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

The role of insurance status in the association between short-term temperature exposure and myocardial infarction hospitalizations in New York State

Myocardial infarction (MI) is a leading cause of morbidity and mortality in the United States and its risk increases with extreme temperatures. Climate change causes variability in weather patterns, including extreme temperature events that disproportionately affect socioeconomically disadvantaged communities. Many studies on the health effects of extreme temperatures have considered community-level socioeconomic disadvantage.

Objectives

To evaluate effect modification of the relationship between short-term ambient temperature and MI, by individual-level insurance status (insured vs. uninsured).

Methods

We identified MI hospitalizations and insurance status across New York State (NYS) hospitals from 1995 to 2015 in the New York Department of Health Statewide Planning and Research Cooperative System database, using International Classification of Diseases codes. We linked short-term ambient temperature (averaging the 6 hours preceding the event [MI hospitalization]) or nonevent control period in patient residential zip codes. We employed a time-stratified case-crossover study design for both insured and uninsured strata, and then compared the group-specific rate ratios.

Results

Over the study period, there were 1,095,051 primary MI admissions, 966,475 (88%) among insured patients. During extremely cold temperatures (<5.8 °C) insured patients experienced reduced rates of MI; this was not observed among the uninsured counterparts. At warmer temperatures starting at the 65th percentile (15.7 °C), uninsured patients had higher rates than insured patients (e.g., for a 6-hour pre-event average temperature increase from the median to the 75th percentile, the rate of MI increased was 2.0% [0.0%-4.0%] higher in uninsured group).

Conclusions

Uninsured individuals may face disproportionate rates of MI hospitalization during extreme temperatures.

Data Challenges for Externally Controlled Trials: Viewpoint

The preferred evidence of a large randomized controlled trial is difficult to adopt in scenarios, such as rare conditions or clinical subgroups with high unmet needs, and evidence from external sources, including real-world data, is being increasingly considered by decision makers. Real-world data originate from many sources, and identifying suitable real-world data that can be used to contextualize a single-arm trial, as an external control arm, has several challenges. In this viewpoint article, we provide an overview of the technical challenges raised by regulatory and health reimbursement agencies when evaluating comparative efficacy, such as identification, outcome, and time selection challenges. By breaking down these challenges, we provide practical solutions for researchers to consider through the approaches of detailed planning, collection, and record linkage to analyze external data for comparative efficacy.

Effect of Race and Ethnicity on In-Hospital Mortality in Patients with COVID-19

Objective

To identify differences in short-term outcomes of patients with coronavirus disease 2019 (COVID-19) according to various racial/ethnic groups.

Design

Analysis of Cerner de-identified COVID-19 dataset.

Setting

A total of 62 health care facilities.

Participants

The cohort included 49,277 adult COVID-19 patients who were hospitalized from December 1, 2019 to November 13, 2020.

Main Outcome Measures

The primary outcome of interest was in-hospital mortality. The secondary outcome was non-routine discharge (discharge to destinations other than home, such as short-term hospitals or other facilities including intermediate care and skilled nursing homes).

Methods

We compared patients' age, gender, individual components of Charlson and Elixhauser comorbidities, medical complications, use of do-not-resuscitate, use of palliative care, and socioeconomic status between various racial and/or ethnic groups. We further compared the rates of in-hospital mortality and non-routine discharges between various racial and/or ethnic groups.

Results

Compared with White patients, in-hospital mortality was significantly higher among African American (OR 1.5; 95%CI:1.3-1.6, P<.001), Hispanic (OR1.4; 95%CI:1.3-1.6, P<.001), and Asian or Pacific Islander (OR 1.5; 95%CI: 1.1-1.9, P=.002) patients after adjustment for age and gender, Elixhauser comorbidities, do-not-resuscitate status, palliative care use, and socioeconomic status.

Conclusions

Our study found that, among hospitalized patients with COVID-2019, African American, Hispanic, and Asian or Pacific Islander patients had increased mortality compared with White patients after adjusting for sociodemographic factors, comorbidities, and do-not-resuscitate/palliative care status. Our findings add additional perspective to other recent studies.

Using Electronic Health Records in Longitudinal Studies: Estimating Patient Attrition

BACKGROUND

Electronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.

OBJECTIVE

The objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.

RESEARCH DESIGN

A retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.

SUBJECTS

A total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.

MEASURES

Percentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.

RESULTS

Attrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.

CONCLUSION

This study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Following Uninsured Patients Through Medicaid Expansion: Ambulatory Care Use and Diagnosed Conditions

PURPOSE

The Patient Protection and Affordable Care Act (ACA) has improved access to health insurance, yet millions remain uninsured. Many patients who remain uninsured access care at community health centers (CHCs); however, little is known about their health conditions and health care use. We assessed ambulatory care use and diagnosed health conditions among a cohort of CHC patients uninsured before enactment of the ACA (pre-ACA: January 1, 2012 to December 31, 2013) and followed them after enactment (post-ACA: January 1, 2014 to December 31, 2015).

