Comparative Analysis of SARS-CoV-2 Reproduction Rates in the Dialysis and General Populations
Authors: Alhaji Cherif, Joanna L. Willetts, Len Usvyat, Yuedong Wang, Peter Kotanko
Journal: Journal of the American Society of Nephrology (2021)

Chronic hemodialysis is a life-sustaining therapy, delivered mostly thrice weekly in dialysis clinics. For patients on dialysis who appear to have a heightened likelihood of severe infection or death from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), travel to these facilities and proximity to fellow patients and health care providers during hemodialysis may pose an increased risk for SARS-CoV-2 infection. To prevent contagion, dialysis providers have implemented several interventions. Our goal was to better understand the dynamics of SARS-CoV-2 spread among patients on hemodialysis compared with the general population in the United States. We used aggregated daily counts of confirmed coronavirus disease 2019 (COVID-19) cases for the general population in the United States1 and from Fresenius Medical Care North America, a large dialysis provider in the country, from March 1 to July 29, 2020. Following screenings in dialysis units, patients showing any relevant signs and symptoms or who had exposure to an infected person underwent nasopharyngeal or oropharyngeal swabs for RT-PCR testing for SARS-CoV-2. All patients included in the study were receiving in-center hemodialysis; patient characteristics are shown in Table 1. The time-varying reproduction number Rt, which represents the expected number of secondary cases arising from each new infectious individual at a particular time, was calculated using the method of Thompson et al.2Rt is estimated from daily cases and an uncertain serial interval with a mean of 4.7 days (SD of 2.9 days) and a 14-day moving average. Table 1. - Demographics of patients on hemodialysis with confirmed COVID-19 Variable Patient Characteristics P Valuea COVID-19–Positive Patients COVID-19–Negative Patients All Patients (%) Population, n (%) 10,558 (100) 201,364 (100) 211,922 (100) Age, yr, n (%) <18 3 (0) 52 (0) 55 (0) >0.99 18–44 1121 (10.6) 21,139 (10.5) 22,260 (10.5) 0.71 45–54 1687 (16) 29,273 (14.5) 30,960 (14.6) <0.001 55–64 2728 (25.8) 48,621 (24.1) 51,349 (24.2) <0.001 65–74 2852 (27) 55,132 (27.4) 57,984 (27.4) 0.42 75+ 2167 (20.5) 47,147 (23.4) 49,314 (23.3) <0.001 Age, yr (SD) 62.6 (14) 63.5 (14.3) 63.5 (14.2) <0.001 Dialysis vintage, mo (SD) 47.3 (49.8) 45.4 (50) 45.5 (50) <0.001 Men, n (%) 5822 (55.1) 116,165 (57.7) 121,987 (57.6) <0.001 Race, n (%) White 3981 (37.7) 81,995 (40.7) 85,976 (40.6) <0.001 Black 3126 (29.6) 50,847 (25.3) 53,973 (25.5) <0.001 Unknown 3155 (29.9) 60,808 (30.2) 63,963 (30.2) 0.50 Other 296 (2.8) 7714 (3.8) 8010 (3.8) <0.001 Ethnicity, n (%) Non-Hispanic 5537 (52.4) 113,217 (56.2) 118,754 (56) <0.001 Hispanic 1675 (15.9) 21,689 (10.8) 23,364 (11) <0.001 Unknown 3346 (31.7) 66,458 (33) 69,804 (32.9) 0.005 Comorbid conditions, n (%) Diabetes 4891 (46.3) 82,739 (41.1) 87,630 (41.4) <0.001 IHD 2101 (19.9) 41,898 (20.8) 43,999 (20.8) 0.03 PAD/PVD 967 (9.2) 17,918 (8.9) 18,885 (8.9) 0.37 CHF 2435 (23.1) 44,968 (22.3) 47,403 (22.4) 0.08 CVD 802 (7.6) 12,492 (6.2) 13,294 (6.3) <0.001 COPD 973 (9.2) 20,185 (10) 21,158 (10) 0.007 Hypertension 7534 (71.4) 141,367 (70.2) 148,901 (70.3) 0.01 The patients received in-center hemodialysis in US Fresenius Medical Care North America dialysis facilities up to July 29, 2020. IHD, ischemic heart disease; PAD, peripheral arterial disease; PVD, peripheral vascular disease; CHF, congestive heart failure; CVD, cerebrovascular disease; COPD, chronic obstructive pulmonary disease.aFor continuous variables, P values result from the two-sided t test H0: mean values for COVID-19–negative patients are equal to mean values for COVID-19–positive patients in the full population. For categorical variables, P values result from the chi-squared test H0: proportion in COVID-19–negative patients is equal to the proportion in COVID-19–positive patients. We fit cubic splines to the estimated Rt values and the logarithm ratio of general and dialysis populations using penalized inverse variance–weighted least squares with the smoothing parameter selected by the generalized maximum likelihood method.3 We used the SSR function in the ASSIST R package4 (R Foundation for Statistical Computing, Vienna, Austria). We tested the null hypothesis that the Rt of the general population and the Rt of the dialysis population are equal for the observation period; this is equivalent to testing that the logarithm ratio equals to zero. We constructed an F statistic and applied the bootstrap procedure to compute the P value.5 Institutional review board review was not required because we used aggregated data without identifiable personal health information, per Common Rule 45 CFR §102(e). We found that the SARS-CoV-2 Rt for the dialysis population and the Rt for the general population follow a similar trend; both of them reach the Rt=1 threshold within a few days of each other (Figure 1A). Similar trends are also seen in three of the largest states, California (Figure 1D), Florida (Figure 1E), and Texas (Figure 1F). Figure 1B shows the log ratio of the general population Rt and the dialysis population Rt over time. A value above zero corresponds to days when the general population Rt is larger. Over the entire observation period, the Rt for the general population and the Rt for the dialysis population are significantly different on the basis of an F test (P<0.001). The general population Rt was mostly larger than the dialysis population Rt, except between April 5 and April 24, 2020. Figure 1C shows the percentage