Kara E. Rudolph

Incubation Periods of Mosquito-Borne Viral Infections: A Systematic Review

Mosquito-borne viruses are a major public health threat, but their incubation periods are typically uncited, non-specific, and not based on data. We systematically review the published literature on six mosquito-borne viruses selected for their public health importance: chikungunya, dengue, Japanese encephalitis, Rift Valley fever, West Nile, and yellow fever viruses. For each, we identify the literatures consensus on the incubation period, evaluate the evidence for this consensus, and provide detailed estimates of the incubation period and distribution based on published experimental and observational data. We abstract original data as doubly interval-censored observations. Assuming a log-normal distribution, we estimate the median incubation period, dispersion, 25th and 75th percentiles by maximum likelihood. We include bootstrapped 95% confidence intervals for each estimate. For West Nile and yellow fever viruses, we also estimate the 5th and 95th percentiles of their incubation periods.

Association Between Connecticut’s Permit-to-Purchase Handgun Law and Homicides

OBJECTIVES We sought to estimate the effect of Connecticuts implementation of a handgun permit-to-purchase law in October 1995 on subsequent homicides. METHODS Using the synthetic control method, we compared Connecticuts homicide rates after the laws implementation to rates we would have expected had the law not been implemented. To estimate the counterfactual, we used longitudinal data from a weighted combination of comparison states identified based on the ability of their prelaw homicide trends and covariates to predict prelaw homicide trends in Connecticut. RESULTS We estimated that the law was associated with a 40% reduction in Connecticuts firearm homicide rates during the first 10 years that the law was in place. By contrast, there was no evidence for a reduction in nonfirearm homicides. CONCLUSIONS Consistent with prior research, this study demonstrated that Connecticuts handgun permit-to-purchase law was associated with a subsequent reduction in homicide rates. As would be expected if the law drove the reduction, the policys effects were only evident for homicides committed with firearms.

Greenness and Birth Outcomes in a Range of Pennsylvania Communities

Living in communities with more vegetation during pregnancy has been associated with higher birth weights, but fewer studies have evaluated other birth outcomes, and only one has been conducted in the Eastern United States, in regions with a broad range, including high levels, of greenness. We evaluated associations between prenatal residential greenness and birth outcomes (term birth weight, small for gestational age, preterm birth, and low 5 min Apgar score) across a range of community types using electronic health record data from 2006–2013 from the Geisinger Health System in Pennsylvania. We assigned greenness based on mother’s geocoded address using the normalized difference vegetation index from satellite imagery. We used propensity scores to restrict the study population to comparable groups among those living in green vs. less-green areas. Analyses were adjusted for demographic, clinical, and environmental covariates, and stratified by community type (city, borough, and township). In cities, higher greenness (tertiles 2–3 vs. 1) was protective for both preterm (OR = 0.78, 95% CI: 0.61–0.99) and small for gestational age birth (OR = 0.73, 95% CI: 0.58–0.97), but not birth weight or Apgar score. We did not observe associations between greenness and birth outcomes in adjusted models in boroughs or townships. These results add to the evidence that greener cities might be healthier cities.

The incubation period of cholera: A systematic review

OBJECTIVES Recent large cholera outbreaks highlight the need for improved understanding of the pathogenesis and epidemiology of cholera. The incubation period of cholera has important implications for clinical and public health decision-making, yet statements of the incubation period of cholera are often imprecise. Here we characterize the distribution of choleras incubation period. METHODS We conducted a systematic review of the literature for statements of the incubation period of cholera and data that might aid in its estimation. We extracted individual-level data, parametrically estimated the distribution of toxigenic choleras incubation period, and evaluated evidence for differences between strains. RESULTS The incubation period did not differ by a clinically significant margin between strains (except O1 El Tor Ogawa). We estimate the median incubation period of toxigenic cholera to be 1.4 days (95% CI, 1.3-1.6). Five percent of cholera cases will develop symptoms by 0.5 days (95% CI 0.4-0.5), and 95% by 4.4 days (95% CI 3.9-5.0) after infection. CONCLUSIONS We recommend that cholera investigations use a recall period of at least five days to capture relevant exposures; significantly longer than recent risk factor studies from the Haitian epidemic. This characterization of choleras incubation period can help improve clinical and public health practice and advance epidemiologic research.

The association between cortisol and neighborhood disadvantage in a U.S. population-based sample of adolescents.

