Download developing an index for the coverage of essential health services

Technical note
Developing an index for the coverage of essential health services
Daniel Hogan, Ahmad Reza Hosseinpoor, and Ties Boerma
Department of Evidence, Information and Research (WHO, Geneva)
May 2016
Preamble
Monitoring coverage of essential health services is a key component of the WHO/World Bank
framework for monitoring Universal Health Coverage (UHC). One approach to summarizing coverage
of essential health services is to compute an index which combines tracer indicators of health service
coverage into a single metric. This technical note documents the approach used to construct a
service coverage index, focusing on national coverage levels, which was published in WHO’s World
Health Statistics 2016: Monitoring Health for the SDGs, and was consistent with the wording of SDG
indicator 3.8.1 as of May 2016. It is anticipated that the inputs and methods used to compute the
index will evolve in the future, in particular to capture inequality in service coverage and to improve
the relevance of the index to higher income countries, as well as to incorporate further feedback
from various stakeholders.
Background
Universal health coverage (UHC) has recently been adopted as Target 3.8 of the Sustainable
Development Goals, which will require regular reporting on progress. UHC is a process of progressive
realization in which all people receive the quality, essential health services they need, without being
exposed to financial hardship. Monitoring UHC therefore requires measuring health service coverage
and financial protection within countries, including coverage among disadvantaged populations to
track equity. In this technical note, we describe a methodological approach to monitoring the health
service coverage component of the UHC target within the SDGs (indicator 3.8.1), which was
published in WHO’s World Health Statistics 2016: Monitoring Health for the SDGs (1). The
measurement approach will likely be refined in the future.
Countries provide a wide range of services for health protection, promotion, prevention, treatment
and care, but it is possible to define a set of tracer indicators that provide a good picture of overall
service coverage (2,3). These tracer indicators can then be combined in a systematic way into an
index that summarizes national service coverage with a single numerical value (4). Ideally, this index
would be constructed so that it incorporates information on how equitably health service coverage
is distributed across subpopulations.
Tracer indicators were selected considering several criteria (3). First, an indicator should be relevant,
reflecting epidemiological burden and the presence of (cost) effective interventions. Second, an
indicator should be conceptually sound, with a measurable numerator and denominator, a clear
target, and a definition that captures effective coverage (5). Third, it must also be feasible, with
current, comparable data available for most countries, which can be disaggregated for equity
analysis. Lastly, indicators should be usable, in the sense they are easy to communicate; indicators
that are already reported across countries are appealing as they reduce reporting burden.
Currently, no potential tracer indicators meet all of these criteria, and therefore proxy indicators, to
approximate ideal quantities of interest, and statistical models, to bridge data gaps, are necessary in
many cases. Ideal data sources are typically nationally representative, population-based surveys,
which enable the measurement of those who need an intervention, in addition to counting those
who receive it, and allow for disaggregation of coverage by different subpopulations for equity
analysis. In other cases, administrative data collected from health facilities may be used, but often
require additional analytic steps to estimate denominators or conduct equity analyses.
A set of tracer indicators for service coverage were selected following an extensive review and
discussion of potential indicators (2,6-8). These indicators are grouped into four main categories: (1)
reproductive, maternal, newborn and child health, (2) infectious diseases, (3) noncommunicable
diseases and (4) service capacity and access and health security. Coverage estimates for these tracer
indicators are combined into a UHC service coverage index, which ultimately should be adjusted to
reflect the degree of inequity in service coverage within a country. In the following sections, we
summarize the tracer indicators for monitoring UHC service coverage (including detailed metadata),
describe how the UHC service coverage index was computed for World Health Statistics 2016, and
discuss areas for future work to further refine the measurement approach.
Description of methods
Tracer Indicators
A total of 16 tracer indicators were selected, with four indicators chosen for each of the four service
coverage categories (Table 1, with metadata in Annex 1). Effective coverage, which captures the
extent to which those in need of health services receive quality care that obtains the desired result,
was the guiding principle for choosing tracer indicators. However, as can be seen in Table 1, in
several cases the selected indicator is only a proxy for effective coverage, which is currently not
measurable for many important health service areas. Perhaps surprisingly, given the proliferation of
health surveys in developing countries over the past 20 years, for several coverage indicators we
have more readily available comparable data from poorer countries than wealthier ones. This is
particularly true for estimating sub-national coverage across key inequality dimensions, as surveys
typically collect the variables necessary for disaggregated analysis, as compared to administrative
data, which may play a greater role in monitoring service coverage in high income countries. The
availability of data and comparable coverage estimates at country level, and our ability to
disaggregate estimates by key inequality dimensions, are presented in Table 2.
1. Reproductive, maternal, newborn and child health
In the area of reproductive, maternal, newborn and child health (RMNCH), data from a large number
of population-based health surveys exist, for example from the Demographic and Health Surveys
(DHS) and the Multiple Indicator Cluster Surveys (MICS). The selected indicators are therefore wellmeasured in many countries. These include indicators for family planning (demand satisfied with a
modern method), pregnancy care (the average coverage of 4 or more antenatal care visits and
skilled birth attendance/institutional delivery rates), full immunization for infants (for now measured
with DTP3 coverage as a proxy), and child treatment (care seeking for suspected pneumonia).
2. Infectious diseases
For infectious diseases, indicators were selected for treatment of TB and HIV, use of insecticide
treated bed nets (ITN) among populations at risk of malaria, and household access to improved
water and sanitation. National estimates of TB and HIV treatment coverage are derived from a
mixture of facility data, for the numerator, and model-based estimates of the need for treatment,
for the denominator (Annex 1). More work is required to improve the accuracy of ART coverage
estimates in high income countries and other countries with low HIV burden. ITN coverage is
estimated by combining household survey data with information on purchasing and distribution of
bed nets for countries with a high burden of malaria, and data on coverage of improved water and
sanitation sources are collected during household surveys.
