Country-level indicators and data
In view of the scope of variables and indicators outlined above, we scanned the range of poten-tial data sources from WHO (World Health Statistics; Global Health Observatory) and World Bank (World Development Indicators (WDI), including the Worldwide Governance Indicators) databases. Country-level data were obtained, in most cases for the years 2002-03. For respon-siveness, we needed to calculate country-level measures from individual-level datasets from the World Health Survey (WHS). The WHO WHS data is the only large publically available cross-country and region source with information on a range of health system responsiveness domains. Implemented between 2002 and 2004, the WHS data, acquired through nationally representative and quality-controlled surveys, have been widely used in the peer-review health literature.29 Its data on responsiveness cover 57 countries and 151,848 respondents (using pub-lic and private sector providers). The selection of the remaining indicators were made for these 57 countries classified by the United Nations Development Agency in 200330: 23 low income countries; 13 lower middle-income countries; 11 upper middle-income countries; and 10 high income countries.
Table 7.1 lists the final indicator names, the number of observations obtained, descriptive sta-tistics, and data sources.31-34 All data except for responsiveness were obtained as country-lev-el indicators. The estimation of country-levcountry-lev-el responsiveness indicators from the World Health Survey individual-level dataset35 is described in detail below.
Acquiring and linking data from different sources took place between August and December 2014. Two consolidated datasets were used for analyses. The final 6 health and coverage aver-age levels dataset contained between 52 (coveraver-age) and 57 (health) country-level records The final dataset for health and coverage inequalities consisted of 23 records (country-level).
Responsiveness indicators were derived from health service user responses to the WHS for all 57 countries as indicated earlier. Responsiveness level indicators were calculated by averaging domain summations of individual-level responses dichotomized from a 5-point verbal response scale (“very good”, “good” [0, no problem]; “moderate”, “bad”, “very bad” [1, problem]). Dichoto-mizing the scale and standardizing by education and self-reported health status makes results less susceptible to ‘reporting behaviour’ bias and more comparable across countries.36
Chapter 7
Table 7.1 Variables used in regression models: descriptive statistics and data sources Analytic model
categories Variable or indicator names Descriptive statistics Data source POPULATION
HEALTH LEVELS ALL (n=57) Mean Std.
Dev Minimum Maximum Refer-ence Year Maternal mortality per 100,000 live births
(2005) 308 368 1 1500 (32) 2005
Under five child mortality per 1000 live births
(2005) 63 66 4 220 (33) 2005
TB cause of death per 100,000 (2004) 36 50 0.5 269 (34) 2004
POPULATION HEALTH SERVICE COVERAGE LEVELS
ALL (n=52)
Percentage of births attended by skilled
health personnel (2000-06) 76 28 6 100 (32)
2000-2006 Percentage of population covered with 1 dose
of measles vaccination (2003) 84 15 42 99 (35) 2003
Percentage of women receiving a pap smear
(2000-06) 31 29 0.1 82 (32)
Child mortality: absolute difference by wealth quintile (poor quintile (I) less wealthy quintile
(V)) -57.6 32.8 -157 -15 (32)
1996-2006 Child mortality: relative ratio (wealthy
quintile(I) / poor quintile (V)) 0.5 0.2 0.3 0.8 (32)
1996-2006 Percent population with 1 dose measles
vaccination: absolute difference by wealth quintile (wealthy quintile (I) less poor quintile (V) )
24.7 13.6 1.9 46.9 (32)
1996-2006 Percent population with 1 dose measles
vaccination: relative ratio (wealthy quintile /
poor quintile) 1.7 0.8 1 4.6 (32)
1996-2006 Percent live births with skilled personnel:
absolute difference by wealth quintile (wealthy
quintile (I) less poor quintile (V)) 48.7 18.4 5.8 78.1 (32)
1996-2006 Percent live births with skilled personnel:
relative ratio (wealthy quintile (I) / poor quintile
(V)) 6.3 8.2 1.1 38 (32) Control in limited regressions: Number of
Lower income countries (2002) (n) 23 n/a n/a n/a (31) 2002
Control in limited regressions: Number of
Lower middle income countries (2002) (n) 13 n/a n/a n/a (31) 2002
Control in limited regressions: Number of
Upper middle-income countries (2002) (n) 11 n/a n/a n/a (31) 2002
Control in limited regressions: Number of High
income countries (2002) (n) 10 n/a n/a n/a (31) 2002
HEALTH AND
Access to improved drinking water (%) 92 12 40 100 (32) 2000
Education (mean number of years ) 7.1 3 1 12.4 (31) 2000
Percentage of the population below the
national poverty line (%) (n= 34) (2000-2006) 37 15 6 69 (31)
2000-2006
Analytic model
categories Variable or indicator names Descriptive statistics Data source DETERMINANTS
EQUITY MEASURES
Mean Std.
Dev Minimum Maximum Refer-ence Year Absolute difference in access to improved
sources of drinking water (urban-rural )
(n=23) 27 17 -6 70 (32) 2000
Gini coefficient (0-1 index (1- highest income
inequality) (n=23) 0.43 0.9 0.3 0.64 (31)
2000-2005
Dignity (n=57) 22 11 6 53 (36) 2002/3
Prompt Attention
Health expenditure per capita (International
Dollars) (n=57) 624 837 21 3409 (35) 2002
Out-of-pocket health expenditure as a percentage of total health expenditure per
cap. (n=25) 47 18 3 71 (31) 2002
POPULATION DEMOGRAPHICS AND PREVALENT DISEASES
Population more than 60 years (%) (2006)
(n=57) 11 7 2 24 (32) 2006
a All wealth inequalities are based on household asset index quintiles (country-specific) calculated and provided by the data source listed
Table 7.1 Variables used in regression models: descriptive statistics and data sources (continued)
Chapter 7
The final indicator calculated for the average level of responsiveness was: the frequency of re-porting ‘a problem’ or ‘poor responsiveness’ in a particular domain. The domains of prompt at-tention and dignity were selected as they were among the two most important domains across a wide range of countries37, and illustrated two different faces of responsiveness as described in the original WHO work (1): prompt attention, a “client orientation” domain, and dignity, a
“respect for persons” domain. A composite responsiveness equity indicator was used for out-patient services rather than having domain-specific indicators. The responsiveness equity indi-cator was the average percentage across domains of responsiveness problems reported in the bottom two wealth quintiles for the less healthy in the population (those reporting moderate, poor, very poor health). Because of small numbers, the bottom two wealth quintiles were used rather than just the bottom). The wealth quintiles were based on cross-country comparable asset indices and made available by WHO as part of the World Health Survey dataset.38 Like the poverty measure, this is not strictly speaking an inequality measure. But it does measure the responsiveness experiences of disadvantaged groups, which could explain inequities in health and coverage outcomes. Neither the relative gap or absolute gap measures of inequality for responsiveness showed any correlation with the dependent variables.