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The increased threat of in-hospital mortality in patients without COVID-19 involves COVID-19 outbreaks of compromised quality of care. No large-scale adjustments were observed in releases to other facilities.
SUMMARY
Objectives: To read about the effect of COVID-19 outbreaks on hospital outcomes, i. e. in patients without COVID-19.
Studio design: an interrupted time design.
Methods: Using information from a large insurance claims clearing house, the study estimates the impact of the onset of the pandemic and the proportion of hospitalized COVID-19 patients on the likelihood of (1) death in the hospital, (2) death in the hospital or discharge to hospice, (3) discharge to other hospitals, (4) discharge to skilled nursing services (NFS), and (5) discharge to home care.
Results: The threat of death in the hospital was approximately 1. 7 times higher than before the onset of the pandemic in all patients and 1. 2 times higher than in patients without COVID-19. A higher percentage of COVID-19 patients was associated with an increased likelihood of in-hospital death among all patients and non-COVID-19 patients. The effects were most pronounced in patients older than forty-five and those with sepsis or pneumonia, and months when COVID-19 cases were highest were also more potent. While no significant changes were observed in the chances of discharge to other hospitals or NFCs, transfers to home care have increased the pandemic.
Conclusions: The negative effect of the pandemic on mortality among patients without COVID-19 confirms existing considerations about patient care. There is no evidence to recommend large-scale adjustments in practices related to discharge/transfer to other facilities. The findings highlight long-term efforts to monitor and improve hospital care as the pandemic evolves.
Suis J Managing Care. 2022;28(11):e399-e404. https://doi. org/10. 37765/ajmc. 2022. 89264
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Takeaway points
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The quality of hospital care is a growing fear during the pandemic, as hospitals have been affected by the influx of COVID-19 patients and staff shortages. However, monitoring quality of care during the pandemic has proved challenging. 1 Evidence from the pandemic has an effect on hospital care, i. e. the regime’s hospital care outcomes in patients who do not have COVID-19, remains limited.
Most existing studies of hospital outcomes in the U. S. UU. la pandemic focuses on COVID-19 care. 2-8 Some studies that included patients who did not have COVID-19 tested trends in death rates. in hospital mortality from non-COVID-19 admissions in April 2020. 7 The rate then returned to the previous point in May 2020. A more recent study, which exclusively evaluated patients without COVID-19,8 found a similar trend in mortality rate for the first months of the pandemic and noted that the rate increased from October to December 2020. Both studies mean a corresponding dating between death rates and pandemic outbreaks.
This study builds on the existing literature and further explores the correspondence between pandemic outbreaks and hospital outcomes. To measure the impact of the pandemic, the study begins with an indicator of the appearance of COVID-19, then introduces a measure of the hospital percentage of patients with COVID-19. The latter is used to quantify the “dose effect” of COVID-19 outbreaks, reflected through the admission of COVID-19 patients to hospitals. The study also looks at a broader range of hospital outcomes. In addition to hospital mortality, as used in existing studies,2-8 this review assesses outcomes: discharge or transfer to other hospitals, skilled nursing facilities (SNFs), or home careArray Given the limited capacity and resources that In many hospitals, discharging patients or transferring them to other less crowded services are more likely to be used as methods to balance patient flows. One example is NYC Health + Hospitals, the nation’s largest public health care formula, which moved patients between its formula services to lessen the burden on defeated hospitals and potentially improve patient outcomes. 9 It’s unclear to what extent methods like these have been used across the country and how hospital outcomes have been affected.
This study looked at all-cause hospitalizations in 2019 and 2020. Insurance claims data from a large claims clearinghouse. With a discontinued time series design, it tested hospital outcomes on all patients and patients who did not have COVID-19 as responses to the onset of the pandemic. and adjustments in the hospital percentage of COVID-19 patients.
METHODS
Data
Data from a giant insurance claims clearinghouse was accessed through the COVID-19 Research Database, a pro bono cross-industry collaboration. 10 Inpatient claims were pulled from institutional claims based on the type of billing codes (011x-012x). Admission dates were limited to a 2-year era from January 1, 2019 to December 31, 2020. Research was conducted at the admission level. Each admission is a unique mix of patient, hospital, and admission date. If the same patient was admitted to the same hospital on another date, it was considered another admission. When claims were related to an admission, the data was compressed into a single record for that admission. Patients in the dataset were highlighted according to Soundex for first and last name, date of birth, and gender. Hospitals have been highlighted through the National Provider Identifier. The review excluded admissions from hospitals that filed fewer than 100 claims in 2019 or 2020. This is to lessen the effect of in-and-out users, or abnormal average usage sharing for claims filing, so that hospital admissions included in the review come from a strong set of hospitals. This resulted in a total of more than 570 hospitals with a total of more than one million admissions in 2019 and 2020.
