developed using a Bayesian learning algorithm. Sensitivity and ROC analysis were performed to explain the importance of input variables and the performance of the neural network.
Results
There were 329 patients without significant fibrosis and 126 with significant fibrosis in the study. All markers except gender, HB, ALP and TP were found to be statistically significant factors associated with significant fibrosis. The sensitivity analysis showed that the most important factors in the predictive model were age, AST, platelet, and GGT, and the influence on the output variable among coal miners were 22.3-24.6%. The AUROC in 3 sets was 0.883, 0.884, and 0.920. In the testing set, for a decision threshold of 0.33, sensitivity and negative predictive values were 100% and all CHB patients with significant fibrosis would be identified.
Conclusions
The artificial neural network model based on routine and serum markers would predict the risk for liver fibrosis with a high accuracy. 47.4% of CHB patients at a decision threshold of 0.33 would be free of liver biopsy and wouldn't be missed.
Source
BMC Infectious Diseases 2010, 10:251
doi:10.1186/1471-2334-10-251
Published: 24 August 2010
Abstract (provisional)
Background
Liver fibrosis progression is commonly found in patients with CHB. Liver biopsy is a gold standard for identifying the extent of liver fibrosis, but has many draw-backs. It is essential to construct a noninvasive model to predict the levels of risk for liver fibrosis. It would provide very useful information to help reduce the number of liver biopsies of CHB patients.
Methods
339 chronic hepatitis B patients with HBsAg-positive were investigated retrospectively, and divided at random into 2 subsets with twice as many patients in the training set as in the validation set; 116 additional patients were consequently enrolled in the study as the testing set. A three-layer artificial neural network was developed using a Bayesian learning algorithm. Sensitivity and ROC analysis were performed to explain the importance of input variables and the performance of the neural network.
Results
There were 329 patients without significant fibrosis and 126 with significant fibrosis in the study. All markers except gender, HB, ALP and TP were found to be statistically significant factors associated with significant fibrosis. The sensitivity analysis showed that the most important factors in the predictive model were age, AST, platelet, and GGT, and the influence on the output variable among coal miners were 22.3-24.6%. The AUROC in 3 sets was 0.883, 0.884, and 0.920. In the testing set, for a decision threshold of 0.33, sensitivity and negative predictive values were 100% and all CHB patients with significant fibrosis would be identified.
Conclusions
The artificial neural network model based on routine and serum markers would predict the risk for liver fibrosis with a high accuracy. 47.4% of CHB patients at a decision threshold of 0.33 would be free of liver biopsy and wouldn't be missed.
Source
Reducing HIV Risks Among Active Injection Drug and Crack Users: The Safety Counts Program
AIDS Behav. 2010 June; 14(3): 658–668.
Published online 2009 September 11.
doi: 10.1007/s10461-009-9606-2.
PMCID: PMC2865646
Copyright © The Author(s) 2009
Mary Jane Rotheram-Borus, 1,2 Fen Rhodes, 1 Katherine Desmond, 1 and Robert E. Weiss 1
1 Center for HIV Identification, Prevention, and Treatment Services, University of California, Los Angeles, CA USA
2 UCLA, 10920 Wilshire Blvd., Suite #350, Los Angeles, CA 90024-6521 USA
Mary Jane Rotheram-Borus, Phone: +1-310-7948278, Fax: +1-310-7948297, Email: Rotheram@ucla.edu.
Received September 11, 2008; Accepted August 10, 2009.
Abstract
The efficacy of Safety Counts, a CDC-diffused intervention, was reanalyzed. In a quasi experimental, cross-over design, injection drug users (IDU) and crack users in two neighborhoods were assigned by neighborhood to receive individual Voluntary HIV Counseling and Testing or Safety Counts and 78% were reassessed at 5–9 months. Drug users in the Safety Counts program reported significantly greater reductions in risky sex, crack and hard drug use, and risky drug injection. The more sessions of Safety Counts attended, the greater were the reductions in risky acts. Different analytic decisions result in very different findings for the same intervention. Safety Counts is an effective intervention for IDU and crack users. Analytic decision of intervention outcomes is highly related to evaluations of an intervention’s efficacy.
Electronic supplementary material
The online version of this article (doi:10.1007/s10461-009-9606-2) contains supplementary material, which is available to authorized users.
Keywords: HIV, Risk reduction, Drug use, IDU, VCT
Introduction
Injecting drug users (IDU) and crack users remain at high-risk for HIV in the United States, especially on the East Coast [1]. A recent study estimated that there are over 1.8 million IDU in the US, of whom 16% are HIV+ [2]. Nearly a quarter of currently prevalent HIV cases are attributable to injection drug use or both IDU and male-to-male sexual contact [3]. Concurrently, there are 1.5 million crack users [4], each having a threefold risk of acquiring HIV [5]. Among drug users, HIV risk emerges not only from drug use (i.e., by sharing needles), but also from sexual behaviors [6]. Therefore, it is critical to design and evaluate evidence-based interventions for IDU and crack users. This article re-evaluates the efficacy of Safety Counts, an intervention to reduce both drug and sexual risk among serious drug users.
Needle exchange and methadone maintenance are two effective strategies for reducing HIV risk for IDU [7]. However, despite multiple scientific reviews on its efficacy, most communities in the US do not have access to needle exchange and there are many parts of the world without such access. Given the political realities of acceptable treatments, this study examines an intervention strategy built concurrently with the street-outreach models of the AIDS Community Demonstration Projects [8].
Drug use is concentrated within specific neighborhoods [1], leading consequent HIV risk to be closely linked to geography [9]. Within neighborhoods with high drug use, there are local “hang-out” sites that are frequented by drug users (shooting galleries, squats). Coincident with drug use and HIV risk, drug-infested neighborhoods have high rates of sexually transmitted diseases and crimes. Because of this geographic concentration, street outreach prevention strategies are desirable [10, 11]. By intervening in neighborhoods with high rates of drug use, it is easier and more efficient to target prevention resources, maximizing the cost-effectiveness of prevention funding. Simultaneously, there are likely to be spill-over benefits from interventions that concentrate on neighborhoods: peers and social networks exert a strong influence on individuals’ high-risk behaviors [12–14].
Street outreach workers can contact drug users in their local hang-outs to deliver HIV prevention messages, both about reducing drug use and increasing condom use. Outreach workers can also facilitate the engagement of drug users into ongoing group and community activities, increasing the exposure to prevention messages, increasing the practice of new behaviors, and building positive social networks. Street-based recruitment and prevention strategies address both the physical and the social dimensions of drug abuse [10] and require that evaluations be based upon interventions delivered at the level of the site, even if tailored to the individual [15, 16].
This study implemented a prevention program for street-based drug users, called Safety Counts. Drug users do not usually access health services or seek care at institutions such as schools, churches, or community centers [13]. Therefore, Safety Counts built on a street outreach approach. The intervention was based on several related theories of behavior change, with the Transtheoretical Stages of Change Model of Prochaska and DiClemente [17, 18] forming the core framework. The intervention also drew on behavior change principles and techniques articulated by social cognitive theories [19–21]. Further information about the Safety Counts intervention can be found in the Program Implementation Manual [22] and an earlier article by Rhodes and Humfleet [23].
Following the protocol of the National Institute on Drug Abuse (NIDA) Cooperative Agreement, this study used the NIDA Standard Intervention for drug users [24] as the control condition. Labeled as VCT in this paper, the control condition delivered a didactic voluntary counseling and testing program over two sessions, in order to inform individuals regarding their HIV serostatus and to motivate them to reduce existing sexual risk acts. The enhanced intervention, Safety Counts, included the VCT sessions plus street outreach, skills-building workshops, individual counseling, and social events. Based on these components and the social cognitive theoretical models common to evidence-based interventions [25], the 7-session Safety Counts Program was delivered to neighborhood drug users. Both self-report and urine screens for active drug use were utilized as outcome measures for the intervention.
Hershberger and colleagues evaluated the Safety Counts intervention in this journal in 2003, finding few significant outcomes of the Safety Counts intervention compared to the standard VCT condition. Our analyses adopt a different perspective towards the data. The prior analyses emphasized as-treated comparisons of compliers in each condition (although intent-to-treat results were presented in the text as well). Those who did not complete the full intervention schedule were discarded from the primary analyses. A key difference between that paper and the present one is our emphasis on intent-to-treat comparisons of those assigned to the VCT control condition and those assigned to Safety Counts.
Beyond the difference in emphasis, the determination of which potential participants to include in the intent-to-treat analyses was not the same in the two studies. In the present study, completion of the NIDA Standard Intervention (attending two sessions) was a criterion for eligibility, and we excluded individuals who did not complete both sessions. In addition, we excluded from analysis those who reported lengthy incarcerations at either the baseline or follow-up assessment, as that reduces the individual’s behavioral autonomy. The Hershberger et al. analyses did not make these exclusions.
Another difference between the two studies is that we analyzed the data using some additional outcome markers. The authors of the prior paper analyzing Safety Counts chose several dichotomous outcome measures such as any sexual activity or not, any unprotected sex or not, multiple partners or not, using crack or not, and any injection drug use or not. They also measured selected risky behaviors as percentages: percent of times injected with dirty works, percent of times used condoms. We analyzed a subset of the dichotomous outcome measures from the prior study, with some modifications to definition or analytic method.
However, in addition to these measures, we also used count measures of how frequently the behaviors were practiced, such as number of times injecting (also used in the prior study), number of times using dirty works, number of times using crack, number of times having sex, and number of unprotected sexual risk acts. We did not use outcomes expressed as percentages. We believe that counts provide a useful measure of absolute risk that is superior to percentages. In the case of condoms, for example, count measures capture frequency of sexual activity, as well as relative frequency of condom use. A person using condoms 30 times out of 60 is at greater risk than a person using condoms two times out of four, yet a dichotomous indicator of practicing unsafe sex, or a percentage measure of condom use would not differentiate between these two individuals.
We also chose different methods to analyze the frequency measures. Data plots showed that these measures were not normally distributed, but in fact followed a Poisson distribution. We therefore used random effects models assuming a Poisson distribution for count variables. We believe that this analytic method provides a better fit to the data than did the methods used by the authors of the prior published analyses. For frequency measures, they used a two-way ANOVA approach, which assumes that variables follow a normal distribution. This was not the case for the count (times injected) or percentage variables examined in that study. We feel that the choice of appropriate sample and outcome measures in conjunction with correct statistical methods will provide a more accurate evaluation of the actual intervention impact.
Methods
Sites
Seventeen ZIP codes were identified, within the greater Los Angeles, California metropolitan area, that were anticipated to be similar in ethnic composition and socioeconomic status. The ZIP codes were expected to have many drug users based on having high rates of drug-related deaths, arrests, arrestees with positive drug screening cases of HIV and AIDS, drug-related emergency room admissions and drug intake data and sexually transmitted diseases. From government data sets and existing research projects ongoing in the 17 ZIP codes, two sets of three contiguous ZIP codes were identified that were similar in the number of anticipated drug users and in the types of drugs being used (details available upon request). Each site consisted of about 50,000 households of about the same size, age, and ethnicity. Table 1 describes the demographics of the two sites. After Site A and Site B were identified, the sites were randomly assigned to receive either the VCT or the Safety Counts intervention in the first phase of the study. In the second phase, each site received the alternative intervention condition. Figure 1 outlines the recruitment and assignment to intervention condition that took place in Sites A and B over the 3.5 years of the study, during 1992–1995.
Table 1
Characteristics of the neighborhoods from which participants were recruited
Fig. 1
Study design assignment by neighborhood, with crossover. Each site (neighborhood) comprised three contiguous ZIP codes. Participants enrolled in Phase 2 are new to the study (cross-over is by neighborhood, not individual)
Participants
All drug users hanging out in either Site A or Site B were potentially eligible for participation. Most participants lived in the ZIP codes in which they were recruited, although some participants hung out frequently in the study sites with drug abusing peers, but actually lived in nearby areas. Outreach workers visited common hang-out sites and screened all persons at the site individually. If the potential participant reported IDU or crack use in the last 30 days, he or she was taken to a field office where an interviewer conducted the baseline interview. In order for potential participants to be eligible for the study, their self-reported crack or injection drug use had to be confirmed, either by visible track marks or positive urine tests for opiates or cocaine. Upon completion of the baseline interview, all participants were offered VCT, and those who completed both sessions were enrolled in the study. All participants testing HIV+ were offered an additional counseling session focused on linking participants to health and mental health care.
As outlined in Fig. 1, in the first phase of the study, all eligible participants in Site A next received the Safety Counts intervention; those in Site B received no additional intervention beyond the VCT. In the second phase, a new wave of participants was recruited; new participants in Site B received the Safety Counts intervention, and those in Site A received only VCT. Separate groups of participants were enrolled in each phase of the study; cross-over of treatment occurred by neighborhood, but not by individual.
Figure 2 describes the participant flow through the study. Some otherwise eligible participants were excluded from the analyses if their responses to the baseline or follow-up interview indicated that they had been jailed for more than five of the previous 30 days (n = 121, 10%). Being jailed reduces the individual’s autonomy in choosing to engage in sexual and drug use behaviors. In order to accurately evaluate behavioral changes in response to the interventions, we excluded such jailed participants.
Fig. 2
Flow of participants, combined over both phases of the study
Assessments
The baseline and follow-up assessments were similar and administered by interviewers based in the field office. Interviewers were ethnically diverse and well trained; the quality of the interviews was supervised on an ongoing basis. In addition to these interviews, urine samples were collected. Urine was tested for the presence of cocaine and heroin, using OnTrak [26]. These tests can detect indicators of heroin and indicators of cocaine for about 2–3 days after use. “Recent” drug and sexual risk behaviors are defined as occurring in the 30 days prior to the assessment for self-report measures.
Sexual Risk Acts
Participants reported the recent number of vaginal and anal sexual acts performed, and condom use per sexual act. From these reports, we constructed counts of the number of vaginal and anal sexual acts, and the number of these sexual acts that were unprotected. Safer sex was defined as 100% condom use or abstinence.
Substance Use
Participants reported use of the following substances: alcohol, marijuana, crack, cocaine, heroin, speedball, nonprescription methadone, other opiates, amphetamines, and other drugs. Total times used as well as times injected in the past month were ascertained. From these reports, we constructed counts of times having injected any substance, and times having used crack. For injection drug use, participants also reported the number of times they injected with dirty works (needles/syringes) that had not been cleaned with bleach. We also constructed counts of the number of times having used alcohol or marijuana, and number of times having used drugs other than alcohol or marijuana (via any method). As noted above, we obtained urine samples for each participant at the baseline and follow-up assessments which were tested for crack/cocaine and opiate metabolites.
Demographic Characteristics
Age, gender, ethnicity, education, marital status, employment and housing status, HIV status, and days incarcerated were reported at each interview.
Intervention Conditions
Participants completing the two VCT sessions were included in the study regardless of HIV status. Thus, there were both HIV+ and HIV− participants in both conditions. Depending upon the site and phase of the study, participants were assigned to either the control (VCT only) or Safety Counts condition.
