Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontraditional, digital information to perform disease surveillance. We introduce a novel method of using multiple data sources for predicting the number of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, ED preparedness, and targeted patient interventions.
Under longstanding federal law, pregnancy-related Medicaid coverage is only guaranteed through 60-days postpartum, at which point many women become uninsured. Barriers to care, including lack of insurance, contribute to maternal mortality and morbidity. Leveraging the Families First Coronavirus Response Act, a federal law requiring that states provide continuous coverage to Medicaid enrollees during the COVID-19 pandemic as a condition of receiving enhanced federal financial support, we examine whether postpartum women seek additional care, and what types of care they use, with extended coverage. We analyze claims from the Parkland Community Health Plan (a Texas Medicaid Health Maintenance Organization) before and after implementation of the pandemic-related Medicaid extension. We find that after implementation of the coverage extension, women used twice as many postpartum services, 2 × to 10 × as many preventive, contraceptive, and mental/behavioral health services, and 37% fewer services related to short interval pregnancies within the first-year postpartum. Our findings provide timely insights for state legislators, Medicaid agencies, and members of Congress working to improve maternal health outcomes. We add empirical evidence to support broad extension of Medicaid coverage throughout the first-year postpartum.
Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.
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