3. Noncommunicable diseases
A growing set of surveys, such as the WHO-supported STEPs surveys, include biomarkers that allow
for important new possibilities for monitoring treatment coverage of some NCDs. However,
currently no comparable national estimates of effective coverage of cardiovascular disease and
diabetes treatment, nor treatment for elevated cardiovascular risk, are available. Until they become
available, the prevalence of hypertension and raised blood glucose (diabetes) are used as proxy
measures, which are meaningful in their own right as indicators of both the success of prevention
efforts and screening and treatment programs. Non-use of tobacco is included as an effective
coverage indicator of interventions to reduce tobacco use through the health sector and in other
sectors, which has large implications for the control of cardiovascular disease, chronic obstructive
lung diseases, and cancer. Finally, in addition to non-use of tobacco, cervical cancer screening was
selected as an indicator of coverage of interventions against cancer. Currently, data for this indicator
are very limited across countries.
4. Service capacity and access and health security
The final indicator category, which attempts to represent more general features of capacity and
access to care within a health system, includes indicators on hospital care, the density and
distribution of core health professionals, psychiatrists and surgeons, access to essential medicines,
and a measure of health security. These indicators depart from the effective coverage paradigm that
has been used to motivate the selection of tracer indicators for monitoring UHC progress (3). This
choice is largely due to data limitations, which leave major gaps in the measurement of coverage for
critical areas such as treatment for mental illnesses, emergency care, and surgical procedures. Data
are limited because for many conditions of interest, such as treatment of major depression or
emergency care for a road traffic injury, even if we knew the number of people who received care, it
is difficult to define the denominator, as it requires diagnosis of need for treatment from survey
questions, and severity can differ substantially. Second, even with effective survey instruments for
determining need, the incidence of some conditions are likely to be too rare to be precisely
estimated from population-based surveys. Thus, while indicators like effective coverage of trauma
surgery are unlikely to be widely measured in the next several years, the capacity of the health
system to provide life-saving acute care is clearly an essential feature of UHC.
An additional challenge of measuring service capacity and access is that we often do not know what
the optimal level of coverage is, and high values may be disadvantageous. Indeed, in high income
countries over use of health services may be a significant problem. We also do not know if those
who receive a health service are those who need it, e.g. for Caesarean section rates. Despite this, in
developing countries, low utilization rates or health worker density are indicative of poor access and
use of essential health services. In the absence of better data, tracking utilization rates and health
worker density up to some threshold is therefore considered useful for tracking UHC progress. These
thresholds are set based on lower bounds observed in OECD countries; once a country exceeds the
threshold, the value is fixed at 100%. Thus, these are only indicators of under-use and low capacity;
no attempt is made to measure overuse.
As mentioned above, a final indicator in this category is for health security, namely the International
Health Regulations core capacity index for responding to epidemics and other health threats. This is
also an indicator of general capacity, which is presumably more meaningful than, for example,
effective coverage against a pandemic, as it is unclear how to measure the latter on a regular basis.
Data availability
Ten of the 16 tracer indicators have recent, comparable estimates of national coverage (Table 2). For
another four, including pregnancy care, care seeking for suspected pneumonia in children, hospital
in-patient admission rates, and health worker density, well-maintained databases of country data
points from either survey or administrative data are available. For the remaining two indicators,
cervical cancer screening and access to essential medicines, there are currently no comprehensive
data bases or comparable estimates available. These two indicators are left out of the calculation of
the index presented in this technical note; in future years more information on these indicators is
expected to be available.
Missing country indicator values for year 2015 were imputed in one of three ways. The first option
was to carry forward the most recently available estimate for a country from previous years. If this
was not possible, in most cases a regional value, based on World Bank regions, was assigned as a
placeholder. The use of regional averages, as opposed to regression-based imputation, was chosen
to increase the accessibility of the index calculations for those interested in computing the index
with different data inputs. Despite this choice to avoid modeling to impute missing values, there
were three instances where there were not enough country data to allow for sensible regional
estimates across all regions. These were inpatient admission rates and surgeons per capita, for
which regressions fit to the log of GDP per capita (2011 PPP) were used to impute missing country
values, and care-seeking for pneumonia, for which missing values were imputed from a regression fit
to the log of the pneumonia mortality rate for children under five (9). Finally, given that ITN use is
only relevant for a subset of countries, ITN coverage was only included in index calculations for
countries in Africa with available estimates (Box 1).
UHC Health Service Coverage Index
A diagram of the calcluations used to construct an index of health service coverage is presented in
Box 1. While the ultimate goal is to construct an index that is adjusted for inequalities in coverage by
socio-economic status or other key inequality dimensions, given current data limitations we focus on
constructing an index based on average national levels of coverage in this Technical Note.
All indicators are structured so they occur on a scale of 0 to 100%, with 100% the target. For
example, non-use of tobacco, rather than use of tobacco, is used, and as described above, in-patient
admission rates and health professional density are rescaled onto a scale of 0 to 100%. The index is
constructed from geometric means of component indicators, first within each of the four categories,
and then across those category-specific means to obtain the final summary index. 1 Geometric means
are used instead of arithmetic means because they are more sensitive to extreme values, thus
implicitly giving more weight to health services with lower coverage, and because the country
rankings implied by an index constructed from geometric means are less sensitive to the scale on
which input variables are measured, as compared to when using arithmetic means. The Human
Development Index was recently revised to use geometric means (10), and geometric means were
used in previous work on an UHC index (4).
