Health spending by state of residence, 1991-2009

Does economic shocks influence household's healthcare expenditure? Evidence from rural Nigeria

Health is profoundly influenced by several factors outside the traditional realm of healthcare. This paper employed a two-step Heckman selectivity model to examine factors influencing the decision to spend on health and the effects of economic shocks on health expenditure. The results from the first stage shows that the likelihood of spending on health increased with age, education, income, and decreases if the household is living in the northern region of Nigeria and uses mosquito bed-net. The findings from the second stage estimation show that a fall in the price of food items, an increase in the price of inputs for household enterprises and loss of job are the significant shocks that affect household health expenditure. Based on these findings, this study concluded that fall in the prices of major food items consumed within the household increased income available for health care among the farmers. Going forward, the study recommends the provision of holistic health-economic-welfare interventions for the marginalized rural populace in Nigeria.

Geographic Variation in Costs of Transsphenoidal Pituitary Surgery in the United States

BACKGROUND

Geographic variations in health care costs have been reported for many surgical specialties.

OBJECTIVE

In this study, we sought to describe national and regional costs associated with transsphenoidal pituitary surgery (TPS).

METHODS

Data from the Truven-MarketScan 2010-2014 were analyzed. We examined overall total, hospital/facility, physician, and out-of-pocket payments in patients undergoing TPS including technique-specific costs. Mean payments were obtained after risk adjustment for patient-level and system-level confounders and estimated differences across regions.

RESULTS

The estimated overall annual burden was $43 million/year in our cohort. The average overall total payment associated with TPS was $35,602.30, hospital/facility payment was $26,980.45, physician payment was $4685.95, and out-of-pocket payment was $2330.78. Overall total and hospital/facility costs were highest in the West and lowest in the South (both P < 0.001), whereas physician reimbursements were highest in the North-east and lowest in the South (P < 0.001). There were no differences in out-of-pocket expenses across regions. On a national level, there were significantly higher overall total and hospital/facility payments associated with endoscopic compared with microscopic procedures (both P < 0.001); there were no significant differences in physician payments or out-of-pocket expenses between techniques. There were also significant within-region cost differences in overall total, hospital/facility, and physician payments in both techniques as well as in out-of-pocket expenses associated with microsurgery. There were no significant regional differences in out-of-pocket expenses associated with endoscopic surgery.

CONCLUSIONS

Our results show significant geographic cost disparities associated with TPS. Understanding factors behind disparate costs is important for developing cost containment strategies.

Does the Great Recession Contribute to the Convergence of Health Care Expenditures in the US States?

This paper examines whether the Great Recession has altered the disparities of the US regional health care expenditures. We test the null hypothesis of convergence for the US real per capita health expenditure for the period 1980–2014. Our results indicate that the null hypothesis of convergence is clearly rejected for the total sample as well as for the pre-Great Recession period. Thus, no changes are found in this regard. However, we find that the Great Recession has modified the composition of the estimated convergence clubs, offering a much more concentrated picture in 2014 than in 2008, with most of the states included in a big club, and only 5 (Nevada, Utah, Arizona, Colorado and Georgia) exhibiting a different pattern of behavior. These two estimated clubs diverge and, consequently, the disparities in the regional health sector have increased.

Economic Costs Attributable to Diabetes in Each U.S. State

OBJECTIVE

To estimate direct medical and indirect costs attributable to diabetes in each U.S. state in total and per person with diabetes.

RESEARCH DESIGN AND METHODS

We used an attributable fraction approach to estimate direct medical costs using data from the 2013 State Health Expenditure Accounts, 2013 Behavioral Risk Factor Surveillance System, and the Centers for Medicare & Medicaid Services' 2013-2014 Minimum Data Set. We used a human capital approach to estimate indirect costs measured by lost productivity from morbidity (absenteeism, presenteeism, lost household productivity, and inability to work) and premature mortality, using the 2008-2013 National Health Interview Survey, 2013 daily housework value data, 2013 mortality data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research, and mean wages from the 2014 Bureau of Labor Statistics. Costs were adjusted to 2017 U.S. dollars.

