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. 2023 Jul 31;54(11-12):508–515. doi: 10.1159/000532105

Impact of the COVID-19 Pandemic on the Provision of Dialysis Service and Mortality in Veterans Receiving Maintenance Hemodialysis in the VA: An Interrupted Time-Series Analysis

Samir Patel a,b, Eduardo A Trujillo Rivera a,c, Venkatesh K Raman a,d, Charles Faselis a,b,e, Virginia Wang f,g, Jeffrey C Fink h,i, Jeffrey M Roseman j, Charity J Morgan k, Sijian Zhang a, Helen M Sheriff a,l, Michael S Heimall a, Wen-Chih Wu m,n, Qing Zeng-Treitler a,l, Ali Ahmed a,b,d,l,✉
PMCID: PMC10959175  PMID: 37524062

Abstract

Introduction

According to the US Renal Data System (USRDS), patients with end-stage kidney disease (ESKD) on maintenance dialysis had higher mortality during early COVID-19 pandemic. Less is known about the effect of the pandemic on the delivery of outpatient maintenance hemodialysis and its impact on death. We examined the effect of pandemic-related disruption on the delivery of dialysis treatment and mortality in patients with ESKD receiving maintenance hemodialysis in the Veterans Health Administration (VHA) facilities, the largest integrated national healthcare system in the USA.

Methods

Using national VHA electronic health records data, we identified 7,302 Veterans with ESKD who received outpatient maintenance hemodialysis in VHA healthcare facilities during the COVID-19 pandemic (February 1, 2020, to December 31, 2021). We estimated the average change in the number of hemodialysis treatments received and deaths per 1,000 patients per month during the pandemic by conducting interrupted time-series analyses. We used seasonal autoregressive moving average (SARMA) models, in which February 2020 was used as the conditional intercept and months thereafter as conditional slope. The models were adjusted for seasonal variations and trends in rates during the pre-pandemic period (January 1, 2007, to January 31, 2020).

Results

The number (95% CI) of hemodialysis treatments received per 1,000 patients per month during the pre-pandemic and pandemic periods were 12,670 (12,525–12,796) and 12,865 (12,729–13,002), respectively. Respective all-cause mortality rates (95% CI) were 17.1 (16.7–17.5) and 19.6 (18.5–20.7) per 1,000 patients per month. Findings from SARMA models demonstrate that there was no reduction in the dialysis treatments delivered during the pandemic (rate ratio: 0.999; 95% CI: 0.998–1.001), but there was a 2.3% (95% CI: 1.5–3.1%) increase in mortality. During the pandemic, the non-COVID hospitalization rate was 146 (95% CI: 143–149) per 1,000 patients per month, which was lower than the pre-pandemic rate of 175 (95% CI: 173–176). In contrast, there was evidence of higher use of telephone encounters during the pandemic (3,023; 95% CI: 2,957–3,089), compared with the pre-pandemic rate (1,282; 95% CI: 1,241–1,324).

Conclusions

We found no evidence that there was a disruption in the delivery of outpatient maintenance hemodialysis treatment in VHA facilities during the COVID-19 pandemic and that the modest rise in deaths during the pandemic is unlikely to be due to missed dialysis.

Keywords: COVID-19, Dialysis, Mortality, Veterans Affairs healthcare system

Introduction

The coronavirus disease 2019 (COVID-19) pandemic caused substantial disruption in the delivery of essential healthcare services, including that of the hemodialysis delivery system [1–4]. Kidney replacement therapy with maintenance dialysis is life-saving for patients with end-stage kidney disease (ESKD), and nonadherence to dialysis has been shown to be associated with a higher risk of death in patients with ESKD [5, 6]. A report by the US Renal Data System (USRDS) suggested higher all-cause mortality in patients with ESKD during the pandemic [7]. Less is known whether these changes in mortality were affected by reductions in dialysis frequency. The US Department of Veterans Affairs (VA) healthcare system is the largest integrated healthcare system in the nation, providing care to over 9 million Veterans at 1,298 health care facilities. VA is also required by law to ensure continued service to Veterans during national emergencies and natural disasters [8]. VA also has one of the most comprehensive research-ready electronic health record systems [9, 10]. Thus, we used VA data to implement our study objective which was to examine the impact of the COVID-19 pandemic on maintenance hemodialysis treatment and death in Veterans with ESKD receiving outpatient maintenance hemodialysis therapy at the VA.

