Being uninsured is bad for your health: can medical homes play a role in treating the uninsurance ailment?

Social and Systemic Barriers to Transition-Related Surgical Procedures for Transgender Americans

Purpose: Transgender and gender-diverse (TGD) individuals in the United States face disproportionate barriers to health care access. This study compared characteristics of individuals who have and have not undergone gender-affirming surgery with the goal of identifying social and systemic barriers to transition-related surgery. Methods: Data were extracted from the 2015 United States Transgender Survey, a cross-sectional nonprobability sample of nearly 28,000 TGD adults. The primary outcome was having undergone gender-affirming surgery. Multivariable logistic regression models were constructed to determine correlates of receipt of gender-affirming surgery. A subgroup analysis was performed to explore differences by insurance types regarding coverage of surgical procedures and presence of in-network providers. Results: In total, 6009 (21.7%) participants underwent transition-related procedures. Increased odds of undergoing surgery were associated with older age, living in congruent gender, higher education attainment, and greater income. Decreased odds were linked with male sex assignment at birth, first recognizing TGD status at older ages, living in states without trans-protective health laws, no close transgender-knowledgeable health care provider, nonbinary status, and identifying as sexual minority. Residing in states without trans-protective health laws correlated with increased surgery denials over the previous 12-month period. Compared to White TGD individuals, TGD individuals who were Black, Latinx, or Another Race were significantly more likely to encounter health equity-related barriers to surgery. Conclusions: Gender-affirming surgery access is differentially distributed across demographic and modifiable equity-related factors amenable to interventions. Efforts are needed to address the number and geographic distribution of transgender health-competent providers, improve TGD legal protections, and increase access to health insurance for minority TGD individuals, who are disproportionately under/uninsured.

Where Do Oregon Medicaid Enrollees Seek Outpatient Care Post-affordable Care Act Medicaid Expansion?

BACKGROUND

Previous studies suggest the newly Medicaid insured are more likely to use the emergency department (ED) however they did not differentiate between patients established or not established with primary care.

OBJECTIVES

To understand where Oregon Medicaid beneficiaries sought care after the Patient Protection and Affordable Care Act (PPACA) Medicaid expansion (ED, primary care, or specialist) and the interaction between primary care establishment and outpatient care utilization.

RESEARCH DESIGN

A retrospective cohort study.

SUBJECTS

Adults continuously insured from 2014 through 2015 who were either newly, returning, or continuously insured post-PPACA.

MEASURES

Site of first and last outpatient visit, established with primary care status, and outpatient care utilization.

RESULTS

The odds of being established with primary care at their first visit were lower among newly [odds ratio (OR), 0.18; 95% confidence interval (CI), 0.18-0.19] and returning insured (OR, 0.22; 95% CI, 0.22-0.23) than the continuously insured. Continuously insured, new patients with primary care had higher odds of visiting the ED (OR, 2.15; 95% CI, 2.01-2.30) at their first visit than newly or returning insured. Patients established with a single primary care provider in all insurance groups had lower rates of ED visit, whereas those established with multiple primary care providers had the highest ED visit rates.

CONCLUSIONS

Most newly and returning insured Medicaid enrollees sought primary care rather than ED services and most became established with primary care. Our findings suggest that both insurance and primary care continuity play a role in where patients seek health care services.

Identifying and characterizing cancer survivors in the US primary care safety net

BACKGROUND

Primary care providers must understand the use patterns, clinical complexity, and primary care needs of cancer survivors to provide quality health care services. However, to the authors' knowledge, little is known regarding the prevalence and health care needs of this growing population, particularly in safety net settings.

METHODS

The authors identified adults with a history of cancer documented in primary care electronic health records within a network of community health centers (CHCs) in 19 states. The authors estimated cancer history prevalence among >1.2 million patients and compared sex-specific site distributions with national estimates. Each survivor was matched to 3 patients without cancer from the same set of clinics. The demographic characteristics, primary care use, and comorbidity burden then were compared between the 2 groups, assessing differences with absolute standardized mean differences (ASMDs). ASMD values >0.1 denote meaningful differences between groups. Generalized estimating equations yielded adjusted odds ratios (aORs) for select indicators.

RESULTS

A total of 40,266 cancer survivors were identified (prevalence of 3.0% of adult CHC patients). Compared with matched cancer-free patients, a higher percentage of survivors had ≥6 primary care visits across 3 years (62% vs 48%) and were insured (83% vs 74%) (ASMD, >0.1 for both). Cancer survivors had excess medical complexity, including a higher prevalence of depression, asthma/chronic obstructive pulmonary disease, and liver disease (ASMD, >0.1 for all). Survivors had higher odds of any opioid prescription (aOR, 1.23; 95% CI, 1.19-1.27) and chronic opioid therapy (aOR, 1.27; 95% CI, 1.23-1.32) compared with matched controls (P < .001 for all).

CONCLUSIONS

Identifying cancer survivors and understanding their patterns of utilization and physical and mental comorbidities present an opportunity to tailor primary health care services to this population.

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.

