American Indian and Non-Hispanic White Midlife Mortality Is Associated With Medicaid Spending
An Oklahoma Ecological Study (1999–2016)
Bibliographic Data
| ID | 22068320 |
|---|---|
| Authors | Mark A Brandenburg (0000-0001-8912-2693, corresponding author) |
| Year | 2020 |
| Volume | 8 |
| Pages | 139-139 |
| Publication date | 2020-04-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2020.00139 |
| PMID | 32411646 |
| OpenAlex | W3023810288 |
| Language | EN |
| References cited | 30 |
Objective A one third reduction of premature deaths from non-communicable diseases by 2030 is a target of the United Nations Sustainable Development Goal for Health. Unlike in other developed nations, premature mortality in the United States (US) is increasing. The state of Oklahoma suffers some of the greatest rates in the US of both all-cause mortality and overdose deaths. Medicaid opioids are associated with overdose death at the patient level, but the impact of this exposure on population all-cause mortality is unknown. The objective of this study was to look for an association between Medicaid spending, as proxy measure for Medicaid opioid exposure, and all-cause mortality rates in the 45-54-year-old American Indian/Alaska Native (AI/AN45-54) and non-Hispanic white (NHW45-54) populations. Methods All-cause mortality rates were collected from the US Centers for Disease Control & Prevention Wonder Detailed Mortality database. Annual per capita (APC) Medicaid spending, and APC Medicare opioid claims, smoking, obesity, and poverty data were also collected from existing databases. County-level multiple linear regression (MLR) analyses were performed. American Indian mortality misclassification at death is known to be common, and sparse populations are present in certain counties; therefore, the two populations were examined as a combined population (AI/NHW45-54), with results being compared to NHW45-54 alone. Results State-level simple linear regressions of AI/NHW45-54 mortality and APC Medicaid spending show strong, linear correlations: females, coefficient 0.168, (R2 0.956; P < 0.0001; CI95 0.15, 0.19); and males, coefficient 0.139 (R2 0.746; P < 0.0001; CI95 0.10, 0.18). County-level regression models reveal that AI/NHW45-54 mortality is strongly associated with APC Medicaid spending, adjusting for Medicare opioid claims, smoking, obesity, and poverty. In females: [R2 0.545; (F)P < 0.0001; Medicaid spending coefficient 0.137; P < 0.004; 95% CI 0.05, 0.23]. In males: [R2 0.719; (F)P < 0.0001; Medicaid spending coefficient 0.330; P < 0.001; 95% CI 0.21, 0.45]. Conclusions In Oklahoma, per capita Medicaid spending is a very strong risk factor for all-cause mortality in the combined AI/NHW45-54 population, after controlling for Medicare opioid claims, smoking, obesity, and poverty
Economic growth · Environmental health · Health care · Medicaid · Mortality rate · Per capita · Population · Poverty · Demography · Heme Oxygenase-1 and Carbon Monoxide · Medicine · Opioid Use Disorder Treatment · Prenatal Substance Exposure Effects · Gerontology
A Simple Test for Heteroscedasticity and Random Coefficient Variation
The State of US Health, 1990-2016
Increases in Drug and Opioid-Involved Overdose Deaths — United States, 2010–2015
The Association Between Income and Life Expectancy in the United States, 2001-2014
Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century
Factors responsible for mortality variation in the United States
Does Medical Expansion Improve Population Health
| Citation velocity | historical |
|---|---|
| Highly cited | No |