Medical Care

Insurance Loss in the Era of the Affordable Care Act: Association With Access to Health Services Background: Every year, millions of Americans lose their health insurance and remain uninsured for various reasons, potentially impacting access to medical services. Objective: To examine trends in health insurance loss in the periods shortly before and after implementation of Patient Protection and Affordable Care Act (ACA) and to assess the association of past-year health insurance loss with access to health services and medications. Research Design and Subjects: Trends in health insurance loss were examined in 176,961 nonelderly adult participants of the National Health Interview Survey 2011–2017—a representative cross-sectional annual survey of US general population. Multivariable logistic regression models were used to examine access to health services and medications. Measures: Loss of private insurance or Medicaid in the past year; use of emergency room services and hospitalizations; contact with medical providers; affording medical care or medications; cost-related medication nonadherence. Results: Private health insurance loss decreased from 3.9%–4.0% in 2011–2013 to 2.7% to 3.1% in 2014–2017 (P<0.001); Medicaid loss decreased from 8.5%–8.9% to 4.6%–6.4% in this period (P<0.001). Nevertheless, as late as 2017, ∼6 million uninsured adults reported having lost private insurance or Medicaid in the past year. Loss of either type of health insurance was associated with lower odds of accessing medical providers, but higher odds of not affording medical care and poor adherence to medication regimens to save costs. Conclusions: Implementation of ACA was associated with lower risk of health insurance loss. Nevertheless, health insurance loss remains a major barrier to accessing health services and prescribed medications.

Effect of Medicaid Disenrollment on Health Care Utilization Among Adults With Mental Health Disorders Background: Medicaid is an important source of insurance coverage for those with mental health (MH) disorders in the United States. Although disruptions in Medicaid coverage are common, little is known about the dynamic relationship between Medicaid disenrollment and MH care utilization. Objective: We estimated changes in all-cause and MH-related health care use post Medicaid disenrollment among a nationwide cohort of adults with MH disorders. Subjects: We identified 8841 persons (197,630 person-months) ages 18–64 with MH disorders and Medicaid coverage from Panels 4 to 19 Medical Expenditure Panel Survey. Methods: Using a quasi-experimental design and propensity weighting, we estimated logit models examining changes in service utilization per-person-per-month. We used a "post" indicator to estimate average differences in service use postdisenrollment (vs. those with continuous Medicaid coverage) and a count variable measuring total months since coverage loss to estimate changes over time. Outcome Measures: All-cause outpatient visits, MH-related outpatient visits, and acute care visits. Results: Becoming uninsured after Medicaid disenrollment was associated with average reductions of 52% [−14.75 percentage-points, 95% confidence interval (CI): −17.59, −11.91] in the likelihood of receiving any outpatient service, 35% (−2.23 percentage-points, 95% CI: −3.71, −0.75) in the likelihood of receiving any MH-related outpatient service, and 52% (−2.44 percentage-points; 95% CI: −3.35, −1.52) in the likelihood of receiving any acute service in a month. Health care use declined the most in the month immediately postdisenrollment, and declines continued over the next half-year (while uninsured). Conclusions: Insurance loss after disenrollment from Medicaid led to a persistent disruption in the receipt of health care services for beneficiaries with MH disorders.

