Easy to use and validated predictive models to identify beneficiaries experiencing homelessness in Medicaid administrative data.

OBJECTIVE: To develop easy to use and validated predictive models to identify beneficiaries experiencing homelessness from administrative data. DATA SOURCES: We pooled enrollment and claims data from enrollees of the California Whole Person Care (WPC) Medicaid demonstration program that coordinated the care of a subset of Medicaid beneficiaries identified as high utilizers in 26 California counties (25 WPC Pilots). We also used public directories of social service and health care facilities. STUDY DESIGN: Using WPC Pilot-reported homelessness status, we trained seven supervised learning algorithms with different specifications to identify beneficiaries experiencing homelessness. The list of predictors included address- and claims-based indicators, demographics, health status, health care utilization, and county-level homelessness rate. We then assessed model performance using measures of balanced accuracy (BA), sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (area under the curve [AUC]). DATA COLLECTION/EXTRACTION METHODS: We included 93,656 WPC enrollees from 2017 to 2018, 37,441 of whom had a WPC Pilot-reported homelessness indicator. PRINCIPAL FINDINGS: The random forest algorithm with all available indicators had the best performance (87% BA and 0.95 AUC), but a simpler Generalized Linear Model (GLM) also performed well (74% BA and 0.83 AUC). Reducing predictors to the top 20 and top five most important indicators in a GLM model yields only slightly lower performance (86% BA and 0.94 AUC for the top 20 and 86% BA and 0.91 AUC for the top five). CONCLUSIONS: Large samples can be used to accurately predict homelessness in Medicaid administrative data if a validated homelessness indicator for a small subset can be obtained. In the absence of a validated indicator, the likelihood of homelessness can be calculated using county rate of homelessness, address- and claim-based indicators, and beneficiary age using a prediction model presented here. These approaches are needed given the rising prevalence of homelessness and the focus of Medicaid and other payers on addressing homelessness and its outcomes

Towards universal health coverage: achievements and challenges of 10 years of healthcare reform in China.

Universal health coverage (UHC) has been identified as a priority for the global health agenda. In 2009, the Chinese government launched a new round of healthcare reform towards UHC, aiming to provide universal coverage of basic healthcare by the end of 2020. We conducted a secondary data analysis and combined it with a literature review, analysing the overview of UHC in China with regard to financial protection, coverage of health services and the reported coverage of the WHO and the World Bank UHC indicators. The results include the following: out-of-pocket expenditures as a percentage of current health expenditures in China have dropped dramatically from 60.13% in 2000 to 35.91% in 2016; the health insurance coverage of the total population jumped from 22.1% in 2003 to 95.1% in 2013; the average life expectancy increased from 72.0 to 76.4, maternal mortality dropped from 59 to 29 per 100 000 live births, the under-5 mortality rate dropped from 36.8 to 9.3 per 1000 live births, and neonatal mortality dropped from 21.4 to 4.7 per 1000 live births between 2000 and 2017; and so on. Our findings show that while China appears to be well on the path to UHC, there are identifiable gaps in service quality and a requirement for ongoing strengthening of financial protections. Some of the key challenges remain to be faced, such as the fragmented and inequitable health delivery system, and the increasing demand for high-quality and value-based service delivery. Given that China has committed to achieving UHC and 'Healthy China 2030', the evidence from this study can be suggestive of furthering on in the UHC journey and taking the policy steps necessary to secure change

Towards universal health coverage: lessons from 10 years of healthcare reform in China.