It is noted that weights could be assigned to each indicator, each broad category, or both when
computing the index. Potential approaches could involve weighting based on the population in need
of a particular service, the burden of disease represented by a particular service area, the expected
health impacts that each service is expected to impart on population health, or even the costeffectiveness of interventions within each health service area. There is, however, no clear way to
identify weighting values for any of these approaches, and so a simple equal weighting approach was
selected.
In World Health Statistics 2016, we present the distribution of country quintiles for the UHC index by
WHO region based on provisional estimates using the above methods. These methods will be refined
in the coming year, with baseline country estimates of UHC service coverage index scheduled for
publication in December 2016. We also note that a single index cannot possibly characterize all of
the necessary and critical health services that are required to achieve universal health coverage,
however selecting a manageable set of tracer indicators and constructing an index provides a
concrete summary of health service coverage within UHC, which can serve as a point of reference
for policy discussions, such as for SDG target 3.8, and help highlight specific issues and patterns
across countries. This monitoring framework is also flexible enough for countries to add additional
indicators that they deem to be important.
Ongoing methodological work
Incorporating Equity
Wagstaff et al (4) demonstrated how average national health service coverage estimates could be
adjusted to account for the unequal distribution of coverage across economic status groups. This
was operationalized by computing a concentration index, which summarizes the distribution of
coverage of a particular health service in relation to household economic status. This approach is
conceptually related to the Gini coefficient, but is expanded in the sense that it summarizes the
distribution of one variable (coverage) in relation to a second one (economic status). The
1
In rare cases where coverage was 0%, it was replaced by a value of 1% to allow for the calculation of a
geometric mean.
concentration index is a useful concept, but simpler stratification approaches may be more
accessible and provide qualitatively similar information, for example comparing coverage among the
poorest quintile (or two poorest quintiles) to the national average. Depending on the indicator, it
may also be desirable to define a “disadvantaged” population for summarizing inequality in coverage
in terms of other inequality dimensions, e.g., sex, age, education, or place of residence. In some
countries, race, ethnicity or caste may be particularly important dimensions for monitoring equity.
However defined, once coverage among the disadvantage population is computed, it can be used to
adjust the national coverage value. One way to do this is to compute the service coverage index
separately for the national population and the disadvantaged population, and then average them to
obtain an equity-adjusted UHC coverage index (or, relatedly, to compute equity adjusted averages
for each indicator separately, and then combine those values with an index). While this approach is
appealing, we currently do not have the data necessary to compute coverage levels in the
disadvantaged population for all tracer indicators. Until more data are available, an alternative
approach could focus on computing an index based on the national coverage rates for all 16 tracer
indicators, and then adjusting that index value by a single equity adjustment factor. This equity
adjustment factor would be computed for each country from the subset of indicators that have
sufficient data available to compute coverage across disadvantaged populations. Currently, this is
largely confined to RMNCH coverage indicators in low- and middle-income countries.
Ensuring relevance for all countries
The process of selecting tracer indicators highlighted the massive data gaps that currently exist for
monitoring health service coverage across countries. Some important health areas are left wanting,
for example, there are currently no strong indicators for coverage of interventions against cancer,
mental health and injuries. In general, there are few indicators of national health service coverage
that are estimated consistently across most countries, and even fewer indicators that actually
measure the effective coverage of health interventions.
A limitation of the current UHC index is that it is arguably more relevant for low and middle income
countries. High income countries have coverage levels approaching 100% for most of the RMNCH
and service capacity and access indicators, and the infectious disease indicators may be poorly
measured (ART) or not relevant (ITN and WASH). Perhaps surprisingly, however, the lack of
standardized surveys across high income countries inhibits our ability to monitor equity in coverage.
We arguably have more internationally comparable information on the equity of service coverage in
developing countries as compared to developed ones, yet many of the gains required to achieve
UHC in developed countries must come through improving coverage in disadvantaged populations.
Going forward, one way to make a single index more relevant across all countries could be to
employ a two-pronged approach for some indicators. For example, once hospital admission rates
reach the threshold value, a second indicator of hospital care could be used, such as waiting times
for hip-fracture surgery. These indicators could potentially be combined into one scale, with hospital
admissions making up the scale from 0 to 50%, and waiting times contributing to the scale from 51
to 100%.
References
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(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
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Table 1. UHC tracer indicators for monitoring progress on health service coverage.
Tracer area
Tracer indicator
Reproductive, maternal, newborn and child health
a. Family planning
Demand satisfied with modern method among women 1549 who are married or in a union (%)
b. Pregnancy care
Average coverage of 4 or more antenatal visits and skilled
birth attendance (%)
c. Full child immunization
One year old children who have received 3 doses of a
vaccine containing diphtheria, tetanus and pertussis (%)
d. Child treatment
Care seeking behaviour for children with suspected
pneumonia (%)
Infectious diseases
a. Tuberculosis treatment
TB cases detected and cured (%)
b. HIV treatment
People living with HIV receiving ART (%)
c. Malaria prevention
Population at risk sleeping under insecticide treated bed
nets (%)
d. Improved water and sanitation
Average coverage of households with access to improved
water and sanitation (%)
Noncommunicable diseases
a. Treatment of cardiovascular disease
Prevalence of raised blood pressure (%)1
b. Management of diabetes
Prevalence of raised blood glucose (%)1
c. Cervical cancer screening
Cervical cancer screening among women 30-49 (%)
d. Tobacco control
Adults age >=15 years not smoking tobacco in last 30 days
(%)
Service capacity and access
a. Hospital access2
In-patient admissions per capita (w/ threshold)
2
b. Health worker density
Health professionals per capita (w/ threshold): physicians,
psychiatrists, and surgeons
c. Access to essential medicines
Average proportion of WHO-recommended core list of
essential medicines present in health facilities
d. Health security
International Health Regulations core capacity index
1. As more data become available, these will likely be replaced by either (i) proportion of population with
hypertension or diabetes receiving effective treatment (ii) proportion of eligible persons (defined as aged 40
years and older with a 10-year cardiovascular risk ≥30%, including those with existing cardiovascular disease)
receiving drug therapy and counselling (including glycaemic control) to prevent heart attacks and strokes.
