Did Health Care Reform Help Kentucky Address Disparities in Coverage and Access to Care among the Poor?

Impact of the presence of private hospitals on the spatial equality of healthcare accessibility in Beijing, China

Providing equal geographical access to hospitals, either in the public or private healthcare sector, is vital and will benefit public health in general. Against the background of the partial privatization of the healthcare sector, the impact of private hospitals on equal healthcare access has been a highly neglected issue. We have applied an assessment methodology to study this situation by comparing the status quo scenario with one without private hospitals, based on accessibility analysis and spatial equality measurements. The case study of Beijing, China revealed a double-sided impact. With the presence of private hospitals, the Gini coefficient of spatial accessibility in urban districts was reduced from 0.03391 to 0.03211, while it increased from 0.1734 to 0.1914 in suburban districts. Thus, private hospitals improved spatial equality in urban districts in Beijing but jeopardized it in suburban districts. These research findings should enlighten policymakers to promote healthcare equality but would also need to be repeated in some other big cities.

Obamacare: A bibliometric perspective

Obamacare is the colloquial name given to the Affordable Care Act (ACA) signed into law by President Obama in the USA, which ultimately aims to provide universal access to health care services for US citizens. The aim of this paper is to provide an overview of the political-legal, economic, social, management (or administrative), and medical (or health) repercussions of this law, using a bibliometric methodology as a basis. In addition, the main contributors to research on ACA issues have been identified in terms of authors, organizations, journals, and countries. The downward trend in scientific production on this law has been noted, and it has been concluded that a balance has not yet been reached between the coexistence of private and public health care that guarantees broad social coverage without economic or other types of barriers. The law requires political consensus to be implemented in a definitive and global manner for the whole of the United States.

Health Insurance Literacy Perceptions and the Needs of a Working-Class Community

Background:

Despite increases in the number of insured Americans, consumers continue to face barriers in accessing care. Low levels of health insurance literacy (HIL) are associated with suboptimal use of health insurance coverage. There remains a need to further contextualize the relationship between HIL and access to care, especially among insured working-class people.

Objective

This study was conducted to understand the pathways through which HIL affects health care decision-making and access to care in an urban working-class population.

Methods:

Using a qualitative descriptive approach, we conducted five focus groups with 45 adult residents of South Louisville, Kentucky. The questions explored participants' experiences of insurance enrollment, use, and health care system navigation, as well as their perceptions and needs regarding accessing health insurance information. Using inductive thematic analysis, transcripts were open coded independently by team members, a focused coding framework was agreed upon, and emergent themes were identified through constant comparison techniques.

Key Results:

Most participants placed high value on their insurance, considering it the most important benefit employers offer. Despite having adequate HIL, most participants expressed frustration with the amount of time and effort they spent to determine the best insurance plan, obtain covered health services, and settle claims, often with ineffective results. Despite having insurance, cost considerations influenced many participants' decisions to accept certain tests or treatments, in some cases resulting in delayed or foregone care.

Conclusions:

The findings of this qualitative study indicate that obtaining health care is strongly influenced both by the individual context of HIL knowledge, experience, and life circumstances, and by the structural context of the complex, bureaucratic, and costly health care system. Interventions to improve HIL should include practical examples and real-life scenarios, because HIL gained from experiential narratives was the most useful in navigating the health care system.[HLRP: Health Literacy Research and Practice. 2022;6(2):e61–e69.]

Plain Language Summary:

By conducting focus groups with a working-class population, this study contextualizes the pathways through which HIL affects consumers' health care decision-making and access to care. Findings demonstrate that obtaining health care is strongly influenced both by the individual context of HIL knowledge, experience, and life circumstances, and by the structural context of the complex, bureaucratic, and costly health care system.

Determinants of Diabetes Disease Management, 2011-2019

This study estimated the effects of Medicaid Expansion, demographics, socioeconomic status (SES), and health status on disease management of diabetes over time. The hypothesis was that the introduction of the ACA and particularly Medicaid Expansion would increase the following dependent variables (all proportions): (1) provider checks of HbA1c, (2) provider checks of feet, (3) provider checks of eyes, (4) patient education, (5) annual physician checks for diabetes, (6) patient self-checks of blood sugar. Data were available from the Behavioral Risk Factor Surveillance System for 2011 to 2019. We filtered the data to include only patients with diagnosed non-gestational diabetes of age 45 or older (n = 510,991 cases prior to weighting). Linear splines modeled Medicaid Expansion based on state of residence as well as implementation status. Descriptive time series plots showed no major changes in proportions of the dependent variables over time. Quasibinomial analysis showed that implementation of Medicaid Expansion had a statistically negative effect on patient self-checks of blood sugar (odds ratio = 0.971, p < 0.001), a statistically positive effect on physician checks of HbA1c (odds ratio = 1.048, p < 0.001), a statistically positive effect on feet checks (odds ratio = 1.021, p < 0.001), and no other significant effects. Evidence of demographic, SES, and health status disparities existed for most of the dependent variables. This finding was especially significant for HbA1c checks by providers. Barriers to achieving better diabetic care remain and require innovative policy interventions.

