Risk of delivery complications among pregnant people experiencing housing insecurity.

BACKGROUND: Housing insecurity is increasingly being recognized as an important social determinant of health. Pregnant individuals experiencing housing insecurity may represent a particularly vulnerable subset of this population, but few studies have examined this population nationally. In particular, racial and ethnic minority individuals may be at risk for poor outcomes within this group because of structural racism and discrimination. The introduction of the International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes related to social determinants of health represent a new opportunity to identify patients with housing insecurity nationally. OBJECTIVE: This study aimed to evaluate the prevalence of and delivery outcomes for pregnant people experiencing housing insecurity, both nationally and by race and ethnicity. STUDY DESIGN: This was a retrospective cohort study using data from the 2016 to 2018 National Inpatient Sample. Delivery hospitalizations for people experiencing housing insecurity were identified using the International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code Z59. Among hospitals that coded at least 1 delivery for a patient with housing insecurity, logistic regression models were used to assess the odds of severe maternal morbidity associated with housing insecurity, adjusting for clinical risk and pregnancy characteristics. RESULTS: Of 539,950 delivery hospitalizations, 1820 hospitalizations (0.3%) were for patients with housing insecurity. Compared to deliveries for patients with housing security, deliveries for patients with housing insecurity were more likely for patients who identified as Black (34.8% vs 18.1%; P<.001) and who had Medicaid insurance (83.5% vs 46.2%; P<.001). People with housing insecurity were more likely to have comorbidities and higher-risk pregnancies, including higher rates of substance use disorders (54.0% vs 6.9%), major mental health disorders (37.5% vs 8.7%), preeclampsia with severe features (7.4% vs 4.3%), and preterm birth <37 weeks gestation (23.7% vs 11.6%) (all P<.001). In regression analyses, patients with housing insecurity had more than twice the odds of severe maternal morbidity than patients with housing security during the delivery hospitalization (odds ratio, 2.17; 95% confidence interval, 1.75-2.68). After adjusting for clinical risk and pregnancy characteristics, the differences were attenuated overall (adjusted odds ratio, 1.17; 95% confidence interval, 0.94-1.47) and among racial and ethnic groups (White patients: adjusted odds ratio, 1.39; 95% confidence interval, 0.95-2.03; Black patients: adjusted odds ratio, 1.05; 95% confidence interval, 0.73-1.52; Hispanic patients: adjusted odds ratio, 1.04; 95% confidence interval, 0.59-1.84; Asian or Pacific Islander or Native American or other race patients: adjusted odds ratio, 1.08; 95% confidence interval, 0.45-2.58). CONCLUSION: Pregnant individuals experiencing housing insecurity were more likely to be from groups that have been marginalized historically, had higher rates of comorbidities, and worse delivery outcomes. After risk adjustment, differences in the odds of severe maternal mortality were attenuated. Screening for housing insecurity may identify these patients earlier and connect them to services that could improve disparities in outcomes.

Racial disparities in pregnancy options counseling and referral in the US South.

OBJECTIVE: The objective of this study is to examine racial variation in receipt of counseling and referral for pregnancy options (abortion, adoption, and parenting) following pregnancy confirmation. Equitable offering of such information is a professional and ethical obligation and an opportunity to prevent racial disparities in maternal and child health. DATA SOURCE: Primary data from patients at southern United States publicly funded family planning clinics, October 2018-June 2019. STUDY DESIGN: Patients at 14 clinics completed a survey about their experiences with pregnancy options counseling and referral following a positive pregnancy test. The primary predictor variable was patients' self-reported racial identity. Outcomes included discussion of pregnancy options, referral for those options, and for support services. DATA COLLECTION: Data from eligible patients with non-missing information for key variables (n = 313) were analyzed using descriptive statistics, χ2 tests, and multivariable logistic regression. PRINCIPAL FINDINGS: Patients were largely Black (58%), uninsured (64%), and 18-29 years of age (80%). Intention to continue pregnancy and receipt of prenatal care referral did not differ significantly among Black as compared to non-Black patients. However, Black patients had a higher likelihood of wanting an abortion or adoption referral and not receiving one (abortion: marginal effect [ME] = 7.68%, p = 0.037; adjusted ME [aME] = 9.02%, p = 0.015; adoption: ME = 7.06%, p = 0.031; aME = 8.42%, p = 0.011). Black patients intending to end their pregnancies had a lower probability of receiving an abortion referral than non-Black patients (ME = -22.37%, p = 0.004; aME = -19.69%, p = 0.023). In the fully adjusted model, Black patients also had a higher probability of wanting access to care resources (including transportation, childcare, and financial support) and not receiving them (aME = 5.38%, p = 0.019). CONCLUSIONS: Clinical interactions surrounding pregnancy confirmation provide critical opportunities to discuss options, coordinate care, and mitigate risk, yet are susceptible to systemic bias. These findings add to limited evidence around pregnancy counseling and referral disparities. Ongoing assessment of pregnancy counseling and referral disparities can provide insight into organizational strengths or the potential to increase structural equity.

