Changes in insurance status and access to care in an integrated safety net healthcare system

Racial and ethnic disparities in influenza vaccination coverage among pregnant women in the United States: The contribution of vaccine-related attitudes

OBJECTIVE

Racial and ethnic disparities in influenza vaccination coverage among pregnant women in the United States have been documented. This study assessed the contribution of vaccine-related attitudes to coverage disparities.

METHODS

Surveys were conducted following the 2019-2020 and 2020-2021 influenza seasons in a US research network. Using electronic health record data to identify pregnant women, random samples were selected for surveying; non-Hispanic Black women and influenza-unvaccinated women were oversampled. Regression-based decomposition analyses were used to assess the contribution of vaccine-related attitudes to racial and ethnic differences in influenza vaccination. Data were combined across survey years, and analyses were weighted and accounted for survey design.

RESULTS

Survey response rate was 41.2% (721 of 1748) for 2019-2020 and 39.3% (706 of 1798) for 2020-2021. Self-reported influenza vaccination was higher among non-Hispanic White respondents (79.4% coverage, 95% CI 73.1%-85.7%) than Hispanic (66.2% coverage, 95% CI 52.5%-79.9%) and non-Hispanic Black (55.8% coverage, 95% CI 50.2%-61.4%) respondents. For all racial and ethnic groups, a high proportion (generally >80%) reported being seen for care, recommended for influenza vaccination, and offered vaccination. In decomposition analyses, vaccine-related attitudes (e.g., worry about vaccination causing influenza; concern about vaccine safety and effectiveness) explained a statistically significant portion of the observed racial and ethnic disparities in vaccination. Maternal age, education, and health status were not significant contributors after controlling for vaccine-related attitudes.

CONCLUSIONS

In a setting with relatively high influenza vaccination coverage among pregnant women, racial and ethnic disparities in coverage were identified. Vaccine-related attitudes were associated with the disparities observed.

Influenza Vaccination Among Pregnant Women: Self-report Compared With Vaccination Data From Electronic Health Records, 2018-2020 Influenza Seasons

OBJECTIVES

Having accurate influenza vaccination coverage estimates can guide public health activities. The objectives of this study were to (1) validate the accuracy of electronic health record (EHR)-based influenza vaccination data among pregnant women compared with survey self-report and (2) assess whether survey respondents differed from survey nonrespondents by demographic characteristics and EHR-based vaccination status.

METHODS

This study was conducted in the Vaccine Safety Datalink, a network of 8 large medical care organizations in the United States. Using EHR data, we identified all women pregnant during the 2018-2019 or 2019-2020 influenza seasons. Surveys were conducted among samples of women who did and did not appear vaccinated for influenza according to EHR data. Separate surveys were conducted after each influenza season, and respondents reported their influenza vaccination status. Analyses accounted for the stratified design, sampling probability, and response probability.

RESULTS

The survey response rate was 50.5% (630 of 1247) for 2018-2019 and 41.2% (721 of 1748) for 2019-2020. In multivariable analyses combining both survey years, non-Hispanic Black pregnant women had 3.80 (95% CI, 2.13-6.74) times the adjusted odds of survey nonresponse; odds of nonresponse were also higher for Hispanic pregnant women and women who had not received (per EHR data) influenza vaccine during current or prior influenza seasons. The sensitivity, specificity, and positive predictive value of EHR documentation of influenza vaccination compared with self-report were ≥92% for both survey years combined. The negative predictive value of EHR-based influenza vaccine status was 80.5% (95% CI, 76.7%-84.0%).

CONCLUSIONS

EHR-based influenza vaccination data among pregnant women were generally concordant with self-report. New data sources and novel approaches to mitigating nonresponse bias may be needed to enhance influenza vaccination surveillance efforts.

Comparison of Risk-Standardized Readmission Rates of Surgical Patients at Safety-Net and Non-Safety-Net Hospitals Using Agency for Healthcare Research and Quality and American Hospital Association Data

Importance

Medical patients discharged from safety-net hospitals (SNHs) experience higher readmission rates compared with those discharged from non-SNHs. However, little is known about whether this association persists for surgical patients.

