Assignment 1..

MONITORING HEALTH DETERMINANTS WITH AN EQUITY FOCUS

Exploring models for the roles of health systems’ responsiveness and social determinants in explaining universal health coverage and health outcomes

Nicole Britt Valentine1,2 and Gouke J. Bonsel2,3*

1World Health Organization, Geneva, Switzerland; 2Department of Public Health, Erasmus University Rotterdam, Rotterdam, The Netherlands; 3Division Mother & Child, University Medical Center Utrecht, Utrecht, The Netherlands

Background: Intersectoral perspectives of health are present in the rhetoric of the sustainable development goals. Yet its descriptions of systematic approaches for an intersectoral monitoring vision, joining deter-

minants of health, and barriers or facilitators to accessing healthcare services are lacking.

Objective: To explore models of associations between health outcomes and health service coverage, and health determinants and health systems responsiveness, and thereby to contribute to monitoring, analysis, and

assessment approaches informed by an intersectoral vision of health.

Design: The study is designed as a series of ecological, cross-country regression analyses, covering between 23 and 57 countries with dependent health variables concentrated on the years 2002�2003. Countries cover a range of development contexts. Health outcome and health service coverage dependent variables were derived

from World Health Organization (WHO) information sources. Predictor variables representing determinants

are derived from the WHO and World Bank databases; variables used for health systems’ responsiveness are

derived from the WHO World Health Survey. Responsiveness is a measure of acceptability of health services to

the population, complementing financial health protection.

Results: Health determinants’ indicators � access to improved drinking sources, accountability, and average years of schooling � were statistically significant in particular health outcome regressions. Statistically sig- nificant coefficients were more common for mortality rate regressions than for coverage rate regressions.

Responsiveness was systematically associated with poorer health and health service coverage. With respect to

levels of inequality in health, the indicator of responsiveness problems experienced by the unhealthy poor

groups in the population was statistically significant for regressions on measles vaccination inequalities between

rich and poor. For the broader determinants, the Gini mattered most for inequalities in child mortality;

education mattered more for inequalities in births attended by skilled personnel.

Conclusions: This paper adds to the literature on comparative health systems research. National and international health monitoring frameworks need to incorporate indicators on trends in and impacts of other

policy sectors on health. This will empower the health sector to carry out public health practices that promote

health and health equity.

Keywords: health information; health determinants; health systems; gender

Responsible Editor: Jennifer Stewart Williams, Umeå University, Sweden.

*Correspondence to: Gouke J. Bonsel, Division Mother & Child Room KE.04. 153.0, University Medical

Center Utrecht, P.O. 85090, 3508 AB Utrecht, The Netherlands, Email: [email protected]

This paper is part of the Special Issue: Monitoring health determinants with an equity focus.

More papers from this issue can be found at www.globalhealthaction.net

Received: 2 August 2015; Revised: 27 October 2015; Accepted: 27 October 2015; Published: 1 March 2016

Introduction In the first decade of the twenty-first century, the World

Health Organization (WHO) played a leading role in

harmonizing health systems’ performance assessment ap-

proaches through the development of relevant conceptual

frameworks (1�4). These frameworks refer to five health system goals that are achieved through the intermediate goal

of coverage of the population with needed health services.

According to these frameworks, different combinations of

health systems’ functions such as stewardship, financing, or

service delivery can be evaluated based on how well they

improve the intermediate and final goals. The final goals are

improvements in: population health levels, population health

equity, levels of health systems’ responsiveness to the

Global Health Action �

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Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329 (page number not for citation purpose)

legitimate expectations of the population, responsiveness

equity, and fairness in financial contributions. When con-

sidering these frameworks and the associated monitoring

approaches they have generated in the context of the

sustainable development goals (SDGs) (5, 6), one can

make several observations concerning potential areas for

improvement. We focus on two areas for improvement for

the purposes of this paper.

A first area for improvement in these frameworks is to

address the neglect of the critical role of determinants

beyond the health sector on population health. From

the leading nineteenth century German doctor, Rudolf

Virchow, to the present-day discussions on sustainable

development, there is general recognition that average

levels of population health and health inequities arise from

factors beyond health care and the health systems’ direct

control. This implies augmentation of the original WHO

frameworks mentioned above to include causal pathways

beyond service coverage. As Hippocrates observed, social

and environmental factors affect health directly. Yet social

and environmental factors may give rise to additional

problems with access to health services, thus modifying or

even augmenting their direct effects on population health.

In order to be comprehensive and efficient, health per-

formance frameworks and associated monitoring should

track trends in these broader determinants. This will allow

the health sector to detect, understand, influence, antici-

pate, and possibly even alter the health impacts of decisions

in other sectors. The WHO Commission on Social Determi-

nants of Health argued in 2008 that impacts of health

determinants, in particular social determinants related

to the distribution of power, money, and resources, were

even more important for addressing health equity (7).

