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Convergence in health care spending across counties in New York from 2007 through 2016.

ABSTRACT: BACKGROUND:One approach considered for reducing health care spending is to narrow the gap in spending between high- and low-spending areas. The goal would be to reduce spending in the high areas to similar levels achieved in areas that use health care more efficiently. This paper examined the degree to which high-spending areas remain high-spending and which types of service lead to convergence or divergence in spending in New York State. METHODS:This analysis utilized publicly available data on county-level spending trends for the Medicare fee-for-service population from 2007 to 2016. The study applied methods previously used to evaluate changes in the regional variation of health care spending nationally to county-level data within New York. RESULTS:The spread of health care spending converged slightly over the ten-year period analyzed. There was also evidence for regression to the mean-effects and changes in the relative rankings of spending across counties during this time. While there was strong evidence for convergence, many high-spending counties in 2007 remained high-spending in 2016. There were also differences in which services drove spending variation at the national level compared to within New York. CONCLUSIONS:These findings point to counties with consistently high spending as a potential focus for health care cost-control efforts. Moreover, efforts to reduce unwarranted variation in spending may need to be tailored to the circumstances of particular regions as there are geographic differences in which services drive spending variation. Regression to the mean effects also have important implications for the specifications of alternative provider payment models, such as accountable care organizations, which promote convergence in spending by utilizing spending targets.

SUBMITTER: Zezza MA

PROVIDER: S-EPMC6481841 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Convergence in health care spending across counties in New York from 2007 through 2016.

Zezza Mark A MA

PloS one 20190424 4

<h4>Background</h4>One approach considered for reducing health care spending is to narrow the gap in spending between high- and low-spending areas. The goal would be to reduce spending in the high areas to similar levels achieved in areas that use health care more efficiently. This paper examined the degree to which high-spending areas remain high-spending and which types of service lead to convergence or divergence in spending in New York State.<h4>Methods</h4>This analysis utilized publicly av ...[more]

PMID: 31017951

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Project description:Importance:US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. Objective:To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Design and Setting:Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Exposures:Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. Main Outcomes and Measures:National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Results:Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Conclusions and Relevance:Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably.

Project description:PurposeTo estimate temporal trends in total and out-of-pocket (OOP) expenditures for ophthalmic prescription medications among adults in the United States.DesignRetrospective, longitudinal cohort study.ParticipantsParticipants in the 2007 through 2016 Medical Expenditure Panel Survey (MEPS) 18 years of age or older. The MEPS is a nationally representative survey of the noninstitutionalized, civilian United States population.MethodsWe estimated trends in national and per capita annual ophthalmic prescription expenditures by pooling data into 2-year cycles and using weighted linear regressions. We also identified characteristics associated with greater total or OOP expenditures with multivariate weighted linear regression. Costs were adjusted to 2016 United States dollars using the gross domestic product price index.Main outcome measuresTrends in total and OOP annual expenditures for ophthalmic medications from 2007 through 2016 as well as factors associated with greater expenditures.ResultsFrom 2007 through 2016, 9989 MEPS participants (4.2%) reported ophthalmic medication prescription use. Annual ophthalmic medication use increased from 10.0 to 12.2 million individuals from 2007 and 2008 through 2015 and 2016. In this same period, national expenditures for ophthalmic medications increased from $3.39 billion to $6.08 billion and OOP expenditures decreased from $1.34 to $1.18 billion. Per capita expenditure increased from $338.72 to $499.42 (P < 0.001), and per capita OOP expenditure decreased from $133.48 to $96.67 (P < 0.001) from 2007 and 2008 through 2015 and 2016, respectively. From 2015 through 2016, dry eye (29.5%) and glaucoma (42.7%) medications accounted for 72.2% of all ophthalmic medication expenditures. Patients who were older than 65 years (P < 0.001), uninsured (P < 0.001), and visually impaired (P < 0.001) were significantly more likely to have greater OOP spending on ophthalmic medications.ConclusionsTotal ophthalmic medication expenditure in the United States increased significantly over the last decade, whereas OOP expenses decreased. Increases in coverage, copayment assistance, and use of expensive brand drugs may be contributing to these trends. Policy makers and physicians should be aware that rising overall drug expenditures ultimately may increase indirect costs to the patient and offset a decline in OOP prescription drug spending.

