Search

Within epidemiological research, estimating treatment effects from observational data presents notable challenges. Targeted Maximum Likelihood Estimation (TMLE) emerges as a robust method, addressing these challenges by accurately modeling treatment effects. This approach uniquely combines the precision of correctly specified models with the versatility of data-adaptive, flexible machine learning algorithms. Despite its effectiveness, TMLE's integration of complex algorithms can introduce bias and under-coverage. This issue is addressed through the Double Cross-fit TMLE (DC-TMLE) approach, enhancing accuracy and reducing biases inherent in observational studies. However, DC-TMLE's potential remains underexplored in epidemiological research, primarily due to the lack of comprehensive methodological guidance and the complexity of its computational implementation. Recognizing this gap, our paper contributes a detailed, reproducible guide for implementing DC-TMLE in R, aimed specifically at epidemiological applications. We demonstrate the utility of this method using an openly available clinical dataset, underscoring its relevance and adaptability for robust epidemiological analysis. This guide aims to facilitate broader adoption of DC-TMLE in epidemiological studies, promoting more accurate and reliable treatment effect estimations in observational research., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Introduction: Men are vulnerable to ambient heat-related kidney disease burden; however, limited evidence exists on how vulnerable women are when exposed to high ambient heat. We evaluated the sex-specific association between ambient temperature and urine electrolytes, and 24-hour urine total protein, and volume., Methods: We pooled a longitudinal 5624 person-visits data of 1175 participants' concentration and 24-hour excretion of urine electrolytes and other biomarkers (24-hour urine total protein and volume) from southwest coastal Bangladesh (Khulna, Satkhira, and Mongla districts) during November 2016 to April 2017. We then spatiotemporally linked ambient temperature data from local weather stations to participants' health outcomes. For evaluating the relationships between average ambient temperature and urine electrolytes and other biomarkers, we plotted confounder-adjusted restricted cubic spline (RCS) plots using participant-level, household-level, and community-level random intercepts. We then used piece-wise linear mixed-effects models for different ambient temperature segments determined by inflection points in RCS plots and reported the maximum likelihood estimates and cluster robust standard errors. By applying interaction terms for sex and ambient temperature, we determined the overall significance using the Wald test. Bonferroni correction was used for multiple comparisons., Results: The RCS plots demonstrated nonlinear associations between ambient heat and urine biomarkers for males and females. Piecewise linear mixed-effects models suggested that sex did not modify the relationship of ambient temperature with any of the urine parameters after Bonferroni correction ( P  < 0.004)., Conclusion: Our findings suggest that women are as susceptible to the effects of high ambient temperature exposure as men., (© 2024 International Society of Nephrology. Published by Elsevier Inc.)

Background: Supply driven programs that are not closely connected to community demand and demand-driven programs that fail to ensure supply both risk worsening inequity. Understanding patterns of uptake of behaviors among the poorest under ideal experimental conditions, such as those of an efficacy trial, can help identify strategies that could be strengthened in routine programmatic conditions for more equitable uptake. WASH Benefits Bangladesh was a randomized controlled efficacy trial that provided free-of cost WASH hardware along with behavior change promotion. The current paper aimed to determine the impact of the removal of supply and demand constraints on the uptake of handwashing and sanitation behaviors across wealth and education levels., Methods: The current analysis selected 4 indicators from the WASH Benefits trial- presence of water and soap in household handwashing stations, observed mother's hand cleanliness, observed visible feces on latrine slab or floor and reported last child defecation in potty or toilet. A baseline assessment was conducted immediately after enrolment and endline assessment was conducted approximately 2 years later. We compared change in uptake of these indicators including wealth quintiles (Q) between intervention and control groups from baseline to endline., Results: For hand cleanliness, the poorest mothers improved more [Q1 difference in difference, DID: 16% (7, 25%)] than the wealthiest mothers [Q5 DID: 7% (- 4, 17%)]. The poorest households had largest improvements for observed presence of water and soap in handwashing station [Q1 DID: 82% (75, 90%)] compared to the wealthiest households [Q5 DID: 39% (30, 50%)]. Similarly, poorer household demonstrated greater reductions in visible feces on latrine slab or floor [Q1DID, - 25% (- 35, - 15) Q2: - 34% (- 44, - 23%)] than the wealthiest household [Q5 DID: - 1% (- 11, 8%). For reported last child defecation in potty or toilet, the poorest mothers showed greater improvement [Q1-4 DID: 50-54% (44, 60%)] than the wealthier mothers [Q5 DID: 39% (31, 46%)., Conclusion: By simultaneously addressing supply and demand-constraints among the poorest, we observed substantial overall improvements in equity. Within scaled-up programs, a separate targeted strategy that relaxes constraints for the poorest can improve the equity of a program., Trial Registration: WASH Benefits Bangladesh: ClinicalTrials.gov , identifier: NCT01590095 . Date of registration: April 30, 2012 'Retrospectively registered'.

