Category Archives: Health Care

Under Affordable Care Act, Growing Use of ‘Community Health Workers’

Source: Michael Ollove, Stateline, July 8, 2016

….Many now recognize that providing good health care has to go beyond the doctor’s office — especially for minorities and low-income people. Limited access to healthy food, environmental perils, crime, insecure housing, insufficient recreational opportunities and the absence of affordable transportation all can have a huge effect on a person’s health. These factors, often called the social determinants of health, are hard for clinicians to address during medical appointments. To contend with them, hospitals, community health clinics, public health agencies and some health plans are increasingly turning to community health workers like Nelson. Thanks in part to federal grants awarded under the Affordable Care Act, the number of community health workers is growing. In 2015, there were 48,000 of them working in the U.S., up from 38,000 three years earlier, a 27 percent increase, according to the U.S. Department of Labor. But many insurers still don’t cover their services, limiting their potential impact…..

Making It Safe to Grow Old: A Financial Simulation Model for Launching MediCaring Communities for Frail Elderly Medicare Beneficiaries

Source: Antonia K. Bernhardt, Joanne Lynn, Gregory Berger, James A. Lee, Kevin Reuter, Joan Davanzo, Anne Montgomery and Allen Dobson, Milbank Quarterly, Early View, July 4, 2016
(subscription required)

From the abstract:
Context:
The Altarum Institute Center for Elder Care and Advanced Illness has developed a reform model, MediCaring Communities, to improve services for frail elderly Medicare beneficiaries through longitudinal care planning, better-coordinated and more desirable medical and social services, and local monitoring and management of a community’s quality and supply of services. This study uses financial simulation to determine whether communities could implement the model within current Medicare and Medicaid spending levels, an important consideration to enable development and broad implementation.

Methods:
The financial simulation for MediCaring Communities uses 4 diverse communities chosen for adequate size, varying health care delivery systems, and ability to implement reforms and generate data rapidly: Akron, Ohio; Milwaukie, Oregon; northeastern Queens, New York; and Williamsburg, Virginia. For each community, leaders contributed baseline population and program effect estimates that reflected projections from reported research to build the model.

Findings:
The simulation projected third-year savings between $269 and $537 per beneficiary per month and cumulative returns on investment between 75% and 165%.

Conclusions:
The MediCaring Communities financial simulation demonstrates that better care at lower cost for frail elderly Medicare beneficiaries is possible within current financing levels. Long-term success of the initiative will require reinvestment of Medicare savings to bolster nonmedical supportive services in the community. Successful implementation will necessitate waiving certain regulations and developing new infrastructure in pilot communities. This financial simulation methodology will help leadership in other communities to project fiscal performance. Since the MediCaring Communities model also achieves the Centers for Medicare and Medicaid Services’ vision for care for frail elders (better care, healthier people, smarter spending) and since these reforms can proceed with limited waivers from Medicare, willing communities should explore implementation and share best practices about how to achieve fundamental service delivery changes that can meet the challenges of a much older population in the 21st century.

Hispanic Children Least Likely to Have Health Insurance: Citizenship, Ethnicity, and Language Barriers to Coverage

Source: Michael J. Staley, Jessica Carson, National Issue Brief #101, Spring 2016

From the summary:
This policy brief examines health insurance coverage of Hispanic children and its relationship to their citizenship status, their parents’1 citizenship status, parents’ insurance coverage, language spoken at home, and their state’s Medicaid expansion policies.

Key Findings:
– Hispanic children are less likely to have health insurance than black or white children, a gap that is explained by differences in citizenship status between Hispanic and non-Hispanic children.
– Noncitizen Hispanic children are nearly three times more likely to be uninsured than Hispanic citizen children living with citizen parents.
– Hispanic children who do not have an insured parent are seven times more likely to be uninsured than Hispanic children with at least one insured parent.
– Children in states that expanded Medicaid are less likely to be uninsured than children in non-expansion states, although low and moderate income children are more likely to be uninsured regardless of state expansion status.

