From exposures to population interventions: pregnancy and response to HIV therapy

Combined oral contraceptive utilization and uterine fibroid incidence: A prospective study in a cohort of African-American women

Published associations between combined oral contraceptive use and uterine fibroid development have lacked prospective imaging with ultrasound to distinguish between incident and prevalent fibroids. The Study of Environment, Lifestyle, and Fibroids prospectively followed fibroid-free, African-American women (the group with the highest disease burden in the U.S.) to identify incident cases. We examined associations between combined oral contraceptive use and the 40-month cumulative risk of fibroids. History of hormonal contraceptive use was collected via telephone interview at enrollment. Fibroid identification was performed using transvaginal ultrasonography at enrollment, and at 20 and 40-months of follow-up. Inverse probability weights for exposures and censoring were used to construct weighted risk ratios (wRR) and weighted risk different (wRD) estimators which control for differences in fibroid risk factors between exposure groups. In addition, unweighted fully adjusted log-binomial regression models (aRR) were run for comparison. Of the 1,308 participants in the analysis sample, 70% had used combined oral contraceptives and 17% developed fibroids by 40 months. We observed an inverse association between ever use of combined oral contraceptives and cumulative fibroid incidence (wRR: 0.78; 95% Confidence Interval (CI): 0.60, 1.00; wRD: -0.05, 95% CI: -0.11, 0; aRR: 0.76, 95% CI: 0.60, 0.98). Fibroid incidence was greater in participants who started using combined oral contraceptives after age 17 years than among younger initiators, though the restriction to ever-users made this estimate less precise (wRR: 1.25; 95% CI: 0.89, 1.76; wRD: 0.04, 95% CI: -0.02, 0.10). No consistent patterns of fibroid incidence were seen among ever-users for duration of, or years since, last combined oral contraceptives use.

Different effects for different questions: An illustration using short cervix and the risk of preterm birth

OBJECTIVE

To illustrate the difference between exposure effects and population attributable effects.

METHODS

We examined the effect of mid-pregnancy short cervical length (<25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g-computation.

RESULTS

Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80-4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02-1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth.

CONCLUSION

Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective.

A Framework for Descriptive Epidemiology

In this paper, we propose a framework for thinking through the design and conduct of descriptive epidemiologic studies. A well-defined descriptive question aims to quantify and characterize some feature of the health of a population and must clearly state: 1) the target population, characterized by person and place, and anchored in time; 2) the outcome, event, or health state or characteristic; and 3) the measure of occurrence that will be used to summarize the outcome (e.g., incidence, prevalence, average time to event, etc.). Additionally, 4) any auxiliary variables will be prespecified and their roles as stratification factors (to characterize the outcome distribution) or nuisance variables (to be standardized over) will be stated. We illustrate application of this framework to describe the prevalence of viral suppression on December 31, 2019, among people living with human immunodeficiency virus (HIV) who had been linked to HIV care in the United States. Application of this framework highlights biases that may arise from missing data, especially 1) differences between the target population and the analytical sample; 2) measurement error; 3) competing events, late entries, loss to follow-up, and inappropriate interpretation of the chosen measure of outcome occurrence; and 4) inappropriate adjustment.

Targeted maximum likelihood estimation of causal effects with interference: A simulation study

Interference, the dependency of an individual's potential outcome on the exposure of other individuals, is a common occurrence in medicine and public health. Recently, targeted maximum likelihood estimation (TMLE) has been extended to settings of interference, including in the context of estimation of the mean of an outcome under a specified distribution of exposure, referred to as a policy. This paper summarizes how TMLE for independent data is extended to general interference (network-TMLE). An extensive simulation study is presented of network-TMLE, consisting of four data generating mechanisms (unit-treatment effect only, spillover effects only, unit-treatment and spillover effects, infection transmission) in networks of varying structures. Simulations show that network-TMLE performs well across scenarios with interference, but issues manifest when policies are not well-supported by the observed data, potentially leading to poor confidence interval coverage. Guidance for practical application, freely available software, and areas of future work are provided.

Impact of hypothetical improvements in the psychosocial work environment on sickness absence rates: a simulation study

Background

The association between psychosocial working environments and sickness absence is well-known. However, the potential for reducing sickness absences of different lengths through improvements in psychosocial work factors is not fully understood. We aim to quantify the potential for reducing short-, intermediate- and long-term sickness absence rates, respectively, through hypothetical improvements in several psychosocial work factors.

Methods

This longitudinal study includes 24 990 public hospital employees from the 2014 wave of the Well-being in Hospital Employees study. The 1-year sickness absence rate was divided into short- (1–3 days), intermediate- (4–28 days) and long-term (29 days or more) periods. We simulated hypothetical scenarios with improvements in 17 psychosocial work factors using the parametric g-formula and estimated resulting changes in sickness absence rate ratios (RRs) with 95% confidence intervals (95% CIs).

Results

Setting all 17 psychosocial work factors to their most desirable levels (vs. least desirable levels) was associated with an overall 54% lower rate of sickness absence (95% CI: 48–60%). Reducing bullying (no vs. yes RR: 0.86, 95% CI: 0.83–0.90) and perceived stress (low vs. high RR: 0.90, 95% CI: 0.87–0.92), and increasing skill discretion (high vs. low RR: 0.91, 95% CI: 0.89–0.94) held the largest potential for reducing the total sickness absence rate. Overall, associations were similar for short-, intermediate- and long-term sickness absence.

Conclusions

The psychosocial working environment was strongly associated with sickness absence. Improving the working environment may have a great impact on short-, intermediate- and long-term sickness absence rates.

Effect of early life antibiotic use on serologic responses to oral rotavirus vaccine in the MAL-ED birth cohort study

BACKGROUND

Oral rotavirus vaccine efficacy is lower in low- and middle-income countries (LMICs) than in high-income countries. The degree to which antibiotic use impacts rotavirus vaccine immunogenicity in LMICs is unknown. Using data from a multisite prospective birth cohort study of malnutrition and enteric disease, MAL-ED, we examined the effect of early life antibiotic use on the immune response to rotavirus vaccine.

METHODS

We assessed whether antibiotic use from birth up to 7 days following rotavirus vaccine series completion was associated with rotavirus seropositivity at 7 months of age in Brazil, Peru, and South Africa using a modified Poisson regression. We then used parametric g-computation to estimate the impact of hypothetical interventions that treated all children and alternatively prevented inappropriate antibiotic treatments on seropositivity.

RESULTS

Of 537 children, 178 (33%) received at least one antibiotic course during the exposure window. Probability of seropositivity was 40% higher among children who had at least one course of antibiotics compared with those with no antibiotic exposure (PR: 1.40, 95% CI: 1.04, 1.89). There was no significant difference by the number of antibiotic courses received or total duration of antibiotics. Treating all children with antibiotics would be associated with a 19% (95% CI: 18%, 21%) absolute increase in seropositivity at 7 months. In contrast, removing inappropriate antibiotics would result in a 4% absolute reduction (95% CI: -5%, -2%) in seropositivity.