change of the daily growth rate on the logarithmic scale; this metric declined below the 1% mark sooner in the dialysis population. By the end of July, the dialysis population's daily growth rates were below or at the 1% threshold, whereas the general population's daily growth rates were still fluctuating above the 1% mark. Although the trends in Figure 1 are similar for Rt and daily growth rates for both populations, for most days, Rt for the dialysis population is lower (Figure 1, Table 2). Table 2 shows that on July 29, the end of the observation period, the Rt for the dialysis population was better (lower) for the United States in general and in some states (e.g., Texas and California) and worse (higher) for other states (e.g., Arizona and North Carolina). More than 60% of all COVID-19 cases among the dialysis population occur in the states shown in Table 2.Figure 1.: Comparison of key metrics describing the dynamics of COVID-19 spread. Reproduction and daily growth rates were significantly lower in the hemodialysis population but mirrored the background contagion of the general population. (A) Time-varying reproduction rate, R t, for the United States general (red) and dialysis populations (blue). The general population R t is below one between April 18th and June 10th, 2020, and at the threshold on June 29th, 2020. The dialysis population R t is below one between April 22nd and June 15th, 2020, and from July 22nd, 2020 onward. (B) Logarithm ratio of general and dialysis population reproduction numbers. A value above the zero line indicates that the general population R t is larger. (C) Daily case growth rates for the general and dialysis populations; at the 5% threshold, the daily case counts for both dialysis and general populations reach the first initial peaks. Prior to reaching the first peak, the daily growth rates for both populations were essentially the same. After reaching the initial peak (5% mark), the growth rates for the two populations started to diverge while maintaining similar trends. The dialysis population growth rates continued to decrease to be below the 1% threshold, whereas the general population decreased slowly and stayed above the 1% mark, which may be necessarily to mitigate and/or control the spread in the population. Time-varying reproduction rates for three of the largest states in the United States: (D) California, (E) Florida, and (F) Texas. These show similar trends as in the United States–wide trend but with substantial fluctuations at or near the R t close to one mark. The shaded areas in (A–F) correspond to 95% confidence intervals. Table 2. - National and state-level Rt (SD) estimates on July 29, 2020 Geography Dialysis Population General Population P Valuea Nationwide 0.9136 (0.0077) 0.9944 (0.0005) <0.001 Pennsylvania 0.7400 (0.0648) 1.0501 (0.0030) <0.001 California 0.8187 (0.0266) 1.0104 (0.0015) <0.001 Texas 0.8221 (0.0175) 0.9246 (0.0045) <0.001 Michigan 0.8497 (0.0924) 1.0287 (0.0095) 0.05 Alabama 0.9172 (0.0264) 0.9730 (0.0051) 0.04 New Jersey 0.9534 (0.0749) 1.1115 (0.0157) 0.04 Florida 0.9563 (0.0170) 0.9454 (0.0047) 0.54 Georgia 1.0208 (0.0320) 1.0122 (0.0052) 0.79 Arizona 1.0405 (0.0368) 0.9081 (0.0095) <0.001 New York 1.0444 (0.0996) 0.9590 (0.0051) 0.39 North Carolina 1.0753 (0.0360) 0.9971 (0.0027) 0.03 Illinois 1.0795 (0.0497) 1.0964 (0.0097) 0.74 Ohio 1.1291 (0.0513) 1.0242 (0.0014) 0.04 The patients received in-center hemodialysis in US Fresenius Medical Care North America dialysis facilities up to July 29, 2020. States are ranked by their dialysis population Rt (SD) estimates.aP values refer to the difference of Rt between dialysis and general populations. The Rt and daily growth rates indicate less SARS-CoV-2 spread among patients on chronic hemodialysis. We hypothesize that this finding is related to patients on hemodialysis having a more sedentary lifestyle and to such prevention measures as system-wide mandatory entrance screening for patients and staff, universal masking, testing of patients and staff with suspected SARS-CoV-2 infection, and dialysis shifts and clinics dedicated to patients with suspected or diagnosed COVID-19.6 However, because patients on hemodialysis are part of the overall general population, the curves tend to follow similar trends of Rt and daily case growth factors. The strength of our analysis is the large number of patients included. One potential limitation is the use of a backward estimator, in which the Rt computation is only on the basis of current and previous data points and does not account for future changes in the calculated Rt.2 The consistency of our results for most states suggests that lifestyle of patients undergoing hemodialysis and interventions to prevent SARS-CoV-2 spread in this patient population were effective in reducing risk, as indicated by a SARS-CoV-2 reproduction number that was significantly lower than, but mirrored the background contagion of, the general population. Disclosures A. Cherif and P. Kotanko are employees of the Renal Research Institute, a wholly owned subsidiary of Fresenius Medical Care North America. P. Kotanko holds stock in Fresenius Medical Care; reports research funding from Fresenius Medical Care; reports honoraria from HSTalks and UpToDate; and reports scientific advisor or membership via editorial board of Blood Purification and editorial board of Kidney and Blood Pressure Research. L. Usvyat and J.L. Willetts are employees of Fresenius Medical Care North America. L. Usvyat has ownership interest in Fresenius Medical Care. The remaining author has nothing to disclose. Funding This study was supported in part by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK130067.