Dispersion of contraceptive access policies across the United States from 2006 to 2021.

Person-centered contraceptive access benefits reproductive autonomy, sexual wellbeing, menstrual regulation, and other preventive health. However, contraceptive access varies by social and geographic position, with policies either perpetuating or alleviating health inequities. We describe geographic and time-trend variation in an index from fewer (less expansive) to greater (more expansive) aggregation of U.S. state-level contraceptive access policies across 50 states and Washington, D.C. (collectively, states) from 2006 to 2021. We collected data from primary and secondary sources on 23 policies regulating contraceptive education, insurance coverage, minor's rights, provider authority, and more. As of 2021, the most enacted policies expanded contraceptive access through: 1) prescribing authority for nurse practitioners, certified nurse-midwives (n = 50, 98 % of states), and clinical nurse specialists (n = 38, 75 %); 2) Medicaid expansion (n = 38, 75 %); 3) prescription method insurance coverage (n = 30, 59 %); and 4) dispensing authority for nurse practitioners and certified nurse-midwives (n = 29, 57 %). The average overall U.S. policy index value increased in expansiveness from 6.9 in 2006 to 8.6 in 2021. States in the West and Northeast regions had the most expansive contraceptive access landscapes (average index values of 9.0 and 8.2, respectively) and grew more expansive over time (increased by 4-5 policies). The Midwest and South had least expansive landscapes (average index values of 5.0 and 6.1, respectively). Regions with more expansive sexual and reproductive health policy environments further expanded access, whereas least expansive environments were maintained. More nuanced understanding of how contraceptive policy diffusion affects health outcomes and equity is needed to inform public health advocacy and law making.

Helpline Calls Associated With Preventable Emergency Department Utilization.

INTRODUCTION: Unmet social needs are linked with greater healthcare utilization, but most studies lack timely and granular data on these needs. The 2-1-1 helpline is a telephone helpline focused on social needs. The objective of the study is to determine whether the number of 2-1-1 requests per 1,000 people is associated with preventable emergency department visits and compare the strength of the association with another commonly used predictor, Area Deprivation Index. METHODS: This cross-sectional study linked 2-1-1 requests to emergency department visits from uninsured and Medicaid-insured patients by ZIP code for a large urban hospital system from January 1, 2016 to August 31, 2019. Negative binomial regression analysis was used to estimate the association of 2-1-1 service requests and Area Deprivation Index with preventable emergency department visits. RESULTS: A total of 233,146 preventable emergency department visits and 520,308 2-1-1 requests were analyzed. For every 1-SD increase in 2-1-1 requests per 1,000 population, preventable emergency department visits increased by a factor of 3.05, even after controlling for local area deprivation and other population characteristics (p<0.001). CONCLUSIONS: Requests to 2-1-1 helplines are strongly associated with preventable emergency department visits. This information may help hospital leaders and policymakers target social needs interventions to the neighborhoods with the greatest need.

How Do Social Needs Cluster Among Low-Income Individuals?

Social needs screening in health care settings reveals that many low-income individuals have multiple unmet social needs at the same time. Having multiple simultaneous social needs greatly increases the odds of experiencing adverse health outcomes. To better understand how and which social needs cluster in these cases, the authors examined data from 14,749 low-income adults who completed a social needs assessment in one of 4 separate studies conducted between 2008 and 2019 in the United States. Participants were Medicaid beneficiaries, helpline callers, and daily smokers. Findings were strikingly consistent across the 4 studies. Participants with ≥2 social needs (n = 5621; 38% of total) experienced more stress, depressive symptoms, sleep problems, and chronic diseases and were more likely to rate their health as fair or poor. Social needs reflecting financial strain were highly correlated, such as needing help paying utility bills and needing money for necessities such as food, shelter, and clothing (r = .49 to .71 across studies). Participants experienced 351 distinct combinations of ≥2 social needs. The 10 most common combinations accounted for more than half of all participants with ≥2 needs. Clusters of social needs varied by subgroups. Women with children were more likely than others to need more space in their home and help paying utility bills; low-income men were more likely to be physically threatened and need a place to stay; older, sicker adults were more likely to need money for necessities and unexpected expenses, as well as transportation. Findings are discussed in the context of creating smarter, more efficient social needs interventions.