The study also included county-level demographic data and economic signs drawn from U. S. census data. UU. de 2010. County-level features related to admissions based on hospital zip codes received from CMS national plan data and provider directory system. A record of crosswalks between the zip code and county of the Dartmouth Atlas Data online page used to facilitate linking.
The measures
Hospital study outcomes come with in-hospital mortality and prestige of discharge. For mortality, we consider (1) death in the hospital and (2) death in the hospital or discharge from hospice. The timing measure aggregates the final results of discharge from palliative care and takes into account patients who are near the end of life. With death in hospital, the combined measure can provide a more complete picture of the prestige of worst-case discharge. 6,11-13 Examining possible adjustments in discharge practices, such as increased use of point load to balance patient flow, the examination was considered as 3 types of provisions: discharge/transfers to hospitals (general acute hospitals), NFC and home care (specialized care provided through home fitness service organizations organized in patients’ homes). ). All designs were known for coming out codes of prestige.
To read about the effect of the pandemic, the study uses a pandemic onset indicator, which is as of March 1, 2020, as well as a percentage of COVID-19 patients at the hospital-month level, explained as the percentage of similar COVID-19 admissions (with International Classification of Diseases codes U071 or U072, 10th revision, clinical amendment [ICD-10-CM] as primary, secondary or tertiary diagnoses) among all hospital admissions in a given month.
The review also controlled for patient composition, hospitalization characteristics, and socioeconomic characteristics at the county level. Specifically, the covariates come with the patient’s age at admission, gender, comorbidities, whether admission was similar to COVID-19, whether the patient was transferred from some other hospital or NFS, time of admission (after hours, weekends or normal hours), whether it was an urgent/urgent admission, racial/ethnic composition of the county’s population (percentage of Americans who were non-Hispanic black, Hispanic and other races/ethnicities), percentage of rural population in the county, county unemployment rate, and median household income. Patients’ comorbidities were measured through the Elixhauser comorbidity index14 (with separate signs for 31 comorbidities), the ICD-10-CM codes of all known diagnoses County-level characteristics were based on hospital zip codes.
Analytical approach
The study adopts an interrupted time series design and examines changes in discharge outcomes using inpatient claims data from 2019-2020. In the following logit model, logit(Pijkt) represents the logarithm of the probability of a specific end result (eg, death in hospital, death in hospital or hospice discharge, hospital discharge, SNF discharge, or hospital discharge). home care) for patient i, at hospital j in state k, at month t. Xijkt is a vector of covariates, which aggregates patient mix, admission characteristics, and demographic and economic points at the county level, as shown above. Our variable of interest is COVIDt. In some specifications, it represents the COVID-19 outbreak indicator, which is equal to 1 for March 2020 and beyond. In election specifications, it represents the percentage of patients with COVID-19 at the hospital level. The state constant effect δk was included for unobservable and time-invariant state-specific features. Constant month and year effects λt were included for conversion of points each month (January,. . . , December) or year (2019 and 2020) that were not unusual for all patient admissions. The error term εijkt was pooled at the hospital level. Regressions were performed separately among all patients and among patients without COVID-19.
logit(Pijkt) = α Xijktβ γCOVIDt δk λt εijkt
To explore prospective heterogeneous effects of the pandemic, mortality outcomes among other age teams (< 1, 1-17, 18-44, 45-64, 65-84, and ≥ 85), team months (January -February, March-April, May-June, July-August, September-October and November-December) and certain non-unusual diagnoses (sepsis, central failure, pneumonia [except that caused by tuberculosis], acute myocardial infarction [AMI] , and unspecified renal failure and cerebral infarction). We used death in hospital or hospice discharge as the measure and focused on the effect in patients without COVID-19. Within the organization of the month, admissions that occurred in the corresponding 2 months of 2019 and 2020 were included. The indicator COVIDt, in this case, is superimposed on the constant effect of 2020 in regressions involving monthly teams from March to April, May to June, and so on through November to December. The estimated constant effect of 2020 is reported for the organization regressions of all months, accounting for the differences between the 2 years. Decided non-unusual diagnoses were among the top number one non-unusual diagnoses for hospital remains (excluding maternal/neonatal remains) based on the 2018 National Inpatient Sample (NIS) from the Cost and Utilization of Care Project (HCUP)15 and with the maximum number of deaths in our data. Diagnoses were categorized using the ICD-10-CM Refined Clinical Classification Software default categorization formula for diagnosis number one. 16
All analyses were performed in the Stata edition (StataCorp).
