Chapter 5. Monitoring considerations across different contexts

Overview

Monitoring health inequalities is essential to identify and track the health experiences of population subgroups and to provide decision-makers with an evidence base to formulate equity-oriented policies, programmes and practices. This book elaborates on a general approach to monitoring health inequalities that is widely applicable, acknowledging that every iteration of monitoring, however, requires contextualization – that is, different contexts raise unique considerations for adapting and applying the approach. For example, how are the base populations for monitoring defined? Are there health topics, indicators and inequality dimensions that are of particular importance? What sources are likely to contain relevant data for inequality monitoring? What capacity exists for analysing data? What are relevant considerations for reporting key messages about health inequalities and using evidence to inform action?

The objective of this chapter is to discuss challenges and opportunities for health inequality monitoring in selected contexts. The contexts highlighted in this chapter include lower- and higher-resourced settings, rural and remote settings, refugee and migrant populations, and emergency contexts. Specifically, the chapter addresses pertinent considerations related to the scope of monitoring, data availability, technical capacity and knowledge translation (as applicable). The populations and contexts featured are intended to highlight a selection of the possible contexts for the application of health inequality monitoring approaches and are not intended to be comprehensive. Health inequality monitoring is warranted across many other diverse contexts to capture ongoing and evolving issues, such as climate change (see Annex 5) and urbanization (see Annex 6), which have cross-cutting implications for the contexts and populations discussed in the chapter.

Inequality monitoring in lower- and higher-resourced settings

This section addresses health inequality monitoring considerations across lower- and higher-resourced settings. These designations are meant to be approximate and descriptive of settings that share commonalities. Attributes of countries or other administrative areas may be variably reflected in descriptions of both lower- and higher-resourced settings.

The WHO Health inequality monitoring workbook contains exercises that guide the application of health inequality monitoring approaches across different contexts (1).

Lower-resourced settings are areas where health needs tend to be high, alongside scarce resources, rudimentary or damaged health infrastructure, or weak governmental institutions. Population-wide access to basic and essential health services may be a pressing concern. These settings may include humanitarian emergencies or be facing protracted crises. They may be characterized by poverty, general lack of infrastructure, remote or mobile populations, emergencies of different natures, or other circumstances that compromise resource availability for high-quality health data collection and analysis.

Higher-resourced settings tend to have more developed infrastructure, including health data infrastructure and technology, and more stable governments and institutions. The capacity of people to collect, store, analyse and use data may be more advanced. With an abundance of data, health inequality monitoring approaches may be more specialized and technically advanced than in lower-resourced settings – and as a result, the results may be less comparable across settings.

Health information systems

In general, health information systems are less functional in lower-resourced settings than in higher-resourced settings. Weaker governance structures, standardization practices and coordination mechanisms in lower-resourced settings often mean that certain data sources are incomplete or of variable quality. For example, arduous requirements for collecting, reporting and managing data on a large number of indicators may be beyond the capacity of data systems and health workers, resulting in poor adherence to protocols and low-quality data.

Lower-resourced settings commonly rely on international donor agencies to support data collection efforts or data source development. Although country ownership and leadership has been a priority in some settings (Box 5.1), in the absence of strong policy and legal environments, the interests and priorities of external funding organizations – such as the private sector or international nongovernmental organizations – may weigh heavily into which data are collected, from whom, and at what frequency. Data governance concerns, such as those related to data-sharing, data ownership and digital interoperability, may arise. See Chapter 4 for more on health data governance.

BOX 5.1. Ethiopia’s One Plan, One Budget, One Report

Ethiopia’s One Plan, One Budget, One Report is part of a health-sector goal to promote government leadership in improving harmonization and alignment across all levels of the health sector, reduce transaction costs of delivering services, and enhance coordination across stakeholders, including donor agencies (2). An overarching priority is for all stakeholder activities and budgets to be reflected in one strategic plan, which is implemented according to an agreed set of indicators and reporting formats:

• One Plan refers to the health sector having one countrywide, shared, agreed strategic plan. The plan is developed through extensive consultation between the Government, donors and other stakeholders. All plans at regional, zonal, district and facility levels are to be subsets of the plan. Programmes and donors may have their own detailed plans, but they should be consistent with the priorities and activities of the public sector.

• One Budget ideally means all funding for health activities, including from the Government, donors, nongovernmental organizations and others, is pooled and routed through Government channels. (At the subnational level, a less ideal realization of One Budget entails that all funds for health activities are reflected in one plan and one documented budget but disbursed through separate channels.) Every cost centre at the federal, regional, zonal, district and facility levels will know about all financial and non-financial resources allocated and spent in the health sector across all levels. This facilitates more comprehensive planning, avoids duplication of efforts, reduces wastage, and increases programme effectiveness.

• One Report means using one monitoring system and one monitoring calendar. A set of indicators has been identified to monitor progress in achieving the health-sector strategy. Reports should be based on these indicators without duplicating the channels of reporting.

A roadmap for accelerating progress towards the implementation of One Plan, One Budget, One Report was developed in 2012 through a participatory approach, enhancing stakeholder buy-in and embodying the premise of the approach, working together (3).