RESULTS

The estimated median state economic cost was $5.9 billion, ranging from $694 million to $55.5 billion, in total and $18,248, ranging from $15,418 to $30,915, per person with diabetes. The corresponding estimates for direct medical costs were $2.8 billion (range $0.3-22.9) and $8,544 (range $6,591-12,953) and for indirect costs were $3.0 billion (range $0.4-32.6) and $9,672 (range $7,133-17,962). In general, the estimated state median indirect costs resulting from morbidity were larger than costs from mortality both in total and per person with diabetes.

CONCLUSIONS

Economic costs attributable to diabetes were large and varied widely across states. Our comprehensive state-specific estimates provide essential information needed by state policymakers to monitor the economic burden of the disease and to better plan and evaluate interventions for preventing type 2 diabetes and managing diabetes in their states.

How Do Pharmacists Assist Medicare Beneficiaries with Limited Income? A Cross-Sectional Study of Community Pharmacies in Alabama

BACKGROUND

Many Medicare beneficiaries have limited income and report problems paying for their medications. Programs are available to assist these low-income individuals. However, these programs are underused because of lack of general awareness and perceived complexity of program applications.

OBJECTIVES

To (a) determine the frequency of encounters by pharmacists with Medicare beneficiaries who cannot afford prescription drugs; (b) identify strategies that pharmacists use to assist Medicare beneficiaries who cannot afford prescription drugs; and (c) explore what pharmacists know about programs for Medicare beneficiaries with limited income.

METHODS

This study used a mixed-mode survey of 350 randomly sampled community pharmacies located in 32 counties in Alabama with a high proportion of Medicare beneficiaries who were potentially eligible for low-income subsidy programs. Measures included frequency of encounters by pharmacists with Medicare beneficiaries who could not afford their medications, strategies used to assist Medicare beneficiaries, and pharmacists' knowledge of programs for Medicare beneficiaries with limited income.

RESULTS

Of 350 surveys sent, 12 were nondeliverable, and 151 were completed (response rate=44.6%). About 50% of respondents reported encountering Medicare beneficiaries who could not afford their medications at least weekly. Various strategies were reported, including refiling claims that were previously denied every day (40.7%), contacting insurance companies at least once per week (43.2%), and loaning medications at least 2-3 times per month (29.1%). Only 12.6% reported referring beneficiaries to the Aging and Disability Resource Centers (ADRCs) to assess eligibility for limited-income programs. When asked about programs for beneficiaries with limited income, the answers were predominantly "don't know for sure."

CONCLUSIONS

Several strategies were used by pharmacists in an attempt to help limited-income Medicare beneficiaries obtain their medications. Lack of knowledge about financial assistance programs for limited-income individuals and the role of ADRCs in helping to screen individuals for benefits and complete applications warrants immediate attention. Improving pharmacists' knowledge on this topic may be an effective mechanism for providing a long-term solution for their patients.

DISCLOSURES

This study was funded by the Alabama Department of Senior Services. The sponsor played no active role in the design, methods, data collection, analysis, or preparation of this manuscript. The authors have nothing to disclose. This paper was presented at the American Pharmacists Association meeting in Baltimore, Maryland, in 2016. Study concept and design were contributed by Westrick. Hastings and McFarland were responsible for data collection, along with Westrick. Data interpretation was performed by Westrick, L. Hohmann, and Hastings, with assistance from McFarland. All authors were involved with manuscript preparation.