Objectives

To examine differences in readmission rates between SNHs and non-SNHs among surgical patients after discharge and determine whether hospital characteristics might account for some of the variation.

Design, Setting, and Participants

This observational retrospective study linked the Healthcare Cost and Utilization Project State Inpatient Databases of the Agency for Healthcare Research and Quality from January 1, 2011, through December 31, 2014, for 4 states (New York, Florida, Iowa, and Washington) with data from the 2014 American Hospital Association annual survey. After identifying surgical discharges, SNHs were defined as those with the top quartile of inpatient stays paid by Medicaid or self-paid. Hospital-level risk-standardized readmission rates (RSRRs) for surgical discharges were calculated. The association between hospital RSRRs and hospital characteristics was evaluated with bivariate analyses. An estimated multivariable hierarchical linear regression model was used to examine variation in hospital RSRRs, adjusting for hospital characteristics, state, year, and SNH status. Data were analyzed from June 1, 2017, through March 1, 2018.

Exposures

Surgical care at an SNH.

Main Outcomes and Measures

Readmission after an index surgical admission.

Results

A total of 1 252 505 patients across all 4 years and states were included in the analysis (51.7% women; mean [SD] age, 52.7 [18.1] years). Bivariate analyses found that SNHs had higher mean (SD) surgical RSRRs compared with non-SNHs; significant differences were found for New York (9.6 [0.1] vs 10.9 [0.1]; P < .001) and Florida (11.6 [0.1] vs 12.1 [0.1]; P = .001). The SNHs also had higher RSRRs in these 2 states when stratified by hospital funding (nonfederal government SNHs in New York, 11.9 [0.2]; for-profit, private SNHs in Florida, 13.1 [0.2]; P < .001 for both); however, bed size was a significant factor for higher mean (SD) RSRRs only for New York (200 to 399 beds, 12.0 [0.4]; P = .006). Similar results were found for multivariable linear regression models; RSRRs were 1.02% higher for SNHs compared with non-SNHs (95% CI, 0.75%-1.29%; P < .001). Increased RSRRs were observed for hospitals in New York and Florida, teaching hospitals, and investor-owned hospitals. Factors associated with reduced RSRRs included presence of an ambulatory surgery center, cardiac catheterization capabilities, and high surgical volume.

Conclusions and Relevance

According to results of this study, surgical patients treated at SNHs experienced slightly higher RSRRs compared with those treated at non-SNHs. This association persisted after adjusting for year, state, and hospital factors, including teaching status, hospital bed size, and hospital volume.

Impacts of the Affordable Care Act on Community Health Centers: Characteristics of New Patients and Early Changes in Delivery of Care

The aim of this study was to assess the impact of the Affordable Care Act (ACA) on community health centers (CHCs). Using electronic health records from the Community Health Applied Research Network, we assessed new patient characteristics, office visit volume, and payer distribution among CHC patients before and after ACA implementation, 2011-2014 (n = 442 455). New patients post-ACA were younger, more likely to be female and have chronic health conditions, and utilized more primary care (P < .05 for each). Post-ACA, clinics delivered 19% more office visits and more visits were reimbursed by Medicaid. The support of CHCs is needed to meet increased demand post-ACA.

Community Health Center Utilization Following the 2008 Medicaid Expansion in Oregon: Implications for the Affordable Care Act

OBJECTIVES

To assess longitudinal patterns of community health center (CHC) utilization and the effect of insurance discontinuity after Oregon's 2008 Medicaid expansion (the Oregon Experiment).

METHODS

We conducted a retrospective cohort study with electronic health records and Medicaid data. We divided individuals who gained Medicaid in the Oregon Experiment into those who maintained (n = 788) or lost (n = 944) insurance coverage. We compared these groups with continuously insured (n = 921) and continuously uninsured (n = 5416) reference groups for community health center utilization rates over a 36-month period.

RESULTS

Both newly insured groups increased utilization in the first 6 months. After 6 months, use among those who maintained coverage stabilized at a level consistent with the continuously insured, whereas it returned to baseline for those who lost coverage.

CONCLUSIONS

Individuals who maintained coverage through Oregon's Medicaid expansion increased long-term utilization of CHCs, whereas those with unstable coverage did not.

