Patient adverse financial outcomes before and after COVID-19 infection.

Adverse financial outcomes after COVID-19 infection and hospitalization have not been assessed with appropriate comparators to account for other financial disruptions of 2020-2021. Using credit report data from 132,109 commercially insured COVID-19 survivors, we compared the rates of adverse financial outcomes for two cohorts of individuals with credit outcomes measured before and after COVID-19 infection, using an interaction term between cohort and hospitalization to test whether adverse credit outcomes changed more for hospitalized than nonhospitalized COVID-19 patients. Covariates included age group, gender, and several area-level social determinants of health. Adverse financial outcomes were significantly more common after COVID-19 infection than before COVID-19 infection, with greater increases among those hospitalized with COVID-19 (5-8 percentage points) than among nonhospitalized patients (1-3 percentage points). Future work examining longitudinal financial outcomes before and after COVID-19 infection is needed to determine the causal mechanisms of this association to reduce financial hardship from COVID-19 and other conditions.

Utilization of Women's Preventive Health Services During the COVID-19 Pandemic.

Importance: The association of the COVID-19 pandemic with women's preventive health care use is unknown. Objective: To describe utilization of women's preventive health services. Design Setting and Participants: Cross-sectional study of women aged 18 to 74 years enrolled in a commercial health maintenance organization in Michigan. Exposures: COVID-19 pandemic (2019-2020). Main Outcomes and Measures: Adjusted odds ratios (AORs) of receiving breast cancer screening, cervical cancer screening, sexually transmitted infection (STI) screening, long-acting reversible contraception (LARC) insertions, and pharmacy-obtained contraception, adjusted for month, age, county, zip code characteristics (per-capita income, non-White percentage of population, non-English-proficient percentage of population), and plan designation (primary plan holder vs dependent). Results: The study population included 685 373 women aged 18 to 74 years, enrolled for 13 000 715 person-months, of whom 10 061 275 person-months (77.4%) were among women aged 25 to 64 years and 8 020 215 (61.7%) were the primary plan holder, with mean zip code per capita income of $33 708, 20.2% mean zip code non-White population, and 3.4% mean zip code non-English-speaking population. For services requiring an in-person visit (breast cancer screening, cervical cancer screening, STI testing, and LARC insertions), utilization declined by 60% to 90% during the spring of 2020, with a nadir in April 2020, after which utilization for all services recovered to close to 2019 levels by July 2020. Claims for pharmacy-obtained hormonal contraceptives in 2020 were consistently 15% to 30% lower than 2019. The AORs of a woman receiving a given preventive service in 2020 compared with 2019 were significantly lower for breast cancer screening (AOR, 0.80; 95% CI, 0.79-0.80), cervical cancer screening (AOR, 0.80; 95% CI, 0.80-0.81), STI screening (AOR, 0.83; 95% CI, 0.82-0.84), LARC insertion (AOR, 0.87; 95% CI, 0.84-0.90), and pharmacy-obtained contraception (AOR, 0.73; 95% CI, 0.72-0.74) (all P < .001). Conclusions and Relevance: In this cross-sectional study of women enrolled in a large US commercial health maintenance organization plan, the COVID-19 pandemic was associated with large but transient declines in rates of breast cancer screening, cervical cancer screening, STI screening, and LARC insertions, and moderate persistent declines in pharmacy-obtained hormonal contraceptives. The overall odds of a woman receiving a given preventive service in 2020 was 20% to 30% lower than 2019. Further research into disparities in access to care and the health outcomes of decreased use of these key health services is warranted.

Trends in Hospitalizations for Ambulatory Care-Sensitive Conditions During the COVID-19 Pandemic.

Importance: The association of the COVID-19 pandemic with the quality of ambulatory care is unknown. Hospitalizations for ambulatory care-sensitive conditions (ACSCs) are a well-studied measure of the quality of ambulatory care; however, they may also be associated with other patient-level and system-level factors. Objective: To describe trends in hospital admissions for ACSCs in the prepandemic period (March 2019 to February 2020) compared with the pandemic period (March 2020 to February 2021). Design, Setting, and Participants: This cross-sectional study of adults enrolled in a commercial health maintenance organization in Michigan included 1 240 409 unique adults (13 011 176 person-months) in the prepandemic period and 1 206 361 unique adults (12 759 675 person-months) in the pandemic period. Exposure: COVID-19 pandemic (March 2020 to February 2021). Main Outcomes and Measures: Adjusted relative risk (aRR) of ACSC hospitalizations and intensive care unit stays for ACSC hospitalizations and adjusted incidence rate ratio of the length of stay of ACSC hospitalizations in the prepandemic (March 2019 to February 2020) vs pandemic (March 2020 to February 2021) periods, adjusted for patient age, sex, calendar month of admission, and county of residence. Results: The study population included 1 240 409 unique adults (13 011 176 person-months) in the prepandemic period and 1 206 361 unique adults (12 759 675 person-months) in the pandemic period, in which 51.3% of person-months (n = 6 547 231) were for female patients, with a relatively even age distribution between the ages of 24 and 64 years. The relative risk of having any ACSC hospitalization in the pandemic period compared with the prepandemic period was 0.72 (95% CI, 0.69-0.76; P < .001). This decrease in risk was slightly larger in magnitude than the overall reduction in non-ACSC, non-COVID-19 hospitalization rates (aRR, 0.82; 95% CI, 0.81-0.83; P < .001). Large reductions were found in the relative risk of respiratory-related ACSC hospitalizations (aRR, 0.54; 95% CI, 0.50-0.58; P < .001), with non-statistically significant reductions in diabetes-related ACSCs (aRR, 0.91; 95% CI, 0.83-1.00; P = .05) and a statistically significant reduction in all other ACSC hospitalizations (aRR, 0.79; 95% CI, 0.74-0.85; P < .001). Among ACSC hospitalizations, no change was found in the percentage that included an intensive care unit stay (aRR, 0.99; 95% CI, 0.94-1.04; P = .64), and no change was found in the length of stay (adjusted incidence rate ratio, 1.02; 95% CI, 0.98-1.06; P = .33). Conclusions and Relevance: In this cross-sectional study of adults enrolled in a large commercial health maintenance organization plan, the COVID-19 pandemic was associated with reductions in both non-ACSC and ACSC hospitalizations, with particularly large reductions seen in respiratory-related ACSCs. These reductions were likely due to many patient-level and health system-level factors associated with hospitalization rates. Further research into the causes and long-term outcomes associated with these reductions in ACSC admissions is needed to understand how the pandemic has affected the delivery of ambulatory and hospital care in the US.

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.

OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing. DESIGN: Retrospective cohort study. SETTING: One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21. PARTICIPANTS: 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME MEASURES: An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early. RESULTS: 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge. CONCLUSION: A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19.

Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53-75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19.