CONCLUSIONS

Early life antibiotic use was associated with increased seropositivity. However, a hypothetical intervention to remove inappropriate antibiotics would have little effect on overall seropositivity. Further investigation into the underlying mechanisms of antibiotic use on the infant gut microbiome and immune response are needed.

Modeling Combination Interventions to Prevent Human Immunodeficiency Virus in Adolescent Girls and Young Women in South Africa (HIV Prevention Trials Network 068)

BACKGROUND

Combination interventions may be an effective way to prevent human immunodeficiency virus (HIV) in adolescent girls and young women. However, current studies are not designed to understand which specific interventions and combinations will be most effective. We estimate the possible impacts of interventions on a combination of factors associated with HIV.

METHODS

We used the g-formula to model interventions on combinations of HIV risk factors to identify those that would prevent the most incident HIV infections, including low school attendance, intimate partner violence, depression, transactional sex, and age-disparate partnerships. We used data from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa from 2011 to 2017. We estimated HIV incidence under a potential intervention that reduced each risk factor and compared this to HIV incidence under the current distribution of these risk factors.

RESULTS

Although many factors had strong associations with HIV, potential intervention estimates did not always suggest large reductions in HIV incidence because the prevalence of risk factors was low. When modeling combination effects, an intervention to increase schooling, decrease depression, and decease transactional sex showed the largest reduction in incident infection (risk difference, -1.4%; 95% confidence interval [CI], -2.7% to -.2%), but an intervention on only transactional sex and depression still reduced HIV incidence by -1.3% (95% CI, -2.6% to -.2%).

CONCLUSIONS

To achieve the largest reductions in HIV, both prevalence of the risk factor and strength of association with HIV must be considered. Additionally, intervening on more risk factors may not necessarily result in larger reductions in HIV incidence.

Union Burying Ground: Mortality, Mortality Inequities, and Sinking Labor Union Membership in the United States

BACKGROUND

Over the last several decades in the United States, socioeconomic life-expectancy inequities have increased 1-2 years. Declining labor-union density has fueled growing income inequities across classes and exacerbated racial income inequities. Using Panel Study of Income Dynamics (PSID) data, we examined the longitudinal union-mortality relationship and estimated whether declining union density has also exacerbated mortality inequities.

METHODS

Our sample included respondents ages 25-66 to the 1979-2015 PSID with mortality follow-up through age 68 and year 2017. To address healthy-worker bias, we used the parametric g-formula. First, we estimated how a scenario setting all (versus none) of respondents' employed-person-years to union-member employed-person-years would have affected mortality incidence. Next, we examined gender, racial, and educational effect modification. Finally, we estimated how racial and educational mortality inequities would have changed if union-membership prevalence had remained at 1979 (vs. 2015) levels throughout follow-up.

RESULTS

In the full sample (respondents = 23,022, observations = 146,681), the union scenario was associated with lower mortality incidence than the nonunion scenario (RR = 0.90, 95% CI = 0.80, 0.99; RD per 1,000 = -19, 95% CI = -37, -1). This protective association generally held across subgroups, although it was stronger among the more-educated. However, we found little evidence mortality inequities would have lessened if union membership had remained at 1979 levels.

CONCLUSIONS

To our knowledge, this was the first individual-level US-based study with repeated union-membership measurements to analyze the union-mortality relationship. We estimated a protective union-mortality association, but found little evidence declining union density has exacerbated mortality inequities; importantly, we did not incorporate contextual-level effects. See video abstract at, http://links.lww.com/EDE/B839.

Population intervention effects in observational studies to emulate target trial results: reconciling the effects of improved sanitation on child growth

BACKGROUND

Improved sanitation has been associated with improved child growth in observational studies, but multiple randomized trials that delivered improved sanitation found no effect on child growth. We assessed to what extent differences in the effect estimated in the two study designs (the effect of treatment in observational studies and the effect of treatment assignment in trials) could explain the contradictory results.

METHODS

We used parametric g-computation in five prospective studies (n = 21 524) and 59 cross-sectional Demographic and Health Surveys (DHS; n = 158 439). We compared the average treatment effect (ATE) for improved sanitation on mean length-for-age z-score (LAZ) among children aged <2 years to population intervention effects (PIEs), which are the observational analogue of the effect estimated in trials in which some participants are already exposed.

RESULTS

The ATE was >0.15 z-scores, a clinically meaningful difference, in most prospective studies but in <20% of DHS surveys. The PIE was always smaller than the ATE, and the magnitude of difference depended on the baseline prevalence of the improved sanitation. Interventions with suboptimal coverage and interventions delivered in populations with higher mean LAZ had a smaller effect on population-level LAZ.

CONCLUSIONS

Estimates of PIEs corresponding to anticipated trial results were often smaller than clinically meaningful effects. Incongruence between observational associations and null trial results may in part be explained by expected differences between the effects estimated. Using observational ATEs to set expectations for trials may overestimate the impact that sanitation interventions can achieve. PIEs predict realistic effects and should be more routinely estimated.

Comparative Effectiveness and Harms of Antibiotics for Outpatient Diverticulitis : Two Nationwide Cohort Studies

BACKGROUND

Outpatient diverticulitis is commonly treated with either a combination of metronidazole and a fluoroquinolone (metronidazole-with-fluoroquinolone) or amoxicillin-clavulanate alone. The U.S. Food and Drug Administration advised that fluoroquinolones be reserved for conditions with no alternative treatment options. The comparative effectiveness of metronidazole-with-fluoroquinolone versus amoxicillin-clavulanate for diverticulitis is uncertain.

OBJECTIVE

To determine the effectiveness and harms of metronidazole-with-fluoroquinolone versus amoxicillin-clavulanate for outpatient diverticulitis.

DESIGN

Active-comparator, new-user, retrospective cohort studies.

SETTING

Nationwide population-based claims data on U.S. residents aged 18 to 64 years with private employer-sponsored insurance (2000 to 2018) or those aged 65 years or older with Medicare (2006 to 2015).

PARTICIPANTS

Immunocompetent adults with diverticulitis in the outpatient setting.

INTERVENTION

Metronidazole-with-fluoroquinolone or amoxicillin-clavulanate.

MEASUREMENTS

1-year risks for inpatient admission, urgent surgery, and Clostridioides difficile infection (CDI) and 3-year risk for elective surgery.

RESULTS

In MarketScan (IBM Watson Health), new users of metronidazole-with-fluoroquinolone (n = 106 361) and amoxicillin-clavulanate (n = 13 160) were identified. There were no differences in 1-year admission risk (risk difference, 0.1 percentage points [95% CI, -0.3 to 0.6]), 1-year urgent surgery risk (risk difference, 0.0 percentage points [CI, -0.1 to 0.1]), 3-year elective surgery risk (risk difference, 0.2 percentage points [CI, -0.3 to 0.7]), or 1-year CDI risk (risk difference, 0.0 percentage points [CI, -0.1 to 0.1]) between groups. In Medicare, new users of metronidazole-with-fluoroquinolone (n = 17 639) and amoxicillin-clavulanate (n = 2709) were identified. There were no differences in 1-year admission risk (risk difference, 0.1 percentage points [CI, -0.7 to 0.9]), 1-year urgent surgery risk (risk difference, -0.2 percentage points [CI, -0.6 to 0.1]), or 3-year elective surgery risk (risk difference, -0.3 percentage points [CI, -1.1 to 0.4]) between groups. The 1-year CDI risk was higher for metronidazole-with-fluoroquinolone than for amoxicillin-clavulanate (risk difference, 0.6 percentage points [CI, 0.2 to 1.0]).

