Healthcare utilization 9 months pre- and post- COVID-19 hospitalization among patients discharged alive

Mohammed Zaidan; Daniel Puebla Neira; Efstathia Polychronopoulou; Kuo Yong-Fang; Gulshan Sharma

doi:10.1371/journal.pone.0303509

. 2024 Jun 20;19(6):e0303509. doi: 10.1371/journal.pone.0303509

Healthcare utilization 9 months pre- and post- COVID-19 hospitalization among patients discharged alive

Mohammed Zaidan ^1,^‡,^*, Daniel Puebla Neira ^2,^‡, Efstathia Polychronopoulou ³, Kuo Yong-Fang ^3,⁴, Gulshan Sharma ¹

Editor: Robert Jeenchen Chen⁵

PMCID: PMC11189225 PMID: 38900737

Abstract

Background

Emerging evidence suggests that there is an increase in healthcare utilization (HCU) in patients due to Coronavirus Disease 2019 (COVID-19). We investigated the change in HCU pre and post hospitalization among patients discharged home from COVID-19 hospitalization for up to 9 months of follow up.

Study design and methods

This retrospective study from a United States cohort used Optum® de-identified Clinformatics Data Mart; it included adults discharged home post hospitalization with primary diagnosis of COVID-19 between April 2020 and March 2021. We evaluated HCU of patients 9 months pre and post -discharge from index hospitalization. We defined HCU as emergency department (ED), inpatient, outpatient (office), rehabilitation/skilled nursing facility (SNF), telemedicine visits, and length of stay, expressed as number of visits per 10,000 person-days.

Results

We identified 63,161 patients discharged home after COVID-19 hospitalization. The cohort of patients was mostly white (58.8%) and women (53.7%), with mean age 72.4 (SD± 12) years. These patients were significantly more likely to have increased HCU in the 9 months post hospitalization compared to the 9 months prior. Patients had a 47%, 67%, 65%, and 51% increased risk of ED (rate ratio 1.47; 95% CI 1.45–1.49; p < .0001), rehabilitation (rate ratio 1.67; 95% CI 1.61–1.73; p < .0001), office (rate ratio1.65; 95% CI 1.64–1.65; p < .0001), and telemedicine visits (rate ratio 1.5; 95% CI 1.48–1.54; p < .0001), respectively. We also found significantly different rates of HCU for women compared to men (women have higher risk of ED, rehabilitation, and telemedicine visits but a lower risk of inpatient visits, length of stay, and office visits than men) and for patients who received care in the intensive care unit (ICU) vs those who did not (ICU patients had increased risk of ED, inpatient, office, and telemedicine visits and longer length of stay but a lower risk of rehabilitation visits). Outpatient (office) visits were the highest healthcare service utilized post discharge (64.5% increase). Finally, the risk of having an outpatient visit to any of the specialties studied significantly increased post discharge. Interestingly, the risk of requiring a visit to pulmonary medicine was the highest amongst the specialties studied (rate ratio 3.35, 95% CI 3.26–3.45, p < .0001).

Conclusion

HCU was higher after index hospitalization compared to 9 months prior among patients discharged home post-COVID-19 hospitalization. The increases in HCU may be driven by those patients who received care in the ICU.

Introduction

Most patients are discharged alive from hospitalization due to Coronavirus Disease 2019 (COVID-19). With over 6 million COVID-19 hospitalizations in the United States (US), there is growing concern regarding the health care utilization (HCU) of patients post discharge [1–4]. Prior reports of non-COVID-19 patients show high HCU post hospitalization and post care in an intensive care unit (ICU) [5–8]. Based on this evidence, HCU is expected to be high for patients discharged from COVID-19 admission.

Overall, studies have found that patients who tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) have greater HCU than patients who tested negative [9, 10]. Also, the 30-day and 60-day readmission rate for patients discharged alive after a hospitalization due to COVID has been reported as 13% to 24% and 19.9%, respectively [2, 11–17]. Similarly, the rates of ambulatory visits, emergency department (ED) visits, and hospitalizations vary following discharge from COVID-19 in socially disadvantaged patients compared to those who are socially advantaged [18, 19]. It has been reported that 82.1% of patients had follow-up visits with a primary care provider in the 60 days following discharge from COVID-19 hospitalization [20]. However, these observations were obtained from cohorts of patients who were ambulatory and/or post-hospital discharge. The HCU of patients discharged home post-COVID-19 hospitalization is not well understood. We, therefore, investigated the HCU of patients discharged home from COVID-19 hospitalization for 9 months post-discharge, using a national US database. We hypothesized that HCU will be high post discharge and that certain specialties will see a disproportionate increase in their share of post-discharge HCU. Some of the results of this study were previously presented in the form of a conference abstract [21].

Study design and methods

Data source

We used de-identified data from Optum’s Clinformatics Data Mart (CDM), a database of administrative health claims for members of large commercial and Medicare Advantage health plans. Optum’s CDM is a comprehensive database that includes claims from enrollees with either commercial insurance or Medicare Advantage Plans, encompassing over 63 million unique enrollees across the United States from all states. Notably, more than 95% of the enrollees in Optum’s CDM possess commercial insurance. This database does not include traditional Medicare or Medicaid enrollees, implying a potential underrepresentation of the older or low socioeconomic status populations. A significant aspect of the data from Optum’s CDM is its source diversity; since the data originates from insurance claims. This inclusivity means data can come from a variety of healthcare settings, whether rural or urban, academic or community-based. Whenever a CDM patient files a claim with their insurance, this information is captured in the database, regardless of the specific medical center or office they visited. This wide-ranging data collection provides a broad and diverse view of healthcare utilization and patterns across different demographics and geographies in the United States. The University of Texas Medical Branch Institutional Review Board (IRB) approved this study (IRB# 20–0180). The need for informed consent was waived by the IRB due to the de-identified nature of the study.

Cohort selection

Our sample included all adults hospitalized with a primary diagnosis of COVID-19 who were discharged home (with or without home health care) between April 2020 and March 2021, with at least 12 months of continuous enrollment before this diagnosis. COVID-19 cases were identified by the International Classification for Diseases, tenth revision, clinical modification (ICD-10-CM) diagnosis code U07.1 or from a positive test. We excluded patients who were not discharged home or whose insurance coverage ended during the inpatient stay (Fig 1).

Fig 1 — COVID-19 was identified by the ICD-10-CM diagnosis code U07.1 or from a positive test. Patients were excluded if they were not discharged home or if their insurance coverage ended during the inpatient stay. *Abbreviations*: COVID-19: Coronavirus Disease 2019; Optum CDM: Optum’s Clinformatics Data Mart. ICD-10-CM: International Classification for Diseases, tenth revision, clinical modification.

Variables

We collected information on patient demographics: age at time of COVID-19 diagnosis, sex, race/ethnicity, and region of residence. We identified comorbidities by ICD-10-CM diagnosis codes, with a 12-month lookback period before COVID-19 hospitalization.