Control Condition: VCT Only
VCT was conducted in a one-on-one setting by trained counselors in a field office. The initial counseling session was didactic and based on the NIDA Standard Intervention, outlining the benefits and risks of HIV testing, drug use, and sexual risk. All participants accepted the HIV test that was offered to them. A second post-HIV test session was also conducted in an individual setting, at which time HIV test results were delivered, and additional information was provided regarding strategies for reducing sexual and drug-related risks.
Safety Counts: VCT Plus Seven Sessions/Activities
The enhanced intervention consisted of the two VCT sessions outlined above plus the following activities: two small-group, skill-focused workshops and one individual counseling session. In addition, participants were expected to engage in at least two structured contacts conducted by outreach workers in street hang-outs, focusing on providing support to participants for achieving personal risk reduction goals. Finally, participants were expected to attend at least two of the monthly social events provided for participants and their invited guests. It took four months for the participants to complete this intervention sequence. High participation in Safety Counts was considered to be a total of at least five sessions or activities (two workshops plus the individual counseling session in the office and at least two additional activities, either outreach contacts or social events).
The two small-group workshops focused on skill-building at which participants were presented with role model vignettes [27] and strategies for reducing sexual and drug use actions, and were asked to commit to changing a specific sexual or drug use risk reduction behavior. Participants wrote risk reduction goals on cards kept by both the outreach workers and the participants.
The two workshops were followed by an individual counseling session delivered in the field office to plan how to implement the risk reduction goals. Street outreach workers followed up at least two times on the planning and implementation of the goals during street contacts.
The social events were held monthly for all participants and included a meal, risk-reduction games and skits, personal testimonials, and recognition of participants’ progress in achieving their personal risk reduction goals.
Four to six ethnically diverse outreach workers delivered the intervention. Outreach workers were similar to participants in ethnicity and many were themselves former drug users, familiar with patterns of daily drug use. Outreach workers received extensive training prior to intervention delivery. First, there was educational training on HIV, transmission risks, and protective behaviors, as well as the theory of behavior change and the Prochaska theory of change [8]. Second, street outreach workers were provided scripts that demonstrated the intervention in role play and with mock clients. Both the individual and the small group sessions were modeled for and practiced by the outreach workers. Finally, outreach workers demonstrated their intervention skills with drug users in the field, accompanied by a supervisor who critiqued their skills.
Participants were compensated $10 for participation in the baseline interview, $10 for additional assessments at the second VCT session, and $20 for participation in the follow-up interview. Non-monetary incentives included food coupons, bus tokens, and personal hygiene kits that were provided at intervention sessions. Other non-monetary incentives included meals at social events, snacks at the intervention workshops, and drawings with prizes.
Data Analyses
Chi-square and ANOVA tests were used to compare baseline characteristics of participants: completing both assessments versus lost to follow-up, assigned to the Safety Counts condition versus assigned to the VCT condition, lost to follow-up in the Safety Counts condition versus lost to follow-up in the VCT condition, and jailed more than five days in the Safety Counts condition versus jailed in the VCT condition.
Crack cocaine or injecting drug use were criteria for participation in this study. We evaluated the effectiveness of the Safety Counts intervention in stopping each behavior, using logistic regression. Separate models were used to estimate the probabilities of stopping crack use among those reporting crack use at baseline and for stopping injection drug use among IDU at baseline.
We compared drug users in the Safety Counts and the VCT condition on counts of risky sexual behaviors, counts of drug use, the probability of practicing safe sex (being abstinent or using condoms at all times), and the probability of having clean urinalysis results for cocaine and opiates using generalized linear mixed-effects models (GLMM). Each participant’s self-reported count was modeled as a Poisson distribution with a log link and a random intercept. Practicing safe sex or having a clean urinalysis was modeled in a logistic analysis with a random intercept. Baseline and follow-up assessments had separate means in the intervention and control conditions. To test for differences between Safety Counts and the VCT control conditions, we evaluated the statistical significance of a treatment-by-time interaction.
The treatment-by-time effect estimated in our Poisson models for count variables is, when exponentiated, a measure of the additional change in the outcome measure from baseline to follow-up that is attributable to Safety Counts. It is equivalent to the ratio:
If the change from baseline to follow-up is the same for both groups, this ratio would be 1. Values less than 1 indicate that the participants in the Safety Counts condition reduced more than the VCT condition (e.g., 0.5 reflects twice as much change in Safety Counts compared to VCT).
The treatment-by-time effect in the logistic models for binary outcomes (negative urine tests for opiates and cocaine, practicing safe sex) is, when exponentiated, a measure of the increased odds of the outcome occurring at follow-up due to Safety Counts. It is equivalent to the ratio:
To test the sensitivity of our findings to extreme values, we reran the mixed-effects models of count variables, omitting both baseline and follow-up observations for participants with unusually high baseline measures. Participants were omitted if their baseline measure was in the top 1% of observations; this resulted in dropping 11 participants from the analysis. Estimates of treatment effect ratios and significance did not change in any important way, and we report only the complete data analysis.
In addition, to determine whether there might be any effects on our findings caused by the groupings of observations into two locations (Sites A and B) and two study phases (pre and post crossover), we reran all analyses controlling for site and phase. Results were unchanged, and we present results from the simpler models.
Finally, as noted above, we compared baseline characteristics of participants assigned to the VCT and Safety Counts arms of the study. We found one characteristic where there was a significant difference (unemployment, P < 0.05), and reran the models controlling for that characteristic. The results did not differ, and findings are shown without this variable in the model.
SAS Proc GLIMMIX (GLIMMIX 9.1 add-on procedure, June 2006 release, SAS Institute, Cary, NC) was used to fit the models. We duplicated the analysis using WinBUGS [27], a Bayesian modeling program that uses Markov chain Monte Carlo computing. We used uninformative but proper priors; results were generally similar to GLIMMIX. Therefore, we present only the GLIMMIX results here.
Results
Sample Description
As shown on Fig. 2, 1,728 individuals were identified through street outreach and received the initial baseline interview. From that group, 1,237 (72%) had their drug use confirmed by way of visible track marks or positive urine screens, and completed both VCT sessions; these individuals were enrolled in the study. One hundred twenty-one enrolled participants (61 VCT, 60 Safety Counts) were excluded from analysis due to having been jailed for more than five days in the month prior to either assessment. There were no significant differences in background characteristics by study arm among those who were excluded due to incarceration. This exclusion resulted in a final sample of 1,116, with 558 in the VCT condition and 558 in the Safety Counts condition, as shown in Fig. 2.
Except where noted, all participants were included in our analyses; however, we did not have complete follow-up data. Across both study conditions, 241 eligible participants did not complete the follow-up within the window of five to nine months after the baseline interview (213 not returned at all, 28 returned too late). The follow-up rate was 80% among those assigned to VCT, and 76% among those receiving Safety Counts. Among those with timely follow-ups, the mean length of time from the baseline to follow-up interview was 186 days (SD = 36 days), about 18 days longer in the Safety Counts condition compared to the VCT condition.
We analyzed the differences between eligible participants who did and did not complete a follow-up on time (complete results available from the authors). Participants lost to follow-up were significantly more likely to be male (76 vs. 65%), White (43 vs. 27%), younger (36 vs. 39 years old; 20% under the age of 30 years vs. 12%), never married (46 vs. 37%), less educated (high school education or less: 83 vs. 74%), or homeless (51 vs. 40%). They were more likely to use both crack and injection drugs, in contrast to only one or the other substance (44 vs. 33%), to have had a positive urine screen for opiates (60 vs. 44%), and to have injected more times in the last 30 days (61 vs. 42 times). Sexual behavior, HIV status, crack use, sharing needles, and employment did not differ significantly between those analyzed and those lost to follow-up. If a significant difference emerged on sociodemographic characteristics, we compared those without follow-ups between the VCT and Safety Counts conditions. There were no significant differences between these two groups.
Table 2 summarizes the baseline demographic and risk behavior profiles of the participants in the Safety Counts and the VCT conditions. Similar across conditions, participants were mostly male (67%); unpartnered (74%); predominantly African American (44%), White (30%), and Latino (20%); and had a mean age of 38 years old (range 18–65 years old). Most had a high school education or less (76%), 43% were homeless, and 4% were identified as HIV+, again, similar across conditions. Overall, 88% were unemployed, with slightly more in the VCT condition than in Safety Counts (90 vs. 85%, P < 0.05).
Table 2
Baseline characteristics of the sample grouped by intervention condition
Risk behaviors at baseline were similar across conditions with one exception: the number of times using crack was higher (47 vs. 39 times in the last 30 days) among the Safety Counts participants in contrast to those in the VCT condition. There were three subgroups of drug users: 30% were injectors only, 35% used only crack, and 35% were both heroin injectors and crack users. On average, 28% of IDU used dirty works (needles/syringes) during the last month; participants used dirty works about 4.9 times in the last month, but there was a large variance on the number of uses of a dirty needle.
Positive urinalysis results for cocaine were found at baseline among 85% of participants and 47% were positive for heroin. About 70% were sexually active in the last 30 days, with a mean of 10 sexual acts; only 25% of sex acts were protected by condoms. Among those who were sexually active, 66% had never used a condom in the past month.
Sex and Drug Outcomes Across Intervention Conditions Over Time
Logistic regressions were used to examine the effect of the Safety Counts intervention on the likelihood of stopping injection drug use among the 537 participants with follow-ups who were IDU at baseline, and on the likelihood of stopping use of crack among the 622 participants with follow-ups using crack at baseline [not shown]. Among baseline IDU in the Safety Counts condition, 31% (80/258) reported no injection drug use at follow-up, compared to 23% (63/279) among participants in VCT (estimated OR = 1.6, P = 0.02). Twenty-nine percent (89/302) of baseline crack users in the Safety Counts condition reported no crack use at follow-up, which was not significantly different from the 27% rate (85/320) found among baseline crack users in VCT.
Table 3 summarizes the rates of risk behaviors at baseline and follow-up, and provides the results of random effects analyses, modeling counts with a Poisson distribution, and using a logistic model for practicing safe sex and for having clean urinalysis results. The Safety Counts intervention resulted in significantly greater reductions in the numbers of sexual acts (ratio = 0.78, P < .001), unprotected sexual acts (ratio = 0.58, P < .001), times injecting (ratio = 0.93, P < .001), times injecting with dirty works (ratio = 0.51, P < .001), times using crack (ratio = 0.90, P < .001), and times using drugs other than alcohol or marijuana (ratio = 0.93, P < .001). On only one measure did those in the Safety Counts condition tend to show less reduction over time than those in the VCT condition: use of alcohol or marijuana (ratio = 1.03, P = 0.14). The logistic analyses of urinanalysis results and practicing safer sex did not show any significant differences between the Safety Counts and VCT conditions.
Table 3
Observed values at each assessment by safety counts or VCT
High Participation
Participants in Safety Counts who demonstrated high adherence to the intervention (participating in five or more intervention activities) showed greater improvement than did those who participated less. For those outcomes where there was a significant intervention effect in the intention-to-treat analyses, Table 4 reports the decreases in risky behaviors among those with low participation in Safety Counts versus high participation. With the exception of number of sexual acts, in which those with less participation showed a slightly greater reduction, decreases in risky behaviors are, overall, markedly greater among those who have better attendance at intervention events.
Table 4
Decreases in risky behaviors from baseline to follow-up by level of participation in the Safety Counts program
Discussion
In contrast to the Hershberger et al. [28] analyses, we found that Safety Counts results in substantial reductions in both sex and drug use. Hard drug users, those actively using crack or injecting drugs in the past month, demonstrated significant reductions in sex and drug use risk acts after receiving the Safety Counts intervention. The effect sizes are relatively large and the greatest reductions are in the types of behavior most likely to result in HIV transmission: unprotected sexual acts and injection with dirty works. Almost all evidence-based HIV-related interventions have been based on a social cognitive approach to behavior change [19] in which change occurs slowly over time in small steps, in response to opportunity and social rewards. Drug users in the Safety Counts condition were much more likely to stop injecting than those in the VCT: 35% more stopped injecting completely. Abstinence from injecting is a substantial shift in behavior. In addition to stopping IDU, there were significantly greater reductions over time in the number of times injecting, injecting with dirty works, and the number of times of using crack in the last month (ratios = 0.51–0.93). These differences are important among serious drug users.
Simultaneous to stopping or reducing drug use, there were also significant reductions in the amount of sexual activity overall, and the number of unprotected sex acts. While only 4% of the participants were known to be HIV+ in the sample, many communities of IDU have much higher rates. Combined with the reductions in injection use, the reductions in sexual risk are also substantial (ratios = 0.58–0.78).
An important aspect of the program was its simultaneous focus both on personal tailoring and the community-level outreach. Counseling at the workshops and individual sessions were adapted to the individual’s risk background. However, the street outreach targeted a neighborhood, and the social activities were aimed at building social networks within the community. Many interventions have been labeled “individual,” “family,” or “community” level. Yet, each individual intervention has a site or institutional component, and every successful community-level intervention has also included training individuals to be more efficacious in their personal lives. Safety Counts targets hang-out spots in communities with high rates of drug use. Since the beginning of the HIV epidemic, geography has been destiny [9]; this intervention provides a packaged solution based on local epidemiology of HIV.
It is not possible to identify which are the components of the Safety Counts intervention most responsible for changing habitual risk behaviors. Skill-building workshops, goal setting in one-on-one counseling sessions, street contacts and social events were all included in Safety Counts. These are common elements in many evidence-based interventions [25, 29]. Future research must identify which components of the intervention are most effective in reducing the risk behaviors of active drug users.
Limitations
This study’s significant results rely on self-reports. No significant treatment effects were found in urine screens for heroin and cocaine. However, the metabolites of heroin and cocaine only represent a sample of use in the last three days. In addition, several significant intervention effects were found that involved reduction rather than elimination of risky behaviors; such reductions may not have been reflected in urinalysis results, which test for any use. Considerable effort was made to obtain reliable self-reports from participants at both the baseline and the follow-up assessments, and the interviewers and the street outreach workers were different.
The follow-up rate was 78%, an acceptable rate for such a high-risk target population, but not optimal. The participants who were lost to follow-up were even riskier than the participants that we were able to follow successfully and reassess. It is not clear whether this led to us assessing those most or least likely to change over time in response to the intervention.
One outcome measure differed significantly by treatment arm at baseline: times used crack. Safety Counts participants demonstrated both greater use at baseline (47 vs. 38 times used crack) and significantly greater improvement over the course of the study than did those in VCT only. Some of the greater improvement may be attributable to Safety Counts participants experiencing more regression to the mean, as they started out at higher baseline levels.
We chose to exclude those who were in jail for five or more days prior to either the baseline or the follow-up interview. Therefore, we define our population of interest to be the non-incarcerated. Because of similarities across intervention arm in the number and characteristics of those excluded, there is little bias anticipated due to exclusion of the incarcerated.
It is notable that this study was conducted in West Coast communities, neighborhoods with much larger geographic areas, less density than the drug neighborhoods along the East Coast of the United States that have much higher rates of HIV infection [3]. Safety Counts, available from the CDC (www.effectiveinterventions.org), may be useful in communities with street hang-outs that facilitate injection use by networks of persons at high-risk for HIV.
