The 2014–2015 Ebola virus disease outbreak and primary healthcare delivery in Liberia: Time-series analyses for 2010–2016

Abstract

BackgroundThe aim of this study is to estimate the immediate and lasting effects of the 2014–2015 Ebola virus disease (EVD) outbreak on public-sector primary healthcare delivery in Liberia using 7 years of comprehensive routine health information system data.Methods and findingsWe analyzed 10 key primary healthcare indicators before, during, and after the EVD outbreak using 31,836 facility-month service outputs from 1 January 2010 to 31 December 2016 across a census of 379 public-sector health facilities in Liberia (excluding Montserrado County). All indicators had statistically significant decreases during the first 4 months of the EVD outbreak, with all indicators having their lowest raw mean outputs in August 2014. Decreases in outputs comparing the end of the initial EVD period (September 2014) to May 2014 (pre-EVD) ranged in magnitude from a 67.3% decrease in measles vaccinations (95% CI: −77.9%, −56.8%, p < 0.001) and a 61.4% decrease in artemisinin-based combination therapy (ACT) treatments for malaria (95% CI: −69.0%, −53.8%, p < 0.001) to a 35.2% decrease in first antenatal care (ANC) visits (95% CI: −45.8%, −24.7%, p < 0.001) and a 38.5% decrease in medroxyprogesterone acetate doses (95% CI: −47.6%, −29.5%, p < 0.001). Following the nadir of system outputs in August 2014, all indicators showed statistically significant increases from October 2014 to December 2014. All indicators had significant positive trends during the post-EVD period, with every system output exceeding pre-Ebola forecasted trends for 3 consecutive months by November 2016. Health system outputs lost during and after the EVD outbreak were large and sustained for most indicators. Prior to exceeding pre-EVD forecasted trends for 3 months, we estimate statistically significant cumulative losses of −776,110 clinic visits (95% CI: −1,480,896, −101,357, p = 0.030); −24,449 bacille Calmette–Guérin vaccinations (95% CI: −45,947, −2,020, p = 0.032); −9,129 measles vaccinations (95% CI: −12,312, −5,659, p < 0.001); −17,191 postnatal care (PNC) visits within 6 weeks of birth (95% CI: −28,344, −5,775, p = 0.002); and −101,857 ACT malaria treatments (95% CI: −205,839, −2,139, p = 0.044) due to the EVD outbreak. Other outputs showed statistically significant cumulative losses only through December 2014, including losses of −12,941 first pentavalent vaccinations (95% CI: −20,309, −5,527, p = 0.002); −5,122 institutional births (95% CI: −8,767, −1,234, p = 0.003); and −45,024 acute respiratory infections treated (95% CI: −66,185, −24,019, p < 0.001). Compared to pre-EVD forecasted trends, medroxyprogesterone acetate doses and first ANC visits did not show statistically significant net losses. ACT treatment for malaria was the only indicator with an estimated net increase in system outputs through December 2016, showing an excess of +78,583 outputs (95% CI: −309,417, +450,661, p = 0.634) compared to pre-EVD forecasted trends, although this increase was not statistically significant. However, comparing December 2013 to December 2017, ACT malaria cases h...

Treatment outcomes of TB‐infected individuals attending public sector primary care clinics in rural Liberia from 2015 to 2017: a retrospective cohort study

“…Liberia is only 12 years removed from 14 years of civil conflict which devastated an already weak health system. In addition, Liberia recently emerged from the worst EVD epidemic known to date . Our findings that an 76% reduction in LTFU rates can be obtained over a relatively short time period immediately after the EVD outbreak is a testament to the strength of CHW interventions, and their potential for positive impacts on TB care and treatment.…”

“…There is an estimated 16% national treatment default rate . The 2014‐2015 Ebola virus disease (EVD) outbreak exacerbated the situation , causing an estimated 11 533 additional TB‐related deaths in Liberia (compared with 4769 deaths directly from EVD) as modelled using decision trees by Parpia et al . .…”

Using Routine Health Information Data for Research in Low- and Middle-Income Countries: A Systematic Review

“…Some of these studies found large discrepancies between RHIS data and an estimated disease burden in populations or highlighted the lack of service provision. A few studies also used RHIS data to describe speci c programs [60][61][62][63][64] , conduct impact evaluations (non-programmatic) [65][66][67][68] , and estimate costs 69,70 .…”

“…Time series analysis using RHIS data was most often applied to evaluate programs and identify disease epidemiology, with one study assessing the impact of an infectious disease outbreak on primary health service utilization 73 . Studies analyzed indicators using large quantities of monthly or yearly data to estimate change (range of time units: 5 -168).…”

“…These strategies consisted of exclusion, imputation, interpolation, veri cation, and accounting for missing data in modeling. Exclusion of missing data was the most common practice, and among studies that used this technique, they excluded facilities from the analytic samples 38,41,45,52,65,91,[93][94][95][96][97][98] , restricted the study period based on explicit criteria 54,99 , or applied sensitivity analysis to compare various exclusion criteria 41,100,101 . Imputation methods varied from assigning speci c values to the missing observation 42,86,91,[102][103][104] , to various modeling strategies such as conditional autoregressive model 87 , generalized linear regression 103 , and iterative singular value decomposition 103 .…”