Our Methodology and User Guides

The FairChoices tool develops tailored mathematical models using country-specific data like:

Population Dynamics

Disease Burden

Health Expenditure

Health Service Provision

Population Dynamics

Disease
Burden

Health Expenditure

Health Service Provision

General Overview

Demography

Demography refers to the study of populations and their characteristics, such as the population growth rate and age structure, and exploring how these factors influence health outcomes and healthcare needs. Understanding the demographics of a population is essential for developing effective health policies and interventions that can address the unique health challenges and needs of different population groups.

For its demography model, the FairChoices tool utilizes a cohort-component method. The method divides the population into cohorts based on age and sex, and then projects the future size and composition of each cohort based on assumptions about future trends in fertility, mortality, and migration. If the forecast horizon is ten years, for example, FairChoices moves the current population through time and estimates the size of each age-sex cohort every year, as well as the sizes of the cohorts being born every year, for the next ten years.

The assumptions about future trends in fertility, mortality, and migration are based on estimates from the Global Burden of Disease (GBD) study and the World Population Prospects (WPP), which themselves are based on past trends and expert opinion. For example, the WPP model assumes that fertility rates will continue to decline in many parts of the world, that mortality rates will continue to decline due to advances in medical technology and public health, and that migration patterns will continue to be influenced by economic and political factors; the GBD model assumes that total mortality in a cohort can be subdivided into cause-specific mortalities from a fixed set of causes. FairChoices allows for users to input key demographic variables if users have more updated or accurate data than GBD or WPP or if users want to use FairChoices on a subnational region that is not covered by WPP or GBD.

Epidemiology

Epidemiology is the study of the distribution and determinants of health and disease in populations, and how this knowledge can be used to develop evidence-based health policies that can improve the health of populations, prevent disease, and reduce health disparities. Epidemiological data provides critical information for policymakers to identify priority health issues, allocate resources, and design effective interventions.

In FairChoices, data on epidemiology is based on the Global Burden of Disease (GBD) study, a comprehensive and ongoing effort to quantify the burden of different diseases and injuries for populations around the world. GBD provides critical information epidemiological trends and patterns of disease occurrence, as well as the impact of various risk factors on health outcomes. In low- and lower-middle-income countries (LLMICs), the GBD study is particularly important because these regions often have limited resources disease surveillance and data collection. GBD helps to fill this gap by providing estimates of disease burden that can be used to guide public health policies and allocate resources effectively. To estimate disease burden in LLMICs, GBD uses a range of data sources, including surveys, vital registration systems, administrative data. These data sources are often incomplete or of low quality, so GBD uses a range of statistical models to estimate disease burden in areas where data is lacking. One key component of the GBD study is the calculation of age-, sex-, and cause-specific mortality rates and disability weights for a wide range of conditions. FairChoices use these rates to calculate the impact of a health care intervention on the health of a population.

Some conditions that are included in FairChoices are not among the GBD causes. The epidemiology of such conditions is therefore based on similar or related causes. For example, “obstetric fistula” is not included among the GBD causes, but “obstructed labor” is. FairChoices takes data on obstructed labor provided by GBD and applies an assumption that approximately 20% of obstructed labors result in a fistula. Users of FairChoices can also contribute information on epidemiology based on local data and context.

Severity

Severity of disease is a critical consideration in health policy as it helps policymakers prioritize health issues and allocate resources to prevent or treat diseases that have the greatest impact on lifespan, suffering, and overall quality of life. When applied in a priority setting framework, interventions that address more severe diseases are assigned higher weight, reflecting greater importance in the decision making process. A quantitative measure of severity of disease is used as an explicit criterion in health priority setting in Netherlands and Norway.

