Rural-urban disparities in health care delivery for children with medical complexity and moderating effects of payer, disability, and community poverty.

PURPOSE: Children with medical complexity (CMC) may be at increased risk of rural-urban disparities in health care delivery given their multifaceted health care needs, but these disparities are poorly understood. This study evaluated rural-urban disparities in health care delivery to CMC and determined whether Medicaid coverage, co-occurring disability, and community poverty modified the effects of rurality on care delivery. METHODS: This retrospective cohort study of 2012-2017 all-payer claims data from Colorado, Massachusetts, and New Hampshire included CMC <18 years. Health care delivery measures (ambulatory clinic visits, emergency department visits, acute care hospitalizations, total hospital days, and receipt of post-acute care) were compared for rural- versus urban-residing CMC in multivariable regression models, following established methods to evaluate effect modification. FINDINGS: Of 112,475 CMC, 7307 (6.5%) were rural residing and 105,168 (93.5%) were urban residing. A total of 68.9% had Medicaid coverage, 33.9% had a disability, and 39.7% lived in communities with >20% child poverty. In adjusted analyses, rural-residing CMC received significantly fewer ambulatory visits (risk ratio [RR] = 0.95, 95% confidence interval [CI]: 0.94-0.96), more emergency visits (RR = 1.12, 95% CI: 1.08-1.16), and fewer hospitalization days (RR = 0.90, 95% CI = 0.85-0.96). The estimated modification effects of rural residence by Medicaid coverage, disability, and community poverty were each statistically significant. Differences in the odds of having a hospitalization and receiving post-acute care did not persist after incorporating sociodemographic and clinical characteristics and interaction effects. CONCLUSIONS: Rural- and urban-residing CMC differed in their receipt of health care, and Medicaid coverage, co-occurring disabilities, and community poverty modified several of these effects. These modifying effects should be considered in clinical and policy initiatives to ensure that such initiatives do not widen rural-urban disparities.

Association between a network-based physician linchpin score and cancer patient mortality: a SEER-Medicare analysis.

BACKGROUND: Patients with cancer frequently require multidisciplinary teams for optimal cancer outcomes. Network analysis can capture relationships among cancer specialists, and we developed a novel physician linchpin score to characterize "linchpin" physicians whose peers have fewer ties to other physicians of the same oncologic specialty. Our study examined whether being treated by a linchpin physician was associated with worse survival. METHODS: In this cross-sectional study, we analyzed Surveillance, Epidemiology, and End Results-Medicare data for patients diagnosed with stage I to III non-small cell lung cancer or colorectal cancer (CRC) in 2016-2017. We assembled patient-sharing networks and calculated linchpin scores for medical oncologists, radiation oncologists, and surgeons. Physicians were considered linchpins if their linchpin score was within the top 15% for their specialty. We used Cox proportional hazards models to examine associations between being treated by a linchpin physician and survival, with a 2-year follow-up period. RESULTS: The study cohort included 10 081 patients with non-small cell lung cancer and 9036 patients with CRC. Patients with lung cancer treated by a linchpin radiation oncologist had a 17% (95% confidence interval = 1.04 to 1.32) greater hazard of mortality, and similar trends were observed for linchpin medical oncologists. Patients with CRC treated by a linchpin surgeon had a 22% (95% confidence interval = 1.03 to 1.43) greater hazard of mortality. CONCLUSIONS: In an analysis of Medicare beneficiaries with nonmetastatic lung cancer or CRC, those treated by linchpin physicians often experienced worse survival. Efforts to improve outcomes can use network analysis to identify areas with reduced access to multidisciplinary specialists.

The role of comorbidities, medications, and social determinants of health in understanding urban-rural outcome differences among patients with heart failure.

PURPOSE: There is now a 20% disparity in all-cause, excess deaths between urban and rural areas, much of which is driven by disparities in cardiovascular death. We sought to explain the sources of these disparities for Medicare beneficiaries with heart failure with reduced ejection fraction (HFrEF). METHODS: Using a sample of Medicare Parts A, B, and D, we created a cohort of 389,528 fee-for-service beneficiaries with at least 1 heart failure hospitalization from 2008 to 2017. The primary outcome was 30-day mortality after discharge; 1-year mortality, readmissions, and return emergency room (ER) admissions were secondary outcomes. We used hierarchical, logistic regression modeling to determine the contribution of comorbidities, guideline-directed medical therapy (GDMT), and social determinants of health (SDOH) to outcomes. RESULTS: Thirty-day mortality rates after hospital discharge were 6.3% in rural areas compared to 5.7% in urban regions (P < .001); after adjusting for patient health and GDMT receipt, the 30-day mortality odds ratio for rural residence was 1.201 (95% CI 1.164-1.239). Adding the SDOH measure reduced the odds ratio somewhat (1.140, 95% CI 1.103-1.178) but a gap remained. Readmission rates in rural areas were consistently lower for all model specifications, while ER admissions were consistently higher. CONCLUSIONS: Among patients with HFrEF, living in a rural area is associated with an increased risk of death and return ER visits within 30 days of discharge from HF hospitalization. Differences in SDOH appear to partially explain mortality differences but the remaining gap may be the consequence of rural-urban differences in HF treatment.