Higher-resourced settings have stronger health information systems. They tend to have more comprehensive health data about the population due to better infrastructure, implementation of stronger standards, and greater availability of resources for data collection. Strong coordination across well-established sources of health and population data may provide opportunities for linking data, presenting numerous possibilities for monitoring across diverse dimensions of inequality. With more advanced digitization and expanding passive data collection (e.g. through mobile applications), higher-resourced settings may face data overload, requiring advances in technology and data management practices to process large amounts of data. Issues related to the digital divide, privacy and security may compromise the quality of data derived from digital sources (see Chapter 16).

Data sources

Household surveys are conducted across all settings, and they are often the primary source of health inequality data in lower-resourced settings. In such settings, household surveys tend to be funded wholly or in part by international donor agencies, whose interests may be limited to a specific disease or programme area and may not be aligned with local priorities. According to a 2020 WHO global report on health data systems and capacity, only 6% of surveys in low- and lower-middle-income countries were funded solely by the national government. Upper-middle-income countries received less support from international donor agencies but also had limited ability to fund their own survey programmes (4). Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) are major international household health surveys supported by the United States Agency for International Development and the United Nations Children’s Fund, respectively, and conducted primarily in low- and middle-income countries (5, 6). Chapter 12 contains more information about household surveys and their use as a data source for health inequality monitoring.

In 2020, two-thirds of high-income countries had well-developed and sustainable capacity for conducting population-level surveys, but only half of middle- and low-income countries had this capacity (4).

Civil registration and vital statistics (CRVS) systems collect data about vital events, namely births, deaths and causes of death (see Chapter 12). They require accurate and continuous registration of events and associated details, in adherence with established legal frameworks and standards and supported by strong centralized and decentralized administration (7). CRVS systems can serve as a source of vital statistics for health inequality monitoring in higher-resourced settings, but lower-resourced settings often have poorly functioning CRVS systems, with only partial coverage. In 2020, fewer than one in 10 low-income countries achieved complete registration of births (defined as over 90% of births registered); by comparison, a third of lower-middle-income countries, two-thirds of upper-middle-income countries, and almost all high-income countries had complete registration. Regarding death registration, most low-income countries reported no data or had completeness below 50%, but over 90% of high-income countries had complete registration (4).

Census data are available across most countries and can provide information about population demographic, socioeconomic and geographic characteristics (see Chapter 12). The quality of census data and the ability to derive disaggregated population projections, however, vary (4). Census data in lower-resourced settings may be old or fail to meet minimum standards. This contributes to issues stemming from a lack of reliable denominator data for health inequality monitoring – that is, data that provide information about the size of the base population for health inequality monitoring. Higher-resourced settings, however, are increasingly applying sophisticated and efficient methodologies to conduct censuses, allowing for more frequent censuses with greater potential for linking data across sources.

Technical capacity

Technical capacity for health inequality monitoring encompasses the skills, knowledge and expertise required to carry out monitoring activities (which include, broadly, determining the scope of monitoring, obtaining data, analysing and interpreting data, reporting results and translating knowledge into action – see Chapter 2). Capacity-strengthening efforts for health inequality monitoring are often focused on lower-resourced settings, where institutional support and political will for inequality monitoring may be weak or lacking, and there may be less access to technology and other infrastructure limitations. Lower-resourced settings may have more sporadic review cycles and less transparent mechanisms for effecting changes based on the results of monitoring. In some cases, weak national institutions may result in an increased reliance on regional or global institutes to support technical capacity-strengthening.

Inequality monitoring in rural and remote settings

Populations in rural and remote areas make important economic, social and cultural contributions to countries, but they experience various forms of disadvantage. Characterized by dispersed populations and weaker health systems, rural and remote areas around the globe face shortages of well-trained, skilled and motivated health workers (8). Populations in these settings experience a unique set of social and environmental determinants of health, including high rates of extreme and multidimensional poverty (9, 10). Climate change, natural disasters, droughts, fires and conflicts may disproportionally affect these populations. The United Nations General Assembly resolution Eradicating Rural Poverty to Implement the 2030 Agenda for Sustainable Development brought attention to the urgent need to accelerate rural poverty eradication and strengthen health service provision in rural areas (11). It also underscored the global nature of the issue, acknowledging that rural poverty exists in countries across all stages of development, although the extent of disadvantage may be different from country to country.

As many countries experience rapid rates of urbanization, health and development attention has shifted towards urban areas and away from rural areas (12). A governmental commitment to balanced urban/rural territorial development is absent in many contexts. As a result, there may be a declining economic incentive to invest in rural health systems. There is, however, a strong rationale for investing in rural health systems, especially in consideration of broader, intersectoral factors and the cost of neglecting such investments. For example, disinvestment in rural populations can create conditions ripe for discontent, conflict and insecurity.

Scope of monitoring

Monitoring inequalities by urban/rural place of residence is a common practice, although unique considerations and limitations arise when further distilling rural settings and defining parameters for monitoring that are relevant within these settings. One pertinent issue pertains to how rural and remote areas are defined and classified. This is key for establishing the base population for monitoring and for categorizing rurality as a dimension of inequality. A second issue when exploring inequalities in rural and remote settings relates to the selection of relevant health indicators and dimensions of inequalities.