LIMITATION

Residual confounding is possible, and not all harms associated with these antibiotics, most notably drug-induced liver injury, could be assessed.

CONCLUSION

Treating diverticulitis in the outpatient setting with amoxicillin-clavulanate may reduce the risk for fluoroquinolone-related harms without adversely affecting diverticulitis-specific outcomes.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Evidence From a Multistate Cohort: Enrollment in Affordable Care Act Qualified Health Plans' Association With Viral Suppression

Background

Healthcare delivery changes associated with viral suppression (VS) could contribute to the United States’ “Ending the HIV Epidemic” (EtHE) initiative. This study aims to determine whether Qualified Health Plans (QHPs) purchased by AIDS Drug Assistance Programs (ADAPs) are associated with VS for low-income people living with HIV (PLWH) across 3 states.

Methods

A multistate cohort of ADAP clients eligible for ADAP-funded QHPs were studied (2014–2015). A log-binomial model was used to estimate the association of demographics and healthcare delivery factors with QHP enrollment prevalence and 1-year risk of VS. A number needed to treat/enroll (NNT) for 1 additional person to achieve viral suppression was calculated.

Results

Of the cohort (n = 7776), 52% enrolled in QHPs. QHP enrollment in 2015 was associated with QHP coverage in 2014 (adjusted PR [aPR], 3.28; 95% confidence intervals [CIs], 3.06–3.53) and engagement in care in 2014 (aPR, 1.16; 1.04–1.28). PLWH who were engaged in care (n = 4597) and had QHPs had a higher VS rate than those who received medications from Direct ADAP (86.0% vs 80.2%). QHPs’ NNT for an additional person to achieve VS is 20 (14.1–34.5). Starting undetectable (adjusted risk ratio [aRR], 1.39; 1.28–1.52) and enrolling in QHPs in 2015 (aRR, 1.06; 0.99–1.14) was associated with VS.

Conclusions

Once enrolled in ADAP-funded QHPs, ADAP clients stay enrolled. Enrollment is associated with VS across states/demographic groups. ADAPs, especially in the South and in Medicaid nonexpansion states, should consider investing in QHPs because increased enrollment could improve VS rates. This evidence-based intervention could be part of EtHE.

Mortality Effects of Hypothetical Interventions on Physical Activity and TV Viewing

INTRODUCTION

Long-term effects of physical activity and television (TV) viewing on mortality have been inferred from observational studies. The associations observed do not allow for inferences about the effects of population interventions and could be subject to bias due to time-varying confounding.

METHODS

Using data from the Australian Diabetes, Obesity and Lifestyle Study, collected in 1999-2000 (T0), 2004-2005 (T1), and 2011-2012 (T2), we applied the parametric g-formula to estimate cumulative risks of death under hypothetical interventions on physical activity and/or TV viewing determined from self-report while adjusting for time-varying confounding.

RESULTS

In the 6377 participants followed up for 13 yr from 2004 to 2005 to death or censoring in 2017, 781 participants died. The observed cumulative risk of death was 12.2%. The most effective hypothetical intervention was to increase weekly physical activity to >300 min (risk ratio (RR), 0.66 (0.46-0.86) compared with a "worst-case" scenario; RR, 0.83 (0.73-0.94) compared with no intervention). Reducing daily TV viewing to <2 h in addition to physical activity interventions did not show added survival benefits. Reducing TV viewing alone was least effective in reducing mortality (RR, 0.85 (0.60-1.10) compared with the worst-case scenario; RR, 1.06 (0.93-1.20) compared with no intervention).

CONCLUSIONS

Our findings suggested that sustained interventions to increase physical activity could lower all-cause mortality over a 13-yr period, and there might be limited gain from intervening to reduce TV viewing time in a relatively healthy population.

Two-stage g-computation: Evaluating Treatment and Intervention Impacts in Observational Cohorts When Exposure Information Is Partly Missing

Illustrations of the g-computation algorithm to evaluate population average treatment and intervention effects have been predominantly implemented in settings with complete exposure information. Thus, worked examples of approaches to handle missing data in this causal framework are needed to facilitate wider use of these estimators. We illustrate two-stage g-computation estimators that leverage partially observed information on the full study sample and complete exposure information on a subset to estimate causal effects. In a hypothetical cohort of 1,623 human immunodeficiency virus (HIV)-positive women with 30% complete opioid prescription information, we illustrate a two-stage extrapolation g-computation estimator for the average treatment effect of shorter or longer duration opioid prescriptions; we further illustrate two-stage inverse probability weighting and imputation g-computation estimators for the average intervention effect of shortening the duration of prescriptions relative to the status quo. Two-stage g-computation estimators approximated the true risk differences for the population average treatment and intervention effects while g-computation fit to the subset of complete cases was biased. In 10,000 Monte Carlo simulations, two-stage approaches considerably reduced bias and mean squared error and improved the coverage of 95% confidence limits. Although missing data threaten validity and precision, two-stage g-computation designs offer principled approaches to handling missing information.

From Epidemiologic Knowledge to Improved Health: A Vision for Translational Epidemiology

Epidemiology should aim to improve population health; however, no consensus exists regarding the activities and skills that should be prioritized to achieve this goal. We performed a scoping review of articles addressing the translation of epidemiologic knowledge into improved population health outcomes. We identified 5 themes in the translational epidemiology literature: foundations of epidemiologic thinking, evidence-based public health or medicine, epidemiologic education, implementation science, and community-engaged research (including literature on community-based participatory research). We then identified 5 priority areas for advancing translational epidemiology: 1) scientific engagement with public health; 2) public health communication; 3) epidemiologic education; 4) epidemiology and implementation; and 5) community involvement. Using these priority areas as a starting point, we developed a conceptual framework of translational epidemiology that emphasizes interconnectedness and feedback among epidemiology, foundational science, and public health stakeholders. We also identified 2-5 representative principles in each priority area that could serve as the basis for advancing a vision of translational epidemiology. We believe an emphasis on translational epidemiology can help the broader field to increase the efficiency of translating epidemiologic knowledge into improved health outcomes and to achieve its goal of improving population health.

The Effects of Hepatitis C Treatment Eligibility Criteria on All-cause Mortality Among People With Human Immunodeficiency Virus

BACKGROUND

The cost of direct-acting antivirals (DAAs) for hepatitis C virus (HCV) prompted many payers to restrict treatment to patients who met non-evidence-based criteria. These restrictions have implications for survival of people with HCV, especially for people with human immunodeficiency virus (HIV)/HCV coinfection who are at high risk for liver disease progression. The goal of this work was to estimate the effects of DAA access policies on 10-year all-cause mortality among people with HIV.

