INTRODUCTION

High-quality data are necessary for making high-quality decisions. Without complete, timely, and accurate data (particularly about causes of mortality), public health officials have limited ability to make informed choices to improve policies and programs. However, better sources of public health data, while necessary, are not sufficient to ensure that policy decisions are regularly guided by data. Without mechanisms to get the right insights to the right people at the right time, improved data sources are not guaranteed to lead to improved public health.

A main objective of Data for Health (an initiative in low- and middle-income countries co-funded by Bloomberg Philanthropies and the Australian government) is to enhance the quality of cause of death data¹1. Bloomberg Philanthropies. Data for health [citado em 10 out. 2019]. Disponível em: Disponível em: https://www.bloomberg.org/program/public-health/data-health/#overview .
https://www.bloomberg.org/program/public... ; this volume reports on such efforts implemented in Brazil as part of this initiative. Moreover, Data for Health recognized the vital importance of addressing the gap between data and decisions by supporting “Data Impact” activities that purposively linked data sources to the strategic and operational choices that public health officials routinely face. This article presents two important lessons learned from these activities that can guide public health leaders to develop strategies to optimize the use of mortality data for health policy-making.

LESSON #1: DATA MUST BE RIGOROUSLY TRANSFORMED TO PRODUCE POLICY IMPACT

Public health data, even high-quality data, does not immediately lead to policies that improve public health²2. AbouZahr C, Adjei S, Kanchanachitra C. From data to policy: good practices and cautionary tales. Lancet. 2007;369(9566): 1039-46, 2007. https://doi.org/10.1016/S0140-6736(07)60463-2
https://doi.org/10.1016/S0140-6736(07)60... . Instead, good data must undergo a transformation process in order to produce good policy.

Data-to-policy transformation is only as strong as the weakest element of this process. For instance, well-prepared data sources rigorously analyzed will not produce more effective policies if findings are not synthesized into action and communicated convincingly. Data for Health has created an intensive “Data-to-Policy” (D2P) program that supports health ministry staff in navigating the entirety of the transformation process on selected issues. In Shanghai, for example, D2P program participants leveraged data to develop legislative/policy changes to address five different issues and to convince senior government leadership to push for adoption of these changes. As a result, new data-driven initiatives have been implemented in Shanghai, such as one to increase helmet use among e-bike users.

LESSON #2: THREE PILLARS - PEOPLE, PROCESSES, PRODUCTS - ARE THE FOUNDATION FOR WIDESPREAD AND SUSTAINABLE DATA-TO-POLICY TRANSFORMATION

Most health ministries can mobilize in the short-term to utilize data to inform a public health decision that is a priority for a prominent elected official or the health minister. However, these ad hoc efforts can only be done for a limited set of issues and are driven entirely by specific individuals (who may only remain in their roles for a short time).

Data for Health promotes widespread and sustainable data use through institutional changes that affect all (or most) of a ministry and are not dependent on a single leader. These activities focus on three broad domains:

The impact of institutional strengthening is illustrated in Peru, where Data for Health supported the publication of a new report (“Analysis of the Causes of Mortality in Peru, 1986-2016”), the first long-term trend analysis of the country’s causes of mortality. This report (together with its companion interactive “Mortality Atlas”) is becoming an essential source of information for decision-makers in Peru. Moreover, during this effort, Data for Health helped enhance the capacity of staff in technical/communication skills (ex. correcting for underreported data) that leave them better equipped them for future efforts to update this report and to create other robust data products.

CONCLUSION

Data for Health provides a strategic road map for ministries to strengthen the use of sources such as cause of death data to guide health policy-making. That strategy will require not only a structured transformation of data into specific decision(s) but also a purposeful redesign of a ministry’s infrastructure and culture in order to facilitate and sustain that transformation process. This change, although neither quick nor easy, will ensure that data drives more effective public health policies and programs that improve the lives of citizens.

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