Methods

Study Design, Data, and Population

We conducted an interrupted time-series analysis of Veterans receiving in-center maintenance hemodialysis. Interrupted time series is an observational study design which is useful for the evaluation of the effectiveness of the start or implementation of an intervention or a new policy at the population level and has been used to examine the effect of COVID-19 on health and outcomes [11, 12]. We used VA’s national electronic health record data stored in the Corporate Data Warehouse for the current analysis [13, 14]. The cohort consisted of Veterans who received outpatient maintenance hemodialysis in the VA from January 1, 2007, through December 31, 2021. Maintenance hemodialysis was defined by the receipt of 7 or more hemodialysis treatments per month for 3 months in a row. We identified 16,411 Veterans who received outpatient maintenance hemodialysis as ascertained by stop codes (602 and 603) for hemodialysis encounters in the VA [15]. When multiple codes were present on the same day, only one was counted. To identify Veterans treated with hemodialysis during prolonged inpatient encounters that may not be captured by stop codes, we used CPT codes 90935 and 90937, which identified an additional 173 patients. Thus, our final cohort of Veterans with ESKD who received outpatient maintenance hemodialysis in the VA consisted of 16,584 patients. The study cohort was assembled in blocks of months starting January 2007 through December 2021 (Table 1). We used February 2020 as the index COVID month based on VA’s initial response to the evolving pandemic [16]. We divided the study population into three cohorts: pre-COVID (Jan 1, 2007, to Jan 31, 2020; n = 14,682), COVID-index (February 2020; n = 2,902), and COVID (Mar 1, 2020, to Dec 31, 2021; n = 4,418) cohorts (Table 1).

Table 1.

Characteristics of Veteran patients receiving maintenance hemodialysis in the Veterans Affairs (VA) healthcare system, before and during the COVID-19 pandemic

Pre-COVID cohort: COVID-index cohort: COVID cohort:
January 1, 2007 to January 31, 2020 (n = 14,682) February 2020 (n = 2,902) March 1, 2020 to December 31, 2021 (n = 4,418)
Age, mean (SD), years 71.9 (10.3) 69.7 (9.6) 69.6 (10.1)
Female, % (95% CI) 2.9 (2.7, 3.2) 3.5 (2.8, 4.2) 3.4 (2.9.4.0)
African American, % (95% CI) 45.0 (44.2, 45.9) 56.9 (55.1, 58.7) 54.0 (52.5.55.4)
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The confidence intervals (CI) for the percentages were computed individually for each year using the Pearson-Klopper method. Patients are shared across cohorts; therefore, a single p value does not adequately describe the differences.

Outcomes

Our primary outcomes were all-cause mortality and frequency of hemodialysis received, and secondary outcomes were non-COVID hospitalizations, all face-to-face outpatient encounters (including dialysis), and remote tele-medicine encounters, observed from electronic health record and administrative data. Unadjusted rates were estimated as events per month per 1,000 patients. Denominators for deaths were the total number of patients alive on the first day of each month, and those for non-mortality outcomes were patients alive at the end of each month.

Statistical Analysis

Demographic characteristics of patients in the pre-COVID, index COVID, and COVID cohorts are described in Table 1. Because patients are shared across these cohorts, between-cohort differences were not tested for statistical significance. We estimated rates per 1,000 patients per month for all outcomes and presented the results in Figure 1. We then conducted interrupted time-series analysis to quantify the impact of COVID-19 pandemic on all-cause mortality and the number of hemodialysis received [11, 17–20]. Based on the finding from augmented Dickey-Fuller tests (p value <0.01) which suggested that our time series are stationary at the alpha level of 0.01, we used seasonal autoregressive moving average (SARMA) models for time-series analyses [21]. SARMA models incorporate seasonal patterns in autoregressive moving average models which are suitable when time series is stationary.

Fig. 1.

Fig. 1.