We evaluated HCU in the 9 months pre and post hospitalization from COVID-19. We divided the study period into pre-COVID-19 hospitalization (-0-9 months) and post-COVID-19 discharge (+0–9 months). We also subdivided into 3-month periods for pre-COVID-19 hospitalization (-0-3 months, -3-6 months, and -6-9 months before hospitalization) and post-COVID-19 discharge (+0–3 months, +3–6 months, and +6–9 months post discharge). HCU included ED visits, inpatient admissions, rehabilitation/skilled nursing facility (SNF) admissions, outpatient visits, and telemedicine visits. Based on billing information, we examined outpatient visits (from here on out referred to as office visits) to primary care providers, including family medicine, internal medicine, and nurse practitioner visits, and certain sub-specialties, such as cardiology, pulmonary medicine, endocrinology, neurology, physical medicine and rehabilitation, psychiatry, and other mental health professionals.

Statistical analysis

Cohort characteristics are presented as mean and standard deviation (SD), median and interquartile range, or frequency and percentage. HCU in the pre-COVID-19 hospitalization and post-COVID-19 discharge periods was expressed as the number of visits per 10,000 (10k) person-days, where each patient contributed person-days until death, loss of insurance eligibility, or the end of the 9-month follow-up period. To identify the risk of HCU post-COVID-19 compared to pre-COVID-19 hospitalization incidence rate ratios (number of observed events over total person-days contributed) and exact Poisson confidence intervals were calculated for each period [22]. All findings were considered statistically significant if the P value was <0.05. To address the concern of survivor bias, the study required participants to have at least 12 months of data prior to their primary COVID-related hospitalization for accurate comorbidity assessment, without mandating post-hospitalization follow-up, and utilized a person-days approach to accommodate variable follow-up durations. As a sensitivity analysis, we evaluated HCU post discharge in women and compared it to that of men. In addition, we analyzed HCU in patients who received care in the ICU and compared it to those who did not. We also examined the distribution of office visits per 10K person-days by provider specialty (select specialties) in each period and calculated rate ratios to identify the risk of having a visit with that specialist post-COVID-19 hospitalization compared to the pre-hospitalization period. All analyses were conducted using SAS version 9.2 (SAS Institute, Inc., Cary NC).

Results

In this retrospective study, we identified 63,161 patients discharged home after hospitalization due to COVID-19. This cohort was comprised of patients who were mostly white (58.8%), women (53.7%), and with mean age of 72.4 (SD± 12.8) years. The most common comorbidities of these patients were hypertension (75.8%), diabetes mellitus (36.5%), congestive heart failure (25.8%), coronary artery disease (25.2%), and chronic obstructive pulmonary disease (23%) (Table 1).

Table 1. Characteristics of patients discharged home from COVID-19 hospitalization from April 2020 to March 2021 in the United States.

Characteristics	N = 63,161 (100%)^a^,^b^,^c
Age, mean (SD)	72.4 (12.8)
Sex	-
Women	33897 (53.7)
Men	29263 (46.3)
Race/Ethnicity ^d^,^e	-
White	37135 (58.8)
Hispanic	9843 (15.6)
Black	10443 (16.5)
Asian	1473 (2.3)
Other/Unknown	4261 (6.8)
Region	-
Midwest	13897 (22)
Northeast	8465 (13.4)
South	30145 (47.7)
West	9299 (14.7)
Unknown	1355 (2.1)
Comorbidities	-
DM	23,065 (36.5)
HTN	47,890 (75.8)
Asthma	6,285 (10.0)
COPD	14,534 (23.0)
CKD	6,793 (10.8)
ESRD	2,032 (3.2)
Stroke	5,547 (8.8)
CHF	16,303 (25.8)
Cancer	7,982 (12.6)
CAD	15,925 (25.2)
Liver disease	3,871 (6.1)
ICU use	21760 (34.5)

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^a Our cohort consists of 63,161 patients discharged home after hospitalization due to COVID-19.

^b COVID-19 and comorbidities were identified by ICD-10-CM diagnosis codes(S7 Table in S1 File).

^c Patients were excluded if they were not discharged home or if their insurance coverage ended during the inpatient stay.

^d Patients self-identifying as non-Hispanic ethnicity were categorized based on race (White, Black, Asian, other/unknown).

^e Patients self-identifying as Hispanic ethnicity were included in the Hispanic group regardless of race.

Definition of abbreviations: COVID-19: Coronavirus Disease 2019; SD: Standard Deviation; DM: Diabetes Mellitus; HTN: Hypertension; COPD: Chronic Obstructive Pulmonary Disease; CKD: Chronic Kidney Disease; ESRD: End-Stage Renal Disease; CHF: Congestive Heart Failure; CAD: Coronary Artery Disease; ICU: Intensive Care Unit; ICD-10-CM International Classification for Diseases, tenth revision, clinical modification.

Health care utilization after discharge home from hospitalization due to COVID-19

Patients discharged home from COVID-19 hospitalization were significantly more likely to have increased HCU in the 9 months post hospitalization compared to the 9 months prior to such hospitalization (Tables 2, 3, S1-S6, S8 Tables in S1 File). For example, these patients had 47%, 67%, 65%, and 51% increased risk of ED (rate ratio 1.47; 95% CI 1.45–1.49; p < .0001), rehabilitation (rate ratio 1.67; 95% CI 1.61–1.73; p < .0001), office (rate ratio1.65; 95% CI 1.64–1.65; p < .0001), and telemedicine visits (rate ratio 1.5; 95% CI 1.48–1.54; p < .0001), respectively. Also, the post-discharge risk of inpatient visits (rate ratio 2.20; 95% CI 2.14–2.25; p < .0001) and longer length of stay (rate ratio 2.62; 95% CI 2.59–2.64; p < .0001) doubled compared to the pre-COVID-19 hospitalization period (Table 2 and S1 Table in S1 File).

Table 2. Health care utilization of patients pre and post-hospitalization due to COVID-19^,c,d,e.

-	Pre-COVID-19 Hospitalization ^a^,^b	Post-COVID-19 Hospitalization ^a^,^b	-	-	-
-	-0-9 months	+0–9 months	Percent change ^c	Rate ratio (95% CI)	p-value
ED visits	22.0	32.3	47.0	1.47 (1.45–1.49)	< .0001
Inpatient visits	7.0	15.4	119.6	2.20 (2.14–2.25)	< .0001
Inpatient admission (LOS)^d	42.7	111.6	161.6	2.62 (2.59–2.64)	< .0001
Rehabilitation visits	3.3	5.5	66.7	1.67 (1.61–1.73)	< .0001
Office visits	156.5	257.5	64.5	1.65 (1.64–1.65)	< .0001
Telemedicine visits	11.2	17.0	51.3	1.51 (1.48–1.54)	< .0001

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^a Our cohort consists of 63,161 patients discharged home after hospitalization due to COVID-19.

^b Health care utilization of patients, measured by number of visits per 10k person-days.

^c Percent change of HCU pre- and post- hospitalization due to COVID-19.

^d LOS was defined as the number of days of inpatient status after hospital admission.

Definition of abbreviations: COVID-19: Coronavirus Disease 2019; ED: Emergency Department; ICD-10-CM: International Classification for Diseases, tenth revision, clinical modification; LOS: length of stay. 10k = 10,000

Table 3. Health care utilization of patients pre and post hospitalization due to COVID-19^,, by medical specialty.