Determinants of U.S. Prescription Drug Utilization using County Level Data

Prescription drugs are the third largest component of U.S. healthcare expenditures. The 2006 Medicare Part D and the 2010 Affordable Care Act are catalysts for further growths in utilization becuase of insurance expansion effects. This research investigating the determinants of prescription drug utilization is timely, methodologically novel, and policy relevant. Differences in population health status, access to care, socioeconomics, demographics, and variations in per capita number of scripts filled at retail pharmacies across the U.S.A. justify fitting separate econometric models to county data of the states partitioned into low, medium, and high prescription drug users. Given the skewed distribution of per capita number of filled prescriptions (response variable), we fit the variance stabilizing Box-Cox power transformation regression models to 2011 county level data for investigating the correlates of prescription drug utilization separately for low, medium, and high utilization states. Maximum likelihood regression parameter estimates, including the optimal Box-Cox λ power transformations, differ across high (λ = 0.214), medium (λ = 0.942), and low (λ = 0.302) prescription drug utilization models. The estimated income elasticities of -0.634, 0.031, and -0.532 in high, medium, and low utilization models suggest that the economic behavior of prescriptions is not invariant across different utilization levels.

Geographic Variation in Antidiabetic Agent Adherence and Glycemic Control Among Patients with Type 2 Diabetes

BACKGROUND

Medication nonadherence is an imperative public health concern. Among patients with type 2 diabetes mellitus (T2DM), poor adherence to antidiabetic agents is strongly associated with suboptimal glycemic control. Poor adherence and hyperglycemia greatly increase diabetes-related morbidity and mortality. At a national level, diabetes drug adherence using average proportion of days covered (PDC) is estimated to range between 36% and 81%, with an estimated range for diabetes control between 38% and 47%. At a state level no such studies exist.

OBJECTIVE

To estimate the level of medication adherence to antidiabetic agents and of diabetes control, and their association among patients with T2DM receiving medication treatment at the state and the Metropolitan Statistical Area (MSA) levels among the populations covered by commercial insurance, Medicare, or Medicaid.

METHODS

The study population included adults with T2DM aged ≥18 years who were identified using ICD-9-CM code 250.xx, who received diabetes medication, and who were covered by private insurance, Medicare, or Medicaid in each state, the District of Columbia, and the top 50 MSAs. Medication adherence was measured by average PDC and the percentage of population that had a PDC ≥ 80%. Diabetes control was identified using ICD-9-CM diagnosis codes. Patients who were not diagnosed with uncontrolled diabetes (250.x2 and 250.x3) were identified as being under control. The administrative claims databases used for this study included the 2012 medical and pharmacy claims from a large U.S. health plan, the complete 2011 Medicare Standard Analytical File linked with Part D claims, and the 2008 Mini-Medicaid Analytic eXtract (Mini-Max). Medication adherence and diabetes control were adjusted for age and sex to allow comparison across insurance coverage, states, and MSAs.

RESULTS

For an insured patient population with T2DM that received diabetic drug treatment, average PDC was 79%. However, 35% of patients did not achieve an adherence of at least 80% of PDC. In addition, at least 40% of patients did not have their diabetes under control. Across insurance types, we found that patients insured with Medicare had relatively high average PDC and adherence levels (83% and 71%) in comparison with the commercially insured population (77% and 60%) and Medicaid patients (75% and 57%). In contrast, commercially insured patients had relatively better diabetes control (69%) than those insured with Medicare and Medicaid (54% and 53%, respectively). At a state level, we found that commercially insured and Medicare populations have relatively smaller geographic variation in drug adherence than the Medicaid population.

CONCLUSIONS

This study identified gaps in T2DM drug adherence and pinpointed geographic areas that lag in terms of diabetes drug adherence or diabetes control and would benefit from implementing strategies to increase drug adherence.

Compensating wage differentials and the impact of health insurance in the public sector on wages and hours

This paper examines the trade-off between wages and employer spending on health insurance for public sector workers, and the relationship between coverage and hours worked. Our primary approach compares trends in wages and hours for public employees with and without state/local government provided health insurance using individual-level micro-data from the 1992-2011 CPS. To adjust for differences between insured and uninsured public sector employees, we create a matched sample based on an employee's propensity to receive health insurance. We assess the relationship between state contribution to the health plan premium, state-level healthcare spending, and the wages and hours of state and local government employees. We find modest reductions in wages are associated with having employer-sponsored health insurance (ESHI), although this effect is not precisely measured. The reduction in wages associated with having ESHI is larger among non-unionized workers. Further, we find little evidence that provision of health insurance increases hours worked.