Trends in Hospital Mortality for Uninsured Rural and Urban Populations, 2012-2016.

PURPOSE: Rural-urban health disparities have received increasing scrutiny as rural individuals continue to have worse health outcomes. However, little is known about how insurance status contributes to urban-rural disparities. This study characterizes how rural uninsured patients compare to the urban uninsured, determines whether rurality among the uninsured is associated with worse clinical outcomes, and examines how clinical outcomes based on rurality have changed over time. METHODS: We conducted a retrospective cohort study of the 2012-2016 National Inpatient Sample hospital discharge data including 1,478,613 uninsured patients, of which 233,816 were rural. Admissions were broken into 6 rurality categories. Logistic regression models were used to determine the independent association between rurality and hospital mortality. FINDINGS: Demographic and clinical characteristics differed significantly between rural and urban uninsured patients: rural patients were more often white, lived in places with lower median household income, and were more often admitted electively and transferred. Rurality was associated with significantly higher in-hospital mortality rates (1.44% vs 1.89%, OR 1.32, P < .001). This association strengthened after adjusting for medical comorbidities and hospital characteristics. Further, disparities between urban and rural mortality were found to be growing, with the gap almost doubling between 2012 and 2016. CONCLUSIONS: Rural and urban uninsured patients differed significantly, specifically in terms of race and median income. Among the uninsured, rurality was associated with higher in-hospital mortality, and the gap between urban and rural in-hospital mortality was widening. Our findings suggest the rural uninsured are a vulnerable population in need of informed, tailored policies to reduce these disparities.

Association of Stratification by Proportion of Patients Dually Enrolled in Medicare and Medicaid With Financial Penalties in the Hospital-Acquired Condition Reduction Program.

Importance: The Hospital-Acquired Condition Reduction Program (HACRP) is a value-based payment program focused on safety events. Prior studies have found that the program disproportionately penalizes safety-net hospitals, which may perform more poorly because of unmeasured severity of illness rather than lower quality. A similar program, the Hospital Readmissions Reduction Program, stratifies hospitals into 5 peer groups for evaluation based on the proportion of their patients dually enrolled in Medicare and Medicaid, but the effect of stratification on the HACRP is unknown. Objective: To characterize the hospitals penalized by the HACRP and the distribution of financial penalties before and after stratification. Design, Setting, and Participants: This economic evaluation used publicly available data on HACRP performance and penalties merged with hospital characteristics and cost reports. A total of 3102 hospitals participating in the HACRP in fiscal year 2020 (covering data from July 1, 2016, to December 31, 2018) were studied. Exposures: Hospitals were divided into 5 groups based on the proportion of patients dually enrolled, and penalties were assigned to the lowest-performing quartile of hospitals in each group rather than the lowest-performing quartile overall. Main Outcomes and Measures: Penalties in the prestratification vs poststratification schemes. Results: The study identified 3102 hospitals evaluated by the HACRP. Safety-net hospitals received $111 333 384 in penalties before stratification compared with an estimated $79 087 744 after stratification-a savings of $32 245 640. Hospitals less likely to receive penalties after stratification included safety-net hospitals (33.6% penalized before stratification vs 24.8% after stratification, Δ = -8.8 percentage points [pp], P < .001), public hospitals (34.1% vs 30.5%, Δ = -3.6 pp, P = .003), hospitals in the West (26.8% vs 23.2%, Δ = -3.6 pp, P < .001), hospitals in Medicaid expansion states (27.3% vs 25.6%, Δ = -1.7 pp, P = .003), and hospitals caring for the most patients with disabilities (32.2% vs 28.3%, Δ = -3.9 pp, P < .001) and from racial/ethnic minority backgrounds (35.1% vs 31.5%, Δ = -3.6 pp, P < .001). In multivariate analyses, safety-net status and treating patients with highly medically complex conditions were associated with higher odds of moving from penalized to nonpenalized status. Conclusions and Relevance: This economic evaluation suggests that stratification of hospitals would be associated with a narrowing of disparities in penalties and a marked reduction in penalties for safety-net hospitals. Policy makers should consider adopting stratification for the HACRP.