Street outreach is a community-level intervention program, with individually tailored components included within the community model. It would have been desirable to gather evaluation data at the level of the community rather than the individual. In particular, the social activities created an opportunity for community members to gather, to set norms that oppose drug use and unprotected sex, and to foster relationships that build community social capital. However, we evaluated the program at the level of the individual participants. Given that there were only two sites, we could not use site as the unit of analysis.
It is noteworthy that the results of these analyses are different from those presented in the 2003 paper of Hershberger and colleagues. While that analysis found some significant intervention effects, it was far fewer than the current analytic strategy demonstrated. The primary differences between our findings and theirs are attributable to the choice of inclusion criteria, outcome measures, and statistical methods. The contrasting findings point to the importance of the decisions made when conducting data analyses. This is problematic for researchers. It is seldom that the same data set is examined by more than one team. The fact that our findings differ points to the importance of taking more than one look at a given research question.
This study identifies an intervention approach that can result in successful reduction of drug use and sexual risk-taking in a targeted community, a key strategy for the next generation of preventive interventions. It is being broadly implemented through the CDC DEBI programs currently. The next generation of outcome analyses will indicate whether the benefits are sustained as Safety Counts is adopted at the local level.
Electronic supplementary material
Below is the link to the electronic supplementary material.
(DOC 52 kb)(53K, doc)
Acknowledgments
This paper was completed with the support of National Institute of Health grants # R18-DA05747, U01-DA07474, 1ROI MH49958-04 and P30 MH58107.
Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
References
1. Steele CB, Melendez-Morales L, Campoluci R, DeLuca N, Dean HD. Health disparities in HIV/AIDS, viral hepatitis, sexually transmitted diseases, and tuberculosis in the United States: issues, burden, and response, a retrospective review, 2000–2004. Atlanta: Department of Health and Human Services, Centers for Disease Control and Prevention; 2007.
2. Mathers BM, Degenhardt L, Phillips B, et al. Global epidemiology of injecting drug use and HIV among people who inject drugs: a systematic review. Lancet. 2008;372:1733–1745. doi: 10.1016/S0140-6736(08)61311-2.
3. Centers for Disease Control. Morbidity and mortality weekly report, October 3, 2008: HIV prevalence estimates—United States. 2006. Available at: www.cdc.gov/mmwr/preview/mmwrhtml/mm5739a2.htm. Accessed 23 Dec 2008.
4. Substance Abuse and Mental Health Services Administration, Office of Applied Studies. Results from the 2007 National Survey on Drug Use and Health: National findings. Rockville. 2008. [NSUDH Series H-34, DHHS Publication No. SMA 08-4343, Table G.3].
5. Edlin BR, Irwin KL, Faruque S, et al. Intersecting epidemics—Crack cocaine use and HIV infection among inner-city young adults. NEJM. 1994;331(21):1422–1427. doi: 10.1056/NEJM199411243312106.
6. Des Jarlais DC, Dehne K, Casabona J. HIV surveillance among injecting drug users. AIDS. 2001;15(suppl 3):S13–S22. doi: 10.1097/00002030-200104003-00003.
7. Strathdee SA, Patterson TL. Behavioral interventions for HIV-positive and HCV-positive drug users. AIDS Behav. 2006;10(2):115–130. doi: 10.1007/s10461-005-9055-5.
8. CDC AIDS Community Demonstration Projects Research Group Community-level HIV intervention in 5 cities: final outcome data from the CDC AIDS Community Demonstration Projects. Am J Public Health. 1999;89(3):336–345. doi: 10.2105/AJPH.89.3.336.
9. Rotheram-Borus MJ, Koopman C. HIV and adolescents. J Prim Prev. 1991;2(1):65–82. doi: 10.1007/BF01326542.
10. Bloom HS, editor. Learning more from social experiments: evolving analytic approaches. New York: Sage; 2006.
11. Murdoch J. Post-structuralist geography: a guide to relational space. London: Sage; 2006.
12. Friedman SR, Curtis R, Neaigus A, Jose B, Des Jarlais DC. Social networks, drug injectors’ lives, and HIV/AIDS. New York: Springer Publications; 1999.
13. Latkin C, Mandell W, Oziemkowska M, et al. Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS. Drug Alcohol Depend. 1995;38:1–9. doi: 10.1016/0376-8716(94)01082-V.
14. Christakis NA, Fowler JH. The collective dynamics of smoking in a large social network. NEJM. 2008;358:2249–2258. doi: 10.1056/NEJMsa0706154.
15. Murray DM, McKinlay SM, Martin D, et al. Design and analysis issues in community trials. Eval Rev. 1994;18(4):493–514. doi: 10.1177/0193841X9401800407.
16. Boruch RF, Foley E. The honestly experimental society: using organization and other entities as units in randomized experiments. In: Bickman L, editor. Donald F. Campbell’s legacy. Thousand Oaks: Sage; 2000.
17. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51:390–395. doi: 10.1037/0022-006X.51.3.390.
18. Prochaska JO, DiClemente CC. Toward a comprehensive model of change. In: Miller WR, Heather N, editors. Treating addictive behaviors: processes of change. New York: Plenum Press; 1986.
19. Bandura A. Social foundations of thought and action: a social cognitive theory. Englewood Cliffs: Prentice Hall; 1986.
20. Becker MH. The health belief model and personal health behavior. Health Educ Monogr. 1974;2:324–508.
21. Maddux JE, Rogers RW. Protection motivation and self-efficacy: a revised theory of fear appeals and attitude change. J Exp Soc Psychol. 1983;19:469–479. doi: 10.1016/0022-1031(83)90023-9.
22. Safety counts: a cognitive-behavioral intervention to reduce HIV/Hepatitis risks among drug users who are not in drug treatment—program manual. Washington, DC: Academy for Educational Development; 2003.
23. Rhodes F, Humfleet GL. Using goal-oriented counseling and peer support to reduce HIV/AIDS risk among drug users not in treatment. Drugs Soc. 1993;7:185–204. doi: 10.1300/J023v07n03_13.
24. Coyle S. The NIDA HIV counseling and education intervention model: intervention manual (NIH Pub. No. 93-3508) Rockville: National Institute on Drug Abuse; 1993.
25. Rotheram-Borus MJ, Swendeman D, Flannery D, Rice E, Adamson DM, Ingram B. Common factors in effective HIV prevention programs. AIDS Behav. 2009;13(3):399–408. doi: 10.1007/s10461-008-9464-3.
26. Varian, Inc. Diagnostic products. 2009. Available at: http://www.varianinc.com/cgi-bin/nav?products/dat/index&cid=KMKPIHHHFJ. Accessed 5 Feb 2009.
27. Lunn DJ, Whittaker JC, Best N. A Bayesian toolkit for genetic association studies. Genet Epidemiol. 2006;30(3):231–247. doi: 10.1002/gepi.20140.
28. Hershberger S, Wood M, Fisher D. A cognitive-behavioral intervention to reduce HIV risk behaviors in crack and injection drug users. AIDS Behav. 2003;7(3):229–243. doi: 10.1023/A:1025487501743.
29. Ingram BL, Flannery D, Elkavich A, Rotheram-Borus MJ. Common processes in evidence-based adolescent HIV prevention programs. AIDS Behav. 2008;12(3):374–383. doi: 10.1007/s10461-008-9369-1.
Source
Published online 2009 September 11.
doi: 10.1007/s10461-009-9606-2.
PMCID: PMC2865646
Copyright © The Author(s) 2009
Mary Jane Rotheram-Borus, 1,2 Fen Rhodes, 1 Katherine Desmond, 1 and Robert E. Weiss 1
1 Center for HIV Identification, Prevention, and Treatment Services, University of California, Los Angeles, CA USA
2 UCLA, 10920 Wilshire Blvd., Suite #350, Los Angeles, CA 90024-6521 USA
Mary Jane Rotheram-Borus, Phone: +1-310-7948278, Fax: +1-310-7948297, Email: Rotheram@ucla.edu.
Received September 11, 2008; Accepted August 10, 2009.
Abstract
The efficacy of Safety Counts, a CDC-diffused intervention, was reanalyzed. In a quasi experimental, cross-over design, injection drug users (IDU) and crack users in two neighborhoods were assigned by neighborhood to receive individual Voluntary HIV Counseling and Testing or Safety Counts and 78% were reassessed at 5–9 months. Drug users in the Safety Counts program reported significantly greater reductions in risky sex, crack and hard drug use, and risky drug injection. The more sessions of Safety Counts attended, the greater were the reductions in risky acts. Different analytic decisions result in very different findings for the same intervention. Safety Counts is an effective intervention for IDU and crack users. Analytic decision of intervention outcomes is highly related to evaluations of an intervention’s efficacy.
Electronic supplementary material
The online version of this article (doi:10.1007/s10461-009-9606-2) contains supplementary material, which is available to authorized users.
Keywords: HIV, Risk reduction, Drug use, IDU, VCT
Introduction
Injecting drug users (IDU) and crack users remain at high-risk for HIV in the United States, especially on the East Coast [1]. A recent study estimated that there are over 1.8 million IDU in the US, of whom 16% are HIV+ [2]. Nearly a quarter of currently prevalent HIV cases are attributable to injection drug use or both IDU and male-to-male sexual contact [3]. Concurrently, there are 1.5 million crack users [4], each having a threefold risk of acquiring HIV [5]. Among drug users, HIV risk emerges not only from drug use (i.e., by sharing needles), but also from sexual behaviors [6]. Therefore, it is critical to design and evaluate evidence-based interventions for IDU and crack users. This article re-evaluates the efficacy of Safety Counts, an intervention to reduce both drug and sexual risk among serious drug users.
Needle exchange and methadone maintenance are two effective strategies for reducing HIV risk for IDU [7]. However, despite multiple scientific reviews on its efficacy, most communities in the US do not have access to needle exchange and there are many parts of the world without such access. Given the political realities of acceptable treatments, this study examines an intervention strategy built concurrently with the street-outreach models of the AIDS Community Demonstration Projects [8].
Drug use is concentrated within specific neighborhoods [1], leading consequent HIV risk to be closely linked to geography [9]. Within neighborhoods with high drug use, there are local “hang-out” sites that are frequented by drug users (shooting galleries, squats). Coincident with drug use and HIV risk, drug-infested neighborhoods have high rates of sexually transmitted diseases and crimes. Because of this geographic concentration, street outreach prevention strategies are desirable [10, 11]. By intervening in neighborhoods with high rates of drug use, it is easier and more efficient to target prevention resources, maximizing the cost-effectiveness of prevention funding. Simultaneously, there are likely to be spill-over benefits from interventions that concentrate on neighborhoods: peers and social networks exert a strong influence on individuals’ high-risk behaviors [12–14].
Street outreach workers can contact drug users in their local hang-outs to deliver HIV prevention messages, both about reducing drug use and increasing condom use. Outreach workers can also facilitate the engagement of drug users into ongoing group and community activities, increasing the exposure to prevention messages, increasing the practice of new behaviors, and building positive social networks. Street-based recruitment and prevention strategies address both the physical and the social dimensions of drug abuse [10] and require that evaluations be based upon interventions delivered at the level of the site, even if tailored to the individual [15, 16].
This study implemented a prevention program for street-based drug users, called Safety Counts. Drug users do not usually access health services or seek care at institutions such as schools, churches, or community centers [13]. Therefore, Safety Counts built on a street outreach approach. The intervention was based on several related theories of behavior change, with the Transtheoretical Stages of Change Model of Prochaska and DiClemente [17, 18] forming the core framework. The intervention also drew on behavior change principles and techniques articulated by social cognitive theories [19–21]. Further information about the Safety Counts intervention can be found in the Program Implementation Manual [22] and an earlier article by Rhodes and Humfleet [23].
Following the protocol of the National Institute on Drug Abuse (NIDA) Cooperative Agreement, this study used the NIDA Standard Intervention for drug users [24] as the control condition. Labeled as VCT in this paper, the control condition delivered a didactic voluntary counseling and testing program over two sessions, in order to inform individuals regarding their HIV serostatus and to motivate them to reduce existing sexual risk acts. The enhanced intervention, Safety Counts, included the VCT sessions plus street outreach, skills-building workshops, individual counseling, and social events. Based on these components and the social cognitive theoretical models common to evidence-based interventions [25], the 7-session Safety Counts Program was delivered to neighborhood drug users. Both self-report and urine screens for active drug use were utilized as outcome measures for the intervention.
Hershberger and colleagues evaluated the Safety Counts intervention in this journal in 2003, finding few significant outcomes of the Safety Counts intervention compared to the standard VCT condition. Our analyses adopt a different perspective towards the data. The prior analyses emphasized as-treated comparisons of compliers in each condition (although intent-to-treat results were presented in the text as well). Those who did not complete the full intervention schedule were discarded from the primary analyses. A key difference between that paper and the present one is our emphasis on intent-to-treat comparisons of those assigned to the VCT control condition and those assigned to Safety Counts.
Beyond the difference in emphasis, the determination of which potential participants to include in the intent-to-treat analyses was not the same in the two studies. In the present study, completion of the NIDA Standard Intervention (attending two sessions) was a criterion for eligibility, and we excluded individuals who did not complete both sessions. In addition, we excluded from analysis those who reported lengthy incarcerations at either the baseline or follow-up assessment, as that reduces the individual’s behavioral autonomy. The Hershberger et al. analyses did not make these exclusions.
Another difference between the two studies is that we analyzed the data using some additional outcome markers. The authors of the prior paper analyzing Safety Counts chose several dichotomous outcome measures such as any sexual activity or not, any unprotected sex or not, multiple partners or not, using crack or not, and any injection drug use or not. They also measured selected risky behaviors as percentages: percent of times injected with dirty works, percent of times used condoms. We analyzed a subset of the dichotomous outcome measures from the prior study, with some modifications to definition or analytic method.
However, in addition to these measures, we also used count measures of how frequently the behaviors were practiced, such as number of times injecting (also used in the prior study), number of times using dirty works, number of times using crack, number of times having sex, and number of unprotected sexual risk acts. We did not use outcomes expressed as percentages. We believe that counts provide a useful measure of absolute risk that is superior to percentages. In the case of condoms, for example, count measures capture frequency of sexual activity, as well as relative frequency of condom use. A person using condoms 30 times out of 60 is at greater risk than a person using condoms two times out of four, yet a dichotomous indicator of practicing unsafe sex, or a percentage measure of condom use would not differentiate between these two individuals.
We also chose different methods to analyze the frequency measures. Data plots showed that these measures were not normally distributed, but in fact followed a Poisson distribution. We therefore used random effects models assuming a Poisson distribution for count variables. We believe that this analytic method provides a better fit to the data than did the methods used by the authors of the prior published analyses. For frequency measures, they used a two-way ANOVA approach, which assumes that variables follow a normal distribution. This was not the case for the count (times injected) or percentage variables examined in that study. We feel that the choice of appropriate sample and outcome measures in conjunction with correct statistical methods will provide a more accurate evaluation of the actual intervention impact.