Medicaid expansion and accessibility to healthcare: The Illinois experience

OBJECTIVE

The study examined the impact that the Medicaid expansion in Illinois had upon insurance rates, access to medical care, dental care, pharmaceuticals, and mental-health counseling between rural and urban counties.

DESIGN AND SAMPLE

A serial cross-sectional design was used to assess the health perceptions of adults living in Illinois.

MEASURES

Survey data were collected in 2012 (n = 6,149) before the Medicaid expansion in Illinois and in 2015 (n = 3,532) after the expansion from rural (n = 4) and urban counties (n = 4).

INTERVENTION

Medicaid expansion reduced the uninsured rate in both rural (16.39%-4.87%) and urban counties (17.05%-5.2%) and improved self-reported health. It also increased access to all types of healthcare, with the biggest increase in dental coverage.

RESULTS

Path analysis indicated that the Medicaid expansion β = -1.03 (p < .01) and poor versus not poor β = -1.50 (p < .01) were a significant predictor to no healthcare access. Rural verses urban location was not significant (β = 0.04); however, race/ethnicity was significantly different (p < .01).

CONCLUSION

Findings suggest that although the expansion has increased access to care overall, those who are the most vulnerable are still not benefiting equally from the expansion. Therefore, strategies to assist high-risk adults in enrolling and using their Medicaid coverage need to developed and implemented.

Change and Stability in the Characteristics of the Population Without Health Insurance

INTRODUCTION

The uninsured population faces greater health risks than the insured population. Although prior research has examined how the uninsured rate has changed for various sociodemographic groups, less is known about how the characteristics of the uninsured population have changed in recent years.

METHODS

The analyses used 1-year American Community Survey data from 2013 through 2018 on the noninstitutionalized civilian population aged 19-64 years to examine trends in the characteristics of the U.S. uninsured population. Analyses also explored the importance of social and demographic change in the overall U.S. population by decomposing the change in the uninsured rate between 2013 and 2018.

RESULTS

In 2018, the profile of the uninsured population differed from that of the noninstitutionalized civilian population aged 19-64 years with respect to a number of characteristics, including age, sex, and socioeconomic resources. Between 2013 and 2018, southern individuals and those with less than a high school education comprised a disproportionate share of the uninsured population. However, compositional changes did not drive the overall decline in the uninsured rate.

CONCLUSIONS

Although prior research has considered changes in the uninsured rate for key sociodemographic groups, fewer studies have considered how these changes affected the composition of the uninsured population in the U.S. The profile of the uninsured population, which has changed over time, can help to inform interventions to target this group.

Unmet Medical Needs of Patients with Benign Prostate Enlargement

This study aimed to analyze the factors affecting the unmet medical needs of patients with benign prostate enlargement (BPE) based on Andersen’s behavioral model. The data were taken from the 2009–2016 Korea Health Panel Study and 3003 participants were used for analysis. “Unmet medical needs” was used as a dependent variable. Independent variables were predisposing variables: age, educational attainment, and marital status; enabling factors: income, job type, and insurance type; and need factors: lying in a sickbed, activity limitation, subjective health status, and having chronic diseases. Results showed that younger patients experienced a higher probability of unmet medical needs. Those with higher educational attainment had a lower chance of experiencing unmet medical needs. Patients with national health insurance were less likely to experience unmet medical needs. In addition, patients who experienced lying in a sickbed had a higher probability of experiencing unmet medical needs. Therefore, in order to reduce the unmet medical needs of patients with BPE, it is necessary to allow patients to be treated early and give them accurate information about the disease. In addition, access to medical care should be strengthened through continuous care focused on primary care.

A comparison of methods for health policy evaluation with controlled pre-post designs

Objective

To compare interactive fixed effects (IFE) and generalized synthetic control (GSC) methods to methods prevalent in health policy evaluation and re‐evaluate the impact of the hip fracture best practice tariffs introduced for hospitals in England in 2010.

Data Sources

Simulations and Hospital Episode Statistics.

Study Design

Best practice tariffs aimed to incentivize providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using difference‐in‐differences (DiD), synthetic control (SC), IFE, and GSC methods. We contrast the estimation methods' performance in a Monte Carlo simulation study.