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Aggregate unadjusted rates (95% CI) per 1,000 patients per month for all-cause mortality, number of hemodialysis treatments received, non-COVID hospitalizations, and telephone encounters in Veterans with end-stage kidney disease receiving chronic dialysis therapy in the VA healthcare system during the COVID-19 pandemic (February 1, 2020 to December 31, 2021) and before the COVID-19 pandemic (January 1, 2007 to January 31, 2020).

For both outcomes, we fitted all possible SARMA models with a maximum of 10 autoregressive and moving average components and at most 3 seasonal autoregressive and 3 seasonal moving average components. We repeated this process for no season and seasons of 4 and 12 months. In the models, we included an intercept and a slope effect starting on February 1, 2020, to test for the COVID effect on the time series. Two final models, one for each outcome, were chosen using the Akaike Information Criteria. Both models were adjusted for age, race, and log total number of patients with ESKD in each month, overall and by age and race.

To assess the impact of other healthcare services (viz., in-person vs. telehealth) or outcomes (viz., non-COVID hospitalization) on death and hemodialysis, we repeated both models, adjusting for face-to-face and telehealth encounters and non-COVID hospitalizations. Finally, to assess the impact of the number of hemodialysis treatments received on death, we repeated the mortality model with additional adjustment for the number of dialysis received. The effect of these variables was not significant. The Ljung-Box test for the two final SARMA models had a p value >0.01, and all the coefficients were statistically significant at the 0.05 alpha level. Using the fitted SARMA models, we conducted one-step ahead prediction and 95% prediction interval for all-cause mortality and the number of hemodialysis therapy received and presented the results in Figure 2. All statistical tests were two-tailed with a p value <0.05 considered significant. R Statistical Software version 4.1.2 (Bird Hippie) was used for data analyses.

Fig. 2.

Fig. 2.

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Predicted changes in rates and 95% prediction intervals of all-cause mortality (top panel) and number of dialysis treatments received (bottom panel) per 1,000 patients per month during the COVID-19 pandemic (February 1, 2020, to December 31, 2021) in Veterans with end-stage kidney disease receiving dialysis in the VA healthcare system, compared with average trend-adjusted pre-pandemic (January 1, 2007, to January 31, 2020) rate. The findings from the seasonal autoregressive moving average (SARMA) models suggest that there was no change in the number of dialysis treatments provided during the pandemic (rate ratio: 0.999; 95% CI: 0.998–1.001), but there was a 2.3% increase in the mortality rate (rate ratio: 1.023; 95% CI: 1.015–1.031). Points represent mean unadjusted rates, the solid lines represent adjusted predicted rates, and the gray areas represent 95% prediction intervals. Both models used February 1, 2020, as a conditional intercept and were adjusted for the monthly number of patients in different age and race groups and the number of patients with end-stage kidney disease.

Results

Baseline Characteristics

Patients in the COVID cohorts had younger mean age, and a greater proportion were African Americans and women compared with those in the pre-COVID cohort (Table 1). As mentioned above, because of overlap of patients in the two cohorts, between-cohort differences in these characteristics were not tested for statistical significance.

All-Cause Mortality

All-cause mortality (95% CIs) per month per 1,000 patients in the COVID and pre-COVID cohorts were 19.6 (18.5–20.7) and 17.1 (16.7–17.5), respectively, representing a 14.6% relative increase (Fig. 1). Findings from our SARMA seasonal models demonstrated that compared with average trend-adjusted pre-pandemic rate, average adjusted monthly mortality was 2.3% (95% CI: 1.5–3.1%) higher during the pandemic, up to December 2021, which was statistically significant (Fig. 2). The adjusted mortality rate in February 2020 was 4.6% higher but was not statistically significant (95% CI: −8.9–20.1%).

Maintenance Hemodialysis Treatment

The number (95% CIs) of hemodialysis treatments per month per 1,000 patients in the COVID and pre-COVID cohorts was 12,865 (12,729–13,002) and 12,667 (12,525–12,796), respectively. These translate into nearly 13 hemodialysis treatments per patient per month, a number which is consistent with the standard of care for patients with ESKD receiving maintenance hemodialysis. Findings from our SARMA seasonal models of 4 months demonstrate that there was no significant change in the average adjusted monthly hemodialysis rate during the pandemic (Fig. 2).