Sociodemographic differences in utilization and outcomes for temporary cardiovascular mechanical support in the setting of cardiogenic shock.

BACKGROUND: Temporary mechanical circulatory support (MCS) devices are increasingly used in cardiogenic shock, but whether sociodemographic differences by sex, race and/or ethnicity, insurance status, and neighborhood poverty exist in the utilization of these devices is unknown. METHODS: Retrospective cross-sectional study using the National Inpatient Sample for 2012-2017. Logistic regression models were used to examine predictors of use of temporary MCS devices and for in-hospital mortality, clustering by hospital-year. RESULTS: Our study population included 109,327 admissions for cardiogenic shock. Overall, 14.3% of admissions received an intra-aortic balloon pump, 4.2% a percutaneous ventricular assist device, and 1.8% extracorporeal membranous oxygenation (ECMO). After adjusting for age, comorbidities, and hospital characteristics, use of temporary MCS was lower in women compared to men (adjusted odds ratio [aOR] = 0.76, P < .001), Black patients compared to white ones (aOR = 0.73, P < .001), those insured by Medicare (aOR = 0.75, P < .001), Medicaid (aOR = 0.74, P < .001), or uninsured (aOR = 0.90, P = .015) compared to privately insured, and those in the lowest income neighborhoods (aOR = 0.94, P = .003) versus other neighborhoods. Women, admissions covered by Medicare, Medicaid, or uninsured, and those from low-income neighborhoods also had higher mortality rates even after adjustment for MCS implantation. CONCLUSIONS: There are differences in the use of temporary MCS in the setting of cardiogenic shock among specific populations within the United States. The growing use of MCS for treating cardiogenic shock highlights the need to better understand its impact on outcomes.

Severe Maternal Morbidity, Race, and Rurality: Trends Using the National Inpatient Sample, 2012-2017.

Background: Severe maternal morbidity is related to maternal mortality and an important measure of maternal health outcomes. Our objective was to evaluate differences in rates of severe maternal morbidity and mortality (SMM&M) by rurality and race, and examine these trends over time. Materials and Methods: It involves the retrospective cohort study of delivery hospitalizations from January 1, 2012 to December 31, 2017 from the National Inpatient Sample. We identified delivery hospitalizations using International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis and procedure codes and diagnosis-related groups. We used hierarchical regression models controlling for insurance status, income, age, comorbidities, and hospital characteristics to model odds of SMM&M. Results: The eligible cohort contained 4,494,089 delivery hospitalizations. Compared with women from small cities, women in the most urban and most rural areas had higher odds of SMM&M (urban adjusted odds ratio [aOR] 1.09, 95% confidence interval [1.04-1.14]; noncore rural aOR 1.24 [1.18-1.31]). Among White women, the highest odds of SMM&M were in noncore rural counties (aOR 1.20 [1.12-1.27]), while among Black women the highest odds were in urban (aOR 1.21 [1.11-1.31]) and micropolitan areas (aOR 1.36 [1.19-1.57]). Findings were similar for Hispanic, Native American, and other race women. Rates of SMM&M increased from 2012 to 2017, especially among urban patients. Conclusions: Women in the most urban and most rural counties experienced higher odds of SMM&M, and these relationships differed by race. These findings suggest particular areas for clinical leaders and policymakers to target to reduce geographic and racial disparities in maternal outcomes.

Utilization of Social Determinants of Health ICD-10 Z-Codes Among Hospitalized Patients in the United States, 2016-2017.

