tetano
Editor, Senior Moderator
J Am Soc Nephrol
. 2020 Jun 19;ASN.2020040534.
doi: 10.1681/ASN.2020040534. Online ahead of print.
Epidemiology of COVID-19 in an Urban Dialysis Center
Richard W Corbett[SUP] 1 [/SUP], Sarah Blakey[SUP] 1 [/SUP], Dorothea Nitsch[SUP] 2 3 4 [/SUP], Marina Loucaidou[SUP] 1 [/SUP], Adam McLean[SUP] 1 [/SUP], Neill Duncan[SUP] 1 [/SUP], Damien R Ashby[SUP] 5 [/SUP], West London Renal and Transplant Centre
Affiliations
Abstract
Background: During the coronavirus disease 2019 (COVID-19) epidemic, many countries have instituted population-wide measures for social distancing. The requirement of patients on dialysis for regular treatment in settings typically not conducive to social distancing may increase their vulnerability to COVID-19.
Methods: Over a 6-week period, we recorded new COVID-19 infections and outcomes for all adult patients receiving dialysis in a large dialysis center. Rapidly introduced control measures included a two-stage routine screening process at dialysis entry (temperature and symptom check, with possible cases segregated within the unit and tested for SARS-CoV-2), isolated dialysis in a separate unit for patients with infection, and universal precautions that included masks for dialysis nursing staff.
Results: Of 1530 patients (median age 66 years; 58.2% men) receiving dialysis, 300 (19.6%) developed COVID-19 infection, creating a large demand for isolated outpatient dialysis and inpatient beds. An analysis that included 1219 patients attending satellite dialysis clinics found that older age was a risk factor for infection. COVID-19 infection was substantially more likely to occur among patients on in-center dialysis compared with those dialyzing at home. We observed clustering in specific units and on specific shifts, with possible implications for aspects of service design, and high rates of nursing staff illness. A predictive epidemic model estimated a reproduction number of 2.2; cumulative cases deviated favorably from the model from the fourth week, suggesting that the implemented measures controlled transmission.
Conclusions: The COVID-19 epidemic affected a large proportion of patients at this dialysis center, creating service pressures exacerbated by nursing staff illness. Details of the control strategy and characteristics of this epidemic may be useful for dialysis providers and other institutions providing patient care.
Keywords: COVID-19; clinical epidemiology; haemodialysis.
. 2020 Jun 19;ASN.2020040534.
doi: 10.1681/ASN.2020040534. Online ahead of print.
Epidemiology of COVID-19 in an Urban Dialysis Center
Richard W Corbett[SUP] 1 [/SUP], Sarah Blakey[SUP] 1 [/SUP], Dorothea Nitsch[SUP] 2 3 4 [/SUP], Marina Loucaidou[SUP] 1 [/SUP], Adam McLean[SUP] 1 [/SUP], Neill Duncan[SUP] 1 [/SUP], Damien R Ashby[SUP] 5 [/SUP], West London Renal and Transplant Centre
Affiliations
- PMID: 32561681
- DOI: 10.1681/ASN.2020040534
Abstract
Background: During the coronavirus disease 2019 (COVID-19) epidemic, many countries have instituted population-wide measures for social distancing. The requirement of patients on dialysis for regular treatment in settings typically not conducive to social distancing may increase their vulnerability to COVID-19.
Methods: Over a 6-week period, we recorded new COVID-19 infections and outcomes for all adult patients receiving dialysis in a large dialysis center. Rapidly introduced control measures included a two-stage routine screening process at dialysis entry (temperature and symptom check, with possible cases segregated within the unit and tested for SARS-CoV-2), isolated dialysis in a separate unit for patients with infection, and universal precautions that included masks for dialysis nursing staff.
Results: Of 1530 patients (median age 66 years; 58.2% men) receiving dialysis, 300 (19.6%) developed COVID-19 infection, creating a large demand for isolated outpatient dialysis and inpatient beds. An analysis that included 1219 patients attending satellite dialysis clinics found that older age was a risk factor for infection. COVID-19 infection was substantially more likely to occur among patients on in-center dialysis compared with those dialyzing at home. We observed clustering in specific units and on specific shifts, with possible implications for aspects of service design, and high rates of nursing staff illness. A predictive epidemic model estimated a reproduction number of 2.2; cumulative cases deviated favorably from the model from the fourth week, suggesting that the implemented measures controlled transmission.
Conclusions: The COVID-19 epidemic affected a large proportion of patients at this dialysis center, creating service pressures exacerbated by nursing staff illness. Details of the control strategy and characteristics of this epidemic may be useful for dialysis providers and other institutions providing patient care.
Keywords: COVID-19; clinical epidemiology; haemodialysis.