Prenatal protease inhibitor use and risk of preterm birth among HIV-infected women initiating antiretroviral drugs during pregnancy.

BACKGROUND: Conflicting results have been reported among studies of protease inhibitor (PI) use during pregnancy and preterm birth. Uncontrolled confounding by indication may explain some of the differences among studies. METHODS: In total, 777 human immunodeficiency virus (HIV)-infected pregnant women in a prospective cohort who were not receiving antiretroviral (ARV) treatment at conception were studied. Births <37 weeks gestation were reviewed, and deliveries due to spontaneous labor and/or rupture of membranes were identified. Risk of preterm birth and low birth weight (<2500 g) were evaluated by using multivariable logistic regression. RESULTS: Of the study population, 558 (72%) received combination ARV with PI during pregnancy, and a total of 130 preterm births were observed. In adjusted analyses, combination ARV with PI was not significantly associated with spontaneous preterm birth, compared to ARV without PI (odds ratio [OR], 1.22; 95% confidence interval [CI], 0.70-2.12). Sensitivity analyses that included women who received ARV prior to pregnancy also did not identify a significant association (OR, 1.34; 95% CI, 0.84-2.16). Low birth weight results were similar. CONCLUSIONS: No evidence of an association between use of combination ARV with PI during pregnancy and preterm birth was found. Our study supports current guidelines that promote consideration of combination ARV for all HIV-infected pregnant women.

Long-term dynamics of death rates of emphysema, asthma, and pneumonia and improving air quality.

BACKGROUND: The respiratory tract is a major target of exposure to air pollutants, and respiratory diseases are associated with both short- and long-term exposures. We hypothesized that improved air quality in North Carolina was associated with reduced rates of death from respiratory diseases in local populations. MATERIALS AND METHODS: We analyzed the trends of emphysema, asthma, and pneumonia mortality and changes of the levels of ozone, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matters (PM2.5 and PM10) using monthly data measurements from air-monitoring stations in North Carolina in 1993-2010. The log-linear model was used to evaluate associations between air-pollutant levels and age-adjusted death rates (per 100,000 of population) calculated for 5-year age-groups and for standard 2000 North Carolina population. The studied associations were adjusted by age group-specific smoking prevalence and seasonal fluctuations of disease-specific respiratory deaths. RESULTS: Decline in emphysema deaths was associated with decreasing levels of SO2 and CO in the air, decline in asthma deaths-with lower SO2, CO, and PM10 levels, and decline in pneumonia deaths-with lower levels of SO2. Sensitivity analyses were performed to study potential effects of the change from International Classification of Diseases (ICD)-9 to ICD-10 codes, the effects of air pollutants on mortality during summer and winter, the impact of approach when only the underlying causes of deaths were used, and when mortality and air-quality data were analyzed on the county level. In each case, the results of sensitivity analyses demonstrated stability. The importance of analysis of pneumonia as an underlying cause of death was also highlighted. CONCLUSION: Significant associations were observed between decreasing death rates of emphysema, asthma, and pneumonia and decreases in levels of ambient air pollutants in North Carolina.

Potential Cost-effectiveness of Early Identification of Hospital-acquired Infection in Critically Ill Patients.

RATIONALE: Limitations in methods for the rapid diagnosis of hospital-acquired infections often delay initiation of effective antimicrobial therapy. New diagnostic approaches offer potential clinical and cost-related improvements in the management of these infections. OBJECTIVES: We developed a decision modeling framework to assess the potential cost-effectiveness of a rapid biomarker assay to identify hospital-acquired infection in high-risk patients earlier than standard diagnostic testing. METHODS: The framework includes parameters representing rates of infection, rates of delayed appropriate therapy, and impact of delayed therapy on mortality, along with assumptions about diagnostic test characteristics and their impact on delayed therapy and length of stay. Parameter estimates were based on contemporary, published studies and supplemented with data from a four-site, observational, clinical study. Extensive sensitivity analyses were performed. The base-case analysis assumed 17.6% of ventilated patients and 11.2% of nonventilated patients develop hospital-acquired infection and that 28.7% of patients with hospital-acquired infection experience delays in appropriate antibiotic therapy with standard care. We assumed this percentage decreased by 50% (to 14.4%) among patients with true-positive results and increased by 50% (to 43.1%) among patients with false-negative results using a hypothetical biomarker assay. Cost of testing was set at $110/d. MEASUREMENTS AND MAIN RESULTS: In the base-case analysis, among ventilated patients, daily diagnostic testing starting on admission reduced inpatient mortality from 12.3 to 11.9% and increased mean costs by $1,640 per patient, resulting in an incremental cost-effectiveness ratio of $21,389 per life-year saved. Among nonventilated patients, inpatient mortality decreased from 7.3 to 7.1% and costs increased by $1,381 with diagnostic testing. The resulting incremental cost-effectiveness ratio was $42,325 per life-year saved. Threshold analyses revealed the probabilities of developing hospital-acquired infection in ventilated and nonventilated patients could be as low as 8.4 and 9.8%, respectively, to maintain incremental cost-effectiveness ratios less than $50,000 per life-year saved. CONCLUSIONS: Development and use of serial diagnostic testing that reduces the proportion of patients with delays in appropriate antibiotic therapy for hospital-acquired infections could reduce inpatient mortality. The model presented here offers a cost-effectiveness framework for future test development.

Individual Proton Pump Inhibitors and Outcomes in Patients With Coronary Artery Disease on Dual Antiplatelet Therapy: A Systematic Review.

BACKGROUND: Observational studies evaluating the possible interaction between proton pump inhibitors (PPIs) and clopidogrel have shown mixed results. We conducted a systematic review comparing the safety of individual PPIs in patients with coronary artery disease taking clopidogrel. METHODS AND RESULTS: Studies performed from January 1995 to December 2013 were screened for inclusion. Data were extracted, and study quality was graded for 34 potential studies. For those studies in which follow-up period, outcomes, and multivariable adjustment were comparable, meta-analysis was performed.The adjusted odds or hazard ratios for the composite of cardiovascular or all-cause death, myocardial infarction, and stroke at 1 year were reported in 6 observational studies with data on individual PPIs. Random-effects meta-analyses of the 6 studies revealed an increased risk for adverse cardiovascular events for those taking pantoprazole (hazard ratio 1.38; 95% CI 1.12-1.70), lansoprazole (hazard ratio 1.29; 95% CI 1.09-1.52), or esomeprazole (hazard ratio 1.27; 95% CI 1.02-1.58) compared with patients on no PPI. This association was not significant for omeprazole (hazard ratio 1.16; 95% CI 0.93-1.44). Sensitivity analyses for the coronary artery disease population (acute coronary syndrome versus mixed) and exclusion of a single study due to heterogeneity of reported results did not have significant influence on the effect estimates for any PPIs. CONCLUSIONS: Several frequently used PPIs previously thought to be safe for concomitant use with clopidogrel were associated with greater risk of adverse cardiovascular events. Although the data are observational, they highlight the need for randomized controlled trials to evaluate the safety of concomitant PPI and clopidogrel use in patients with coronary artery disease.

Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.