Dataset Information

Public Health Spending and Medicare Resource Use: A Longitudinal Analysis of U.S. Communities.

ABSTRACT: To examine whether local expenditures for public health activities influence area-level medical spending for Medicare beneficiaries.Six census surveys of the nation's 2,900 local public health agencies were conducted between 1993 and 2013, linked with contemporaneous information on population demographics, socioeconomic characteristics, and area-level Medicare spending estimates from the Dartmouth Atlas of Health Care.Measures derive from agency survey data and aggregated Medicare claims.A longitudinal cohort design follows the geographic areas served by local public health agencies. Multivariate, fixed-effects, and instrumental-variables regression models estimate how area-level Medicare spending changes in response to shifts in local public health spending, controlling for observed and unmeasured confounders.A 10 percent increase in local public health spending per capita was associated with 0.8 percent reduction in adjusted Medicare expenditures per person after 1 year (p < .01) and a 1.1 percent reduction after 5 years (p < .05). Estimated Medicare spending offsets were larger in communities with higher rates of poverty, lower health insurance coverage, and health professional shortages.Expanded financing for public health activities may provide an effective way of constraining Medicare spending, particularly in low-resource communities.

SUBMITTER: Mays GP

PROVIDER: S-EPMC5682130 | biostudies-other | 2017 Dec

REPOSITORIES: biostudies-other

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Public Health Spending and Medicare Resource Use: A Longitudinal Analysis of U.S. Communities.

Mays Glen P GP Mamaril Cezar B CB

Health services research 20171201

<h4>Objective</h4>To examine whether local expenditures for public health activities influence area-level medical spending for Medicare beneficiaries.<h4>Data sources and setting</h4>Six census surveys of the nation's 2,900 local public health agencies were conducted between 1993 and 2013, linked with contemporaneous information on population demographics, socioeconomic characteristics, and area-level Medicare spending estimates from the Dartmouth Atlas of Health Care.<h4>Data collection/extract ...[more]

PMID: 29130263

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Project description:ImportanceCurrent approaches to predicting health care costs generally rely on a single composite value of spending and focus on short time horizons. By contrast, examining patients' spending patterns using dynamic measures applied over longer periods may better identify patients with different spending and help target interventions to those with the greatest need.ObjectiveTo classify patients by their long-term, dynamic health care spending patterns using a data-driven approach and assess the ability to predict spending patterns, particularly using characteristics that are potentially modifiable through intervention.Design, setting, and participantsThis cohort study used a retrospective cohort design from a random nationwide sample of Medicare fee-for-service administrative claims data to identify beneficiaries aged 65 years or older with continuous eligibility from 2011 to 2013. Statistical analysis was performed from August 2018 to December 2019.Main outcomes and measuresGroup-based trajectory modeling was applied to the claims data to classify the Medicare beneficiaries by their total health care spending patterns over a 2-year period. The ability to predict membership in each trajectory spending group was assessed using generalized boosted regression, a data mining approach to model building and prediction, with split-sample validation. Models were estimated using (1) prior-year predictors and (2) prior-year predictors potentially modifiable through intervention measured in the claims data. These models were evaluated using validated C-statistics. The relative influence of individual predictors in the models was evaluated.ResultsAmong the 329 476 beneficiaries, the mean (SD) age was 76.0 (7.2) years and 190 346 (57.8%) were female. This final 5-group model included a minimal-user group (group 1, 37 572 individuals [11.4%]), a low-cost group (group 2, 48 575 individuals [14.7%]), a rising-cost group (group 3, 24 736 individuals [7.5%]), a moderate-cost group (group 4, 83 338 individuals [25.3%]), and a high-cost group (group 5, 135 255 individuals [41.2%]). Potentially modifiable characteristics strongly predicted these patterns (C-statistics range: 0.68-0.94). For groups with progressively increasing spending in particular, the most influential factors were number of medications (relative influence: 29.2), number of office visits (relative influence: 30.3), and mean medication adherence (relative influence: 33.6).Conclusions and relevanceUsing a data-driven approach, distinct spending patterns were identified with high accuracy. The potentially modifiable predictors of membership in the rising-cost group represent important levers for early interventions that may prevent later spending increases. This approach could be adapted by organizations to target quality improvement interventions, particularly because numerous health care organizations are increasingly using these routinely collected data.

Project description:Importance:The degree to which the presence of mental health disorders is associated with additional medical spending on non-mental health conditions is largely unknown. Objective:To determine the proportion and degree of total spending directly associated with mental health conditions vs spending on other non-mental health conditions. Design, Setting, and Participants:This retrospective cohort study of 4 358 975 fee-for-service Medicare beneficiaries in the US in 2015 compared spending and health care utilization among Medicare patients with serious mental illness (SMI; defined as bipolar disease, schizophrenia or related psychotic disorders, and major depressive disorder), patients with other common mental health disorders (defined as anxiety disorders, personality disorders, and posttraumatic stress disorder), and patients with no known mental health disorders. Data analysis was conducted from February to October 2019. Exposure:Diagnosis of an SMI or other common mental health disorder. Main Outcomes and Measures:Risk-adjusted, standardized spending and health care utilization. Multivariable linear regression models were used to adjust for patient characteristics, including demographic characteristics and other medical comorbidities, using hospital referral region fixed effects. Results:Of 4 358 975 Medicare beneficiaries, 987 379 (22.7%) had an SMI, 326 991 (7.5%) had another common mental health disorder, and 3 044 587 (69.8%) had no known mental illness. Compared with patients with no known mental illness, patients with an SMI were younger (mean [SD] age, 72.3 [11.6] years vs 67.4 [15.7] years; P < .001) and more likely to have dual eligibility (633 274 [20.8%] vs 434 447 [44.0%]; P < .001). Patients with an SMI incurred more mean (SE) spending on mental health services than those with other common mental health disorders or no known mental illness ($2024 [3.9] vs $343 [6.2] vs $189 [2.1], respectively; P < .001). Patients with an SMI also had substantially higher mean (SE) spending on medical services for physical conditions than those with other common mental health disorders or no known mental illness ($17 651 [23.6] vs $15 253 [38.2] vs $12 883 [12.8], respectively; P < .001), reflecting $4768 (95% CI, $4713-$4823; 37% increase) more in costs for patients with an SMI and $2370 (95% CI, $2290-$2449; 18.4% increase) more in costs for patients with other common mental health disorders. Among Medicare beneficiaries, $2 686 016 110 of $64 326 262 104 total Medicare spending (4.2%) went to mental health services and an additional $5 482 791 747 (8.5%) went to additional medical spending associated with mental illness, representing a total of 12.7% of spending associated with mental health disorders. Conclusions and Relevance:In this study, having a mental health disorder was associated with spending substantially more on other medical conditions. These findings quantify the extent of additional spending in the Medicare fee-for-service population associated with a diagnosis of a mental health disorder.

Dataset Information

Public Health Spending and Medicare Resource Use: A Longitudinal Analysis of U.S. Communities.

Publications

Public Health Spending and Medicare Resource Use: A Longitudinal Analysis of U.S. Communities.

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