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The application of social network analysis to the organization of healthcare delivery is a relatively new area of research that may not be familiar to health services statisticians and other methodologists. We present a methodological introduction to social network analysis with a case study of physicians’ adherence to clinical guidelines regarding use of implantable cardioverter defibrillators (ICDs) for the prevention of sudden cardiac death. We focus on two hospital referral regions (HRRs) in Indiana, Gary and South Bend, characterized by different rates of evidence-based ICD use (86% and 66%, respectively). Using Medicare Part B claims, we construct a network of physicians who care for cardiovascular disease patients based on patient-sharing relationships. Approaches for weighting physician dyads and aggregating physician dyads by hospital are discussed. Then, we obtain a set of weighted network statistics for the positions of hospitals in their referral region, global statistics for the physician network within each hospital, and of the network positions of individual physicians within hospitals, providing the mathematical specification and sociological intuition underlying each measure. We find that adjusting for network measures can reduce the observed differences between referral regions for evidence-based ICD therapy. This study supports previous reports on how variation in physician network structure relates to utilization of care, and motivates future work using physician network measures to examine variation in evidence-based medicine.

Most women with incident urinary incontinence continued to experience symptoms over 10 years; few had complete remission. Identification of risk factors for urinary incontinence progression, such as body mass index and physical activity, could be important for reducing symptoms over time.

Our findings suggest claims-based algorithms identify incident cancer with variable reliability when measured against an observational cohort study reference standard. Self-reported baseline information available in cohort studies is more effective in removing prevalent cancer cases than are claims data algorithms. Use of claims-based algorithms should be tailored to the research question at hand and the nature of available observational cohort data.

Theoretical models of competition with fixed prices suggest that hospitals should compete by increasing quality of care for diseases with the greatest profitability and demand elasticity. Most empirical evidence regarding hospital competition is limited to heart attacks, which in the U.S. generate positive profit margins but exhibit very low demand elasticity -ambulances usually take patients to the closest (or affiliated) hospital. In this paper, we derive a theoretically appropriate measure of market concentration in a fixed-price model, and use differential travel-time to hospitals in each of the 306 U.S. regional hospital markets to instrument for market concentration. We then estimate the model using risk-adjusted Medicare data for several different population cohorts: heart attacks (low demand elasticity), hip and knee replacements (high demand elasticity) and dementia patients (low demand elasticity, low or negative profitability). First, we find little correlation within hospitals across quality measures. And second, while we replicate the standard result that greater competition leads to higher quality in some (but not all) measures of heart attack quality, we find essentially no association between competition and quality for what should be the most competitive markets -elective hip and knee replacements. Consistent with the model, competition is associated with lower quality care among dementia patients, suggesting that competition could induce hospitals to discourage unprofitable patients.

Background/Objective Most older adults have multiple chronic conditions which lead to costly care that requires coordination across specialties. Yet many in the U.S. use a specialist physician rather than primary care as their predominant provider of ambulatory visits (PPC). As new physician payment models are designed under the Medicare and Chip Reauthorization Act (MACRA), information on whether specialists deliver care as efficiently as primary care to this high cost, high need population is needed. We test whether primary care versus specialty PPC is associated with better outcomes for older adults with multimorbidity. Design Observational study using propensity-score matching. Setting Fee-for-service Medicare, 2011–2012. Participants Beneficiaries over age 65 with multimorbidity. Measurements The independent variable was an indicator for having a specialty (versus primary) care PPC. Main outcomes were one-year mortality, hospitalization, and standardized expenditures, ambulatory visit patterns. Results In 3,934,942 beneficiaries with multimorbidity, two-thirds had a primary care provider as their PPC. Patients with a specialty PPC compared to primary care PPC had higher hospitalizations (40.3 more per 1,000) and higher spending ($1,781 more per beneficiary) but little meaningful difference in mortality (0.2% higher) or preventable hospitalizations. Spending differences stemmed from professional fees ($769 higher per beneficiary), inpatient stays ($572 higher per beneficiary) and outpatient facilities ($510 higher per beneficiary). All p-values <.001. In addition, people with a specialist versus primary care PPC had lower continuity of care and saw a greater number of providers. Conclusions Older adults with multimorbidity with a specialist as their main ambulatory care provider had higher spending and lower continuity of care but similar clinical outcomes as patients whose PPC was in primary care.