METHODS

This retrospective cohort analysis used electronic health record data from CHCs in 11 US states that expanded Medicaid eligibility. We assessed ambulatory care visits and documented health conditions among a cohort of 138,246 patients (aged 19 to 64 years) who were uninsured pre-ACA and either remained uninsured, gained Medicaid, gained other health insurance, or did not have a visit post-ACA. We estimated adjusted predicted probabilities of ambulatory care use using an ordinal logistic mixed-effects regression model.

RESULTS

Post-ACA, 20.9% of patients remained uninsured, 15.0% gained Medicaid, 12.4% gained other insurance, and 51.7% did not have a visit. The majority of patients had ≥1 diagnosed health condition. The adjusted proportion of patients with high use (≥6 visits over 2 years) increased from pre-ACA to post-ACA among those who gained Medicaid (pre-ACA: 23%, post-ACA: 34%, P <.001) or gained other insurance (pre-ACA: 29%, post-ACA: 48%, P <.001), whereas the percentage fell slightly for those continuously uninsured.

CONCLUSIONS

A significant percentage of CHC patients remained uninsured; many who remained uninsured had diagnosed health conditions, and one-half continued to have ≥3 visits to CHCs. CHCs continue to be essential providers for uninsured patients.

Medicaid coverage accuracy in electronic health records

Health insurance coverage facilitates access to preventive screenings and other essential health care services, and is linked to improved health outcomes; therefore, it is critical to understand how well coverage information is documented in the electronic health record (EHR) and which characteristics are associated with accurate documentation. Our objective was to evaluate the validity of EHR data for monitoring longitudinal Medicaid coverage and assess variation by patient demographics, visit types, and clinic characteristics. We conducted a retrospective, observational study comparing Medicaid status agreement between Oregon community health center EHR data linked at the patient-level to Medicaid enrollment data (gold standard). We included adult patients with a Medicaid identification number and ≥1 clinic visit between 1/1/2013-12/31/2014 [>1 million visits (n = 135,514 patients)]. We estimated statistical correspondence between EHR and Medicaid data at each visit (visit-level) and for different insurance cohorts over time (patient-level). Data were collected in 2016 and analyzed 2017-2018. We observed excellent agreement between EHR and Medicaid data for health insurance information: kappa (>0.80), sensitivity (>0.80), and specificity (>0.85). Several characteristics were associated with agreement; at the visit-level, agreement was lower for patients who preferred a non-English language and for visits missing income information. At the patient-level, agreement was lower for black patients and higher for older patients seen in primary care community health centers. Community health center EHR data are a valid source of Medicaid coverage information. Agreement varied with several characteristics, something researchers and clinic staff should consider when using health insurance information from EHR data.

Insurance Coverage and Acute Care Revisits After Pediatric Ambulatory Tonsillectomy

We investigated whether patterns of health insurance coverage were associated with 30-day all-cause acute care revisits after ambulatory tonsillectomy at a free-standing quaternary-care pediatric hospital. Insurance patterns were classified from past encounters as continuous private, continuous Medicaid, Medicaid-to-private change, or private-to-Medicaid change. Among 478/675 boys/girls (age 9 ± 4 years) selected for analysis, 148 (13%) had 30-day revisits, whereas 96 (8%) changed from Medicaid to private insurance, and 99 (9%) changed from private insurance to Medicaid. Revisits were most common in the private-to-Medicaid group, compared with continuous private coverage (19% vs 10%; 95% CI of difference: 1%-18%; P = .007). The private-to-Medicaid group was most likely to be overweight, have symptoms of sleep disordered breathing, and have more past clinical encounters. In multivariable analysis, the greater risk of acute care revisits among children with private-to-Medicaid change in coverage was attributable to greater comorbidity burden and past health care utilization.

Measures of SES for Electronic Health Record-based Research

INTRODUCTION

Although infrequently recorded in electronic health records (EHRs), measures of SES are essential to describe health inequalities and account for confounding in epidemiologic research. Medical Assistance (i.e., Medicaid) is often used as a surrogate for SES, but correspondence between conventional SES and Medical Assistance has been insufficiently studied.

METHODS

Geisinger Clinic EHR data from 2001 to 2014 and a 2014 questionnaire were used to create six SES measures: EHR-derived Medical Assistance and proportion of time under observation on Medical Assistance; educational attainment, income, and marital status; and area-level poverty. Analyzed in 2016-2017, associations of SES measures with obesity, hypertension, type 2 diabetes, chronic rhinosinusitis, fatigue, and migraine headache were assessed using weighted age- and sex-adjusted logistic regression.