2. There is no clear optimal level of service capacity and access. Therefore, these indicators are measured
against a threshold defined by minimum rates observed in OECD countries. Thus, countries with low rates of
service capacity and access will score below 100% on these indicators, and countries that are above the
threshold will be set at 100%. It is currently unclear how to estimate the “over-use” of health services.
Table 2. Tracer indicators for UHC service coverage, with data availability
Indicator
Data sources
Number of
countries with
national data since
2010
Reproductive, maternal, newborn and child health
Family planning coverage
Surveys
Antenatal and delivery care Surveys
Full child immunization
Surveys, Admin
Health-seeking behaviour
Surveys
for child pneumonia
Infectious diseases
Tuberculosis effective
Admin
treatment
HIV antiretroviral
Admin,
treatment
Surveys,
Surveillance
ITN coverage for malaria
Surveys, Admin
prevention
Improved water source and Surveys
adequate sanitation
Noncommunicable diseases
Prevalence of raised blood Surveys
pressure
Prevalence of raised blood Surveys
glucose
Cervical cancer screening
Surveys
Tobacco (non-use)
Surveys
Service capacity and access
Basic hospital access
Facility data
Health-worker density
Administrative
data
Access to essential
Facility surveys
medicines
Health security: IHR
Country
compliance
reported
a
Number of
countries
with
comparable
estimates
Measurability
of key
dimensions of
inequalitya,b
98
121
193
72
184
194
194
None
W,E,R,(A)
W,E,R,(A)
W,E,R,S
W,E,R,S
190
190
(R)
118
118
(R)
40c
40c
W,E,R,S
156
192
W,R
86
192
(E),(R),S,A
76
192
(E),(R),S,A
<30
146
None
123
(W),(E),(R),S,(A)
105
166
None
None
(R)
(R)
<30
None
(R)
191
None
-
W = household wealth quintile; E = educational attainment; R = place of residence (typically urban
vs. rural); S = sex; and A = age. Letters in parentheses indicate that data sources exist to estimate
coverage by the indicated dimension but that more analytical work is needed to prepare
disaggregated estimates.
b
Information to estimate coverage across key inequality dimensions typically comes from
population-based surveys. Standardized population-based surveys are typically only conducted in
developing countries, and therefore there is currently a lack consistent data sources to characterize
equity for service coverage in many high-income countries.
c
Only pertains to countries with highly endemic malaria.
Box 1. Calculation of UHC service coverage index based on national levels for coverage
Notes
1. Cervical cancer screening and access to essential medicines are excluded due to low data availability.
2. The percentage of the adult population with non-elevated blood pressure and non-elevated blood glucose are based on
age-standardized estimates. These distributions are rescaled to provide finer resolution for the index, based on looking at
observed minima across countries. For non-elevated blood pressure, the rescaled indicator = 100-(100-X)/(100-66.67), and
for non-elevated blood pressure, the rescaled indicator = 100-(100-X)/(100-75).
3. Similar to (2), non-use of tobacco is also based on age-standard estimates, and rescaled to provide a finer resolution
using a minimum bound of 50%, so that the rescaled indicator = 100-(100-X)/(100-50).
4. In-patient hospital admission rates are rescaled and capped based on a threshold of 8 per 100, based on minimum rates
observed in OECD countries. Values below 0.08 are rescaled as X/0.08*100, and values above 0.08 are set to 100%.
5. Similar to (4), health worker density is rescaled and capped based on threshold values. Physician density has a threshold
of 0.9 per 1000, psychiatrists have a threshold of 1 per 100 000, and surgeons have a threshold of 14 per 100 000. After
rescaling these values (i.e., max(100, X/threshold*100), they are combined into a HWD composite variable for entry into
1/3
the above index calculations, computed as = (physicians * psychiatrists * surgeons) .
1/2
1/2
6. Pregnancy care is computed as (ANC4 * SAB) and WASH is computed as (water * sanitation) . In the future these
could be replaced by co-coverage estimates, e.g., fraction of women receiving both ANC4 and SAB.
Annex 1
Tracer area
Family planning
Indicator name
Demand for family planning satisfied with modern methods
Indicator definition
Percentage of women of reproductive age (15−49 years) who are married or inunion who have their need for family planning satisfied with modern methods.
Numerator
Number of women aged 15-49 who are married or in-union who use modern
methods
Total number of women aged 15-49 who are married or in-union in need of family
planning
Population-based health surveys
Household surveys include a series of questions to measure modern contraceptive
prevalence rate and demand for family planning. Total demand for family planning
is defined as the sum of the number of women of reproductive age (15–49 years)
who are married or in a union and who are currently using, or whose sexual
partner is currently using, at least one contraceptive method, and the unmet need
for family planning. Unmet need for family planning is the proportion of women of
reproductive age (15–49 years) either married or in a consensual union, who are
fecund and sexually active but who are not using any method of contraception
(modern or traditional), and report not wanting any more children or wanting to
delay the birth of their next child for at least two years. Included are:
1. all pregnant women (married or in a consensual union) whose pregnancies
were unwanted or mistimed at the time of conception;
2. all postpartum amenorrhoeic women (married or in consensual union) who
are not using family planning and whose last birth was unwanted or mistimed;
3. all fecund women (married or in consensual union) who are neither pregnant
nor postpartum amenorrhoeic, and who either do not want any more children
(want to limit family size), or who wish to postpone the birth of a child for at
least two years or do not know when or if they want another child (want to
space births), but are not using any contraceptive method.
