Safety Net Primary Care Capabilities After the COVID-19 Pandemic.

Importance: Federally qualified health centers (FQHCs) provide care to 30 million patients in the US and have shown better outcomes and processes than other practice types. Little is known about how the COVID-19 pandemic contributed to FQHC capabilities compared with other practices. Objective: To compare postpandemic operational characteristics and capabilities of FQHCs with non-FQHC safety net practices and non-FQHC, non-safety net practices. Design, Setting, and Participants: This nationally representative survey conducted from June 2022 to February 2023 with an oversampling of safety net practices in the US included practice leaders working in stratified random selection of practices based on FQHC status, Area Deprivation Index category, and ownership type per a health care network dataset. Exposures: Practice type: FQHC vs non-FQHC safety net and non-FQHC practices. Main Outcomes and Measures: Primary care capabilities, including 2 measures of access and 11 composite measures. Results: A total of 1245 practices (221 FQHC and 1024 non-FQHC) responded of 3498 practices sampled. FQHCs were more likely to be independently owned and have received COVID-19 funding. FQHCs and non-FQHC safety net practices were more likely to be in rural areas. FQHCs significantly outperformed non-FQHCs on several capabilities even after controlling for practice size and ownership, including behavioral health provision (mean score, 0.53; 95% CI, 0.51-0.56), culturally informed services (mean score, 0.55; 95% CI, 0.53-0.58), screening for social needs (mean score, 0.43; 95% CI, 0.39-0.47), social needs referrals (mean score, 0.53; 95% CI, 0.48-0.57), social needs referral follow-up (mean score, 0.31; 95% CI, 0.27-0.36), and shared decision-making and motivational interviewing training (mean score, 0.53; 95% CI, 0.51-0.56). No differences were found in behavioral and substance use screening, care processes for patients with complex and high levels of need, use of patient-reported outcome measures, decision aid use, or after-hours access. Across all practices, most of the examined capabilities showed room for improvement. Conclusions and Relevance: The results of this survey study suggest that FQHCs outperformed non-FQHC practices on important care processes while serving a patient population with lower incomes who are medically underserved compared with patients in other practice types. Legislation to expand funding for the FQHC program should improve services for underserved populations and target current non-FQHC safety net practices to serve these populations. Increased support for these practices could improve primary care for rural populations.

Regional variation in diagnostic intensity of dementia among older U.S. adults: An observational study.

INTRODUCTION: Geographic variation in diagnosed cases of Alzheimer's disease and related dementias (ADRD) could be due to underlying population risk or differences in intensity of new case identification. Areas with low ADRD diagnostic intensity could be targeted for additional surveillance efforts. METHODS: Medicare claims were used for a cohort of older adults across hospital referral regions (HRRs). ADRD-specific regional diagnosis intensity was measured as the ratio of expected new ADRD cases (estimated using population demographics, risk factors, and practice intensity) compared to observed ADRD-diagnosed cases. RESULTS: Crude new ADRD diagnosis rate ranged from 1.7 to 5.4 per 100 across HRRs. ADRD-specific diagnosis intensity ranged from 0.69 to 1.47 and varied most for Black, Hispanic, and the youngest (66-74) subgroups. Across all subgroups, ADRD diagnosis intensity was associated with 2-fold difference in receiving an ADRD diagnosis. DISCUSSION: Where one resides influences the likelihood of receiving an ADRD diagnosis, particularly among those 66-74 years of age and minoritized groups. HIGHLIGHTS: Rate of new Alzheimer's disease and related dementias (ADRD) case identification varies geographically across the United States. Variation in case identification is greatest in Black, Hispanic, and young-old groups. Intensity of diagnosis (ie, case identification) unrelated to population risk differs across place. Likelihood of receiving an ADRD diagnosis varies 2-fold based on place of residence.

Neurodevelopmental and Mental Health Conditions in Children With Medical Complexity.

BACKGROUND AND OBJECTIVES: Children with medical complexity (CMC) may be at a high risk of neurodevelopmental and mental health conditions given disease comorbidities and lived experiences. Little is known about the prevalence of these conditions at a population level. In this study, we estimated the prevalence of neurodevelopmental and mental health diagnoses in CMC relative to children without medical complexity and measured associations between these diagnoses in CMC and subsequent health care utilization and in-hospital mortality. METHODS: We applied the Child and Adolescent Mental Health Disorders Classification System to identify neurodevelopmental and mental health diagnoses using all-payer claims data from three states (2012-2017). Poisson regression was used to compare outcomes in CMC with neurodevelopmental and mental health diagnoses to CMC without these diagnoses, adjusting for sociodemographic and clinical characteristics. RESULTS: Among 85 581 CMC, 39 065 (45.6%) had ≥1 neurodevelopmental diagnoses, and 31 703 (37.0%) had ≥1 mental health diagnoses, reflecting adjusted relative risks of 3.46 (3.42-3.50) for neurodevelopmental diagnoses and 2.22 (2.19-2.24) for mental health diagnoses compared with children without medical complexity. CMC with both neurodevelopmental and mental health diagnoses had 3.00 (95% confidence interval [CI]: 2.98-3.01) times the number of ambulatory visits, 69% more emergency department visits (rate ratio = 1.69, 95% CI: 1.66-1.72), 58% greater risk of hospitalization (rate ratio = 1.58, 95% CI: 1.50-1.67), and 2.32 times (95% CI: 2.28-2.36) the number of hospital days than CMC without these diagnoses. CONCLUSIONS: Neurodevelopmental and mental health diagnoses are prevalent among CMC and associated with increased health care utilization across the continuum of care. These findings illustrate the importance of recognizing and treating neurodevelopmental and mental health conditions in this population.

