Methodology

Overview

The Exemplars in Primary Health Care study aims to systematically and comprehensively identify countries whose primary health care (PHC) systems have performed exceptionally well over the past 20 years relative to their total health spending.

Country selection

Exemplar countries were selected through a tiered process combining econometric and descriptive analyses, validated by expert input.

The selection process aimed to identify countries based on core assumptions about what makes for a strong PHC system. PHC Exemplar countries get more for each dollar spent on health, protect the most vulnerable segments of their populations, and can offer PHC improvement lessons to other countries (Figure 25).

We identified four “archetypes” of PHC performance (Figure 26):

• Countries that have strong sustained performance
• Countries that have increased spending and improved performance
• Countries that have improved performance while keeping spending near constant
• Countries that have improved performance while spending has been disrupted

Rwanda falls into the second group: countries that improved their universal health coverage effective coverage while also increasing total health expenditure per capita. Rwanda’s universal health coverage effective coverage index increased by 137% between 2000 and 2018, and its total health spending per capita increased by 262%.¹ For reference, global health spending per capita increased 55% during the same period.

To determine Exemplar countries, a group of low- and middle-income countries (per World Bank classifications) were ranked by technical efficiency, which is defined as overall health system performance relative to total health expenditure. Performance was measured using the Global Burden of Disease 2019 universal health coverage effective index, which comprises 23 “effective coverage” indicators (including promotion, prevention, and treatment) and five age groups.² Effective coverage of an intervention is defined as the fraction of the potential population gains from it, adjusted for quality.

As a result, 60 countries with the highest technical efficiency—measured by a stochastic frontier analysis regressing effective coverage index data against total health expenditure for the study period—were shortlisted for further analysis.

Next, we examined health system performance equity, via PHC services coverage and financial protection. Equity in PHC was assessed using a published indicator of geographical inequality DTP3 coverage (calculated as the weighted absolute mean difference from the best performing subnational unit within a country) and out-of-pocket spending as a proportion of total health expenditure, respectively.

The final step was to identify the countries most influential to study based on the transferability of the findings—namely, population size, geographic diversity, data availability—to ensure that lessons from the Exemplar countries would be widely applicable.

Conceptual framework

Country analyses were informed by the WHO-UNICEF Operational Framework for PHC’s definition of PHC as “a whole-of-society approach to health that aims to maximize the level and distribution of health and wellbeing” through three components (Figure 28).

• Primary care and essential public health at the core of integrated health services
• Multisectoral policy and action
• Empowered people and communities

To identify the key reform strategies in each country that improved efficiency in these domains, and the subsystems driving positive change, researchers adopted a mixed methods approach triangulating data from a variety of sources.

Country-specific retrospective study methods

The work reflected within this narrative relies on mixed methods for data collection and analysis, across national and subnational systems levels.

• Researchers first identified policies and sub-systems that were critical to achieving high PHC efficiency, and linkages between them at the national level. Data extraction included time series analysis of secondary data with a focus on efficiency and equity measures, literature reviews of published and gray sources, key informant interviews to understand stakeholder perceptions of drivers of efficiency, and qualitative systems dynamic modeling—a technique to specify the dynamic relationships and feedback loops that improved PHC efficiency within the system.
• Researchers then stratified subnational units (districts) to understand high and low performance between them—and to analyze the critical, sub-systems, linkages and operations down to the health facility level. Stochastic frontier analysis was leveraged to rank subnational units according to PHC efficiency and to identify exogenous drivers (sociodemographic characteristics) that may have affected it. Site visits, primary data collection with the development and adaptation of a survey protocol, and document review were leveraged to capture operational data and details.
• Researchers then synthesized findings and transferable lessons. Findings were synthesized and integrated, controlling for context and constraints in each setting.

For more detailed and country-specific information on the PHC Exemplars methods, please refer to our research partner’s published academic manuscripts.

Data disclaimer

The country selection analytical process took place in 2020 and primarily leveraged the Institute for Health Metrics and Evaluation’s Global Burden of Disease data through 2018, the latest publicly available data at the time. Therefore, country selection graphics and references reflect 2000–2018 trend data. Other references can reflect updated data, including data collection from dates outside the range of country selection years, as well as more recent Global Burden of Disease estimates.