RESULTS

Among 5,550 participants (interquartile range, 39.6-57.5 years, 65.9% female), 83% never used Medical Assistance. All SES measures were correlated (Spearman's p≤0.4). Medical Assistance was significantly associated with all six health outcomes in adjusted models. For example, the OR for prevalent type 2 diabetes associated with Medical Assistance was 1.7 (95% CI=1.3, 2.2); the OR for high school versus college graduates was 1.7 (95% CI=1.2, 2.5). Medical Assistance was an imperfect proxy for SES: associations between conventional SES measures and health were attenuated <20% after adjustment for Medical Assistance.

CONCLUSIONS

Because systematically collected SES measures are rarely available in EHRs and are unlikely to appear soon, researchers can use EHR-based Medical Assistance to describe inequalities. As SES has many domains, researchers who use Medical Assistance to evaluate the association of SES with health should expect substantial unmeasured confounding.

Introductory Overview of the Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network: Examining the Impact of US Health Policies and Practices to Prevent Diabetes and Its Complications

PURPOSE OF REVIEW

Diabetes incidence is rising among vulnerable population subgroups including minorities and individuals with limited education. Many diabetes-related programs and public policies are unevaluated while others are analyzed with research designs highly susceptible to bias which can result in flawed conclusions. The Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network includes eight research centers and three funding agencies using rigorous methods to evaluate natural experiments in health policy and program delivery.

RECENT FINDINGS

NEXT-D2 research studies use quasi-experimental methods to assess three major areas as they relate to diabetes: health insurance expansion; healthcare financing and payment models; and innovations in care coordination. The studies will report on preventive processes, achievement of diabetes care goals, and incidence of complications. Some studies assess healthcare utilization while others focus on patient-reported outcomes. NEXT-D2 examines the effect of public and private policies on diabetes care and prevention at a critical time, given ongoing and rapid shifts in the US health policy landscape.

Medicaid's Impact on Chronic Disease Biomarkers: A Cohort Study of Community Health Center Patients

BACKGROUND

Understanding the impact of health insurance is critical, particularly in the era of Affordable Care Act Medicaid expansion. The electronic health record (EHR) provides new opportunities to quantify health outcomes.

OBJECTIVE

To assess changes in biomarkers of chronic disease among community health center (CHC) patients who gained Medicaid coverage with the Oregon Medicaid expansion (2008-2011).

DESIGN

Prospective cohort. Patients were followed for 24 months, and rate of mean biomarker change was calculated. Time to a controlled follow-up measurement was compared using Cox regression models.

SETTING/PATIENTS

Using EHR data from OCHIN (a non-profit network of CHCs) linked to state Medicaid data, we identified three cohorts of patients with uncontrolled chronic conditions (diabetes, hypertension, and hyperlipidemia). Within these cohorts, we included patients who gained Medicaid coverage along with a propensity score-matched comparison group who remained uninsured (diabetes n = 608; hypertension n = 1244; hyperlipidemia n = 546).

MAIN MEASURES

Hemoglobin A1c (HbA1c) for the diabetes cohort, systolic and diastolic blood pressure (SBP and DBP, respectively) for the hypertension cohort, and low-density lipoprotein (LDL) for the hyperlipidemia cohort.

KEY RESULTS

All cohorts improved over time. Compared to matched uninsured patients, adults in the diabetes and hypertension cohorts who gained Medicaid coverage were significantly more likely to have a follow-up controlled measurement (hazard ratio [HR] =1.26, p = 0.020; HR = 1.35, p < 0.001, respectively). No significant difference was observed in the hyperlipidemia cohort (HR = 1.09, p = 0.392).

CONCLUSIONS

OCHIN patients with uncontrolled chronic conditions experienced objective health improvements over time. In two of three chronic disease cohorts, those who gained Medicaid coverage were more likely to achieve a controlled measurement than those who remained uninsured. These findings demonstrate the effective care provided by CHCs and the importance of health insurance coverage within a usual source of care setting.

CLINICAL TRIALS REGISTRATION

NCT02355132 [ https://clinicaltrials.gov/ct2/show/NCT02355132 ].

A new role for primary care teams in the United States after "Obamacare:" Track and improve health insurance coverage rates

Maintaining continuous health insurance coverage is important. With recent expansions in access to coverage in the United States after "Obamacare," primary care teams have a new role in helping to track and improve coverage rates and to provide outreach to patients. We describe efforts to longitudinally track health insurance rates using data from the electronic health record (EHR) of a primary care network and to use these data to support practice-based insurance outreach and assistance. Although we highlight a few examples from one network, we believe there is great potential for doing this type of work in a broad range of family medicine and community health clinics that provide continuity of care. By partnering with researchers through practice-based research networks and other similar collaboratives, primary care practices can greatly expand the use of EHR data and EHR-based tools targeting improvements in health insurance and quality health care.