Rapid urbanization has led to a growing sanitation crisis in urban areas of Bangladesh and potential exposure to fecal contamination in the urban environment due to inadequate sanitation and poor fecal sludge management. Limited data are available on environmental fecal contamination associated with different exposure pathways in urban Dhaka. We conducted a cross-sectional study to explore the magnitude of fecal contamination in the environment in low-income, high-income, and transient/floating neighborhoods in urban Dhaka. Ten samples were collected from each of 10 environmental compartments in 10 different neighborhoods (4 low-income, 4 high-income and 2 transient/floating neighborhoods). These 1,000 samples were analyzed with the IDEXX-Quanti-Tray technique to determine most-probable-number (MPN) of E. coli. Samples of open drains (6.91 log10 MPN/100 mL), surface water (5.28 log10 MPN/100 mL), floodwater (4.60 log10 MPN/100 mL), produce (3.19 log10 MPN/serving), soil (2.29 log10 MPN/gram), and street food (1.79 log10 MPN/gram) had the highest mean log10 E. coli contamination compared to other samples. The contamination concentrations did not differ between low-income and high-income neighborhoods for shared latrine swabs, open drains, municipal water, produce, and street foodsamples. E. coli contamination levels were significantly higher (p <0.05) in low-income neighborhoods compared to high-income for soil (0.91 log10 MPN/gram, 95% CI, 0.39, 1.43), bathing water (0.98 log10 MPN/100 mL, 95% CI, 0.41, 1.54), non-municipal water (0.64 log10 MPN/100 mL, 95% CI, 0.24, 1.04), surface water (1.92 log10 MPN/100 mL, 95% CI, 1.44, 2.40), and floodwater (0.48 log10 MPN/100 mL, 95% CI, 0.03, 0.92) samples. E. coli contamination were significantly higher (p<0.05) in low-income neighborhoods compared to transient/floating neighborhoods for drain water, bathing water, non-municipal water and surface water. Future studies should examine behavior that brings people into contact with the environment and assess the extent of exposure to fecal contamination in the environment through multiple pathways and associated risks., Competing Interests: The authors have declared that no competing interests exist.

Generalized estimating equation (GEE) is a popular approach for analyzing correlated binary data. However, the problems of separation in GEE are still unknown. The separation created by a covariate often occurs in small correlated binary data and even in large data with rare outcome and/or high intra-cluster correlation and a number of influential covariates. This paper investigated the consequences of separation in GEE and addressed them by introducing a penalized GEE, termed as PGEE. The PGEE is obtained by adding Firth-type penalty term, which was originally proposed for generalized linear model score equation, to standard GEE and shown to achieve convergence and provide finite estimate of the regression coefficient in the presence of separation, which are not often possible in GEE. Further, a small-sample bias correction to the sandwich covariance estimator of the PGEE estimator is suggested. Simulations also showed that the GEE failed to achieve convergence and/or provided infinitely large estimate of the regression coefficient in the presence of complete or quasi-complete separation, whereas the PGEE showed significant improvement by achieving convergence and providing finite estimate. Even in the presence of near-to-separation, the PGEE also showed superior properties over the GEE. Furthermore, the bias-corrected sandwich estimator for the PGEE estimator showed substantial improvement over the standard sandwich estimator by reducing bias in estimating type I error rate. An illustration using real data also supported the findings of simulation. The PGEE with bias-corrected sandwich covariance estimator is recommended to use for small-to-moderate size sample (N ≤ 50) and even can be used for large sample if there is any evidence of separation or near-to-separation., (© 2019 John Wiley & Sons, Ltd.)