Racial differences in low value care among older adult Medicare patients in US health systems: retrospective cohort study.

OBJECTIVE: To characterize racial differences in receipt of low value care (services that provide little to no benefit yet have potential for harm) among older Medicare beneficiaries overall and within health systems in the United States. DESIGN: Retrospective cohort study SETTING: 100% Medicare fee-for-service administrative data (2016-18). PARTICIPANTS: Black and White Medicare patients aged 65 or older as of 2016 and attributed to 595 health systems in the United States. MAIN OUTCOME MEASURES: Receipt of 40 low value services among Black and White patients, with and without adjustment for patient age, sex, and previous healthcare use. Additional models included health system fixed effects to assess racial differences within health systems and separately, racial composition of the health system's population to assess the relative contributions of individual patient race and health system racial composition to low value care receipt. RESULTS: The cohort included 9 833 304 patients (6.8% Black; 57.9% female). Of 40 low value services examined, Black patients had higher adjusted receipt of nine services and lower receipt of 20 services than White patients. Specifically, Black patients were more likely to receive low value acute diagnostic tests, including imaging for uncomplicated headache (6.9% v 3.2%) and head computed tomography scans for dizziness (3.1% v 1.9%). White patients had higher rates of low value screening tests and treatments, including preoperative laboratory tests (10.3% v 6.5%), prostate specific antigen tests (31.0% v 25.7%), and antibiotics for upper respiratory infections (36.6% v 32.7%; all P<0.001). Secondary analyses showed that these differences persisted within given health systems and were not explained by Black and White patients receiving care from different systems. CONCLUSIONS: Black patients were more likely to receive low value acute diagnostic tests and White patients were more likely to receive low value screening tests and treatments. Differences were generally small and were largely due to differential care within health systems. These patterns suggest potential individual, interpersonal, and structural factors that researchers, policy makers, and health system leaders might investigate and address to improve care quality and equity.

Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals.

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.

Who's Accountable? Low-Value Care Received By Medicare Beneficiaries Outside Of Their Attributed Health Systems.

Policy makers and payers increasingly hold health systems accountable for spending and quality for their attributed beneficiaries. Low-value care-medical services that offer little or no benefit and have the potential for harm in specific clinical scenarios-received outside of these systems could threaten success on both fronts. Using national Medicare data for fee-for-service beneficiaries ages sixty-five and older and attributed to 595 US health systems, we describe where and from whom they received forty low-value services during 2017-18 and identify factors associated with out-of-system receipt. Forty-three percent of low-value services received by attributed beneficiaries originated from out-of-system clinicians: 38 percent from specialists, 4 percent from primary care physicians, and 1 percent from advanced practice clinicians. Recipients of low-value care were more likely to obtain that care out of system if age 75 or older (versus ages 65-74), male (versus female), non-Hispanic White (versus other races or ethnicities), rural dwelling (versus metropolitan dwelling), more medically complex, or experiencing lower continuity of care. However, out-of-system service receipt was not associated with recipients' health systems' accountable care organization status. Health systems might improve quality and reduce spending for their attributed beneficiaries by addressing out-of-system receipt of low-value care-for example, by improving continuity.

The impact of adding cost information to a conversation aid to support shared decision making about low-risk prostate cancer treatment: Results of a stepped-wedge cluster randomised trial.