FairChoices uses the health-adjusted age at death (HAAD) metric in its calculations of severity of disease. HAAD, an absolute lifetime measure of severity, was recommended as the metric of severity of disease or equity by The Lancet Commission on NCDs and injuries (NCDIs) in 2020. Zanzibar used HAAD when designing its regional essential health benefit package as a means of quantitatively balancing efficiency and equity. In the academic literature, Johansson and Haaland present nine different metrics to measure severity of disease and how these can be calculated.

HAAD measures lifetime health for individuals with specific conditions. It consists of two components: past health and future expected health. Starting from the average age of a given disease’s onset in a population, past health is estimated by summing the number of healthy life years (HLYs) that the affected population has lived without the disease. Future expected health is estimated by summing the estimated HLYs the affected population will live (in the future) with the disease. A disease characterized by a low HAAD (or a low health-adjusted age at death) indicates severe disease, whereas a high HAAD indicates less severe disease. As an example, let’s conceptualize the HAAD of two conditions: dementia and schizophrenia. Schizophrenia is a highly disabling, chronic condition that starts in early adulthood; dementia is a highly disabling, chronic condition that starts in late adulthood or elderly years. Based on the epidemiology of these two conditions, we would expect the HAAD to be lower (indicating a higher severity) for schizophrenia compared to dementia, since schizophrenia starts earlier in life and is a chronic, lifelong condition.

A detailed description of the HAAD methodology can be found here.

Effectiveness

The effectiveness of health interventions refers to the extent to which interventions reduce mortality and disability. Accounting for the effectiveness of interventions is essential for ensuring that limited resources are used efficiently and that interventions chosen for the health benefit package (HBP) provide the maximum benefit possible. Information on intervention effectiveness is collected from the published literature, including randomized trials, observational studies, cost-effectiveness analyses, and systematic reviews and meta-analyses, as well as expert opinion when relevant literature is not available. In FairChoices, the effectiveness of an intervention is given as the reduction in the age- and sex-specific mortality and disability provided by the Global Burden of Disease (GBD) study. Interventions that reduce the prevalence or incidence of a condition will affect both mortality and disability. For example, if we assume that a measles vaccine is 100% effective in protecting against measles, the effectiveness of measles vaccine is 1 for mortality and 1 for disability (i.e., the vaccine provides 100% reduction in measles-related mortality and disability). If all individuals in a cohort were vaccinated, the contribution of measles would be removed from that cohort’s mortality and disability.

Once an HBP is selected, FairChoices estimates how scaling up coverage of HBP interventions will affect the future mortality and disability of the population over the selected time horizon (i.e., ten years) using data on the population in need of the interventions and the interventions’ effectiveness. FairChoices produces several metrics to assess the population-level benefits of the HBP. First, FairChoices uses cohort life-expectancies to estimate the health-adjusted life expectancy (HALE) and life expectancy (LE) for each cohort, with and without the HBP. Next, FairChoices calculates the healthy life-years (HLYs) and life-years (LYs) gained from adopting the HBP. FairChoices uses a lifetime perspective on health in the sense that effects beyond the selected time horizon are also counted. For example, if surgery to treat obstetric fistula is included in the HBP, the disability of the affected cohorts is reduced for decades; if a cohort receives HPV-vaccines, mortality from cervix cancer is reduced in affected cohorts for as long as they live. The total health benefits of the HBP is the sum of health benefits accrued across the lifetime of individuals alive or born over the time horizon (i.e., ten years).

Budget Space

Budget space for health (also called fiscal space for health) refers to how much money the government has available to spend on health. In general, most low- and lower-middle income countries seek to expand their budget space for health in order to achieve their health system objectives. However, this is not easy or straightforward, and it is an inherently political process, as there are many competing priorities for government resources.

This page of the FairChoices tool helps users understand and envision their country’s budget space for health. The graphs below showcase data from the recent past (years 2010 and later) to provide important context for future budget planning. Users can define their budget space planning period using the inputs below. To promote accuracy, feasibility, and realism, it is advised that users parameterize this tab in collaboration with staff from both the ministries of health and finance, as well as health financing experts and relevant stakeholders.