To inform HHS, States, Congress, and the public about the results and accomplishments of the State Medicaid Fraud Control Units.
Medicaid Fraud Control Units (MFCU or Unit) investigate and prosecute Medicaid fraud as well as patient abuse and neglect in health care facilities. OIG certifies, and annually recertifies, each MFCU. OIG collects information about MFCU operations and assesses whether they comply with statutes, regulations, and OIG policy. OIG also analyzes MFCU performance based on 12 published performance standards and recommends program improvements where appropriate.
This dataset tracks the updates made on the dataset "Medicaid Fraud Control Units (MFCU) Annual Spending and Performance Statistics" as a repository for previous versions of the data and metadata.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
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To inform HHS, States, Congress, and the public about the results and accomplishments of the State Medicaid Fraud Control Units.