The relationship between health spending and social spending In high-income countries: how does the US compare?

There is broad consensus that the US spends too much on health care. One proposed driver of the high US spending is low investment in social services. We examined the relationship between health spending and social spending across high-income countries. We found that US social spending (at 16.1 percent of gross domestic product [GDP] in 2015) is slightly below the average for Organization for Economic Cooperation and Development (OECD) countries (17.0 percent of GDP) and above that average when education spending is included (US: 19.7 percent of GDP; OECD: 17.7 percent of GDP). We found that countries that spent more on social services tended to spend more on health care. Adjusting for poverty and unemployment rates and the proportion of people older than age sixty-five did not meaningfully change these associations. In addition, when we examined changes over time, we found additional evidence for a positive relationship between social and health spending: Countries with the greatest increases in social spending also had larger increases in health care spending

Influence of social deprivation index on in-hospital outcomes of COVID-19

Abstract While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient’s residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70–0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71–0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79–0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79–0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important