Methods
Sites
Seventeen ZIP codes were identified, within the greater Los Angeles, California metropolitan area, that were anticipated to be similar in ethnic composition and socioeconomic status. The ZIP codes were expected to have many drug users based on having high rates of drug-related deaths, arrests, arrestees with positive drug screening cases of HIV and AIDS, drug-related emergency room admissions and drug intake data and sexually transmitted diseases. From government data sets and existing research projects ongoing in the 17 ZIP codes, two sets of three contiguous ZIP codes were identified that were similar in the number of anticipated drug users and in the types of drugs being used (details available upon request). Each site consisted of about 50,000 households of about the same size, age, and ethnicity. Table 1 describes the demographics of the two sites. After Site A and Site B were identified, the sites were randomly assigned to receive either the VCT or the Safety Counts intervention in the first phase of the study. In the second phase, each site received the alternative intervention condition. Figure 1 outlines the recruitment and assignment to intervention condition that took place in Sites A and B over the 3.5 years of the study, during 1992–1995.
Table 1
Characteristics of the neighborhoods from which participants were recruited
Fig. 1
Study design assignment by neighborhood, with crossover. Each site (neighborhood) comprised three contiguous ZIP codes. Participants enrolled in Phase 2 are new to the study (cross-over is by neighborhood, not individual)
Participants
All drug users hanging out in either Site A or Site B were potentially eligible for participation. Most participants lived in the ZIP codes in which they were recruited, although some participants hung out frequently in the study sites with drug abusing peers, but actually lived in nearby areas. Outreach workers visited common hang-out sites and screened all persons at the site individually. If the potential participant reported IDU or crack use in the last 30 days, he or she was taken to a field office where an interviewer conducted the baseline interview. In order for potential participants to be eligible for the study, their self-reported crack or injection drug use had to be confirmed, either by visible track marks or positive urine tests for opiates or cocaine. Upon completion of the baseline interview, all participants were offered VCT, and those who completed both sessions were enrolled in the study. All participants testing HIV+ were offered an additional counseling session focused on linking participants to health and mental health care.
As outlined in Fig. 1, in the first phase of the study, all eligible participants in Site A next received the Safety Counts intervention; those in Site B received no additional intervention beyond the VCT. In the second phase, a new wave of participants was recruited; new participants in Site B received the Safety Counts intervention, and those in Site A received only VCT. Separate groups of participants were enrolled in each phase of the study; cross-over of treatment occurred by neighborhood, but not by individual.
Figure 2 describes the participant flow through the study. Some otherwise eligible participants were excluded from the analyses if their responses to the baseline or follow-up interview indicated that they had been jailed for more than five of the previous 30 days (n = 121, 10%). Being jailed reduces the individual’s autonomy in choosing to engage in sexual and drug use behaviors. In order to accurately evaluate behavioral changes in response to the interventions, we excluded such jailed participants.
Fig. 2
Flow of participants, combined over both phases of the study
Assessments
The baseline and follow-up assessments were similar and administered by interviewers based in the field office. Interviewers were ethnically diverse and well trained; the quality of the interviews was supervised on an ongoing basis. In addition to these interviews, urine samples were collected. Urine was tested for the presence of cocaine and heroin, using OnTrak [26]. These tests can detect indicators of heroin and indicators of cocaine for about 2–3 days after use. “Recent” drug and sexual risk behaviors are defined as occurring in the 30 days prior to the assessment for self-report measures.
Sexual Risk Acts
Participants reported the recent number of vaginal and anal sexual acts performed, and condom use per sexual act. From these reports, we constructed counts of the number of vaginal and anal sexual acts, and the number of these sexual acts that were unprotected. Safer sex was defined as 100% condom use or abstinence.
Substance Use
Participants reported use of the following substances: alcohol, marijuana, crack, cocaine, heroin, speedball, nonprescription methadone, other opiates, amphetamines, and other drugs. Total times used as well as times injected in the past month were ascertained. From these reports, we constructed counts of times having injected any substance, and times having used crack. For injection drug use, participants also reported the number of times they injected with dirty works (needles/syringes) that had not been cleaned with bleach. We also constructed counts of the number of times having used alcohol or marijuana, and number of times having used drugs other than alcohol or marijuana (via any method). As noted above, we obtained urine samples for each participant at the baseline and follow-up assessments which were tested for crack/cocaine and opiate metabolites.
Demographic Characteristics
Age, gender, ethnicity, education, marital status, employment and housing status, HIV status, and days incarcerated were reported at each interview.
Intervention Conditions
Participants completing the two VCT sessions were included in the study regardless of HIV status. Thus, there were both HIV+ and HIV− participants in both conditions. Depending upon the site and phase of the study, participants were assigned to either the control (VCT only) or Safety Counts condition.
Control Condition: VCT Only
VCT was conducted in a one-on-one setting by trained counselors in a field office. The initial counseling session was didactic and based on the NIDA Standard Intervention, outlining the benefits and risks of HIV testing, drug use, and sexual risk. All participants accepted the HIV test that was offered to them. A second post-HIV test session was also conducted in an individual setting, at which time HIV test results were delivered, and additional information was provided regarding strategies for reducing sexual and drug-related risks.
Safety Counts: VCT Plus Seven Sessions/Activities
The enhanced intervention consisted of the two VCT sessions outlined above plus the following activities: two small-group, skill-focused workshops and one individual counseling session. In addition, participants were expected to engage in at least two structured contacts conducted by outreach workers in street hang-outs, focusing on providing support to participants for achieving personal risk reduction goals. Finally, participants were expected to attend at least two of the monthly social events provided for participants and their invited guests. It took four months for the participants to complete this intervention sequence. High participation in Safety Counts was considered to be a total of at least five sessions or activities (two workshops plus the individual counseling session in the office and at least two additional activities, either outreach contacts or social events).
The two small-group workshops focused on skill-building at which participants were presented with role model vignettes [27] and strategies for reducing sexual and drug use actions, and were asked to commit to changing a specific sexual or drug use risk reduction behavior. Participants wrote risk reduction goals on cards kept by both the outreach workers and the participants.
The two workshops were followed by an individual counseling session delivered in the field office to plan how to implement the risk reduction goals. Street outreach workers followed up at least two times on the planning and implementation of the goals during street contacts.
The social events were held monthly for all participants and included a meal, risk-reduction games and skits, personal testimonials, and recognition of participants’ progress in achieving their personal risk reduction goals.
Four to six ethnically diverse outreach workers delivered the intervention. Outreach workers were similar to participants in ethnicity and many were themselves former drug users, familiar with patterns of daily drug use. Outreach workers received extensive training prior to intervention delivery. First, there was educational training on HIV, transmission risks, and protective behaviors, as well as the theory of behavior change and the Prochaska theory of change [8]. Second, street outreach workers were provided scripts that demonstrated the intervention in role play and with mock clients. Both the individual and the small group sessions were modeled for and practiced by the outreach workers. Finally, outreach workers demonstrated their intervention skills with drug users in the field, accompanied by a supervisor who critiqued their skills.
Participants were compensated $10 for participation in the baseline interview, $10 for additional assessments at the second VCT session, and $20 for participation in the follow-up interview. Non-monetary incentives included food coupons, bus tokens, and personal hygiene kits that were provided at intervention sessions. Other non-monetary incentives included meals at social events, snacks at the intervention workshops, and drawings with prizes.
Data Analyses
Chi-square and ANOVA tests were used to compare baseline characteristics of participants: completing both assessments versus lost to follow-up, assigned to the Safety Counts condition versus assigned to the VCT condition, lost to follow-up in the Safety Counts condition versus lost to follow-up in the VCT condition, and jailed more than five days in the Safety Counts condition versus jailed in the VCT condition.
Crack cocaine or injecting drug use were criteria for participation in this study. We evaluated the effectiveness of the Safety Counts intervention in stopping each behavior, using logistic regression. Separate models were used to estimate the probabilities of stopping crack use among those reporting crack use at baseline and for stopping injection drug use among IDU at baseline.
We compared drug users in the Safety Counts and the VCT condition on counts of risky sexual behaviors, counts of drug use, the probability of practicing safe sex (being abstinent or using condoms at all times), and the probability of having clean urinalysis results for cocaine and opiates using generalized linear mixed-effects models (GLMM). Each participant’s self-reported count was modeled as a Poisson distribution with a log link and a random intercept. Practicing safe sex or having a clean urinalysis was modeled in a logistic analysis with a random intercept. Baseline and follow-up assessments had separate means in the intervention and control conditions. To test for differences between Safety Counts and the VCT control conditions, we evaluated the statistical significance of a treatment-by-time interaction.
The treatment-by-time effect estimated in our Poisson models for count variables is, when exponentiated, a measure of the additional change in the outcome measure from baseline to follow-up that is attributable to Safety Counts. It is equivalent to the ratio:
If the change from baseline to follow-up is the same for both groups, this ratio would be 1. Values less than 1 indicate that the participants in the Safety Counts condition reduced more than the VCT condition (e.g., 0.5 reflects twice as much change in Safety Counts compared to VCT).
The treatment-by-time effect in the logistic models for binary outcomes (negative urine tests for opiates and cocaine, practicing safe sex) is, when exponentiated, a measure of the increased odds of the outcome occurring at follow-up due to Safety Counts. It is equivalent to the ratio:
To test the sensitivity of our findings to extreme values, we reran the mixed-effects models of count variables, omitting both baseline and follow-up observations for participants with unusually high baseline measures. Participants were omitted if their baseline measure was in the top 1% of observations; this resulted in dropping 11 participants from the analysis. Estimates of treatment effect ratios and significance did not change in any important way, and we report only the complete data analysis.
In addition, to determine whether there might be any effects on our findings caused by the groupings of observations into two locations (Sites A and B) and two study phases (pre and post crossover), we reran all analyses controlling for site and phase. Results were unchanged, and we present results from the simpler models.
Finally, as noted above, we compared baseline characteristics of participants assigned to the VCT and Safety Counts arms of the study. We found one characteristic where there was a significant difference (unemployment, P < 0.05), and reran the models controlling for that characteristic. The results did not differ, and findings are shown without this variable in the model.
SAS Proc GLIMMIX (GLIMMIX 9.1 add-on procedure, June 2006 release, SAS Institute, Cary, NC) was used to fit the models. We duplicated the analysis using WinBUGS [27], a Bayesian modeling program that uses Markov chain Monte Carlo computing. We used uninformative but proper priors; results were generally similar to GLIMMIX. Therefore, we present only the GLIMMIX results here.
Results
Sample Description
As shown on Fig. 2, 1,728 individuals were identified through street outreach and received the initial baseline interview. From that group, 1,237 (72%) had their drug use confirmed by way of visible track marks or positive urine screens, and completed both VCT sessions; these individuals were enrolled in the study. One hundred twenty-one enrolled participants (61 VCT, 60 Safety Counts) were excluded from analysis due to having been jailed for more than five days in the month prior to either assessment. There were no significant differences in background characteristics by study arm among those who were excluded due to incarceration. This exclusion resulted in a final sample of 1,116, with 558 in the VCT condition and 558 in the Safety Counts condition, as shown in Fig. 2.
Except where noted, all participants were included in our analyses; however, we did not have complete follow-up data. Across both study conditions, 241 eligible participants did not complete the follow-up within the window of five to nine months after the baseline interview (213 not returned at all, 28 returned too late). The follow-up rate was 80% among those assigned to VCT, and 76% among those receiving Safety Counts. Among those with timely follow-ups, the mean length of time from the baseline to follow-up interview was 186 days (SD = 36 days), about 18 days longer in the Safety Counts condition compared to the VCT condition.
We analyzed the differences between eligible participants who did and did not complete a follow-up on time (complete results available from the authors). Participants lost to follow-up were significantly more likely to be male (76 vs. 65%), White (43 vs. 27%), younger (36 vs. 39 years old; 20% under the age of 30 years vs. 12%), never married (46 vs. 37%), less educated (high school education or less: 83 vs. 74%), or homeless (51 vs. 40%). They were more likely to use both crack and injection drugs, in contrast to only one or the other substance (44 vs. 33%), to have had a positive urine screen for opiates (60 vs. 44%), and to have injected more times in the last 30 days (61 vs. 42 times). Sexual behavior, HIV status, crack use, sharing needles, and employment did not differ significantly between those analyzed and those lost to follow-up. If a significant difference emerged on sociodemographic characteristics, we compared those without follow-ups between the VCT and Safety Counts conditions. There were no significant differences between these two groups.
Table 2 summarizes the baseline demographic and risk behavior profiles of the participants in the Safety Counts and the VCT conditions. Similar across conditions, participants were mostly male (67%); unpartnered (74%); predominantly African American (44%), White (30%), and Latino (20%); and had a mean age of 38 years old (range 18–65 years old). Most had a high school education or less (76%), 43% were homeless, and 4% were identified as HIV+, again, similar across conditions. Overall, 88% were unemployed, with slightly more in the VCT condition than in Safety Counts (90 vs. 85%, P < 0.05).
Table 2
Baseline characteristics of the sample grouped by intervention condition
Risk behaviors at baseline were similar across conditions with one exception: the number of times using crack was higher (47 vs. 39 times in the last 30 days) among the Safety Counts participants in contrast to those in the VCT condition. There were three subgroups of drug users: 30% were injectors only, 35% used only crack, and 35% were both heroin injectors and crack users. On average, 28% of IDU used dirty works (needles/syringes) during the last month; participants used dirty works about 4.9 times in the last month, but there was a large variance on the number of uses of a dirty needle.
Positive urinalysis results for cocaine were found at baseline among 85% of participants and 47% were positive for heroin. About 70% were sexually active in the last 30 days, with a mean of 10 sexual acts; only 25% of sex acts were protected by condoms. Among those who were sexually active, 66% had never used a condom in the past month.
Sex and Drug Outcomes Across Intervention Conditions Over Time
Logistic regressions were used to examine the effect of the Safety Counts intervention on the likelihood of stopping injection drug use among the 537 participants with follow-ups who were IDU at baseline, and on the likelihood of stopping use of crack among the 622 participants with follow-ups using crack at baseline [not shown]. Among baseline IDU in the Safety Counts condition, 31% (80/258) reported no injection drug use at follow-up, compared to 23% (63/279) among participants in VCT (estimated OR = 1.6, P = 0.02). Twenty-nine percent (89/302) of baseline crack users in the Safety Counts condition reported no crack use at follow-up, which was not significantly different from the 27% rate (85/320) found among baseline crack users in VCT.
Table 3 summarizes the rates of risk behaviors at baseline and follow-up, and provides the results of random effects analyses, modeling counts with a Poisson distribution, and using a logistic model for practicing safe sex and for having clean urinalysis results. The Safety Counts intervention resulted in significantly greater reductions in the numbers of sexual acts (ratio = 0.78, P < .001), unprotected sexual acts (ratio = 0.58, P < .001), times injecting (ratio = 0.93, P < .001), times injecting with dirty works (ratio = 0.51, P < .001), times using crack (ratio = 0.90, P < .001), and times using drugs other than alcohol or marijuana (ratio = 0.93, P < .001). On only one measure did those in the Safety Counts condition tend to show less reduction over time than those in the VCT condition: use of alcohol or marijuana (ratio = 1.03, P = 0.14). The logistic analyses of urinanalysis results and practicing safer sex did not show any significant differences between the Safety Counts and VCT conditions.