Non-COVID Hospitalization and Telephone Encounters

Non-COVID hospitalizations per month per 1,000 patients (95% CIs) in the pre-COVID and COVID cohorts were 174.6 (173.4–175.8) and 145.9 (143.1–148.7), respectively (Fig. 1). There was an increase in telehealth encounters but no significant change in face-to-face outpatient encounters during the pandemic.

Discussion

The findings from our study demonstrate that during the COVID-19 pandemic, Veterans with ESKD receiving outpatient maintenance hemodialysis in VA healthcare facilities received hemodialysis therapy at a rate similar to the average trend-adjusted pre-pandemic rate. We also observed that these patients had a statistically significant, albeit small, increase in adjusted rate of death when compared with average trend-adjusted pre-pandemic rate, which was not affected by the delivery of hemodialysis or other healthcare services. To the best of our knowledge, this is the first study of quality and outcomes of care of Veterans receiving maintenance hemodialysis in the VA during the pandemic that suggest an uninterrupted delivery of hemodialysis service. This is consistent with the VA’s commitment to ensure continued service to Veterans during national emergencies and natural disasters. Taken together with other pandemic-related responses such as an increase in telehealth use and a decrease in non-COVID hospitalizations, these findings suggest that by providing uninterrupted dialysis services, the VA was able to attenuate COVID-related higher mortality in Veterans receiving maintenance hemodialysis in the VA than those receiving maintenance hemodialysis in non-VA settings [7].

According to USRDS, all-cause mortality among patients undergoing dialysis during the first 3 months of 2020 varied between 3.0 and 3.4 per 1,000 patients per week, which was similar to that observed during the same period in 2017–2019 [7]. Thereafter, mortality increased, peaking in early April 2020 to a rate of 4.2 deaths per 1,000 patients per week, which was 37% higher than that during the same period in 2017–2019. During the months of May and June 2020, all-cause mortality was 26% higher than that during 2017–2019. The higher mortality during the early pandemic months is likely due to missed dialysis treatments [22–26]. Thus, the more modest increase in mortality in Veteran patients receiving maintenance hemodialysis in the VA is likely due to lack of disruption in the delivery and receipt of dialysis treatments which involve both patient and system-related factors. Veterans receiving maintenance hemodialysis in the VA have lower mortality than Veterans receiving maintenance hemodialysis in non-VA facilities [27]. Better outcomes in the VA are likely due to Veterans’ resilience as well as VA’s ability to provide high-quality, effective, and timely care, which is attributed to advances made in the use of information technology, performance measurement, and integration of services [28, 29]. Another potential explanation of the ability of the VA to provide uninterrupted dialysis services is its preparedness to provide continued service during public emergencies [8, 30–32]. VA’s preparedness is also evidenced by the rapid and robust expansion of telehealth services during the pandemic [33, 34]. In our study, the number of telehealth encounters increased by 1,500 per 1,000 Veterans in the first 2 months of the pandemic (from ∼2,000 to ∼3,500), which is greater than that observed between 2011 and 2019 (from ∼1,000 to ∼2,000). Findings from a study comparing excess death during the first year of the pandemic in Veterans receiving care in the VA versus the overall US population suggest that despite being on average 23 years older, Veterans had a relatively lower excess mortality [35].

The lack of interruption of hemodialysis services in the VA suggests that missed or delayed dialysis likely did not contribute to the higher death rate during the pandemic in Veterans receiving hemodialysis in the VA. It has been suggested that lack of hospitalization for conditions requiring emergency treatment during the pandemic may have contributed to higher mortality [36]. However, hospitalization is also associated with a higher risk of death [37–39]. Thus, a lower non-COVID hospitalization may also have attenuated the risk of death associated with hospitalizations [40]. Considering that the findings from our SARMA model suggest that hospitalization was not a predictor of death, the higher mortality rate during the pandemic is most likely attributable to COVID-19 [41–44]. Although not directly comparable, the pandemic-related higher mortality observed in our study is similar to that reported for the US general population [45]. According to the US Census Bureau, unadjusted mortality in the US general population increased by 18.7% from 2019 to 2020 [45]. Which is higher than 14.6% higher mortality observed in our study.

