Risk of Transfusion in Isolated Coronary Artery Bypass Graft: Models developed from the STS Database.

BACKGROUND: Perioperative blood transfusion is associated with adverse outcomes and higher costs following coronary artery bypass graft surgery (CABG). We developed risk assessments for patients' probability of perioperative transfusion and the expected transfusion volume, to improve clinical management and resource use. METHODS: Among 1,266,545 consecutive (2008-2016) isolated-CABG operations in STS's Adult Cardiac Surgery Database, 657,821 (51.9%) received perioperative blood transfusions (red blood cell [RBC], fresh frozen plasma [FFP], cryoprecipitate, and/or platelets). We developed "full" models to predict perioperative transfusion of any blood product, and of RBC, FFP, or platelets. Using least absolute shrinkage and selection operator model selection, we built a rapid risk score based on 5 variables (age, body surface area, sex, preoperative hematocrit and use of intra-aortic balloon pump). RESULTS: Full model C-statistics were 0.785, 0.815, 0.707, and 0.699 for any blood product, RBC, FFP, and platelets. Rapid risk assessments' C-statistics were 0.752, 0.785, 0.670, and 0.661 for any blood product, RBC, FFP, and platelets. The observed versus expected risk plots showed strong calibration for full models and risk assessment tools; absolute differences between observed and expected risks of transfusion were <10.8% in each percentile of expected risk. Risk-assessments' predicted probabilities of transfusion were strongly and non-linearly associated (p<.0001) with total units transfused. CONCLUSIONS: These robust and well-calibrated risk assessment tools for perioperative transfusion in CABG can inform surgeons regarding patients' risks and number of RBC, FFP, and platelets units they can expect to need. This can aid in optimizing outcomes and increasing efficient use of blood products.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

PURPOSE: By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. METHODS: We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. FINDINGS: Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. CONCLUSIONS: US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

A Simple Risk Adjustment for Hospital-Level Nulliparous, Term, Singleton, Vertex, Cesarean Delivery Rates and Its Implications for Public Reporting.

BACKGROUND: The Joint Commission uses nulliparous, term, singleton, vertex, cesarean delivery (NTSV-CD) rates to assess hospitals' perinatal care quality through the Cesarean Birth measurement (PC-02). However, these rates are not risk-adjusted for maternal health factors, putting this measure at odds with the risk adjustment paradigm of most publicly reported hospital quality measures. Here, the authors tested whether risk adjustment for readily documented maternal risk factors affected hospital-level NTSV-CD rates in a large health system. METHODS: Included were all consecutive NTSV pregnancies from January 2019 to April 2023 across 10 hospitals in one health system. Logistic regression, adjusting for age, obesity, diabetes, and hypertensive disorders. was used to calculate hospital-level risk-adjusted NTSV-CD rates by multiplying observed vs. expected ratios for each hospital by the systemwide unadjusted NTSV-CD rate. The authors calculated intrahospital risk differences between unadjusted and risk-adjusted rates and calculated the percentage of hospitals qualifying for different reporting status after risk adjustment using the 30% Joint Commission reporting threshold rate. RESULTS: Of 23,866 pregnancies, 6,550 (27.4%) had cesarean deliveries. Across 10 hospitals, the number of deliveries ranged from 393 to 7,671, with unadjusted NTSV-CD rates ranging from 21.0% to 30.5%. Risk-adjusted NTSV-CD rates ranged from 21.5% to 30.4%, with absolute intrahospital differences in risk-adjusted vs. unadjusted rates ranging from -1.33% (indicating lower rate after risk adjustment) to 3.37% (indicating higher rate after risk adjustment). Three of 10 (30.0%) hospitals qualified for different reporting statuses after risk adjustment. CONCLUSION: Risk adjustment for age, obesity, diabetes, and hypertensive disorders is feasible and resulted in meaningful changes in hospital-level NTSV-CD rates with potentially impactful consequences for hospitals near The Joint Commission reporting threshold.

An SGO commentary: U.S. News and World Report gynecologic oncology procedural ratings-Do they reflect high-quality care?

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Missed opportunities in hospital quality measurement during the COVID-19 pandemic: a retrospective investigation of US hospitals' CMS Star Ratings and 30-day mortality during the early pandemic.