Defining rurality

There are various ways to define rural areas and capture the extent of remoteness within them (i.e. the degree of rurality). Defining rural areas too broadly can mask inequalities within these areas, while defining them too narrowly may fail to fully capture the population experiencing spatial disadvantage. For example, of the 103 countries that use a minimum population size threshold to define rural and urban areas, 84 use a threshold of 5000 or fewer inhabitants to define rural areas (13). In some cases, this threshold of 5000 inhabitants represents too broad a grouping to capture the diverse health experiences of people living in smaller settlements.

Detailed classifications of remoteness can provide a breakdown of how disadvantage may be experienced as a gradient within rural contexts. Ideally, the definition of rurality for a particular monitoring application should allow for the results of monitoring to inform effective and efficient policy responses in rural areas to advance equity.

A rudimentary approach to urban/rural classification might consider the capital city of a country as urban and the rest of the country as rural. This approach, however, poses major limitations in many settings, especially if the capital city is not the main urban centre or if there are multiple large cities in the country. Further, binary urban/rural categories do not differentiate between remote rural areas and rural areas close to a city. Other approaches to defining rurality might consider population size and density, administrative designations, sectoral employment and economic activities, proximity to services and infrastructure (sometimes captured by satellite imagery), land use or other factors. The applicability of these different approaches varies across countries. Box 5.2 describes an example in New Zealand.

BOX 5.2. Developing a relevant classification of rurality in New Zealand

New Zealand has undertaken efforts to develop meaningful urban/rural classifications for the analysis of health data and exploration of rural health inequalities in the country (14). Although anecdotal experiences surrounding health and determinants of health point to important differences between rural and urban areas, generic classification schemes have traditionally underestimated inequality between these settings. One contributing factor is that classification approaches may inappropriately designate urban fringe areas as rural, while medium-sized isolated communities are considered urban. An alternative approach to defining rurality in New Zealand is focused on integrating factors relevant for measuring inequalities in health such as proximity in terms of travel time to larger urban areas. The Geographic Classification for Health is a “fit for health purpose” rural/urban classification for analysis of health data at the national and local levels (15). The Rural Health Strategy 2023 for New Zealand relies on this refined approach to measuring rurality (16).

Although global-level monitoring may rely on country-level specifications, the Degree of Urbanisation methodology, endorsed by the United Nations Statistical Commission in 2020, provides a common set of thresholds that can be applied across countries. Covering all territories within countries, it specifies the three classes of cities, towns and semi-dense areas, and rural areas. Within rural areas, further classification can be applied to divide local units into villages, dispersed rural areas, and mostly uninhabited areas, which are determined based on population thresholds and clustering (17).

Health indicators and dimensions of inequality

Because a major challenge in rural contexts relates to the physical accessibility of health services, health indicators with a spatial component are of special importance. These include indicators related to health workforce density and distribution, health facility density and distribution, geographical access to essential medicines and health services, and household expenditure on health. To date, the exploration of inequalities in rural areas has focused largely on reproductive, maternal, newborn and child health topics. Further efforts are needed to better understand inequalities across a wider range of indicators and topics.

AccessMod (version 5) is a tool that facilitates analyses to support universal health coverage “by modelling physical accessibility to health care”, with particular relevance to rural and remote settings. The tool contains accessibility analysis, geographic coverage analysis, referral analysis, zonal statistics and scaling up analysis (18).

Poverty and its worst manifestations are overwhelmingly rural. In many settings, however, a lack of available data limits the extent to which economic-related inequalities in health within rural and remote populations can be unpacked. Approaches to measuring economic status may need to be adapted to reflect indirect aspects (e.g. assets, housing and access to services) that are relevant in rural and remote settings.

Within rural areas, intersecting forms of disadvantage relate to age, indigenous status, migration status, occupation and sex (see Chapter 3). These constitute important and overlapping dimensions of inequality in many contexts, which may also have implications for remedial action. For example, inequality monitoring might expose how intersecting sources of disadvantage compound in rural agricultural workers, informing entry points for health programming and social protection policies.

Data availability and quality

A lack of adequate data (and particularly disaggregated data) in rural and remote areas is a critical limitation for inequality monitoring. Data collection through CRVS systems, censuses, health facility-based records and registries may have low coverage and quality constraints. Household surveys that include data collection in rural areas may help to fill data gaps because they tend to gather a range of information about relevant dimensions of inequality. Data collection in remote rural areas with low population density is resource-intensive, however, and resulting small sample sizes may limit inequality analysis capabilities.

Data quality is also a key consideration in rural and remote settings. Issues related to training and capacity, information technology, communication and task prioritization may contribute to incomplete or unreliable data collection and lead to underreporting or biased reporting. Quality issues may also emerge during data preparation and analysis. For example, data about rural health-system performance indicators may be excluded from reporting due to small sample sizes, or they may be aggregated across regions (which may have distinct characteristics). The periodic collection of quantitative data may not be sufficient to understand the variations in health indicators across rural and remote settings, which may be highly variable over time.

Generating demand for data in rural areas is key to addressing data scarcity. A strategic entry point lies in securing government and donor commitments to promoting health and well-being in rural areas, for example, through balanced territorial growth initiatives. Commitments linked to monitoring activities create a mandate for data collection and health information system strengthening in rural areas and promote regular reporting on rural health inequalities. They can also bolster political will for monitoring and follow-up action, public support and engagement.

