BACKGROUND/OBJECTIVES: To determine the associations of nursing home registered nurse (RN) staffing, overall quality of care, and concentration of Medicaid or racial and ethnic minority residents with 2019 coronavirus disease (COVID‐19) confirmed cases and deaths by April 16, 2020, among Connecticut nursing home residents. DESIGN: Cross‐sectional analysis on Connecticut nursing home (n = 215) COVID‐19 report, linked to other nursing home files and county counts of confirmed cases and deaths. Multivariable two‐part models determined the associations of key nursing home characteristics with the likelihood of at least one confirmed case (or death) in the facility, and with the count of cases (deaths) among facilities with at least one confirmed case (death). SETTING: All Connecticut nursing homes (n = 215). PARTICIPANTS: None. INTERVENTION: None. MEASUREMENTS: Numbers of COVID‐19 confirmed cases and deaths among residents. RESULTS: The average number of confirmed cases was eight per nursing home (zero in 107 facilities), and the average number of confirmed deaths was 1.7 per nursing home (zero in 131 facilities). Among facilities with at least one confirmed case, every 20‐minute increase in RN staffing (per resident day) was associated with 22% fewer confirmed cases (incidence rate ratio [IRR] = .78; 95% confidence interval [CI] = .68–.89; P < .001); compared with one‐ to three‐star facilities, four‐ or five‐star facilities had 13% fewer confirmed cases (IRR = .87; 95% CI = .78–.97; P < .015), and facilities with high concentration of Medicaid residents (IRR = 1.16; 95% CI = 1.02–1.32; P = .025) or racial/ethnic minority residents (IRR = 1.15; 95% CI = 1.03–1.29; P = .026) had 16% and 15% more confirmed cases, respectively, than their counterparts. Among facilities with at least one death, every 20‐minute increase in RN staffing significantly predicted 26% fewer COVID‐19 deaths (IRR = .74; 95% CI = I .55–1.00; P = .047). Other focused characteristics did not show statistically significant associations with deaths. CONCLUSION: Nursing homes with higher RN staffing and quality ratings have the potential to better control the spread of the novel coronavirus and reduce deaths. Nursing homes caring predominantly for Medicaid or racial and ethnic minority residents tend to have more confirmed cases.

Rural-urban inequalities in health status and access to care are a significant issue in China, especially among older adults. However, the rural-urban differences in health outcomes, healthcare use, and expenditures among insured elders following China’s comprehensive healthcare reforms in 2009 remain unclear. Using the Chinese Longitudinal Healthy Longevity Surveys data containing a sample of 2,624 urban and 6,297 rural residents aged 65 and older, we performed multivariable regression analyses to determine rural-urban differences in physical and psychological functions, self-reported access to care, and healthcare expenditures, after adjusting for individual socio-demographic characteristics and health conditions. Nonparametric tests were used to evaluate the changes in rural-urban differences between 2011 and 2014. Compared to rural residents, urban residents were more dependent on activities of daily living (ADLs) and instrumental ADLs. Urban residents reported better adequate access to care, higher adjusted total expenditures for inpatient, outpatient, and total care, and higher adjusted out-of-pocket spending for outpatient and total care. However, rural residents had higher adjusted self-payment ratios for total care. Rural-urban differences in health outcomes, adequate access to care, and self-payment ratio significantly narrowed, but rural-urban differences in healthcare expenditures significantly increased from 2011 to 2014. Our findings revealed that although health and healthcare access improved for both rural and urban older adults in China between 2011 and 2014, rural-urban differences showed mixed trends. These findings provide empirical support for China’s implementation of integrated rural and urban public health insurance systems, and further suggest that inequalities in healthcare resource distribution and economic development between rural and urban areas should be addressed to further reduce the rural-urban differences.

BACKGROUND/OBJECTIVES: To determine racial/ethnic disparities in weekly counts of new COVID‐19 cases and deaths among nursing home residents or staff. DESIGN: Cross‐sectional analysis of national nursing home COVID‐19 reports linked to other data. Multivariable two‐part models modeled disparities in count of cases or deaths, and logistic regressions modeled disparities in self‐reported shortages in staff and personal protective equipment (PPE), across nursing home groups with varying proportions of racial/ethnic minority residents, defined as low‐, medium‐, medium‐high–, and high‐proportion groups. SETTING: A total of 12,576 nursing homes nationally. PARTICIPANTS: None. INTERVENTION: None. MEASUREMENTS: Numbers of incident COVID‐19 confirmed cases among residents and staff, and incident COVID‐19 related deaths among residents (primary outcomes); and nursing home reported shortages in staff and PPE (secondary outcomes). All outcomes were reported for the week of May 25, 2020. RESULTS: The number of weekly new COVID‐19 confirmed cases among residents ranged from an average of 0.4 cases per facility (standard deviation (SD) = 2.5) for the low‐proportion group (93.0% had zero new cases) to 1.5 cases per facility (SD = 6.3) for the high‐proportion group (78.9% had zero new cases). Multivariable regression estimated that compared with the low‐proportion group, the likelihood of having at least one new resident case was 76% higher (odds ratio = 1.76; 95% confidence interval = 1.38–2.25; P = .000) for the high‐proportion group. Similar across‐facility disparities were found for the weekly count of new COVID‐19 deaths among residents (ranging from 0.1 deaths per facility (SD = 1.1) for the low‐proportion group to 0.4 deaths (SD = 2.0) for the high‐proportion group) and in the weekly count of new COVID‐19 confirmed cases among staff (ranging from 0.3 cases (SD = 1.4] to 1.3 cases (SD = 4.4) per facility). No substantial disparities in self‐reported shortages of staff or PPE were found. CONCLUSION: Nursing homes caring for disproportionately more racial/ethnic minority residents reported more weekly new COVID‐19 confirmed cases and/or deaths. Immediate actions are needed to address these system‐level disparities.