Getting Out of the Weeds: Medical Marijuana and the Workplace

Source: Diana M. Bardes and Paul A. Green, Benefits Magazine, Vol. 52 no. 10, October 2015
(subscription required) (scroll down)

Although 24 states and the District of Columbia have decriminalized or legalized the use of medical marijuana, marijuana is still illegal under federal law. What are employers, employees and health plans to do?

Health Costs, But Not Obamacare, Dominate The Future Of Federal Spending

Source: Eugene Steuerle, Health Affairs blog, June 27, 2016

From all the political discussion about health care, you’d think that government health policy generally lives or dies by what happens to the Affordable Care Act (ACA), aka Obamacare. One side offers almost nothing apart from saying Obamacare must (somehow) be abandoned. The other side tells us that health costs, partly thanks to Obamacare, might be under control. Neither side faces up to the continuing dominance of health costs in projections of future federal spending.

Meanwhile, a recent study suggests yet again that spending a huge amount more on health care may do little to improve mortality and opportunity for the disadvantaged.

Like most debates that become political, the discussion tends to be numberless. Numbers aren’t always popular for those whose facts must fit their storylines, as opposed to those whose storylines evolve from the facts.

So, what do the numbers tell us? The chart below shows that health care spending comprises the majority of all projected increases in non-interest outlays of the federal government, but that Obamacare for those under age 65 is only a moderate cause of this growth…..

The Association Between Income and Life Expectancy in the United States, 2001-2014

Source: Raj Chetty, Michael Stepner, Sarah Abraham, Shelby Lin, Benjamin Scuderi, Nicholas Turner, Augustin Bergeron, David Cutler, JAMA: Journal of the MAerican Medical Association, Vol. 315 no. 16, Special Communication, April 26, 2016
(subscription required)

From the abstract:
Importance: The relationship between income and life expectancy is well established but remains poorly understood.

Objectives: To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation.

Design and Setting: Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.

Exposure: Pretax household earnings as a measure of income.

Main Outcomes and Measures: Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas.

Results: The sample consisted of 1 408 287 218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61 175 per year). There were 4 114 380 deaths among men (mortality rate, 596.3 per 100 000) and 2 694 808 deaths among women (mortality rate, 375.1 per 100 000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = −0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001). Conclusions and Relevance: In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

The Uninsured in America: Estimates of the Percentage of Non-Elderly Adults Uninsured throughout Each Calendar Year, by Selected Population Subgroups and State Medicaid Expansion Status: 2013 and 2014

Source: Jessica P. Vistnes and Brandy J. Lipton, Agency for Healthcare Research and Quality, Statistical Brief #488, June 2016

Highlights:
The percentage of non-elderly adults ages 18-64, uninsured for the entire calendar year (“the uninsured rate”) declined from 18.8 percent (35.6 million adults) to 14.4 percent (27.4 million adults) between 2013 and 2014.
The uninsured rate declined between 2013 and 2014 for adults ages 18-35, 36-54, and 55-64 in both Medicaid expansion and non-expansion states. In states that expanded Medicaid, the decline was larger for adults ages 18-35, than for adults ages 55-64, narrowing the percentage point difference in the uninsured rates between these two age groups in 2014.
Uninsured rates declined between 2013 and 2014 for Hispanic, white, black and Asian non-elderly adults ages 18-64. The percentage point difference in the uninsured rates for Hispanic non-elderly adults and white non-Hispanic adults decreased between 2013 and 2014, overall and in Medicaid expansion states.

Out-of-Pocket Spending for Hospitalizations Among Nonelderly Adults

Source: Emily R. Adrion, Andrew M. Ryan, Amanda C. Seltzer, Lena M. Chen, John Z. Ayanian, Brahmajee K. Nallamothu, JAMA internal Medicine, Online First, June 27, 2016
(subscription required)

From the abstract:
Importance: Patients’ out-of-pocket spending for major health care expenses, such as inpatient care, may result in substantial financial distress. Limited contemporary data exist on out-of-pocket spending among nonelderly adults.

