State of inequality: HIV, tuberculosis and malaria
A visual summary
HIV, tuberculosis (TB) and malaria together account for more than two million deaths per year. Malaria alone causes hundreds of millions of people to fall ill.
HIV, TB and malaria are diseases of poverty and marginalization – diseases characterized by inequality. They take their heaviest toll among populations that are chronically disadvantaged. That is, populations consisting of the poorest, least educated and rural residing people.
Although remarkable progress has been made in reducing the overall burden of each disease, particularly in the past decade, certain groups still have persistently higher disease mortality and morbidity, and lower access to life-saving interventions.
Yet, the extent of inequalities in HIV, TB and malaria remains poorly documented and understood.
State of inequality: HIV, tuberculosis and malaria is the first monitoring report devoted to systematically assessing the global state of inequality in the three diseases. It quantifies the latest situation of inequalities within countries and the change in inequality over time.
This report is timely. The global community is working to achieve the bold commitments of the Sustainable Development Goals and global HIV, TB and malaria strategies: to end the epidemics of AIDS, TB and malaria by 2030.
Unfair and remediable inequalities are increasingly acknowledged as barriers to progress, especially as the devastating implications of the COVID-19 pandemic play out with disproportionate impacts among disadvantaged populations.
The report illustrates widespread inequalities across most aspects of the diseases where data are available.
Findings show that, in most countries, inequalities have not narrowed over the past decade. In some cases, inequalities have worsened.
However, there are situations where some health indicators show low inequality or where gaps between population groups are narrowing.
The report demonstrates possibilities for faster improvement of intervention coverage by addressing inequalities that are unfair and remediable. It emphasizes that targeting action to disadvantaged groups and improving relevant social determinants of health are part of accelerated efforts to end these epidemics.
Community launch of an indoor mosquito spraying campaign on World Malaria Day 2019 in Nhamatanda, Mozambique.
Why are disaggregated data important?
Where we lack data, we lack understanding. Particularly when it comes to understanding the state of inequality. Data blind spots leave us in the dark. Even when data are available, there can be limitations. Data are too often incomplete, out of date or otherwise unreliable. Data that cannot be disaggregated (broken down by population subgroups) cannot be used for inequality monitoring.
With data at its heart, the novelty of this report lies in its comprehensive and systematic approach to presenting the state of inequality across three of the world’s most destructive diseases: HIV, TB and malaria.
It uses the latest available global data for 32 health indicators and up to 186 countries.
Where possible, the report quantifies within-country inequalities by five inequality dimensions: sex, economic status, education, place of residence and age.
For certain disease topics, there were insufficient disaggregated data to fully analyse the state of inequality. Notably, a lack of data limited the extent of results reported for TB and, to a lesser extent, malaria.
A field visit in Darbhanga, India during Polio National Immunization Days 2020.
Where are inequalities high?
Inequalities in HIV, TB and malaria exist across population subgroups. This is true for the majority of the countries included in the analysis.
For example, within-country inequality is observed when the richest and poorest subgroups in a country report different levels of health service coverage.
DATA VISUALIZATION
This animated data visualization shows the inequality among females in HIV testing between the ‘richest 20% of the population’ and ‘poorest 20% of the population’. The first frame shows one country plotted as dots on two different axes, labelled ‘poorest 20%’ and ‘richest 20%’. Both axes range from 0 to 100%. The next frame shows 52 countries positioned on each axis as dots by the estimated median percentage. A bigger dot on each axis appears showing the estimated median across the 52 countries. In the next frame, the axes merge, showing the global median of the ‘richest 20%’ at 51% and ‘poorest 20%’ at 26.4% with a line connecting them to highlight the inequality.