Universal health coverage (UHC) is driving the global health agenda. Many countries have embarked on national policy reforms towards this goal, including China. In 2009, the Chinese government launched a new round of healthcare reform towards UHC, aiming to provide universal coverage of basic healthcare by the end of 2020. The year of 2019 marks the 10th anniversary of China's most recent healthcare reform. Sharing China's experience is especially timely for other countries pursuing reforms to achieve UHC. This study describes the social, economic and health context in China, and then reviews the overall progress of healthcare reform (1949 to present), with a focus on the most recent (2009) round of healthcare reform. The study comprehensively analyses key reform initiatives and major achievements according to four aspects: health insurance system, drug supply and security system, medical service system and public health service system. Lessons learnt from China may have important implications for other nations, including continued political support, increased health financing and a strong primary healthcare system as basis

Failure to recognize Low non-treponemal titer syphilis infections in pregnancy May lead to widespread under-treatment

Objectives: Rates of maternal syphilis have increased five-fold in Brazil in the past decade. While penicillin remains the only appropriate treatment for maternal syphilis, we hypothesized that low non-treponemal titers (<1:16) may lead to reduced penicillin treatment in Brazil. Methods: Using Brazilian Ministry of Health data on women diagnosed with maternal syphilis between January 1, 2010, and December 31, 2018, we conducted a random-effects logistic regression model with a cluster correction at the state level to evaluate predictive factors of penicillin treatment. Results: We observed yearly increases in cases of pregnant women with syphilis from 2010 to 2018. There was significant variation by state: 52,451 cases were reported in São Paulo, followed by 26,838 in Rio de Janeiro. Among 215,937 cases of maternal syphilis, 91·3% received penicillin. In the random-effects model, a non-treponemal titer ≥1:16 was associated with 1·44 higher odds of receiving penicillin (95% confidence interval [CI]: 1·391·48), and prenatal care was associated with a 2·12 increased odds of receiving penicillin (95% CI: 2·022·21). Although there is an association between the absence of prenatal care and inadequate treatment for syphilis, 83·2% of women in this cohort who did not receive penicillin were engaged in prenatal care. Conclusions: Providers may inappropriately exclude low non-treponemal titers and thereby fail to use penicillin treatment in maternal syphilis. While the cause of the maternal syphilis epidemic in Brazil is multifactorial, we believe our findings can be used to develop targeted interventions throughout Brazil as well as shape public health initiatives globally.National Institute of Mental HealthRevisión por pare

Educational Attainment and Hospital Admissions: New Evidence from the Health and Retirement Study