OBJECTIVES: In the USA and UK, pandemic-era outcome data have been excluded from hospital rankings and pay-for-performance programmes. We assessed the relationship between US hospitals' pre-pandemic Centers for Medicare and Medicaid Services (CMS) Overall Hospital Star ratings and early pandemic 30-day mortality among both patients with COVID and non-COVID to understand whether pre-existing structures, processes and outcomes related to quality enabled greater pandemic resiliency. DESIGN AND DATA SOURCE: A retrospective, claim-based data study using the 100% Inpatient Standard Analytic File and Medicare Beneficiary Summary File including all US Medicare Fee-for-Service inpatient encounters from 1 April 2020 to 30 November 2020 linked with the CMS Hospital Star Ratings using six-digit CMS provider IDs. OUTCOME MEASURE: The outcome was risk-adjusted 30-day mortality. We used multivariate logistic regression adjusting for age, sex, Elixhauser mortality index, US Census Region, month, hospital-specific January 2020 CMS Star rating (1-5 stars), COVID diagnosis (U07.1) and COVID diagnosis×CMS Star Rating interaction. RESULTS: We included 4 473 390 Medicare encounters from 2533 hospitals, with 92 896 (28.2%) mortalities among COVID-19 encounters and 387 029 (9.3%) mortalities among non-COVID encounters. There was significantly greater odds of mortality as CMS Star Ratings decreased, with 18% (95% CI 15% to 22%; p<0.0001), 33% (95% CI 30% to 37%; p<0.0001), 38% (95% CI 34% to 42%; p<0.0001) and 60% (95% CI 55% to 66%; p<0.0001), greater odds of COVID mortality comparing 4-star, 3-star, 2-star and 1-star hospitals (respectively) to 5-star hospitals. Among non-COVID encounters, there were 17% (95% CI 16% to 19%; p<0.0001), 24% (95% CI 23% to 26%; p<0.0001), 32% (95% CI 30% to 33%; p<0.0001) and 40% (95% CI 38% to 42%; p<0.0001) greater odds of mortality at 4-star, 3-star, 2-star and 1-star hospitals (respectively) as compared with 5-star hospitals. CONCLUSION: Our results support a need to further understand how quality outcomes were maintained during the pandemic. Valuable insights can be gained by including the reporting of risk-adjusted pandemic era hospital quality outcomes for high and low performing hospitals.

Hospital quality reporting in the pandemic era: to what extent did hospitals' COVID-19 census burdens impact 30-day mortality among non-COVID Medicare beneficiaries?

OBJECTIVES: Highly visible hospital quality reporting stakeholders in the USA such as the US News & World Report (USNWR) and the Centers for Medicare & Medicaid Services (CMS) play an important health systems role via their transparent public reporting of hospital outcomes and performance. However, during the pandemic, many such quality measurement stakeholders and pay-for-performance programmes in the USA and Europe have eschewed the traditional risk adjustment paradigm, instead choosing to pre-emptively exclude months or years of pandemic era performance data due largely to hospitals' perceived COVID-19 burdens. These data exclusions may lead patients to draw misleading conclusions about where to seek care, while also masking genuine improvements or deteriorations in hospital quality that may have occurred during the pandemic. Here, we assessed to what extent hospitals' COVID-19 burdens (proportion of hospitalised patients with COVID-19) were associated with their non-COVID 30-day mortality rates from March through November 2020 to inform whether inclusion of pandemic-era data may still be appropriate. DESIGN: This was a retrospective cohort study using the 100% CMS Inpatient Standard Analytic File and Master Beneficiary Summary File to include all US Medicare inpatient encounters with admission dates from 1 April 2020 through 30 November 2020, excluding COVID-19 encounters. Using linear regression, we modelled the association between hospitals' COVID-19 proportions and observed/expected (O/E) ratios, testing whether the relationship was non-linear. We calculated alternative hospital O/E ratios after selective pandemic data exclusions mirroring the USNWR data exclusion methodology. SETTING AND PARTICIPANTS: We analysed 4 182 226 consecutive Medicare inpatient encounters from across 2601 US hospitals. RESULTS: The association between hospital COVID-19 proportion and non-COVID O/E 30-day mortality was statistically significant (p<0.0001), but weakly correlated (r2=0.06). The median (IQR) pairwise relative difference in hospital O/E ratios comparing the alternative analysis with the original analysis was +3.7% (-2.5%, +6.7%), with 1908/2571 (74.2%) of hospitals having relative differences within ±10%. CONCLUSIONS: For non-COVID patient outcomes such as mortality, evidence-based inclusion of pandemic-era data is methodologically plausible and must be explored rather than exclusion of months or years of relevant patient outcomes data.

Real-world performance of SARS-Cov-2 serology tests in the United States, 2020.

BACKGROUND: Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. METHODS: Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. RESULTS: A total of 15,615 people were observed to have at least one serology test 14-90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79-96%) compared to non-Hispanic (60-89%) patients; in those presenting with at least one COVID-19 related symptom (69-93%) as compared to no such symptoms (63-91%); and in inpatient (70-97%) and emergency department (93-99%) compared to outpatient (63-92%) settings across datasets. PPA was highest in those with diabetes (75-94%) and kidney disease (83-95%); and lowest in those with auto-immune conditions or who are immunocompromised (56-93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59-3.86), patients with diabetes (1.49-1.56), and obesity (1.63-2.23); and lower in those with immunocompromised or autoimmune conditions (0.25-0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. CONCLUSION: Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States.