	Pre-COVID-19 Hospitalization^a^,^b	Post-COVID-19 Hospitalization^a,b	-	-	-
	-0-9 months	+0–9 months	Percent change ^c	Rate ratio (95% CI)	p-value
PCP	78.8	133.1	68.9	1.69 (1.67–1.70)	< .0001
Cardiology	12.2	21.9	79.2	1.79 (1.76–1.83)	< .0001
Pulmonary Medicine	3.8	12.9	235.5	3.35 (3.26–3.45)	< .0001
Endocrinology	2.3	3.7	63.8	1.64 (1.57–1.71)	< .0001
Neurology	3.0	4.5	51.6	1.51 (1.46–1.58)	< .0001
Phys Med & Rehab	2.0	2.5	25.8	1.26 (1.20–1.32)	< .0001
Psychiatry	1.9	2.7	41.4	1.41 (1.35–1.49)	< .0001
Mental Health Professional	0.8	1.3	66.1	1.66 (1.54–1.79)	< .0001

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^a Our cohort consists of 63,161 patients discharged home after hospitalization due to COVID-19.

^b Health care utilization of patients, measured by number of visits per 10k person-days.

^c Percent change of HCU from pre- to post-hospitalization periods due to COVID-19.

Definition of abbreviations: HCU: health care utilization; COVID-19: Coronavirus Disease 2019; PCP: Primary Care Provider; ICD-10-CM: International Classification for Diseases, tenth revision, clinical modification; Phys Med & Rehab: Physical Medicine and Rehabilitation; 10k = 10,000.

As a sensitivity analysis, we evaluated HCU post discharge in women and compared it to that of men patients (S5 Table in S1 File). We also analyzed HCU in patients who received care in the ICU and compared it to those who did not (S6 Table in S1 File). We found that women have higher risk of ED (rate ratio 1.03; 95% CI 1.01–1.05), rehabilitation (rate ratio 1.23; 95% CI 1.17–1.30), and telemedicine visits (rate ratio 1.12; 95% CI 1.1–1.16) than men. But women have a lower risk of inpatient visits (Rate ratio 0.91, 95% CI 0.88–0.94), and office visits (rate ratio 0.92; 95% CI 0.91–0.93) than men. Additionally, women had a shorter length of stay (rate ratio 0.84, 95% CI 0.84–0.86) (S5 Table in S1 File).

Compared to patients who did not receive ICU care, those who were admitted to the ICU have increased risk of ED visits (19%, rate ratio 1.19; 95% CI 1.17–1.22), inpatient visits (27%, rate ratio 1.27; 95% CI 1.23–1.31); shorter length of stay (34%, rate ratio 1.34; 95% CI 1.32–1.35), office visits (46%, rate ratio 1.46; 95% CI1.44–1.47), and telemedicine visits (38%, rate ratio 1.38; 95%1.35–1.43).

Contrary to the elevated risks stated above, patients discharged home from COVID-19 hospitalization who received care in the ICU were less likely to have rehabilitation visits (rate ratio 0.92; 95% CI 0.87–0.97) compared to those who did not receive care in the ICU (S6 Table in S1 File).

Outpatient office visits were the most utilized health care service by patients post discharge (275.5 visits-10k person-days). All included specialties showed more visits in the 9 months after discharge than in the 9 months prior to hospitalization. Primary care providers had the highest number of visits post discharge (133.1 visits/10k persons-days), followed by cardiology (21.9 visits/10k persons-days) and pulmonary medicine (12.9 visits/10k persons-days). Interestingly, pulmonary medicine saw the highest percent change in the number of pre- (3.8 visits/10k person-days) vs post-discharge visits (235.5% change). Similarly, the risk of having a visit to any specialty significantly increased post discharge, but the risk of having a visit with pulmonary medicine was the highest (rate ratio 3.35, 95% CI 3.26–3.45, p < .0001) (Table 3, S3, S4 Tables in S1 File).

We also found that the risk of having visits with neurology (rate ratio 1.51; 95% CI 1.46–1.58; p < .0001), psychiatry (rate ratio 1.41; 1.35–1.49; p<0.0001) and other mental health professionals (rate ratio 1.66; 95% CI 1.54–1.79; p<0.0001) significantly increased post discharge compared to the pre-hospitalization period (Table 3).

Discussion

In our retrospective cohort study of patients discharged home after a COVID-19 hospitalization in the US, we found that these patients have an increased risk of post-discharge HCU compared to the pre-hospitalization period. Our results are similar to studies of HCU in patients post discharge from non-COVID-19 and COVID-19 admissions [3]. Our findings advance the knowledge about HCU post-COVID hospitalization in patients discharged home. Additionally, our observations of different HCU in women compared to men and in those who received care in the ICU compared to those who did not may help health systems and policy makers identify potential disparities and at-risk populations who may benefit from targeted interventions to improve their HCU post discharge. Furthermore, we found that all medical specialties studied had high use that varied by specialty, which can help us identify medical providers needed to meet patient care demands during the next respiratory pandemic.

We must consider why patients discharged home from a hospitalization for severe COVID-19 have high HCU. Acute infection by SARS-COV-2, leading to hospitalization due to severe COVID-19, has been associated with conditions that have multi-organ involvement and dysfunction, most commonly pneumonia but also including cardiac injury, acute liver injury, acute kidney injury, venous and arterial thrombotic events, and a variety of neurological and psychiatric manifestations [23]. Multisystem infection of the virus may explain a variety of persistent organ dysfunctions and may result in chronic clinical symptoms [23, 24]. These persistent symptoms likely lead patients to seek medical care post hospitalization and health systems to develop multi-disciplinary clinics, facilitating referrals to multiple specialists [25]. Our findings of increase in post-discharge HCU suggest an increased demand for hospital-centered care.

The differences in HCU in women compared to men are intriguing. Prior literature showed that women had lower risk of adverse outcomes and mortality due to COVID-19 compared to men [26, 27]. In our study, women have higher risk of ED visits, rehabilitation visits, and telemedicine visits, and lower risk of inpatient visits, office visits compared to men. Women also had a shorter length of stay compared to men. Although we do not know why these differences exist, our findings are consistent with prior non-COVID-19 literature of fewer hospital admissions, shorter length of stay, and fewer physician visits in women compared to men [28, 29]. The differences in HCU by sex may be explained by a variety of factors, including demographics and social factors, such as health care needs (limitation in mobility, disability, specific chronic comorbidities) and economic access factors (overall health, income, education, etc.) [30].

Our finding of higher HCU in patients post-discharge home from COVID-19 may be primarily driven by patients who received care in the ICU. Pre-COVID-19, research showed that patients with sepsis, pneumonia, central line associated blood stream infections, and ventilator associated pneumonia had increased post-discharge mortality and high HCU [8]. Also, patients who receive prolonged mechanical ventilation (>21 days) have high risk of mortality, readmissions to the hospital and ICU, and high HCU [5]. Finally, patients who survive ARDS have impaired functional status, and their quality of life is affected even 2 years after discharge from the ICU [7]. In our cohort, 34.5% of patients received care in the ICU, which is in accordance to published findings that nearly 1 in 3 (33%) hospitalized patients with COVID-19 develop ARDs and 1 in 4 hospitalized patients require transfer to the ICU (26%) [31]. We also know that patients with COVID-19 admitted to the ICU have longer length of stay in the hospital and ICU, longer length of mechanical ventilation, and therefore may be at increased risk of nosocomial infections [32, 33]. If we are to extrapolate those percentages to the over 6 million people hospitalized due to COVID 19, it may be expected that over 1.5 million people may experience the elevated HCU described in our study.

