Regression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials

When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007–2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45–0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology

Beyond the binary: a proposal for uniform standards for gender identity and more descriptive sex classifications in electronic medical records

Sex and gender are commonly thought to be synonymous, and both are generally classified using the binary categories of male and female. Existing standards applying to electronic health records (EHRs) all use, or until very recently have used, only these two categories plus “unknown” or “not-specified” with the latter options existing more for irretrievable or absent information than for purposes of documenting a diverse set of possible expected answers. Evidence is mounting that the binary options may not be adequate for many populations, including but not limited to intersex and transgendered patients. This paper examines the supporting basis for more inclusive and clear gender and sex classifications in EHRs, provides an argument that Meaningful Use Stage 3 should include a requirement for such a classification scheme, and proposes a framework for such requirements that would meet the needs of the affected population, providers, and vendors taking into account the experiences gathered by trailblazing providers and vendors since the IoM report of 2011

Health Behaviors and Behavioral Economics in the Context of HIV, Malaria, and Exercise

Although the challenges of population health differ widely between rich and poor countries, fundamental features of health behavior shed light on how individuals make choices about their health. These insights that can cut across countries and cultures. In this thesis, I apply concepts from behavioral economics to provide insights into how cognitive biases and social influences guide health behavior. Paper 1 addresses inter-household spillovers and knowledge of HIV status. Using regression discontinuity design and a population-based dataset from South Africa, I estimate how a person's ART eligibility affects their household member’s HIV status knowledge. ART led to a large increase in HIV status knowledge among the patient's male household members. Although prior studies have noted a correlation between ART expansion and testing rates, this study is among the first to causally link ART initiation to increased awareness of HIV status among household members. Paper 2 assesses the role of present bias and salience in malaria prevention behavior and risk perception in northern Ghana. Using lab-in-the-field measurement and high-frequency surveys of market vendors in Tamale, Ghana, I find that time preferences do not predict spending on malaria prevention or bednet utilization, but recent illnesses are associated with malaria prevention spending. I investigate the role of beliefs about malaria risk and find that respondents whose children had been ill in the past two weeks report higher subjective expectations of malaria risk, suggesting that recent episodes of illness may increase an individual's perception of risk and lead to increase spending on malaria prevention. Paper 3 uses a behavioral field experiment to evaluate whether personal, goal-oriented reminders are an effective means to increase exercise frequency. I ran a 12-month randomized controlled trial on members of a chain of gyms in Montreal, Quebec. The trial compared generic SMS reminders with personalized reminders that recalled members' own exercise goals, which were elicited via a questionnaire at the time of study enrollment. I find that individuals who received personalized reminders did not exercise more frequently than the general reminder group and present suggestive evidence that recalling their goals generated a discouragement effect

Un Estudio de la Palma de Ramos, Ceroxylum alpinum, en la Zona Intag, Ecuador

This study addresses the endangered wax palm species, Ceroxylum alpinum, in the Ecuadorian cloud forest zone of Intag. Attention has recently arisen regarding this plant because the yellow-eared parrot, Ognorhynchus icterotis, which depends on it for food and nesting, is in critical danger of extinction. Much of this attention has targeted the use of wax palm fronds on Palm Sunday, but this investigation shows that this is only one of the many threats to the palm. Environmentalists in Intag are currently undertaking reforestation projects in community reserves to save the palm, but little is known about its development. This study seeks to learn about the communities’ knowledge about and relations with the palm and the conditions of the palm’s natural habitat. It uses this information to provide the foundation for continuing studies of the reforestation projects so that the wax palm populations can be saved

Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review

OBJECTIVE: Motivated by recent calls to use electronic health records for research, we reviewed the application and development of methods for addressing the bias from unmeasured confounding in longitudinal data. DESIGN: Methodological review of existing literature SETTING: We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomised longitudinal health data through quasi-experimental analysis. RESULTS: Among the 121 studies included for review, 84 used instrumental variable analysis (IVA), of which 36 used lagged or historical instruments. Difference-in-differences (DiD) and fixed effects (FE) models were found in 29 studies. Five of these combined IVA with DiD or FE to try to mitigate for time-dependent confounding. Other less frequently used methods included prior event rate ratio adjustment, regression discontinuity nested within pre-post studies, propensity score calibration, perturbation analysis and negative control outcomes. CONCLUSIONS: Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in non-randomised, longitudinal studies, but researchers often fail to take full advantage of available longitudinal information. A range of promising new methods have been developed, but further studies are needed to understand their relative performance in different contexts before they can be recommended for widespread use

Behavioral Aspects of Healthy Longevity

Addressing the growing burden of noncommunicable diseases to achieve healthy longevity for an aging population has become central to global health policy goals. New policy tools are needed for effectively and efficiently tackling health and lifestyle behaviors and habits linked to the development of noncommunicable disease risk factors. Behavioral science offers insights into psychological barriers, mental models, biases, and other factors that influence decision making and habit formation. Applying these insights can support current policy efforts toward healthy longevity. This paper develops a framework to clarify the relationships between noncommunicable disease formation, detection, and management and behavioral determinants at the individual, community, and health system levels. Following the framework, the paper documents frequently identified behavioral barriers at the three key stages of patients’ noncommunicable disease trajectories. It identifies policy lessons from the behavioral science literature to address such barriers and, together with other policies, reduce the incidence of noncommunicable diseases and improve treatment effectiveness

There is a large disparity between what people see in social media about health research and the underlying strength of evidence

Our social media feeds are full of articles shared by friends and family that make claims about how something can prevent a particular health condition. But how robust is the scientific evidence base underpinning these claims? Noah Haber, Alexander Breskin, Ellen Moscoe and Emily R. Smith, on behalf of the CLAIMS team, report on a systematic review of the state of causal inference in media articles and academic studies at the point of consumption on social media. There is a large disparity between what people see in social media about health research compared with the underlying strength of evidence, both in the studies themselves and in the media articles describing their findings. The studies tend to imply stronger causal inference than their methods merit, while media articles reporting on them were found to be further overstated and inaccurate

Regression discontinuity designs in health: a systematic review

Background: Regression discontinuity designs are non-randomized study designs that permit strong causal inference with relatively weak assumptions. Interest in these designs is growing but there is limited knowledge of the extent of their application in health. We aimed to conduct a comprehensive systematic review of the use of regression discontinuity designs in health research. Methods: We included studies that used regression discontinuity designs to investigate the physical or mental health outcomes of any interventions or exposures in any populations. We searched 32 health, social science, and grey literature databases (1 January 1960-1 January 2019). We critically appraised studies using eight criteria adapted from the What Works Clearinghouse Standards for regression discontinuity designs. We conducted a narrative synthesis, analyzing the forcing variables and threshold rules used in each study. Results: The literature search retrieved 7658 records, producing 325 studies that met the inclusion criteria. A broad range of health topics were represented. The forcing variables used to implement the design were age; socioeconomic measures; date or time of exposure or implementation; environmental measures such as air quality; geographic location; and clinical measures that act as a threshold for treatment. Twelve percent of the studies fully met the eight quality appraisal criteria. Fifteen percent of studies reported a pre-specified primary outcome or study protocol. Conclusions: This systematic review demonstrates that regression discontinuity designs have been widely applied in health research and could be used more widely still. Shortcomings in study quality and reporting suggest that the potential benefits of this method have not yet been fully realized