METHODS

The study population included 3056 adults with HIV in the Women's Interagency HIV Study and Multicenter AIDS Cohort Study from 1 October 1994 through 30 September 2015. We used the parametric g-formula to estimate 10-year all-cause mortality under DAA access policies that included treating (i) all people with HCV; (ii) only people with suppressed HIV; (iii) only people with severe fibrosis; and (iv) only people with HIV suppression and severe fibrosis.

RESULTS

The 10-year risk difference of treating all coinfected persons with DAAs compared with no treatment was -3.7% (95% confidence interval [CI], -9.1% to .6%). Treating only those with suppressed HIV and severe fibrosis yielded a risk difference of -1.1% (95% CI, -2.8% to .6%), with 51% (95% CI, 38%-59%) of coinfected persons receiving DAAs. Treating a random selection of 51% of coinfected persons at baseline decreased the risk by 1.9% (95% CI, -4.7% to .3%).

CONCLUSIONS

Restrictive DAA access policies may decrease survival compared to treating similar proportions of people with HIV/HCV coinfection with DAAs at random. These findings suggest that lives could be saved by thoughtfully revising access policies.

The Effects of Hepatitis C Infection and Treatment on All-cause Mortality Among People Living With Human Immunodeficiency Virus

BACKGROUND

Persons living with human immunodeficiency virus (HIV; PLwH) are commonly co-infected with hepatitis C virus (HCV). Most co-infected individuals can achieve a sustained HCV virologic response after treatment with direct-acting antivirals (DAA). However, the effect of HCV co-infection and DAA treatment on mortality after initiating antiretroviral therapy (ART) is unknown for PLwH.

METHODS

We analyzed data from the Women's Interagency HIV Study and the Multicenter AIDS Cohort Study. Participants included those who had prevalent HIV or seroconverted during follow-up; all were antiretroviral-naive and acquired immunodeficiency syndrome (AIDS)-free prior to their first visit after 1 October 1994. The follow-up lasted 10 years or until 30 September 2015. We used parametric g-computation to estimate the effects of HCV infection and DAA treatment on mortality had participants initiated ART at study entry.

RESULTS

Of the 3056 eligible participants, 58% were female and 18% had HCV. The estimated 10-year all-cause mortality risk in the scenario in which no PLwH had HCV was 10.4% (95% confidence interval [CI] 6.0-18.0%). The 10-year mortality risk difference for HCV infection was 4.3% (95% CI 0.4-8.9%) and the risk ratio was 1.4 (95% CI 1.0-1.9). The risk difference for DAA treatment was -3.8% (95% CI -9.2-0.9%) and the risk ratio was 0.8 (95% CI 0.6-1.1).

CONCLUSIONS

HCV co-infection remains an important risk factor for mortality among PLwH after initiating ART according to modern guidelines, and DAAs are effective at reducing mortality in this population. HCV prevention and treatment interventions should be prioritized to reduce mortality among PLwH.

"Complete Streets" and Adult Bicyclist Fatalities: Applying G-Computation to Evaluate an Intervention That Affects the Size of a Population at Risk

"Complete streets" policies require transportation engineers to make provisions for pedestrians, bicyclists, and mass transit users. These policies may make bicycling safer for individual cyclists while increasing the overall number of bicycle fatalities if more people cycle due to improved infrastructure. We merged county-level records of complete streets policies with Fatality Analysis Reporting System counts of cyclist fatalities occurring between January 2000 and December 2015. Because comprehensive county-level estimates of numbers of cyclists were not available, we used bicycle commuter estimates from the American Community Survey and the US Census as a proxy for the cycling population and limited analysis to 183 counties (accounting for over half of the US population) for which cycle commuting estimates were consistently nonzero. We used G-computation to estimate the effect of complete streets policies on overall numbers of cyclist fatalities while also accounting for potential policy effects on the size of the cycling population. Over a period of 16 years, 5,254 cyclists died in these counties, representing 34 fatalities per 100,000 cyclist-years. We estimated that complete streets policies made cycling safer, averting 0.6 fatalities per 100,000 cyclist-years (95% confidence interval: -1.0, -0.3) by encouraging a 2.4% increase in cycling but producing only a 0.7% increase in cyclist fatalities. G-computation is a useful tool for understanding the impact of policy on risk and exposure.

A review of time scale fundamentals in the g-formula and insidious selection bias

PURPOSE OF REVIEW

We review recent examples of data analysis with the g-formula, a powerful tool for analyzing longitudinal data and survival analysis. Specifically, we focus on the common choices of time scale and review inferential issues that may arise.

RECENT FINDINGS

Researchers are increasingly engaged with questions that require time scales subject to left-truncation and right-censoring. The assumptions necessary for allowing right-censoring are well defined in the literature, whereas similar assumptions for left-truncation are not well defined. Policy and biologic considerations sometimes dictate that observational data must be analyzed on time scales that are subject to left-truncation, such as age.

SUMMARY

Further consideration of left-truncation is needed, especially when biologic or policy considerations dictate that age is the relevant time scale of interest. Methodologic development is needed to reduce potential for bias when left-truncation may occur.

Estimating the Impact of Changes to Occupational Standards for Silica Exposure on Lung Cancer Mortality

BACKGROUND

Respiratory exposure to silica is associated with the risk of death owing to malignant and nonmalignant disease. 2.3 million US workers are exposed to silica. Occupational exposure limits for silica are derived from a number of lines of evidence, including observational studies. Observational studies may be subject to healthy worker survivor bias, which could result in underestimates of silica's impact on worker mortality and, in turn, bias risk estimates for occupational exposure limits.

METHODS

Using data on 65,999 workers pooled across multiple industries, we estimate the impacts of several hypothetical occupational exposure limits on silica exposure on lung cancer and all-cause mortality. We use the parametric g-formula, which can account for healthy worker survivor bias.

RESULTS

Assuming we could eliminate occupational exposure, we estimate that there would be 20.7 fewer deaths per 1,000 workers in our pooled study by age 80 (95% confidence interval = 14.5, 26.8), including 3.91 fewer deaths owing to lung cancer (95% CI = 1.53, 6.30). Less restrictive interventions demonstrated smaller but still substantial risk reductions.

CONCLUSIONS

Our results suggest that occupational exposure limits for silica can be further strengthened to reduce silica-associated mortality and illustrate how current risk analysis for occupational limits can be improved.

Opportunities for Epidemiologists in Implementation Science: A Primer

The field of epidemiology has been defined as the study of the spread and control of disease. However, epidemiology frequently focuses on studies of etiology and distribution of disease at the cost of understanding the best ways to control disease. Moreover, only a small fraction of scientific discoveries are translated into public health practice, and the process from discovery to translation is exceedingly slow. Given the importance of translational science, the future of epidemiologic training should include competency in implementation science, whose goal is to rapidly move evidence into practice. Our purpose in this paper is to provide epidemiologists with a primer in implementation science, which includes dissemination research and implementation research as defined by the National Institutes of Health. We describe the basic principles of implementation science, highlight key components for conducting research, provide examples of implementation studies that encompass epidemiology, and offer resources and opportunities for continued learning. There is a clear need for greater speed, relevance, and application of evidence into practice, programs, and policies and an opportunity to enable epidemiologists to conduct research that not only will inform practitioners and policy-makers of risk but also will enhance the likelihood that evidence will be implemented.