There is an increasing use of technologies to facilitate data collection in rural and remote settings, such as mobile and web-based surveys or geospatial data collected through satellite imagery (see Chapter 16). There are, however, limitations in these approaches, such as the introduction of bias due to differences in the access and use of digital technologies (the “digital divide”) (19).

Knowledge translation

Inequality monitoring in rural settings can have direct implications for policies and programmes, especially if it is integrated into ongoing monitoring and evaluation cycles (20). Moreover, inequality monitoring that aligns with rural and remote administrative boundaries contains clear entry points for targeted, intersectoral interventions (21). Subpopulations experiencing disadvantage are easy to identify and locate, and health information specific to a geographical setting can be considered alongside information across diverse sectors. Too often, however, inequality monitoring in rural areas is limited to situation analyses with insufficient follow-through in terms of designing and deploying equity-oriented interventions that address differentiated population needs. In general, capacity for generating action in rural and remote settings from inequality evidence is often lacking. There are several success stories, however, where evidence from inequality monitoring has been used to inform action (Box 5.3).

BOX 5.3. Addressing rural inequalities: country examples

Over the past decades, Thailand has successfully worked to narrow gaps in health service provision in rural and remote areas, using inequality monitoring to inform and refine health workforce interventions (22, 23). The country has adopted integrated, multipronged strategies to address the inequitable distribution of human resources for health between urban and rural areas. These have focused on:

• prioritizing medical education admission for students from rural areas;

• locating health profession schools outside the capital city;

• providing health profession training and preparation specifically to practise in rural settings;

• financial incentives to attract health professionals to work in rural areas;

• personal and professional support interventions, including improved health facility infrastructure, logistics support, housing and transportation;

• opportunities for career advancement and enhancement of professional networks;

• social recognition and job perquisites to sustain motivation.

India faces shortages of human resource for health, especially in rural and remote settings (24). The predominantly rural state of Chhattisgarh, for example, has quantified the vacancy of health professionals across divisions and districts within the state, reporting more severe challenges and shortages in rural and remote areas. Additionally, there are inequalities in health and access to health services across population groups defined by geography, socioeconomic status, gender, class and social group (23). Policy interventions to strengthen human resources for health include educational interventions, regulatory interventions, financial incentives, and personal and professional support systems. Notably, a three-year medical diploma course was established, focused on service in rural and remote areas. The initiation of the Chhattisgarh Rural Medical Corps incentivizes health professionals to work in difficult and less accessible areas, including rural, remote and conflict-affected settings (25). The scheme was given sustained funding over a period of time and placed emphasis on financial incentives, gradually expanding rural residency incentives while ensuring health system functioning improved in rural areas (26).

Despite rapid industrialization and economic growth during the 1960s and 1970s, rural areas in the Islamic Republic of Iran remained underdeveloped. The rural population was characterized by poor health status, with many areas lacking basic health-care infrastructure (27). In response, the country initiated a rural development strategy, focusing on providing primary health care (28). The programme aimed to address the immediate health-care needs of rural populations by creating a network of rural health centres and health houses (28). These health houses, staffed by trained community health workers (Behvarz), became the backbone of the country’s rural health-care system. The responsibilities of Behvarz include a wide range of preventive and basic health-care services, such as providing maternal and child health care, family planning, immunization, noncommunicable disease control, supporting health education, environmental health activities, and annual population censuses (27, 28). By addressing basic health-care needs and promoting healthy lifestyles, the Behvarz programme has contributed to a reduction in the burden of disease and an increase in life expectancy among rural populations (29). The success of the programme in reaching remote rural areas has been attributed to its decentralized structure and the extensive network of health houses (30). As of 2022, the programme covered about 85% of the Iranian rural population through more than 17 000 health houses spread across villages and settlements (31).

Inequality monitoring among refugee and migrant populations

One in eight individuals globally is a migrant or forcibly displaced person, meaning over 1 billion people are on the move worldwide. Refugees and migrants comprise diverse groups of people with complex and varied health experiences. The term “refugee” is defined in the 1951 Refugee Convention and the 1967 Convention and Protocol Relating to the Status of Refugees as “any person outside their country of origin who needs international protection because they fear persecution or a serious threat to their life, physical integrity or freedom in their country of origin as a result of persecution, armed conflict, violence or serious public disorder” (32). There is no formal legal definition of a migrant, but a widely used definition is “a person who moves from one place to another, whether across or within international boundaries” (33), acknowledging there are a variety of further specifications that could be made (Box 5.4). Drivers for migration and displacement include climate change, collective expulsion, human rights violations, natural or human disasters, armed conflict, situations of generalized violence, family reunification, freedom of movement, and labour and economic factors.

BOX 5.4. Descriptions of migrants

Recognizing the diversity within the general classification of migrants, the International Organization for Migration (IOM), the United Nations High Commissioner for Refugees (UNHCR) and WHO have developed specific descriptions for migrants in different situations (34).

An international migrant is any person who changes their country of usual residence. In 2020, there were an estimated 281 million international migrants (35).

Internal migrants are migrants who stay within their country of origin. In 2005, there were an estimated 763 million internal migrants, although this number may be much higher due to the informal nature of much of this movement (36).

Internally displaced people are those who have been “forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border” (33). As of 2023, there were a total of 75.9 million internally displaced people in 116 countries and territories (37).

