Are Drug-Free School Zones Effective? Evidence From Matching Schools and School-like Entities

Using a combination of spatial and statistical analysis, this paper focuses on analyzing the effectiveness of drug-free school zones (DFSZ) around K-12 schools in Los Angeles County. A propensity score matching model is employed to match schools and school-like entities to compare the amount of drug crimes in two distinct 1000-foot buffers surrounding them. The model is then compared to a coarsened exact matching model. The average treatment effects (ATE) and average treatment effects on the treated (ATT) are estimated. Our results indicate that there are 2.7 and 1.7 fewer drug crimes and non–marijuana-related drug crimes respectively near schools, as a result of the policy. The total effect of the policy is estimated to reduce drug crime near schools by between 1065 to 1643 fewer incidences per year. Furthermore, we find no significant differences in gang-related drug crimes, gang-related violent crimes, or property crimes as a result of the policy.

Clarifying selection bias in cluster randomized trials

Background In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads to differential recruitment and consequently systematic differences in baseline characteristics of the recruited patients between intervention and control arms, inducing post-randomization selection bias. We aim to rigorously define causal estimands in the presence of selection bias. We elucidate the conditions under which standard covariate adjustment methods can validly estimate these estimands. We further discuss the additional data and assumptions necessary for estimating causal effects when such conditions are not met. Methods Adopting the principal stratification framework in causal inference, we clarify there are two average treatment effect (ATE) estimands in cluster randomized trials: one for the overall population and one for the recruited population. We derive analytical formula of the two estimands in terms of principal-stratum-specific causal effects. Furthermore, using simulation studies, we assess the empirical performance of the multivariable regression adjustment method under different data generating processes leading to selection bias. Results When treatment effects are heterogeneous across principal strata, the average treatment effect on the overall population generally differs from the average treatment effect on the recruited population. A naïve intention-to-treat analysis of the recruited sample leads to biased estimates of both average treatment effects. In the presence of post-randomization selection and without additional data on the non-recruited subjects, the average treatment effect on the recruited population is estimable only when the treatment effects are homogeneous between principal strata, and the average treatment effect on the overall population is generally not estimable. The extent to which covariate adjustment can remove selection bias depends on the degree of effect heterogeneity across principal strata. Conclusion There is a need and opportunity to improve the analysis of cluster randomized trials that are subject to post-randomization selection bias. For studies prone to selection bias, it is important to explicitly specify the target population that the causal estimands are defined on and adopt design and estimation strategies accordingly. To draw valid inferences about treatment effects, investigators should (1) assess the possibility of heterogeneous treatment effects, and (2) consider collecting data on covariates that are predictive of the recruitment process, and on the non-recruited population from external sources such as electronic health records.

Evaluation of Treatment Outcome for Pneumonia among Pre-Vaccinated COVID-19 Patients with/without Comorbidity in a Public Hospital in Bengkulu, Indonesia

Pneumonia is one of the common complications of SARS-CoV-2 infection where most patients have moderate to severe symptoms that pose a higher risk for death. This study aims to evaluate the treatment outcome of COVID-19-associated Pneumonia among patients with/without comorbidity in a public hospital in Indonesia. This is a retrospective cohort study involving unvaccinated confirmed COVID-19 patients admitted to the hospital between March and December 2020. All confirmed COVID-19 patients with Pneumonia (n = 1522) treated at the hospital were included. The majority of patients (99%) had mild COVID-19 symptoms while the remaining had moderate symptoms. The median age was about 32 years old and the average treatment duration was 6.25 ± 1.83 days. Most patients (88.8%) received a combination of azithromycin and oseltamivir. There was a very significant relationship (p < 0.001) between comorbidities with treatment and duration of treatment of Pneumonia in COVID-19 patients. Although most patients had Pneumonia and comorbidities, they were successfully treated with azithromycin and oseltamivir combination following approximately five days of treatment.

Sociodemographic determinants of internet use and its impact on family planning behavior among young male in Ethiopia: evidence from EDHS 2016

<p class="abstract"><strong>Background:</strong> Internet was preferred source of health information to retrieve relevant information. The study investigated sociodemographic determinants of internet and its impact on family planning behavior among young male in Ethiopia, 2016.</p><p class="abstract"><strong>Methods:</strong> The study was based on data from Ethiopia health and demographic survey 2016. The sample size was 4550 male youth age between 15-24 years include in EDHS 2016. Multilevel logistic regression was used to determine sociodemographic factor associated with internet utilization. The impact of internet use on family planning behaviors was assessed by propensity score matching using average treatment effect.</p><p class="abstract"><strong>Results:</strong> The magnitude of internet use in Ethiopia was 14% and factor associated with internet use included age between 20-24 years (AOR=0.60; CI=0.46-0.79), urban resident (AOR=3.94; CI=2.75-5.64), higher educational level (AOR=12.98; CI=2.69-62.66), lived in city administrative region (AOR=2.18; CI=1.46-3.28), respondent own mobile phone (AOR=8.00; CI=95.58-11.48), respondent who able to read whole sentence (AOR=4.04; CI=2.54-6.44) and respondent who has computer in household (AOR=2.03, CI=1.22-3.40) more likely to use internet. In other hand respondent occupation of agricultural and manual has 56% (AOR=0.44; CI=0.3-0.61) and 46% (AOR=54, CI=0.36-0.81) less likely to use internet. The average treatment impact of internet use on exposure to internet about FP, male condom uses and knowledge about FP increase by 12%, 7.2% and 4.5% percent point respectively.</p><p class="abstract"><strong>Conclusions:</strong> In spite of fast-growing nature of internet and popularity among youth people. In the future, integrating health information to different platforms on the internet will be a most effective way of health promotion.</p>

A Quasi-Experimental Study of Medicaid Expansion and Urban Mortality in the American Northeast

Objectives: To investigate the association of state-level Medicaid expansion and non-elderly mortality rates from 1999 to 2018 in Northeastern urban settings.Methods: This quasi-experimental study utilized a synthetic control method to assess the association of Medicaid expansion on non-elderly urban mortality rates [1999–2018]. Counties encompassing the largest cities in the Northeastern Megalopolis (Washington D.C., Baltimore, Philadelphia, New York City, and Boston) were selected as treatment units (n = 5 cities, 3,543,302 individuals in 2018). Cities in states without Medicaid expansion were utilized as control units (n = 17 cities, 12,713,768 individuals in 2018).Results: Across all cities, there was a significant reduction in the neoplasm (Population-Adjusted Average Treatment Effect = −1.37 [95% CI −2.73, −0.42]) and all-cause (Population-Adjusted Average Treatment Effect = −2.57 [95%CI −8.46, −0.58]) mortality rate. Washington D.C. encountered the largest reductions in mortality (Average Treatment Effect on All-Cause Medical Mortality = −5.40 monthly deaths per 100,000 individuals [95% CI −12.50, −3.34], −18.84% [95% CI −43.64%, −11.67%] reduction, p = &lt; 0.001; Average Treatment Effect on Neoplasm Mortality = −1.95 monthly deaths per 100,000 individuals [95% CI −3.04, −0.98], −21.88% [95% CI −34.10%, −10.99%] reduction, p = 0.002). Reductions in all-cause medical mortality and neoplasm mortality rates were similarly observed in other cities.Conclusion: Significant reductions in urban mortality rates were associated with Medicaid expansion. Our study suggests that Medicaid expansion saved lives in the observed urban settings.