Predictors of children's health insurance coverage discontinuity in 1998 versus 2009: parental coverage continuity plays a major role

To identify predictors of coverage continuity for United States children and assess how they have changed in the first 12 years since implementation of the Children's Health Insurance Program in 1997. Using data from the nationally-representative Medical Expenditure Panel Survey, we used logistic regression to identify predictors of discontinuity in 1998 and 2009 and compared differences between the 2 years. Having parents without continuous coverage was the greatest predictor of a child's coverage gap in both 1998 and 2009. Compared to children with at least one parent continuously covered, children whose parents did not have continuous coverage had a significantly higher relative risk (RR) of a coverage gap [RR 17.96, 95 % confidence interval (CI) 14.48-22.29 in 1998; RR 12.88, 95 % CI 10.41-15.93 in 2009]. In adjusted models, parental continuous coverage was the only significant predictor of discontinuous coverage for children (with one exception in 2009). The magnitude of the pattern was higher for privately-insured children [adjusted relative risk (aRR) 29.17, 95 % CI 20.99-40.53 in 1998; aRR 25.54, 95 % CI 19.41-33.61 in 2009] than publicly-insured children (aRR 5.72, 95 % CI 4.06-8.06 in 1998; aRR 4.53, 95 % CI 3.40-6.04 in 2009). Parental coverage continuity has a major influence on children's coverage continuity; this association remained even after public health insurance expansions for children. The Affordable Care Act will increase coverage for many adults; however, 'churning' on and off programs due to income fluctuations could result in coverage discontinuities for parents. If parental coverage instability persists, these discontinuities may continue to have a negative impact on children's coverage stability as well.

Supporting health insurance expansion: do electronic health records have valid insurance verification and enrollment data?

OBJECTIVE

To validate electronic health record (EHR) insurance information for low-income pediatric patients at Oregon community health centers (CHCs), compared to reimbursement data and Medicaid coverage data.

MATERIALS AND METHODS

Subjects Children visiting any of 96 CHCs (N = 69 189) from 2011 to 2012. Analysis The authors measured correspondence (whether or not the visit was covered by Medicaid) between EHR coverage data and (i) reimbursement data and (ii) coverage data from Medicaid.

RESULTS

Compared to reimbursement data and Medicaid coverage data, EHR coverage data had high agreement (87% and 95%, respectively), sensitivity (0.97 and 0.96), positive predictive value (0.88 and 0.98), but lower kappa statistics (0.32 and 0.49), specificity (0.27 and 0.60), and negative predictive value (0.66 and 0.45). These varied among clinics.

DISCUSSION/CONCLUSIONS

EHR coverage data for children had a high overall correspondence with Medicaid data and reimbursement data, suggesting that in some systems EHR data could be utilized to promote insurance stability in their patients. Future work should attempt to replicate these analyses in other settings.

Receipt of diabetes preventive services differs by insurance status at visit

BACKGROUND

Lack of insurance is associated with suboptimal receipt of diabetes preventive care. One known reason for this is an access barrier to obtaining healthcare visits; however, little is known about whether insurance status is associated with differential rates of receipt of diabetes care during visits.

PURPOSE

To examine the association between health insurance and receipt of diabetes preventive care during an office visit.

METHODS

This retrospective cohort study used electronic health record and Medicaid data from 38 Oregon community health centers. Logistic regression was used to test the association between insurance and receipt of four diabetes services during an office visit among patients who were continuously uninsured (n=1,117); continuously insured (n=1,466); and discontinuously insured (n=336) in 2006-2007. Generalized estimating equations were used to account for within-patient correlation. Data were analyzed in 2013.

RESULTS

Overall, continuously uninsured patients had lower odds of receiving services at visits when due, compared to those who were continuously insured (AOR=0.73, 95% CI=0.66, 0.80). Among the discontinuously insured, being uninsured at a visit was associated with lower odds of receipt of services due at that visit (AOR=0.77, 95% CI=0.64, 0.92) than being insured at a visit.

CONCLUSIONS

Lack of insurance is associated with a lower probability of receiving recommended services that are due during a clinic visit. Thus, the association between being uninsured and receiving fewer preventive services may not be completely mediated by access to clinic visits.

Health information technology: an untapped resource to help keep patients insured

The recent confluence of: (1) changing state and national insurance-related policies, and (2) the rapid growth in electronic health record (EHR) use, yields an unprecedented opportunity for patient-centered medical homes (PCMHs) and other primary care practices or care settings to use health information technology (HIT) and health information exchange (HIE) in novel ways to impact patient health. We propose that HIT is an untapped resource for supporting clinic-based efforts to help eligible patients obtain and maintain insurance coverage. This commentary presents a conceptual model and guiding principles for this idea. Additionally, it describes insurance support tools that could be used to conduct 'inreach' and 'outreach' with patients around health insurance, similar to how HIT is used to manage chronic disease and panels of patients, and to improve population health outcomes.

Using geographic information systems (GIS) to identify communities in need of health insurance outreach: An OCHIN practice-based research network (PBRN) report

BACKGROUND

Our practice-based research network (PBRN) is conducting an outreach intervention to increase health insurance coverage for patients seen in the network. To assist with outreach site selection, we sought an understandable way to use electronic health record (EHR) data to locate uninsured patients.

METHODS

Health insurance information was displayed within a web-based mapping platform to demonstrate the feasibility of using geographic information systems (GIS) to visualize EHR data. This study used EHR data from 52 clinics in the OCHIN PBRN. We included cross-sectional coverage data for patients aged 0 to 64 years with at least 1 visit to a study clinic during 2011 (n = 228,284).

RESULTS

Our PBRN was successful in using GIS to identify intervention sites. Through use of the maps, we found geographic variation in insurance rates of patients seeking care in OCHIN PBRN clinics. Insurance rates also varied by age: The percentage of adults without insurance ranged from 13.2% to 86.8%; rates of children lacking insurance ranged from 1.1% to 71.7%. GIS also showed some areas of households with median incomes that had low insurance rates.

DISCUSSION

EHR data can be imported into a web-based GIS mapping tool to visualize patient information. Using EHR data, we were able to observe smaller areas than could be seen using only publicly available data. Using this information, we identified appropriate OCHIN PBRN clinics for dissemination of an EHR-based insurance outreach intervention. GIS could also be used by clinics to visualize other patient-level characteristics to target clinic outreach efforts or interventions.