South Africa faced repeated episodes of temporary power shutdowns, or load shedding, in 2014/2015. The effect of load shedding on children’s health is unknown.
We determined periods of load shedding using Twitter, Facebook, and data from the City of Cape Town. We obtained the number of unscheduled hospital admissions between June 2014 and May 2015 from Red Cross Children’s Hospital, Cape Town, and weather data from the South African Weather Service. We used quasi-Poisson regression models to explore the relationship between number of hospital admissions and load shedding, adjusted for season, weather, and past admissions. Based on assumptions about the causal process leading to hospital admissions, we estimated the average treatment effect, that is, the difference in expected number of admissions per day had there been load shedding each day or on any of the preceding 2 days compared with if there had not been any load shedding.
We found a 10% increase (95% confidence interval: 4%, 15%) in hospital admissions for days where load shedding was experienced on the same day, or no more than 2 days prior, compared with when there was no load shedding in the past 2 days. The increase was more pronounced during weekdays (12% [7%, 18%] vs. 1% [−9%, 11%]), and for specific diagnoses (e.g., respiratory system: 14% [2%, 26%]). The average treatment effect was estimated as 6.50 (5.12, 7.87) highlighting that about 6 additional admissions a day could be attributed to load shedding.
The association we measured is consistent with our hypothesis that failures of the power infrastructure increase risk to children’s health. See video abstract at, http://links.lww.com/EDE/B409.
From the aDivision of Internal Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
bRed Cross War Memorial Children’s Hospital, Cape Town, South Africa
cCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Submitted December 6, 2017; accepted July 26, 2018.
Supported by the US National Institute of Allergy and Infectious Diseases through the International epidemiological Databases to Evaluate AIDS, Southern Africa (IeDEA-SA), grant 5U01AI069924-05.
The authors report no conflicts of interest.
Availability of code and data: The R-code for this analysis is part of the supplementary material; http://links.lww.com/EDE/B389. The data are available from the authors upon request to verify the results.
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Michael Schomaker, Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Anzio Road, Observatory 7925, Cape Town, South Africa. E-mail: michael.schomaker@uct.ac.za.