Table 3
Observed values at each assessment by safety counts or VCT
High Participation
Participants in Safety Counts who demonstrated high adherence to the intervention (participating in five or more intervention activities) showed greater improvement than did those who participated less. For those outcomes where there was a significant intervention effect in the intention-to-treat analyses, Table 4 reports the decreases in risky behaviors among those with low participation in Safety Counts versus high participation. With the exception of number of sexual acts, in which those with less participation showed a slightly greater reduction, decreases in risky behaviors are, overall, markedly greater among those who have better attendance at intervention events.
Table 4
Decreases in risky behaviors from baseline to follow-up by level of participation in the Safety Counts program
Discussion
In contrast to the Hershberger et al. [28] analyses, we found that Safety Counts results in substantial reductions in both sex and drug use. Hard drug users, those actively using crack or injecting drugs in the past month, demonstrated significant reductions in sex and drug use risk acts after receiving the Safety Counts intervention. The effect sizes are relatively large and the greatest reductions are in the types of behavior most likely to result in HIV transmission: unprotected sexual acts and injection with dirty works. Almost all evidence-based HIV-related interventions have been based on a social cognitive approach to behavior change [19] in which change occurs slowly over time in small steps, in response to opportunity and social rewards. Drug users in the Safety Counts condition were much more likely to stop injecting than those in the VCT: 35% more stopped injecting completely. Abstinence from injecting is a substantial shift in behavior. In addition to stopping IDU, there were significantly greater reductions over time in the number of times injecting, injecting with dirty works, and the number of times of using crack in the last month (ratios = 0.51–0.93). These differences are important among serious drug users.
Simultaneous to stopping or reducing drug use, there were also significant reductions in the amount of sexual activity overall, and the number of unprotected sex acts. While only 4% of the participants were known to be HIV+ in the sample, many communities of IDU have much higher rates. Combined with the reductions in injection use, the reductions in sexual risk are also substantial (ratios = 0.58–0.78).
An important aspect of the program was its simultaneous focus both on personal tailoring and the community-level outreach. Counseling at the workshops and individual sessions were adapted to the individual’s risk background. However, the street outreach targeted a neighborhood, and the social activities were aimed at building social networks within the community. Many interventions have been labeled “individual,” “family,” or “community” level. Yet, each individual intervention has a site or institutional component, and every successful community-level intervention has also included training individuals to be more efficacious in their personal lives. Safety Counts targets hang-out spots in communities with high rates of drug use. Since the beginning of the HIV epidemic, geography has been destiny [9]; this intervention provides a packaged solution based on local epidemiology of HIV.
It is not possible to identify which are the components of the Safety Counts intervention most responsible for changing habitual risk behaviors. Skill-building workshops, goal setting in one-on-one counseling sessions, street contacts and social events were all included in Safety Counts. These are common elements in many evidence-based interventions [25, 29]. Future research must identify which components of the intervention are most effective in reducing the risk behaviors of active drug users.
Limitations
This study’s significant results rely on self-reports. No significant treatment effects were found in urine screens for heroin and cocaine. However, the metabolites of heroin and cocaine only represent a sample of use in the last three days. In addition, several significant intervention effects were found that involved reduction rather than elimination of risky behaviors; such reductions may not have been reflected in urinalysis results, which test for any use. Considerable effort was made to obtain reliable self-reports from participants at both the baseline and the follow-up assessments, and the interviewers and the street outreach workers were different.
The follow-up rate was 78%, an acceptable rate for such a high-risk target population, but not optimal. The participants who were lost to follow-up were even riskier than the participants that we were able to follow successfully and reassess. It is not clear whether this led to us assessing those most or least likely to change over time in response to the intervention.
One outcome measure differed significantly by treatment arm at baseline: times used crack. Safety Counts participants demonstrated both greater use at baseline (47 vs. 38 times used crack) and significantly greater improvement over the course of the study than did those in VCT only. Some of the greater improvement may be attributable to Safety Counts participants experiencing more regression to the mean, as they started out at higher baseline levels.
We chose to exclude those who were in jail for five or more days prior to either the baseline or the follow-up interview. Therefore, we define our population of interest to be the non-incarcerated. Because of similarities across intervention arm in the number and characteristics of those excluded, there is little bias anticipated due to exclusion of the incarcerated.
It is notable that this study was conducted in West Coast communities, neighborhoods with much larger geographic areas, less density than the drug neighborhoods along the East Coast of the United States that have much higher rates of HIV infection [3]. Safety Counts, available from the CDC (www.effectiveinterventions.org), may be useful in communities with street hang-outs that facilitate injection use by networks of persons at high-risk for HIV.
Street outreach is a community-level intervention program, with individually tailored components included within the community model. It would have been desirable to gather evaluation data at the level of the community rather than the individual. In particular, the social activities created an opportunity for community members to gather, to set norms that oppose drug use and unprotected sex, and to foster relationships that build community social capital. However, we evaluated the program at the level of the individual participants. Given that there were only two sites, we could not use site as the unit of analysis.
It is noteworthy that the results of these analyses are different from those presented in the 2003 paper of Hershberger and colleagues. While that analysis found some significant intervention effects, it was far fewer than the current analytic strategy demonstrated. The primary differences between our findings and theirs are attributable to the choice of inclusion criteria, outcome measures, and statistical methods. The contrasting findings point to the importance of the decisions made when conducting data analyses. This is problematic for researchers. It is seldom that the same data set is examined by more than one team. The fact that our findings differ points to the importance of taking more than one look at a given research question.
This study identifies an intervention approach that can result in successful reduction of drug use and sexual risk-taking in a targeted community, a key strategy for the next generation of preventive interventions. It is being broadly implemented through the CDC DEBI programs currently. The next generation of outcome analyses will indicate whether the benefits are sustained as Safety Counts is adopted at the local level.
Electronic supplementary material
Below is the link to the electronic supplementary material.
(DOC 52 kb)(53K, doc)
Acknowledgments
This paper was completed with the support of National Institute of Health grants # R18-DA05747, U01-DA07474, 1ROI MH49958-04 and P30 MH58107.
Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
References
1. Steele CB, Melendez-Morales L, Campoluci R, DeLuca N, Dean HD. Health disparities in HIV/AIDS, viral hepatitis, sexually transmitted diseases, and tuberculosis in the United States: issues, burden, and response, a retrospective review, 2000–2004. Atlanta: Department of Health and Human Services, Centers for Disease Control and Prevention; 2007.
2. Mathers BM, Degenhardt L, Phillips B, et al. Global epidemiology of injecting drug use and HIV among people who inject drugs: a systematic review. Lancet. 2008;372:1733–1745. doi: 10.1016/S0140-6736(08)61311-2.
3. Centers for Disease Control. Morbidity and mortality weekly report, October 3, 2008: HIV prevalence estimates—United States. 2006. Available at: www.cdc.gov/mmwr/preview/mmwrhtml/mm5739a2.htm. Accessed 23 Dec 2008.
4. Substance Abuse and Mental Health Services Administration, Office of Applied Studies. Results from the 2007 National Survey on Drug Use and Health: National findings. Rockville. 2008. [NSUDH Series H-34, DHHS Publication No. SMA 08-4343, Table G.3].
5. Edlin BR, Irwin KL, Faruque S, et al. Intersecting epidemics—Crack cocaine use and HIV infection among inner-city young adults. NEJM. 1994;331(21):1422–1427. doi: 10.1056/NEJM199411243312106.
6. Des Jarlais DC, Dehne K, Casabona J. HIV surveillance among injecting drug users. AIDS. 2001;15(suppl 3):S13–S22. doi: 10.1097/00002030-200104003-00003.
7. Strathdee SA, Patterson TL. Behavioral interventions for HIV-positive and HCV-positive drug users. AIDS Behav. 2006;10(2):115–130. doi: 10.1007/s10461-005-9055-5.
8. CDC AIDS Community Demonstration Projects Research Group Community-level HIV intervention in 5 cities: final outcome data from the CDC AIDS Community Demonstration Projects. Am J Public Health. 1999;89(3):336–345. doi: 10.2105/AJPH.89.3.336.
9. Rotheram-Borus MJ, Koopman C. HIV and adolescents. J Prim Prev. 1991;2(1):65–82. doi: 10.1007/BF01326542.
10. Bloom HS, editor. Learning more from social experiments: evolving analytic approaches. New York: Sage; 2006.
11. Murdoch J. Post-structuralist geography: a guide to relational space. London: Sage; 2006.
12. Friedman SR, Curtis R, Neaigus A, Jose B, Des Jarlais DC. Social networks, drug injectors’ lives, and HIV/AIDS. New York: Springer Publications; 1999.
13. Latkin C, Mandell W, Oziemkowska M, et al. Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS. Drug Alcohol Depend. 1995;38:1–9. doi: 10.1016/0376-8716(94)01082-V.
14. Christakis NA, Fowler JH. The collective dynamics of smoking in a large social network. NEJM. 2008;358:2249–2258. doi: 10.1056/NEJMsa0706154.
15. Murray DM, McKinlay SM, Martin D, et al. Design and analysis issues in community trials. Eval Rev. 1994;18(4):493–514. doi: 10.1177/0193841X9401800407.
16. Boruch RF, Foley E. The honestly experimental society: using organization and other entities as units in randomized experiments. In: Bickman L, editor. Donald F. Campbell’s legacy. Thousand Oaks: Sage; 2000.
17. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51:390–395. doi: 10.1037/0022-006X.51.3.390.
18. Prochaska JO, DiClemente CC. Toward a comprehensive model of change. In: Miller WR, Heather N, editors. Treating addictive behaviors: processes of change. New York: Plenum Press; 1986.
19. Bandura A. Social foundations of thought and action: a social cognitive theory. Englewood Cliffs: Prentice Hall; 1986.
20. Becker MH. The health belief model and personal health behavior. Health Educ Monogr. 1974;2:324–508.
21. Maddux JE, Rogers RW. Protection motivation and self-efficacy: a revised theory of fear appeals and attitude change. J Exp Soc Psychol. 1983;19:469–479. doi: 10.1016/0022-1031(83)90023-9.
22. Safety counts: a cognitive-behavioral intervention to reduce HIV/Hepatitis risks among drug users who are not in drug treatment—program manual. Washington, DC: Academy for Educational Development; 2003.
23. Rhodes F, Humfleet GL. Using goal-oriented counseling and peer support to reduce HIV/AIDS risk among drug users not in treatment. Drugs Soc. 1993;7:185–204. doi: 10.1300/J023v07n03_13.
24. Coyle S. The NIDA HIV counseling and education intervention model: intervention manual (NIH Pub. No. 93-3508) Rockville: National Institute on Drug Abuse; 1993.
25. Rotheram-Borus MJ, Swendeman D, Flannery D, Rice E, Adamson DM, Ingram B. Common factors in effective HIV prevention programs. AIDS Behav. 2009;13(3):399–408. doi: 10.1007/s10461-008-9464-3.
26. Varian, Inc. Diagnostic products. 2009. Available at: http://www.varianinc.com/cgi-bin/nav?products/dat/index&cid=KMKPIHHHFJ. Accessed 5 Feb 2009.
27. Lunn DJ, Whittaker JC, Best N. A Bayesian toolkit for genetic association studies. Genet Epidemiol. 2006;30(3):231–247. doi: 10.1002/gepi.20140.
28. Hershberger S, Wood M, Fisher D. A cognitive-behavioral intervention to reduce HIV risk behaviors in crack and injection drug users. AIDS Behav. 2003;7(3):229–243. doi: 10.1023/A:1025487501743.
29. Ingram BL, Flannery D, Elkavich A, Rotheram-Borus MJ. Common processes in evidence-based adolescent HIV prevention programs. AIDS Behav. 2008;12(3):374–383. doi: 10.1007/s10461-008-9369-1.
Source
Chronic and cleared hepatitis C viral infection: A nationwide cohort study
Download the PDF here
Jnl of Hepatology July 2010
"Five-year survival was 86% (95% CI: 84-87%) among patients in the chronic HCV group and 92% (95% CI: 91-94%) among those in the cleared HCV group (Fig. 1)...... In HCV RNA positive patients, the 8-year risks of death were: 5.5% from liver-related death, 5.5% from non-liver-related natural death, 8.8% from unnatural death, and 0.8% from other death. In HCV RNA negative patients these estimates were 2.0%, for liver-related death, 5.0% for non-liver-related natural death, 6.6% for unnatural death, and 0.2% for other death (Fig. 2).....Chronic HCV-infection was primarily associated with liver-related death (SDHR: 2.42, 95% CI: 1.51-3.88)"
Lars Haukali Omland 1 Corresponding Author Information email address, Henrik Krarup 2, Peter Jepsen 3, Jorgen Georgsen 4, Lene Holm Harritshoj 5, Kirsten Riisom 6, Svend Erik Hove Jacobsen 7, Per Schouenborg 8, Peer Brehm Christensen 9, Henrik Toft Sorensen 310, Niels Obe l1, On behalf of the DANVIR Cohort Study
ABSTRACT
Background & Aims
It is unknown whether mortality differs between patients with chronic hepatitis C virus (HCV) replication and those who cleared the virus after infection. We examined the impact of chronic HCV replication on mortality among Danish patients testing positive for HCV antibodies.
Methods
This nationwide cohort study focused on Danish patients with at least one HCV RNA measurement available after testing positive for HCV antibodies between 1996 and 2005. To capture long-term prognosis, eligible patients needed to be alive 1year after HCV RNA assessment. We estimated mortality rate ratios (MRRs) using Cox regression (for overall mortality) and subdistribution hazard ratios (SDHRs) for cause-specific mortality, controlling for gender, age, comorbidity, calendar period, alcohol abuse, injection drug use, and income.
Results
Of the 6292 patients under study, 63% had chronic HCV-infection and 37% had cleared the virus. Five-year survival was 86% (95% confidence interval (CI): 84-87%) in the chronic HCV group and 92% (95% CI: 91-94%) in the cleared HCV group. Chronic HCV-infection was associated with higher overall mortality (MRR: 1.55, 95% CI: 1.28-1.86) and liver-related death (SDHR: 2.42, 95% CI: 1.51-3.88). Chronic HCV-infection greatly increased the risk of death from primary liver cancer (SDHR: 16.47, 95% CI: 2.24-121.00).
Conclusions
Patients with chronic HCV-infection are at higher risk of death than patients who cleared the infection. The substantial association found between chronic HCV-infection and death from primary liver cancer supports early initiation of antiviral treatment in chronically HCV-infected patients.
Results
From the DANVIR cohort we identified 13,005 patients diagnosed with HCV, of whom 6292 met the studyÕs inclusion criteria. Of these, 3969 patients (63%) were classified as chronically HCV-infected and 2323 (37%) as having cleared the infection. Compared to patients in the cleared group, patients with chronic HCV-infection were more likely to be male, and they also were older and had lower income, more hospitalizations, and a higher prevalence of non-HCV-related liver disease (Table 1).