Intermittent Preventive Therapy in Pregnancy and Incidence of Low Birth Weight in Malaria-Endemic Countries

OBJECTIVES

To estimate the impact of hypothetical antimalarial and nutritional interventions (which reduce the prevalence of low midupper arm circumference [MUAC]) on the incidence of low birth weight (LBW).

METHODS

We analyzed data from 14 633 pregnancies from 13 studies conducted across Africa and the Western Pacific from 1996 to 2015. We calculated population intervention effects for increasing intermittent preventive therapy in pregnancy (IPTp), full coverage with bed nets, reduction in malaria infection at delivery, and reductions in the prevalence of low MUAC.

RESULTS

We estimated that, compared with observed IPTp use, administering 3 or more doses of IPTp to all women would decrease the incidence of LBW from 9.9% to 6.9% (risk difference = 3.0%; 95% confidence interval = 1.7%, 4.0%). The intervention effects for eliminating malaria at delivery, increasing bed net ownership, and decreasing low MUAC prevalence were all modest.

CONCLUSIONS

Increasing IPTp uptake to at least 3 doses could decrease the incidence of LBW in malaria-endemic countries. The impact of IPTp on LBW was greater than the effect of prevention of malaria, consistent with a nonmalarial effect of IPTp, measurement error, or selection bias.

Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation

In a town located in a desert area of Northern Chile, gold and copper open-pit mining is carried out involving explosive processes. These processes are associated with increased dust exposure, which might affect children's respiratory health. Therefore, we aimed to quantify the causal attributable risk of living close to the mines on asthma or allergic rhinoconjunctivitis risk burden in children. Data on the prevalence of respiratory diseases and potential confounders were available from a cross-sectional survey carried out in 2009 among 288 (response: 69 % ) children living in the community. The proximity of the children's home addresses to the local gold and copper mine was calculated using geographical positioning systems. We applied targeted maximum likelihood estimation to obtain the causal attributable risk (CAR) for asthma, rhinoconjunctivitis and both outcomes combined. Children living more than the first quartile away from the mines were used as the unexposed group. Based on the estimated CAR, a hypothetical intervention in which all children lived at least one quartile away from the copper mine would decrease the risk of rhinoconjunctivitis by 4.7 percentage points (CAR: - 4.7 ; 95 % confidence interval ( 95 % CI): - 8.4 ; - 0.11 ); and 4.2 percentage points (CAR: - 4.2 ; 95 % CI: - 7.9 ; - 0.05 ) for both outcomes combined. Overall, our results suggest that a hypothetical intervention intended to increase the distance between the place of residence of the highest exposed children would reduce the prevalence of respiratory disease in the community by around four percentage points. This approach could help local policymakers in the development of efficient public health strategies.

Mortality under plausible interventions on antiretroviral treatment and depression in HIV-infected women: an application of the parametric g-formula

PURPOSE

Among HIV-infected persons, antiretroviral therapy (ART) and depression are strongly associated with mortality. We estimated reductions in 5-year mortality in Women's Interagency HIV Study participants under plausible hypothetical increases in ART initiation and reductions in depression (CES-D score≥16).

METHODS

We followed 885 ART-naïve Women's Interagency HIV Study participants for 5 years from their first study visit after April 1998 to death or censoring. We used the parametric extended g-formula to estimate cumulative mortality under the natural course (NC) and alternative exposure distributions.

RESULTS

Baseline prevalence of depression was 52% and 62% initiated ART by 5 years. Compared with mortality under NC (13.2%), immediate ART and elimination of 36% or 67% of depressive episodes were associated with risk differences (RDs) of -5.2% (95% CI: -7.7%, -2.6%) and -5.7 (95% CI: -8.7, -2.7). Compared with immediate ART and NC for depression, additionally eliminating 67% of the depressive episodes was associated with RD = -1.6 (95% CI: -3.9, 0.8). Compared with 5-year mortality under NC for ART and elimination of 67% of depression, also initiating ART immediately was associated with RD = -2.6 (95% CI: -5.0, -0.3).

CONCLUSIONS

Increasing ART initiation and reducing depression were associated with moderate reductions in 5-year mortality among HIV-infected women.

Effects of Health Insurance Interruption on Loss of Hypertension Control in Women With and Women Without HIV

BACKGROUND

Among low-income women with and without HIV, it is a priority to reduce age-related comorbidities, including hypertension and its sequelae. Because consistent health insurance access has been identified as an important factor in controlling many chronic diseases, we estimated the effects of coverage interruption on loss of hypertension control in a cohort of women in the United States.

METHODS

We analyzed prospective, longitudinal data from the Women's Interagency HIV Study. HIV-infected and HIV-uninfected women were included between 2005 and 2014 when they reported health insurance at consecutive biannual visits and had controlled hypertension, and were followed for any insurance break and loss of hypertension control. We estimated hazard ratios (HRs) by Cox proportional hazards regression with inverse-probability-of-treatment-and censoring weights (marginal structural models), and plotted the cumulative incidence of hypertension control loss.

RESULTS

Among 890 HIV-infected women, the weighted HR for hypertension control loss comparing health insurance interruption to uninterrupted coverage was 1.37 (95% confidence interval [CI], 0.99-1.91). Inclusion of AIDS Drug Assistance Program (ADAP) participation with health insurance modestly increased the HR (1.47; 95% CI, 1.04-2.07). Analysis of 272 HIV-uninfected women yielded a similar HR (1.39; 95% CI, 0.88-2.21). Additionally, there were indications of uninterrupted coverage having a protective effect on hypertension when compared with the natural course in HIV-infected (HR, 0.82; 95% CI, 0.61-1.11) and HIV-uninfected (HR, 0.78; 95% CI, 0.52-1.19) women.

CONCLUSIONS

This study provides evidence that health insurance continuity promotes hypertension control in key populations. Interventions that ensure coverage stability and ADAP access should be a policy priority.

From Patients to Policy: Population Intervention Effects in Epidemiology

Interest in implementation science and recent calls for consequentialist epidemiology urge epidemiologists to produce work more immediately applicable to public health practice. A clear vocabulary for such approaches is lacking. Here, we present a potential taxonomy of causal effects, distinguishing between "exposure effects" more relevant to patients and individuals; and "population intervention effects" more relevant to public health policy. We discuss this range of effects using figures and a simple numerical example.

Scaling Up a Water, Sanitation, and Hygiene Program in Rural Bangladesh: The Role of Program Implementation

OBJECTIVES

To evaluate whether the quality of implementation of a water, sanitation, and hygiene program called SHEWA-B and delivered by UNICEF to 20 million people in rural Bangladesh was associated with health behaviors and sanitation infrastructure access.

