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During Sierra Leone’s 2014–2015 Ebola virus disease (EVD) epidemic, early reports warned of health system collapse and potential effects on other-cause mortality. These same warnings are reverberating during the COVID-19 pandemic. Consideration of the impacts of EVD on maternal and child health services from facility data can be instructive during COVID-19. We surveyed all peripheral healthcare units (PHUs) in Sierra Leone in October 2014 and March 2015 to assess closures, staffing, amenities, medicines, supplies, and service utilization during May 2014–January 2015 and October 2013–January 2014. We report PHU characteristics and service utilization changes for equivalent 4-month periods during the epidemic and the prior year. We present utilization changes by district and service type, and model excess child mortality. PHU closures (−8%) and staff attrition (−3%) were limited, but many facilities lacked amenities, medicines, and supplies. Utilization of preventive and scheduled services fell more than individualized, clinical care interventions, aside from malaria treatment which declined significantly. Ebola virus disease intensity in districts was weakly associated with utilization, aside from two districts that were severely affected. Modeling suggests utilization declines resulted in 6,782 excess under-five deaths (an increase of 21%) between 2014 and 2015. Ebola virus disease negatively affected service provision, but utilization declined relatively more, particularly for preventive and scheduled interventions. Although these findings are specific to Sierra Leone’s EVD epidemic, they illustrate the magnitude of possible effects in other settings due to COVID-19–induced service disruptions, where collateral impacts on child mortality from other preventable causes may far outweigh COVID-19 mortality.
Sierra Leone is a small West African nation that had about seven million people and a female adult literacy rate of 35.5% in 2013.14 Although very high, under-five child mortality was declining before EVD15 and coverage of key MCH services (antenatal care [ANC], institutional delivery, the third dose of DPT vaccine, and seeking treatment for fever) ranged from 55% to 99.5% across districts (Table 1). Ebola virus disease was first detected in Sierra Leone in May 2014 in Kailahun district. By October 2014, it spread to all districts with nearly 2000 reported cases per month in November 2014 (Table 2). The epidemic subsided rapidly thereafter but was not declared over until March 2016. District cumulative EVD incidence rates over the period May 2014–January 2015 ranged from 12.5/100,000 in Pujehun to 205.9/100,000 in Western Area (Table 2). Sierra Leone district-level population (2015) and literacy and coverage of select maternal and child health interventions (2013) EVD = Ebola virus disease; ICF = International Community Foundation; Sources: Statistics Sierra Leone. Population and Housing Census 2015, Statistics Sierra Leone and ICF International 2014, Sierra Leone Demographic and Health Survey 2013. Freetown, Sierra Leone, and Rockville, MA: Statistics Sierra Leone and ICF International. Monthly number of confirmed EVD cases by district, May 2014–January 2015 Data accessed on April 2015 (Sierra Leone Ministry of Health and Sanitation): 1–50 cases, 51–100 cases, 101–200 cases, 201–500 cases, and 500+ cases. Two HFAs of all PHUs in Sierra Leone were conducted in October 2014 and March 2015. The first aimed to understand PHU operational status and service utilization during the epidemic. The second expanded on the first, to help plan for health system recovery. Both HFAs documented PHU closure, the availability of human resources, and monthly service visits for 10 essential MCH services: family planning, ANC, prevention of mother-to-child transmission of HIV, institutional delivery, postnatal care, pediatric HIV treatment, pentavalent vaccination (third dose), pediatric malaria treatment, growth monitoring, and treatment for severe acute malnutrition in children. The March 2015 HFA also collected information on the availability of medicines, supplies, and amenities (water, sanitation, and electricity). During the March 2015 HFA, service visit data were abstracted from facility records for two comparable 4-month periods before and during the epidemic: October 2013–January 2014 (period 1, before EVD) and October 2014–January 2015 (period 2, during EVD). Because seasonal variation can affect service utilization patterns, in this article, we only assess changes in service uptake for these two periods and do not include service utilization data collected during the October 2014 