BACKGROUND: Decision aids help patients consider the benefits and drawbacks of care options but rarely include cost information. We assessed the impact of a conversation-based decision aid containing information about low-risk prostate cancer management options and their relative costs. METHODS: We conducted a stepped-wedge cluster randomised trial in outpatient urology practices within a US-based academic medical center. We randomised five clinicians to four intervention sequences and enroled patients newly diagnosed with low-risk prostate cancer. Primary patient-reported outcomes collected postvisit included the frequency of cost conversations and referrals to address costs. Other patient-reported outcomes included: decisional conflict postvisit and at 3 months, decision regret at 3 months, shared decision-making postvisit, financial toxicity postvisit and at 3 months. Clinicians reported their attitudes about shared decision-making pre- and poststudy, and the intervention's feasibility and acceptability. We used hierarchical regression analysis to assess patient outcomes. The clinician was included as a random effect; fixed effects included education, employment, telehealth versus in-person visit, visit date, and enrolment period. RESULTS: Between April 2020 and March 2022, we screened 513 patients, contacted 217 eligible patients, and enroled 117/217 (54%) (51 in usual care, 66 in the intervention group). In adjusted analyses, the intervention was not associated with cost conversations (ß = .82, p = .27), referrals to cost-related resources (ß = -0.36, p = .81), shared decision-making (ß = -0.79, p = .32), decisional conflict postvisit (ß = -0.34, p= .70), or at follow-up (ß = -2.19, p = .16), decision regret at follow-up (ß = -9.76, p = .11), or financial toxicity postvisit (ß = -1.32, p = .63) or at follow-up (ß = -2.41, p = .23). Most clinicians and patients had positive attitudes about the intervention and shared decision-making. In exploratory unadjusted analyses, patients in the intervention group experienced more transient indecision (p < .02) suggesting increased deliberation between visit and follow-up. DISCUSSION: Despite enthusiasm from clinicians, the intervention was not significantly associated with hypothesised outcomes, though we were unable to robustly test outcomes due to recruitment challenges. Recruitment at the start of the COVID-19 pandemic impacted eligibility, sample size/power, study procedures, and increased telehealth visits and financial worry, independent of the intervention. Future work should explore ways to support shared decision-making, cost conversations, and choice deliberation with a larger sample. Such work could involve additional members of the care team, and consider the detail, quality, and timing of addressing these issues. PATIENT OR PUBLIC CONTRIBUTION: Patients and clinicians were engaged as stakeholder advisors meeting monthly throughout the duration of the project to advise on the study design, measures selected, data interpretation, and dissemination of study findings.

Association of Functional Status, Cognition, Social Support, and Geriatric Syndrome With Admission From the Emergency Department.

Importance: The role of patient-level factors that are unrelated to the specific clinical condition leading to an emergency department (ED) visit, such as functional status, cognitive status, social supports, and geriatric syndromes, in admission decisions is not well understood, partly because these data are not available in administrative databases. Objective: To determine the extent to which patient-level factors are associated with rates of hospital admission from the ED. Design, Setting, and Participants: This cohort study analyzed survey data collected from participants (or their proxies, such as family members) enrolled in the Health and Retirement Study (HRS) from January 1, 2000, to December 31, 2018. These HRS data were linked to Medicare fee-for-service claims data from January 1, 1999, to December 31, 2018. Information on functional status, cognitive status, social supports, and geriatric syndromes was obtained from the HRS data, whereas ED visits, subsequent hospital admission or ED discharge, and other claims-derived comorbidities and sociodemographic characteristics were obtained from Medicare data. Data were analyzed from September 2021 to April 2023. Main Outcomes and Measures: The primary outcome measure was hospital admission after an ED visit. A baseline logistic regression model was estimated, with a binary indicator of admission as the dependent variable of interest. For each primary variable of interest derived from the HRS data, the model was reestimated, including the HRS variable of interest as an independent variable. For each of these models, the odds ratio (OR) and average marginal effect (AME) of changing the value of the variable of interest were calculated. Results: A total of 42 392 ED visits by 11 783 unique patients were included. At the time of the ED visit, patients had a mean (SD) age of 77.4 (9.6) years, and visits were predominantly for female (25 719 visits [60.7%]) and White (32 148 visits [75.8%]) individuals. The overall percentage of patients admitted was 42.5%. After controlling for ED diagnosis and demographic characteristics, functional status, cognition status, and social supports all were associated with the likelihood of admission. For instance, difficulty performing 5 activities of daily living was associated with an 8.5-percentage point (OR, 1.47; 95% CI, 1.29-1.66) AME increase in the likelihood of admission. Having dementia was associated with an AME increase in the likelihood of admission of 4.6 percentage points (OR, 1.23; 95% CI, 1.14-1.33). Living with a spouse was associated with an AME decrease in the likelihood of admission of 3.9 percentage points (OR, 0.84; 95% CI, 0.79-0.89), and having children living within 10 miles was associated with an AME decrease in the likelihood of admission of 5.0 percentage points (OR, 0.80; 95% CI, 0.71-0.89). Other common geriatric syndromes, including trouble falling asleep, waking early, trouble with vision, glaucoma or cataract, use of hearing aids or trouble with hearing, falls in past 2 years, incontinence, depression, and polypharmacy, were not meaningfully associated with the likelihood of admission. Conclusion and Relevance: Results of this cohort study suggest that the key patient-level characteristics, including social supports, cognitive status, and functional status, were associated with the decision to admit older patients to the hospital from the ED. These factors are critical to consider when devising strategies to reduce low-value admissions among older adult patients from the ED.