METHODS

We surveyed 33 027 households targeted by SHEWA-B and 1110 SHEWA-B hygiene promoters in 2011 and 2012. We developed an implementation quality index and compared the probability of health behaviors and sanitation infrastructure access in counterfactual scenarios over the range of implementation quality.

RESULTS

Forty-seven percent of households (n = 14 622) had met a SHEWA-B hygiene promoter, and 47% of hygiene promoters (n = 527) could recall all key program messages. The frequency of hygiene promoter visits was not associated with improved outcomes. Higher implementation quality was not associated with better health behaviors or infrastructure access. Outcomes differed by only 1% to 3% in scenarios in which all clusters received low versus high implementation quality.

CONCLUSIONS

SHEWA-B did not meet UNICEF's ideal implementation quality in any area. Improved implementation quality would have resulted in marginal changes in health behaviors or infrastructure access. This suggests that SHEWA-B's design was suboptimal for improving these outcomes.

Smoking, HIV, and risk of pregnancy loss

OBJECTIVE

Cigarette smoking during pregnancy increases risks of poor pregnancy outcomes including miscarriage and stillbirth (pregnancy loss), but the effect of smoking on pregnancy loss among HIV-infected women has not been explored. Here, investigated the impact of smoking on risk of pregnancy loss among HIV-positive and HIV-negative women, and estimated the potential impact of realistic smoking cessation interventions on risk of pregnancy loss among HIV-positive women.

DESIGN

We analyzed pregnancy outcomes in HIV-positive and HIV-negative participants in the Women's Interagency HIV Study between 1994 and 2014.

METHODS

We estimated effects of current smoking at or immediately before pregnancy on pregnancy loss; we controlled for confounding using regression approaches, and estimated potential impact of realistic smoking cessation interventions using a semiparametric g-formula approach.

RESULTS

Analysis examined 1033 pregnancies among 659 women. The effect of smoking on pregnancy loss differed dramatically by HIV status: adjusted for confounding, the risk difference comparing current smokers to current nonsmokers was 19.2% (95% confidence limit 10.9-27.5%) in HIV-positive women and 9.7% (95% confidence limit 0.0-19.4%) in HIV-negative women. These results were robust to sensitivity analyses. We estimated that we would need to offer a realistic smoking cessation intervention to 36 women to prevent one pregnancy loss.

CONCLUSION

Smoking is a highly prevalent exposure with important consequences for pregnancy in HIV-positive pregnant women in the United States, even in the presence of potent highly active antiretroviral therapy. This evidence supports greater efforts to promote smoking cessation interventions among HIV-positive women, especially those who desire to become pregnant.

Maternal Malaria and Malnutrition (M3) initiative, a pooled birth cohort of 13 pregnancy studies in Africa and the Western Pacific

PURPOSE

The Maternal Malaria and Malnutrition (M3) initiative has pooled together 13 studies with the hope of improving understanding of malaria-nutrition interactions during pregnancy and to foster collaboration between nutritionists and malariologists.

PARTICIPANTS

Data were pooled on 14 635 singleton, live birth pregnancies from women who had participated in 1 of 13 pregnancy studies. The 13 studies cover 8 countries in Africa and Papua New Guinea in the Western Pacific conducted from 1996 to 2015.

FINDINGS TO DATE

Data are available at the time of antenatal enrolment of women into their respective parent study and at delivery. The data set comprises essential data such as malaria infection status, anthropometric assessments of maternal nutritional status, presence of anaemia and birth weight, as well as additional variables such gestational age at delivery for a subset of women. Participating studies are described in detail with regard to setting and primary outcome measures, and summarised data are available from each contributing cohort.

FUTURE PLANS

This pooled birth cohort is the largest pregnancy data set to date to permit a more definite evaluation of the impact of plausible interactions between poor nutritional status and malaria infection in pregnant women on fetal growth and gestational length. Given the current comparative lack of large pregnancy cohorts in malaria-endemic settings, compilation of suitable pregnancy cohorts is likely to provide adequate statistical power to assess malaria-nutrition interactions, and could point towards settings where such interactions are most relevant. The M3 cohort may thus help to identify pregnant women at high risk of adverse outcomes who may benefit from tailored intensive antenatal care including nutritional supplements and alternative or intensified malaria prevention regimens, and the settings in which these interventions would be most effective.

Prepregnancy obesity and the racial disparity in infant mortality

OBJECTIVE

To estimate the extent to which prepregnancy obesity explains the Black-White disparity in stillbirth and infant mortality.

METHODS

A population-based study of linked Pennsylvania birth-infant death certificates (2003-2011; n = 1,055,359 births) and fetal death certificates (2006-2011; n = 3,102 stillbirths) for all singleton pregnancies in non-Hispanic (NH) White and NH Black women was conducted. Inverse probability weighted regression was used to estimate the role of prepregnancy obesity in explaining the race-infant/fetal death association.

RESULTS

Compared with NH White women, NH Black women were more likely to have obesity (≥30 kg/m² ) and experienced a higher rate of stillbirth (8.3 vs. 3.6 stillbirths per 1,000 live-born and stillborn infants) and infant death (8.5 vs. 3.0 infant deaths per 1,000 live births). When the contribution of prepregnancy obesity was removed, the difference in risk between NH Blacks and NH Whites decreased from 6.2 (95% CI: 5.6-6.7) to 5.5 (95% CI: 4.9-6.2) excess stillbirths per 1,000 and 5.8 (95% CI: 5.3-6.3) to 5.2 (95% CI: 4.7-5.7) excess infant deaths per 1,000.

CONCLUSIONS

For every 10,000 live births in Pennsylvania (2003-2011), 6 of the 61 excess infant deaths in NH Black women and 5 of the 44 excess stillbirths (2006-2011) were attributable to prepregnancy obesity.

Predicting the Population Health Impacts of Community Interventions: The Case of Alcohol Outlets and Binge Drinking

A substitution estimator can be used to predict how shifts in population exposures might change health. We illustrated this method by estimating how an upper limit on alcohol outlet density might alter binge drinking in the New York Social Environment Study (n = 4000), and provided statistical code and sample data. The largest differences in binge drinking were for an upper limit of 70 outlets per square mile; there was a -0.7% difference in binge drinking prevalence for New York City overall (95% confidence interval [CI] = -0.2%, -1.3%) and a -2.4% difference in binge drinking prevalence for the subset of communities the intervention modified (95% CI = -0.5%, -4.0%). A substitution estimator is a flexible tool for estimating population intervention parameters and improving the translation of public health research results to practitioners.

