Population Health Monitoring pp 151-173 | Cite as
Health Inequality Monitoring: A Practical Application of Population Health Monitoring
Abstract
This chapter draws from contemporary examples to illustrate one application of population monitoring: health inequality monitoring. It demonstrates how monitoring of health inequalities forms a central part of population health initiatives across global and national contexts. The primary aims of the chapter are to demonstrate the importance of health inequality monitoring for equity-oriented policies, programmes and practices; provide examples of how all steps of the health inequality monitoring cycle have been applied; discuss practical challenges of monitoring health inequalities; and suggest strategies for strengthening health inequality monitoring.
Keywords
Health inequality Population groups Socioeconomic status Monitoring cycle Health inequality measure Monitoring tool Survey8.1 About This Chapter
This chapter draws from contemporary examples to illustrate one application of population monitoring: health inequality monitoring. It demonstrates how monitoring of health inequalities has been integrated as a central part of population health initiatives across global and national contexts. The primary aims of the chapter are to demonstrate the importance of health inequality monitoring for equity-oriented policies, programmes and practices; provide examples of how all steps of the health inequality monitoring cycle have been applied; discuss practical challenges of monitoring health inequalities; and suggest strategies for strengthening health inequality monitoring.
The chapter begins by setting the scene, introducing health inequality monitoring, describing how health inequality monitoring is part of major global initiatives, and highlighting the benefits of institutionalizing health inequality monitoring as part of a national health information system. Next, building on the content presented in Chaps. 3, 4, 5, 6, and 7, the five-step cycle of health inequality monitoring is presented, covering (1) determining the scope of monitoring, (2) data collection, (3) analysing and interpreting the data, (4) reporting results and (5) knowledge translation. For each step of the cycle, a brief outline is provided of what the step entails and examples of its application are given. Finally, some of the practical challenges of health inequality monitoring are reviewed, and forthcoming opportunities to strengthen the practice of health inequality monitoring are discussed. The chapter concludes by suggesting resources for further reading on contemporary applications of the cycle of health inequality monitoring.
8.2 Setting the Scene
In the past, most efforts to measure, understand and improve population health have focused on national averages. For example, ministries of health have collected data in order to quantify the national levels of infant morbidity and mortality in their country. The media have commonly reported on average disease rates in a country of interest and have speculated about why it may have increased or decreased over time. International organizations often have compared the average life expectancies across countries as a way to advocate for increased resources to improve the situation in poorly performing countries. While national averages provide valuable and necessary information about a population, they do not tell the whole story. They do not capture health inequalities that exist within a population.
Gap in life expectancy at age 65 by sex and educational level, 2013 (or nearest year). (Republished with permission of OECD Publishing, from OECD/EU. Health at a glance: Europe 2016 – State of health in the EU cycle. 2016. Paris: OECD Publishing; released under a Creative Commons Attribution 3.0 IGO License [CC BY 3.0 IGO]). Note: The figure shows the gap in expected years of life remaining at age 65 between adults with the highest level (“tertiary education”) and the lowest level (“below upper secondary education”) of education. (Source: Eurostat Database completed with OECD Health Statistics 2016 for Austria and Latvia)
Box 8.1 From the Millennium Development Goals to the Sustainable Development Goals: Looking Beyond National Averages
The United Nations Millennium Development Goals, which set targets for progress between 1990 and 2015, tracked changes in national averages. For instance, the goal pertaining to the reduction of child mortality called on countries to reduce, by two-thirds, the under-five mortality rate. Several countries made remarkable progress in improving national gains in health, which should not be understated. In some cases, however, certain population subgroups actually fell further behind – a trend which was masked by tracking national progress alone (World Health Organization 2015). The Sustainable Development Goals (2016–2030) have an explicit focus on the reduction of inequalities, including goals and targets that track progress among vulnerable population subgroups (United Nations General Assembly 2015).