POLICY IMPLICATIONS

This study predicts long-term increase in CHC utilization following Affordable Care Act Medicaid expansion and emphasizes the need for policies that support insurance retention.

Using the electronic health record for assessment of health insurance in community health centers

OBJECTIVE

To demonstrate use of the electronic health record (EHR) for health insurance surveillance and identify factors associated with lack of coverage.

MATERIALS AND METHODS

Using EHR data, we conducted a retrospective, longitudinal cohort study of adult patients (n = 279 654) within a national network of community health centers during a 2-year period (2012-2013).

RESULTS

Factors associated with higher odds of being uninsured (vs Medicaid-insured) included: male gender, age >25 years, Hispanic ethnicity, income above the federal poverty level, and rural residence (P < .01 for all). Among patients with no insurance at their initial visit (n = 114 000), 50% remained uninsured for every subsequent visit.

DISCUSSION

During the 2 years prior to 2014, many patients utilizing community health centers were unable to maintain stable health insurance coverage.

CONCLUSION

As patients gain access to health insurance under the Affordable Care Act, the EHR provides a novel approach to help track coverage and support vulnerable patients in gaining and maintaining coverage.

Understanding how low-income families prioritize elements of health care access for their children via the optimal care model

Background

Insurance coverage alone does not guarantee access to needed health care. Few studies have explored what “access” means to low-income families, nor have they examined how elements of access are prioritized when availability, affordability, and acceptability are not all achievable. Therefore, we explored low-income parents’ perspectives on accessing health care.

Methods

In-depth interviews with a purposeful sample of 29 Oregon parents who responded to a previously administered statewide survey about health insurance. Transcribed interviews were analyzed by a multidisciplinary team using a standard iterative process.

Results

Parents highlighted affordability and limited availability as barriers to care; a continuous relationship with a health care provider helped them overcome these barriers. Parents also described the difficult decisions they made between affordability and acceptability in order to get the best care they could for their children. We present a new conceptual model to explain these experiences accessing care with health insurance: the Optimal Care Model. The model shows a transition from optimal care to a breaking point where affordability becomes the driving factor, but the care is perceived as unacceptable because it is with an unknown provider.

Conclusions

Even when covered by health insurance, low-income parents face barriers to accessing health care for their children. As the Affordable Care Act and other policies increase coverage options across the United States, many Americans may experience similar barriers and facilitators to health care access. The Optimal Care Model provides a useful construct for better understanding experiences that may be encountered when the newly insured attempt to access available, acceptable, and affordable health care services.

Continuity of Public Insurance Coverage

Publicly financed insurance programs are tasked with maintaining coverage for eligible children, but published measures to assess coverage have not been evaluated. Therefore, we sought to identify and categorize measures of health insurance continuity for children and adolescents. We conducted a systematic review of Medline and HealthStar databases, review of reference lists of eligible articles, and contact with experts. We categorized measures into 8 domains based on a conceptual framework. We identified 147 measures from 84 eligible articles. Most measures evaluated the following domains: always insured (41%), repeatedly uninsured (36%), and transition out of coverage (29%), while fewer assessed single gap in coverage, always uninsured, transition into coverage, change in coverage, and eligibility. Only 18% of measures assessed associations between continuity of coverage and child and adolescent health outcomes. These results suggest that a number of measures of continuity of coverage exist, but few measures have assessed impact on outcomes.

Access to care provided by better safety net systems for the uninsured: measuring and conceptualizing adequacy

This descriptive study assesses the access to care provided by five model and diverse safety net programs that enroll uninsured adults in a coordinated system offering primary care, hospital care, prescription drugs, and most specialist services. Physician use by safety net program members was similar to insured groups. However, there was less use of hospitals in the two programs that relied on uncompensated charity care. Considering access measures commonly used in population-based surveys, the uninsured in these five communities fared no better than uninsured elsewhere. However, respondents may consider enrollment in a well-structured safety net program to be equivalent to insurance. If so, population surveys may be least accurate in identifying uninsured people in the very communities that have the best safety net programs. On balance the five safety net systems profiled here meet the needs of low-income uninsured residents at a level that is roughly similar to that for people with insurance.