The United Nations Population Division produces a systematic and comprehensive
series of annual estimates and projections of the percentage of demand for family
planning that is satisfied among married or in-union women. A Bayesian
hierarchical model combined with country-specific data are used to generate the
estimates, projections and uncertainty assessments from survey data. The model
accounts for differences by data source, sample population, and contraceptive
methods.
See here for details:
http://www.un.org/en/development/desa/population/theme/familyplanning/cp_model.shtml
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
Tracer area
Pregnancy care
Indicator definition
Percentage of women aged 15-49 years with a live birth in a given time period who
received antenatal care four or more times and who had skilled health personnel
attending the birth
Numerator
Number of women aged 15−49 years with a live birth in a given time period who
(1) received antenatal care four or more times as well as (2) had skilled health
personnel attending the birth
Total number of women aged 15−49 years with a live birth in the same period.
Household surveys and routine facility information systems.
Data on four or more antenatal care visits is based on questions that ask if and
how many times the health of the woman was checked during pregnancy. Data on
skilled birth attendance come from questions that ask respondents about who
helped during delivery.
Note that the definition of skilled birth attendant varies between countries, but
should include doctors, nurses or midwives, who are trained in providing livesaving obstetric care giving the necessary supervision, care and advice for women
during pregnancy, childbirth and postpartum, to conduct deliveries on their own,
and to care for newborns.
Household surveys that can generate this indicator include DHS, MICS, RHS and
other surveys based on similar methodologies. Service/facility reporting systems
can be used where the coverage is high, usually in industrialized countries.
WHO and UNICEF maintain data bases on coverage of antenatal care and births
attended by skilled health personnel. Considerable effort is spent on verifying
skilled birth attendant definitions from survey data; in many cases survey reports
will present coverage of “skilled birth attendance” but use cadres that are not
considered skilled. These figures must be adjusted to ensure comparability for
global monitoring purposes. During 2016, WHO and UNICEF are collaborating to
conduct a full review and country consultation on this issue to obtain a final set of
data sources and estimates.
This indicator is intended to provide a more comprehensive measure of pregnancy
care as compared to monitoring antenatal care and skill birth attendance coverage
separately. To calculate it, original survey data must be re-analyzed to determine
the joint distribution of ANC4 and SAB coverage across respondents. To date, this
exercise has not been completed; as a proxy, the geometric mean of national
estimates of ANC4 and SAB coverage is computed, which is an overestimate. Note
that this indicator could later be expanded to incorporate a measure of postdelivery care if desired.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
Tracer area
Full child immunization
Indicator definition
Percentage of infants receiving three doses of diphtheria-tetanus-pertussis
containing vaccine
Numerator
Children 1 year of age who have received three doses of diphtheria-tetanuspertussis containing vaccine
Denominator
Main data sources
Method of measurement
All children 1 year of age
Household surveys and facility information systems.
For survey data, the vaccination status of children aged 12–23 months is
collected from child health cards or, if there is no card, from recall by the caretaker. For administrative data, the total number of doses administered to the
target population is extracted.
Together, WHO and UNICEF derive estimates of DTP3 coverage based on data
officially reported to WHO and UNICEF by Member States, as well as data
reported in the published and grey literature. They also consult with local
experts - primarily national EPI managers and WHO regional office staff - for
additional information regarding the performance of specific local
immunization services. Based on the available data, consideration of potential
biases, and contributions from local experts, WHO/UNICEF determine the
most likely true level of immunization coverage.
For details, see here:
http://www.who.int/bulletin/volumes/87/7/08-053819/en/
http://www.who.int/immunization/monitoring_surveillance/routine/coverage
/en/index4.html
There is variability in national vaccine schedules across countries. Given this,
one option for monitoring full child immunization is to monitor the fraction of
children receiving vaccines included in their country’s national schedule. A
second option, which may be more comparable across countries and time, is
to monitor DTP3 coverage as a proxy for full child immunization. Diphtheriatetanus-pertussis containing vaccine often includes other vaccines, e.g.,
against Hepatitis B and Haemophilus influenza type B, and is a reasonable
measure of the extent to which there is a robust vaccine delivery platform
within a country.
Method of estimation
UHC-related notes
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Child treatment (care-seeking for symptoms of pneumonia)
Indicator definition
Percentage of children under 5 years of age with suspected pneumonia (cough and
difficult breathing NOT due to a problem in the chest and a blocked nose) in the
two weeks preceding the survey taken to an appropriate health facility or provider.
Numerator
Number of children with suspected pneumonia in the two weeks preceding the
survey taken to an appropriate health provider.
Number of children with suspected pneumonia in the two weeks preceding the
survey.
Household surveys
During the UNICEF/WHO Meeting on Child Survival Survey-based Indicators, held
in New York, 17–18 June 2004, it was recommended that acute respiratory
infections (ARI) be described as “presumed pneumonia” to better reflect probable
cause and the recommended interventions. The definition of presumed
pneumonia used in the Demographic and Health Surveys (DHS) and in the Multiple
Indicator Cluster Surveys (MICS) was chosen by the group and is based on mothers’
perceptions of a child who has a cough, is breathing faster than usual with short,
quick breaths or is having difficulty breathing, excluding children that had only a
blocked nose. The definition of “appropriate” care provider varies between
countries.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
WHO maintains a data base of country-level observations from household surveys
that can be accessed here:
http://www.who.int/gho/child_health/prevention/pneumonia/en/
There are currently no internationally comparable estimates for this indicator.
This indicator is not typically measured in higher income countries with wellestablished health systems.
For countries without observed data, coverage was estimated from a regression
that predicts coverage of care-seeking for symptoms of pneumonia (on the logit
scale), obtained from the WHO data base described above, as a function of the log
of the under-five pneumonia mortality rate, which can be found here:
http://www.who.int/healthinfo/global_burden_disease/estimates_child_cod_201
5/en/.