Increasingly, major global initiatives have recognized the importance of addressing health inequality (see Box 8.1). Alongside improvements in national average, faster improvements in health among disadvantaged population subgroups – a so-called narrowing of the gap – are emerging as a hallmark of success. Policies, programmes and practices that are specifically designed to improve health while simultaneously reducing health inequalities are said to be equity oriented. The final report of the Commission on the Social Determinants of Health provided a strong consensus that the global health community needed to take action to reduce health inequalities (Commission on Social Determinants of Health 2008). Subsequently, the United Nations 2030 Agenda for Sustainable Development (the ‘2030 SDG Agenda’), adopted in 2015, demonstrated a commitment to the reduction of inequality. This commitment is evident in its slogan: to ‘leave no one behind’ (United Nations General Assembly 2015). The Sustainable Development Goal (SDG) on health aims to ensure healthy lives and promote well-being for all at all ages and includes a call for the advancement of universal health coverage (UHC ). The two core components of UHC are to extend the coverage of good-quality, essential health services and to ensure financial protection through reducing dependence on out-of-pocket payments for health services. The progressive realization of UHC means that progress in these two areas will be prioritized and accelerated among the most disadvantaged population subgroups (Hosseinpoor et al. 2014). Health inequality monitoring can indicate whether disadvantaged population subgroups are improving over time and thus help countries to track whether UHC is being realized progressively.
As discussed in Chap. 2, a main function of national health information systems is to produce intelligence about health that enables evidence-informed policy. Policies – as well as programmes and practices – that are equity oriented (such as UHC) should be informed by intelligence about the nature, magnitude and trends of health inequalities within the population. Health inequality monitoring contributes evidence to produce this intelligence. It helps to answer questions such as: Are there differences in health based on income level? Education level? Place of residence? Other important dimensions of inequality? Which of these differences are meaningful? Have health inequalities widened or narrowed over time? By identifying where health inequalities exist, health inequality monitoring can provide a base for further quantitative and qualitative research. Further research can explore the underlying factors that contribute to generating and perpetuating health inequality in a population and get a better grasp on why health inequality exists. A comprehensive and multifaceted understanding of health inequalities and their root causes is necessary to strengthen the equity orientation of policies, programmes and practices (WHO 2013, 2017d, 2018).
Health inequality monitoring should be institutionalized as a regular practice of national health information systems. What does it mean to institutionalize health inequality monitoring? National health information systems should collect data about health as well as data about diverse dimensions of inequality (income level, education, area of residence, sex/gender, age, etc.), and data collection should be done on a regular and ongoing basis. Additionally, national health information systems should have the technical capacity to analyse and report health inequality data. Resources should be allocated to maintaining and building upon these capacities. Institutionalizing health inequality monitoring also means that there are established mechanisms for knowledge translation; that is, there are regular opportunities to integrate the results of health inequality monitoring into policy decisions.
8.3 Cycle of Health Inequality Monitoring
The cycle of health inequality monitoring (adapted from (WHO 2013)). (Reproduced with permission of the World Health Organization; released under a Creative Commons Attribution 3.0 IGO License [CC BY 3.0 IGO])
8.3.1 Step 1: Determine the Scope of Monitoring
The first step of health inequality monitoring is to determine the scope of monitoring. As detailed in Chap. 3, the use of frameworks and models can help to identify key questions or information gaps that monitoring efforts can then address. Depending on the type of questions and information gaps that are identified, health inequality monitoring may have an expansive scope (e.g. encompassing multiple health topics, different aspects of the health sector and their intersection) or a narrow scope (e.g. focusing on a select number of health topics or even a single health topic). Like other types of population health monitoring, health inequality monitoring requires the selection of a set of relevant health indicators that aptly reflect the scope of monitoring.
Health inequality monitoring also requires the selection of relevant dimensions of inequality, which serve as the basis for forming population subgroups. Dimensions of inequality may stem from any factor that constitutes a source of discrimination or social exclusion that is detrimental to health. The types of dimensions of inequality that can be applied in health inequality monitoring are vast, encompassing socioeconomic, demographic, geographic and other characteristics. Common socioeconomic dimensions of inequality include economic status/wealth, education level and deprivation; common demographic dimensions include sex and age; and common geographic dimensions include place of residence and subnational division (region, district, etc.). Other dimensions may include disability status, religion, migration status, aboriginal status, etc.
For every dimension of inequality, there are numerous ways in which subgroups might be constructed. In practice, however, decisions about how to conceptualize subgroups may need to consider the characteristics and availability of data and the monitoring context. For example, wealth indices based on household asset ownership are commonly used in low- and middle-income countries, where population-based surveys collect data about these variables; high-income countries tend to conceptualize economic status according to income level. Deprivation indices are a common way to capture socioeconomic inequality at the small-area level, which typically derive from census indicators (see Box 8.2). Subnational geographic dimensions of inequality are highly dependent on the country context and may reflect regional divisions (provinces, states, districts, etc., which are usually indicated in all data sources) or divisions that correspond with the organization of the health system (health regions, facility catchment areas, etc., which are included in administrative/facility records).