BACKGROUND: As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. METHODS: Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. RESULTS: Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. CONCLUSION: Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Association of Neutralizing Antispike Monoclonal Antibody Treatment With Coronavirus Disease 2019 Hospitalization and Assessment of the Monoclonal Antibody Screening Score.

Objective: To test the hypothesis that the Monoclonal Antibody Screening Score performs consistently better in identifying the need for monoclonal antibody infusion throughout each "wave" of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant predominance during the coronavirus disease 2019 (COVID-19) pandemic and that the infusion of contemporary monoclonal antibody treatments is associated with a lower risk of hospitalization. Patients and Methods: In this retrospective cohort study, we evaluated the efficacy of monoclonal antibody treatment compared with that of no monoclonal antibody treatment in symptomatic adults who tested positive for SARS-CoV-2 regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients with a diagnosis of COVID-19 from November 19, 2020, through May 12, 2022. Results: Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (eg, relative risk, 0.15; 95% CI, 0.14-0.17). Conclusion: Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic.

A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic.

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

A Simple and Interpretable Mortality-Based Value Metric for Condition- or Procedure-Specific Hospital Performance Reporting.

Objective: To develop a simple, interpretable value metric (VM) to assess the value of care of hospitals for specific procedures or conditions by operationalizing the value equation: Value = Quality/Cost. Patients and Methods: The present study was conducted on a retrospective cohort from 2015 to 2018 drawn from the 100% US sample of Medicare inpatient claims. The final cohort comprised 637,341 consecutive inpatient encounters with a cancer-related Medicare Severity-Diagnosis Related Grouping and 13,307 consecutive inpatient encounters with the International Classification of Diseases, Ninth Revision or International Classification of Diseases, Tenth Revision procedure code for partial or total gastrectomy. Claims-based demographic and clinical variables were used for risk adjustment, including age, sex, year, dual eligibility, reason for Medicare entitlement, and binary indicators for each of the Elixhauser comorbidities used in the Elixhauser mortality index. Risk-adjusted 30-day mortality and risk-adjusted encounter-specific costs were combined to form the VM, which was calculated as follows: number needed to treat = 1/(Mortalitynational - Mortalityhospital), and VM = number needed to treat × risk-adjusted cost per encounter. Results: Among hospitals with better-than-average 30-day cancer mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient cancer encounter ranged from $71,000 (best value) to $1.4 billion (worst value), with a median value of $543,000. Among hospitals with better-than-average 30-day gastrectomy mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient gastrectomy encounter ranged from $710,000 (best value) to $95 million (worst value), with a median value of $1.8 million. Conclusion: This simple VM may have utility for interpretable reporting of hospitals' value of care for specific conditions or procedures. We found substantial inter- and intrahospital variation in value when defined as the costs of preventing 1 excess cancer or gastrectomy mortality compared with the national average, implying that hospitals with similar quality of care may differ widely in the value of that care.

State and Government Administrative Databases: Medicare, National Inpatient Sample (NIS), and State Inpatient Databases (SID) Programs.

The availability of large state and federally run administrative health-care databases provides potentially comprehensive population-wide information that can dramatically impact both medical and health-policy decision-making. Specific opportunities and important limitations exist with all administrative databases based on what information is collected and how reliably specific data elements are reported. Access to patient identifiable-level information can be critical for certain long-term outcome studies but can be difficult (although not impossible) due to patient privacy protections, while more easily available de-identified information can provide important insights that may be more than sufficient for some short-term operative or in-hospital outcome questions. The first section of this paper by Sarah K. Meier and Benjamin D. Pollock discusses Medicare and the different data files available to health-care researchers. They describe what is and is not generally available from even the most granular Medicare Standard Analytic Files, and provide an analysis of the strengths and weaknesses of Medicare administrative data as well as the resulting best and inappropriate uses of these data. In the second section, the Nationwide Inpatient Sample and complementary State Inpatient Database programs are reviewed by Steven M. Kurtz and Edmund Lau, with insights into the origins of these programs, the data elements that are recorded relating to the operative procedure and hospital stay, and examples of the types of studies that optimally utilize these data sources. They also detail the limitations of these databases and identify studies that they are not well-suited for, especially those involving linkage or longitudinal studies over time. Both sections provide useful guidance on the best uses and pitfalls related to these important large representative national administrative data sources.

A Learning Health System Approach to Hospital Quality Performance Benchmarking: The Composite Hospital Quality Index.

