Readiness of health facilities to provide safe childbirth in Liberia: a cross-sectional analysis of population surveys, facility censuses and facility birth records

Background: The provision of quality obstetric care in health facilities is central to reducing maternal mortality, but simply increasing childbirth in facilities not enough, with evidence that many facilities in sub-Saharan Africa do not fulfil even basic requirements for safe childbirth care. There is ongoing debate on whether to recommend a policy of birth in hospitals, where staffing and capacity may be better, over lower level facilities, which are closer to women's homes and more accessible. Little is known about the quality of childbirth care in Liberia, where facility births have increased in recent decades, but maternal mortality remains among the highest in the world. We will analyse quality in terms of readiness for emergency care and referral, staffing, and volume of births. Methods: We assessed the readiness of the Liberian health system to provide safe care during childbirth use using three data sources: Demographic and Health Surveys (DHS), Service Availability and Readiness Assessments (SARA), and the Health Management Information System (HMIS). We estimated trends in the percentage of births by location and population caesarean-section coverage from 3 DHS surveys (2007, 2013 and 2019-20). We examined readiness for safe childbirth care among all Liberian health facilities by analysing reported emergency obstetric and neonatal care signal functions (EmONC) and staffing from SARA 2018, and linking with volume of births reported in HMIS 2019. Results: The percentage of births in facilities increased from 37 to 80% between 2004 and 2017, while the caesarean section rate increased from 3.3 to 5.0%. 18% of facilities could carry out basic EmONC signal functions, and 8% could provide blood transfusion and caesarean section. Overall, 63% of facility births were in places without full basic emergency readiness. 60% of facilities could not make emergency referrals, and 54% had fewer than one birth every two days. Conclusions: The increase in proportions of facility births over time occurred because women gave birth in lower-level facilities. However, most facilities are very low volume, and cannot provide safe EmONC, even at the basic level. This presents the health system with a serious challenge for assuring safe, good-quality childbirth services

Community health workers during the Ebola outbreak in Guinea, Liberia, and Sierra Leone.

BACKGROUND: The role of community health workers (CHWs) in the West Africa Ebola outbreak has been highlighted to advocate for increasing numbers of CHWs globally to build resilience, strengthen health systems, and provide emergency response capacity. However, the roles CHWs played, the challenges they faced, and their effectiveness during the outbreak are not well documented. This study assessed the impact of Ebola on community-based maternal, newborn, and child health (MNCH) services, documented the contribution of CHWs and other community-based actors to the Ebola response, and identified lessons learned to strengthen resilience in future emergencies. METHODS: This mixed methods study was conducted in Guinea, Liberia, and Sierra Leone, with data collected in four Ebola-affected districts of each country. Qualitative data were collected through in-depth interviews and focus group discussions with stakeholders at national, district, and community levels. Quantitative program data were used to assess trends in delivery of community-based MNCH services. RESULTS: There was a sharp decline in MNCH service provision due to weak service delivery, confusion over policy, and the overwhelming nature of the outbreak. However, many CHWs remained active in their communities and were willing to continue providing services. When CHWs received clear directives and were supported, service provision rebounded. Although CHWs faced mistrust and hostility from community members because of their linkages to health facilities, the relationship between CHWs and communities proved resilient over time, and CHWs were more effectively able to carry out Ebola-related activities than outsiders. Traditional birth attendants, community health committees, community leaders, and traditional healers also played important roles, despite a lack of formal engagement or support. Service delivery weaknesses, especially related to supply chain and supervision, limited the effectiveness of community health services before, during, and after the outbreak. CONCLUSIONS: CHWs and other community-level actors played important roles during the Ebola outbreak. However, maintenance of primary care services and the Ebola response were hampered because community actors were engaged late in the response and did not receive sufficient support. In the future, communities should be placed at the forefront of emergency preparedness and response plans and they must be adequately supported to strengthen service delivery

PLoS Negl Trop Dis

BACKGROUND: During the Ebola virus disease (EVD) epidemic in Liberia, contact tracing was implemented to rapidly detect new cases and prevent further transmission. We describe the scope and characteristics of contact tracing in Liberia and assess its performance during the 2014-2015 EVD epidemic. METHODOLOGY/PRINCIPAL FINDINGS: We performed a retrospective descriptive analysis of data collection forms for contact tracing conducted in six counties during June 2014-July 2015. EVD case counts from situation reports in the same counties were used to assess contact tracing coverage and sensitivity. Contacts who presented with symptoms and/or died, and monitoring was stopped, were classified as "potential cases". Positive predictive value (PPV) was defined as the proportion of traced contacts who were identified as potential cases. Bivariate and multivariate logistic regression models were used to identify characteristics among potential cases. We analyzed 25,830 contact tracing records for contacts who had monitoring initiated or were last exposed between June 4, 2014 and July 13, 2015. Contact tracing was initiated for 26.7% of total EVD cases and detected 3.6% of all new cases during this period. Eighty-eight percent of contacts completed monitoring, and 334 contacts were identified as potential cases (PPV = 1.4%). Potential cases were more likely to be detected early in the outbreak; hail from rural areas; report multiple exposures and symptoms; have household contact or direct bodily or fluid contact; and report nausea, fever, or weakness compared to contacts who completed monitoring. CONCLUSIONS/SIGNIFICANCE: Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history. While there were notable improvements in implementation over time, these data suggest there were limitations to its performance-particularly in urban districts and during peak transmission. Recommendations for improving performance include integrated surveillance, decentralized management of multidisciplinary teams, comprehensive protocols, and community-led strategies