Box 8.2 Examples of Deprivation Indices
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Various deprivation indices have been developed that combine several different types of socioeconomic characteristics (e.g. income, employment, housing, crime, education, access to services and living environment) by small-area geographical units (such as census tracts, electoral wards, postcode areas or municipalities) (Morris and Carstairs 1991; Carstairs 1995).
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The Carstairs index was developed by Carstairs and Morris in 1991 for use in Scotland. It combines four census indicators – male unemployment, household overcrowding, lack of car ownership and low social class – which are split by postcode (Carstairs and Morris 1989).
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The Townsend index, developed by Townsend in the late 1980s is based on four variables: unemployment, lack of car ownership, lack of home ownership and household overcrowding. Data are typically derived from census indicators and applied to census tracts (or aggregated to higher area levels) (Townsend 1987).
Globally, sets of health indicators with relevance to a particular health topic or topics have been defined to promote a systematic and comparable approach to monitoring (see Sect. 3.5 of Chap. 3). In some cases, common dimensions of inequality are also suggested, and approaches have been developed to standardize how they are measured. In general, global sets of health indicators and dimensions of inequality are usually proposed with the caveat that countries should also integrate additional, setting-specific measures that may be relevant in their jurisdiction, but not necessarily universally.
In the case of UHC, the World Bank and the World Health Organization have developed a framework to guide monitoring efforts. The framework includes a summary measure for the coverage of essential health services, which is an index comprised of 16 tracer indicators. The 16 tracer indicators reflect the coverage of essential health services within four categories: reproductive, maternal, newborn and child health; infectious diseases; noncommunicable diseases; and service capacity and access (see Table 8.1) (Boerma et al. 2014; Hogan et al. 2018).Sustainable Development Goal indicators should be disaggregated, where relevant, by income, sex, age, race, ethnicity, migratory status, disability and geographic location, or other characteristics, in accordance with the Fundamental Principles of Official Statistics. (United Nations General Assembly 2014)
Framework of tracer indicators to measure UHC service coverage (adapted from (WHO 2017d)). (Reproduced with permission of the World Health Organization; released under a Creative Commons Attribution 3.0 IGO License [CC BY 3.0 IGO])
Category | Indicator area: indicator |
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Reproductive, maternal, newborn and child health | Family planning: demand satisfied with a modern method among women aged 15–49 years (%) Pregnancy and delivery care: antenatal care – four or more visits (%) Child immunization: 1-year-old children who have received three doses of a vaccine containing diphtheria, tetanus and pertussis (%) Child treatment: care-seeking behaviour for children with suspected pneumonia (%) |
Infectious diseases | Tuberculosis (TB) treatment: TB cases detected and treated (%) HIV treatment: people living with HIV receiving antiretroviral therapy (ART) (%) Malaria prevention: population at risk sleeping under insecticide-treated bed nets (%) Water and sanitation: households with access to improved sanitation (%) |
Noncommunicable diseases | Treatment of cardiovascular diseases: prevalence of non-raised blood pressure (%) Management of diabetes: mean fasting plasma glucose (FPG) (mmol/l) Cervical cancer screening: cervical cancer screening among women aged 30–49 years (%) Tobacco control: adults aged ≥15 years not smoking tobacco in the last 30 days (%) |
Service capacity and access | Hospital access: hospital beds per capita (in relation to a minimum threshold) Health worker density: health professionals per capita (in relation to a minimum threshold) – physicians, psychiatrists and surgeons Essential medicines: proportion of health facilities with basket of essential medicines available Health security: International Health Regulations (IHR) core capacity index |
8.3.2 Step 2: Collect Data
After determining the scope for monitoring, the next step of health inequality monitoring is to collect data. Recall that two types of data are required for health inequality monitoring: data about health and data about relevant dimensions of inequality. These two streams of data are the cornerstone for health inequality monitoring, and national health information systems should collect diverse information that covers both types of data. If the data are obtained from different sources, there should be a way to link them together (e.g. through common personal identification numbers or small-area identifiers) (see Chap. 4). For example, a study of multi-morbidities in Scotland used postcodes as a way to link health data from personal medical records with information about socioeconomic status of the area (Barnett et al. 2012).