HFA. In addition, we reviewed EVD incidence over the period May 2014–January 2015. Assessments were approved and managed by the MoHS and UNICEF. They comprised close-ended questionnaires administered to senior onsite health workers, direct observation of amenities and stocks, and abstraction of data for 10 MCH service indicators from facility-based monthly health management information system (HMIS) reports. Data were collected by trained teams of two using paper-based questionnaires (October 2014) and tablets with KoBo Toolbox (version 1.4.3 [1,039]; Harvard Humanitarian Initiative, Cambridge, MA) open-source software (March 2015). Quality control measures included field-based supervision and review, back-up paper questionnaires in the event of tablet malfunction, and centralized review of values. Ministry of Health and Sanitation confirmed EVD case data were used to assess EVD intensity by district. We report descriptive PHU characteristics, including closures and availability of staff, amenities, medicines, and supplies. We used MCH Aides (junior nurses), which according to our survey were the largest cadre of health workers, to gauge attrition of clinical health workers during the EVD epidemic. We calculate the percent change in median cumulative MCH service visits between period 1 and period 2 by district for five tracer services: pentavalent three vaccine administration, malaria treatment (artemisinin-based combination therapy) for children younger than 5 years, four ANC visits (ANC 4), institutional deliveries, and growth monitoring. We use median as the central tendency measure because data are not normally distributed with high skewness and kurtosis, likely because of data quality issues common to routinely collected health data. Where data were missing for 1 month in a series, the 3-month average was imputed for the missing value. Where data were missing for more than 1 month, the PHU record was dropped for analysis of that intervention. Dropped values account for less than 3% of PHUs. We also excluded extreme outliers, which accounted for 3–5% of PHUs (Supplemental Webappendix 1). We evaluate whether changes in service utilization between periods 1 and 2 are statistically significant at P < 0.05 using the Wilcoxon signed-rank test (Supplemental Webappendix 2). We also assess whether changes in service use differed across districts using pairwise nonparametric Bonferoni-adjusted Dunn tests (Supplemental Webappendix 3). To determine if some interventions were more affected than others, we compared median percentage change in cumulative visits by intervention type and applied the Wilcoxon matched-pairs signed-rank test. To assess the relationship between service utilization and EVD intensity, we examine the correlation between the cumulative EVD incidence rate using MoHS confirmed case data from May 2014 to January 2015 and changes in service utilization across districts. Statistical analyses were conducted in STATA (STATA IC 12·1, StataCorp., College Station, TX). Last, we used the Lives Saved Tool (LiST) within spectrum analyzer (version 5.07; Avenir Health, Glastonbury, CT)16 to model excess under-five mortality attributable to the observed reductions in MCH service uptake during a 12-month period during 2014 and 2015. Lives Saved Tool is designed to estimate the impact of changes in health intervention coverage on mortality. We used annual country data available from the LiST website and incorporated additional data for 2013 and 2014 (Supplemental Webappendix 4). We created a baseline projection that assumed 2014 coverage estimates continued along the 2013 trajectory. We then developed a second projection that used the HFA-detected percent change in service utilization for 10 interventions to adjust annual MCH intervention coverage levels. Because the HFA did not collect data on all interventions included in LiST, we assumed the same change in coverage for interventions that are similar or operate on the same platform. For instance, changes in institutional deliveries were applied to newborn care services. Where no related data were obtained, we did not adjust 2014 coverage levels unless new estimates were published. Because much of the underlying data used in LiST are based on annual estimates, we are unable to present a more detailed estimate than expected excess mortality over a 12-month time period within 2014 and 2015. UNICEF and the Sierra Leone MoHS supported the cost of the HFAs. UNICEF and MoHS staff conducted this analysis and wrote the manuscript. The authors had access to all data reported in this article and have final responsibility for the decision to publish. Ethics approval was not required/sought.
