IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity.

Utilizing electronic health records (EHR) for machine learning-driven clinical research has great potential to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary use of EHR data is regulated, constraining researchers' access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the privacy risks associated with real patient data.

Multiple "spaces": Using wildlife surveillance, climatic variables, and spatial statistics to identify and map a climatic niche for endemic plague in California, U.S.A.

Regional climatic features in endemic areas can help inform surveillance for plague, a bacterial disease typically transmitted by fleas and maintained in mammals. We use 7,954 coyotes (Canis latrans), a sentinel species for plague, screened for plague exposure by the California Department of Public Health - Vector-Borne Disease Section (CDPH-VBDS; 1983-2015) to identify and map plague-suitable local climates within California to empirically inform ongoing sampling and surveillance plans. Using spatial point processes, we compare the distributions of seropositive and seronegative coyotes within the "space" defined by the first two principal components of PRISM Climate Group 30-year average climate variables (primarily temperature and moisture).

Anopheles gambiae Re-Emergence and Resurgent Malaria Transmission in Eastern Rwanda, 2010-2020.

Rwanda achieved unprecedented malaria control gains from 2000 to 2010, but cases increased 20-fold between 2011 and 2017. Vector control challenges and environmental changes were noted as potential explanations, but no studies have investigated causes of the resurgence or identified which vector species drove transmission. We conducted a retrospective study in four sites in eastern Rwanda that conducted monthly entomological surveillance and outpatient malaria care.

The TIRS trial: Enrollment procedures and baseline characterization of a pediatric cohort to quantify the epidemiologic impact of targeted indoor residual spraying on Aedes-borne viruses in Merida, Mexico.

Aedes mosquito-borne viruses (ABVs) place a substantial strain on public health resources in the Americas. Vector control of Aedes mosquitoes is an important public health strategy to decrease or prevent spread of ABVs. The ongoing Targeted Indoor Residual Spraying (TIRS) trial is an NIH-sponsored clinical trial to study the efficacy of a novel, proactive vector control technique to prevent dengue virus (DENV), Zika virus (ZIKV), and chikungunya virus (CHIKV) infections in the endemic city of Merida, Yucatan, Mexico.

A state-level history of opioid overdose deaths in the United States: 1999-2021.

We examined a natural history of opioid overdose deaths from 1999-2021 in the United States to describe state-level spatio-temporal heterogeneity in the waves of the epidemic. We obtained overdose death counts by state from 1999-2021, categorized as involving prescription opioids, heroin, synthetic opioids, or unspecified drugs. We developed a Bayesian multivariate multiple change point model to flexibly estimate the timing and magnitude of state-specific changes in death rates involving each drug type.

The Population-level Effect of Adjuvant Therapies on Breast Cancer Recurrence: Application of the Trend-in-Trend Design.

Purpose: Breast cancer has an average 10-year relative survival reaching 84%. This favorable survival is due, in part, to the introduction of biomarker-guided therapies. We estimated the population-level effect of the introduction of two adjuvant therapies-tamoxifen and trastuzumab-on recurrence using the trend-in-trend pharmacoepidemiologic study design.

The impact of COVID-19 national lockdowns on drug-resistant tuberculosis in KwaZulu-Natal, South Africa: A spatial analysis.

Purpose: We sought to understand the impact of the initial COVID-19 mitigation strategies in 2020 on drug-resistant (DR) TB diagnoses in KwaZulu-Natal province (KZN), South Africa.

Methods: We compared the number, spatial distribution, and characteristics of DR TB diagnoses before and after the initial COVID-19 lockdown on March 26th, 2020. Information on DR TB diagnoses was collected from the CONTEXT prospective cohort study and municipality characteristics were collected from Statistics South Africa.

Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors.

Epidemiologic screening programs often make use of tests with small, but non-zero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation.

Cooking with liquefied petroleum gas or biomass and fetal growth outcomes: a multi-country randomised controlled trial.

Background: Household air pollution might lead to fetal growth restriction during pregnancy. We aimed to investigate whether a liquefied petroleum gas (LPG) intervention to reduce personal exposures to household air pollution during pregnancy would alter fetal growth.

Methods: The Household Air Pollution Intervention Network (HAPIN) trial was an open-label randomised controlled trial conducted in ten resource-limited settings across Guatemala, India, Peru, and Rwanda.

Disparities in Unmet Health Care Needs Among US Children During the COVID-19 Pandemic.

Purpose: The COVID-19 pandemic disrupted pediatric health care in the United States, and this disruption layered on existing barriers to health care. We sought to characterize disparities in unmet pediatric health care needs during this period.

Methods: We analyzed data from Wave 1 (October through November 2020) and Wave 2 (March through May 2021) of the COVID Experiences Survey, a national longitudinal survey delivered online or via telephone to parents of children aged 5 through 12 years using a probability-based sample representative of the US household population.

Joint effects of air pollution and neighborhood socioeconomic status on cognitive decline - Mediation by depression, high cholesterol levels, and high blood pressure.

Air pollution and neighborhood socioeconomic status (N-SES) are associated with adverse cardiovascular health and neuropsychiatric functioning in older adults. This study examines the degree to which the joint effects of air pollution and N-SES on the cognitive decline are mediated by high cholesterol levels, high blood pressure (HBP), and depression. In the Emory Healthy Aging Study, 14,390 participants aged 50+ years from Metro Atlanta, GA, were assessed for subjective cognitive decline using the cognitive function instrument (CFI).

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters.

Heat Exposure among Adult Women in Rural Tamil Nadu, India.

Exposure to heat is associated with a substantial burden of disease and is an emerging issue in the context of climate change. Heat is of particular concern in India, which is one of the world's hottest countries and also most populous, where relatively little is known about personal heat exposure, particularly in rural areas. Here, we leverage data collected as part of a randomized controlled trial to describe personal temperature exposures of adult women (40-79 years of age) in rural Tamil Nadu.

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies.

The industrial revolution and urbanization fundamentally restructured populations' living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation.

Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda.

Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (CCHFV), serosampling among herds of livestock was conducted in 28 Uganda districts in 2017.

Fairness as an afterthought: An American perspective on fairness in model developer-clinician user collaborations.

Numerous ethics guidelines have been handed down over the last few years on the ethical applications of machine learning models. Virtually every one of them mentions the importance of "fairness" in the development and use of these models. Unfortunately, though, these ethics documents omit providing a consensually adopted definition or characterization of fairness.

Association Between Clinician-Level Factors and Patient Outcomes in Virtual and In-Person Outpatient Treatment for Substance Use Disorders: Multilevel Analysis.

Background: The use of virtual treatment services increased dramatically during the COVID-19 pandemic. Unfortunately, large-scale research on virtual treatment for substance use disorder (SUD), including factors that may influence outcomes, has not advanced with the rapidly changing landscape.

Objective: This study aims to evaluate the link between clinician-level factors and patient outcomes in populations receiving virtual and in-person intensive outpatient services.

Space-time clustering of COVID-19 cases in the United States veteran population.

Purpose: To assess the distribution and clustering of coronavirus disease 2019 (COVID-19) testing and incidence over space and time, U.S. Department of Veteran's Affairs (VA) data were used to describe where and when veterans experienced highest proportions of test positivity.