Research Objective: Education is one of the most significant correlates of health. However, the extent to which this relationship is causal is yet to be established. Additionally, there is a dearth of studies investigating the effect of education on health care utilization. This dissertation’s overall objective was to examine the relationship between educational attainment and hospitalizations using a large longitudinal database and more efficient estimation methods. The three specific aims were: 1) to investigate determinants of attrition due to death and non-response in the Health and Retirement Study (first study); 2) to examine the association between education and hospitalizations based on a pre-set conceptual model and assess the impact of attrition on the estimation of the education-hospitalization relationship (second study); and 3) to determine the causal effect of education on hospitalizations (third study).Methods: The primary data source was the Health and Retirement Study (HRS) with restricted files, including state-identifiers from 1992 to 2016. This database was further merged with data consisting of 1919-1973 state-level compulsory schooling laws and the quality of schooling measures to study the causal effects of education on hospitalizations. I used a multinomial logistic regression model to investigate the determinants of attrition status in 2016 as well as the between-wave attrition. I then constructed weights to account for attrition bias in the relationship between education and hospitalizations using the inverse probability weighting approach. To determine the causal effects of education on hospitalizations, I used compulsory schooling laws as instruments for years of completed education. A Post-Double-Selection method based on the Least Absolute Shrinkage and Selection Operator (LASSO) regressions was used to select optimal instruments and a parsimonious set of controls, which yields more efficient but still consistent instrumental variable (IV) estimators. Population Studied: The study population included eligible respondents and their spouses in the HRS survey from 1992 to 2016. The first study excluded the Later Baby Boomer cohort that entered the HRS in 2016. The second study focused on those born in the United States. The third study further restricted the study population to white respondents who had high school or lower educational attainment and were born in the 48 contiguous states and the District of Columbia (excluding Hawaii and Alaska) between 1905 and 1959.Results: Respondents who were female, white, Hispanic, married, who had more living children, who had more years of education, and who were healthier, and financially better off during childhood were more likely to remain in the survey and respond in every follow-up wave. These variables had different impacts on attrition due to death and attrition due to non-response. On average, compared to individuals with less than a high school education, individuals with a high school education or some college had a 3.37 percentage point (pp) (95% CI, -3.93 pp to -2.80 pp) lower likelihood of being hospitalized, and individuals with a college degree or above had an 8.39 pp (95% CI, -9.10 pp to -7.67 pp) lower likelihood of hospitalization over the past two years, controlling for demographics, childhood socioeconomic conditions, childhood health status, state-of-birth fixed effects, year-of-birth fixed effects, state-specific linear time trends, and accounting for attrition bias. After age 78, the probability of hospitalization for those with a high school education was not significantly different from that of those with less than a high school education; the estimate was -0.96 pp and not statistically significant. The preferred IV estimator (LASSO-IV estimator) implies that a one year increase in schooling lowered the probability of two-year hospitalization by 6.5 pp (95% CI: - 9.1 pp to -3.9 pp), which is much larger than that of the OLS estimator (-1.1 pp, 95% CI: -1.4 pp to -0.7 pp) without correcting for the endogeneity of education.Conclusions: Individuals with more years of schooling had a lower probability of two-year hospitalizations compared to their counterparts with fewer years of education. These effects would be underestimated if attrition bias was not accounted for. Moreover, age modifies the relationship. After age 78, the effect of a high school or some college education became indistinguishable from zero, but the effect of higher education remained statistically significant. Importantly, when accounting for the endogeneity of education, I found a relatively large and significant effect of education on hospitalizations.Implications for Research and Policy: My main finding that educational attainment has a large effect on hospitalizations contributes to the growing literature on the social determinants of health. Results from this study should inform policymakers and suggest that providing more health care resources to the low-education group might be an effective means for reducing health disparities. It also provides rigorous evidence for health care payment reforms that consider incorporating education into the risk-adjustment models. In a broader context, it suggests that investing in the educational system could be a more cost-effective way to reduce intensive health care use and health care costs. Furthermore, the analytic framework constructed in this dissertation to account for attrition bias and produce efficient estimators by selecting optimal instruments and controls with LASSO regression models should guide further research for evaluating the effects of education in other similar studies, and, more generally, longitudinal studies involving many instruments and/or many controls

The impact of the health care workforce on under-five mortality in rural China.

BackgroundPrevious studies have focused on the relationship between increases in the health care workforce and child health outcomes, but little is known about how this relationship differs in contexts where economic growth differs by initial level and pace. This study evaluates the association between increased health professionals and the under-five mortality rate (U5MR) in rural Chinese counties from 2008 to 2014 and examines whether this relationship differs among counties with different patterns of economic growth over this period.MethodsWe estimated fixed effects models with rural counties as the unit of analysis to evaluate the association between health professional density and U5MR. Covariates included county-level gross domestic product (GDP) per capita, female illiteracy rate, value of medical equipment per bed, and province-level health expenditures (measured as a proportion of provincial GDP). To explore modification effects, we assessed interactions between health professionals and county types defined by county poverty status and county-level trajectories of growth in GDP per capita. U5MR data have been adjusted for county-level underreporting, and all other data were obtained from administrative and official sources.ResultsThe U5MR dropped by 36.19% during the study period. One additional health professional per 1000 population was associated with a 2.6% reduction in U5MR, after controlling for other covariates. County poverty status and GDP trajectories moderated this relationship: the U5MR reductions attributed to a one-unit increase in health professionals were 6.8% among poor counties, but only 1.1% among non-poor ones. These reductions were, respectively, 6.7%, 0.7%, and 4.3% in counties with initially low GDP that slowly increased, medium-level GDP that rose at a moderate pace, and high GDP that rose rapidly.ConclusionsThis study demonstrates that increased health professionals were associated with reductions in U5MR. The&nbsp;largest association was seen in poor counties and those with low and slowly increasing GDP per capita, which justifies further expansion of the health care workforce in these areas. This study could be instructive for other developing countries to achieve Sustainable Development Goal 3 by helping them identify where additional health professionals would make the greatest contribution