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Tuberculosis detection and treatment
Indicator definition
Percentage of incidence TB cases that are detected and successfully treated in a
given year
Numerator
Denominator
Main data sources
Number of new and relapse cases detected in a given year and successfully treated
Number of new and relapse cases in the same year
Facility information systems, surveillance systems, population-based health
surveys with TB diagnostic testing, TB register and related quarterly reporting
system (or electronic TB registers)
This indicator requires three main inputs:
(1) The number of new and relapse TB cases diagnosed and treated in national TB
control programmes and notified to WHO in a given year.
(2) The number of incident TB cases for the same year, typically estimated by
WHO.
(3) Percentage of TB cases successfully treated (cured plus treatment completed)
among TB cases notified to the national health authorities.
Method of measurement
The final indicator = (1)/(2) x (3)
Method of estimation
UHC-related notes
Estimates of TB incidence are produced through a consultative and analytical
process led by WHO and are published annually. These estimates are based on
annual case notifications, assessments of the quality and coverage of TB
notification data, national surveys of the prevalence of TB disease and information
from death (vital) registration systems. Estimates of incidence for each country are
derived, using one or more of the following approaches depending on available
data:
1. incidence = case notifications/estimated proportion of cases detected;
2. incidence = prevalence/duration of condition;
3. incidence = deaths/proportion of incident cases that die.
These estimates of TB incidence are combined with country-reported data on the
number of cases detected and treated, and the percentage of cases successfully
treated, as described above.
To compute the indicator using WHO estimates, one can access necessary files
here: http://www.who.int/tb/country/data/download/en/, and compute the indicator
as = c_cdr x c_new_tsr
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HIV treatment
Indicator definition
Percentage of people living with HIV currently receiving antiretroviral therapy
(ART)
Numerator
Number of adults and children who are currently receiving ART at the end of the
reporting period
Number of adults and children living with HIV during the same period
Facility reporting systems, sentinel surveillance sites, population-based surveys
Numerator: The numerator can be generated by counting the number of adults
and children who received antiretroviral combination therapy at the end of the
reporting period. Data can be collected from facility-based ART registers or drug
supply management systems. These are then tallied and transferred to cross
sectional monthly or quarterly reports which can then be aggregated for national
totals. Patients receiving ART in the private sector and public sector should be
included in the numerator.
Denominator: Data on the number of people with HIV infection may come from
population-based surveys or, as is common in sub-Saharan Africa, surveillance
systems based on antenatal care clinics.
The numerator is calculated using the above methods by WHO/UNAIDS. To
estimate the number of people living with HIV across time, UNAIDS in
collaboration with countries uses an epidemic model (Spectrum) that combines
surveillance data on prevalence with the current number of patients receiving ART
and assumptions about the natural history of HIV disease progression.
Since ART is now recommended for all individuals living with HIV, monitoring ART
coverage is less complicated than before, when only those with a certain level of
disease severity were eligible to receive ART.
Estimates of ART coverage can be found here:
http://apps.who.int/gho/data/node.main.626
There are currently no comparable estimates of ART coverage in high income
countries, but estimates are expected within the next year.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
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ITN coverage for malaria prevention
Indicator definition
Percentage of population in malaria-endemic areas who slept under an ITN the
previous night.
Numerator
Denominator
Main data sources
Number of people in malaria-endemic areas who slept under an ITN.
Total number of people in malaria endemic areas.
Data on household access and use of ITNs come from nationally representative
household surveys such as Demographic and Health Surveys, Multiple Indicator
Cluster Surveys, and Malaria Indicator Surveys. Data on the number of ITNs
delivered by manufacturers to countries are compiled by Milliner Global
Associates, and data on the number of ITNs distributed within countries are
reported by National Malaria Control Programs.
Many recent national surveys report the number of ITNs observed in each
respondent household. Ownership rates can be converted to the proportion of
people sleeping under an ITN using a linear relationship between access and use
that has been derived from 62 surveys that collect information on both indicators.
Mathematical models can be used to combine data from household surveys on
access and use with information on ITN deliveries from manufacturers and ITN
distribution by national malaria programmes to produce annual estimates of ITN
coverage. WHO uses this approach in collaboration with the Malaria Atlas Project.
Methodological details can be found in the Annex of the World Malaria Report
2015: http://www.who.int/malaria/publications/world-malaria-report2015/report/en/.
WHO produces comparable ITN coverage estimates for 40 high burden countries.
For other countries, ITN coverage is not included in the UHC service coverage
index.
Method of measurement
Method of estimation
UHC-related notes
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Improved water and adequate sanitation source
Indicator definition
Percentage of households using improved water and improved sanitation facilities
Numerator
Population living in a household with drinking water from: piped water into
dwelling, plot or yard; public tap/stand pipe; tube well/borehole; protected dug
well; protected spring; or rainwater collection AND living in a household with: flush
or pour-flush to piped sewer system, septic tank or pit latrine; ventilated improved
pit latrine; pit latrine with slab; or composting toilet.
Total population
Population-based household surveys and censuses
Household-level responses, weighted by household size, are used to compute
population coverage.
The WHO/UNICEF Joint Monitoring Programme has produced regular estimates of
coverage of improved water and improved sanitation for MDG monitoring. After
compiling a database of available data sources, for each country, simple linear
regressions are fitted to the country’s data series to obtain an in-sample estimate,
as well as to produce a 2-year extrapolation beyond the last available data point,
after which coverage is held constant for 4 years and then assumed missing. This is
done separately for urban and rural regions, and then combined to obtain national
coverage estimates. Details of the methodology and most recent estimates can be
found here: http://www.wssinfo.org/
This indicator is intended to provide a more comprehensive measure of water and
sanitation coverage than estimating use of improved water and sanitation
separately as two indicators. To calculate it, original survey data must be reanalyzed to determine the joint distribution of improved water and sanitation
coverage across households. To date, this exercise has not been completed; as a
proxy, the geometric mean of national estimates of improved water and improved
sanitation coverage is computed, which is an overestimate.