Strengths and limitations of data sources used in health inequality monitoring (adapted from (WHO 2013)). (Reproduced with permission of the World Health Organization; released under a Creative Commons Attribution 3.0 IGO License [CC BY 3.0 IGO])
Data source type and examples | Strengths | Limitations |
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Health surveys | ||
Population household survey Examples: Demographic and Health Survey (DHS ) Multiple Indicator Cluster Survey (MICS ) Global Adult Tobacco Survey (GATS ) Survey of Health, Ageing and Retirement in Europe (SHARE ) European Health Interview Survey (EHIS) | Often collect data on a specific health topic as well as dimensions of inequality Usually repeated over time, allowing for monitoring of time trends Conducted in multiple countries, allowing for benchmarking Data are representative for a specific population (often national) | Sampling and non-sampling errors can be important Survey may not be representative of small subpopulations of interest (so it cannot be used to assess cross-district inequality) |
Health registers | ||
Administrative registers Examples: Individual health records Service records Resource records | Data are readily and quickly available Can be used at lower administrative levels (e.g. district level) and may be useful for monitoring inequalities between geographical areas | Data may be fragmented or of poor quality Often data cannot be linked to other sources, limiting the ability to disaggregate by diverse dimensions of inequality Data may not be representative of the whole population |
Registers on vital statistics and civil registration Examples: Birth and death registries Municipal records (marital status, ethnicity, etc.) | Can be used to generate reliable estimates for mortality rate, life expectancy and sometimes cause-of-death statistics May contain identifiers that can be linked to information on sex, geographical region, occupation, education | Incomplete in most low- and middle-income countries Does not regularly include information on dimensions of inequality other than sex |
Surveillance systems Examples: Demographic surveillance Disease registries Sentinel surveillance | Can provide detailed data on a single condition or from selected sites Sentinel surveillance site data are useful for correction of over-reporting or under-reporting in other sources | Not always representative of population Some systems may collect little information relevant dimensions of inequality |
Censuses | Data cover the entire population (or nearly so), providing accurate denominator counts for population subgroups | Contains only limited information on health Conducted infrequently (every 10 years in many countries) |
Global efforts to conduct health inequality monitoring across countries have benefited from the widespread data collection efforts of multi-country, population-wide household surveys such as the Demographic and Health Surveys (DHS ); the Multiple Indicator Cluster Surveys (MICS ); the Global Adult Tobacco Survey (GATS ); the Survey of Health, Ageing and Retirement in Europe (SHARE ); and the European Health Interview Survey (EHIS) (US Agency for International Development 2017; UNICEF 2017b; SHARE 2017; European Commission 2017; WHO 2017a). DHS and MICS, which operate in several low- and middle-income countries, use a standardized and rigorous approach to collect data at regular time intervals. As a result, reliable and comparable data about certain health topics such as reproductive, maternal, newborn and child health (RMNCH ) are available across many low- and middle-income countries. GATS, a nationally representative household survey, enables countries to collect data about adult tobacco use and key tobacco control measures. Covering more than 120,000 individuals aged 50 years or more, SHARE collects data that capture health and socioeconomic status and social/family networks. The EHIS is part of the European Commission’s data collection activities to produce public health statistics in Europe. The EHIS includes those aged 15 years or older living in private households and covers four modules: health status, health-care utilization, health determinants and demographic/socioeconomic information (see Chap. 4 for more information on EHIS).
At the data collection step, the practice of health inequality monitoring may be aided by data source mapping. Data source mapping is an exercise that helps to assess data availability for health inequality monitoring through organizing and cataloguing the contents of existing data sources. The exercise involves preparing four connected tables. The first table lists the data sources available for a given jurisdiction, such as a country. The second table shows the dimensions of inequality data contained within each data source. The third table lists the health indicator data contained within each data source. In some cases, data sources may collect different types of data in different years; any such inconsistencies should be noted in the second and third tables. Finally, the fourth table integrates the information from the second and third tables, indicating the data sources that contain both health indicator and dimension of inequality data. This exercise can also be helpful in exposing gaps that indicate where additional data collection is required or where means for facilitating data links may be introduced. For detailed explanation and examples of how data source mapping has been applied for health inequality monitoring, refer to the Handbook on Health Inequality Monitoring: With a Special Focus on Low- and Middle-Income Countries (WHO 2013) and an article showcasing Indonesia (Hosseinpoor et al. 2018b).