Asylum-seekers are people who seek international protection. In countries where asylum is judged on a case-by-case basis using specific eligibility criteria, asylum-seekers are people whose claim has not been finally decided on by the country in which they have submitted it. Not every asylum-seeker will ultimately be recognized as a refugee, but every recognized refugee is initially an asylum-seeker. As of end of 2023, there were 6.9 million asylum-seekers globally (38).

Other designations, which have various uses, cases and contextual specifications, include international migrant workers, migrants in irregular situations, stateless people, environmental migrants, and documented and undocumented migrants.

Refugee and migrant populations face many of the same health and health-related challenges as host populations, but they may encounter additional barriers and risks due to their migratory status and circumstance (e.g. linked to substandard living and working conditions). Migration and displacement are considered key determinants of health and are often associated with worse health and well-being outcomes compared with those in non-migrant host populations. Migratory status and the ensuing conditions may be compounded by other social determinants of health, such as education, income, housing and access to services, as well as linguistic, cultural, legal and other health-system barriers (34). Racism, discrimination and xenophobia may exacerbate disadvantages experienced by refugees and migrants (39). In some circumstances, refugees and migrants lack access to essential health services or have low levels of health literacy, hindering health-care seeking, access and treatment adherence.

Xenophobia refers to “attitudes, prejudices and behaviour that reject, exclude and often vilify persons, based on the perception that they are outsiders or foreigners to the community, society or national identity” (39).

Data availability

In general, data disaggregated by migratory status are lacking from major global health datasets, including official Sustainable Development Goal data. The absence of these data hinders the ability to make comparisons of refugee and migrant populations against host populations within and across countries. The 2022 World Report on the Health of Refugees and Migrants underscored a need for “higher-quality and standardized, disaggregated data and definitions around health and migration” (34), which was further highlighted in the subsequent WHO Global research agenda on health, migration and displacement (Box 5.5) (40).

BOX 5.5. WHO Global Research Agenda on Health, Migration and Displacement

The 2023 WHO Global research agenda on health, migration and displacement identified data collection and knowledge exchange as a key implementation challenge to strengthening research on the topic globally (40). It noted the following limitations and challenges and also highlighted possible actions to address these: limited availability, granularity, quality and comparability of data sources; challenges with accessing migration related data; navigating legal and ethical considerations; lack of trust and sustainable partnerships with migrant communities to collect good-quality data; language and cultural barriers to data collection; time and cost of collecting data; and achieving a balance between qualitative, quantitative and mixed-methods data collection and research.

Another major challenge stems from the unclear definitions of migratory status. Much of the data that exist about refugee and migrant populations are not harmonized, limiting the extent to which data can be compared across countries and over time. The lack of standardized migrant classifications conceals important differences and varied health experiences and risks within refugee and migrant populations. Individuals within these populations encompass a broad spectrum of circumstances and experiences, with shifting and evolving health needs and risks. Definitional ambiguity hinders the ability to compare between different subgroups of refugees and migrants, and to benchmark other forms of inequality analyses. In particular, there is a lack of data about hard-to-reach irregular migrants, refugees residing outside of camps, people who have been trafficked, deportees and stateless individuals.

Refugee and migrant populations, inherently transient and mobile, present challenges for data collection. Data may be collected at different phases of migration journeys, by different institutions and for different purposes. Considerations for collecting data from these populations include ensuring language accessibility, cultural sensitivity and awareness, equitable reach of people on the move, and addressing concerns such as lack of trust, fear of mistreatment or discrimination. Refugees and migrants have the right to detailed explanations of the purposes and use of data collection, in languages that they understand, to obtain informed consent (34). Data collection agencies must ethically ensure adequate data protections, including upholding privacy, confidentiality, safeguarding of health data from immigrant enforcement, and the appropriate use of data.

Data sharing and linking in refugee and migrant populations require coordination among multiple stakeholders nationally and globally. Entities such as IOM, UNHCR and WHO, together with other United Nations agencies and other national and international organizations, work collaboratively to promote the health, well-being and safety of refugees and migrants (41). Fragmentation of data and information systems at the national level needs to be addressed – but incompatible software systems and data protection regulations limit the extent to which data may be shared between agencies.

For example, structures do not typically exist to link migrant health assessment upon entry to a country with the public health system of the country of origin. This poses challenges to understanding the health status and needs of people on the move, and to the continuity of treatment and care across borders.

Data sources

Acknowledging the complexities described above, a substantial challenge across all data sources lies in introducing or expanding the collection of data about migration indicators in existing data sources.

The WHO European Region has published technical guidance for integrating migration health data into national health information systems and for creating a basis for harmonization of data reporting across Member States in the Region (42).

Household surveys remain a common and relevant source for population-based data and monitoring of global goals and targets. WHO works with countries to help improve their health information systems, but it is imperative to look at information systems for health in a holistic manner and to invest in population-based data-collection instruments and approaches. Household survey programmes, including multicountry initiatives such as DHS and MICS, collect some relevant but limited information related to migration (43). For example, DHS includes questions related to rural-to-urban migration and international migration, such as length of stay or date of entry, citizenship and country of birth – although countries can opt out of migration questions. The World Bank Living Standards Measurement Study includes a migration module with questions about place of birth, most recent place of residence, reasons for moving, number of times moved, and specified types of migration (including inter-district, rural-to-urban and international migration) (44). Nevertheless, the use of data disaggregated by migration variables may be limited if these populations are not adequately accounted for in the survey sampling frame (see Chapter 12).