US hospital quality rankings and ratings use disparate methodologies and are weakly correlated. This causes confusion for patients and hospital quality staff. At the authors' institution, a Composite Hospital Quality Index (CHQI) was developed to combine hospital quality ratings. This approach is described and a calculator is shared here for other health systems to explore their performance. Among the US News and World Report Top 50 Hospitals, hospital-specific numeric summary scores were aggregated from the 2021 Centers for Medicare and Medicaid Services (CMS) Hospital Overall Star Rating, the Spring 2021 Leapfrog Safety Grade, and the April 2021 Hospital Consumer Assessment of Healthcare Providers and Systems Star Rating. The CHQI is the hospital-specific sum of the national percentile-rankings across these 3 ratings. In this example, mean (SD) percentiles were as follows: CMS Stars 74 (19), Hospital Consumer Assessment of Healthcare Providers and Systems 63 (19), Leapfrog 65 (24), with mean (SD) CHQI of 202 (49). The CHQI is used at the authors' institution to identify improvement opportunities and ensure that high-quality care is delivered across the health system.

The association of acute COVID-19 infection with Patient Safety Indicator-12 events in a multisite healthcare system.

BACKGROUND: Patient Safety Indicator (PSI)-12, a hospital quality measure designed by Agency for Healthcare Research and Quality (AHRQ) to capture potentially preventable adverse events, captures perioperative venous thromboembolism (VTE). It is unclear how COVID-19 has affected PSI-12 performance. OBJECTIVE: We sought to compare the cumulative incidence of PSI-12 in patients with and without acute COVID-19 infection. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study including PSI-12-eligible events at three Mayo Clinic medical centers (4/1/2020-10/5/2021). EXPOSURE, MAIN OUTCOMES, AND MEASURES: We compared the unadjusted rate and adjusted risk ratio (aRR) for PSI-12 events among patients with and without COVID-19 infection using Fisher's exact χ2  test and the AHRQ risk-adjustment software, respectively. We summarized the clinical outcomes of COVID-19 patients with a PSI-12 event. RESULTS: Our cohort included 50,400 consecutive hospitalizations. Rates of PSI-12 events were significantly higher among patients with acute COVID-19 infection (8/257 [3.11%; 95% confidence interval {CI}, 1.35%-6.04%]) compared to patients without COVID-19 (210/50,143 [0.42%; 95% CI, 0.36%-0.48%]) with a PSI-12 event during the encounter (p < .001). The risk-adjusted rate of PSI-12 was significantly higher in patients with acute COVID-19 infection (1.50% vs. 0.38%; aRR, 3.90; 95% CI, 2.12-7.17; p < .001). All COVID-19 patients with PSI-12 events had severe disease and 4 died. The most common procedure was tracheostomy (75%); the mean (SD) days from surgical procedure to VTE were 0.12 (7.32) days. CONCLUSION: Patients with acute COVID-19 infection are at higher risk for PSI-12. The present definition of PSI-12 does not account for COVID-19. This may impact hospitals' quality performance if COVID-19 infection is not accounted for by exclusion or risk adjustment.

Clinical and laboratory profiles of the SARS-CoV-2 Delta variant compared with pre-Delta variants.

OBJECTIVES: The emergence of SARS-CoV-2 variants of concern has led to significant phenotypical changes in transmissibility, virulence, and public health measures. Our study used clinical data to compare characteristics between a Delta variant wave and a pre-Delta variant wave of hospitalized patients. METHODS: This single-center retrospective study defined a wave as an increasing number of COVID-19 hospitalizations, which peaked and later decreased. Data from the United States Department of Health and Human Services were used to identify the waves' primary variant. Wave 1 (August 8, 2020-April 1, 2021) was characterized by heterogeneous variants, whereas Wave 2 (June 26, 2021-October 18, 2021) was predominantly the Delta variant. Descriptive statistics, regression techniques, and machine learning approaches supported the comparisons between waves. RESULTS: From the cohort (N = 1318), Wave 2 patients (n = 665) were more likely to be younger, have fewer comorbidities, require more care in the intensive care unit, and show an inflammatory profile with higher C-reactive protein, lactate dehydrogenase, ferritin, fibrinogen, prothrombin time, activated thromboplastin time, and international normalized ratio compared with Wave 1 patients (n = 653). The gradient boosting model showed an area under the receiver operating characteristic curve of 0.854 (sensitivity 86.4%; specificity 61.5%; positive predictive value 73.8%; negative predictive value 78.3%). CONCLUSION: Clinical and laboratory characteristics can be used to estimate the COVID-19 variant regardless of genomic testing availability. This finding has implications for variant-driven treatment protocols and further research.

Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study.

OBJECTIVE: We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. DESIGN: We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. INTERVENTIONS: Issuance of stay-at-home orders. PRIMARY AND SECONDARY OUTCOME MEASURES: Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. RESULTS: Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. CONCLUSION: Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.