8.3.3 Step 3: Analyse and Interpret Data
After data are collected, the next step of health inequality monitoring is to analyse and interpret the data. This step begins by preparing disaggregating data estimates, which demonstrate the level of health in each population subgroup. Disaggregated data may include the most recent available data, reflecting the current situation, or they may include data from two or more points in time, permitting consideration of changes over time. Examining disaggregated data is an important part of understanding the patterns of health inequality across population subgroups.
RMNCH indicators in Armenia: coverage data disaggregated by economic status and place of residence ( DHS 2000, 2005 and 2010). (Source: Health Equity Assessment Toolkit (HEAT ): Software for exploring and comparing health inequalities in countries. Built-in database edition, Version 2. Geneva, World Health Organization, 2017. Data source: the disaggregated data used in this version were drawn from the WHO Health Equity Monitor database (2016 update))
After inspecting patterns in disaggregated data, summary measures of inequality can be calculated. Summary measures of inequality yield a single number to reflect the level of inequality between two or more population subgroups. As the term suggests, they are a useful way to summarize the multiple points of disaggregated data. Different summary measures of inequality are appropriate for different applications. While certain summary measures of inequality are simple to calculate and intuitive to understand, others require advanced technical skills and/or data analysis software. Box 8.3 describes some of the common summary measures of inequality and their defining characteristics.
Box 8.3 Defining Characteristics of Summary Measures
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Summary measures express absolute or relative inequality. Absolute inequality measures reflect the magnitude of difference in health between population subgroups and retain the same unit of measure as the health indicator. Relative inequality measures show the proportional differences in health among subgroups and are unit-less.
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Summary measures may be described as simple or complex measures of health inequality. Simple measures of inequality make pairwise comparisons of health between two population subgroups (e.g. the most and least wealthy quintiles), whereas complex measures of inequality draw on data from all population subgroups (e.g. all five wealth quintiles) to produce a single number that expresses inequality.
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Complex summary measures may be applied to ordered or non-ordered dimensions of inequality. Ordered dimensions of inequality have an inherent positioning, and population subgroups can be logically ranked (e.g. wealth or education level). Non-ordered dimensions of inequality are not based on criteria that can be logically ranked (e.g. region or ethnicity).
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Summary measures may be weighted or unweighted. Weighted measures take into account the population size of each subgroup, whereas unweighted measures treat each population subgroup as if it were equally sized.
Summary measures of inequality and associated characteristics. (Adapted from (WHO 2017b)). (Reproduced with permission of the World Health Organization; released under a Creative Commons Attribution 3.0 IGO License [CC BY 3.0 IGO])
Name of summary measure | Absolute versus relative | Simple versus complex measure | Ordered versus non-ordered complex measure | Weighted versus unweighted measure |
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Absolute concentration index | Absolute | Complex | Ordered | Weighted |
Between-group variance | Absolute | Complex | Non-ordered | Weighted |
Difference | Absolute | Simple | – | Unweighted |
Index of disparity | Relative | Complex | Non-ordered | Unweighted |
Mean difference from best performing subgroup | Absolute | Complex | Non-ordered | Weighted |
Mean difference from mean | Absolute | Complex | Non-ordered | Weighted |
Mean log deviation | Relative | Complex | Non-ordered | Weighted |
Population attributable fraction | Relative | Complex | Either ordered or non-ordered | Weighted |
Population attributable risk | Absolute | Complex | Either ordered or non-ordered | Weighted |
Ratio | Relative | Simple | – | Unweighted |
Relative concentration index | Relative | Complex | Ordered | Weighted |
Relative index of inequality | Relative | Complex | Ordered | Weighted |
Slope index of inequality | Absolute | Complex | Ordered | Weighted |
Theil index | Relative | Complex | Non-ordered | Weighted |
Two of the most straightforward types of summary measures of inequality are difference and ratio. Difference shows absolute inequality between two population subgroups. The difference in health service coverage by place of residence, for instance, can be calculated as the level of coverage in the urban area (%) minus the level of coverage in the rural area (%), resulting in the difference (percentage points). Ratio shows relative inequality between two population subgroups. The ratio in health service coverage by place of residence can be calculated as the level of coverage in the urban area (%) divided by the level of coverage in the rural area (%), resulting in the ratio (unit-less). In the same manner, difference and ratio can be calculated between the level of coverage in the richest and poorest subgroups.