If they are functional, censuses and CRVS systems may include information such as a person’s length of stay or date of entry into a country, citizenship and country of birth, allowing for some characterization of migration flows and potential use in inequality monitoring (see Chapter 12). The United Nations Expert Group on Migration Statistics has proposed additional questions for censuses and household surveys to improve the quality and comparability of migration data (45).

In general, institution-based data sources, including medical records, have limited utility for monitoring in refugee and migrant populations, unless information about migration status is routinely recorded and accessible in a format that can be linked with health data to yield disaggregated estimates. Some disease control surveillance programmes, such as HIV, tuberculosis and malaria, collect migration data as part of their efforts.

Recognizing the need for continued efforts to address the health needs of refugees and migrants, the WHO 2019–2023 Promoting the health of refugees and migrants: global action plan (subsequently extended to 2030) highlights the importance of strengthening health monitoring and health information systems (41). This includes collaborating with countries to develop disaggregated data on the health of refugees and migrants and supporting the development of approaches for data collection. A commitment to “collect and utilize disaggregated data as a basis for evidence-based policies” is also evident in the first objective of the Global Compact for Safe, Orderly and Regular Migration, a comprehensive framework for managing international migration (46).

Promoting the health of refugees and migrants: experiences from around the world is a WHO compendium of 49 country examples the illustrates how countries are advancing health monitoring and information systems to promote the health of refugees and migrants (47). Box 5.6 describes an example of an initiative in Peru.

BOX 5.6. Strengthening health monitoring and health information systems to promote the health of refugees and migrants in Peru

In response to the increasing migrant populations within Peru, the Ministry of Health established the Functional Health Unit of Migrant and Border Populations in 2020 (47). Recognizing that the majority of migrants may not access health services, the unit focuses on promoting the integration of migrants into the health system. Its mandate includes proposing public policy strategies and monitoring their implementation and impact. The introduction of nationality variables in health records has enabled closer monitoring of the health-care needs of migrants.

Expanding the depth and scope of inequality monitoring

Due to the lack of disaggregated data on refugee and migrant populations, health inequality monitoring has focused on comparisons between refugee and migrant versus non-migrant (host) populations. Although there are cases where refugees and migrants are found to be healthier than host populations, limited available evidence is often generalized to give a notion of a healthy migrant effect (which may be misleading). Analyses of health inequalities within refugee and migrant populations have been more limited and fragmented, but they are crucial for understanding how disadvantages are experienced within these populations. Box 5.7 overviews the availability of disaggregated migration data in the WHO Health Inequality Data Repository (48).

BOX 5.7. Migration data featured in the WHO Health Inequality Data Repository

The WHO Health Inequality Data Repository includes disaggregated data pertaining to migration (48). As of 2024, the Data Repository contained indicators disaggregated by migratory status in European countries, sourced from Eurostat. These indicators are related to health status, health behaviours and risk factors, health care and health determinants.

Disaggregated data within refugee and migrant populations are also available. This includes Eurostat data from European countries, disaggregated by age, sex and type of migrant. Globally, data from 197 countries, sourced from the United Nations Statistical Department International Migrant Stock 2020 (35) and the UNHCR Refugee Data Finder (38), pertain to the number of international migrants and refugees per 1000 population, disaggregated by age, place of residence and sex.

Collecting data about nationality is a good step towards inequality monitoring for health of refugees and migrants, but data sources also need to include information about variables such as time of arrival in the country, country of citizenship and nationality of parents. The expanded collection of data disaggregated by gender or sex can help elucidate gender influences on migration experiences (49). Targeted explorations of inequality are warranted to address specific circumstances. For example, monitoring occupational accidents, injuries and deaths is relevant for migrant worker populations who often work in jobs that are “dirty, dangerous and demanding” (34).

Inequality monitoring should also attend to the various factors that influence the physical and mental health of refugees and migrants throughout the phases of migration, which have implications for the types of relevant health indicators (43). Notably, pre-migration events and trauma, especially common in forced migration and displacement flows, can have significant impact on mental health and well-being. Conditions during transit, arrival and integration phases vary, with potential exposure to communicable diseases. Barriers or delays in accessing timely testing, diagnostic or treatment services could further exacerbate negative health outcomes. Unique factors during the return phase, where applicable, further affect health outcomes. For example, refugees and migrants are particularly vulnerable to the effects of antimicrobial resistance throughout various phases of migration and displacement, due to factors such as exposure to infections, limited access to diagnostics and therapeutics, and inappropriate use of antibiotics (50).

Developing a global health indicator framework tailored to refugee and migrant health would enhance inequality monitoring efforts, ensuring comprehensive tracking of their diverse health needs and challenges, and informing tailored health policies and interventions in origin, transit and host countries.