Background We calculated the performance of National Cancer Institute (NCI)/National Comprehensive Cancer Network (NCCN) cancer centers’ end‐of‐life (EOL) quality metrics among minority and white decedents to explore center‐attributable sources of EOL disparities. Methods We conducted a retrospective cohort study of Medicare beneficiaries with poor‐prognosis cancers who died between April 1, 2016 and December 31, 2016 and had any inpatient services in the last 6 months of life. We attributed patients’ EOL treatment to the center at which they received the preponderance of EOL inpatient services and calculated eight risk‐adjusted metrics of EOL quality (hospice admission ≤3 days before death; chemotherapy last 14 days of life; ≥2 emergency department (ED) visits; intensive care unit (ICU) admission; or life‐sustaining treatment last 30 days; hospice referral; palliative care; advance care planning last 6 months). We compared performance between patients across and within centers. Results Among 126,434 patients, 10,119 received treatment at one of 54 NCI/NCCN centers. In aggregate, performance was worse among minorities for ED visits (10.3% vs 7.4%, P < .01), ICU admissions (32.9% vs 30.4%, P = .03), no hospice referral (39.5% vs 37.0%, P = .03), and life‐sustaining treatment (19.4% vs 16.2%, P < .01). Despite high within‐center correlation for minority and white metrics (0.61‐0.79; P < .01), five metrics demonstrated worse performance as the concentration of minorities increased: ED visits (P = .03), ICU admission (P < .01), no hospice referral (P < .01), and life‐sustaining treatments (P < .01). Conclusion EOL quality metrics vary across NCI/NCCN centers. Within center, care was similar for minority and white patients. Minority‐serving centers had worse performance on many metrics.

IMPORTANCEChildren with medical complexity (CMC) have substantial health care needs and frequently experience poor health care quality. Understanding the population prevalence and associated health care needs can inform clinical and public health initiatives.OBJECTIVE To estimate the prevalence of CMC using open-source pediatric algorithms, evaluate performance of these algorithms in predicting health care utilization and in-hospital mortality, and identify associations between medical complexity as defined by these algorithms and clinical outcomes. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study used all-payer claims data from Colorado, Massachusetts, and New Hampshire from 2012 through 2017. Children and adolescents younger than 18 years residing in these states were included if they had 12 months or longer of enrollment in a participating health care plan. Analyses were conducted from March 12, 2021, to January 7, 2022.EXPOSURES The pediatric Complex Chronic Condition Classification System, Pediatric Medical Complexity Algorithm, and Children With Disabilities Algorithm were applied to 3 years of data to identify children with complex and disabling conditions, first in their original form and then using more conservative criteria that required multiple health care claims or involvement of 3 or more body systems.MAIN OUTCOMES AND MEASURES Primary outcomes, examined over 2 years, included in-hospital mortality and a composite measure of health care services, including specialized therapies, specialized medical equipment, and inpatient care. Outcomes were modeled using logistic regression. Model performance was evaluated using C statistics, sensitivity, and specificity. RESULTSOf 1 936 957 children, 48.4% were female, 87.8% resided in urban core areas, and 45.1% had government-sponsored insurance as their only primary payer. Depending on the algorithm and coding criteria applied, 0.67% to 11.44% were identified as CMC. All 3 algorithms had adequate discriminative ability, sensitivity, and specificity to predict in-hospital mortality and composite health care services (C statistic = 0.76 [95% CI, 0.73-0.80] to 0.81 [95% CI, 0.78-0.84] for mortality and 0.77 [95% CI, 0.76-0.77] to 0.80 [95% CI, 0.79-0.80] for composite health care services). Across algorithms, CMC had significantly greater odds of mortality (adjusted odds ratio [aOR], 9.97; 95% CI, 7.70-12.89; to aOR, 69.35; 95% CI, 52.52-91.57) and composite health care services (aOR, 4.59; 95% CI, to aOR, 18.87; 95% CI,) than children not identified as CMC.CONCLUSIONS AND RELEVANCE In this study, open-source algorithms identified different cohorts of CMC in terms of prevalence and magnitude of risk, but all predicted increased health care utilization and in-hospital mortality. These results can inform research, programs, and policies for CMC.