RMNCH indicators in Armenia: difference in coverage by economic status (richest-poorest) and place of residence (urban-rural), in percentage points ( DHS 2000, 2005 and 2010). (Source: Health Equity Assessment Toolkit (HEAT ): Software for exploring and comparing health inequalities in countries. Built-in database edition, Version 2. Geneva, World Health Organization, 2017. Data source: the disaggregated data used in this version were drawn from the WHO Health Equity Monitor database (2016 update))
RMNCH indicators in Armenia: ratio in coverage by economic status (richest/poorest) and place of residence (urban/rural) ( DHS 2000, 2005 and 2010). (Source: Health Equity Assessment Toolkit (HEAT ): Software for exploring and comparing health inequalities in countries. Built-in database edition, Version 2. Geneva, World Health Organization, 2017. Data source: the disaggregated data used in this version were drawn from the WHO Health Equity Monitor database (2016 update))
Overall, the same general patterns of increasing or decreasing inequality for these three RMNCH indicators tended to be the same when looking at the ratios (relative inequality, shown in Fig. 8.4) as indicated by the differences (absolute inequality). In the case of wealth-related inequality in contraceptive prevalence, however, absolute inequality increased slightly between 2000 and 2010, whereas relative inequality was about the same in 2000 and 2010. This demonstrates how absolute and relative inequality may not necessarily demonstrate changes in the same direction.
Simple summary measures like difference and ratio cannot take into account population shifts between subgroups over time. For instance, many countries face a situation where the percentage of the population with a low level of education is decreasing, and the percentage of the population with a high level of education is increasing. In these situations, weighted summary measures, such as the slope index of inequality (absolute measure) and the relative index of inequality (relative measure), can help to account for population shift and are interpreted like difference and ratio, respectively.
For more information about summary measures of inequality and their applications for assessing the change of within-country inequalities over time, refer to the Handbook of Health Inequality Monitoring: With a Special Focus on Low- and Middle-Income Countries (WHO 2013).
8.3.4 Step 4: Report Results
Reporting the results of health inequality monitoring follows the same basic tenets as other types of public health reporting. As outlined in Chap. 6, the general considerations within the three domains of public health reporting – content, production process and marketing – can be aptly applied to enhance reporting. When reporting the results of health inequality monitoring, however, certain special considerations arise. Reporting on health inequalities can quickly become complicated by the extensive nature of the underlying datasets: multiple health indicators disaggregated by various dimensions of inequality at several time points yield a lot of data! (The use of summary measures of inequality can be applied for a concise presentation of the data, though the characteristics of the measures should be taken into account – see step 3.) In addition, the results of health inequality monitoring are often of interest to a wide range of diverse stakeholders across health and non-health sectors such as education, environment, agriculture, business and others. Stakeholders may have different levels of expertise and exposure to the health topics, data sources and analytical approaches that are applied in health inequality monitoring; diverse preferences and norms across stakeholder groups may affect how the results can be effectively marketed.
The following approach to preparing reports of health inequality monitoring works through five sequential tasks, which highlight key decisions and action points that promote coherent, relevant and robust reporting. The application of this approach is illustrated using the WHO report State of Inequality: Childhood Immunization (WHO 2016b)1.
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The State of Inequality: Childhood Immunization report was developed for a broad audience with variable levels of experience in the area of health inequality monitoring. The primary target audience includes technical staff as well as public health professionals and researchers.
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The purpose of the State of Inequality: Childhood Immunization report is to serve ‘as source of high-quality data for those involved in making policy decisions affecting health or those working to improve childhood immunization coverage’.
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The scope of reporting in the State of Inequality: Childhood Immunization report stems from two overarching questions: What inequalities in childhood immunization coverage exist? And how have childhood immunization inequalities changed over the past 10 years?
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The report contains data from 69 countries and makes comparisons of the levels of within-country inequality (benchmarking). The best and worst performing countries are identified, and an extended analysis of poor performing countries is provided.
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The content of the State of Inequality: Childhood Immunization report centres around four pertinent dimensions of inequality: household economic status, mother’s education, place of residence and sex.