DATA VISUALIZATION - The poorest, least educated and rural populations tend to experience disadvantage
This interactive data visualization shows the global inequality across ‘Economic Status’, ‘Education’ and ‘Place of Residence’ for HIV, Tuberculosis (TB) and Malaria indicators. The primary view is Economic status with one indicator each for HIV, TB and Malaria. Under each disease indicator, there is one axis for females and another for males, ranging from 0 to 90%. On the Female axis for the indicator ‘Testing for HIV and receiving results (ever), the median estimate is plotted as a dot for the ‘richest 20% of the population’ at 51% and 26.4% for ‘poorest 20% of the population’. You can toggle between the primary view of Economic status to Education or Place of Residence. When Education is selected, the median estimate for ‘No or primary education’ and ‘Secondary or higher education’ are plotted on each Female and Male axis. If you choose Place of Residence, the median estimate for ‘Rural’ and ‘Urban’ are plotted on each Female and Male axis. Malaria has one axis for both sexes.
The poorest, least educated and rural subgroups are at a disadvantage across most HIV, TB and malaria indicators. For nearly every indicator, at least one country reported high inequality* by economic status, education or place of residence.
For example, in many countries, testing for HIV is much lower among the poorest, least educated or rural subgroups. Fifty-six per cent of countries (27 of 48) reported high inequality in at least one of these dimensions of inequality for HIV testing among males.
A TB knowledge indicator tracks the percentage of people who report TB is spread through coughing. Here, the richest scored higher than the poorest in nearly all countries in both females and males. TB knowledge tends to be higher among older, more educated and urban subgroups.
For malaria, subgroups that are poorer, less educated and rural are more likely to report delayed care-seeking for febrile children. There was high inequality in more than half of countries (16 of 28) for this indicator.
*High inequality refers to a difference of at least 20 percentage points between population subgroups.
DATA VISUALIZATION - Many inequalities remain largely unchanged, compared to a decade ago
This interactive data visualization shows the lack of change over time in global inequality for ‘Economic Status’ across HIV, Tuberculosis (TB) and Malaria. The primary view is HIV with the indicator ‘Testing for HIV and receiving results (ever)’, broken down by females with ‘2001-2010’ and ‘2011-2020’ on two different axes, ranging from 0 to 90%. This is repeated under males. Under females on the ‘2001-2010’ axis, the median estimate is plotted as a dot for ‘richest 20% of the population’ at 21.2% and 3% for ‘poorest 20% of the population’. On the ‘2011-2020’ axis, ‘richest 20%’ is 53.5% and ‘poorest 20%’ is 35.2%, which is a similar gap to the previous decade. You can toggle between the primary view of HIV to TB or Malaria.
Inequalities have not narrowed in the past decade in the majority of countries. National averages of HIV, TB and malaria indicators have generally improved over the past 10 years. Yet, in most indicators, inequalities are largely unchanged: poorer, less educated and rural subgroups remain at a disadvantage.
In some cases, inequalities have increased over the past decade. Among males, the gap between the richest and poorest increased for HIV testing and TB knowledge. Economic-related inequalities remained high over the past decade for prompt care-seeking for febrile children.
WHO working with partners at a hospital in South Sudan to ensure there are medical supplies and equipment for patients.
Where are inequalities low or narrowing?
Despite the general state of high inequality, low inequality* was reported for certain indicators and dimensions. For many indicators with sex-disaggregated data, the global median across countries showed no or low differences between women and men.
*Low inequality refers to a difference of less than 5 percentage points between population subgroups.
DATA VISUALIZATION - Sex-related inequality is low in certain indictators
This interactive data visualization shows low inequality between females and males across different indicators for HIV, Tuberculosis (TB) and Malaria. The primary view is HIV with three different indicators. Each indicator has its own axis, ranging from 0 to 80%, with the estimated median for females and males plotted as separate dots. For the HIV indicator ‘Accepting attitudes (would buy fresh vegetables from shopkeeper living with HIV)’ females have an estimated median of 48.5% and 52.8% for males. You can toggle between the primary view of HIV to TB or Malaria.
In HIV, around half of countries reported a low difference between females and males for indicators of accepting attitudes, comprehensive knowledge and people living with HIV with suppressed viral load.
In TB, nearly all countries reported no or low female–male differences in TB case detection rate, and there were low overall sex-related differences in TB knowledge and attitudes.
In malaria, there was little difference between boys and girls aged under 5 years in most (or all) countries with available data for use of insecticide-treated nets, prompt care-seeking for fever, and prompt treatment for fever with antimalarial medicines.
