Transportability without positivity: a synthesis of statistical and simulation modeling

When estimating an effect of an action with a randomized or observational study, that study is often not a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions are ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches were able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge

Beyond the Boxes: Guiding Questions for Thoughtfully Measuring and Interpreting Race in Population Health Research

Race and ethnicity are key constructs underpinning social stratification and health in the US, but the use of race and ethnicity in population health research can be ritualistic and lacking careful consideration. Our team collaborated on a research project evaluating how population health research conceptualizes and uses race. We drew from lessons learned during this project to develop a blog series for the Interdisciplinary Association of Population Health Science. This six part series proposes guiding questions and considerations for how researchers can more thoughtfully define, measure, code, analyze, and interpret race and ethnicity in their own work

Streptococcus pneumoniae outbreaks and implications for transmission and control: a systematic review

Abstract Background Streptococcus pneumoniae is capable of causing multiple infectious syndromes and occasionally causes outbreaks. The objective of this review is to update prior outbreak reviews, identify control measures, and comment on transmission. Methods We conducted a review of published S. pneumoniae outbreaks, defined as at least two linked cases of S. pneumoniae. Results A total of 98 articles (86 respiratory; 8 conjunctivitis; 2 otitis media; 1 surgical site; 1 multiple), detailing 94 unique outbreaks occurring between 1916 to 2017 were identified. Reported serotypes included 1, 2, 3, 4, 5, 7F, 8, 12F, 14, 20, and 23F, and serogroups 6, 9, 15, 19, 22. The median attack rate for pneumococcal outbreaks was 7.0% (Interquartile range: 2.4%, 13%). The median case-fatality ratio was 12.9% (interquartile range: 0%, 29.2%). Age groups most affected by outbreaks were older adults (60.3%) and young adults (34.2%). Outbreaks occurred in crowded settings, such as universities/schools/daycares, military barracks, hospital wards, and long-term care facilities. Of outbreaks that assessed vaccination coverage, low initial vaccination or revaccination coverage was common. Most (73.1%) of reported outbreaks reported non-susceptibility to at least one antibiotic, with non-susceptibility to penicillin (56.0%) and erythromycin (52.6%) being common. Evidence suggests transmission in outbreaks can occur through multiple modes, including carriers, infected individuals, or medical devices. Several cases developed disease shortly after exposure (< 72 h). Respiratory outbreaks used infection prevention (55.6%), prophylactic vaccination (63.5%), and prophylactic antibiotics (50.5%) to prevent future cases. PPSV23 covered all reported outbreak serotypes. PCV13 covered 10 of 16 serotypes. For conjunctival outbreaks, only infection prevention strategies were used. Conclusions To prevent the initial occurrence of respiratory outbreaks, vaccination and revaccination is likely the best preventive measure. Once an outbreak occurs, vaccination and infection-prevention strategies should be utilized. Antibiotic prophylaxis may be considered for high-risk exposed individuals, but development of antibiotic resistance during outbreaks has been reported. The short period between initial exposure and development of disease indicates that pneumococcal colonization is not a prerequisite for pneumococcal respiratory infection

Resource Packet for "Clear communication of race and ethnicity for public health: best practices & common failings"

This work was created as a resource packet for attendees of the Interdisciplinary Association for Population Health Science (IAPHS)'s pre-conference workshop "Clear communication of race and ethnicity for public health: Best practices & common failings." The pre-conference workshop was held virtually on September 23rd, 2021 and facilitated by the author team

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators

Postpartum depressive symptoms following implementation of the 10 steps to successful breastfeeding program in Kinshasa, Democratic Republic of Congo: A cohort study.