Emergency Department Visits And Hospital Capacity In The US: Trends In The Medicare Population During The COVID-19 Pandemic.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25 percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2 percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks.

Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing.

Network spillover effects associated with the ChooseWell 365 workplace randomized controlled trial to promote healthy food choices.

Food choices are closely linked to culture, social relationships, and health. Because many adults spend up to half their time at work, the workplace provides a venue for changing population health-related behaviors and norms. It is unknown whether the effects of a workplace intervention to improve health behaviors might spread beyond participating employees due to social influence. ChooseWell 365 was a randomized controlled trial testing a 12-month healthy eating intervention grounded in principles of behavioral economics. This intervention leveraged an existing cafeteria traffic-light labeling system (green = healthy; red = unhealthy) in a large hospital workplace and demonstrated significant improvements in healthy food choices by employees in the intervention vs. control group. The current study used data from over 29 million dyadic purchasing events during the trial to test whether social ties to a trial participant co-worker (n = 299 intervention, n = 302 control) influenced the workplace food choices of non-participants (n = 7900). There was robust evidence that non-participants who were socially tied to more intervention group participants made healthier workplace food purchases overall, and purchased a greater proportion of healthy (i.e., green) food and beverages, and fewer unhealthy (i.e., red) beverages and modest evidence that the benefit of being tied to intervention participants was greater than being tied to control participants. Although individual-level effect sizes were small, a range of consistent findings indicated that this light-touch intervention yielded spillover effects of healthy eating behaviors on non-participants. Results suggest that workplace healthy eating interventions could have population benefits extending beyond participants.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

Communicating visit information to family caregivers: How does method matter? A national survey.

OBJECTIVES: The clinic visit is a critical point of contact for family caregivers. However, only 37% of family caregivers are able to accompany patients to visits. When they cannot attend, caregivers receive visit information to assist with their caregiving. However, little is known about how method of receiving information from clinic visits is associated with important caregiver outcomes. This study sought to determine whether mode of receiving clinic visit information (speaking with the patient, attending the visit, or using an after-visit summary [AVS]) was associated with changes in caregiver burden, caregiver preparedness, and the positive aspects of caregiving. METHODS: Cross-sectional web-based survey of a national sample of adult family caregivers. Multiple linear regression models determined associations between communication modes and caregivers' burden, preparedness, and positive aspects of caregiving, adjusting for sociodemographic covariates. RESULTS: Respondents (N = 340) were mostly male (58%), White (59%), ranged from 18 to 85 years old, and supported patients with conditions including diabetes, dementia, and cancer. Speaking with patients was associated with increases in positive aspects of caregiving (95% CI = 2.01, 5.42) and an AVS was associated with increases in positive aspects of caregiving (95% CI = 0.4, 3.56) and preparedness for caregiving (95% CI = 0.61, 3.15). Using any method of receiving information from visits was associated with the greatest increase in preparedness, compared to not receiving visit information. We did not observe an association between method of communication and caregiver burden. SIGNIFICANCE OF RESULTS: Method of communicating visit information is associated with improvements in caregiver preparedness and the positive aspects of caregiving, though caregiver burden may be unaffected by information exchange. Given the limitations of current communication methods, future work should explore directionality of the associations we found and identify visit communication strategies with caregivers that optimize caregiver and patient outcomes.

Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients.

When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effect pathways between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study is used to assess the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply our Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.

The Association Between Oncology Outreach and Timely Treatment for Rural Patients with Breast Cancer: A Claims-Based Approach.