The SDG indicators for drinking water and sanitation are expanded versions of the
MDG indicators, incorporating the safety/quality of drinking water and sanitation
facilities. Once country data and estimates are available for these new indicators,
they could be used for UHC monitoring in lieu of those based on the MDG indicator
definitions described above.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
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Treatment of cardiovascular disease
Indicator definition
Age-standardized prevalence of raised blood pressure among adults aged 18+
Numerator
Number of adults aged 18 or older with systolic blood pressure >=140 mm Hg or
diastolic blood pressure >=90 mm Hg
Number of adults aged 18 or older
Population-based surveys and surveillance systems
Data sources recording measured blood pressure are used (self-reported data are
excluded). If multiple blood pressure readings are taken per participant, the first
reading is dropped and the remaining readings are averaged.
For producing comparable national estimates, data observations of prevalence
defined in terms of alternate SBP and/or DBP cutoffs are converted into
prevalence of systolic blood pressure >=140 mm Hg or diastolic blood pressure
>=90 mm Hg using regression equations. A Bayesian hierarchical model is then
fitted to these data to calculate age-sex-year-country specific prevalences, which
accounts for national vs. subnational data sources, urban vs. rural data sources,
and allows for variation in prevalence across age and sex. Age-standardized
estimates are then produced by applying the crude estimates to the WHO
Standard Population. Details on the statistical methods are here:
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(10)620363/abstract
WHO and the Global Burden of Metabolic Risk Factors of Chronic Diseases
Collaborating Group has produced comparable estimates for this indicator for year
2014, which are available here:
http://apps.who.int/gho/data/node.main.A875?lang=en
As more data become available, this indicator will likely be replaced by the fraction
of population with hypertension receiving effective treatment. For now,
prevalence of raised blood pressure is used as a proxy.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
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Management of diabetes
Indicator definition
Age-standardized prevalence of raised blood glucose among adults 18+
Numerator
Number of adults aged 18 or older with fasting plasma glucose >= 7.0 mmol/l or on
medication for raised blood glucose
Number of adults aged 18 or older
Population-based surveys and surveillance systems
Fasting plasma glucose (FPG) levels are determined by taking a blood sample from
participants who have fasted for at least 8 hours. Other related bio-indicators can
also be incorporated into estimates (see below), in particular the two-hour oral
glucose tolerance test (OGTT) or hemoglobin A1c (HbA1c) percentage. Selfreported data on diabetes diagnosis are excluded, but self-reported data are used
for identifying whether or not a respondant is currently on medication for raised
blood glucose.
For producing comparable national estimates, data observations of diabetes
prevalence defined in terms of FPG, OGTT, HbA1c, or combinations therein, are all
converted into diabetes prevalence in terms of FPG >= 7.0mmol/l. A Bayesian
hierarchical model is then fitted to these data to calculate age-sex-year-country
specific prevalences, which accounts for national vs. subnational data sources,
urban vs. rural data sources, and allows for variation in prevalence across age and
sex. Age-standardized estimates are then produced by applying the crude
estimates to the WHO Standard Population. Methodological details can be found
here: http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)006188/fulltext
WHO and the Global Burden of Metabolic Risk Factors of Chronic Diseases
Collaborating Group has produced comparable estimates for this indicator for year
2014, which are available here:
http://apps.who.int/gho/data/node.main.A869?lang=en
As more data become available, this indicator will likely be replaced by the fraction
of population with diabetes receiving effective treatment. For now, prevalence of
raised blood glucose is used as a proxy.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
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Cervical cancer screening
Indicator definition
Percentage of women aged 30−49 years who report ever having been screened for
cervical cancer
Numerator
Number of women aged 30−49 years who report ever having had a screening test
for cervical cancer using any of these methods: VIA, pap smear and HPV test.
All women aged 30-49 years
Population-based surveys
Self-reported data on respondents’ cervical cancer screening history are collected
through surveys.
There are currently no comparable estimates of cervical cancer screening
coverage.
There are currently few countries with recent data for this indicator and it is
therefore excluded from the 2016 UHC service coverage index calculations. An
additional challenge for international comparability is that data sources may use
different time periods (ever screened vs. screened in past 5 years) and different
age groups.
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
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Tobacco control
Indicator definition
Age-standardized prevalence of adults >=15 years not smoking tobacco in last 30
days
Numerator
Denominator
Main data sources
Method of measurement
Adults 15 years and older who have not smoked tobacco in the last 30 days
Adults 15 years and older
Household surveys
“Current tobacco smoking" includes cigarettes, cigars, pipes or any other smoked
tobacco products used in the past 30 days. Data are collected via self-report in
surveys.
Method of estimation
WHO estimates prevalence of current tobacco (non) smoking with a negative
binomial meta-regression model, which generates comparable estimates by
adjusting for differences in age groups and indicator definition across national
surveys included in the analysis. These estimates are done separately for men and
women. Methodological details can be found here:
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)602641/supplemental.
WHO estimates of the prevalence of tobacco smoking can be accessed here (see
“current smoking of any tobacco product”):
http://apps.who.int/gho/data/node.main.1250?lang=en
Prevalence of not smoking tobacco is computed as 1 minus the prevalence of
tobacco smoking.