There are several limitations to our study. As in any observational study, findings from our study are subject to potential bias due to unmeasured or residual confounding. Unlike regression analyses that use two independent samples, time-series analyses are often based on non-independent samples. This limits investigators’ ability to adjust for confounders shared by patients in pre- and post-pandemic cohorts, which in turn limits the ability of time-series analyses to establish independence of associations. Our study excluded patients receiving acute hemodialysis for COVID-19-related acute kidney injury [46–51]. However, that is unlikely to affect the results of our study as the delivery of maintenance hemodialysis was not interrupted despite the additional demand of providing acute hemodialysis. Although our study is based on a national sample, results of our study may not be generalized to Veterans with ESKD receiving hemodialysis in the non-VA system, non-Veterans with ESKD, or Veterans without ESKD.

In conclusion, we found no evidence of disruption in the hemodialysis service in the VA healthcare system during the COVID-19 pandemic, which in turn may explain the relatively modest increase in mortality in Veterans receiving maintenance hemodialysis. Considering that the vast majority of Veterans with ESKD receive their maintenance hemodialysis in non-VA facilities [27], it is important to replicate these findings in that group of Veterans. Future studies also need to replicate these findings in patients receiving maintenance hemodialysis under Medicare.

Statement of Ethics

This study protocol was reviewed and approved by the VA Central Institutional Review Board, approval number (1613783-4). The study is based on existing electronic health records, and written informed consent was not required. The study was granted an exemption from requiring written informed consent as well as an exemption from a full review.

Conflict of Interest Statement

None of the other authors report any conflicts of interest related to this manuscript. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans Affairs.

Funding Sources

The work was supported by supplemental funding from an award (I01HX002422) from the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, to the Center for Data Science and Outcomes Research, Washington DC VA Medical Center (principal investigators: Ali Ahmed, Wen-Chi Wu, and Qing Zeng). This material is the result of work supported with resources and the use of facilities at the Washington, DC, VA Medical Center. Virginia Wang is supported by the Center of Innovation to Accelerate Discovery and Practice Transformation (CIN 13-410) at the Durham VA Health Care System.

Author Contributions

Drs. Samir Patel, Ali Ahmed, Wen-Chih Wu, and Qing Zeng-Treitler conceived and designed the study. Drs. Eduardo Trujillo Rivera and Sijian Zhang performed data analysis. Drs. Samir Patel and Ali Ahmed drafted the initial manuscript. All authors including Drs. Venkatesh Raman, Charles Faselis, Virginia Wang, Jeffrey Fink, Jeffrey Roseman, Charity Morgan, and Helen Sheriff, and Mr. Michael Heimall contributed important intellectual contents during drafting and critical revision of the manuscript.

Funding Statement

The work was supported by supplemental funding from an award (I01HX002422) from the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, to the Center for Data Science and Outcomes Research, Washington DC VA Medical Center (principal investigators: Ali Ahmed, Wen-Chi Wu, and Qing Zeng). This material is the result of work supported with resources and the use of facilities at the Washington, DC, VA Medical Center. Virginia Wang is supported by the Center of Innovation to Accelerate Discovery and Practice Transformation (CIN 13-410) at the Durham VA Health Care System.

Data Availability Statement

Data used in the current study are based on electronic health records data that include sensitive patient information and are available to researchers in the US. Department of Veterans Affairs (VA) with VA Institutional Review Board-approved study protocols. Additional information can be obtained from the VA Information Resource Center (VIReC) Research User Guides at https://www.virec.research.va.gov/Resources/RUGs.asp.

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Associated Data

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

Data Availability Statement

Data used in the current study are based on electronic health records data that include sensitive patient information and are available to researchers in the US. Department of Veterans Affairs (VA) with VA Institutional Review Board-approved study protocols. Additional information can be obtained from the VA Information Resource Center (VIReC) Research User Guides at https://www.virec.research.va.gov/Resources/RUGs.asp.

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