BACKGROUND: The inclusion of Z-codes for social determinants of health (SDOH) in the 10th revision of the International Classification of Diseases (ICD-10) may offer an opportunity to improve data collection of SDOH, but no characterization of their utilization exists on a national all-payer level. OBJECTIVE: To examine the prevalence of SDOH Z-codes and compare characteristics of patients with and without Z-codes and hospitals that do and do not use Z-codes. RESEARCH DESIGN: Retrospective cohort study using 2016 and 2017 National Inpatient Sample. PARTICIPANTS: Total of 14,289,644 inpatient hospitalizations. MEASURES: Prevalence of SDOH Z-codes (codes Z55-Z65) and descriptive statistics of patients and hospitals. RESULTS: Of admissions, 269,929 (1.9%) included SDOH Z-codes. Average monthly SDOH Z-code use increased across the study period by 0.01% per month (P<0.001). The cumulative number and proportion of hospitals that had ever used an SDOH Z-code also increased, from 1895 hospitals (41%) in January 2016 to 3210 hospitals (70%) in December 2017. Hospitals that coded at least 1 SDOH Z-code were larger, private not-for-profit, and urban teaching hospitals. Compared with admissions without an SDOH Z-code, admissions with them were for patients who were younger, more often male, Medicaid recipients or uninsured. A higher proportion of admissions with SDOH Z-codes were for mental health (44.0% vs. 3.3%, P<0.001) and alcohol and substance use disorders (9.6% vs. 1.1%, P<0.001) compared with those without. CONCLUSIONS: The uptake of SDOH Z-codes has been slow, and current coding is likely poorly reflective of the actual burden of social needs experienced by hospitalized patients.

Urban-Rural Inequities in Acute Stroke Care and In-Hospital Mortality.

BACKGROUND AND PURPOSE: The rural-urban life-expectancy gap is widening, but underlying causes are incompletely understood. Prior studies suggest stroke care may be worse for individuals in more rural areas, and technological advancements in stroke care may disproportionately impact individuals in more rural areas. We sought to examine differences and 5-year trends in the care and outcomes of patients hospitalized for stroke across rural-urban strata. METHODS: Retrospective cohort study using National Inpatient Sample data from 2012 to 2017. Rurality was classified by county of residence according to the 6-strata National Center for Health Statistics classification scheme. RESULTS: There were 792 054 hospitalizations for acute stroke in our sample. Rural patients were more often white (78% versus 49%), older than 75 (44% versus 40%), and in the lowest quartile of income (59% versus 32%) compared with urban patients. Among patients with acute ischemic stroke, intravenous thrombolysis and endovascular therapy use were lower for rural compared with urban patients (intravenous thrombolysis: 4.2% versus 9.2%, adjusted odds ratio, 0.55 [95% CI, 0.51-0.59], P<0.001; endovascular therapy: 1.63% versus 2.41%, adjusted odds ratio, 0.64 [0.57-0.73], P<0.001). Urban-rural gaps in both therapies persisted from 2012 to 2017. Overall, stroke mortality was higher in rural than urban areas (6.87% versus 5.82%, P<0.001). Adjusted in-patient mortality rates increased across categories of increasing rurality (suburban, 0.97 [0.94-1.0], P=0.086; large towns, 1.05 [1.01-1.09], P=0.009; small towns, 1.10 [1.06-1.15], P<0.001; micropolitan rural, 1.16 [1.11-1.21], P<0.001; and remote rural 1.21 [1.15-1.27], P<0.001 compared with urban patients. Mortality for rural patients compared with urban patients did not improve from 2012 (adjusted odds ratio, 1.12 [1.00-1.26], P<0.001) to 2017 (adjusted odds ratio, 1.27 [1.13-1.42], P<0.001). CONCLUSIONS: Rural patients with stroke were less likely to receive intravenous thrombolysis or endovascular therapy and had higher in-hospital mortality than their urban counterparts. These gaps did not improve over time. Enhancing access to evidence-based stroke care may be a target for reducing rural-urban disparities.

Assessment of HF Outcomes Using a Claims-Based Frailty Index.