Principal Findings

Unlike DiD, SC, and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of best practice tariffs led to a 5.9 (confidence interval: 2.0 to 9.9) percentage point increase in the proportion of patients having surgery within 48 hours and a statistically insignificant 0.6 (confidence interval: −1.4 to 0.4) percentage point reduction in 30‐day mortality.

Conclusions

The GSC approach is an attractive method for health policy evaluation. We cannot be confident that best practice tariffs were effective.

A population-based measure of chronic disease severity for health planning and evaluation in the United States

In the healthcare sector, patients can be categorized into clinical risk groups, which are based, in part, on multiple chronic conditions. Population-based measures of clinical risk groups for population health planning, however, are not available. Using responses of working-age adults (19-64 years old) from the Behavioral Risk Factor Surveillance System for survey years 2015-2017, a population-based measure of chronic disease severity (CDS) was developed as a proxy for clinical risk groups. Four categories of CDS were developed: low, medium-low, medium-high, and high, based on self-reported diagnoses of multiple chronic conditions, weighted by hospitalization costs. Prevalence estimates of CDS were prepared, by population demographics and state characteristics, and CDS association with perceived health-related quality of life (HRQOL) was evaluated. Age-adjusted CDS varied from 72.9% (95% CI: 72.7-73.1%) for low CDS, to 21.0% (95% CI: 20.8-21.2%), 4.4% (95% CI: 4.3-4.5%) and 1.7% (95% CI: 1.6-1.8%) for medium-low, medium-high, and high CDS, respectively. The prevalence of high CDS was significantly greater (p < 0.05) among older adults, those living below the federal poverty level, and those with disabilities. The adjusted odds of fair/poor perceived HRQOL among adults with medium-low or medium-high/high CDS were 2.39 times (95% CI: 2.30-2.48) or 6.53 times (95% CI: 6.22-6.86) higher, respectively, than adults with low CDS. Elevated odds of fair/poor HRQOL with increasing CDS coincided with less prevalence of high CDS among men, minority race/ethnicities, and adults without insurance, suggesting a link between CDS and risk of mortality. Prevalence of high CDS was significantly higher (p < 0.05) in states with lower population density, lower per capita income, and in states that did not adopt the ACA. These results demonstrate the relevance of a single continuous population-based measure of chronic disease severity for health planning at the state, regional, and national levels.

Differences in referral patterns for rural primary care physicians from 2005 to 2016

OBJECTIVE

To examine differences in referral patterns in a nationally representative sample between primary care physicians (PCP) practicing in rural vs nonrural areas and changes over time.

STUDY DESIGN

Using the 2005-2016 National Ambulatory Medical Care Survey and multivariate logit regression models, I compare referral patterns of PCPs in rural vs nonrural areas.

DATA COLLECTION

Multiple years of data were combined.

PRINCIPAL FINDINGS

A PCP visit was 1.9 percentage points (95% confidence interval: 0.1 pp, 3.8 pp) more likely to result in a referral in nonrural areas than rural areas, controlling for physician and patient characteristics, a 17 percent increase. This difference is driven by a widening gap in referral rates between nonrural and rural areas over time, with large differences in later periods. The regression-adjusted predicted probability of a PCP visit resulting in a referral was 71 percent higher in nonrural than rural areas in 2013-2014 and 92 percent higher in 2015-2016.

CONCLUSIONS

Recognizing that the optimal PCP referral rate is unknown, referrals are less common in rural areas with a widening gap in recent years. This difference may reflect specialist availability, distance to care, or patient preferences. As changes occur to health care financing and delivery, continuing to monitor practice patterns is important to ensure patients are receiving appropriate levels of care across geographic regions.

Impact of the Medicare hospital readmissions reduction program on vulnerable populations

BACKGROUND

The Hospital Readmissions Reduction Program (HRRP) was established by the 2010 Patient Protection and Affordable Care Act (ACA) in an effort to reduce excess hospital readmissions, lower health care costs, and improve patient safety and outcomes. Although studies have examined the policy's overall impacts and differences by hospital types, research is limited on its effects for different types of vulnerable populations. The aim of this study was to analyze the impact of the HRRP on readmissions for three targeted conditions (acute myocardial infarction, heart failure, and pneumonia) among four types of vulnerable populations, including low-income patients, patients served by hospitals that serve a high percentage of low-income or Medicaid patients, and high-risk patients at the highest quartile of the Elixhauser comorbidity index score.

METHODS

Data on patient and hospital information came from the Nationwide Readmission Database (NRD), which contained all discharges from community hospitals in 27 states during 2010-2014. Using difference-in-difference (DD) models, linear probability regressions were conducted for the entire sample and sub-samples of patients and hospitals in order to isolate the effect of the HRRP on vulnerable populations. Multiple combinations of treatment and control groups and triple difference (DDD) methods were used for testing the robustness of the results. All models controlled for the patient and hospital characteristics.