Does One Size Fit All With the Effects of Payment Reform? Dialysis Facility Payer Mix and Anemia Management Under the Expanded Medicare Prospective Payment System Background: The effects of Medicare payment reforms aiming to improve the efficiency and quality of care by establishing greater financial accountability for providers may vary based on the extent and types of other coverage for their patient populations. Providers who are more resource constrained due to a less favorable payer mix face greater financial risks under such reforms. The impact of the expanded Medicare dialysis prospective payment system (PPS) on quality of care in independent dialysis facilities may vary based on the extent of higher payments from private insurers available for managing increased risks. Objectives: To evaluate whether anemia outcomes for dialysis patients in independent facilities differ under the Medicare PPS based on facility payer mix. Design: We examined changes in anemia outcomes for 122,641 Medicare dialysis patients in 921 independent facilities during 2009–2014 among facilities with differing levels of employer insurance (EI). We performed similar analyses of facilities affiliated with large dialysis organizations, whose practices were not expected to change based on facility-specific payer mix. Results: Among independent facilities, similar modeled trends in low hemoglobin for all 3 facility EI groups in 2009–2010 were followed by increased low hemoglobin during 2012–2014 for facilities with lower EI (P<0.01). Post-PPS standardized blood transfusion ratios were 9% higher for lower EI versus higher EI independent facilities (P<0.01). Among large dialysis organizations facilities, there was no divergence in low hemoglobin by payer mix under the PPS. Conclusions: There is evidence of poorer quality of care for anemia under the PPS in independent facilities with lower versus higher EI. Provider responses to payment reform may vary based on attributes such as payer mix that could have implications for health disparities.

A Prediction Model for Uncontrolled Type 2 Diabetes Mellitus Incorporating Area-level Social Determinants of Health Background: Social determinants of health (SDH) at the area level are understood to influence the likelihood of having poor glycemic control for patients with type 2 diabetes mellitus (T2DM). Objectives: To develop a model for predicting whether a person with T2DM has uncontrolled diabetes (hemoglobin A1c ≥9%), incorporating individual and area-level (census tract) covariates. Research Design: Development and validation of machine learning models. Subjects: Total of N=1,015,808 privately insured persons in claims data with T2DM. Measures: C-statistic, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results: A standard logistic regression model selecting among the available individual-level covariates and area-level SDH covariates (at the census tract level) performed poorly, with a C-statistic of 0.685, sensitivity of 25.6%, specificity of 90.1%, positive predictive value of 56.9%, negative predictive value of 70.4%, and accuracy of 68.4% on a 25% held-out validation subset of the data. By contrast, machine learning models improved upon risk prediction, with the highest performance from a random forest algorithm with a C-statistic of 0.928, sensitivity of 68.5%, specificity of 94.6%, positive predictive value of 69.8%, negative predictive value of 94.3%, and accuracy of 90.6%. SDH variables alone explained 16.9% of variation in uncontrolled diabetes. Conclusions: A predictive model developed through a machine learning approach may assist health care organizations to identify which area-level SDH data to monitor for prediction of diabetes control, for potential use in risk-adjustment and targeting.

Developing and Validating a Measure to Estimate Poverty in Medicare Administrative Data Objective: To develop and validate a measure that estimates individual level poverty in Medicare administrative data that can be used in studies of Medicare claims. Data Sources: A 2008 to 2013 Medicare Current Beneficiary Survey linked to 2008 to 2013 Medicare fee-for-service beneficiary summary file and census data. Study Design and Methods: We used the Medicare Current Beneficiary Survey to define individual level poverty status and linked to Medicare administrative data (N=38,053). We partitioned data into a measure derivation dataset and a validation dataset. In the derivation data, we used a logistic model to regress poverty status on measures of dual eligible status, part D low-income subsidy, and demographic and administrative data, and modeled with and without linked census and nursing home data. Each beneficiary receives a predicted poverty score from the model. Performance was evaluated in derivation and validation data and compared with other measures used in the literature. We present a measure for income-only poverty as well as one for income and asset poverty. Principal Findings: A score (predicted probability of income poverty) >0.5 yielded 58% sensitivity, 94% specificity, and 84% positive predictive value in the derivation data; our score yielded very similar results in the validation data. The model's c-statistic was 0.84. Our poverty score performed better than Medicaid enrollment, high zip code poverty, and zip code median income. The income and asset version performed similarly well. Conclusions: A poverty score can be calculated using Medicare administrative data for use as a continuous or binary measure. This measure can improve researchers' ability to identify poverty in Medicare administrative data.