Objectives: To evaluate out-of-pocket spending associated with hospitalizations and to assess how this spending varied over time and by patient characteristics, region, and type of insurance.

Design, Setting, and Participants: A retrospective analysis of medical claims for 7.3 million hospitalizations using 2009-2013 data from Aetna, UnitedHealthcare, and Humana insurance companies representing approximately 50 million members was performed. Out-of-pocket spending was evaluated by age, sex, type of insurance, region, and principal diagnosis or procedure for hospitalized adults aged 18 to 64 years who were enrolled in employer-sponsored and individual-market health insurance plans from January 1, 2009, to December 31, 2013. The study was conducted between July 1, 2015, and March 1, 2016.

Main Outcomes and Measures: Primary outcomes were total out-of-pocket spending and spending attributed to deductibles, copayments, and coinsurance for all hospitalizations. Other outcomes included out-of-pocket spending associated with 7 commonly occurring inpatient diagnoses and procedures: acute myocardial infarction, live birth, pneumonia, appendicitis, coronary artery bypass graft, total knee arthroplasty, and spinal fusion.

Results: From 2009 to 2013, total cost sharing per inpatient hospitalization increased by 37%, from $738 in 2009 to $1013 in 2013, after adjusting for inflation and case-mix differences. This rise was driven primarily by increases in the amount applied to deductibles, which grew by 86% from $145 in 2009 to $270 in 2013, and by increases in coinsurance, which grew by 33% over the study period from $518 in 2009 to $688 in 2013. In 2013, total cost sharing was highest for enrollees in individual market plans and consumer-directed health plans. Cost sharing varied substantially across regions, diagnoses, and procedures.

Conclusions and Relevance: Mean out-of-pocket spending among commercially insured adults exceeded $1000 per inpatient hospitalization in 2013. Wide variability in out-of-pocket spending merits greater attention from policymakers.

PHI State Data Center

Source: Paraprofessional Healthcare Institute, 2016

From the blog post:
PHI recently updated the PHI State Data Center, the first web-based tool to provide comprehensive, state-by-state profiles of the direct-care workforce — one of the largest and fastest-growing workforces in the nation.

Using Current Population Survey (CPS) data collected from 2012 to 2014 and Occupational Employment Statistics (OES) from 2015, PHI released updates to the following areas:
– trends in wages for home health aides, personal care aides, and nursing assistants
– information on health insurance coverage rates
– worker reliance on public assistance

Key Findings
According to the most recent OES data, 4.4 million direct-care workers were employed nationwide in 2015, an increase of over 125,000 workers from the previous year. Despite increasing demand for paid caregivers, inflation-adjusted wages have fallen by 4 percent over the past 10 years, from an average of $11.60 per hour in 2005 to $11.08 per hour in 2015.

There is some good news, however. The downward trend in wages, which began in 2009, finally ended in 2014. Due in part to successful wage initiatives in some states — including increases to the minimum wage — inflation-adjusted wages rose on average $0.27 from 2014 to 2015.

Still, nearly half (48 percent) of direct-care workers rely on public assistance, including Medicaid (39 percent) and nutrition assistance (31 percent), according to the CPS…..

“Who is a Veteran?” — Basic Eligibility for Veterans’ Benefits

Source: Scott D. Szymendera, Congressional Research Service, R42324, May 25, 2016

The U.S. Department of Veterans Affairs (VA) offers a broad range of benefits to U.S. Armed Forces veterans and certain members of their families. Among these benefits are various types of financial assistance, including monthly cash payments to disabled veterans, health care, education, and housing. Basic criteria must be met to be eligible to receive any of the benefits administered by the VA.

This report examines the basic eligibility criteria for VA administered veterans’ benefits, including the issue of eligibility of members of the National Guard and reserve components….