RESULTS
Descriptive statistics are presented by year of admission in Table 1. The percentage of deaths in the hospital building increased from 1. 7% in 2019 to 2. 7% in 2020. When hospice discharge was added, the combined mortality measure showed an increase from 3. 1% in 2019 to 2. 7% in 2020. 4. 3% in 2020. The frequency of discharges to some other hospital appears to have replaced little from 2019 to 2020 (1. 7% to 1. 6%). The percentage of patients discharged to a SNF fell from 9. 2% in 2019 to 7. 7% in 2020. However, the rate of discharge to home care fell from 10. 2% in 2019 to 11. 8% in 2020. Approximately 81% of all admissions in 2020 occurred after March 2020 (during the pandemic). The average percentage of patients with COVID-19 in the hospital was 5. 5% in 2020. Patient admission and pooled characteristics showed little difference over the 2 years. Median age, percentages of admissions by gender or age group, and mean Elixhauser score in 2019 were similar to 2020. In 2020, 5. 5% of all admissions were COVID-19-like; 6. 6% of all patients were referred from other hospitals (7. 2% in 2019) and 1. 9% were referred from a SNF (1. 5% in 2019). The percentages of after-hours admissions, weekend admissions, and urgent/urgent admissions were similar over the 2 years. County point characteristics were also similar across the 2 years.
Mortality outcomes
The estimated effects of the pandemic on mortality outcomes are shown in Table 2. The pandemic was relevant with higher mortality threats. Among all patients, the risk of dying in hospital was about 1. 7 times higher than before the start of the pandemic (top panel). For deaths at hospital or hospice discharge (lower panel), the odds increase to 1. 6 times what they were before the pandemic. Excluding COVID-19 patients, the odds of dying in hospital and the odds of dying in hospital or being discharged from hospice were even higher than the pandemic (1. 2 and 1. 3 times the previous ones). the pandemic, respectively). When the percentage of admissions similar to COVID-19 was used, the estimates also showed a particularly positive relationship. With a 1 percentage point increase in the percentage of COVID-19-like admissions, the threat of dying in hospital would be 1. 04 times higher than before the pandemic among all patients and 1. 02 times higher than among patients without COVID-19 (top panel). If the percentage of admissions for COVID-19 increased by five percentage points, the threat of dying in hospital would be 1. 22 (= 1. 04 five) times the pre-pandemic threat for all patients and 1. 10 (= 1. 02 five) that of patients without COVID -19 patients. Estimates were similar when in-hospital death or hospital discharge were used.
Disposition on departure
The pandemic has shown other effects on all 3 types of provisions (Table 3). The odds of discharge to another hospital were not significantly replaced. There has been a decreased chance of discharge to SNF and an increasing chance of discharge to home care. after the pandemic. When the percentage of COVID-19 patients was used, estimates showed consistent effects on transfers to the SNF. the pandemic in all patients, but there is no significant replacement in patients who do not have COVID-19.
Heterogeneous effects
Table four shows the estimated effects of the pandemic on mortality among other age groups (upper panel), months of admission (central panel) and some non-unusual diagnoses (lower panel). We use death in the hospital or discharge from hospice as a measure of mortality and targeted to patients without COVID-19.
Significant increases in the dangers of death in hospital or hospice discharge were observed among the 3 oldest teams of non-COVID-19 patients (aged forty-five to 64, 65 to 84 and ≥ 85 years), with the increase among patients older than 85 years (the odds were 1. 4 times higher than before the pandemic).
When admissions were pooled by month, there was no significant difference between the chances of death in hospital or discharge from hospice in January and February 2020 and those in 2019. Across all other months of teams, being admitted in 2020 (or COVID-19) was linked to an increased threat of death in hospital or hospice discharge in patients without COVID-19. Probability indices were higher in March-April, July-August and November-December than in May-June and September-October. the weather largely coincided with peaks and falls in COVID-19 cases. 17
Among the 6 non-unusual diagnoses selected, non-COVID-19 patients with sepsis and pneumonia (except those caused by tuberculosis) as number one diagnoses experienced higher mortality during the pandemic than in 2019. No significant adjustments were found in the center’s death rate. acute renal failure, AMI, acute and unspecified renal failure or cerebral infarction.