The association between neighborhood conditions and cortisol is rarely studied in children or adolescents and has been hampered by small sample size and racial/ethnic and geographic homogeneity. Our objective was to estimate the association between neighborhood disadvantage and salivary cortisol levels in a large, geographically and racially/ethnically diverse sample of adolescents from the National Comorbidity Survey Replication Adolescent Supplement. Salivary cortisol was collected before and after an interview administered in the adolescents home. We used a propensity score approach to match adolescents living in disadvantaged neighborhoods with those in non-disadvantaged neighborhoods to create two similar groups based on the time and day of cortisol collection as well as demographic characteristics. Adolescents living in disadvantaged neighborhoods had higher pre-interview cortisol levels and steeper rates of decline in cortisol levels over the course of the interview than similar adolescents in non-disadvantaged neighborhoods. This bolsters the evidence base suggesting that place may influence the stress response system.

An imputation-based solution to using mismeasured covariates in propensity score analysis

Although covariate measurement error is likely the norm rather than the exception, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We consider a multiple imputation-based approach that uses an external calibration sample with information on the true and mismeasured covariates, multiple imputation for external calibration, to correct for the measurement error, and investigate its performance using simulation studies. As expected, using the covariate measured with error leads to bias in the treatment effect estimate. In contrast, the multiple imputation for external calibration method can eliminate almost all the bias. We confirm that the outcome must be used in the imputation process to obtain good results, a finding related to the idea of congenial imputation and analysis in the broader multiple imputation literature. We illustrate the multiple imputation for external calibration approach using a motivating example estimating the effects of living in a disadvantaged neighborhood on mental health and substance use outcomes among adolescents. These results show that estimating the propensity score using covariates measured with error leads to biased estimates of treatment effects, but when a calibration data set is available, multiple imputation for external calibration can be used to help correct for such bias.

Estimating Population Treatment Effects From a Survey Subsample

We considered the problem of estimating an average treatment effect for a target population using a survey subsample. Our motivation was to generalize a treatment effect that was estimated in a subsample of the National Comorbidity Survey Replication Adolescent Supplement (2001-2004) to the population of US adolescents. To address this problem, we evaluated easy-to-implement methods that account for both nonrandom treatment assignment and a nonrandom 2-stage selection mechanism. We compared the performance of a Horvitz-Thompson estimator using inverse probability weighting and 2 doubly robust estimators in a variety of scenarios. We demonstrated that the 2 doubly robust estimators generally outperformed inverse probability weighting in terms of mean-squared error even under misspecification of one of the treatment, selection, or outcome models. Moreover, the doubly robust estimators are easy to implement and provide an attractive alternative to inverse probability weighting for applied epidemiologic researchers. We demonstrated how to apply these estimators to our motivating example.

Optimizing matching and analysis combinations for estimating causal effects

Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with the best post-matching covariate balance, but this work finds that such an approach does not always minimize mean squared error (MSE). These findings have important implications for future research utilizing matching. To minimize MSE, investigators should consider additional diagnostics, and use of simulations tailored to the study of interest to identify the optimal matching and analysis combination.

Job Strain and the Cortisol Diurnal Cycle in MESA: Accounting for Between- and Within-Day Variability.

Evidence of the link between job strain and cortisol levels has been inconsistent. This could be due to failure to account for cortisol variability leading to underestimated standard errors. Our objective was to model the relationship between job strain and the whole cortisol curve, accounting for sources of cortisol variability. Our functional mixed-model approach incorporated all available data-18 samples over 3 days-and uncertainty in estimated relationships. We used employed participants from the Multi-Ethnic Study of Atherosclerosis Stress I Study and data collected between 2002 and 2006. We used propensity score matching on an extensive set of variables to control for sources of confounding. We found that job strain was associated with lower salivary cortisol levels and lower total area under the curve. We found no relationship between job strain and the cortisol awakening response. Our findings differed from those of several previous studies. It is plausible that our results were unique to middle- to older-aged racially, ethnically, and occupationally diverse adults and were therefore not inconsistent with previous research among younger, mostly white samples. However, it is also plausible that previous findings were influenced by residual confounding and failure to propagate uncertainty (i.e., account for the multiple sources of variability) in estimating cortisol features.

Robust estimation of encouragement design intervention effects transported across sites

We develop robust targeted maximum likelihood estimators (TMLE) for transporting intervention effects from one population to another. Specifically, we develop TMLE estimators for three transported estimands: intent-to-treat average treatment effect (ATE) and complier ATE, which are relevant for encouragement-design interventions and instrumental variable analyses, and the ATE of the exposure on the outcome, which is applicable to any randomized or observational study. We demonstrate finite sample performance of these TMLE estimators using simulation, including in the presence of practical violations of the positivity assumption. We then apply these methods to the Moving to Opportunity trial, a multi-site, encouragement-design intervention in which families in public housing were randomized to receive housing vouchers and logistical support to move to low-poverty neighborhoods. This application sheds light on whether effect differences across sites can be explained by differences in population composition.