Notable insights emerged from our study regarding medical specialty utilization post-COVID-19 discharge. Primary care providers and outpatient office visits were pivotal, while an intriguing surge in pulmonology clinic visits highlighted disproportionate escalating demand for specialized respiratory care. This may be explained by the persistent pulmonary symptoms and complications post-COVID-19. A recent study highlighted the multisystemic impact of COVID-19 and reported that the chronic symptoms linked with SARS-CoV-2 infection involved a variety of organ systems, with chronic cough notably identified as one of the defining symptoms for the new diagnosis of post-acute sequelae of SARS-CoV-2 infection (PACS) [34]. Another study found that SARS-COV-2 infection was associated with an additional 213 health care visits per 1000 patients during the 6 months after the acute stage of illness [8]. Notably, this study found the second highest increase in utilization was observed for pulmonary symptoms (bronchitis, venous thromboembolism, dyspnea upon exertion, hypoxemia, and cough). Another study in France evaluated sequelae in COVID-19 patients post-hospital discharge from March to May 2020. They found that 51% of patients reported persistent respiratory symptoms 4 months after COVID-19 hospitalization. This study found persistent ground glass opacities in 32%, fibrotic lung lesions in 12%, and abnormal diffusion capacity in 13.6% of patients 4 months post-discharge [35]. Other studies have reported persistent reductions in diffusion capacity 3 months to 4 months after acute illness, with rates ranging from 16.4% to 52% [4, 36]. Multivariate analysis studies also found that severe disease during acute illness was associated with a persistently reduced diffusion capacity [36] and worse heart function [24]. Additionally, the most frequent serious manifestation of acute COVID-19 infection is pneumonia [37], with a reported 17% of patients complicated with acute respiratory distress syndrome [38]. All of the above may help explain our finding of 235.5% change in pulmonary visits from the pre- to the post-hospitalization period.

Interestingly, studies have reported persistent pulmonary symptoms in patients without persistent physiologic impairments [39, 40]. One study aimed to investigate the long-term pulmonary effects of severe COVID-19 pneumonia by assessing cardiopulmonary exercise test (CPET) performance in 60 patients 12 months after a COVID-19 infection that required ICU management [41]. Exercise capacity assessed by CPET was within normal limits in most patients 12 months after hospitalization, and impairment was predominantly related to persistent deconditioning or prior respiratory comorbidities. Complementing our findings, other studies have compared hospitalization related to COVID to other causes of hospitalization.

The disproportionate increases in HCU by PCP and pulmonary specialist may also be explained by the establishment of multi-disciplinary post-acute sequelae of COVID-19 (PASC) clinics. One study surveyed healthcare systems in the US participating in the PETAL Network and reported that 70% had established an outpatient clinic for PASC with physicians providing care in 97% of clinics supplemented by Advanced Practice Professionals [25]. Of these systems, 21% automatically referred all patients discharged alive post-COVID-19 hospitalization to the PASC clinic for outpatient follow up while 70% of referrals relied on physician discretion or patient requests. Subspecialties available were pulmonary (97%), general medicine and primary care (58%), cardiology (52%), and psychiatry (30%). Remarkably, 73% of these PASC clinics were distinct from their previously established post-ICU clinics [25].

We acknowledge our study’s limitations, including its retrospective design; therefore, we cannot infer cause and effect but only an association between being discharged from a hospitalization due to COVID-19 and an increase in post-discharge HCU. Also, our patient cohort was obtained from a national commercially-insured and Medicare Advantage claims database. Therefore, we do not provide information on uninsured/out of network patients, and this may underrepresent the number of patients discharged home from COVID-19 hospitalizations and their post-discharge HCU. Also, our study period spans the first year of the pandemic when many COVID-19 treatments and vaccines were in development or in early stages of use. Therefore, we are unable to determine how many of our patients were vaccinated or had received COVID-19-specific therapy and how these two factors mediate post-discharge HCU. Also, the mean age of our population is 72 years, and our results may be more representative of the older adult population. Similarly, based on limitations from our dataset, we cannot determine the post-discharge mortality rate in our cohort; however, with an estimated 7.8% all-cause post-discharge mortality rate [42] reported in the literature, we know most patients discharged from a COVID-19 hospitalization are alive one year post-discharge [42]. To address this limitation, our cohort only included patients enrolled in insurance/database until the end of the study period. Therefore, each patient contributed person-days until death, loss of insurance eligibility, or the end of the 9-month follow-up period. Additionally, we acknowledge the multifaceted influences on healthcare utilization, extending beyond direct medical necessity. Integral factors likely include socioeconomic status, health insurance coverage, access to and availability of care, and clinician referrals. We must also consider the potential for bias due to resource exhaustion amid the pandemic’s economic fallout. Resource limitation might have prompted a decrease in healthcare visits despite ongoing health impairments, thereby affecting our analysis of utilization rates.

Our study has several strengths including a cohort of patients discharged home obtained from a database that spans nationally. The information obtained from our study provides insights in the HCU trends post-COVID-19 hospitalization in the US. We were also able to follow patients for a considerable period, up to 9 months post discharge, and to compare pre- and post-COVID hospitalization HCU in the same patient population, eliminating potential selection bias or the influence of such confounders as demographic characteristics and prior comorbidities.

Conclusions and implications

In our nationally representative retrospective study, we identified that HCU remains high among patients discharged to a home setting after a hospitalization due to COVID-19. Health systems and providers may be able to use this information to better deploy resources in the care of this chronically ill population.

Supporting information

S1 File. This document contains supplementary tables and figures that provide additional details and analyses supporting the findings of the main manuscript.

The tables and figures included herein offer further insights, data points, and visual representations to enhance the understanding and interpretation of the research presented in the primary manuscript.

(DOCX)

pone.0303509.s001.docx^{(32.2KB, docx)}

Data Availability

Optum data is proprietary (and we have purchased access to it under contract), therefore, we are not permitted to share the data. We accessed Clinformatics through the university contract. This contract expired on 6/30/2023 To acquire the same type of data, researchers need to sign a contract with Optum. Contact information for Optum: call: 1-866-3061321; email: connected@optum.com The cohort and analytic results can be replicated by following the inclusion/exclusion criteria as described in the manuscript. Interested researchers would need to sign individual contracts with Optum and receive the data. We did not have any special access privileges to Optum data.

Funding Statement

Dr. Puebla Neira reports support from NHLBI Division of Intramural Research (US), NHLBI Advanced Respiratory Research for Equity (AiRE) - AZ-PRIDE Program grant (5R25HL126140-09) during the conduct of this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0303509.r001

Decision Letter 0

Raymond Nienchen Kuo

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

13 Jun 2023

PONE-D-23-10824Health Care Utilization 9 months Pre- and Post- COVID-19 Hospitalization among Patients Discharged AlivePLOS ONE

Dear Dr. Zaidan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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The authors employed a nationally-representative database from the United States to examine healthcare utilization patterns up to nine months after patients were discharged home following COVID-19 hospitalization. While the findings demonstrate an increase in healthcare utilization post-discharge for individuals hospitalized due to COVID-19 compared to their pre-pandemic healthcare utilization, the authors must address several crucial concerns before this manuscript can be considered suitable for publication.

As highlighted by the reviewer, it is advisable for the authors to conduct additional analyses comparing healthcare utilization across various sub-groups. The authors must implement sufficient statistical testing in order to substantiate their interpretation of the study's findings. Furthermore, the authors are urged to carefully consider and address the study limitations pointed out by the reviewers.