UHC-related notes
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Hospital access
Indicator definition
Hospital (inpatient) admissions per population per year, relative to a maximum
threshold of 8 per 100 population per year
Numerator
Denominator
Main data sources
Method of measurement
Number of inpatient admissions (or discharges) per year
Total population
Administrative systems / Health facility reporting system
Country administrative systems are used to total the number of inpatient
admissions (or discharges) per year, and divided by the total estimated population
for the same year.
Various groups collate information on inpatient admission rates, including:
OECD: http://www.oecd.org/els/health-systems/oecd-health-statistics-2014frequently-requested-data.htm and doi: 10.1787/health_glance_ap-2014-graph74-en
PAHO: http://ais.paho.org/phip/viz/basicindicatorbrowaser.asp
WHO: http://www.euro.who.int/en/data-and-evidence/core-health-indicators-in-thewho-european-region/core-health-indicators-in-the-who-european-region-2015.special-focus-human-resources-for-health
Method of estimation
Using available data, the indicator is computed relative to the minimum inpatient
admission rate observed across OECD countries since 2000, which is 8 per 100 per
year. This indicator is designed to capture low rates of hospital access; the
maximum threshold is used because high rates of inpatient admissions are not
necessarily optimal. The indicator is computed as follows, using country data on
inpatient admission rates (x), which results in values ranging from 0 to 100:
• Country with an admission rate x < 8 per 100 per year, the indicator =
x /0.08*100.
• Country with an admission rate x >= 8 per 100 per year, the indicator = 100.
There are currently no comprehensive and comparable estimates of national
hospital admission rates.
UHC-related notes
There is currently sparse information available on inpatient admission rates for
countries from three WHO regions: Africa, Eastern Mediterranean and South-East
Asia
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Health workforce
Indicator definition
Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to
maximum thresholds for each cadre
Numerator
Denominator
Main data sources
Number of physicians, psychiatrists and surgeons
Total population
National database or registry of health workers, ideally coupled with regular
assessment of completeness using census data, professional association registers,
or facility censuses.
The classification of health workers is based on criteria for vocational education
and training, regulation of health professions, and activities and tasks of jobs, i.e. a
framework for categorizing key workforce variables according to shared
characteristics. The WHO framework largely draws on the latest revisions to the
internationally standardized classification systems of the International Labour
Organization (International Standard Classification of Occupations), United Nations
Educational, Scientific and Cultural Organization (International Standard
Classification of Education), and the United Nations Statistics Division
(International Standard Industrial Classification of All Economic Activities).
Methodological details and data can be found here:
http://www.who.int/hrh/statistics/hwfstats/en/
There are currently no comprehensive and comparable estimates health worker
density across countries. Using available data, the indicator is computed by first
rescaling, separately, health worker density ratios for each of the three cadres
(physicians, psychiatrists and surgeons) relative to the minimum observed values
across OECD countries since 2000, which are as follows: physicians = 0.9 per 1000,
psychiatrists = 1 per 100,000, and surgeons = 14 per 100,000. This rescaling is
done in the same way as that for the hospital inpatient admission rates indicator
described above, resulting in indicator values that range from 0 to 100 for each of
the three cadres. For example, using country data on physicians per 1000
population (x), the cadre-specific indicator would be computed as:
• Country with x < 0.9 per 1000 per year, the cadre-specific indicator = x /0.9*100.
• Country with x >= 0.9 per 1000 per year, the cadre-specific indicator = 100.
As a final step, the geometric mean of the three cadre-specific indicator values is
computed to obtain the final indicator of health workforce density.
The “physicians” category would ideally be expanded to include all “core health
professionals”, such as nurses and midwives. However, no internationally
comparable data base exists that uses consistent definitions of non-physician core
health professionals to allow for meaningful cross-country comparisons.
Method of measurement
Method of estimation
UHC-related notes
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Access to essential medicines
Indicator definition
Percentage of health facilities with essential medicines
Numerator
Denominator
Main data sources
Method of measurement
Number of facilities with essential medicines in stock
Total number of health facilities
Special facility surveys or, potentially, routine facility information systems
Data on the availability of a specific list of medicines are collected from a survey of
a sample of facilities. Availability is reported as the percentage of medicine outlets
where a particular medicine was found on the day of the survey. If routine facility
reporting on stocks is accurate and complete, it may also be possible to use data
from the routine system. Regular independent verification will be required.
This indicator is still under development, both in terms of the core list of medicines
to be monitored and data collection strategies. The Service Availability and
Readiness Assessment (SARA) surveys have collected data for a limited number of
countries, see here: http://www.who.int/healthinfo/systems/sara_methods/en/
There are currently about 30 countries with recent data for this indicator and it is
therefore excluded from the 2016 UHC service coverage index calculations.
Method of estimation
UHC-related notes
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Health security
Indicator definition
International Health Regulations (IHR) core capacity index, which is the average
percentage of attributes of 13 core capacities that have been attained at a specific
point in time.
The 13 core capacities are: (1) National legislation, policy and financing; (2)
Coordination and National Focal Point communications; (3) Surveillance; (4)
Response; (5) Preparedness; (6) Risk communication; (7) Human resources; (8)
Laboratory; (9) Points of entry; (10) Zoonotic events; (11) Food safety; (12)
Chemical events; (13) Radionuclear emergencies.
Numerator
Denominator
Main data sources
Method of measurement
Method of estimation
UHC-related notes
Number of attributes attained
Total number of attributes
Key informant survey
Key informants report on attainment of a set of attributes for each of 13 core
capacities using a standard WHO instrument, as described here:
http://apps.who.int/iris/bitstream/10665/84933/1/WHO_HSE_GCR_2013.2_eng.p
df
Capacity-level indicator values can be found here:
http://www.who.int/gho/ihr/monitoring/legislation/en/index1.html
The indicator is computed by averaging, across the 13 core capacities, the
percentage of attributes for each capacity that have been attained.