Inequality monitoring in emergency contexts

WHO defines an emergency as a “situation impacting the lives and well-being of many people or a significant percentage of a population and requiring substantial multisectoral assistance” (51). Emergencies are diverse. They may be acute or protracted and have a rapid or slow onset. They may be complex, with more than one cause, and have significant public health, social, economic and political impacts that span a single country, or they may be regional or global in scope. Multiple emergencies can occur concurrently and may be the result of, or exacerbated by, pre-existing inequalities. Globally, the number of emergencies with public health impacts is anticipated to continue to rise alongside increases in risk factors such as biological hazards (prompting epidemics and pandemics), humanitarian crises (such as armed conflicts and civil unrest), extreme weather events and natural disasters (52).

The risks, vulnerabilities and impacts of health emergencies fall disproportionally on population groups with longstanding experiences of disadvantage. For example, women and girls are at heightened risk of experiencing sexual violence in conflict situations (53). Children, older people, people living with disabilities, people living with HIV, people from ethnic or religious minorities, internally displaced people and people living in poverty may be more vulnerable to health risks during emergencies. There may be cases where people from certain population subgroups face discrimination or deliberate exclusion on the basis of religion, ethnicity, political affiliation or place of residence. This underscores the importance of equity-oriented health emergency preparedness, response and resilience efforts. Indeed, proposals that emerged as a result of the COVID-19 pandemic to strengthen global efforts emphasized equity as a principle and a goal (54).

The International Health Regulations provide a legal framework that defines countries’ rights and obligations in handling public health events and emergencies that have the potential to cross borders (55). One of the requirements for countries is to establish, strengthen and maintain core capacities for surveillance and response. Relatedly, the notion of “accountability to affected populations”, a key part of a people-centred approach to emergency responses, recognizes the unique situations of population subgroups of different ages, genders, disability status, mental health status and other factors. WHO country offices are accountable for “systematically including accountability to affected populations in all needs assessments and monitoring, review and evaluation processes” (56).

The management of public health information in emergency contexts requires a high level of coordination to avoid duplication, inefficiency, and poor-quality or contradictory information. To this end, the Standards for Public Health Information Services of the Global Health Cluster provide guidance, templates and best practices for integration of all available public health information to support evidence-based operational decision-making (57).

Scope of monitoring

In emergency contexts, monitoring is required to assess the health status of – and health threats to – affected populations; assess the availability of health resources and services; identify potential barriers to health care; and determine health-system performance (58). The nature of public health emergencies varies greatly, and the timing, frequency and general scope of monitoring should have the ability to rapidly surge and adapt to the scale of the emergency. Disease outbreaks may be global or regional in nature, requiring coordinated mechanisms for harmonizing and sharing information, and continuous updates and surveillance in accordance with the spread of the hazard. For example, the frequency of collecting and reporting data about COVID-19 cases and deaths changed over the course of the pandemic (Box 5.8). Protracted humanitarian crises may require ongoing monitoring over longer periods.

BOX 5.8. Frequency of reporting WHO COVID-19 surveillance data

Initially WHO published daily updated numbers of reported cases and deaths, but in August 2023 it moved to weekly reporting (59). Age- and sex-disaggregated data about COVID-19 case rates, death rates and case fatality ratios were available weekly from January 2020 in the WHO COVID-19 Detailed Surveillance Data Dashboard (60). In some countries, such as those affected by conflict, case identification was reported to be low due to detection and testing strategy limitations (as noted in a caveat in the Dashboard).

For environmental disasters with a rapid onset, such as floods or earthquakes, monitoring takes place after the initial event. Monitoring may include the use of needs assessment tools such as the Multi-cluster/sector Initial Rapid Assessment (MIRA), the Multi-sector Needs Assessment (MSNA) and public health situation analysis (Box 5.9).

BOX 5.9. Needs assessment tools

MIRA is a joint needs assessment tool that can be used in sudden-onset emergencies to identify needs, affected areas and affected subpopulations (61).

MSNA is a more detailed versions of MIRA used to identify subpopulations experiencing disadvantage (62). MSNA is led by the United Nations Office for the Coordination of Humanitarian Affairs, usually with support from the REACH initiative (63).

A public health situation analysis draws on secondary data to identify the current health status and all potential health threats that the population may face, the functioning of the health system, barriers for access and utilisation, and the humanitarian health response. It identifies the major areas for health action to respond to and recover from the crisis. It is relevant for preparedness and response-planning (59).

Health indicators and dimensions of inequality

Disaggregated data in emergency contexts are vital to determine who is at greatest risk or in need of health care. Integrated as part of larger barrier assessment analyses, disaggregated data can also assist in identifying and anticipating health system capacity needs. Core health indicators for humanitarian contexts are laid out in the Standards for Public Health Information Services (57), with guidance for disaggregation where relevant (64). Indicators span several health topics and may be applicable in some situations but not others. Examples of these indicators include:

• proportional mortality (Box 5.10);

• average population per functioning health facility, disaggregated by health facility type and administrative unit (permitting assessment of inequality in geographical accessibility and availability of health facilities by administrative unit);

• percentage of children who have received measles vaccination (disaggregated by age and sex, and by displaced versus host population);

• number of community health workers per 500 people in rural and hard-to-reach locations.

BOX 5.10. Population mortality estimation in emergency contexts

Population mortality indicators have important uses in emergency contexts, including informing emergency responses, estimating the magnitude and severity of humanitarian crises, and supporting human rights and internal law advocacy (65). These data are often inadequate, incomplete and of low quality, however, especially in unstable and conflict-affected regions. For example, monitoring proportional mortality (i.e. deaths due to a given cause) by age group can help to assess the appropriateness of a health service package deployed during a crisis and inform subsequent adaptations. Monitoring death rates due to intentional trauma in disadvantaged population subgroups can help to build cases for alleged crimes against humanity or other violations of international law. In some situations, excess mortality and other health impacts attributable to a crisis may require long-term monitoring over decades or even generations.