Overall mortality
During 23,648 person-years of observation (PYR), a total of 601 patients died (MR: 25.4/1000 PYR, 95% CI: 23.5-27.5) with 448 deaths in the chronic group and 153 deaths in the cleared group. Five-year survival was 86% (95% CI: 84-87%) among patients in the chronic HCV group and 92% (95% CI: 91-94%) among those in the cleared HCV group (Fig. 1). The adjusted MRR was 1.55 (95% CI: 1.28-1.86). Chronic HCV-infection was associated with increased mortality in most subgroups, except among patients with severe comorbidity (Table 2). Restricting the cohort to patients whose positive HCV antibody test was confirmed by a 3rd generation diagnostic test prior to HCV RNA measurement (n=2753) did not change the estimated association between chronic HCV-infection and mortality (data not shown).
Specific causes of death
In HCV RNA positive patients, the 8-year risks of death were: 5.5% from liver-related death, 5.5% from non-liver-related natural death, 8.8% from unnatural death, and 0.8% from other death. In HCV RNA negative patients these estimates were 2.0%, for liver-related death, 5.0% for non-liver-related natural death, 6.6% for unnatural death, and 0.2% for other death (Fig. 2). The risk of death other than liver-related death (i.e. non-liver-related death, unnatural death and other death) thereby far exceeded the risk of liver-related death for both HCV RNA positive and negative patients (15.1% vs. 5.5% and 11.8% vs. 2.0%, respectively). The corresponding causes for specific MRs are provided in Supplementary Table 1.
Chronic HCV-infection was primarily associated with liver-related death (SDHR: 2.42, 95% CI: 1.51-3.88), and to some extent with non-liver-related natural causes of death (SDHR: 1.24, 95% CI: 0.91-1.71) and unnatural causes of death (SDHR: 1.28, 95% CI: 0.97-1.69). In the non-liver-related natural death category, none of the detailed causes of death were notably associated with chronic HCV-infection (Table 3). Except for primary liver cancer, there was no substantially increased risk of death due to neoplasms (SDHR: 1.28, 95% CI: 0.65-2.54).
Of the liver-related deaths, death due to alcoholic liver disease was the most frequent (2.3% vs. 1.4% after 8years of follow-up for patients with chronic vs. cleared HCV-infection). Chronic HCV-infection was substantially associated with death from primary liver cancer (SDHR: 16.47, 95% CI: 2.24-121). However, death from primary liver cancer was rather infrequent (28 events vs. 1 event for patients with chronic vs. cleared HCV-infection, corresponding to an 8-year risk of 1.4% in patients with chronic HCV-infection and of 0.0% in patients with cleared HCV-infection) (Fig. 3). There were no deaths due to oesophageal or gastric varices.
Discussion
We observed an increased mortality among patients with chronic HCV-infection compared to patients with cleared infection, based on HCV RNA testing. This effect was observed in all patient subgroups except in those with severe comorbidity. Chronic HCV-infection was associated with liver-related mortality, and in particular death from primary liver cancer. However, the risk of deaths other than liver-related deaths by far exceeded the risk of liver-related deaths in both HCV RNA positive and HCV RNA negative patients. To our knowledge, no previous study has addressed the impact of chronic HCV replication on mortality in an equivalent nationwide setting with a long and complete follow-up and with an extensive control of confounders.
Our study has several limitations. We had access to the exact date of HCV diagnosis, but not the date of HCV-infection [6]. For a substantial proportion of study participants, HCV-infection could have preceded study inclusion by several years, since most HCV-infections occur subclinically [6]. Thus patients in the chronic group could have had more liver damage at the time of study inclusion than patients in the cleared group. We did not have access to liver biopsies or liver function tests, so we could not directly address this question. More patients in the chronic HCV group than in the cleared group were diagnosed with liver diseases other than HCV. However, we were able to demonstrate that chronic HCV-infection was associated with mortality in patients both with and without pre-existing liver diseases, which indicates that severity of liver disease did not explain our findings. Our analyses did not account for spontaneous or treatment-related viral clearance nor HCV re-infection during follow-up. Most patients are IDUs, and probably as a result, regular testing for HCV RNA subsequent to an initial diagnosis is not performed systematically in Denmark. Modelling HCV viraemia as a time-updated variable thus was not possible in this study. However, spontaneous clearance of HCV-infection subsequent to the initial acute phase of the disease occurs infrequently [25] and only a minority of Danish patients receive antiviral treatment [11]. Finally, despite the large study population and long-term follow up, our study had too small power to make statistically significant estimates for most of the detailed categories of causes of death.
Patients with chronic HCV-infection were at an increased risk of liver-related death, with the strongest association observed between chronic HCV-infection and primary liver cancer. This information is important, and suggests that clearance of the virus almost eliminates the risk of developing primary liver cancer, thus confirming the potential benefit of antiviral treatment. However, one patient in the cleared group developed primary liver cancer. This observation agrees with recent findings of cases of hepatocellular carcinoma in long-term viral suppression responders [26]. These data suggest that clearance of the virus substantially decreases but not fully eliminates the risk of primary liver cancer. Chronic HCV-infection was also associated with other liver-related causes of death (viral hepatitis, alcoholic liver disease and non-alcoholic liver disease), also emphasising the potential for antiviral treatment.
The associations between chronic HCV-infection and non-liver-related natural deaths, unnatural deaths and other deaths diminished when we adjusted for confounders. However, we cannot exclude the possibility of unmeasured or residual confounding. The fact that patients with chronic HCV-infection were at increased risk of unnatural deaths (and to some extent death due to infections) indicates certain risk-taking behaviour in this group. As we were unable to adjust for this factor in our models, this could have resulted in unmeasured confounding. We find it likely that the associations found between chronic HCV-infection and non-liver-related natural deaths, unnatural deaths, and other deaths result from unmeasured confounding. In particular, from more injection drug use among chronically HCV-infected patients than among patients who cleared the virus.
The Trent HCV Cohort Study examined predictors of survival among HCV-infected patients treated in secondary care centres. That study, unlike ours, reported no substantial association between HCV RNA positive status (compared to HCV RNA negative status) and an increased all-cause mortality (MRR: 1.1, 95% CI 0.7-1.8) [5]. These inconsistent findings might be a result of lack of precision in the Trent study, which included only 157 deaths in the HCV RNA positive group and 21 deaths in the HCV negative group. More likely, however, these inconsistencies stem from differences in the study populations, as the Trent HCV Cohort only included patients from referral sites, while our study included nearly all patients tested for HCV RNA in Denmark. The patients in the Trent study therefore may have been at a more advanced stage of their liver disease and may have had more comorbidity. In that case, results for the Trent HCV Cohort should be compared to results for the most diseased subgroup of our study population. In fact, we did not observe a substantial impact of chronic HCV-infection among patients with a high comorbidity index, those with alcohol abuse or those who had been hospitalized recently. In a previous study from our group focusing on Danish HIV-infected IDUs with a high level of comorbidity, we also observed no association between chronic vs. cleared HCV-infection and mortality [27]. These findings suggest that chronic HCV-infection, compared to cleared HCV-infection, is associated with increased mortality in most patient groups. However, in high-risk study populations characterised by substantial mortality, the relative impact of chronic HCV-infection is limited.
We conclude that based on HCV RNA assessment, patients with chronic HCV-infection have higher mortality and, in particular, a higher risk of liver-related death than patients who cleared the virus. The pronounced association between chronic HCV-infection and death from primary liver cancer provides a rationale for antiviral treatment in chronically HCV-infected patients. However, our data also underline the importance of a balanced decision, as subgroups characterised by substantial mortality probably have less potential for a treatment benefit.
Source
Jnl of Hepatology July 2010
"Five-year survival was 86% (95% CI: 84-87%) among patients in the chronic HCV group and 92% (95% CI: 91-94%) among those in the cleared HCV group (Fig. 1)...... In HCV RNA positive patients, the 8-year risks of death were: 5.5% from liver-related death, 5.5% from non-liver-related natural death, 8.8% from unnatural death, and 0.8% from other death. In HCV RNA negative patients these estimates were 2.0%, for liver-related death, 5.0% for non-liver-related natural death, 6.6% for unnatural death, and 0.2% for other death (Fig. 2).....Chronic HCV-infection was primarily associated with liver-related death (SDHR: 2.42, 95% CI: 1.51-3.88)"
Lars Haukali Omland 1 Corresponding Author Information email address, Henrik Krarup 2, Peter Jepsen 3, Jorgen Georgsen 4, Lene Holm Harritshoj 5, Kirsten Riisom 6, Svend Erik Hove Jacobsen 7, Per Schouenborg 8, Peer Brehm Christensen 9, Henrik Toft Sorensen 310, Niels Obe l1, On behalf of the DANVIR Cohort Study
ABSTRACT
Background & Aims
It is unknown whether mortality differs between patients with chronic hepatitis C virus (HCV) replication and those who cleared the virus after infection. We examined the impact of chronic HCV replication on mortality among Danish patients testing positive for HCV antibodies.
Methods
This nationwide cohort study focused on Danish patients with at least one HCV RNA measurement available after testing positive for HCV antibodies between 1996 and 2005. To capture long-term prognosis, eligible patients needed to be alive 1year after HCV RNA assessment. We estimated mortality rate ratios (MRRs) using Cox regression (for overall mortality) and subdistribution hazard ratios (SDHRs) for cause-specific mortality, controlling for gender, age, comorbidity, calendar period, alcohol abuse, injection drug use, and income.
Results
Of the 6292 patients under study, 63% had chronic HCV-infection and 37% had cleared the virus. Five-year survival was 86% (95% confidence interval (CI): 84-87%) in the chronic HCV group and 92% (95% CI: 91-94%) in the cleared HCV group. Chronic HCV-infection was associated with higher overall mortality (MRR: 1.55, 95% CI: 1.28-1.86) and liver-related death (SDHR: 2.42, 95% CI: 1.51-3.88). Chronic HCV-infection greatly increased the risk of death from primary liver cancer (SDHR: 16.47, 95% CI: 2.24-121.00).
Conclusions
Patients with chronic HCV-infection are at higher risk of death than patients who cleared the infection. The substantial association found between chronic HCV-infection and death from primary liver cancer supports early initiation of antiviral treatment in chronically HCV-infected patients.
Results
From the DANVIR cohort we identified 13,005 patients diagnosed with HCV, of whom 6292 met the studyÕs inclusion criteria. Of these, 3969 patients (63%) were classified as chronically HCV-infected and 2323 (37%) as having cleared the infection. Compared to patients in the cleared group, patients with chronic HCV-infection were more likely to be male, and they also were older and had lower income, more hospitalizations, and a higher prevalence of non-HCV-related liver disease (Table 1).
Overall mortality
During 23,648 person-years of observation (PYR), a total of 601 patients died (MR: 25.4/1000 PYR, 95% CI: 23.5-27.5) with 448 deaths in the chronic group and 153 deaths in the cleared group. Five-year survival was 86% (95% CI: 84-87%) among patients in the chronic HCV group and 92% (95% CI: 91-94%) among those in the cleared HCV group (Fig. 1). The adjusted MRR was 1.55 (95% CI: 1.28-1.86). Chronic HCV-infection was associated with increased mortality in most subgroups, except among patients with severe comorbidity (Table 2). Restricting the cohort to patients whose positive HCV antibody test was confirmed by a 3rd generation diagnostic test prior to HCV RNA measurement (n=2753) did not change the estimated association between chronic HCV-infection and mortality (data not shown).
Specific causes of death
In HCV RNA positive patients, the 8-year risks of death were: 5.5% from liver-related death, 5.5% from non-liver-related natural death, 8.8% from unnatural death, and 0.8% from other death. In HCV RNA negative patients these estimates were 2.0%, for liver-related death, 5.0% for non-liver-related natural death, 6.6% for unnatural death, and 0.2% for other death (Fig. 2). The risk of death other than liver-related death (i.e. non-liver-related death, unnatural death and other death) thereby far exceeded the risk of liver-related death for both HCV RNA positive and negative patients (15.1% vs. 5.5% and 11.8% vs. 2.0%, respectively). The corresponding causes for specific MRs are provided in Supplementary Table 1.
Chronic HCV-infection was primarily associated with liver-related death (SDHR: 2.42, 95% CI: 1.51-3.88), and to some extent with non-liver-related natural causes of death (SDHR: 1.24, 95% CI: 0.91-1.71) and unnatural causes of death (SDHR: 1.28, 95% CI: 0.97-1.69). In the non-liver-related natural death category, none of the detailed causes of death were notably associated with chronic HCV-infection (Table 3). Except for primary liver cancer, there was no substantially increased risk of death due to neoplasms (SDHR: 1.28, 95% CI: 0.65-2.54).
Of the liver-related deaths, death due to alcoholic liver disease was the most frequent (2.3% vs. 1.4% after 8years of follow-up for patients with chronic vs. cleared HCV-infection). Chronic HCV-infection was substantially associated with death from primary liver cancer (SDHR: 16.47, 95% CI: 2.24-121). However, death from primary liver cancer was rather infrequent (28 events vs. 1 event for patients with chronic vs. cleared HCV-infection, corresponding to an 8-year risk of 1.4% in patients with chronic HCV-infection and of 0.0% in patients with cleared HCV-infection) (Fig. 3). There were no deaths due to oesophageal or gastric varices.
Discussion
We observed an increased mortality among patients with chronic HCV-infection compared to patients with cleared infection, based on HCV RNA testing. This effect was observed in all patient subgroups except in those with severe comorbidity. Chronic HCV-infection was associated with liver-related mortality, and in particular death from primary liver cancer. However, the risk of deaths other than liver-related deaths by far exceeded the risk of liver-related deaths in both HCV RNA positive and HCV RNA negative patients. To our knowledge, no previous study has addressed the impact of chronic HCV replication on mortality in an equivalent nationwide setting with a long and complete follow-up and with an extensive control of confounders.
Our study has several limitations. We had access to the exact date of HCV diagnosis, but not the date of HCV-infection [6]. For a substantial proportion of study participants, HCV-infection could have preceded study inclusion by several years, since most HCV-infections occur subclinically [6]. Thus patients in the chronic group could have had more liver damage at the time of study inclusion than patients in the cleared group. We did not have access to liver biopsies or liver function tests, so we could not directly address this question. More patients in the chronic HCV group than in the cleared group were diagnosed with liver diseases other than HCV. However, we were able to demonstrate that chronic HCV-infection was associated with mortality in patients both with and without pre-existing liver diseases, which indicates that severity of liver disease did not explain our findings. Our analyses did not account for spontaneous or treatment-related viral clearance nor HCV re-infection during follow-up. Most patients are IDUs, and probably as a result, regular testing for HCV RNA subsequent to an initial diagnosis is not performed systematically in Denmark. Modelling HCV viraemia as a time-updated variable thus was not possible in this study. However, spontaneous clearance of HCV-infection subsequent to the initial acute phase of the disease occurs infrequently [25] and only a minority of Danish patients receive antiviral treatment [11]. Finally, despite the large study population and long-term follow up, our study had too small power to make statistically significant estimates for most of the detailed categories of causes of death.