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https://www.jamda.com/article/S1525-8610(21)00762-3/fulltext

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: The authors use a large health system in the US to describe patterns in health care use among individuals who were hospitalized with COVID-19 in the early phases of the pandemic, prior to widespread vaccine access or the newer variants. Although they describe and exposure and outcome, formal comparisons are not included. It would be of potential interest if, for example, they compared post-hospitalization use to other conditions (e.g., sepsis, influenza), pre- vs. post-HCU, or even formally assessed trends in HCU over time. At times the authors refer to people who were hospitalized but not discharged home as being excluded, but most of Table 2 describes alternative dispositions for patients who were not discharged home. Perhaps another formal comparison would be of interest in that regard, e.g., predictors of home vs. not-home discharge.

It is important to note in more detail that health care use in the US is not driven solely by patient need for medical care. It involves a complex interplay of multiple factors, including health insurance, access to care, available medical care, and referrals. Patients cannot access medical care they (or their clinicians) do not know they would benefit from (e.g., increasing evidence for increased risk of diabetes following infection suggests some patients might benefit from endocrinology evaluation; GI complications are also apparent, as are fertility issues, but few were aware of these issues at the time of the study); second, patients who have exhausted available resources do eventually stop going to as many medical appointments, even in cases where their health remains severely impaired. Please comment.

With regards to healthcare encounters, is it possible to include home health encounters?

Did the authors consider looking for potential effect modification by sex? Or by ICU admission during the index hospitalization? What % of cases were re-infections, and if an important %, what if any difference is there in HCU by reinfection?

The authors describe covariates, although no adjusted models were run. Perhaps baseline patient characteristics might a more accurate term. With regards to these variables, why gender (instead of sex), what were the race categories (and were they patient reported or something else), and what about other important potential confounders, e.g., education, insurance, occupation, number o vaccinations? Finally, inclusion of comorbid conditions only in the prior 12 months likely significantly underestimated true co-morbid conditions, is it possible to expand the look-back window to several years?

Table 1: What % were covered by commercial vs. other health insurance?

Table 2 seems more appropriate for study flow- these patients were excluded from the cohort.

There are more limitations than the authors list, including the following:

- Use of only patients insured by a large commercial company or Medicare Advantage with at least 2 months of pre-COVID data means the study population includes people who have relatively good access to medical care. The authors do not report what % had commercial insurance, but those people would have been healthy (and young enough) to have employment. How representative is this of the overall population?

- What about care people may have received in other locations, e.g., out of network?

- The study time is limited to largely pre-vaccination stages of the pandemic, and during early variants, when monoclonals were available and used more widely – would these findings be generalizable or informative now?

Line 181 – There is an interesting but unfounded (by reproducible studies) assumption that patients are ‘merely anxious or depressed’ after SARS-CoV-2 infection, implying they should be able to use CBT or positive self-talk to resolve their symptoms. In an effort to discourage these and other similar implications, please remove the phrase “decline in mental health” from line 181 and instead simply report the numbers.

Reviewer #2: This study provides evidence of healthcare utilization (HCU) trends of COVID-19 patients 9 months post-diagnosis using a sample of the U.S. COVID-19 patient population. The cohort creation process is transparent and the paper is well written. However, the current analysis is not sufficient to merit publication at PLOS ONE. First, it is not entirely clear what is the biggest contribution of the study. Given that there are other studies that extend the time horizon of long COVID HCU to twelve months (Roth et al., 2022), the authors should argue how these results may be more informative to other longitudinal studies of HCU and long COVID on the basis of the quality of their analysis, as opposed to the long time span of the COVID-19 post-diagnosis period only. It is not surprising that HCU rises post-diagnosis; the authors could also try to timestamp when this increased HCU subsides for different age groups (Koumpias et al., 2022).

The authors should consider these easily executable revisions below to enhance the rigor of the analytical approach.

Major comments:

The biggest concern is the lack of any account of patient insurance status. The confluence of Medicare-eligible and Medicare non-eligible population in the study cohort leads to overestimation of the influence of a COVID-19 diagnosis on HCU because the latter group (<65years old) is less likely to engage in HCU due to relatively higher costs they may face. Therefore, the authors should report HCU separately for commercially-insured (<65yrs old) and Medicare/Medicare Advantage patients.

It is also mentioned that this is a descriptive analysis; yet no statistical tests are being used, whatsoever. At a minimum, the authors shoould report whether pre-, post-diagnosis levels are statistically different using t-tests. For instance, the first sentence of the introduction reads: "In our nationally-representative study of patients discharged home after a COVID-19 hospitalization, we found that HCU remained significantly high 9 months after discharge from index hospitalization." It appears highly likely that the increase in HCU is detectable at conventional levels of statistical significance, too. After conducting the statistical analysis, this statement could be revised to explicitly state whether increased HCU remained higher in a statistically significant way.

Another concern is that HCU is driven by intensity of hospitalization which varies in a way that the authors do not sufficiently account for. Separating the results by specialty is certainly a step towards that direction. It would be most informative to show how the trends of post-diagnosis HCU for different subgroups of varying hospitalization intensity. In fact, the authors should consider shedding light to any differential HCU responses by LOS. (e.g. by LOS quartiles). This would make a genuine contribution by further illustrating post-discharge differences in HCU based on severity of COVID-19 infection. However, the authors do not mention changes in patient LOS until page 9. Given that there is relatively less evidence on LOS changes than HCU changes following COVID-19 diagnosis, this novel finding should be discussed earlier if not employed as another variable in cross-tabulations of HCU pre- and post-diagnosis.

Minor comments:

Another source of measurement error causing underestimation of the association of COVID-19 with post-diagnosis HCU is due to sample attrition from death or loss of insurance. To examine the sensitivity of their findings to this potential issue, the authors should use a balanced panel of patients who contribute person-days both pre-diagnosis and during the third post-diagnosis time interval (6-9) as a supplementary robustness check.

Related, the authors should explore whether information regarding COVID-19 diagnosis is available in the secondary and subsequent diagnosis lines and to what extent this leads to significant undercount of COVID-19 patients. It would be very helpful to complement the results a discussion of the frequency of COVID-19 diagnosis non-primary line reporting.

Re: Data Availability - Unsure whether data is available after all given that this is the proprietary OptumInsights de-identified Clinformatics Data Mart. This may need to be corrected.

Finally, are the results robust to the inclusion of the outcome in its raw form, measured in levels? It would be useful to show whether the transformation to person-days has any influence on the results.

A quick literature review identified the following journal articles pertinent to this study:

References:

- Koumpias AM, Schwartzman D, Fleming O. Long-haul COVID: healthcare utilization and medical expenditures 6 months post-diagnosis. BMC Health Services Research. 2022 Aug 8;22(1):1010.

- Roth SE, Govier DJ, Marsi K, Cohen-Cline H. Differences in outpatient health care utilization 12 months after COVID-19 infection by race/ethnicity and community social vulnerability. International Journal of Environmental Research and Public Health. 2022 Mar 15;19(6):3481.