OBJECTIVES: This study used a claims-based frailty index to investigate outcomes of frail patients with heart failure (HF). BACKGROUND: Medicare value-based payment programs financially reward and penalize hospitals based on HF patients' outcomes. Although programs adjust risks for comorbidities, they do not adjust for frailty. Hospitals caring for high proportions of frail patients may be unfairly penalized. Understanding frail HF patients' outcomes may allow improved risk adjustment and more equitable assessment of health care systems. METHODS: Adapting a claims-based frailty index, the study assigned a frailty score to each adult in the National in-patient Sample (NIS) from 2012 through September 2015 with a primary diagnosis of HF and dichotomized frailty by using a cutoff value established in the general NIS population. Multivariate regression models were estimated, controlling for comorbidities and hospital characteristics, to investigate relationships between frailty and outcomes. RESULTS: Of 732,932 patients, 369,298 were frail. Frail patients were more likely than nonfrail patients to die during hospital stay (3.57% vs. 2.37%, respectively; adjusted odds ratio [aOR]: 1.67; 95% confidence interval [CI]: 1.61 to 1.72; p < 0.001); were less likely to be discharged to home (66.5% vs. 79.3%, respectively; aOR: 0.58; 95% CI: 0.57 to 0.58; p < 0.001); were hospitalized for more days (5.89 vs. 4.63 days, respectively; adjusted coefficient: 0.21 days; 95% CI: 0.21 to 0.22; p < 0.001); and incurred higher charges ($47,651 vs. $40,173, respectively; adjusted difference = $9,006; 95% CI: $8,596 to $9,416; p < 0.001). CONCLUSIONS: Frail patients with HF had significantly poorer outcomes than nonfrail patients after accounting for comorbidities. Clinicians should screen for frailty to identify high-risk patients who could benefit from targeted intervention. Policymakers should perform risk adjustments for frailty for more equitable quality measurement and financial incentive allocation.

Disparities and Impact of Medicaid Expansion on Left Ventricular Assist Device Implantation and Outcomes.

BACKGROUND: Left ventricular assist device (LVAD) therapy is an increasingly viable alternative for patients who are not candidates for heart transplantation or who are waiting for a suitable donor. We aimed to determine whether there is an association between sex, race/ethnicity, insurance coverage, and neighborhood income and access to/outcomes of LVAD implantation. We further analyzed whether access to LVAD improved in states that did versus did not expand Medicaid. METHODS AND RESULTS: Retrospective cohort study using State Inpatient Databases to identify patients 18 to 85 years of age admitted for heart failure, cardiogenic shock, or LVAD implantation from 2012 to 2015. Logistic regression analyses adjusting for age, all the sociodemographic factors above, medical comorbidities, and a hospital random effect were used to quantify odds of receipt of LVADs, as well as outcomes conditional on receiving an LVAD, for the sociodemographic groups of interest. A total of 925 770 patients were included; 3972 (0.43%) received LVADs. After adjusting for age, comorbidities, and hospital effects, women (adjusted odds ratio [aOR], 0.45 [0.41-0.49]), black patients (aOR, 0.83 [0.74-0.92]), and Hispanic patients (aOR, 0.74 [0.64-0.87]) were less likely to receive LVADs than whites. Medicare (aOR, 0.79 [0.72-0.86]), Medicaid (aOR, 0.52 [0.46-0.58]), and uninsured patients (aOR, 0.17 [0.11-0.25]) were less likely to receive LVADs than the privately insured, and patients in low-income ZIP codes were less likely than those in higher income areas (aOR, 0.71 [0.65-0.77]). Among those who received LVADs, women (aOR, 1.78 [1.38-2.30]), patients of unknown race or race other than white, black, or Hispanic (aOR, 1.97 [1.42-2.74]), and uninsured patients (aOR, 4.86 [1.92-12.28]) had higher rates of in-hospital mortality. Medicaid expansion was not associated with an increase in LVAD implantation. CONCLUSIONS: There are meaningful sociodemographic disparities in access and outcomes for LVAD implantation. Medicaid expansion was not associated with an increase in LVAD rates.

Performance and Penalties in Year 1 of the Skilled Nursing Facility Value-Based Purchasing Program.

