Supplemental Nutrition Assistance Program Participation is Associated with Lower Health Care Spending among Working Age Adults without Dependents.

Prior evidence suggests an association among food insecurity, poor health, and increased health care spending. In this study, we are using a natural experiment to confirm if longer participation in the Supplemental Nutrition Assistance Program (SNAP) is associated with reduced Medicaid spending among a highly impoverished group of adults. In 2013, the mandatory work requirements associated with SNAP benefits were lifted for able-bodied adults without dependents (ABAWDs). Using 2013 to 2015 Medicaid and SNAP data of 24,181 Minnesotans aged 18-49, we examined if changes in SNAP enrollment duration affect health care expenditures. In fully adjusted within-participant regression models, for each additional month of SNAP, average annual health care spending was $98.8 lower (95% CI: -131.7, -66.0; p<.001) per person. Our data suggests that allowing ABAWDs to receive SNAP even in months they are not working may be critical to their health as well as cost-effective.

Bias in telephone surveys that do not sample cell phones: uses and limits of poststratification adjustments.

OBJECTIVE: To examine how biased health surveys are when they omit cell phone-only households (CPOH) and to explore whether poststratification can reduce this bias. METHODS: We used data from the 2008 National Health Interview Survey (NHIS), which uses area probability sampling and in-person interviews; as a result people of all phone statuses are included. First, we examined whether people living in CPOH are different from those not living in CPOH with respect to several important health surveillance domains. We compared standard NHIS estimates to a set of "reweighted" estimates that exclude people living in CPHO. The reweighted NHIS cases were fitted through a series of poststratification adjustments to NHIS control totals. In addition to poststratification adjustments for region, race or ethnicity, and age, we examined adjustments for home ownership, age by education, and household structure. RESULTS: Poststratification reduces bias in all health-related estimates for the nonelderly population. However, these adjustments work less well for Hispanics and blacks and even worse for young adults (18 to 30 y). Reduction in bias is greatest for estimates of uninsurance and having no usual source of care, and worse for estimates of drinking, smoking, and forgone or delayed care because of costs. CONCLUSIONS: Applying poststratification adjustments to data that exclude CPOH works well at the total population level for estimates such as health insurance, and less well for access and health behaviors. However, poststratification adjustments do not do enough to reduce bias in health-related estimates at the subpopulation level, particularly for those interested in measuring and monitoring racial, ethnic, and age disparities.

Accuracy in self-reported health insurance coverage among Medicaid enrollees.

The largest portion of the Medicaid undercount is caused by survey reporting error--that is, Medicaid recipients misreport their enrollment in health insurance coverage surveys. In this study, we sampled known Medicaid enrollees to learn how they respond to health insurance questions and to document correlates of accurate and inaccurate reports. We found that Medicaid enrollees are fairly accurate reporters of insurance status and type of coverage, but some do report being uninsured. Multivariate analyses point to the prominent role of program-related factors in the accuracy of reports. Our findings suggest that the Medicaid undercount should not undermine confidence in survey-based estimates of uninsurance.

Using silver and bighead carp cell lines for the identification of a unique metabolite fingerprint from thiram-specific chemical exposure.

Conservation biology often requires the control of invasive species. One method is the development and use of biocides. Identifying new chemicals as part of the biocide registration approval process can require screening millions of compounds. Traditionally, screening new chemicals has been done in vivo using test organisms. Using in vitro (e.g., cell lines) and in silico (e.g., computer models) methods decrease test organism requirements and increase screening speed and efficiency. These methods, however, would be greatly improved by better understanding how individual fish species metabolize selected compounds. We combined cell assays and metabolomics to create a powerful tool to facilitate the identification of new control chemicals. Specifically, we exposed cell lines established from bighead carp and silver carp larvae to thiram (7 concentrations) then completed metabolite profiling to assess the dose-response of the bighead carp and silver carp metabolome to thiram. Forty one of the 700 metabolomic markers identified in bighead carp exhibited a dose-response to thiram exposure compared to silver carp in which 205 of 1590 metabolomic markers exhibited a dose-response. Additionally, we identified 11 statistically significant metabolomic markers based upon volcano plot analysis common between both species. This smaller subset of metabolites formed a thiram-specific metabolomic fingerprint which allowed for the creation of a toxicant specific, rather than a species-specific, metabolomic fingerprint. Metabolomic fingerprints may be used in biocide development and improve our understanding of ecologically significant events, such as mass fish kills.

Development and Validation of a Method to Identify Children With Social Complexity Risk Factors.

OBJECTIVES: We sought to develop and validate a method to identify social complexity risk factors (eg, limited English proficiency) using Minnesota state administrative data. A secondary objective was to examine the relationship between social complexity and caregiver-reported need for care coordination. METHODS: A total of 460 caregivers of children with noncomplex chronic conditions enrolled in a Minnesota public health care program were surveyed and administrative data on these caregivers and children were obtained. We validated the administrative measures by examining their concordance with caregiver-reported indicators of social complexity risk factors using tetrachoric correlations. Logistic regression analyses subsequently assessed the association between social complexity risk factors identified using Minnesota's state administrative data and caregiver-reported need for care coordination, adjusting for child demographics. RESULTS: Concordance between administrative and caregiver-reported data was moderate to high (correlation range 0.31-0.94, all P values <.01), with only current homelessness (r = -0.01, P = .95) failing to align significantly between the data sources. The presence of any social complexity risk factor was significantly associated with need for care coordination before (unadjusted odds ratio = 1.65; 95% confidence interval, 1.07-2.53) but not after adjusting for child demographic factors (adjusted odds ratio = 1.53; 95% confidence interval, 0.98-2.37). CONCLUSIONS: Social complexity risk factors may be accurately obtained from state administrative data. The presence of these risk factors may heighten a family's need for care coordination and/or other services for children with chronic illness, even those not considered medically complex.