STEP 5: USE data to inform programmes and policy

The final gap is in the use of data for action. Once data are collated in an accurate and comparable manner for every birth, ensuring that data are used for action will require the that they are accessible to both frontline health workers and policy makers and that they are understood, valued and perceived as useful. Closing this gap will require improved understanding of how data are currently used, and current barriers and enablers to more

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widespread use. Common approaches across data systems are discussed below with further details in Annex A.6.5.

The first step in facilitating data use for action is to promote data ownership and use at a local level. Many routine health systems rely on healthcare workers for the collection of data. Increasing demands on healthcare workers, both in terms of clinical and administrative workloads, can affect the data quality as seen above, but also how data are perceived.387 _Current

data collection systems, even the newly emerging electronic based ones, are commonly designed with the needs of stakeholders higher up the system rather than those recording the data, with data systems frequently not adapted to actual workflow or healthcare worker’s clinical decision-making requirements.388 _{Use of local data is critical for improving quality of care.}

The generation of actionable data, such as through DHIS-2 dashboards, could provide timely information to clinical and local level health staff to improve care, and linked to perinatal audit could be used as a tool to facilitate facility level quality improvement.112 _{Involving healthcare}

workers in the design of dashboards and linking to tools to make clinical data available in real- time could increase data availability for clinical decision-making and improve ownership and use of such data to improve outcomes at a local level.

The next step is to make data accessible and understandable to policy makers to enable it to influence public health policy and programmes and to guide decision making at local, district and national level. This may include a variety of formats such as data dashboards, monthly reporting and annual reports. Data should be presented disaggregated by subnational, equity and other relevant grouping to track progress and enable targeted interventions to those groups at greatest risk. When available, information on stillbirth timing (antepartum or intrapartum) and cause of death can be used to further refine areas to target. High quality tracking in a comparable way, across all data platforms including CRVS, HMIS and surveys, could enable data to be used to monitor investments in programmes, identify areas of concern and set priorities for maternal newborn health or wider health sector 5 year plans. Barriers to including data on stillbirth, preterm birth and low birthweight in formats accessible to policy makers include failure of those responsible for data collating to appreciate the potential of these indicators as markers of health of women and children in their populations, and of indicators of strength of their health systems. The technical maternal–newborn health community, frontline health workers, affected families and communities could all potentially play an important role in raising the profile of the large preventable burden associated with stillbirth, preterm birth and low birthweight on women, families and communities.5 _{This could include knowledge translation to}

communicate the issue more clearly to programmes and policy makers using varying mediums such as reports, policy briefs and infographics and individual and group advocacy efforts. The

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increasing attention given to these outcomes in global institutions with mandates for establishing and maintaining administrative and technical services, such as epidemiological and statistical services, including the setting of normative guidance (WHO) and advocating for the protection of children's rights, to help meet their basic needs and to expand their opportunities to reach their full potential (UNICEF) is increasing the visibility of these health issues in many countries.35 _{Many countries are now reporting on these outcomes as part of sharpened newborn}

plans towards ending preventable stillbirths and newborn deaths.389

Including data in all relevant publicly available reports that include maternal and child health will also allow parent groups and other interested parties the opportunity to advocate and increase political pressure by highlighting these issues and thus further increasing visibility, for example, in the media. One example of this resulting from this work was the Born Too Soon Report published in 2012 alongside the estimates in chapter 4 which received major media coverage with an estimated reach of 1 billion, including 72 million Twitter "impressions”. Parent groups had an important role in raising awareness with activities in over 60 countries, including national events with government and other stakeholders in Bangladesh, India, Malawi, and Uganda and a Facebook page.6,390 _{Data were key to many of the messaging strategies used, and}

provided evidence to show the size of the burden, preventability and to use as inputs to models to estimate how many lives could be saved using different intervention approaches.6,391

However, ultimately data use will depend on how data are perceived and their social robustness, both are linked to data quality and coverage. For example, in CRVS, birth registration data are used for population and health planning purposes. Perinatal mortality data in contrast, whilst collected in most settings, are rarely used. This in part is due to low confidence in and perceived low quality of much of the data collected. As such, the preceding steps to reach every birth, assess, record and collate the data elements will be critical to improve the quality of such data, and facilitate a change in perception about the data, increasing the likeliness of data use. In HMIS, as healthcare data systems are complex the completeness and quality of routinely collected HMIS data remains a challenge for data use. A recent study found that completeness of DHIS-2 data in Kenya was a challenge to data use for decision making.392 _{Improving the quality}

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