Using health-facility data to assess subnational coverage of maternal and child health indicators, Kenya

Maina, Isabella; Wanjala, Pepela; Soti, David; Kipruto, Hillary; Droti, Benson; Boerma, Ties

doi:10.2471/blt.17.194399

Cited by 64 publications

(121 citation statements)

References 14 publications

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“…Health facility data, including routine reporting systems and facility surveys, can be used to improve monitoring of RMNCH indicators at local levels, provided completeness and accuracy of reporting by facilities is good. 63 Linking of population and health facility surveys is at present the best way to measure access to and quality of specific services. Special studies are needed for determining how best to identify and reach underserved groups, to ascertain the quality of care and how to improve it, on reaching women and children with services in conflict situations, and to better understand the role of governance processes and legislative frameworks in improving RMNCH.…”

Section: Universal Coverage For Rmnch Interventions: Still Much To Do\mentioning

confidence: 99%

Countdown to 2030: tracking progress towards universal coverage for reproductive, maternal, newborn, and child health

Boerma¹,

Requejo²,

Victora³

et al. 2018

The Lancet

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340

295

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Building upon the successes of Countdown to 2015, Countdown to 2030 aims to support the monitoring and measurement of women's, children's, and adolescents' health in the 81 countries that account for 95% of maternal and 90% of all child deaths worldwide. To achieve the Sustainable Development Goals by 2030, the rate of decline in prevalence of maternal and child mortality, stillbirths, and stunting among children younger than 5 years of age needs to accelerate considerably compared with progress since 2000. Such accelerations are only possible with a rapid scale-up of effective interventions to all population groups within countries (particularly in countries with the highest mortality and in those affected by conflict), supported by improvements in underlying socioeconomic conditions, including women's empowerment. Three main conclusions emerge from our analysis of intervention coverage, equity, and drivers of reproductive, maternal, newborn, and child health (RMNCH) in the 81 Countdown countries. First, even though strong progress was made in the coverage of many essential RMNCH interventions during the past decade, many countries are still a long way from universal coverage for most essential interventions. Furthermore, a growing body of evidence suggests that available services in many countries are of poor quality, limiting the potential effect on RMNCH outcomes. Second, within-country inequalities in intervention coverage are reducing in most countries (and are now almost non-existent in a few countries), but the pace is too slow. Third, health-sector (eg, weak country health systems) and non-health-sector drivers (eg, conflict settings) are major impediments to delivering high-quality services to all populations. Although more data for RMNCH interventions are available now, major data gaps still preclude the use of evidence to drive decision making and accountability. Countdown to 2030 is investing in improvements in measurement in several areas, such as quality of care and effective coverage, nutrition programmes, adolescent health, early childhood development, and evidence for conflict settings, and is prioritising its regional networks to enhance local analytic capacity and evidence for RMNCH.

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Section: Universal Coverage For Rmnch Interventions: Still Much To Do\mentioning

confidence: 99%

Countdown to 2030: tracking progress towards universal coverage for reproductive, maternal, newborn, and child health

Boerma¹,

Requejo²,

Victora³

et al. 2018

The Lancet

Self Cite

340

295

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“…DHIS2 data includes individuals who may have tested multiple times which can lead to the use of wrong or unrepresentative denominators for individuals tested at health facilities. Studies elsewhere report repeat testing ranging from 3 to 13% [40,41]. We further note that some health facilities, especially privately owned, do not report their data to the national DHIS2 further lowering the representativeness of health facility HIV testing data.…”