BackgroundSocial support and relevant skills training can reduce the risk of postpartum depression (PPD) by reducing the impact of stressors. The 10-step program to encourage exclusive breastfeeding that forms the basis of the Baby-Friendly Hospital Initiative (BFHI) provides both, suggesting it may lessen depressive symptoms directly or by reducing difficulties associated with infant feeding. Our objective was to quantify the association of implementing Steps 1-9 or Steps 1-10 on postpartum depressive symptoms and test whether this association was mediated by breastfeeding difficulties.Methods and findingsWe used data from a breastfeeding promotion trial of all women who gave birth to a healthy singleton between May 24 and August 25, 2012 in 1 of the 6 facilities comparing different BFHI implementations (Steps 1-9, Steps 1-10) to the standard of care (SOC) randomized by facility in Kinshasa, Democratic Republic of Congo. Depressive symptoms, a non-registered trial outcome, was assessed at 14 weeks via the Edinburgh Postnatal Depression Scale (EPDS). Inverse probability weighting (IPW) was used to estimate the association of BFHI implementations on depressive symptoms and the controlled direct association through breastfeeding difficulties at 10 weeks postpartum. A total of 903 mother-infant pairs were included in the analysis. Most women enrolled had previously given birth (76%) and exclusively breastfed at 10 weeks (55%). The median age was 27 (interquartile range (IQR): 23, 32 years). The proportion of women reporting breastfeeding difficulties at week 10 was higher in both Steps 1-9 (75%) and Steps 1-10 (91%) relative to the SOC (67%). However, the number of reported difficulties was similar between Steps 1-9 (median: 2; IQR: 0, 3) and SOC (2; IQR: 0, 3), with slightly more in Steps 1-10 (2; IQR: 1, 3). The prevalence of symptoms consistent with probable depression (EPDS score >13) was 18% for SOC, 11% for Steps 1-9 (prevalence difference [PD] = -0.08; 95% confidence interval (CI): -0.14 to -0.01, p = 0.019), and 8% for Steps 1-10 (PD = -0.11, -0.16 to -0.05; p ConclusionsIn conclusion, in this cohort, the implementation of the BFHI steps was associated with a reduction in depressive symptoms in the groups implementing BFHI Steps 1-9 or 1-10 relative to the SOC, with the implementation of Steps 1-10 associated with the largest decrease. Specifically, the reduction in depressive symptoms was observed for women reporting breastfeeding difficulties. PPD has a negative impact on the mother, her partner, and the baby, with long-lasting consequences. This additional benefit of BFHI steps suggests that renewed effort to scale its implementation globally may be beneficial to mitigate the negative impacts of PPD on the mother, her partner, and the baby.Trial registrationClinicalTrials.gov NCT01428232

Measuring office workplace interactions and hand hygiene behaviors through electronic sensors: A feasibility study.

Office-based workplaces are an important but understudied context for infectious disease transmission. We examined the feasibility of two different sensors (Opos and Bluetooth beacons) for collecting person-to-person contacts and hand hygiene in office-based workplaces. Opo is an interaction sensor that captures sensor-to-sensor interactions through ultrasonic frequencies, which correspond to face-to-face contacts between study participants. Opos were additionally used to measure hand hygiene events by affixing sensors to soap and alcohol-based hand sanitizer dispensers. Bluetooth beacons were used in conjunction with a smartphone application and recorded proximity contacts between study participants. Participants in two office sites were followed for one-week in their workplace in March 2018. Contact patterns varied by time of day and day of the week. Face-to-face contacts were of shorter mean duration than proximity contacts. Supervisors had fewer proximity contacts but more face-to-face contacts than non-supervisors. Self-reported hand hygiene was substantively higher than sensor-collected hand hygiene events and duration of hand washing events was short (median: 9 seconds, range: 2.5-33 seconds). Given that office settings are key environments in which working age populations spend a large proportion of their time and interactions, a better characterization of empirical social networks and hand hygiene behaviors for workplace interactions are needed to mitigate outbreaks and prepare for pandemics. Our study demonstrates that implementing sensor technologies for tracking interactions and behaviors in offices is feasible and can provide new insights into real-world social networks and hygiene practices. We identified key social interactions, variability in hand hygiene, and differences in interactions by workplace roles. High-resolution network data will be essential for identifying the most effective ways to mitigate infectious disease transmission and develop pandemic preparedness plans for the workplace setting