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

Background: The 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 findings We 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 have increased 49.2% (95% CI: 33.9%, 64.5%, p < 0.001). Compared to pre-EVD forecasted trends, there remains a statistically significant loss of −15,144 PNC visits within 6 weeks (95% CI: −29,453, −787, p = 0.040) through December 2016. Conclusions: The Liberian public-sector primary healthcare system has made strides towards recovery from the 2014–2015 EVD outbreak. All primary healthcare indicators tracked have recovered to pre-EVD levels as of November 2016. Yet, for most indicators, it took more than 1 year to recover to pre-EVD levels. During this time, large losses of essential primary healthcare services occurred compared to what would have been expected had the EVD outbreak not occurred. The disruption of malaria case management during the EVD outbreak may have resulted in increased malaria cases. Large and sustained investments in public-sector primary care health system strengthening are urgently needed for EVD-affected countries

Dates system outputs surpassed pre-Ebola forecasted trends for 3 months, total system outputs estimated to be lost due to the Ebola virus disease (EVD) outbreak (June 2014–April 2015), and number of clinics and clinic-months included for 10 key health system outputs across a census of clinics providing services in Liberia excluding Montserrado County, 2010–2016.

<p>Dates system outputs surpassed pre-Ebola forecasted trends for 3 months, total system outputs estimated to be lost due to the Ebola virus disease (EVD) outbreak (June 2014–April 2015), and number of clinics and clinic-months included for 10 key health system outputs across a census of clinics providing services in Liberia excluding Montserrado County, 2010–2016.</p

Mean trends and system losses due to Ebola virus disease (EVD) outbreak (June 2014–April 2015) for institutional births in a census of 379 clinics providing services in Liberia from 2010–2016, excluding Montserrado County.

<p>The black solid line represents the fitted mean from a linear mixed model using a segmented regression parameterization, random intercepts and slopes by facility, monthly indicator variables to adjust for seasonality, a fixed effect to adjust for clinic-level catchment area, and an AR(1) structure to account for autocorrelation in residual errors. Gray dashed lines are 95% confidence intervals around the fitted mean. Red lines are placed at the final month before the start (May 2014) and end (April 2015) of the EVD outbreak in Liberia.</p

Parameter estimates and system losses due to Ebola virus disease (EVD) outbreak (June 2014–April 2015) for postnatal care visits within 6 weeks, artemisinin-based combination therapy (ACT) treatments for malaria, and acute respiratory infections treated across a census of clinics providing services in Liberia excluding Montserrado County, 2010–2016.

<p>Parameter estimates and system losses due to Ebola virus disease (EVD) outbreak (June 2014–April 2015) for postnatal care visits within 6 weeks, artemisinin-based combination therapy (ACT) treatments for malaria, and acute respiratory infections treated across a census of clinics providing services in Liberia excluding Montserrado County, 2010–2016.</p

Mean trends and system losses due to Ebola virus disease (EVD) outbreak (June 2014–April 2015) for first pentavalent vaccinations in a census of 379 clinics providing services in Liberia from 2010–2016, excluding Montserrado County.

<p>The black solid line represents the fitted mean from a linear mixed model using a segmented regression parameterization, random intercepts and slopes by facility, monthly indicator variables to adjust for seasonality, a fixed effect to adjust for clinic-level catchment area, and an AR(1) structure to account for autocorrelation in residual errors. Gray dashed lines are 95% confidence intervals around the fitted mean. Red lines are placed at the final month before the start (May 2014) and end (April 2015) of the EVD outbreak in Liberia.</p

Mean trends and system losses due to Ebola virus disease (EVD) outbreak (June 2014–April 2015) for acute respiratory infections treated in a census of 379 clinics providing services in Liberia from 2010–2016, excluding Montserrado County.

<p>The black solid line represents the fitted mean from a linear mixed model using a segmented regression parameterization, random intercepts and slopes by facility, monthly indicator variables to adjust for seasonality, a fixed effect to adjust for clinic-level catchment area, and an AR(1) structure to account for autocorrelation in residual errors. Gray dashed lines are 95% confidence intervals around the fitted mean. Red lines are placed at the final month before the start (May 2014) and end (April 2015) of the EVD outbreak in Liberia.</p