Quality-quantity decomposition of income elasticity of U.S. hospital care expenditure using state-level panel data

Economic theory suggests that income growth could lead to changes in consumption quantity and quality as the spending on a commodity changes. Similarly, the volume and quality of healthcare consumption could rise with incomes because of demographic changes, usage of innovative medical technologies, and other factors. Hospital healthcare spending is the largest component of aggregate US healthcare expenditures. The novel contribution of our paper is estimating and decomposing the income elasticity of hospital care expenditures (HOCEXP) into its quantity and quality components. By using a 1999-2008 panel dataset of the 50 US states, results from the seemingly unrelated regressions model estimation reveal the income elasticity of HOCEXP to be 0.427 (std. error=0.044), with about 0.391 (calculated std. error=0.044) arising from care quality improvements and 0.035 (std. error=0.050) emanating from the rise in usage volume. Our novel research findings suggest the following: (i) the quantity part of hospital expenditure is inelastic to income change; (ii) almost the entire income-induced rise in hospital expenditure comes from care quality changes; and (iii) the 0.427 income elasticity of HOCEXP, the largest component of total US healthcare expenditure, makes hospital care a normal commodity and a much stronger technical necessity than aggregate healthcare. Policy implications are discussed.

State unemployment in recessions during 1991-2009 was linked to faster growth in Medicare spending

During the US recession of 2007-09, overall health care spending growth fell, but Medicare spending growth increased. Using state-level data from the period 1991-2009, we show that these divergent trends were also observed within states. Furthermore, increases in state unemployment rates were associated with higher Medicare spending per capita and increased hospital use by Medicare beneficiaries. For example, a one-percentage-point point rise in the unemployment rate was associated with a $40 (0.7 percent) increase in Medicare spending per capita. Our results suggest that economic downturns contribute to Medicare spending and use. One of many possible explanations may be that health care providers have greater capacity, inclination, and financial incentive to treat Medicare patients during recessions as a result of slackening demand from the non-Medicare population.

Coverage, access, and affordability under health reform: learning from the Massachusetts model

While the impacts of the Affordable Care Act will vary across the states given their different circumstances, Massachusetts' 2006 reform initiative, the template for national reform, provides a preview of the potential gains in insurance coverage, access to and use of care, and health care affordability for the rest of the nation. Under reform, uninsurance in Massachusetts dropped by more than 50%, due, in part, to an increase in employer-sponsored coverage. Gains in health care access and affordability were widespread, including a 28% decline in unmet need for doctor care and a 38% decline in high out-of-pocket costs.

Modeling per capita state health expenditure variation: state-level characteristics matter

OBJECTIVE

In this paper, we describe the methods underlying the econometric model developed by the Office of the Actuary in the Centers for Medicare & Medicaid Services, to explain differences in per capita total personal health care spending by state, as described in Cuckler, et al. (2011). Additionally, we discuss many alternative model specifications to provide additional insights for valid interpretation of the model.

DATA SOURCE

We study per capita personal health care spending as measured by the State Health Expenditures, by State of Residence for 1991-2009, produced by the Centers for Medicare & Medicaid Services' Office of the Actuary. State-level demographic, health status, economic, and health economy characteristics were gathered from a variety of U.S. government sources, such as the Census Bureau, Bureau of Economic Analysis, the Centers for Disease Control, the American Hospital Association, and HealthLeaders-InterStudy.

PRINCIPAL FINDINGS

State-specific factors, such as income, health care capacity, and the share of elderly residents, are important factors in explaining the level of per capita personal health care spending variation among states over time. However, the slow-moving nature of health spending per capita and close relationships among state-level factors create inefficiencies in modeling this variation, likely resulting in incorrectly estimated standard errors. In addition, we find that both pooled and fixed effects models primarily capture cross-sectional variation rather than period-specific variation.