RESULTS

There have been statistically significant reductions in readmission rates overall as well as for vulnerable populations, especially for acute myocardial infarction patients in hospitals serving the largest percentage of low-income patients and high-risk patients. There is also evidence of spillover effects for non-targeted conditions among Medicare patients compared to privately insured patients.

CONCLUSIONS

The HRRP appears to have created the right incentives for reducing readmissions not only overall but also for vulnerable populations, accruing societal benefits in addition to previously found reductions in costs. As the reduction in the rate of readmissions is not consistent across patient and hospital groups, there could be benefits to adjusting the policy according to the socioeconomic status of a hospital's patients and neighborhood.

Does Primary Care Provider Supply Influence Medicaid Acceptability?

BACKGROUND

Following the Affordable Care Act's Medicaid expansions, access to care improved through elevated coverage rates among the low-income population. In Michigan, a major factor contributing to improved access among low-income patients was increased Medicaid acceptance in primary care settings.

OBJECTIVES

Prior evidence shows substantial geographic variation preacceptance and postacceptance of Medicaid. In this study, we determine whether physician's willingness to accept new Medicaid patients is moderated by the availability of other providers in close proximity.

METHODS

The study uses Michigan simulated patient (ie, "secret shopper") data collected during 2014 and 2015, and applies a difference-in-differences styled event-study regression approach comparing trends in Medicaid acceptability and appointment scheduling between areas in Michigan with higher densities of primary care providers against those with lower densities of providers that could arguably be classified a health professional shortage areas.

RESULTS

Through one year after Michigan's Medicaid expansion, practices in low-supply areas appeared no more likely (P>0.10) to turn away a newly insured Medicaid patient than a practice in a supply-rich area. The wait times for patients in a low-supply area were about a day longer (P<0.05) than for patients in supply-rich areas through 4 months after the expansion, though this difference dissipated through 8 and 12 months after the expansion.

CONCLUSIONS

These results indicate that newly insured Medicaid patients are gaining access to care in settings with limited health care supply.

The impact of ACA Medicaid expansion on socioeconomic inequality in health care services utilization

Objective

We examined whether the Affordable Care Act (ACA) Medicaid expansion reduced socioeconomic inequalities in health care utilization.

Methods

We used data from the Behavioral Risk Factor Surveillance System, covering the 50 U.S. states and the District of Columbia, between 2011 and 2016. We selected outcome indicators, viz. ability to afford needed health care, having a personal doctor, use of health services in the past year (routine check-up, flu shot and dental visits), and attending screenings for breast, cervical, and colon cancers. Socioeconomic status was measured by household income. We calculated two indices of inequality by household income for each outcome: Slope Index of Inequality (SII) and Relative Index of Inequality (RII). We estimated difference-in-differences models to examine the impact of ACA Medicaid expansion on socioeconomic inequality in use of health care services.

Results

The ACA Medicaid expansion appeared to reduce the socioeconomic gap in individuals reporting financial ability in accessing health care (difference-in-differences estimators, -0.045 for SII and RII), having a personal doctor (-0.037 for SII and RII), and receiving routine check-ups (-0.027 for SII and -0.039 for RII). However, the expansion was not associated with reduction in the socioeconomic gap for preventive health care visits or dental care.

Conclusions

The ACA Medicaid expansion had mixed effects on socioeconomic disparities in health care utilization. Medicaid expansion may not be sufficient to address socioeconomic disparities in preventive services uptake.

The three-year impact of the Affordable Care Act on disparities in insurance coverage

Objective

To estimate the impact of the major components of the ACA (Medicaid expansion, subsidized Marketplace plans, and insurance market reforms) on disparities in insurance coverage after three years.

Data Source

The 2011‐2016 waves of the American Community Survey (ACS), with the sample restricted to nonelderly adults.

Design

We estimate a difference‐in‐difference‐in‐differences model to separately identify the effects of the nationwide and Medicaid expansion portions of the ACA using the methodology developed in the recent ACA literature. The differences come from time, state Medicaid expansion status, and local area pre‐ACA uninsured rates. In order to focus on access disparities, we stratify our sample separately by income, race/ethnicity, marital status, age, gender, and geography.

Principal Findings

After three years, the fully implemented ACA eliminated 43% of the coverage gap across income groups, with the Medicaid expansion accounting for this entire reduction. The ACA also reduced coverage disparities across racial groups by 23%, across marital status by 46%, and across age‐groups by 36%, with these changes being partly attributable to both the Medicaid expansion and nationwide components of the law.

Conclusions

The fully implemented ACA has been successful in reducing coverage disparities across multiple groups.