- Zhou X, Andes LJ, Rolka DB, Imperatore G. Changes in health care utilization among Medicare beneficiaries with diabetes two years into the COVID-19 pandemic. Ajpm Focus. 2023 Jun 1:100117.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2024 Jun 20;19(6):e0303509. doi: 10.1371/journal.pone.0303509.r002

Author response to Decision Letter 0

12 Sep 2023

Thank you for the constructive feedback. Please see the "response to reviewers" document for our full responses.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0303509.s002.docx^{(34.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0303509.r003

Decision Letter 1

Raymond Nienchen Kuo

16 Oct 2023

PONE-D-23-10824R1Health Care Utilization 9 months Pre- and Post- COVID-19 Hospitalization among Patients Discharged AlivePLOS ONE

Dear Dr. Zaidan,

Please submit your revised manuscript by Nov 30 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Raymond Nienchen Kuo, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

We acknowledge the effort and dedication you have exhibited in responding to the initial reviews and improving your manuscript. However, it has come to our attention that there are still several significant issues raised by Reviewer #1 that necessitate further elucidation. One such area pertains to how this study could potentially bridge the existing knowledge gap related to inpatient care related to COVID-19 infection. It would be constructive to elaborate on this aspect in your manuscript.

In addition, we suggest that you provide a concise description of the data source that was used in your research. It would also be beneficial to discuss the potential impact of your sample's characteristics on the generalizability of your study findings. This will allow readers to understand the context and implications of your research better.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have responded to some questions and comments to the best of their ability, and comparison of 9 months pre- vs. post HCU hospitalization for COVID was added, which his helpful. However, there remain significant limitations within the available data that perhaps cannot be addressed. Overall, however, it is not surprising that people who were hospitalized required more health care after hospitalization and they did poorly in general, even among those who were only eligible because they survived at least a year after hospital discharge. One key question that this study cannot answer is whether or to what degree hospitalization for COVID may differ from other causes of hospitalization.

Can the authors clearly explain what this study adds beyond a prior publication (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548925/) how it adds materially to our understanding of health care use post hospitalization for COVID (specifically, compared to other reasons for hospitalization).

Otherwise, I have the following additional comments/questions:

Please provide a brief description of the kinds of patients who would be included in Optum’s Clinformatics Data Mart – what % of the US population are generally included? Are these people generally of similar, worse, or better health than the general population? What kinds of medical centers contribute data, e.g., typically community vs. academic? What % are rural?

Readers have a general idea of, for example, the patient population and generalizability for VHA studies, but that is not the case here. For provide a similar summary for patients in this cohort.

Page 7 line 127 – Please clarify that this was the primary admission diagnosis (In Figure 1, I see that this appears to be a hospital admission diagnosis, is that correct?)

In the discussion/limitations, please include the risk of introducing survivor bias by requiring that people have at least 12 months of follow-up. Why not allow variable follow-up and simply account for it in analysis, and not risk survivor bias?

What are the test characteristics for the ICD code for accurate case identification? (e.g., sensitivity, specificity, PPV, NPV)

I initially misread Figure to read that 63,161 people were excluded due to loss of insurance prior to hospital discharge. Please restructure Figure 1 to list the N excluded (%), similar to reporting for clinical trials.

Table 1: Lack of insurance information is an important limitation. How were the comorbid conditions identified (i.e., in a supplement, include ICD codes, etc)

Table 2: Please move the units for each measure of HCU to the row label to make them more clear and please provide more explanation of the measures of health care utilization. E.g., “ED visits per 10,000 person-days”. Are the reported pre- vs. post-hospitalization HCU reflective of the mean or median or some other measure? Please include the appropriate measure of variability around that reported summary (e.g., SD, IQR, etc). Is hospital LOS per capita as well? Why was 9 months before and after chosen? Why not a year? (A year would be easier to compare to other information, e.g., the baseline proportion of people with an ED visit per year, to assess generalizability)

Please confirm that post hospitalization HCU measures do not include the index hospitalization.

Tables 1, 2, and 3 are currently in the Methods section, but shouldn’t they be referred to and located in the Results section?

Results:

Why might people who were in the ICU have shorter length of stay? (How does that translate into a RR>1? Am I interpreting that result incorrectly?)

Discussion:

For people in other countries, please provide some context for health care utilization. In general, how much might an outpatient visit cost, out of pocket? What % of a hospitalization might patients have to pay out of pocket, and approximately how much might that be in absolute USD? This provides context for what might drive patient behavior, specifically health-seeking behaviors. Might some patients avoid outpatient care if it cost $25/visit, while an ED visit or in-patient hospitalization might have $0 out of pocket costs?

Is it possible to include any information about why patients were re-hospitalized? E.g., admission for ACS, neurological, PNA, dyspnea, or other causes?

For consistency, I suggest using the terms male and female throughout, since sex was likely the measured variable in available data, not gender.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2024 Jun 20;19(6):e0303509. doi: 10.1371/journal.pone.0303509.r004

Author response to Decision Letter 1

5 Dec 2023

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

6. Review Comments to the Author

Reviewer #1:

The authors have responded to some questions and comments to the best of their ability, and comparison of 9 months pre- vs. post HCU hospitalization for COVID was added, which his helpful. However, there remain significant limitations within the available data that perhaps cannot be addressed. Overall, however, it is not surprising that people who were hospitalized required more health care after hospitalization and they did poorly in general, even among those who were only eligible because they survived at least a year after hospital discharge.

R1:

One key question that this study cannot answer is whether or to what degree hospitalization for COVID may differ from other causes of hospitalization.

A1:

Thank you for the criticism. Our main purpose of this article was to provide a descriptive analysis of health care utilization of patients post hospitalization for COVID pneumonia. We did not intend to compare how hospitalization for COVID pneumonia may differ from other pneumonias or other causes. Other published works have made such comparisons, such as these 2 articles.

COVID pneumonia hospitalization had increased pulmonary shunts that is not commonly seen in other types of pneumonia. (Novelli et al) Another article noted that patients with COVID pneumonia who required mechanical ventilation had longer times to liberation from mechanical ventilation. (Nolley et al).

Novelli, Malandrino, Balbi, et al. Shunt fraction and radiological involvement in Covid-19 related Acute Respiratory Failure. European Respiratory Journal Sep 2023, 62 (suppl 67) PA5105; DOI: 10.1183/13993003.congress-2023.PA5105

Nolley EP, Sahetya SK, Hochberg CH, et al. Outcomes Among Mechanically Ventilated Patients With Severe Pneumonia and Acute Hypoxemic Respiratory Failure From SARS-CoV-2 and Other Etiologies. JAMA Netw Open. 2023;6(1):e2250401. doi:10.1001/jamanetworkopen.2022.50401

R2:

A2:

Thank you for pointing out this prior publication. This was our poster publication that was presented at the CHEST conference 2022 and published online. Since this abstract, we have performed a statistical analysis and sensitivity analysis on healthcare utilization post discharge the index hospitalization.

Previous studies compared HCU between patients with and without COVID.

In this article, we did within subject comparison (before and after COVID for each patient). We also compared the within subject changes by gender and ICU. We did not intend to compare how hospitalization for COVID pneumonia may differ from other pneumonias or other causes.

R3:

Otherwise, I have the following 3 additional comments/questions:

Please provide a brief description of the kinds of patients who would be included in Optum’s Clinformatics Data Mart – what % of the US population are generally included?

Are these people generally of similar, worse, or better health than the general population?

What kinds of medical centers contribute data, e.g., typically community vs. academic? What % are rural?