Association of rurality, socioeconomic status, and race with pancreatic cancer surgical treatment and survival.

BACKGROUND: Pancreatectomy is a necessary component of curative intent therapy for pancreatic cancer, and patients living in nonmetropolitan areas may face barriers to accessing timely surgical care. We evaluated the intersecting associations of rurality, socioeconomic status (SES), and race on treatment and outcomes of Medicare beneficiaries with pancreatic cancer. METHODS: We conducted a retrospective cohort study, using fee-for-service Medicare claims of beneficiaries with incident pancreatic cancer (2016-2018). We categorized beneficiary place of residence as metropolitan, micropolitan, or rural. Measures of SES were Medicare-Medicaid dual eligibility and the Area Deprivation Index. Primary study outcomes were receipt of pancreatectomy and 1-year mortality. Exposure-outcome associations were assessed with competing risks and logistic regression. RESULTS: We identified 45 915 beneficiaries with pancreatic cancer, including 78.4%, 10.9%, and 10.7% residing in metropolitan, micropolitan, and rural areas, respectively. In analyses adjusted for age, sex, comorbidity, and metastasis, residents of micropolitan and rural areas were less likely to undergo pancreatectomy (adjusted subdistribution hazard ratio = 0.88 for rural, 95% confidence interval [CI] = 0.81 to 0.95) and had higher 1-year mortality (adjusted odds ratio = 1.25 for rural, 95% CI = 1.17 to 1.33) compared with metropolitan residents. Adjustment for measures of SES attenuated the association of nonmetropolitan residence with mortality, and there was no statistically significant association of rurality with pancreatectomy after adjustment. Black beneficiaries had lower likelihood of pancreatectomy than White, non-Hispanic beneficiaries (subdistribution hazard ratio = 0.80, 95% CI = 0.72 to 0.89, adjusted for SES). One-year mortality in metropolitan areas was higher for Black beneficiaries (adjusted odds ratio = 1.15, 95% CI = 1.05 to 1.26). CONCLUSIONS: Rurality, socioeconomic deprivation, and race have complex interrelationships and are associated with disparities in pancreatic cancer treatment and outcomes.

The diffusion of health care fraud: A bipartite network analysis.

Many studies have examined the diffusion of health care innovation but less is known about the diffusion of health care fraud. In this paper, we consider the diffusion of potentially fraudulent Medicare home health care billing in the United States during 2002-16, with a focus on the 21 hospital referral regions (HRRs) covered by local Department of Justice (DOJ) anti-fraud "strike force" offices. We hypothesize that patient-sharing across home health care agencies (HHAs) provides a mechanism for the rapid diffusion of fraudulent strategies. We measure such activity using a novel bipartite mixture (or BMIX) network index, which captures patient sharing across multiple agencies and thus conveys more information about the diffusion process than conventional unipartite network measures. Using a complete population of fee-for-service Medicare claims data, we first find a remarkable increase in home health care activity between 2002 and 2009 in many regions targeted by the DOJ; average billing per Medicare enrollee in McAllen TX and Miami increased by $2127 and $2422 compared to just an average $289 increase in other HRRs not targeted by the DOJ. Second, we establish that the HRR-level BMIX (but not other network measures) was a strong predictor of above-average home health care expenditures across HRRs. Third, within HRRs, agencies sharing more patients with other agencies were predicted to increase billing. Finally, the initial 2002 BMIX index was a strong predictor of subsequent changes in HRR-level home health billing during 2002-9. These results highlight the importance of bipartite network structure in diffusion and in infection and contagion models more generally.

Pediatric Mental Health Hospitalizations at Acute Care Hospitals in the US, 2009-2019.