Background: Little is known about how the geographic variation and disparities in use of elective primary total hip and knee replacements for Medicare beneficiaries have evolved in recent years. The study objectives are to determine these variations and disparities, whether Black Medicare beneficiaries have continued to undergo fewer total hip replacements and total knee replacements across regions, and whether disparities affected all Black beneficiaries or mainly affected socioeconomically disadvantaged Black beneficiaries. Methods: We used 2009 to 2017 Medicare enrollment and claims data to examine Hospital Referral Region (HRR)-level variation and disparities by race (non-Hispanic White and Black) and socioeconomic status (Medicare-only and dual eligibility for both Medicare and Medicaid). The outcomes were HRR-level age and sex-standardized total hip replacement and total knee replacement utilization rates for White Medicare-only beneficiaries, White dual-eligible beneficiaries, Black Medicare-only beneficiaries, and Black dual-eligible beneficiaries, and the differences in rates between these groups as a representation of disparities. The key exposure variables were race-socioeconomic group and year. We constructed multilevel mixed-effects linear regression models to estimate trends in total hip replacement and total knee replacement rates and to examine whether rates were lower in HRRs with high percentages of Black beneficiaries or dual-eligible beneficiaries. Results: The study included 924,844 total hip replacements and 2,075,968 total knee replacements. In 2017, the mean HRR-level total hip replacement rate was 4.64 surgical procedures per 1,000 beneficiaries, and the mean HRR-level total knee replacement rate was 9.66 surgical procedures per 1,000 beneficiaries, with a threefold variation across HRRs. In 2017, the total hip replacement rate was 32% higher for White Medicare-only beneficiaries and 48% higher for Black Medicare-only beneficiaries than in 2009 (p < 0.001). However, because the surgical rates for White and Black dual-eligible beneficiaries remained unchanged over the study period, the 2017 Medicare-only and dual-eligible disparity for White beneficiaries increased by 0.75 surgical procedures per 1,000 from 2009 (40.98% increase; p = 0.03), and the disparity for Black beneficiaries by 1.13 surgical procedures per 1,000 beneficiaries (297.37% increase; p < 0.001). The total knee replacement disparities remained unchanged. Notably, the rates for White dual-eligible beneficiaries were significantly lower than those for Black Medicare-only beneficiaries (p < 0.001 for both total hip replacements and total knee replacements), and fewer surgical procedures were conducted in HRRs with a higher density of Black or dual-eligible beneficiaries. Conclusions: Although the total hip replacement use for Medicare-only beneficiaries of both races increased, disparities for White and Black dual-eligible beneficiaries (compared with their Medicare-only counterparts) are increasing. Efforts to improve equity must identify and address both racial and socioeconomic barriers and focus on regions with high concentrations of disadvantaged beneficiaries. Clinical Relevance: Although total hip replacements and total knee replacements are highly successful surgical procedures for end-stage osteoarthritis, our findings show that, as recently as 2017, Black beneficiaries and those dual eligible for Medicaid (a proxy for socioeconomic status) are less likely to undergo these surgical procedures and that there is profound geographic variation in the use of these surgical procedures. This evidence is essential for the design and implementation of disparity-reduction strategies focused on patients, providers, and geographic areas that can potentially improve the equity in joint replacement care.

Background: The identification of surgical site infections for infection surveillance in hospitals depends on the manual abstraction of medical records and, for research purposes, depends mainly on the use of administrative or claims data. The objective of this study was to determine whether automating the abstraction process with natural language processing (NLP)-based models that analyze the free-text notes of the medical record can identify surgical site infections with predictive abilities that match the manual abstraction process and that surpass surgical site infection identification from administrative data. Methods: We used surgical site infection surveillance data compiled by the infection prevention team to identify surgical site infections among patients undergoing orthopaedic surgical procedures at a tertiary care academic medical center from 2011 to 2017. We compiled a list of keywords suggestive of surgical site infections, and we used NLP to identify occurrences of these keywords and their grammatical variants in the free-text notes of the medical record. The key outcome was a binary indicator of whether a surgical site infection occurred. We estimated 7 incremental multivariable logistic regression models using a combination of administrative and NLP-derived variables. We split the analytic cohort into training (80%) and testing data sets (20%), and we used a tenfold cross-validation approach. The main analytic cohort included 172 surgical site infection cases and 200 controls that were repeatedly and randomly selected from a pool of 1,407 controls. Results: For Model 1 (variables from administrative data only), the sensitivity was 68% and the positive predictive value was 70%; for Model 4 (with NLP 5-grams [distinct sequences of 5 contiguous words] from the medical record), the sensitivity was 97% and the positive predictive value was 97%; and for Model 7 (a combination of Models 1 and 4), the sensitivity was 97% and the positive predictive value was 97%. Thus, NLP-based models identified 97% of surgical site infections identified by manual abstraction with high precision and 43% more surgical site infections compared with models that used administrative data only. Conclusions: Models that used NLP keywords achieved predictive abilities that were comparable with the manual abstraction process and were superior to models that used administrative data only. NLP has the potential to automate and aid accurate surgical site infection identification and, thus, play an important role in their prevention. Clinical Relevance: This study examines NLP’s potential to automate the identification of surgical site infections. This automation can potentially aid the prevention and early identification of these surgical complications, thereby reducing their adverse clinical and economic impact.

Despite the increased use of nursing homes by minority residents, nursing home care remains highly segregated. Compared to whites, racial/ethnic minorities tend to be cared for in facilities with l...