Readers have a general idea of, for example, the patient population and generalizability for VHA studies, but that is not the case here. For provide a similar summary for patients in this cohort.

A3:

Optum’s Clinformatics Data Mart contains claims from enrollees with commercial insurance or Medicare Advantage Plans. The database comprises of over 63 million unique enrollees in the United States from all states.

The majority (>= 95%) of the enrollees in Optum’s CDM have commercial insurance.

Traditional Medicare or Medicaid enrollees are not included in the database, so it is likely that the dataset is less representative of the older or low socioeconomic status population.

Because the data in Optum’s CDM come from insurance claims, there are no limitations on the type of medical center that can contribute data (e.g., rural / urban or academic/community).

When a CDM patient files a claim with their insurance, the data are captured, regardless of the medical center/office he visited.

We have added these points to the Data Source paragraph in the methods section

R4:

Page 7 line 127 – Please clarify that this was the primary admission diagnosis (In Figure 1, I see that this appears to be a hospital admission diagnosis, is that correct?)

A4:

Yes. This was the primary admission diagnosis for the hospitalization.

R5:

A5:

Thank you for your comment. To address the concern of survivor bias, we required participants to have at least 12 months of data prior to their primary COVID-related hospitalization for accurate comorbidity assessment and utilized a person-days approach to accommodate variable follow-up durations. We did not require follow-up AFTER hospitalization. We used person-days to capture to allow for variable follow up and limit survivor bias.

R5:

What are the test characteristics for the ICD code for accurate case identification? (e.g., sensitivity, specificity, PPV, NPV)

A5:

While there are no published studies on ICD code accuracy using Optum’s CDM specifically, a study (Kadri et. Al) using the same ICD-codes for identification of COVID-19 disease in another insurance claims database found the following: "sensitivity was 98.01% (95% CI, 97.62%-98.39%), the specificity was 99.04% (95% CI, 98.95%-99.13%), the positive predictive value was 91.52% (95% CI, 90.77%-92.27%), and the negative predictive value was 99.79% (95% CI, 99.75%-99.83%).”

Kadri SS, Gundrum J, Warner S, et al. Uptake and Accuracy of the Diagnosis Code for COVID-19 Among US Hospitalizations. JAMA. 2020;324(24):2553–2554. doi:10.1001/jama.2020.20323

R6:

A6:

Thank you for the suggestion. We have made this change.

Please see the new Figure 1

R7:

Table 1: Lack of insurance information is an important limitation. How were the comorbid conditions identified (i.e., in a supplement, include ICD codes, etc)

A7:

All patients had insurance under Optum’s insurance contractor (large national commercial insurance or Medicare Advantage). Over 95% had commercial insurance and a little over 4% had Medicare advantage (we unfortunately do not have more specific information on insurance for each patient). We required all patients covered by Optum insurance contracts in the 12 months before hospitalization in order to identify comorbidity.

Additonally, we have added this table in the supplement section

Disease

ICD-10 codes

COVID-19

U07.1

Diabetes

E10.xx, E11.xx, E13.xx

Hypertension

I10.x, I11.xx, I12.xx, I13.xx I15.xx, I67.4

Asthma

J45.xx

COPD

J41.8, J42.xx, J43.xx, J44.xx

CKD

N18.9

ESRD

N18.6

Stroke

I63.xx, I64.xx, I69.3x, G45.9

Heart disease

I09.9, I11.0, I13.xx, I25.5, I42.xx, I43.xx, I50.xx

Cancer

C00.x – C96.x

CAD

I25.1X

Liver disease

K70 – K74, K76.xx

R8:

A8:

Thank you for this suggestion. Completed.

R9:

Are the reported pre- vs. post-hospitalization HCU reflective of the mean or median or some other measure?

A9:

We report healthcare use (e.g. number of visits) as incidence per 10,000 person – days. It is an epidemiologic measure, which accounts for the actual time each participant contributes to the study.

We mention this on lines 147-150 and each table has this mentioned at the bottom.

R10:

Please include the appropriate measure of variability around that reported summary (e.g., SD, IQR, etc). Is hospital LOS per capita as well?

A10:

There is no measure of variability for incidence, other than the reported risk ratio when comparing two incidence rates. We reported LOS as total days of inpatient stay per 10,000 person-days.

R11:

Why was 9 months before and after chosen? Why not a year? (A year would be easier to compare to other information, e.g., the baseline proportion of people with an ED visit per year, to assess generalizability)

A11:

During the time of our initial data acquisition, we didn't have data for 12 months post-COVID hospitalization.

R12:

Please confirm that post hospitalization HCU measures do not include the index hospitalization.

A12:

This is correct. All the HCU measures DID NOT include the index hospitalization.

R13:

Tables 1, 2, and 3 are currently in the Methods section, but shouldn’t they be referred to and located in the Results section?

A13:

Thank you for the suggestion, the tables have been moved to the results section.

R14:

Why might people who were in the ICU have shorter length of stay? (How does that translate into a RR>1? Am I interpreting that result incorrectly?)

A14:

Thank you for your comment. We found that increase of covid HCU from pre to post period in patients with ICU stays was larger than those without, hence the RR>1. Those patients with ICU stays during COVID hospitalization had much large increase of length of stay than those without.

Discussion:

R15:

For people in other countries, please provide some context for health care utilization. In general, how much might an outpatient visit cost, out of pocket?

A15:

Ambulatory care, visit complexity is stratified from Level 1 for minor issues, costing an average of $46, to Level 5 for the most severe cases at $182 per visit. Level 3, the median for common visits, averages $90. The overall outpatient visit cost in 2018 averaged $105.

From 2008 to 2018, the cost for Level 1 visits rose by $15 (52%), while Level 5 visits increased by $49 (37%). Level 3 visits, the most frequent, saw a $20 rise (29%), indicating a notable trend in healthcare expenditure growth.

"How costly are common health services in the United States?" Peterson-KFF Health System Tracker. https://www.healthsystemtracker.org/chart-collection/how-costly-are-common-health-services-in-the-united-states/#item-start. Accessed November 7, 2023.

R16:

What % of a hospitalization might patients have to pay out of pocket, and approximately how much might that be in absolute USD? This provides context for what might drive patient behavior, specifically health-seeking behaviors. Might some patients avoid outpatient care if it cost $25/visit, while an ED visit or in-patient hospitalization might have $0 out of pocket costs?

A16:

Hospitalization costs incur varied out-of-pocket expenses, influenced by insurance coverage. In 2020, average out-of-pocket costs for COVID-19 hospitalizations were approximately $1,653 under traditional plans and $1,961 for consumer-driven plans. A related study indicated a median cost for privately insured patients ranging from $59 to $842 due to COVID-19.

"Assessment of Out-of-Pocket Spending for COVID-19 Hospitalizations in the US in 2020." JAMA Network. https://jamanetwork.com/journals/jama/fullarticle/2780416. Accessed November 7, 2023.

On a broader note, U.S. employees paid an average of $1,763 out-of-pocket before reaching their insurance deductibles last year. Such costs are substantial and may affect decisions regarding follow-up outpatient care, in contrast to some plans covering emergency or inpatient services fully, thereby incurring no out-of-pocket expenses

"Average out-of-pocket healthcare costs." Bankrate. https://www.bankrate.com/insurance/health/average-out-of-pocket-healthcare-costs/

R17:

Is it possible to include any information about why patients were re-hospitalized? E.g., admission for ACS, neurological, PNA, dyspnea, or other causes?