DISCUSSION
The COVID-19 pandemic has created unprecedented demands for hospitals and fitness systems. While central attention has been given to the care and outcomes of patients with COVID-19, it is also imperative to monitor the quality of the hospital care regimen and discharge outcomes of patients who do not have COVID-19. This study uses data from insurance claims and quantifies changes in mortality and discharge disposition in all patients and those without COVID-19. Before the pandemic, the overall hospital mortality rate for all hospital remains was at or below 2. 0% between 2009 and 2019. 18,19 With this strong statement, the increase in mortality was obviously due to the start of the pandemic. The effects raise fears that hospital outcomes have been compromised by the pandemic, after adjusting for patient mix, admission characteristics, and sociodemographic characteristics at the county level. The pandemic has increased the dangers of hospital death (or hospital death and hospice discharge) not only in patients with COVID-19, but also in patients who do not have COVID-19. As the percentage of COVID-19 patients in a hospital building increases, the dangers of death in the hospital increase for all patients, as well as for patients who do not have COVID-19. The effects were more pronounced in patients over the age of forty-five and were also stronger in months when COVID-19 cases accumulated. The location of increased mortality in non-COVID-19 patients with sepsis and pneumonia in the pandemic is broadly consistent with evidence from the United States and other countries. with cardiac acute renal failure, AMI, acute and unspecified renal failure, or cerebral infarction, which is consistent with the effects of some studies,23,24 there is also evidence to recommend cumulative mortalityd20,22 or time-varying mortality all these conditions8, 21.
The reasons for compromised hospital outcomes in patients without COVID-19 during the pandemic are multiple. Patients hospitalized during the pandemic would possibly be sicker than those in the pre-pandemic era due to the delay or abandonment of care during the initial phase of the pandemic25,26. patient care. In this review sample, patients without COVID-19 were younger, with slightly lower Elixhauser scores, and a lower percentage of urgent/urgent admissions (data not shown) during the pandemic than during the pre-pandemic era, which is not shows symptoms of a sicker patient population during the pandemic. The study also incorporated data on HHS hospital capacity and tested its relationship to death rates. Among a subset of hospitals that matched HHS hospital capacity knowledge, intensive care unit (ICU) bed occupancy was particularly associated with mortality (result not shown), supporting pressure on resources as the dominant mechanism for increased mortality in patients without COVID-19. Such an arrangement is also consistent with existing studies, which have found that extensive care bed use is a vital indicator of hospital strain and a strong predictor of higher mortality and worse fitness outcomes. array28-30
The effects of discharge design reflect the extent to which hospitals may have leveled the need for load to balance patient flow. This study found that hospital-to-hospital transfers were no more common during the pandemic. Departures to NFC have decreased, likely due to the already higher incidence of COVID-19 in retirement homes. Only the odds of referrals to home care have increased slightly since the beginning of the pandemic. Overall, there were no significant adjustments in the likelihood of transfers, so there is no evidence that hospitals inflated calls on a giant scale. Compared to NFCs and other hospital facilities, home care appears to be a promising framework of choice for pandemic care. The effects reflect an informed shift in NFN preference for home care among pandemic patients and providers. 31
Boundaries
This study has limitations. First, knowledge of the claims knowledge base would likely not be nationally representative. Therefore, we compared patient characteristics, patient volume stocks per month, and hospital mortalities in our 2019 knowledge with those from the HCUP’s 2018 NIS. The result shows that the patient and hospitalization characteristics to our knowledge closely resemble those of the NIS pattern (eAppendix Table [available at ajmc. com]). Stock volumes per month and hospital mortality rate were also similar across the 2 knowledge sets. Second, some patient characteristics, such as race/ethnicity and payer type, were not to be known. Therefore, similar analyzes for racial/ethnic teams and insurance prestige were not possible. Finally, the study did not read about some choice measures of hospital endpoints, such as length of stay, 30-day mortality, and readmission rates, due to lack of knowledge or knowledge limitations. Since such measures would likely also respond to changes in hospital practice during the pandemic, long-term studies to assess endpoint measures such as these are warranted.
CONCLUSION
The study is one of the first to quantify the effect of COVID-19 outbreaks on hospital discharge outcomes, specifically in patients without COVID-19. The increased risk of mortality among patients without COVID-19 means that the quality of care was compromised as hospitals battled COVID-19. While no significant change was seen in the likelihood of discharging from other hospitals, the expansion of discharges to home care shows the promise of home physical care as a care option of choice during the pandemic. As it evolves, timely feedback on patient outcomes is critical to helping providers maintain and improve the quality of care and be informed for the future.
thank youu
Thanks to the anonymous reviewers for their constructive feedback. The data, generation, and facilities used to generate the effects of those studies were generously provided free of charge through COVID-19 studies database partners, identified in https://covid19studiesdatabase. org/.
Author affiliation: Department of Economics, Finance and Quantitative Analysis, Kennesaw State University, Kennesaw, GA.
Source of funding: None.
Author Disclosures: Does not disclose any date or monetary interest with any entity that may raise a conflict of interest with the subject matter of this article.
Author information: concept and design; Knowledge acquisition; Data research and interpretation; write the manuscript; critical review of the manuscript for intellectual content; and statistical research.
Correspondence to: Weiwei Chen, PhD, Department of Economics, Finance and Quantitative Analysis, Kennesaw State University, 560 Parliament Garden Way, BB 360, Kennesaw, GA 30144. Email: wchen30@kennesaw. edu.
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