Importance: Approximately 1 in 6 youth in the US have a mental health condition, and suicide is a leading cause of death among this population. Recent national statistics describing acute care hospitalizations for mental health conditions are lacking. Objectives: To describe national trends in pediatric mental health hospitalizations between 2009 and 2019, to compare utilization among mental health and non-mental health hospitalizations, and to characterize variation in utilization across hospitals. Design, Setting, and Participants: Retrospective analysis of the 2009, 2012, 2016, and 2019 Kids' Inpatient Database, a nationally representative database of US acute care hospital discharges. Analysis included 4 767 840 weighted hospitalizations among children 3 to 17 years of age. Exposures: Hospitalizations with primary mental health diagnoses were identified using the Child and Adolescent Mental Health Disorders Classification System, which classified mental health diagnoses into 30 mutually exclusive disorder types. Main Outcomes and Measures: Measures included number and proportion of hospitalizations with a primary mental health diagnosis and with attempted suicide, suicidal ideation, or self-injury; number and proportion of hospital days and interfacility transfers attributable to mental health hospitalizations; mean lengths of stay (days) and transfer rates among mental health and non-mental health hospitalizations; and variation in these measures across hospitals. Results: Of 201 932 pediatric mental health hospitalizations in 2019, 123 342 (61.1% [95% CI, 60.3%-61.9%]) were in females, 100 038 (49.5% [95% CI, 48.3%-50.7%]) were in adolescents aged 15 to 17 years, and 103 456 (51.3% [95% CI, 48.6%-53.9%]) were covered by Medicaid. Between 2009 and 2019, the number of pediatric mental health hospitalizations increased by 25.8%, and these hospitalizations accounted for a significantly higher proportion of pediatric hospitalizations (11.5% [95% CI, 10.2%-12.8%] vs 19.8% [95% CI, 17.7%-21.9%]), hospital days (22.2% [95% CI, 19.1%-25.3%] vs 28.7% [95% CI, 24.4%-33.0%]), and interfacility transfers (36.9% [95% CI, 33.2%-40.5%] vs 49.3% [95% CI, 45.9%-52.7%]). The percentage of mental health hospitalizations with attempted suicide, suicidal ideation, or self-injury diagnoses increased significantly from 30.7% (95% CI, 28.6%-32.8%) in 2009 to 64.2% (95% CI, 62.3%-66.2%) in 2019. Length of stay and interfacility transfer rates varied significantly across hospitals. Across all years, mental health hospitalizations had significantly longer mean lengths of stay and higher transfer rates compared with non-mental health hospitalizations. Conclusions and Relevance: Between 2009 and 2019, the number and proportion of pediatric acute care hospitalizations due to mental health diagnoses increased significantly. The majority of mental health hospitalizations in 2019 included a diagnosis of attempted suicide, suicidal ideation, or self-injury, underscoring the increasing importance of this concern.

Comparative Effectiveness of Left Atrial Appendage Occlusion Versus Oral Anticoagulation by Sex.

BACKGROUND: The comparative real-world outcomes of older patients with atrial fibrillation (AF) treated with anticoagulation compared with left atrial appendage occlusion (LAAO) may be different from those in clinical trials because of differences in anticoagulation strategies and patient demographics, including a greater proportion of women. We sought to compare real-world outcomes between older patients with AF treated with anticoagulation and those treated with LAAO by sex. METHODS: Using Medicare claims data from 2015 to 2019, we identified LAAO-eligible beneficiaries and divided them into sex subgroups. Patients receiving LAAO were matched 1:1 to those receiving anticoagulation alone through propensity score matching. The risks of mortality, stroke or systemic embolism, and bleeding were compared between matched groups with adjustment for potential confounding characteristics in Cox proportional hazards models. RESULTS: Among women, 4085 LAAO recipients were matched 1:1 to those receiving anticoagulation; among men, 5378 LAAO recipients were similarly matched. LAAO was associated with a significant reduction in the risk of mortality for women and men (hazard ratio [HR], 0.509 [95% CI, 0.447-0.580]; and HR, 0.541 [95% CI, 0.487-0.601], respectively; P<0.0001), with a similar finding for stroke or systemic embolism (HR, 0.655 [95% CI, 0.555-0.772]; and HR, 0.649 [95% CI, 0.552-0.762], respectively; P<0.0001). Bleeding risk was significantly greater in LAAO recipients early after implantation but lower after the 6-week periprocedural period for women and men (HR, 0.772 [95% CI, 0.676-0.882]; and HR, 0.881 [95% CI, 0.784-0.989], respectively; P<0.05). CONCLUSIONS: In a real-world population of older Medicare beneficiaries with AF, compared with anticoagulation, LAAO was associated with a reduction in the risk of death, stroke, and long-term bleeding among women and men. These findings should be incorporated into shared decision-making with patients considering strategies for reduction in AF-related stroke.

Estimating population-averaged hazard ratios in the presence of unmeasured confounding.

The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR's interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent papers. Several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder from an instrumental variable-based procedure. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitor (Wang et al.) for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary material.

Use of a Novel Network-Based Linchpin Score to Characterize Accessibility to the Oncology Physician Workforce in the United States.