BACKGROUND: Oncology outreach is a common strategy for increasing rural access to cancer care, where traveling oncologists commute across healthcare settings to extend specialized care. Examining the extent to which physician outreach is associated with timely treatment for rural patients is critical for informing outreach strategies. METHODS: We identified a 100% fee-for-service sample of incident breast cancer patients from 2015 to 2020 Medicare claims and apportioned them into surgery and adjuvant therapy cohorts based on treatment history. We defined an outreach visit as the provision of care by a traveling oncologist at a clinic outside of their primary hospital service area. We used hierarchical logistic regression to examine the associations between patient receipt of preoperative care at an outreach visit (preoperative outreach) and > 60-day surgical delay, and patient receipt of postoperative care at an outreach visit (postoperative outreach) and > 60-day adjuvant delay. RESULTS: We identified 30,337 rural-residing patients who received breast cancer surgery, of whom 4071 (13.4%) experienced surgical delay. Among surgical patients, 14,501 received adjuvant therapy, of whom 2943 (20.3%) experienced adjuvant delay. In adjusted analysis, we found that patient receipt of preoperative outreach was associated with reduced odds of surgical delay (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.61-0.91); however, we found no association between patient receipt of postoperative outreach and adjuvant delay (OR 1.04, 95% CI 0.85-1.25). CONCLUSIONS: Our findings indicate that preoperative outreach is protective against surgical delay. The traveling oncologists who enable such outreach may play an integral role in catalyzing the coordination and timeliness of patient-centered care.

Clinician-Spoken Plain Language in Health Care Encounters: A Qualitative Analysis to Assess Measurable Elements.

PURPOSE: Good communication and use of plain language in health care encounters improve outcomes, including emotional health, symptom resolution, and functional status. Yet there is limited research on how to measure and report spoken plain language, which is the use of familiar, clear language. The authors aimed to describe key, measurable elements of spoken plain language that can be assessed and reported back to clinicians for self-reflection. METHOD: The authors conducted secondary analysis of transcripts from recorded encounters between breast cancer surgeons and patients with early-stage breast cancer. Two coders used a hybrid qualitative analysis with a framework based on U.S. Federal Plain Language Guidelines. To develop major themes, they examined (1) alignment with the Guidelines and (2) code frequencies within and across transcripts. They also noted minor themes. RESULTS: From 74 transcripts featuring 13 surgeons, the authors identified 2 major themes representing measurable elements of spoken plain language: (1) clinicians had a propensity to use both explained and unexplained medical terms, and (2) clinicians delivered information using either short turns (one unit of someone speaking) with 1 topic or long turns with multiple topics. There were 3 minor themes that were not indicative of whether or not clinicians used spoken plain language. First, clinicians regularly used absolute risk communication techniques. Second, question-asking techniques varied and included open-ended, close-ended, and comprehension checks. Third, some clinicians used imagery to describe complex topics. CONCLUSIONS: Clinicians' propensity to use medical terms with and without explanation and parse encounters into shorter or longer turns are measurable elements of spoken plain language. These findings will support further research on the development of a tool that can be used in medical education and other settings. This tool could provide direct and specific feedback to improve the plain language practices of clinicians in training and beyond.

Characterizing the Traveling Oncology Workforce and Its Influence on Patient Travel Burden: A Claims-Based Approach.

PURPOSE: Oncology outreach is a common strategy for extending cancer care to rural patients. However, a nationwide characterization of the traveling workforce that enables this outreach is lacking, and the extent to which outreach reduces travel burden for rural patients is unknown. METHODS: This cross-sectional study analyzed a rural (nonurban) subset of a 100% fee-for-service sample of 355,139 Medicare beneficiaries with incident breast, colorectal, and lung cancers. Surgical, medical, and radiation oncologists were linked to patients using Part B claims, and traveling oncologists were identified by observing hospital service area (HSA) transition patterns. We defined oncology outreach as the provision of cancer care by a traveling oncologist outside of their primary HSA. We used hierarchical gamma regression models to examine the separate associations between patient receipt of oncology outreach and one-way patient travel times to chemotherapy, radiotherapy, and surgery. RESULTS: On average, 9,935 of 39,960 oncologists conducted annual outreach, where 57.8% traveled with low frequency (0-1 outreach visits/mo), 21.1% with medium frequency (1-3 outreach visits/mo), and 21.1% with high frequency (>3 outreach visits/mo). Oncologists provided surgery, radiotherapy, and chemotherapy to 51,715, 27,120, and 5,874 rural beneficiaries, respectively, of whom 2.5%, 6.9%, and 3.6% received oncology outreach. Rural patients who received oncology outreach traveled 16% (95% CI, 11 to 21) and 11% (95% CI, 9 to 13) less minutes to chemotherapy and radiotherapy than those who did not receive oncology outreach, corresponding to expected one-way savings of 15.9 (95% CI, 15.5 to 16.4) and 11.9 (95% CI, 11.7 to 12.2) minutes, respectively. CONCLUSION: Our study introduces a novel claims-based approach for tracking the nationwide traveling oncology workforce and supports oncology outreach as an effective means for improving rural access to cancer care.

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.

Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies.

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of "referral paths" - sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital's adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.