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Wednesday, June 23 • 14:00 - 15:00
Use of DHIS2 data in research

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This session will explore various alternative ways of using data from DHIS2. Read more on Caitlin Augustin's presentation here and Yangzi Sherpa's presentation here on the Community of Practice discussion.

Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study
Background: Globally each year, an estimated 2.4 million newborns die in the first 28 days of life - 47% of under five year old deaths. Additionally, more than two million babies are stillborn.  Although most of these deaths are preventable, 99% occur in low & middle income countries (LMICs) with the least data on coverage and quality of care around the time of birth the “inverse data law”
The Every Newborn Action Plan (ENAP) [5, 6] endorsed by the World Health Assembly in 2014 committed to end preventable newborn deaths and stillbirths. Sustainable Development Goal 3 includes the first ever global target for neonatal mortality reduction. To attain universal health coverage countries need to scale up evidence-based interventions for newborn health.
Timely and high-quality data on outcomes and coverage of care are crucial. The majority of comparable data for newborn health indicators are currently collected through population-based surveys (e.g. DHS, MICS). Data from routine health management information systems (HMIS) including DHIS2 is increasingly available for the 80% of global births now occurring in health facilities.  Aggregated data from routine hospital registers is the typical data source for HMIS.
 A multi-partner measurement improvement roadmap [11] was developed for 2015–2020 to improve the ENAP core indicator definitions, to test their measurement validity and inform feasibility of measurement. The linked Every Newborn - Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study was collaborative research by London School of Hygiene & Tropical Medicine, icddr,b Bangladesh, Golden Community Nepal and Ifakara Health Institute Tanzania and funded by Children’s Investment Fund Foundation (CIFF).
Aim: To test validity of measurement of newborn and maternal indicator measurement.  
Objectives: 1) Assess validity of numerator measurement 2) Assess validity of denominator measurement 3) Evaluate content and quality of care 4) Assess barriers and enablers to routine register measurement
Methods: Mixed methods observational study in five public hospitals providing comprehensive emergency obstetric and newborn care hospitals in three high-burden mortality countries: Tanzania, Bangladesh, and Nepal. Direct clinical observation of care and interventions was used as the gold standard to compare against data from routine facility registers and women’s survey report.  Qualitative interviews were conducted with health workers and research data collectors.
The multi-country EN-BIRTH team observed 23,471 births and 840 kangaroo mother care (KMC) mother baby pairs, in addition to collecting information on 1015 admissions for neonatal infection.
Results: The presentation will report selected findings from the EN-BIRTH analyses focusing on accuracy of routine register data elements aggregated for indicator measurement in health information systems e.g. DHIS2.  
Indicators discussed will include: newborn and maternal indicator measurement around the time of birth including: uterotonics to prevent postpartum haemorrhage, neonatal resuscitation (bag-mask-ventilation), birth weight, early initiation of breastfeeding, kangaroo mother care etc.   
Findings will be shared regarding barriers and enablers and what needs to be done to improve data quality on the frontline in health facilities.
Plans for EN-BIRTH phase 2 feasibility and implementation research will be shared aiming to assess whether the validated newborn indicators are feasible to implement in HMIS as the next step to promote broad uptake in (LMIC).
Further details about the EN-BIRTH study including publications can be found at https://www.lshtm.ac.uk/research/centres/march-centre/every-newborn-birth

avatar for Johan Ivar Sæbø

Johan Ivar Sæbø

Associate Professor, University of Oslo
avatar for Mitali Ayyangar

Mitali Ayyangar

Portfolio Manager, Frontline Health Systems, DataKind
avatar for Emily Yelverton

Emily Yelverton

Data Scientist, DataKind
avatar for Yangzi Sherpa

Yangzi Sherpa

Health Information System-Technical Officer, Abt Associates Inc
avatar for Louise Tina Day

Louise Tina Day

London School of Hygiene & Tropical Medicine
Louise is a clinician-scientist combining experience as an obstetric-paediatrician with a focus on health information system research at the London School of Hygiene and Tropical medicine - Maternal Newborn Health Group. She is interested in the sustainable implementation of evidence-based... Read More →
avatar for Caitlin Augustin

Caitlin Augustin

Data strategy, data science applications, measurement & evaluation, tool deployment.

Wednesday June 23, 2021 14:00 - 15:00 CEST
Annual Conference Virtual Room 2