The WHO Handbook for conducting assessments of barriers to effective coverage with health services applies a mixed-methods research approach to identify and understand the supply- and demand-size barriers experienced by potential users and non-users of health services. Across each of its eight modules, the Handbook includes guidance for its adaptation to humanitarian contexts (66).

Data collection in emergency contexts typically focuses on specific affected areas or populations. Disaggregation of health indicators by geographic variables such as subnational regions, neighbourhoods or catchment areas for health facilities can have direct implications for targeted operational responses. In addition, many health indicators should be disaggregated by age group, people living with HIV or other condition, or sex. This information is important to establish the impact of the crisis, plan the scale of the response, inform resource allocations, and target responses towards priority subgroups or areas. For example, WHO guidance states that data about people in need – calculations that seek to determine how many people require assistance to meet their basic health needs – should be disaggregated by age, disability status, displacement status (i.e. refugee, internally displaced person or returnee), sex and subnational region (67).

Data sources

Population surveys often provide a frontline method of collecting key data in real time to guide programmatic decisions (68). They may also be used retrospectively, to assess the health impact of an emergency. Population surveys can be deployed quickly and be tailored to the immediate data requirements of a given situation, potentially capturing diverse dimensions of inequality alongside health information. Reliable lists of people or households are rarely available in emergency situations because there are often large population movements. Survey sampling design may rely on approximations – for example, drawing from IOM and UNHCR data sources – and there may be bias in how respondents are selected. For more information about population surveys, see Chapter 12.

Systems of public health surveillance – defined as the continuous, systematic collection, analysis and interpretation of health-related data (69) – are important data sources in emergency contexts. Disease surveillance data can help to identify impending outbreaks. The WHO Early Warning, Alert and Response System (EWARS) is designed to improve disease outbreak detection in emergency settings, such as in countries in conflict or following a natural disaster (70). More generally, surveillance systems can enable monitoring and evaluation of the impact of interventions and can contribute to priority-setting and planning activities for public health policy and strategies (69). For example, demographic surveillance, which may include weekly or monthly updates from community health workers, can provide information about population size and mortality. Facility-based surveillance may collect information relevant to cases of sexual or gender-based violence or mental health symptoms (68). A limitation of using surveillance data is that they require population or population subgroup estimates as denominators, which may be challenging to obtain in emergency contexts (Box 5.11). For more information about surveillance systems, see Chapter 14.

BOX 5.11. Population denominator estimation in emergency contexts

One data challenge in emergency contexts relates to estimating population sizes – that is, denominators for the whole population and population subgroups, such as age and sex groupings (71). There are numerous reasons why it can be difficult to estimate population denominators, including weak pre-crisis health information systems; hindered physical access to affected populations; overreporting of population sizes to maximize possible access to resources; lack of expertise and resources to apply rigorous methods for population estimation; and crises spanning administrative boundaries. Additionally, fluctuations in the population size and composition (e.g. by age and sex) may occur due to displacement. Geospatial data and technologies and health-tracking applications (see Chapter 16) are of emerging importance for gathering information in emergency contexts.

For acute emergencies, qualitative reports gathered through rapid field assessments are a key source of information and may be used to identify priority populations initially (68). There are also several rapid assessment questionnaires and protocols that can be used to gather quantitative information from key informants and non-representative samples, although the robustness of these assessments, and their usefulness to guide humanitarian responses, may be limited.

One approach to strengthening data availability and quality regarding availability of essential health resources and services during emergencies is the WHO Health Resources and Services Availability Monitoring System (HeRAMS). HeRAMS aims to ensure that information on essential health resources and services is readily available to decision-makers at the country, regional and global levels. The approach, which supports the standardization and continuous collection, analysis and dissemination of information, is rapidly deployable and scalable in emergency contexts and fragile states. As of 2023, HeRAMS supports 30 projects across 27 countries and includes a geospatial modelling service that allows the precise identification, location and quantification of populations lacking access to essential health services (72, 73).

Reporting inequality data

Frequent and centralized reporting of disaggregated data about health emergencies can help to guide the ongoing development and targeting of responses, including prioritization and optimization of resource allocation. Reporting channels include databases, repositories, situation updates, news bulletins or other publications. WHO maintains emergency situation reports for ongoing health emergencies, including outbreaks and humanitarian crises (74). For example, beginning in October 2023, WHO released periodic emergency situation reports on the health impacts of the humanitarian crisis in the occupied Palestinian territory, including east Jerusalem, with disaggregated data for certain indicators (75). Disease outbreak news reports provide information on confirmed or acute public health events or potential events of concern (76). The Weekly epidemiological record publishes epidemiological information on cases and outbreaks of diseases (77).

There may, however, be sensitivities associated with publishing data collected in conjunction with a public health emergency. The practice is often highly political, and governments may not want to acknowledge inequalities or draw attention to certain realities. For example, government ministries may be reluctant to release information that could prompt scrutiny about their preparation and response.

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