An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects

Traditional epidemiologic approaches allow us to compare counterfactual outcomes under 2 exposure distributions, usually 100% exposed and 100% unexposed. However, to estimate the population health effect of a proposed intervention, one may wish to compare factual outcomes under the observed exposure distribution to counterfactual outcomes under the exposure distribution produced by an intervention. Here, we used inverse probability weights to compare the 5-year mortality risk under observed antiretroviral therapy treatment plans to the 5-year mortality risk that would had been observed under an intervention in which all patients initiated therapy immediately upon entry into care among patients positive for human immunodeficiency virus in the US Centers for AIDS Research Network of Integrated Clinical Systems multisite cohort study between 1998 and 2013. Therapy-naïve patients (n = 14,700) were followed from entry into care until death, loss to follow-up, or censoring at 5 years or on December 31, 2013. The 5-year cumulative incidence of mortality was 11.65% under observed treatment plans and 10.10% under the intervention, yielding a risk difference of -1.57% (95% confidence interval: -3.08, -0.06). Comparing outcomes under the intervention with outcomes under observed treatment plans provides meaningful information about the potential consequences of new US guidelines to treat all patients with human immunodeficiency virus regardless of CD4 cell count under actual clinical conditions.

The population impact of eliminating homelessness on HIV viral suppression among people who use drugs

OBJECTIVE

We sought to estimate the change in viral suppression prevalence if homelessness were eliminated from a population of HIV-infected people who use drugs.

DESIGN

Community-recruited prospective cohort of HIV-infected people who use drugs in Vancouver, Canada. Behavioural information was collected at baseline and linked to a province-wide HIV/AIDS treatment database. The primary outcome was viral suppression (<50 copies/ml) measured during subsequent routine clinical care.

METHODS

We employed an imputation-based marginal modelling approach. First, we used modified Poisson regression to estimate the relationship between homelessness and viral suppression (adjusting for sociodemographics, substance use, addiction treatment, and other confounders). Then, we imputed an outcome probability for each individual while manipulating the exposure (homelessness). Population viral suppression prevalence under realized and 'housed' scenarios were obtained by averaging these probabilities across the study population. Bootstrapping was conducted to calculate 95% confidence limits.

RESULTS

Of 706 individuals interviewed between January 2005 and December 2013, the majority were men (66.0%), of white race/ethnicity (55.1%), and had a history of injection drug use (93.6%). At first study visit, 223 (31.6%) reported recent homelessness, and 37.8% were subsequently identified as virally suppressed. Adjusted marginal models estimated a 15.1% relative increase [95% confidence interval (CI) 9.0-21.7%) in viral suppression in the entire population - to 43.5% (95% CI 39.4-48.2%) - if all homeless individuals were housed. Among those homeless, eliminating this exposure would increase viral suppression from 22.0 to 40.1% (95% CI 35.1-46.1%), an 82.3% relative increase.

CONCLUSION

Interventions to house homeless, HIV-positive individuals who use drugs could significantly increase population viral suppression. Such interventions should be implemented as a part of renewed HIV/AIDS prevention and treatment efforts.

The effect of antiretroviral therapy on all-cause mortality, generalized to persons diagnosed with HIV in the USA, 2009-11

BACKGROUND

Although antiretroviral therapy (ART) is known to be protective against HIV-related mortality, the expected magnitude of effect is unclear because existing estimates of the effect of ART may not directly generalize to recently HIV-diagnosed persons.

METHODS

In this study, we estimated 5-year mortality risks for immediate versus no ART initiation among patients (n = 12,547) in the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) using the complement of adjusted Kaplan-Meier survival functions. We subsequently standardized estimates to persons diagnosed with HIV in the USA between 2009 and 2011, who were enumerated using national surveillance data.

RESULTS

The 5-year mortality, had all patients in the CNICS immediately initiated ART, was 10.6% [95% confidence interval (CI): 9.3%, 11.9%] compared with 28.3% (95% CI: 19.1%, 37.5%) had ART initiation been delayed at least 5 years. The 5-year mortality risk difference due to ART among patients in the CNICS was -17.7% (95% CI: -27.0%, -8.4%). Based on methods for generalizing an estimate from a study sample to a different target population, the expected risk difference due to ART initiation among recently HIV-diagnosed persons in the USA was -19.1% (95% CI: -30.5%, -7.8%).

CONCLUSIONS

Immediate ART initiation substantially lowers mortality among persons in the CNICS and this benefit is expected to be similar among persons recently diagnosed with HIV in the USA. We demonstrate a method by which concerns about generalizability can be addressed and evaluated quantitatively.

Reduction in diarrhoeal rates through interventions that prevent unnecessary antibiotic exposure early in life in an observational birth cohort

BACKGROUND

Antibiotic treatment early in life is often not needed and has been associated with increased rates of subsequent diarrhoea. We estimated the impact of realistic interventions, which would prevent unnecessary antibiotic exposures before 6 months of age, on reducing childhood diarrhoeal rates.

METHODS

In data from a prospective observational cohort study conducted in Vellore, India, we used the parametric g-formula to model diarrhoeal incidence rate differences contrasting the observed incidence of diarrhoea to the incidence expected under hypothetical interventions. The interventions prevented unnecessary antibiotic treatments for non-bloody diarrhoea, vomiting and upper respiratory infections before 6 months of age. We also modelled targeted interventions, in which unnecessary antibiotic use was prevented only among children who had already stopped exclusive breast feeding.

RESULTS

More than half of all antibiotic exposures before 6 months (58.9%) were likely unnecessary. The incidence rate difference associated with removing unnecessary antibiotic use before 6 months of age was -0.28 (95% CI -0.46 to -0.08) episodes per 30 child-months. This implies that preventing unnecessary antibiotic exposures in just 4 children would reduce the incidence of diarrhoea by 1 from 6 months to 3 years of age.

CONCLUSIONS

Interventions to reduce unnecessary antibiotic use among young children could result in an important reduction in diarrhoeal rates. This work provides an example application of statistical methods which can further the aim of presenting epidemiological findings that are relevant to public health practice.

Invited commentary: every good randomization deserves observation

Preexposure prophylaxis (PrEP) is a promising approach to prevention of human immunodeficiency virus (HIV) transmission, in which an HIV-negative individual takes a single daily dose of an antiretroviral drug so that, if exposed to HIV, an active antiretroviral drug is already present in his or her system. In this issue of the Journal, Murnane et al. (Am J Epidemiol. 2015;182(10):848-856) use data from the Partners PrEP Study (Kenya and Uganda, 2008-2011) to estimate the efficacy of PrEP under perfect adherence. We discuss Murnane et al.'s work and then examine the larger issues of generalizability or transportability of findings from a randomized trial to a new setting when adherence to an intervention determines its effectiveness. We discuss sufficient conditions for generalizability and use causal directed acyclic graphs to show how these assumptions might play out when trials are used to make inferences about the effect of PrEP in current and future real-world target populations.

Population intervention models to estimate ambient NO2 health effects in children with asthma

Health effects of ambient air pollution are most frequently expressed in individual studies as responses to a standardized unit of air pollution changes (e.g., an interquartile interval), which is thought to enable comparison of findings across studies. However, this approach does not necessarily convey health effects in terms of a real-world air pollution scenario. In the present study, we use population intervention modeling to estimate the effect of an air pollution intervention that makes explicit reference to the observed exposure data and is identifiable in those data. We calculate the association between ambient summertime nitrogen dioxide (NO2) and forced expiratory flow between 25% and 75% of forced vital capacity (FEF25-75) in a cohort of children with asthma in Fresno, California. We scale the effect size to reflect NO2 abatement on a majority of summer days. The effect estimates were small, imprecise, and consistently indicated improved pulmonary function with decreased NO2. The effects ranged from -0.8% of mean FEF25-75 (95% confidence interval (CI): -3.4, 1.7) to -3.3% (95% CI: -7.5, 0.9). We conclude by discussing the nature and feasibility of the exposure change analyzed here given the observed air pollution profile, and we propose additional applications of population intervention models in environmental epidemiology.

Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling

Air pollution epidemiology continues moving toward the study of mixtures and multipollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multipollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in 2 air pollutants (nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5 μm) related to one another within 2 seasons (summer and winter), within 3 cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city season, we examined how realistically IQR scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than 1 IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the copollutant had to remain relatively constant. We found that in some city seasons, there were very few instances in which these conditions were satisfied (eg, 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multipollutant effects that are supported by empirical data.

Standardized binomial models for risk or prevalence ratios and differences

BACKGROUND

Epidemiologists often analyse binary outcomes in cohort and cross-sectional studies using multivariable logistic regression models, yielding estimates of adjusted odds ratios. It is widely known that the odds ratio closely approximates the risk or prevalence ratio when the outcome is rare, and it does not do so when the outcome is common. Consequently, investigators may decide to directly estimate the risk or prevalence ratio using a log binomial regression model.

METHODS

We describe the use of a marginal structural binomial regression model to estimate standardized risk or prevalence ratios and differences. We illustrate the proposed approach using data from a cohort study of coronary heart disease status in Evans County, Georgia, USA.

RESULTS

The approach reduces problems with model convergence typical of log binomial regression by shifting all explanatory variables except the exposures of primary interest from the linear predictor of the outcome regression model to a model for the standardization weights. The approach also facilitates evaluation of departures from additivity in the joint effects of two exposures.

CONCLUSIONS

Epidemiologists should consider reporting standardized risk or prevalence ratios and differences in cohort and cross-sectional studies. These are readily-obtained using the SAS, Stata and R statistical software packages. The proposed approach estimates the exposure effect in the total population.

The Impact of Implementation Fidelity on Mortality Under a CD4-Stratified Timing Strategy for Antiretroviral Therapy in Patients With Tuberculosis

Among patients with tuberculosis and human immunodeficiency virus type 1, CD4-stratified initiation of antiretroviral therapy (ART) is recommended, with earlier ART in those with low CD4 counts. However, the impact of implementation fidelity to this recommendation is unknown. We examined a prospective cohort study of 395 adult patients diagnosed with tuberculosis and human immunodeficiency virus between August 2007 and November 2009 in Kinshasa, Democratic Republic of the Congo. ART was to be initiated after 1 month of tuberculosis treatment at a CD4 count of <100 cells/mm(3) or World Health Organization stage 4 (other than extrapulmonary tuberculosis) and after 2 months of tuberculosis treatment at a CD4 count of 100-350 cells/mm(3). We used the parametric g-formula to estimate the impact of implementation fidelity on 6-month mortality. Observed implementation fidelity was low (46%); 54% of patients either experienced delays in ART initiation or did not initiate ART, which could be avoided under perfect implementation fidelity. The observed mortality risk was 12.0% (95% confidence interval (CI): 8.2, 15.7); under complete (counterfactual) implementation fidelity, the mortality risk was 7.8% (95% CI: 2.4, 12.3), corresponding to a risk reduction of 4.2% (95% CI: 0.3, 8.1) and a preventable fraction of 35.1% (95% CI: 2.9, 67.9). Strategies to achieve high implementation fidelity to CD4-stratified ART timing are needed to maximize survival benefit.

Invited commentary: Estimating population impact in the presence of competing events

The formal approach in the field of causal inference has enabled epidemiologists to clarify several complications that arise when estimating the effect of an intervention on a health outcome of interest. When the outcome is a failure time or longitudinal process, researchers must often deal with competing events. In this issue of the Journal, Picciotto et al. (Am J Epidemiol. 2015;181(8):563-570) use structural nested failure time models to assess potential population effects of hypothetical interventions and censor competing events. In the present commentary, we discuss 2 interpretations that result from treating competing events as censored observations and how they relate to measures of public health impact. We also comment on 2 alternative approaches for handling competing events: an inverse probability weighting estimator of the survivor average causal effect and the parametric g-formula, which can be used to estimate a functional of the subdistribution of the event of interest. We argue that careful consideration of the tradeoff between the interpretation of the parameters from each approach and the assumptions required to estimate these parameters should guide researchers on the various ways to handle competing events in epidemiologic research.

Epidemiology's continuing contribution to public health: The power of "Then and Now"

The 47th annual meeting of the Society for Epidemiologic Research hosted 17 invited speakers charged by the Executive Committee with presenting some of the many ways that epidemiologists have improved the health of the general population. There were 9 "Then and Now" sessions that were structured to focus on how early epidemiologists overcame research hurdles and advanced health through innovative strategies. For most topics, a longstanding expert was paired with an excellent contemporary epidemiologist working in the area, and both were given the freedom to deliver an integrated story about epidemiology's temporal role in protecting and promoting public health. Epidemiologic discoveries in cardiovascular, cancer, and perinatal epidemiology were discussed on day 1, followed by discussions of accomplishments in reducing exposures that adversely impact health (nutrition, environment/occupation, and tobacco use) on day 2. Topics with relevancy for many aspects of epidemiology were presented on day 3, including infectious diseases, social forces, and causal thinking in epidemiologic research. Given the large number of outstanding senior and junior epidemiologists that attended the meeting, choosing speakers was a unique challenge. What became evident from all sessions was the passion that epidemiologists have for population health, tempered with concerns for remaining true to epidemiologic principles, the timely adoption of innovative methods, and the responsible interpretation of research findings.

Point and interval estimation of baseline risk and treatment effect based on logistic model for observational studies

In observational studies with dichotomous outcome of a population, researchers usually report treatment effect alone, although both baseline risk and treatment effect are needed to evaluate the significance of the treatment effect to the population. In this article, we study point and interval estimates including confidence region of baseline risk and treatment effect based on logistic model, where baseline risk is the risk of outcome of the population under control treatment while treatment effect is measured by the risk difference between outcomes of the population under active versus control treatments. Using approximate normal distribution of the maximum-likelihood (ML) estimate of the model parameters, we obtain an approximate joint distribution of the ML estimate of the baseline risk and the treatment effect. Using the approximate joint distribution, we obtain point estimate and confidence region of the baseline risk and the treatment effect as well as point estimate and confidence interval of the treatment effect when the ML estimate of the baseline risk falls into specified range. These interval estimates reflect nonnormality of the joint distribution of the ML estimate of the baseline risk and the treatment effect. The method can be easily implemented by using any software that generates normal distribution. The method can also be used to obtain point and interval estimates of baseline risk and any other measure of treatment effect such as risk ratio and the number needed to treat. The method can also be extended from logistic model to other models such as log-linear model.