Individual-Based Simulation Models of HIV Transmission: Reporting Quality and Recommendations

Background: Individual-based modeling is a growing technique in the HIV transmission and prevention literature, but insufficient attention has been paid to formally evaluate the quality of reporting in this field. We present reporting recommendations for individual-based models for HIV treatment and prevention, assess the quality of reporting in the existing literature, and comment on the contribution of this model type to HIV policy and prediction. Methods: We developed reporting recommendations for individual-based HIV transmission mathematical models, and through a systematic search, used them to evaluate the reporting in the existing literature. We identified papers that employed individual-based simulation models and were published in English prior to December 31, 2012. Articles were included if the models they employed simulated and tracked individuals, simulated HIV transmission between individuals in a particular population, and considered a particular treatment or prevention intervention. The papers were assessed with the reporting recommendations. Findings: Of 214 full text articles examined, 32 were included in the evaluation, representing 20 independent individual-based HIV treatment and prevention mathematical models. Manuscripts universally reported the objectives, context, and modeling conclusions in the context of the modeling assumptions and the model’s predictive capabilities, but the reporting of individual-based modeling methods, parameterization and calibration was variable. Six papers discussed the time step used and one discussed efforts to maintain internal validity in coding. Conclusion: Individual-based models represent detailed HIV transmission processes with the potential to contribute to inference and policy making for many different regions and populations. The rigor in reporting of assumptions, methods, and calibration of individual-based models focused on HIV transmission and prevention varies greatly. Higher standards for reporting of statistically rigorous calibration and model assumption testing need to be implemented to increase confidence in existing and future modeling results

Understanding Treatment Refusal Among Adults Presenting for HIV-Testing in Soweto, South Africa: A Qualitative Study

HIV treatment initiatives have focused on increasing access to antiretroviral therapy (ART). There is growing evidence, however, that treatment availability alone is insufficient to stop the epidemic. In South Africa, only one third of individuals living with HIV are actually on treatment. Treatment refusal has been identified as a phenomenon among people who are asymptomatic, however, factors driving refusal remain poorly understood. We interviewed 50 purposively sampled participants who presented for voluntary counseling and testing in Soweto to elicit a broad range of detailed perspectives on ART refusal. We then integrated our core findings into an explanatory framework. Participants described feeling “too healthy” to start treatment, despite often having a diagnosis of AIDS. This subjective view of wellness was framed within the context of treatment being reserved for the sick. Taking ART could also lead to unintended disclosure and social isolation. These data provide a novel explanatory model of treatment refusal, recognizing perceived risks and social costs incurred when disclosing one’s status through treatment initiation. Our findings suggest that improving engagement in care for people living with HIV in South Africa will require optimizing social integration and connectivity for those who test positive

Comparative Safety of Antiretroviral Drugs to Treat HIV During Pregnancy

Each year, nearly 1.5 million women with HIV become pregnant, and require antiretroviral treatment to reduce risk of perinatal transmission of the virus and improve their own health. The safety of currently approved antiretroviral medications for the fetus is largely unknown; recommendations about preferred regimens during pregnancy are often based on a small body of clinical safety data, expert opinion, and programmatic considerations, including regimen harmonization across sub-populations. Therefore, there is a public health need to identify the specific antiretroviral drugs and combinations of drugs that are safest for use during pregnancy. Across the papers that comprise this Dissertation, we apply different epidemiological methods to observational data sources in order to provide information on clinically-motivated questions. In Chapter II, we used descriptive statistics to explore how substance use, one important risk factor for adverse infant birth outcomes, changed over time among pregnant women with HIV in the US. We found that substance use in this population dramatically decreased from 1990 to 2012, and that substance use had correlations with treatment, which means it may act as an important confounder in subsequent antiretroviral safety studies. In Chapter III, we further investigated the relationship between specific antiretroviral regimens and adverse birth outcomes, providing information on safety questions raised by a recent clinical trial. Pooling data from two prospective cohorts, we compared three antiretroviral regimens and concluded that the use of tenofovir with protease inhibitors not including lopinavir/ritonavir do not increase adverse birth outcomes. In Chapter IV, we examined the relationship between first trimester use of the antiretroviral zidovudine and congenital malformations. We summarized results of published studies using a systematic review and meta-analysis, and used Bayesian methods to incorporate this information with new data from Medicaid. We concluded that there appears to be a link between first trimester zidovudine use and increased risk of male genital malformations. We hope that the research undertaken in this Dissertation will substantially contribute to the growing body of antiretroviral safety information available to women with HIV and their clinicians, ultimately allowing them to make more well-informed treatment decisions

Bar chart of the percentage of identified papers that complied with each reporting guideline item.

<p>Bar chart of the percentage of identified papers that complied with each reporting guideline item.</p

Cascade of papers excluded and included in the systematic review of individual-based HIV transmission, treatment, and prevention models in the literature.

<p>Cascade of papers excluded and included in the systematic review of individual-based HIV transmission, treatment, and prevention models in the literature.</p

Dramatic decline in substance use by HIV-infected pregnant women in the United States from 1990–2012

OBJECTIVE: We aimed to describe temporal changes in substance use among HIV-infected pregnant women in the US from 1990–2012. DESIGN: Data came from two prospective cohort studies (Women and Infants Transmission Study and Surveillance Monitoring for Antiretroviral Therapy Toxicities Study). METHODS: Women were classified as using a substance during pregnancy if they self-reported use or had a positive biological sample. To account for correlation between repeated pregnancies by the same woman, GEE models were used to test for temporal trends and evaluate predictors of substance use. RESULTS: Over the 23-year period, substance use among the 5,451 HIV-infected pregnant women sharply declined; 82% of women reported substance use during pregnancy in 1990, compared to 23% in 2012. Use of each substance decreased significantly (p<0.001 for each substance) in an approximately linear fashion, until reaching a plateau in 2006. Multivariable models showed substance use was inversely associated with receiving antiretroviral therapy. Among the subset of 824 women with multiple pregnancies under observation, women who used a substance in their previous pregnancy were at elevated risk of substance use during their next pregnancy (RR, 5.71; 95% CI, 4.63–7.05). CONCLUSIONS: A substantial decrease in substance use during pregnancy was observed between 1990 and 2012 in two large US cohorts of HIV-infected women. Substance use prevalence in these cohorts became similar to that of pregnant women in the general US population by the mid-2000s, suggesting that the observed decrease may be due to an epidemiological transition of the HIV epidemic among women in the US

Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data.

IntroductionWith increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention.ObjectiveTo develop an algorithm to predict overdose using routinely-collected healthcare databases.MethodsWithin a US commercial claims database (2011-2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance.ResultsWe identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In the final algorithm based on elastic net, the strongest predictors were age 18-25 years (OR: 2.21), prior suicide attempt (OR: 3.68), opioid dependence (OR: 3.14).ConclusionsWe demonstrate that sophisticated algorithms using healthcare databases can be predictive of overdose, creating opportunities for active monitoring and early intervention