Importance: Physician headcounts provide useful information about the cancer care delivery workforce; however, efforts to track the oncology workforce would benefit from new measures that capture how essential a physician is for meeting the multidisciplinary cancer care needs of the region. Physicians are considered linchpins when fewer of their peers are connected to other physicians of the same specialty as the focal physician. Because they are locally unique for their specialty, these physicians' networks may be particularly vulnerable to their removal from the network (eg, through relocation or retirement). Objective: To examine a novel network-based physician linchpin score within nationwide cancer patient-sharing networks and explore variation in network vulnerability across hospital referral regions (HRRs). Design, Setting, and Participants: This cross-sectional study analyzed fee-for-service Medicare claims and included Medicare beneficiaries with an incident diagnosis of breast, colorectal, or lung cancer from 2016 to 2018 and their treating physicians. Data were analyzed from March 2022 to October 2022. Exposures: Physician characteristics assessed were specialty, rurality, and Census region. HRR variables assessed include sociodemographic and socioeconomic characteristics and use of cancer services. Main Outcomes and Measures: Oncologist linchpin score, which examined the extent to which a physician's peers were connected to other physicians of the same specialty as the focal physician. Network vulnerability, which distinguished HRRs with more linchpin oncologists than expected based on oncologist density. χ2 and Fisher exact tests were used to examine relationships between oncologist characteristics and linchpin score. Spearman rank correlation coefficient (ρ) was used to measure the strength and direction of relationships between HRR network vulnerability, oncologist density, population sociodemographic and socioeconomic characteristics, and cancer service use. Results: The study cohort comprised 308 714 patients with breast, colorectal, or lung cancer. The study cohort of 308 714 patients included 161 206 (52.2%) patients with breast cancer, 76 604 (24.8%) patients with colorectal cancer, and 70 904 (23.0%) patients with lung cancer. In our sample, 272 425 patients (88%) were White, and 238 603 patients (77%) lived in metropolitan areas. The cancer patient-sharing network included 7221 medical oncologists and 3573 radiation oncologists. HRRs with more vulnerable networks for medical oncology had a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.19; 95% CI, 0.08 to 0.29). HRRs with more vulnerable networks for radiation oncology had a higher percentage of beneficiaries living in poverty (ρ, 0.17; 95% CI, 0.06 to 0.27), and a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.21; 95% CI, 0.09 to 0.31), and lower rates of cohort patients receiving radiation therapy (ρ, -0.18; 95% CI, -0.28 to -0.06; P = .003). The was no association between network vulnerability for medical oncology and percent of cohort patients receiving chemotherapy (ρ, -0.03; 95% CI, -0.15 to 0.08). Conclusions and Relevance: This study found that patient-sharing network vulnerability was associated with poverty and lower rates of radiation therapy. Health policy strategies for addressing network vulnerability may improve access to interdisciplinary care and reduce treatment disparities.

Association of Rurality, Race and Ethnicity, and Socioeconomic Status With the Surgical Management of Colon Cancer and Postoperative Outcomes Among Medicare Beneficiaries.

Importance: Rural patients with colon cancer experience worse outcomes than urban patients, but the extent to which disparities are explained by social determinants is not known. Objectives: To evaluate the association of rurality with surgical treatment and outcomes of colon cancer and to investigate the intersection of rurality with race and ethnicity and socioeconomic status. Design, Settings, and Participants: This cohort study included fee-for-service Medicare beneficiaries 65 years or older diagnosed with incident, nonmetastatic colon cancer between April 1, 2016, and September 30, 2018, with follow-up until December 31, 2018. Data were analyzed from August 3, 2020, to April 30, 2021. Exposures: Rurality of patient's residence, categorized as metropolitan, micropolitan, or small town or rural, using Rural-Urban Commuting Area codes. Main Outcomes and Measures: Receipt of surgery, emergent surgery, or minimally invasive surgery (MIS); 90-day surgical complications; and 90-day mortality. Results: Among 57 710 Medicare beneficiaries with incident, nonmetastatic colon cancer, 46.6% were men, 53.4% were women, and the mean (SD) age was 76.6 (7.2) years. In terms of race and ethnicity, 3.7% were Hispanic, 6.4% were non-Hispanic Black (hereinafter Black), 86.1% were non-Hispanic White (hereinafter White), and 3.8% were American Indian or Alaska Native, Asian or Pacific Islander, or unknown race or ethnicity. Patients residing in nonmetropolitan areas were more likely to undergo surgical resection than those residing in metropolitan areas (69.2% vs 63.9%; P < .001). Black race was independently associated with lower hazard of surgical resection (hazard ratio, 0.92 [95% CI, 0.88-0.95]). Race and ethnicity and measures of socioeconomic status did not modify the association of rurality with surgery. Beneficiaries from small town and rural areas had higher odds of undergoing emergent surgery (adjusted odds ratio [OR], 1.32 [95% CI, 1.20-1.44]) but lower odds of undergoing MIS (adjusted OR, 0.75 [95% CI, 0.70-0.80]), with similar findings for patients residing in micropolitan areas. Members of racial and ethnic minority groups who resided in small town and rural settings experienced higher odds of postoperative surgical complications (P = .001 for interaction) and mortality (P = .001 for interaction). Notably, White patients who resided in small town and rural areas experienced lower odds of postoperative mortality than their White metropolitan counterparts (adjusted OR, 0.81 [95% CI, 0.71-0.92]), but Black patients who resided in small town and rural areas had significantly higher odds of postoperative mortality (adjusted OR, 1.86 [95% CI, 1.16-2.97]) than their Black metropolitan counterparts. Conclusions and Relevance: These findings suggest that Medicare beneficiaries from small town and rural areas were more likely to undergo surgery for nonmetastatic colon cancer than metropolitan beneficiaries but also more likely to undergo emergent surgery and less likely to have MIS. The experiences of rural patients varied by race; rurality was associated with higher postoperative mortality for Black patients but not for other racial and ethnic groups.