A17:

Unfortunately, our contract of the Optum data set was expired on June 2023 and this is not possible.

R18:

For consistency, I suggest using the terms male and female throughout, since sex was likely the measured variable in available data, not gender.

A18:

Thank you for the suggestions. We have made the appropriate changes.

Reviewer #2: (No Response)

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Attachment

Submitted filename: Response to Reviewers - 11-17-23.docx

pone.0303509.s003.docx^{(35.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0303509.r005

Decision Letter 2

Robert Jeenchen Chen

12 Feb 2024

PONE-D-23-10824R2Health Care Utilization 9 months Pre- and Post- COVID-19 Hospitalization among Patients Discharged AlivePLOS ONE

Dear Dr. Zaidan,

Please submit your revised manuscript by Mar 28 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Robert Jeenchen Chen, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments:

Please revise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: No

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

Reviewer #3: This study by Zaidan et al, described the increase in Health Care Utilization (HCU) in patients post COVID-19 hospital discharge (follow-up = 9 months). The manuscript is well written and includes appropriate analyses and tables.

The tables should be referred to in the results & not the methods.

They also have numerous subscripts within the title of the tables that are unnecessary. Just need a table legend without the addition of subscripts within the title (Minor point).

I am slightly confused about the inclusion/exclusion criteria. In the "Cohort Selection" section of the methods, the authors state that "Our samples included all adults hospitalized with primary diagnosis of COVID-19 who were discharged home (with or without HOME HEALTH care)..." However, in Figure 1 they state that "Not discharged home or with HOME HEALTH" was part of the exclusion criteria. This needs to be clarified, were those with HOME HEALTH included of excluded. This may have an impact on the amount of HCU. If HOME HEALTH participants were included then this would likely have an impact on HCU.

Similarly, another potentially observation that could inform future pandemic preparedness would be analysis on whether particular co-morbidities were associated with increased HCU post-COVID discharge.

Please define OFFICE (mentioned in the results (e.g. p13; line 232, p14; line 248) which was not described within the results.

A limitation of this work is perhaps the limited ability to extrapolate these findings to a larger cohort (throughout the USA), given that this cohort is likely to have a higher socioeconomic status (given the degree of insurance cover) and are of older age (72 years). If the authors were to divide the cohort by age (in a sub analysis) this would add more weight to generalizing to a larger population.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

**********

PLoS One. 2024 Jun 20;19(6):e0303509. doi: 10.1371/journal.pone.0303509.r006

Author response to Decision Letter 2

11 Apr 2024

Reviewers:

We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in your cover letter; we will change the online submission form on your behalf.

Response:

I spoke to Dr. Kuo and she reports that that her grants are related to opioid prescription, cancer, and ADRD. None of these grants are related to this study.

So I will remove her grants from the page and resubmit.

Reviewer #3: 

Comment: The tables should be referred to in the results & not the methods. They also have numerous subscripts within the title of the tables that are unnecessary. Just need a table legend without the addition of subscripts within the title (Minor point).

Response: Thank you for your comment. All tables references have been removed from the methods. Tables are referenced in the results section. Also, subscripts from the titles have been removed and the table legends summarized.

Comment: I am slightly confused about the inclusion/exclusion criteria. In the "Cohort Selection" section of the methods, the authors state that "Our samples included all adults hospitalized with primary diagnosis of COVID-19 who were discharged home (with or without HOME HEALTH care)..." However, in Figure 1 they state that "Not discharged home or with HOME HEALTH" was part of the exclusion criteria. This needs to be clarified, were those with HOME HEALTH included of excluded. This may have an impact on the amount of HCU. If HOME HEALTH participants were included then this would likely have an impact on HCU.

Response: Thank you for your comment. We agree with the reviewer. The way figure one was written lead to potential confusion. To clarify, our study only included patients who were discharged home (with or without home health). So, if a patient was not discharged home, then it was excluded. Figure one has been edited to clarify this point.

Comment: Similarly, another potentially observation that could inform future pandemic preparedness would be analysis on whether particular co-morbidities were associated with increased HCU post-COVID discharge.

Response: PLEASE RESPOND

Comment: Please define OFFICE (mentioned in the results (e.g. p13; line 232, p14; line 248) which was not described within the results.

Response: Thank you for your comment. We have added the definition of office in the methods section. A copy of the paragraph follows “. Based on billing information, we examined outpatient visits (from here on out referred to as office visits) to primary care providers, including family medicine, internal medicine, and nurse practitioner visits, and certain sub-specialties, such as cardiology, pulmonary medicine, endocrinology, neurology, physical medicine and rehabilitation, psychiatry, and other mental health professionals”

Comment: A limitation of this work is perhaps the limited ability to extrapolate these findings to a larger cohort (throughout the USA), given that this cohort is likely to have a higher socioeconomic status (given the degree of insurance cover) and are of older age (72 years). If the authors were to divide the cohort by age (in a sub analysis) this would add more weight to generalizing to a larger population.

Response: Thank you for your comment. Socioeconomic status has been added as a limitation. Please see discussion section under limitations.

Please see the table below. We have made a table that divides the cohort by age. This has been added to the supplemental tables.

Attachment

Submitted filename: Response to reviewers FINAL MZ 3-27 .docx

pone.0303509.s004.docx^{(25.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0303509.r007

Decision Letter 3

Robert Jeenchen Chen

26 Apr 2024

Health Care Utilization 9 months Pre- and Post- COVID-19 Hospitalization among Patients Discharged Alive

PONE-D-23-10824R3

Dear Dr. Zaidan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLoS One. doi: 10.1371/journal.pone.0303509.r008

Acceptance letter

Robert Jeenchen Chen

27 May 2024

PONE-D-23-10824R3

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. This document contains supplementary tables and figures that provide additional details and analyses supporting the findings of the main manuscript.

(DOCX)

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Data Availability Statement

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PERMALINK

Healthcare utilization 9 months pre- and post- COVID-19 hospitalization among patients discharged alive

Mohammed Zaidan

Daniel Puebla Neira

Efstathia Polychronopoulou

Kuo Yong-Fang

Gulshan Sharma

Roles

Abstract

Background

Study design and methods

Results

Conclusion

Introduction

Study design and methods

Data source

Cohort selection

Fig 1. Cohort selection of patients discharged from COVID-19 hospitalization.

Variables

Statistical analysis

Results

Table 1. Characteristics of patients discharged home from COVID-19 hospitalization from April 2020 to March 2021 in the United States.

Health care utilization after discharge home from hospitalization due to COVID-19

Table 2. Health care utilization of patients pre and post-hospitalization due to COVID-19,c,d,e.

Table 3. Health care utilization of patients pre and post hospitalization due to COVID-19,, by medical specialty.

Discussion

Conclusions and implications

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Raymond Nienchen Kuo

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Raymond Nienchen Kuo

Roles

Author response to Decision Letter 1

Decision Letter 2

Robert Jeenchen Chen

Roles

Author response to Decision Letter 2

Decision Letter 3

Robert Jeenchen Chen

Roles

Acceptance letter

Robert Jeenchen Chen

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 2. Health care utilization of patients pre and post-hospitalization due to COVID-19^,c,d,e.

Table 3. Health care utilization of patients pre and post hospitalization due to COVID-19^,, by medical specialty.