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The report contains disaggregated data and draws from two statistical measures – median and interquartile range – to describe patterns in disaggregated data from study countries.
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In addition to disaggregated data, the latest situation is presented using two simple measures of inequality (difference and ratio) and one complex measure of inequality (population attributable risk); absolute excess change is a summary measure used to convey change over time.
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The State of Inequality: Childhood Immunization report uses text, tables and figures to communicate the key messages.
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The report also interfaces with interactive visuals that permit further exploration of the data: all of the static figures in the text are also available as interactive visuals. Additional interactive visuals containing story points and reference tables are available. The interactive visuals are referenced throughout the report using QR codes and URLs to direct the audience to the online visuals.
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The State of Inequality: Childhood Immunization report adheres to the best practices, as listed above. In the cases of flagging results that are based on low sample sizes and reporting statistical significance, these appear in tooltip (pop-up) boxes within the interactive visuals.
Box 8.4 WHO Resources for Analysing and Reporting Data About Health Inequalities: HEAT and HEAT Plus (WHO 2017b; Hosseinpoor et al. 2016; Hosseinpoor et al. 2018c)
The Health Equity Assessment Toolkit (HEAT), developed by the WHO, serves as an interactive application that allows users to explore inequality and make comparisons across countries. Focusing on RMNCH, HEAT uses an existing database of disaggregated data to calculate summary measures of inequality. Users can create customized visuals based on disaggregated data or summary measures. HEAT Plus has the added feature of allowing users to upload their own database. HEAT was recognized as “highly commended” in the British Medical Association’s Medical Book Awards 2018 in the category ‘digital and online resources’.
8.3.5 Step 5: Knowledge Translation
The fifth step of the health inequality monitoring cycle involves knowledge translation, that is, promoting the uptake of monitoring results into the policymaking process. The process of knowledge translation is highly iterative and context specific. Chap. 7 discusses challenges in addressing the evidence-to-action gap and strategies to promote the use of evidence to inform public health policy. In the cycle of health inequality monitoring, the main goal of knowledge translation is for changes – typically in the realm of policies, programmes and practices – to be implemented that improve population health while reducing health inequalities.
Presenting straightforward, evidence-informed priority areas for action may be helpful to encourage the consideration of health inequality monitoring results by policymakers. The Handbook on Health Inequality Monitoring: With a Special Focus on Low- and Middle-Income Countries details one approach to identifying priority areas that involves applying a scoring system to the results of health inequality monitoring (WHO 2013). Briefly, each health indicator and dimension of inequality combination is assigned a score of 1, 2 or 3, based on the results of health inequality monitoring: 1 indicates that no immediate action is warranted; 2 indicates that action is warranted; and 3 indicates that urgent action is warranted. The national average for each indicator is also scored. Then, the average score for each health indicator is calculated to determine the priority areas across health indicators. Similarly, the average score for each dimension of inequality is calculated to determine priority areas across dimensions of inequality. While this approach overlooks important nuances and contextual aspects of policymaking, its simplicity and intuitiveness provide a concrete starting point for further consideration and discussion.
The Innov8 approach for reviewing national health programmes to leave no one behind (WHO 2016a). (Reproduced with permission of the World Health Organization; released under a Creative Commons Attribution 3.0 IGO License [CC BY 3.0 IGO])
Other tools, such as UNICEF’s Equist, have been developed to encourage and facilitate the uptake of health inequality monitoring by policymakers (UNICEF 2017a). With a focus on RMNCH, Equist is an online platform that was designed to assist health policymakers and programme managers in strengthening health systems. Equist provides stakeholders with access to the best available global evidence, data and tools and aims to help them devise strategies and approaches to reduce health inequalities.
8.4 Practical Challenges
Countries, or other jurisdictions where monitoring is carried out, experience unique sets of challenges along the five steps of health inequality monitoring. The challenges encountered depend partly upon the level of development of health information systems, as well as the capacity that exists to conduct monitoring, and the extent to which addressing health inequalities has been prioritized. Here, four common types of challenges are identified (Hosseinpoor et al. 2018a).
One type of challenge pertains to data collection and availability. Commonly, data are collected for health topics that are high profile and well-established on health agendas; data are often lacking for other topics that are less prominent or of emerging interest. Similarly, data about certain dimensions of inequality, such as place of residence, are routinely collected as part of health data sources. Certain others, such as socioeconomic dimensions, are mainly available through household health surveys. These dimensions may be gathered separately from health data and then linked to health data sources through individual or small-area identifiers. Obtaining recent and high-quality data may pose challenges.