Project description:ImportanceMore evidence on associations between mandated paid sick leave and health service utilization among low-income adults is needed to guide health policy and legislation nationwide.ObjectiveTo evaluate the association between New York City's 2014 paid sick leave mandate and health care utilization among Medicaid-enrolled adults.Design setting and participantsThis retrospective cohort study used New York State Medicaid administrative data for adults 18 to 64 years old continuously enrolled in Medicaid from August 1, 2011, through July 31, 2017. A difference-in-differences approach with entropy balancing weights was used to compare New York City with the rest of New York State to assess the association of the paid sick leave mandate with health care utilization, and for those 40 to 64 years old, with preventive care utilization. The data analysis was performed from June through August 2020.ExposuresTemporal and spatial variation in exposure to the mandate.Main outcomes and measuresAnnual health care utilization (emergency care, specialist visits, and primary care clinician visits) per Medicaid-enrolled adult. Secondary outcomes include categories of emergency utilization and utilization of 5 preventive services.ResultsOf 552 857 individuals (mean [SD] age, 43 [12] years; 351 130 [64%] women) who met inclusion criteria, 99 181 (18%) were White, 162 492 (29%) Black, and 138 061 (25%) Hispanic. Paid sick leave was significantly associated with a reduction in the probability of emergency care (-0.6 percentage points [pp]; 95% CI, -0.7 to -0.5 pp; P < .001), including a 0.3 pp reduction (95% CI, -0.4 to -0.2; P < .001) in care for conditions treatable in a primary care setting and an increase in annual outpatient visits (0.124 pp; 95% CI, 0.040 to 0.208 pp; P < .001). Among those 40 to 64 years old, the mandate was significantly associated with increased probabilities of glycated hemoglobin A1c level testing (2.9 pp; 95% CI, 2.5-3.3 pp; P < .001), blood cholesterol testing (2.7 pp; 95% CI, 2.5-2.9 pp; P < .001), and colon cancer screening (0.4 pp; 95% CI, 0.2-0.6 pp; P < .001).Conclusions and relevanceThis retrospective cohort study of nonelderly adults enrolled in Medicaid New York State showed that mandated paid sick leave in New York City was significantly associated with differences in several dimensions of health care services use.

Project description:The five boroughs of New York City (NYC) were early epicenters of the Covid-19 pandemic in the United States, with over 380,000 cases by May 31. High caseloads were also seen in nearby counties in New Jersey (NJ), Connecticut (CT) and New York (NY). The pandemic started in the area in March with an exponential rise in the number of daily cases, peaked in early April, briefly declined, and then, showed clear signs of a second peak in several counties. We will show that despite control measures such as lockdown and restriction of movement during the exponential rise in daily cases, there was a significant net migration of households from NYC boroughs to the neighboring counties in NJ, CT and NY State. We propose that the second peak in daily cases in these counties around NYC was due, in part, to the movement of people from NYC boroughs to these counties. We estimate the movement of people using "Change of Address" (CoA) data from the US Postal Service, provided under the "Freedom of Information Act" of 1967. To identify the timing of the second peak and the number of cases in it, we use a previously proposed SIR model, which accurately describes the early stages of the coronavirus pandemic in European countries. Subtracting the model fits from the data identified, we establish the timing and the number of cases, N CS , in the second peak. We then related the number of cases in the second peak to the county population density, P, and the excess Change of Address, E CoA, into each county using the simple model which fits the data very well with α = 0.68, β = 0.31 (R 2 = 0.74, p = 1.3e-8). We also find that the time between the first and second peaks was proportional to the distance of the county seat from NY Penn Station, suggesting that this migration of households and disease was a directed flow and not a diffusion process. Our analysis provides a simple method to use change of address data to track the spread of an infectious agent, such as SARS-Cov-2, due to migrations away from epicenters during the initial stages of a pandemic.

Dataset Information

Convergence in health care spending across counties in New York from 2007 through 2016.

Publications

Convergence in health care spending across counties in New York from 2007 through 2016.

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