Patients with chronic HCV-infection were at an increased risk of liver-related death, with the strongest association observed between chronic HCV-infection and primary liver cancer. This information is important, and suggests that clearance of the virus almost eliminates the risk of developing primary liver cancer, thus confirming the potential benefit of antiviral treatment. However, one patient in the cleared group developed primary liver cancer. This observation agrees with recent findings of cases of hepatocellular carcinoma in long-term viral suppression responders [26]. These data suggest that clearance of the virus substantially decreases but not fully eliminates the risk of primary liver cancer. Chronic HCV-infection was also associated with other liver-related causes of death (viral hepatitis, alcoholic liver disease and non-alcoholic liver disease), also emphasising the potential for antiviral treatment.
The associations between chronic HCV-infection and non-liver-related natural deaths, unnatural deaths and other deaths diminished when we adjusted for confounders. However, we cannot exclude the possibility of unmeasured or residual confounding. The fact that patients with chronic HCV-infection were at increased risk of unnatural deaths (and to some extent death due to infections) indicates certain risk-taking behaviour in this group. As we were unable to adjust for this factor in our models, this could have resulted in unmeasured confounding. We find it likely that the associations found between chronic HCV-infection and non-liver-related natural deaths, unnatural deaths, and other deaths result from unmeasured confounding. In particular, from more injection drug use among chronically HCV-infected patients than among patients who cleared the virus.
The Trent HCV Cohort Study examined predictors of survival among HCV-infected patients treated in secondary care centres. That study, unlike ours, reported no substantial association between HCV RNA positive status (compared to HCV RNA negative status) and an increased all-cause mortality (MRR: 1.1, 95% CI 0.7-1.8) [5]. These inconsistent findings might be a result of lack of precision in the Trent study, which included only 157 deaths in the HCV RNA positive group and 21 deaths in the HCV negative group. More likely, however, these inconsistencies stem from differences in the study populations, as the Trent HCV Cohort only included patients from referral sites, while our study included nearly all patients tested for HCV RNA in Denmark. The patients in the Trent study therefore may have been at a more advanced stage of their liver disease and may have had more comorbidity. In that case, results for the Trent HCV Cohort should be compared to results for the most diseased subgroup of our study population. In fact, we did not observe a substantial impact of chronic HCV-infection among patients with a high comorbidity index, those with alcohol abuse or those who had been hospitalized recently. In a previous study from our group focusing on Danish HIV-infected IDUs with a high level of comorbidity, we also observed no association between chronic vs. cleared HCV-infection and mortality [27]. These findings suggest that chronic HCV-infection, compared to cleared HCV-infection, is associated with increased mortality in most patient groups. However, in high-risk study populations characterised by substantial mortality, the relative impact of chronic HCV-infection is limited.
We conclude that based on HCV RNA assessment, patients with chronic HCV-infection have higher mortality and, in particular, a higher risk of liver-related death than patients who cleared the virus. The pronounced association between chronic HCV-infection and death from primary liver cancer provides a rationale for antiviral treatment in chronically HCV-infected patients. However, our data also underline the importance of a balanced decision, as subgroups characterised by substantial mortality probably have less potential for a treatment benefit.
Source
Health Law Requires Continued Coverage For Patients In Clinical Trials
By Michelle Andrews
Aug 24, 2010
When Richard Crusoe was diagnosed with a rare form of soft tissue cancer called liposarcoma, the retired firefighter and his family pinned their hopes of slowing the cancer's advance on a drug that was being tested in a clinical trial.
Crusoe, then 57, was approved for the trial, and he and his wife flew from their home in Pembroke Pines, Fla., to the MD Anderson Cancer Center in Houston to get the treatment last September. But the day before he was supposed to begin the trial, researchers told the Crusoes that he wouldn't be able to participate after all. The reason: His health plan was refusing to cover his routine medical care during the trial.
The problem wasn't the costs of the clinical trial itself: The cancer center would pay to administer the drug and analyze the results. But if Crusoe participated in the trial, his health plan would stop covering all the other doctor visits, hospital stays, tests and treatment related to treating his cancer.
The Crusoes were stunned. They appealed to his former employer, the city of Pembroke Pines. Like many large employers, the city pays its workers' health claims directly rather than buying insurance. (Because such employers often use insurance companies to administer claims, workers may not realize that the payments are coming from their employer.) More than a month later, after the family enlisted the Patient Advocate Foundation to fight on its behalf, the city relented and granted $250,000 in coverage. But by that time Crusoe had become too weak to participate in the trial. He died a few weeks later.
Crusoe's widow, Debbie, still lives in Pembroke Pines. She says it's hard to pass City Hall every day. The city honored her husband for his firefighting work, she says, but "when it comes time to save his life, they just blocked it." Daniel Rotstein, the city's human resources director, declined to comment on the case.
The new federal health law will prevent such disputes, beginning in 2014. The law requires health plans to pay the routine care costs of patients who participate in clinical trials for the prevention, detection and treatment of cancer and other life-threatening conditions.
Routine patient care refers to the range of medical services people with a particular diagnosis might need. It includes treatment for side effects and other medical issues that might arise as a result of the trial.
Although Medicare and many private health plans already cover such costs, some plans decline to do so on the grounds that clinical trials are experimental, say experts. More than half of states require coverage of routine costs in a clinical trial, but state requirements vary. The new law sets a minimum standard.
Employers and insurers that decline to cover routine care in clinical trials are often concerned about their financial exposure. It's a legitimate concern, says Nancy Davenport-Ennis, founder and chief executive of the Patient Advocate Foundation. Patients in clinical trials are likely to have additional blood work, scans and tests, not to mention side effects that may be expensive to treat. But other plans view clinical trials in a different light. "They see it as a way to get better results at a better cost," says Davenport-Ennis.
The new law applies to all individual and group health plans, whether self-funded as at Pembroke Pines or fully insured. Plans that were in existence when the law was signed this spring and have "grandfathered" status under its provisions are exempt, but policy experts expect many of these plans to lose their special status over time.
Despite cases like Richard Crusoe's, the insurance industry generally supports coverage of routine care costs, up to a point. Clinical trials are conducted in four phases, adding more patients in each round; they are intended to answer different questions about safety, efficacy, side effects and the like. The new law covers care in all phases.
The industry supports coverage of routine care costs in late-phase clinical trials, says Susan Pisano, a spokeswoman for America's Health Insurance Plans, a trade group. However, it has concerns about covering costs during early-phase trials, she says, when researchers may be evaluating whether a drug is safe rather than testing its therapeutic value.
In addition to helping individuals get potentially life-saving treatment, advocates hope the new law will encourage broader participation in clinical trials, which are essential to developing new drugs and therapies. Nearly 20 percent of cancer patients are eligible for cancer clinical trials, for example, but fewer than 5 percent enroll, according to the American Cancer Society's Cancer Action Network. "Even the perception that costs might not be covered is enough to prevent patients from considering it," says Rebecca Kirch, the network's associate director of policy.
Source
Aug 24, 2010
When Richard Crusoe was diagnosed with a rare form of soft tissue cancer called liposarcoma, the retired firefighter and his family pinned their hopes of slowing the cancer's advance on a drug that was being tested in a clinical trial.
Crusoe, then 57, was approved for the trial, and he and his wife flew from their home in Pembroke Pines, Fla., to the MD Anderson Cancer Center in Houston to get the treatment last September. But the day before he was supposed to begin the trial, researchers told the Crusoes that he wouldn't be able to participate after all. The reason: His health plan was refusing to cover his routine medical care during the trial.
The problem wasn't the costs of the clinical trial itself: The cancer center would pay to administer the drug and analyze the results. But if Crusoe participated in the trial, his health plan would stop covering all the other doctor visits, hospital stays, tests and treatment related to treating his cancer.
The Crusoes were stunned. They appealed to his former employer, the city of Pembroke Pines. Like many large employers, the city pays its workers' health claims directly rather than buying insurance. (Because such employers often use insurance companies to administer claims, workers may not realize that the payments are coming from their employer.) More than a month later, after the family enlisted the Patient Advocate Foundation to fight on its behalf, the city relented and granted $250,000 in coverage. But by that time Crusoe had become too weak to participate in the trial. He died a few weeks later.
Crusoe's widow, Debbie, still lives in Pembroke Pines. She says it's hard to pass City Hall every day. The city honored her husband for his firefighting work, she says, but "when it comes time to save his life, they just blocked it." Daniel Rotstein, the city's human resources director, declined to comment on the case.
The new federal health law will prevent such disputes, beginning in 2014. The law requires health plans to pay the routine care costs of patients who participate in clinical trials for the prevention, detection and treatment of cancer and other life-threatening conditions.
Routine patient care refers to the range of medical services people with a particular diagnosis might need. It includes treatment for side effects and other medical issues that might arise as a result of the trial.
Although Medicare and many private health plans already cover such costs, some plans decline to do so on the grounds that clinical trials are experimental, say experts. More than half of states require coverage of routine costs in a clinical trial, but state requirements vary. The new law sets a minimum standard.
Employers and insurers that decline to cover routine care in clinical trials are often concerned about their financial exposure. It's a legitimate concern, says Nancy Davenport-Ennis, founder and chief executive of the Patient Advocate Foundation. Patients in clinical trials are likely to have additional blood work, scans and tests, not to mention side effects that may be expensive to treat. But other plans view clinical trials in a different light. "They see it as a way to get better results at a better cost," says Davenport-Ennis.
The new law applies to all individual and group health plans, whether self-funded as at Pembroke Pines or fully insured. Plans that were in existence when the law was signed this spring and have "grandfathered" status under its provisions are exempt, but policy experts expect many of these plans to lose their special status over time.
Despite cases like Richard Crusoe's, the insurance industry generally supports coverage of routine care costs, up to a point. Clinical trials are conducted in four phases, adding more patients in each round; they are intended to answer different questions about safety, efficacy, side effects and the like. The new law covers care in all phases.
The industry supports coverage of routine care costs in late-phase clinical trials, says Susan Pisano, a spokeswoman for America's Health Insurance Plans, a trade group. However, it has concerns about covering costs during early-phase trials, she says, when researchers may be evaluating whether a drug is safe rather than testing its therapeutic value.
In addition to helping individuals get potentially life-saving treatment, advocates hope the new law will encourage broader participation in clinical trials, which are essential to developing new drugs and therapies. Nearly 20 percent of cancer patients are eligible for cancer clinical trials, for example, but fewer than 5 percent enroll, according to the American Cancer Society's Cancer Action Network. "Even the perception that costs might not be covered is enough to prevent patients from considering it," says Rebecca Kirch, the network's associate director of policy.
Source
Bloodborne Pathogens and Their Threat to School Safety
23.08.2010 Author: Jim Nulsen
Posted in Health & Fitness
Exposure to bloodborne pathogens could happen on any school campus, at any time, and on any given day. Consider for a moment how often school employees encounter students that have suffered a bloody nose during class. Or how frequently kids on the playground sustain cuts, scrapes, and other bleeding injuries. Also, students becoming ill and vomiting in the classroom is unfortunately not uncommon on school grounds as well. It may not be the most glamorous part of a school employee’s job, but it’s vital that school staff learn and understand how to properly handle bloodborne pathogen exposures to aid others while protecting themselves. Bloodborne pathogens safety training is one of the most crucial parts of a school staff member’s job, which is why it’s an annual school safety training requirement for most school district employees across the country.
Any human bodily fluid containing blood can carry BBPs. School personnel can be at risk when:
>>> providing first aid
>>> intervening in a student fight or
>>> cleaning up bodily fluids
…all common activities in a school community. That’s why all employees – custodians, teachers, and administrators – should be familiar with proper BBP procedures. Even if you are not required to handle bodily fluids as part of your job, you need to be prepared.
Bloodborne pathogens, which are commonly referred to as “BBPs,” can be bacterial (i.e. staph or strep) or viruses (i.e. flu, colds, hepatitis A, B, or C, and HIV). BBPs are present in blood and other bodily fluids, and can be transmitted when blood or body fluid from an infected person enters another person’s body through cuts, abrasions, or body cavities (such as the mouth, eyes, or nose). And while, according to a report from the Centers for Disease Control and Prevention (CDC), there are 300,000 new cases of BBP exposure reported each year, the greatest risk to school personnel is from the hepatitis B virus. It is particularly crucial to stress protection and prevention in regards to hepatitis B, as the signs and symptoms of the virus may not manifest for a long time – often weeks or months. Hepatitis B causes liver inflammation, vomiting, jaundice and sometimes death. However, chronic hepatitis B may eventually cause liver cirrhosis and liver cancer, a fatal disease with very poor response to current chemotherapy treatment. There is no “cure” or specific treatment for hepatitis B, but many people who contract the disease will develop antibodies, which help them get over the infection and protect them from getting it again. It is important to note, however, that there are different kinds of hepatitis, so infection with Hepatitis B will not stop someone from getting another type.
Similarly, the CDC also estimates that there are as many as 3 million people currently infected with hepatitis C and most of them don’t even know it yet! Hepatitis C virus (HCV) infection is the most common chronic bloodborne infection in the United States. Most people with this virus are chronically infected and might not be aware of their infection because they are not clinically ill. Many patients with hepatitis C exhibit no symptoms prior to the development of liver cirrhosis. Those with symptoms exhibit fatigue, loss of appetite, joint and body aches, nausea and abdominal discomfort. That is why it is essential for K-12 staff to be diligent in protecting themselves from any BBP exposure.
No matter the position and its particular duties, the risk of encountering an accident involving bodily fluids on the job, such as blood, is always present for all school district employees. And any human bodily fluid containing blood can carry BBPs. School districts should have key prevention strategies in place such as providing BBP Training to all staff.
A key prevention strategy, therefore, is to always exercise universal precautions – a series of precautious measures designed to prevent against the transmission of bloodborne pathogens. A good example of universal precautions would be the use of personal protective equipment (PPE), such as disposable gloves, when handling blood or other bodily fluids.
SafeSchools, the nation’s leading provider of online safety training for schools, currently offers expertly-authored bloodborne pathogens health safety training courses for school employees: Bloodborne Pathogens: Full Course and Bloodborne Pathogens: Refresher Course. The Full Course version is meant to be taken once, while the Refresher version – a concise overview of the material that is updated annually – is meant to be taken every year thereafter.
A Refresher version of the SafeSchools Bloodborne Pathogens course is also available in Spanish. Each of the SafeSchools’ BBP courses is written by two of the nation’s leading experts on BBP, James Vaughan and Karl Sommers III. Mr. Vaughan is President of Today’s Resources, Inc. (TRI), a full-service safety and health-consulting firm. He also has direct OSHA compliance and loss-control experience. Karl Sommers III, a TRI associate, has experience as a safety director in a manufacturing environment and leadership duties within area professional safety organizations. SafeSchools’ bloodborne pathogens safety training address facts about the disease, give guidelines for the cleanup and handling of potentially infected bodily fluids, wastes, or contaminated materials, discusses the risk associated with school workers exposed to BBPs, and delivers training designed to emphasize the practices of proper exposure control. The courses contain specific important references to OSHA regulations.
The SafeSchools’ suite of BBP courses help school districts manage their school safety compliance which is essential to providing a safe learning environment for staff, students, and parents, alike.
For more information, please call 1-800-434-0154 or info@safeschools.com
Jim Nulsen, SafeSchools, a web based safety training and tracking system designed specifically for school employee training. A powerful library of expertly authored courses, combined with the SafeSchools Compliance Management System, makes it easy to deliver all of the essential health and safety awareness training you need for every employee in your district.