Predicting Primary Care Use Among Patients in a Large Integrated Health System: The Role of Patient Experience Measures Objective: Most Veterans Affairs (VA) Health Care System enrollees age 65+ also have the option of obtaining care through Medicare. Reliance upon VA varies widely and there is a need to optimize its prediction in an era of expanding choice for veterans to obtain care within or outside of VA. We examined whether survey-based patient-reported experiences improved prediction of VA reliance. Methods: VA and Medicare claims in 2013 were linked to construct VA reliance (proportion of all face-to-face primary care visits), which was dichotomized (=1 if reliance >50%). We predicted reliance in 83,143 Medicare-eligible veterans as a function of 61 baseline characteristics in 2012 from claims and the 2012 Survey of Healthcare Experiences of Patients. We estimated predictive performance using the cross-validated area under the receiver operating characteristic (AUROC) curve, and assessed variable importance using the Shapley value decomposition. Results: In 2012, 68.9% were mostly VA reliant. The AUROC for the model including claims-based predictors was 0.882. Adding patient experience variables increased AUROC to 0.890. The pseudo R2 for the full model was 0.400. Baseline reliance and patient experiences accounted for 72.0% and 11.1% of the explained variation in reliance. Patient experiences related to the accessibility of outpatient services were among the most influential predictors of reliance. Conclusion: The addition of patient experience variables slightly increased predictive performance. Understanding the relative importance of patient experience factors is critical for informing what VA reform efforts should be prioritized following the passage of the 2018 MISSION Act.

Comparing Resource Use in Medical Admissions of Children With Complex Chronic Conditions Background: Children with complex chronic conditions (CCCs) utilize a disproportionate share of hospital resources. Objective: We asked whether some hospitals display a significantly different pattern of resource utilization than others when caring for similar children with CCCs admitted for medical diagnoses. Research Design: Using Pediatric Health Information System data from 2009 to 2013, we constructed an inpatient Template of 300 children with CCCs, matching these to 300 patients at each hospital, thereby performing a type of direct standardization. Subjects: Children with CCCs were drawn from a list of the 40 most common medical principal diagnoses, then matched to patients across 40 Children's Hospitals. Measures: Rate of intensive care unit admission, length of stay, resource cost. Results: For the Template-matched patients, when comparing resource use at the lower 12.5-percentile and upper 87.5-percentile of hospitals, we found: intensive care unit utilization was 111% higher (6.6% vs. 13.9%, P<0.001); hospital length of stay was 25% higher (2.4 vs. 3.0 d/admission, P<0.001); and finally, total cost per patient varied by 47% ($6856 vs. $10,047, P<0.001). Furthermore, some hospitals, compared with their peers, were more efficient with low-risk patients and less efficient with high-risk patients, whereas other hospitals displayed the opposite pattern. Conclusions: Hospitals treating similar patients with CCCs admitted for similar medical diagnoses, varied greatly in resource utilization. Template Matching can aid chief quality officers benchmarking their hospitals to peer institutions and can help determine types of their patients having the most aberrant outcomes, facilitating quality initiatives to target these patients.