BACKGROUND/OBJECTIVES: Launched in October 2018, Medicare's Skilled Nursing Facility Value-Based Purchasing (SNF VBP) program mandates financial penalties for SNFs with high 30-day readmission rates. Our objective was to identify characteristics of SNFs associated with provider performance under the program. DESIGN: Retrospective cross-sectional analysis using Nursing Home Compare data for the 2019 SNF VBP. Facility-level regressions examined the relationship between structural characteristics (nursing home size, rurality, profit status, hospital affiliation, region, and Star Ratings) and patient characteristics (neighborhood income, race/ethnicity, dual eligibility, disability, and frailty) and facility performance. SETTING: US Medicare. PARTICIPANTS: A total of 14 558 SNFs. MEASUREMENTS: The 2019 SNF VBP performance scores and penalties. RESULTS: Nationally, 72% (10 436) of SNFs were penalized; 21% (2996) received the maximum penalty of 1.98%. In multivariate analyses, rural SNFs were less likely to be penalized (odds ratio [OR] = 0.85; 95% confidence interval [CI] = 0.78-0.92; P < .001; vs urban), while small SNFs were more likely to be penalized (≤70 beds: OR = 1.28; 95% CI = 1.15-1.42; P < .001; 71-120 beds: OR = 1.15; 95% CI = 1.05-1.26; P = .003; vs >120 beds). SNFs with lower nurse staffing had higher odds of penalties (low: OR = 1.15; 95% CI = 1.03-1.27; P = .010; vs high); nonprofit and government-owned SNFs had lower odds of penalties (OR = 0.79; 95% CI = 0.72-0.87; P < .001; government: OR = 0.72; 95% CI = 0.61-0.84; P < .001; vs for profit); and SNFs with higher Star Ratings had lower odds of penalties (5 stars: OR = 0.47; 95% CI = 0.40-0.54; P < .001; vs 1 star). In terms of patient population, SNFs located in low-income ZIP codes (OR = 1.17; 95% CI = 1.03-1.34; P = .019) or serving a high proportion of frail patients (OR = 1.39; 95% CI = 1.21-1.60; P < .001) were more likely to be penalized than other SNFs. SNFs with high proportions of dual, black, Hispanic, or disabled patients did not have higher odds of penalization. CONCLUSION: Structural and patient characteristics of SNFs may significantly impact provider performance under the SNF VBP. These findings have implications for policy makers and clinical leaders seeking to improve quality and avoid unintended consequences with VBP in SNFs. J Am Geriatr Soc 68:826-834, 2020.

Associations Between Social Risk Factors and Surgical Site Infections After Colectomy and Abdominal Hysterectomy.

Importance: Surgical site infection (SSI) is an important patient safety outcome. Although social risk factors have been linked to many adverse health outcomes, it is unknown whether such factors are associated with higher rates of SSI. Objectives: To determine whether social risk factors, including race/ethnicity, insurance status, and neighborhood income, are associated with higher rates of SSI after colectomy or abdominal hysterectomy, 2 surgical procedures for which SSI rates are publicly reported and included in pay-for-performance programs by Medicare and other groups. Design, Setting, and Participants: This cross-sectional study analyzed adults undergoing colectomy or abdominal hysterectomy, as captured in State Inpatient Databases for Arizona, Florida, Iowa, Massachusetts, Maryland, New York, and Vermont. Operations were performed in 2013 through 2014 at general acute care hospitals in the United States. Data analysis was conducted from October 2018 through June 2019. Exposures: Colectomy or hysterectomy. Main Outcomes and Measures: Postoperative complex SSI rates. Results: A total of 149 741 patients met the inclusion criteria, including 90 210 patients undergoing colectomies (mean [SD] age, 63.4 [15.6] years; 49 029 [54%] female; 74% white, 11% black, 9% Hispanic, and 5% other or unknown race/ethnicity) and 59 531 patients undergoing abdominal hysterectomies (mean [SD] age, 49.8 [11.8] years; 100% female; 52% white, 26% black, 14% Hispanic, and 8% other or unknown race/ethnicity). In the colectomy cohort, 34% had private insurance, 52% had Medicare, 9% had Medicaid, and 5% had other or unknown insurance or were uninsured; 24% were from the lowest quartile of median zip code income. In the hysterectomy cohort, 57% had private insurance, 16% had Medicare, 19% had Medicaid, and 3% had other or unknown insurance or were uninsured; 27% were from the lowest-income zip codes. Within 30 days of surgery, SSI rates were 2.55% for the colectomy cohort and 0.61% for the hysterectomy cohort. For colectomy, black race (adjusted odds ratio [AOR], 0.71; 95% CI, 0.61-0.82) was associated with lower odds of SSI, whereas Medicare (AOR, 1.25; 95% CI, 1.10-1.41), Medicaid (AOR, 1.23; 95% CI, 1.06-1.44), and low neighborhood income (AOR, 1.14; 95% CI, 1.01-1.29) were associated with higher odds of SSI. For hysterectomy, no social risk factors that were examined in this study had statistically significant associations with SSI after adjustment for clinical risk. Conclusions and Relevance: Inconsistent associations between social risk factors and SSIs were found. For colectomy, infection prevention programs targeting low-income groups may be important for reducing disparities in this postoperative outcome, and policy makers could consider taking social risk factors into account when evaluating hospital performance.