Pollock, Benjamin D

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.

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.

AbstractBackgroundPatient 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.ObjectiveWe sought to compare the cumulative incidence of PSI‐12 in patients with and without acute COVID‐19 infection.Design, Setting, and ParticipantsThis 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 MeasuresWe 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.ResultsOur 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.ConclusionPatients 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.

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.

Abstract We followed 106 349 primary care patients for 22 385 3099 person-days across 21 calendar months and documented 69 breakthrough coronavirus disease 2019 (COVID-19) hospitalizations: 65/102,613 (0.06%) among those fully vaccinated, 3/11 047 (0.03%) among those previously infected, and 1/7,313 (0.01%) among those with both statuses. These data give providers real-world context regarding breakthrough COVID-19 hospitalization risk.

AbstractBackgroundWhile COVID‐19 immunization programs attempted to reach targeted rates, cases rose significantly since the emergence of the delta variant. This retrospective cohort study describes the correlation between antispike antibodies and outcomes of hospitalized, breakthrough cases during the delta variant surge.MethodsAll patients with positive SARS‐CoV‐2 polymerase chain reaction hospitalized at Mayo Clinic Florida from 19 June 2021 to 11 November 2021 were considered for analysis. Cases were analyzed by vaccination status. Breakthrough cases were then analyzed by low and high antibody titers against SARS‐CoV‐2 spike protein, with a cut‐off value of ≥132 U/ml. Outcomes included hospital length of stay (LOS), need for intensive care unit (ICU), mechanical ventilation, and mortality. We used 1:1 nearest neighbor propensity score matching without replacement to assess for confounders.ResultsAmong 627 hospitalized patients with COVID‐19, vaccine breakthrough cases were older with more comorbidities compared to unvaccinated. After propensity score matching, the unvaccinated patients had higher mortality (27 [28.4%] vs. 12 [12.6%], p = 0.002) and LOS (7 [1.0–57.0] vs. 5 [1.0–31.0] days, p = 0.011). In breakthrough cases, low‐titer patients were more likely to be solid organ transplant recipients (16 [34.0%] vs. 9 [12.3%], p = 0.006), with higher need for ICU care (24 [51.1%] vs. 22 [11.0%], p = 0.034), longer hospital LOS (median 6 vs. 5 days, p = 0.013), and higher mortality (10 [21.3%] vs. 5 [6.8%], p = 0.025) than high‐titer patients.ConclusionsHospitalized breakthrough cases were more likely to have underlying risk factors than unvaccinated patients. Low‐spike antibody titers may serve as an indicator for poor prognosis in breakthrough cases admitted to the hospital.

Abstract We characterized coronavirus disease 2019 (COVID-19) breakthrough cases admitted to a single center in Florida. With the emergence of delta variant, an increased number of hospitalizations was seen due to breakthrough infections. These patients were older and more likely to have comorbidities. Preventive measures should be maintained even after vaccination.

Abstract Background Several vaccines are now available under emergency use authorization in the United States and have demonstrated efficacy against symptomatic COVID-19. Vaccine impact on asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is largely unknown. Methods We conducted a retrospective cohort study of consecutive, asymptomatic adult patients (n = 39 156) within a large US healthcare system who underwent 48 333 preprocedural SARS-CoV-2 molecular screening tests between 17 December 2020 and 8 February 2021. The primary exposure of interest was vaccination with ≥1 dose of an mRNA COVID-19 vaccine. The primary outcome was relative risk (RR) of a positive SARS-CoV-2 molecular test among those asymptomatic persons who had received ≥1 dose of vaccine compared with persons who had not received vaccine during the same time period. RR was adjusted for age, sex, race/ethnicity, patient residence relative to the hospital (local vs nonlocal), healthcare system regions, and repeated screenings among patients using mixed-effects log-binomial regression. Results Positive molecular tests in asymptomatic individuals were reported in 42 (1.4%) of 3006 tests and 1436 (3.2%) of 45 327 tests performed on vaccinated and unvaccinated patients, respectively (RR, .44; 95% CI, .33–.60; P &lt; .0001). Compared with unvaccinated patients, risk of asymptomatic SARS-CoV-2 infection was lower among those &gt;10 days after the first dose (RR, .21; 95% CI, .12–.37; P &lt; .0001) and &gt;0 days after the second dose (RR, .20; 95% CI, .09–.44; P &lt; .0001) in the adjusted analysis. Conclusions COVID-19 vaccination with an mRNA-based vaccine showed a significant association with reduced risk of asymptomatic SARS-CoV-2 infection as measured during preprocedural molecular screening. Results of this study demonstrate the impact of the vaccines on reduction in asymptomatic infections supplementing the randomized trial results on symptomatic patients.