Section: Strengths and Limitationsmentioning

confidence: 82%

Combining national survey with facility-based HIV testing data to obtain more accurate estimate of HIV prevalence in districts in Uganda

et al. 2020

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Background: National or regional population-based HIV prevalence surveys have small sample sizes at district or sub-district levels; this leads to wide confidence intervals when estimating HIV prevalence at district level for programme monitoring and decision making. Health facility programme data, collected during service delivery is widely available, but since people self-select for HIV testing, HIV prevalence estimates based on it, is subject to selection bias. We present a statistical annealing technique, Hybrid Prevalence Estimation (HPE), that combines a small population-based survey sample with a facility-based sample to generate district level HIV prevalence estimates with associated confidence intervals. Methods: We apply the HPE methodology to combine the 2011 Uganda AIDS indicator survey with the 2011 health facility HIV testing data to obtain HIV prevalence estimates for districts in Uganda. Multilevel logistic regression was used to obtain the propensity of testing for HIV in a health facility, and the propensity to test was used to combine the population survey and health facility HIV testing data to obtain the HPEs. We assessed comparability of the HPEs and survey-based estimates using Bland Altman analysis. Results: The estimates ranged from 0.012 to 0.178 and had narrower confidence intervals compared to surveybased estimates. The average difference between HPEs and population survey estimates was 0.00 (95% CI: − 0.04, 0.04). The HPE standard errors were 28.9% (95% CI: 23.4-34.4) reduced, compared to survey-based standard errors. Overall reduction in HPE standard errors compared survey-based standard errors ranged from 5.4 to 95%. Conclusions: Facility data can be combined with population survey data to obtain more accurate HIV prevalence estimates for geographical areas with small population survey sample sizes. We recommend use of the methodology by district level managers to obtain more accurate HIV prevalence estimates to guide decision making without incurring additional data collection costs.

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“…Learning from these efforts may also inform efforts to strengthen routine health information systems and maximize the use of routine data for monitoring and evaluation of health development efforts. [35][36][37] Outside of humanitarian settings, there are efforts to develop practical methods for improving measurements of perinatal mortality (fresh stillbirth and early neonatal mortality) in health facilities, 38 and strengthen maternal and perinatal death surveillance and response systems which may provide models for replication in refugee camps. [39][40][41]…”

Section: Discussionmentioning

confidence: 99%

Neonatal Mortality Burden and Trends in UNHCR Refugee Camps, 2006-2017: A Retrospective Analysis

Tappis

Ramadan

Vargas

et al. 2020

Preprint

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Abstract Objective: To examine neonatal mortality burden and trends in refugee camps between 2006 and 2017.Methods: Refugee population and mortality data were exported from the United Nations High Commissioner (UNHCR) Health Information System (HIS) database. Tableau was used to export the data. Stata was used for data cleaning and statistical analysis. Neonatal mortality burdens and trends in refugee camps were analyzed and compared to national and subnational neonatal mortality rates captured by household surveys.Findings: 150 refugee camps in 21 countries were included in this study, with an average population of 1,725,433 between 2006 and 2017. A total of 663,892 live births and 3,382 neonatal deaths were captured during this period. Annual country-level refugee camp neonatal mortality rates (NMR) ranged from 12 to 56 neonatal deaths per 1,000 live births. In most countries and years where national population-based surveys are available, refugee camp NMR as reported in the UNHCR HIS was lower than that of the immediate host community.Conclusion: The UNHCR HIS provides insights into the neonatal mortality burden among refugees in camp settings and issues to consider in design and use of routine health information systems to monitor neonatal health in sub-national populations. Increased visibility of neonatal deaths and stillbirths among displaced populations can drive advocacy and inform decisions needed to strengthen health systems. Efforts to count every stillbirth and neonatal death are critical, as well as improvements to reporting systems and mechanisms for data review within broader efforts to improve the quality of neonatal care practices within and outside of health facilities.

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Using health-facility data to assess subnational coverage of maternal and child health indicators, Kenya

Cited by 64 publications

References 14 publications

Countdown to 2030: tracking progress towards universal coverage for reproductive, maternal, newborn, and child health

Countdown to 2030: tracking progress towards universal coverage for reproductive, maternal, newborn, and child health

Combining national survey with facility-based HIV testing data to obtain more accurate estimate of HIV prevalence in districts in Uganda

Neonatal Mortality Burden and Trends in UNHCR Refugee Camps, 2006-2017: A Retrospective Analysis

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