DATA VISUALIZATION - Certain interventions are helping tackle inequities
This interactive data visualization shows the change of inequality over two decades between the ‘poorest 20% of the population’ and ‘richest 20% of the population’ for ‘Households with access to a pesticide-treated net’. For ‘2001-2010’, the estimated median ‘poorest 20%’ is plotted as a dot on an axis at 33.8% and ‘richest 20%’ at 38.9%. ‘2011-2020’ is plotted on a separate axis. The estimated median for ‘poorest 20%’ is higher at 70.8% than the ‘richest 20%’ at 68.2%. Each axis ranges from 0 to 80%.
Some gaps in inequality are narrowing through faster improvement of intervention coverage among disadvantaged population subgroups. For example, household ownership of at least one insecticide-treated net – indicative of malaria prevention efforts – has increased faster among the poorest than the richest households between 2001–2010 and 2011–2020, resulting in a narrowing gap between the richest and poorest.
Reviewing an X-ray during a hospital examination in Bandar Seri Begawan, Brunei Darussalam.
What is the impact of addressing inequality?
Promoting faster improvements in service coverage among the poorest, least educated, and rural – the subgroups that have consistently lower coverage – will bring countries closer to achieving national goals and targets.
But how much would national averages improve if the entire population had the same level of coverage as the most advantaged subgroup? Eliminating economic-related inequality in countries for selected HIV, TB and malaria indicators stands to have a far-reaching global impact.
DATA VISUALIZATION - Eliminating inequalities can accelerate improvements in national averages
This interactive data visualization shows the comparison of the current national average and the potential national average if there were no inequalities across HIV, Tuberculosis (TB) and Malaria. The primary view is HIV with three different indicators broken down by females and males on separate axes, ranging from 0 to 90%. Some disease indicators are not sex-disaggregated. For the indicator ‘Condom use at last high-risk sex’, the current average is plotted as a dot at 36% and 55.3% as the potential. For males, the current average is plotted at 48.1% and 58.3% as the national average. You can toggle between HIV or TB and malaria.
Note: For the TB catastrophic cost indicator, lower averages are desirable; for all other indicators, higher averages are desirable.
For pregnant women tested for HIV during antenatal care, current national averages across 46 countries range from 1% to 98%. That gives an overall weighted average of 40%.
If countries improved the level of HIV testing of all pregnant women to match the level of testing in the richest subgroup, 21 countries would see an improvement in the national average by at least 50%. And the overall weighted national average would increase to 64% (a 60% relative increase).
What would be the impact of eliminating economic-related inequality for families affected by TB? The percentage of families affected by TB facing catastrophic costs due to TB would decrease by at least 50% in half of countries (from a current weighted average of 61% to a potential average of 38%).
For malaria, eliminating economic-related inequalities in prompt care-seeking for children with fever would mean a 26% relative improvement in the weighted average across 28 countries.
Conclusion
Tackling inequities in HIV, TB and malaria is key to accelerating progress towards the Sustainable Development Goals.
Identifying and characterizing inequalities through health inequality monitoring lends important insights to inform differentiation in service provision so that resources are aligned to achieve maximal impact.
Developing technical capacity for health inequality monitoring is important to ensure the process is rigorous, has impact and generates change. There are significant opportunities to strengthen the impact of inequality monitoring in HIV, TB and malaria.
Include inequality indicators and targets in global and national disease strategies and performance assessments
This creates accountability mechanisms for tackling inequities and a strong impetus for regular health inequality monitoring.
Ensure availability of more and better inequality data
Disaggregated data about a range of health indicators are needed to assess and compare the situation across population subgroups.
Conduct regular inequality analysis and reporting
Assess the state of inequality in HIV, TB and malaria as part of routine monitoring and evaluation at global, national and subnational levels.
Use multiple forms of evidence to contextualize inequalities
Draw on findings from other quantitative and qualitative studies to more fully understand why inequalities exist and what needs to be done to address them.
WHO has developed a number of tools and resources to support health inequality monitoring (see the Health Equity Monitor).