Disputes of Self-reported Chronic Disease Over Time: The Role of Race, Ethnicity, Nativity, and Language of Interview Background: Respondents in longitudinal health interview surveys may inconsistently report their chronic diseases across interview waves. Racial/ethnic minority adults have an increased burden of chronic diseases and may dispute chronic disease reports more frequently. Objective: We evaluated the longitudinal association between race/ethnicity, nativity, and language of interview with disputing previously reported chronic diseases. Methods: We performed secondary data analysis of nationally representative longitudinal data (Health and Retirement Study, 1998–2010) of adults 51 years or older (n=23,593). We estimated multilevel mixed-effects logistic models of disputes of previously reported chronic disease (hypertension, heart disease, lung disease, diabetes, cancer, stroke, arthritis). Results: Approximately 22% of Health and Retirement Study respondents disputed prior chronic disease self-reports across the entire study period; 21% of non-Latino white, 20.5% of non-Latino black, and 28% of Latino respondents disputed. In subgroup comparisons of model-predicted odds using postestimation commands, Latinos interviewed in Spanish have 34% greater odds of disputing compared with non-Latino whites interviewed in English and 35% greater odds of dispute relative to non-Latino blacks interviewed in English. Conclusions: The odds of disputing a prior chronic disease report were substantially higher for Latinos who were interviewed in Spanish compared with non-Latino white or black counterparts interviewed in English, even after accounting for other sociodemographic factors, cognitive declines, and time-in-sample considerations. Our findings point toward leveraging of multiple sources of data to triangulate information on chronic disease status as well as investigating potential mechanisms underlying the higher probability of dispute among Spanish-speaking Latino respondents.

Does Early Follow-Up Improve the Outcomes of Sepsis Survivors Discharged to Home Health Care? Background: There is little evidence to guide the care of over a million sepsis survivors following hospital discharge despite high rates of hospital readmission. Objective: We examined whether early home health nursing (first visit within 2 days of hospital discharge and at least 1 additional visit in the first posthospital week) and early physician follow-up (an outpatient visit in the first posthospital week) reduce 30-day readmissions among Medicare sepsis survivors. Design: A pragmatic, comparative effectiveness analysis of Medicare data from 2013 to 2014 using nonlinear instrumental variable analysis. Subjects: Medicare beneficiaries in the 50 states and District of Columbia discharged alive after a sepsis hospitalization and received home health care. Measures: The outcomes, protocol parameters, and control variables were from Medicare administrative and claim files and the home health Outcome and Assessment Information Set (OASIS). The primary outcome was 30-day all-cause hospital readmission. Results: Our sample consisted of 170,571 mostly non-Hispanic white (82.3%), female (57.5%), older adults (mean age, 76 y) with severe sepsis (86.9%) and a multitude of comorbid conditions and functional limitations. Among them, 44.7% received only the nursing protocol, 11.0% only the medical doctor protocol, 28.1% both protocols, and 16.2% neither. Although neither protocol by itself had a statistically significant effect on readmission, both together reduced the probability of 30-day all-cause readmission by 7 percentage points (P=0.006; 95% confidence interval=2, 12). Conclusions: Our findings suggest that, together, early postdischarge care by home health and medical providers can reduce hospital readmissions for sepsis survivors.

Perceived Patient Safety Culture in Nursing Homes Associated With "Nursing Home Compare" Performance Indicators Background: The safety and quality of care provided to nursing home residents is a significant concern. Little is known whether fostering patient safety culture helps improve the safety and quality of nursing home care. Methods: This study determined the associations of nursing home patient safety culture performance, as reported by administrators, directors of nursing, and unit leaders in a large national sample of free-standing nursing homes, with several "Nursing Home Compare" performance indicators. We conducted the survey in 2017 using the Agency for Healthcare Research and Quality Survey on Patient Safety Culture for nursing homes to collect data on 12 core domains of safety culture scores. Survey data were linked to other nursing home files for multivariable regression analyses. Results: Overall, 818 of the 2254 sampled nursing homes had at least 1 completed survey returned for a response rate of 36%. After adjustment for nursing home, market, and state covariates, every 10 percentage points increase in overall positive response rate for safety culture was associated with 0.56 fewer health care deficiencies (P=0.001), 0.74 fewer substantiated complaints (P=0.004), reduced fines by $2285.20 (P=0.059), and 20% increased odds of being designated as 4-star or 5-star (vs. 1 to 3 star) facilities (odds ratio roughly=1.20, P<0.05). Conclusions: Efforts to improve nursing home performance in patient safety culture have the potential to improve broad safety and quality of care measures encapsulated in the Nursing Home Compare publication.

Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos
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2841026182

6948891480

alsfakia@gmail.com