Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China

Abstract Background Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple main-effects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter. Results The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53-0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%). Conclusions The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of real-world applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation

Assessment of traffic-related noise in three cities in the United States

BackgroundTraffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities.MethodsNoise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration's Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input.ResultsWe found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input.ConclusionsAmbient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics

Toward sustainable and comprehensive control of schistosomiasis in China: lessons from Sichuan.

Triggered by a fascinating publication in the New England Journal of Medicine detailing China's new multi-pronged strategy to control and eventually interrupt the transmission of Schistosoma japonicum, this PLoS Neglected Tropical Diseases Debate critically examines the generalizability and financial costs of the studies presented from the marshlands of the lake region. Edmund Seto from the University of California and colleagues emphasize that the epidemiology and control of schistosomiasis varies according to the social-ecological context. They conjecture that the successful intervention packages piloted in the lake region is not fully fit for the hilly and mountainous environments in Sichuan and Yunnan provinces, and hence call for more flexible, setting-specific, and less expensive control strategies. In response, Xiao-Nong Zhou from the National Institute of Parasitic Diseases at the Chinese Center of Disease Control and Prevention and colleagues explain the steps from designing pilot studies to the articulation and implementation of a new national control strategy through a careful process of scaling-up and adaptations. Finally, the two opponents converge. The need for integrated, intersectoral, and setting-specific control measures is stressed, supported by rigorous surveillance and continuous research. Experiences and lessons from China are important for shaping the schistosomiasis elimination agenda