Using a mixed-effect model with a parameter-space of heterogenous dimension to evaluate whether accountable care organizations are associated with greater uniformity across constituent practices.

Accountable care organization (ACO) legislation was designed to improve patient outcomes by inducing greater coordination of care and adoption of best practices. Therefore, it is of interest to assess whether greater uniformity occurs among practices comprising an ACO post ACO formation. We develop a mixed-effect model with a difference-in-difference design to evaluate the effect of a patient receiving care from an ACO on patient outcomes and adapt this model to examine whether an ACO is associated with increased uniformity across its constituent practices. The task is complicated by the organizations within an ACO forming an additional layer in the multilevel model, due to medical practices and hospitals that form an ACOs being nested within the ACO, making the number of levels of the model variable and the dimension of the parameter space time-varying. We develop the model and a procedure for testing the hypothesis that ACO formation was associated with increased uniformity among its constituent practices. We apply our procedure to a cohort of medicare beneficiaries followed over 2009-2014. Although there is extensive heterogeneity of becoming an ACOs across practices, we find that the formation of an ACO appears to be associated with greater uniformity of patient outcomes among its constituent practices.

A national evaluation of geographic accessibility and provider availability of obesity medicine diplomates in the United States between 2011 and 2019.

BACKGROUND/OBJECTIVES: Obesity is a pressing health concern within the United States (US). Obesity medicine "diplomates" receive specialized training, yet it is unclear if their accessibility and availability adequately serves the need. The purpose of this research was to understand how accessibility has evolved over time and assess the practicality of serving an estimated patient population with the current distribution and quantity of diplomates. METHODS: Population-weighted Census tracts in US counties were mapped to the nearest facility on a road network with at least one diplomate who specialized in adult (including geriatric) care between 2011 and 2019. The median travel time for all Census tracts within a county represented the primary geographic access measure. Availability was assessed by estimating the number of diplomates per 100 000 patients with obesity and the number of facilities able to serve assigned patients under three clinical guidelines. RESULTS: Of the 3371 diplomates certified since 2019, 3036 were included. The median travel time (weighted for county population) fell from 28.5 min [IQR: 13.7, 68.1] in 2011 to 9.95 min [IQR: 7.49, 18.1] in 2019. There were distinct intra- and inter-year travel time variations by race, ethnicity, education, median household income, rurality, and Census region (all P < 0.001). The median number of diplomates per 100 000 with obesity grew from 1 [IQR: 0.39, 1.59] in 2011 to 5 [IQR: 2.74, 11.4] in 2019. In 2019, an estimated 1.7% of facilities could meet the recommended number of visits for all mapped patients with obesity, up from 0% in 2011. CONCLUSIONS: Diplomate geographic access and availability have improved over time, yet there is still not a high enough supply to serve the potential patient demand. Future studies should quantify patient-level associations between travel time and health outcomes, including whether the number of available diplomates impacts utilization.

A robust hazard ratio for general modeling of survival-times.

Hazard ratios (HR) associated with the well-known proportional hazard Cox regression models are routinely used for measuring the impact of one factor of interest on a time-to-event outcome. However, if the underlying real model does not fit with the theoretical requirements, the interpretation of those HRs is not clear. We propose a new index, gHR, which generalizes the HR beyond the underlying survival model. We consider the case in which the study factor is a binary variable and we are interested in both the unadjusted and adjusted effect of this factor on a time-to-event variable, potentially, observed in a right-censored scenario. We propose non-parametric estimations for unadjusted gHR and semi-parametric regression-induced techniques for the adjusted case. The behavior of those estimators is studied in both large and finite sample situations. Monte Carlo simulations reveal that both estimators provide good approximations of their respective inferential targets. Data from the Health and Lifestyle Study are used for studying the relationship of the tobacco use and the age of death and illustrate the practical application of the proposed technique. gHR is a promising index which can help facilitate better understanding of the association of one study factor on a time-dependent outcome.