Source
Posted in Health & Fitness
Exposure to bloodborne pathogens could happen on any school campus, at any time, and on any given day. Consider for a moment how often school employees encounter students that have suffered a bloody nose during class. Or how frequently kids on the playground sustain cuts, scrapes, and other bleeding injuries. Also, students becoming ill and vomiting in the classroom is unfortunately not uncommon on school grounds as well. It may not be the most glamorous part of a school employee’s job, but it’s vital that school staff learn and understand how to properly handle bloodborne pathogen exposures to aid others while protecting themselves. Bloodborne pathogens safety training is one of the most crucial parts of a school staff member’s job, which is why it’s an annual school safety training requirement for most school district employees across the country.
Any human bodily fluid containing blood can carry BBPs. School personnel can be at risk when:
>>> providing first aid
>>> intervening in a student fight or
>>> cleaning up bodily fluids
…all common activities in a school community. That’s why all employees – custodians, teachers, and administrators – should be familiar with proper BBP procedures. Even if you are not required to handle bodily fluids as part of your job, you need to be prepared.
Bloodborne pathogens, which are commonly referred to as “BBPs,” can be bacterial (i.e. staph or strep) or viruses (i.e. flu, colds, hepatitis A, B, or C, and HIV). BBPs are present in blood and other bodily fluids, and can be transmitted when blood or body fluid from an infected person enters another person’s body through cuts, abrasions, or body cavities (such as the mouth, eyes, or nose). And while, according to a report from the Centers for Disease Control and Prevention (CDC), there are 300,000 new cases of BBP exposure reported each year, the greatest risk to school personnel is from the hepatitis B virus. It is particularly crucial to stress protection and prevention in regards to hepatitis B, as the signs and symptoms of the virus may not manifest for a long time – often weeks or months. Hepatitis B causes liver inflammation, vomiting, jaundice and sometimes death. However, chronic hepatitis B may eventually cause liver cirrhosis and liver cancer, a fatal disease with very poor response to current chemotherapy treatment. There is no “cure” or specific treatment for hepatitis B, but many people who contract the disease will develop antibodies, which help them get over the infection and protect them from getting it again. It is important to note, however, that there are different kinds of hepatitis, so infection with Hepatitis B will not stop someone from getting another type.
Similarly, the CDC also estimates that there are as many as 3 million people currently infected with hepatitis C and most of them don’t even know it yet! Hepatitis C virus (HCV) infection is the most common chronic bloodborne infection in the United States. Most people with this virus are chronically infected and might not be aware of their infection because they are not clinically ill. Many patients with hepatitis C exhibit no symptoms prior to the development of liver cirrhosis. Those with symptoms exhibit fatigue, loss of appetite, joint and body aches, nausea and abdominal discomfort. That is why it is essential for K-12 staff to be diligent in protecting themselves from any BBP exposure.
No matter the position and its particular duties, the risk of encountering an accident involving bodily fluids on the job, such as blood, is always present for all school district employees. And any human bodily fluid containing blood can carry BBPs. School districts should have key prevention strategies in place such as providing BBP Training to all staff.
A key prevention strategy, therefore, is to always exercise universal precautions – a series of precautious measures designed to prevent against the transmission of bloodborne pathogens. A good example of universal precautions would be the use of personal protective equipment (PPE), such as disposable gloves, when handling blood or other bodily fluids.
SafeSchools, the nation’s leading provider of online safety training for schools, currently offers expertly-authored bloodborne pathogens health safety training courses for school employees: Bloodborne Pathogens: Full Course and Bloodborne Pathogens: Refresher Course. The Full Course version is meant to be taken once, while the Refresher version – a concise overview of the material that is updated annually – is meant to be taken every year thereafter.
A Refresher version of the SafeSchools Bloodborne Pathogens course is also available in Spanish. Each of the SafeSchools’ BBP courses is written by two of the nation’s leading experts on BBP, James Vaughan and Karl Sommers III. Mr. Vaughan is President of Today’s Resources, Inc. (TRI), a full-service safety and health-consulting firm. He also has direct OSHA compliance and loss-control experience. Karl Sommers III, a TRI associate, has experience as a safety director in a manufacturing environment and leadership duties within area professional safety organizations. SafeSchools’ bloodborne pathogens safety training address facts about the disease, give guidelines for the cleanup and handling of potentially infected bodily fluids, wastes, or contaminated materials, discusses the risk associated with school workers exposed to BBPs, and delivers training designed to emphasize the practices of proper exposure control. The courses contain specific important references to OSHA regulations.
The SafeSchools’ suite of BBP courses help school districts manage their school safety compliance which is essential to providing a safe learning environment for staff, students, and parents, alike.
For more information, please call 1-800-434-0154 or info@safeschools.com
Jim Nulsen, SafeSchools, a web based safety training and tracking system designed specifically for school employee training. A powerful library of expertly authored courses, combined with the SafeSchools Compliance Management System, makes it easy to deliver all of the essential health and safety awareness training you need for every employee in your district.
Source
Supplements inadequately regulated, making them risky to users
Published: Tuesday, August 24, 2010, 7:00 AM
Brie Zeltner, The Plain Dealer
Warning: may cause low blood pressure, heart rhythm disorders, fainting, liver damage, kidney damage, rapid heart rate. A nightmare list of side effects for a new erectile dysfunction drug? Nope, it's worse.
These are the possible dangers of taking 12 supplements that are readily available in stores and on the Internet and are purported to help conditions from upset stomach to cancer and heart disease. Many of the supplements, identified by Consumer Reports in its September cover story, have no warning labels.
And that is the crux of the problem: In an effort to protect access to herbal remedies, consumers are left vulnerable to harm by inadequate regulation of the supplement industry. The 12 supplements identified by Consumer Reports represent a tiny portion of the vast supplement market, but they are perfect examples of an ineffective system that forces consumers to act as detectives in order to remain safe.
The Food and Drug Administration regulates dietary supplements under a set of rules laid out in the 1994 Dietary Supplement Health and Education Act. Under these rules, the open market is the testing ground for the safety of supplements: manufacturers are responsible for developing safe products, and the FDA takes action when needed if adverse events crop up.
Representatives of the supplement industry argue that the system has produced a safe marketplace.
"The FDA has the authority to remove products from the market that it believes to be hazardous to consumers," says Duffy MacKay, a naturopathic physician and vice president of scientific and regulatory affairs at the Council for Responsible Nutrition, or CRN, a trade association representing supplement manufacturers.
This happens only rarely, though. The most recent example was ephedra, a weight-loss stimulant banned in 2004 after it was linked to heart attack, stroke, suicide risk and death. More frequently, the FDA notifies the public through consumer warnings.
Eight of the 12 supplements identified by Consumer Reports have been the subject of FDA warnings in the past, some as far back as the early 1990s. Three of them, colloidal silver, kava and yohimbe, are the most widely available.
Colloidal silver, which has been marketed as a natural antibiotic and anti-fungal agent, can cause a permanent bluish skin discoloration when taken in large doses, as well as neurological problems and kidney damage.
Kava, a plant in the pepper family, has been used in many cultures as a nausea remedy and for relaxation purposes. It was widely used in the 1990s for anxiety, until it was linked to cases of liver damage and liver failure. It was subsequently banned in Canada, Germany, France and the United Kingdom.
Yohimbe, an evergreen tree native to Africa, yields the chemical yohimbine from its bark. A drug version of yohimbine has been used to treat erectile dysfunction, and the herb has also been marketed as an aphrodisiac. Normal doses can cause high blood pressure and a rapid heart rate, and high doses can lead to death.
It's hard to know what's in supplements
Vitamin Shoppe, which sells store brands of the three supplements, says there is no need for alarm.
"I think you'd really have to take an excessive amount that is not recommended on the label [to cause harm]," says Susan McLachlan, spokeswoman for the retail supplement chain. "You really have to go over and above to have that happen."
But because of poor enforcement of quality manufacturing standards in the supplement industry, it's currently impossible to tell with any certainty how much of each ingredient is in these products.
Richard Creger, a pharmacist at University Hospitals Case Medical Center who counsels cancer and bone marrow transplant patients, asks all of his patients to stay off supplements during treatment.
"You really aren't sure what these people are getting," he says. "There's no regulation -- so when you look at [a product] and it says there's 700 milligrams of herb in it, you don't know if there's really 700, or if it's 100, or 1,000. You don't know if there's fungus or yeast or bad bacteria in there. You don't know if there are heavy metals like lead and zinc in them, because there's actually no control."
Dr. Carolyn Nemec, a family practice physician at the Cleveland Clinic who specializes in women's health, agrees. She fields daily questions from women about weight-loss supplements, including yohimbe.
"Unfortunately we just don't have a lot of good studies on these products because frankly there isn't a lot of money in it for the pharmaceutical companies," she says.
MacKay of CRN says the FDA and the supplement industry are making moves to improve quality and reporting. In 2007, the Dietary and Supplement Non-Prescription Drug Consumer Act made it mandatory for supplement and over-the-counter-drug manufacturers to report all serious adverse events to the FDA. Regulations regarding Current Good Manufacturing Practices now require domestic and foreign companies producing supplements sold in the United States to adhere to standards of quality, consistency, purity, strength and composition.
An example of poor policing
Regulations are useless without adequate enforcement, and for now, the supplement marketplace is poorly policed.
Take the example of Miracle Mineral Solutions, or MMS, an oral liquid marketed as a lifesaving scientific breakthrough. On July 30, the FDA warned consumers to stop drinking the solution, which turns into a potent bleach when mixed with citrus juice per the instructions on the product.
The agency has received several health reports from consumers who have experienced severe nausea, vomiting and life-threatening low blood pressure from dehydration after using MMS.
The manufacturers claim that the product, which contains 28 percent sodium chlorite, can cure cancer, AIDS, hepatitis, malaria, tuberculosis and acne -- claims that the FDA has repeatedly warned the company to remove from its website.
What is even more shocking than these unsupported and outlandish claims is that one month after the FDA's warning, you can still easily buy a product that instructs you to prepare and drink industrial-strength bleach in the name of good health.
Caveat emptor, indeed.
Source
Brie Zeltner, The Plain Dealer
Warning: may cause low blood pressure, heart rhythm disorders, fainting, liver damage, kidney damage, rapid heart rate. A nightmare list of side effects for a new erectile dysfunction drug? Nope, it's worse.
These are the possible dangers of taking 12 supplements that are readily available in stores and on the Internet and are purported to help conditions from upset stomach to cancer and heart disease. Many of the supplements, identified by Consumer Reports in its September cover story, have no warning labels.
And that is the crux of the problem: In an effort to protect access to herbal remedies, consumers are left vulnerable to harm by inadequate regulation of the supplement industry. The 12 supplements identified by Consumer Reports represent a tiny portion of the vast supplement market, but they are perfect examples of an ineffective system that forces consumers to act as detectives in order to remain safe.
The Food and Drug Administration regulates dietary supplements under a set of rules laid out in the 1994 Dietary Supplement Health and Education Act. Under these rules, the open market is the testing ground for the safety of supplements: manufacturers are responsible for developing safe products, and the FDA takes action when needed if adverse events crop up.
Representatives of the supplement industry argue that the system has produced a safe marketplace.
"The FDA has the authority to remove products from the market that it believes to be hazardous to consumers," says Duffy MacKay, a naturopathic physician and vice president of scientific and regulatory affairs at the Council for Responsible Nutrition, or CRN, a trade association representing supplement manufacturers.
This happens only rarely, though. The most recent example was ephedra, a weight-loss stimulant banned in 2004 after it was linked to heart attack, stroke, suicide risk and death. More frequently, the FDA notifies the public through consumer warnings.
Eight of the 12 supplements identified by Consumer Reports have been the subject of FDA warnings in the past, some as far back as the early 1990s. Three of them, colloidal silver, kava and yohimbe, are the most widely available.
Colloidal silver, which has been marketed as a natural antibiotic and anti-fungal agent, can cause a permanent bluish skin discoloration when taken in large doses, as well as neurological problems and kidney damage.
Kava, a plant in the pepper family, has been used in many cultures as a nausea remedy and for relaxation purposes. It was widely used in the 1990s for anxiety, until it was linked to cases of liver damage and liver failure. It was subsequently banned in Canada, Germany, France and the United Kingdom.
Yohimbe, an evergreen tree native to Africa, yields the chemical yohimbine from its bark. A drug version of yohimbine has been used to treat erectile dysfunction, and the herb has also been marketed as an aphrodisiac. Normal doses can cause high blood pressure and a rapid heart rate, and high doses can lead to death.
It's hard to know what's in supplements
Vitamin Shoppe, which sells store brands of the three supplements, says there is no need for alarm.
"I think you'd really have to take an excessive amount that is not recommended on the label [to cause harm]," says Susan McLachlan, spokeswoman for the retail supplement chain. "You really have to go over and above to have that happen."
But because of poor enforcement of quality manufacturing standards in the supplement industry, it's currently impossible to tell with any certainty how much of each ingredient is in these products.
Richard Creger, a pharmacist at University Hospitals Case Medical Center who counsels cancer and bone marrow transplant patients, asks all of his patients to stay off supplements during treatment.
"You really aren't sure what these people are getting," he says. "There's no regulation -- so when you look at [a product] and it says there's 700 milligrams of herb in it, you don't know if there's really 700, or if it's 100, or 1,000. You don't know if there's fungus or yeast or bad bacteria in there. You don't know if there are heavy metals like lead and zinc in them, because there's actually no control."
Dr. Carolyn Nemec, a family practice physician at the Cleveland Clinic who specializes in women's health, agrees. She fields daily questions from women about weight-loss supplements, including yohimbe.
"Unfortunately we just don't have a lot of good studies on these products because frankly there isn't a lot of money in it for the pharmaceutical companies," she says.
MacKay of CRN says the FDA and the supplement industry are making moves to improve quality and reporting. In 2007, the Dietary and Supplement Non-Prescription Drug Consumer Act made it mandatory for supplement and over-the-counter-drug manufacturers to report all serious adverse events to the FDA. Regulations regarding Current Good Manufacturing Practices now require domestic and foreign companies producing supplements sold in the United States to adhere to standards of quality, consistency, purity, strength and composition.
An example of poor policing
Regulations are useless without adequate enforcement, and for now, the supplement marketplace is poorly policed.
Take the example of Miracle Mineral Solutions, or MMS, an oral liquid marketed as a lifesaving scientific breakthrough. On July 30, the FDA warned consumers to stop drinking the solution, which turns into a potent bleach when mixed with citrus juice per the instructions on the product.
The agency has received several health reports from consumers who have experienced severe nausea, vomiting and life-threatening low blood pressure from dehydration after using MMS.
The manufacturers claim that the product, which contains 28 percent sodium chlorite, can cure cancer, AIDS, hepatitis, malaria, tuberculosis and acne -- claims that the FDA has repeatedly warned the company to remove from its website.
What is even more shocking than these unsupported and outlandish claims is that one month after the FDA's warning, you can still easily buy a product that instructs you to prepare and drink industrial-strength bleach in the name of good health.
Caveat emptor, indeed.
Source
Subscribe to:
Posts (Atom)