Challenges may also stem from a lack of capacity to conduct analyses for health inequality monitoring. In some cases, individuals with the advanced technical knowledge to do health inequality analyses may not have access to the data or resources to do so.
Health inequality monitoring necessitates specialized skills in effectively reporting and communicating the findings – an area of expertise that is distinct from doing analyses. It is too often taken for granted that strong analyses or compelling results will ‘speak for themselves’. The importance of effective communication that is tailored to different target audiences should not be overlooked.
Finally, implementing changes based on the results of health inequality monitoring represents a considerable challenge. Health inequality is one of many considerations taken into account when planning policies, programmes and practices. Further, finding solutions to address health inequalities and their root causes often requires intersectoral action beyond the health sector alone. Planning and coordination across diverse groups of stakeholders is a necessary, but difficult, aspect of moving forward on understanding and addressing the root causes of health inequalities.
8.5 Current and Future Developments
Global health and development initiatives increasingly underscore the importance of monitoring and addressing health inequalities through evidence-informed policy. This includes a growing emphasis on the collection and use of disaggregated data. Globally, solutions to enhance the availability, quality and comparability of data across countries are emerging, driven in part by a growing emphasis on standardized analysis and reporting. While health inequality monitoring for some topics, such as RMNCH, is relatively well-established globally, other topics, such as noncommunicable diseases, are monitored less frequently, with fewer opportunities to make cross-national comparisons. Initiatives such as the Health Data Collaborative, comprised of multiple health partners across the globe, work with countries to enhance the availability, quality and use of data to track progress towards the health-related SDGs and promote evidence-informed policymaking for sustainable development (Health Data Collaborative 2017). Moving forward, the wide adoption of common indicator sets and the collection of high-quality data about those indicators will be key to generating evidence and spurring action on inequalities across a broader range of health topics.
Strengthening national health information systems, as detailed in Chap. 2 and in the ‘Setting the Scene’ section of this chapter, is central to overcome the challenges of health inequality monitoring. The WHO has developed a number of tools and resources for all countries, to support each step of the health inequality monitoring cycle (Hosseinpoor et al. 2015) (also see Box 8.4). Importantly, countries should invest resources in building capacity at each step of the health inequality monitoring cycle and should aim to establish the reduction of inequality as a common priority across sectors and levels of governance. The use of new technologies in how data are collected, linked, analysed and reported will continue to shape the practice of health inequality monitoring. The introduction of electronic records and the use of big data, for example, are two areas that pose exciting opportunities as emerging data sources. Analysis software and tools are increasingly available online and streamlined for a broader base of users that may not have advanced technical expertise. The growing availability of online interactive data visuals is shaping the norms around how results are communicated and making data accessible to wider audiences.
Footnotes
- 1.
The resource received first prize from the British Medical Association’s Medical Book Awards 2017 in the category ‘digital and online resources’.
Notes
Acknowledgement
The open access availability of this chapter was financially supported by the Dutch National Institute for Public Health and the Environment (RIVM). No funding for this work was received from WHO.
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Further Reading
- Hosseinpoor, A. R., Bergen, N., Schlotheuber, A., et al. (2016). Data resource profile: WHO Health Equity Monitor (HEM). International Journal of Epidemiology, 45, 1404–1405e.CrossRefGoogle Scholar
- Hosseinpoor, A. R., Bergen, N, Schlotheuber, A., & Grove, J. (2018). Measuring health inequalities in the context of sustainable development goals. Bulletin of the World Health Organization, 96, 654–659. http://dx.doi.org/10.2471/BLT.18.210401.CrossRefGoogle Scholar
- WHO. (2013). Handbook on health inequality monitoring: With a special focus on low-and middle-income countries. Geneva: World Health Organization. Also available as an eLearning Module.Google Scholar
- WHO. (2016). State of inequality: Childhood immunization. Geneva: World Health Organization.Google Scholar
- WHO. (2017). National health inequality monitoring: A step-by-step manual. Geneva: World Health Organization.Google Scholar
- WHO. (2018). Health Equity Monitor. (http://www.who.int/gho/health_equity/en/). Geneva: World Health Organization.
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