Analysis of Consistency in Emergency Department Physician Variation in Propensity for Admission Across Patient Sociodemographic Groups.

Importance: Sociodemographic disparities in health care and variation in physician practice patterns have been well documented; however, the contribution of variation in individual physician care practices to health disparities is challenging to quantify. Emergency department (ED) physicians vary in their propensity to admit patients. The consistency of this variation across sociodemographic groups may help determine whether physician-specific factors are associated with care differences between patient groups. Objective: To estimate the consistency of ED physician admission propensities across categories of patient sex, race and ethnicity, and Medicaid enrollment. Design, Setting, and Participants: This cross-sectional study analyzed Medicare fee-for-service claims for ED visits from January 1, 2016, to December 31, 2019, in a 10% random sample of hospitals. The allocation of patients to ED physicians in the acute care setting was used to isolate physician-level variation in admission rates that reflects variation in physician decision-making. Multi-level models with physician random effects and hospital fixed effects were used to estimate the within-hospital physician variation in admission propensity for different patient sociodemographic subgroups and the covariation in these propensities between subgroups (consistency), adjusting for primary diagnosis and comorbidities. Main Outcomes and Measures: Admission from the ED. Results: The analysis included 4 567 760 ED visits involving 2 334 361 beneficiaries and 15 767 physicians in 396 EDs. The mean (SD) age of the beneficiaries was 78 (8.2) years, 2 700 661 visits (59.1%) were by women, and most patients (3 839 055 [84.1%]) were not eligible for Medicaid. Of 4 473 978 race and ethnicity reports on enrollment, 103 699 patients (2.3%) were Asian/Pacific Islander, 421 588 (9.4%) were Black, 257 422 (5.8%) were Hispanic, and 3 691 269 (82.5%) were non-Hispanic White. Within hospitals, adjusted rates of admission were higher for men (36.8%; 95% CI, 36.8%-36.9%) than for women (33.7%; 95% CI, 33.7%-33.8%); higher for non-Hispanic White (36.0%; 95% CI, 35.9%-36.0%) than for Asian/Pacific Islander (33.6%; 95% CI, 33.3%-33.9%), Black (30.2%; 95% CI, 30.0%-30.3%), or Hispanic (31.1%; 95% CI, 30.9%-31.2%) beneficiaries; and higher for beneficiaries dually enrolled in Medicaid (36.3%; 95% CI, 36.2%-36.5%) than for those who were not (34.7%; 95% CI, 34.7%-34.8%). Within hospitals, physicians varied in the percentage of patients admitted, ranging from 22.4% for physicians at the 10th percentile to 47.6% for physicians at the 90th percentile of the estimated distribution. Physician admission propensities were correlated between men and women (r = 0.99), Black and non-Hispanic White patients (r = 0.98), and patients who were dually enrolled and not dually enrolled in Medicaid (r = 0.98). Conclusions and Relevance: This cross-sectional study indicated that, although overall rates of admission differ systematically by patient sociodemographic factors, an individual physician's propensity to admit relative to other physicians appears to be applied consistently across sociodemographic groups of patients.

National Trends In ED Visits, Hospital Admissions, And Mortality For Medicare Patients During The COVID-19 Pandemic.

Concerns about avoidance or delays in seeking emergency care during the COVID-19 pandemic are widespread, but national data on emergency department (ED) visits and subsequent rates of hospitalization and outcomes are lacking. Using data on all traditional Medicare beneficiaries in the US from October 1, 2018, to September 30, 2020, we examined trends in ED visits and rates of hospitalization and thirty-day mortality conditional on an ED visit for non-COVID-19 conditions during several stages of the pandemic and for areas that were considered COVID-19 hot spots versus those that were not. We found reductions in ED visits that were largest by the first week of April 2020 (52 percent relative decrease), with volume recovering somewhat by mid-June (25 percent relative decrease). These reductions were of similar magnitude in counties that were and were not designated as COVID-19 hot spots. There was an early increase in hospitalizations and in the relative risk for thirty-day mortality, starting with the first surge of the pandemic, peaking at just over a 2-percentage-point increase. These results suggest that patients were presenting with more serious illness, perhaps related to delays in seeking care.