Advanced Analysis of Health Data Strengthening as Input for the Preparation of the 2025–2029 National Medium-Term Development Plan (Health Sector)
Executive Summary
This study is the result of Reconstra’s work as a technical partner commissioned by Bappenas to support the strengthening of integrated and sustainable data-driven national health development planning and policymaking.
Accurate, consistent, and timely health data are essential for planning development that truly meets people’s needs. This study supports future national health policy directions by strengthening indicators, data sources, and measurement methods to make them more standardized and better integrated. The goal is not only to ensure that data are available, but also that they can be compared across time and across data sources, providing a clearer picture of the population’s health conditions.
Currently, health indicators are derived from various surveys, censuses, and routine program reports. However, differences in definitions, data collection methods, and results across sources often create confusion. This can affect the accuracy of projections and policy decisions. Therefore, harmonizing definitions, data collection methods, and data availability timelines is a key recommendation of this study.
The study also develops a health development logical framework with a clear flow—from inputs, through processes, to outcomes and impacts. This framework helps illustrate how health programs contribute to improved service coverage and, ultimately, to better public health outcomes, particularly in Maternal and Child Health and Nutrition, as well as Disease Prevention and Control.
In addition, operational definitions, coverage, methods, and the best data sources have been formulated for each priority indicator. The objective is to establish a single shared reference to improve measurement consistency and quality. The integration of health information systems also creates significant opportunities to provide routine annual data at the district/city levels. The use of modern statistical methods, such as Bayesian analysis to project various health indicators, supports medium- and long-term planning, enabling development targets to be set in a more realistic, data-driven manner.