A second area for improvement relates to the develop-

ment of measures of non-financial barriers to access to

health services. We use the term ‘non-financial’ to distin-

guish a set of barriers that complement the financing

of direct medical expenses. The so-called non-financial

barriers may have components related to indirect costs

(e.g. food, fear of loss of income), but also include other

barriers related to acceptability and access (e.g. treatment

with dignity and non-discrimination). Non-financial bar-

riers to health service access are related to health deter-

minants. For example, the lack of transport in rural areas

may result in longer travel distances to health facilities and

differential health service access for disadvantaged groups.

At the same time, the lack of transport can affect access

to work with direct impacts on health through reducing

family time or the length of periods of breastfeeding.

Although non-financial outcomes of health systems

were reflected in WHO’s original frameworks by the con-

cept of health systems’ responsiveness, advances in routine

application in measurement and monitoring have been slow.

Responsiveness is the degree to which legitimate expecta-

tions of the population with respect to non-clinical aspects

of health care or public health services were actually

met (1). It is measured through large representative general

population household surveys, or targeted surveys among

recent care users. The responsiveness domains are, in

alphabetical order: autonomy, choice, communication,

confidentiality, dignity, prompt attention, (quality of) basic

amenities, and (access to family and community) social

support. The work of Donabedian, Tanahahsi, and others

suggest that responsiveness has a direct positive relationship

with service coverage and the final target, health (8�11). We therefore plead for a broader measurement and

monitoring framework, incorporating responsiveness and

determinants, to be applied to evaluating health systems

performance. To investigate the case for this empirically,

this paper describes the development of analytical models

that use data on health systems responsiveness and indi-

cators of social and environmental determinants of health

for their association with key outcomes from the original

WHO frameworks. These key outcomes relate to average

levels of population health and health equity and the

intermediate goals of health service coverage and service

coverage equity. These outcomes of interest are important

in light of the SDGs, as several measures of average levels

of health and universal health coverage (UHC) have been

accepted as part of the SDGs monitoring framework (12).

In our paper, responsiveness and determinants are eval-

uated in terms of their instrumental contribution to

health and health service coverage.

This paper investigates the association between popula-

tion health outcomes, UHC, and responsiveness, and the

role of determinants. We explore regression models, vari-

ables, and country-level indicators for determinants and

responsiveness using cross-sectional data for between

23 and 57 countries. We observe whether a small basket

of theory-supported determinants indicators explain

expected linkages at the ecological level to health and

coverage outcomes.

Methods The approach was: 1) to define a hypothesis-driven set

of variables representing health service coverage, health,

health systems responsiveness, health systems financial

protection, and broader societal factors referred to as

health determinants, suitable to test relationships; 2) to

select and link accessible data sets for testing; and 3) to

conduct multiple regression analyses to assess the hy-

pothesized associations. The country set was confined to

those listed in the 57 face-to-face complete surveys of the

World Health Survey (WHS) (2002�2003; see Appendix 1), for which comparable health systems’ responsiveness

information is available.

Model

The analytical model that underpins the variables and

regression analyses of population health and service cover-

age in this paper is represented in Fig. 1. This describes

Nicole Britt Valentine and Gouke J. Bonsel

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Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329

the main pathways related to how determinants and res-

ponsiveness, as instrumental variables, affect population

health and service coverage. It is derived from standard

literature of conceptual models or frameworks related to

how the broader society interacts with health systems

to ‘produce’ health (7, 13�15). In view of the broader analytical focus of this paper at this stage, these broader

conceptual frameworks were considered to provide more

relevant starting points than frameworks for monitoring of

health services (e.g. monitoring of UHC). The societal-level

models tend to show the role of determinants quite strongly.

The unique feature we added is the separate, instrumental

and therefore testable role given to responsiveness, assum-

ing that people-centredness matters for health and coverage

outcomes, thus representing a pathway in itself.

From left to right, Fig. 1 describes the pathways of

influence on population health and universal population

health service coverage. Starting first with the blue block,

the analytic framework assumes that ‘fixed’ characteris-

tics of population, society, and the health systems’ func-

tions determine the context for intermediate factors that

are more directly associated with health and coverage. These

‘fixed’ characteristics (left) are considered unchangeable

for a given period of time and result in multiple influences

on intermediate factors. The intermediate factors shown

in the centre column operate at the individual-level and

include exposures or access to health services, for which

empirical studies have shown more direct causative asso-

ciations with population health (average levels and health

equity) and health service coverage (average levels and

coverage equity) (on the right) (7). The intended analyses

focus on the pathways (i) determinants to health and

coverage (not distinguishing between iA and iB); (ii) res-

ponsiveness to coverage; and (iii) financial resources and

financial health protection to coverage. Pathway (iv) is

assumed as implicit. Given the importance of financial

protection for health service coverage, it was necessary to

model it, although the focus of the paper is on the addi-

tional roles of determinants and health systems responsive-

ness. Below we elaborate several generic implications of this

analytical model for structuring the analyses that follow.

Health outcomes

1. Two outcomes should be considered in the regression

models in order to cover two separate but important

measures of health systems performance: population

health and coverage of the population with essential

health services (population health service coverage).

2. The outcome measures tested as the dependent

variables should cover a spectrum of disease profiles

and health service interventions.

3. Equity measures of these main coverage and health

outcomes should also be considered in order to

assess specific pathways for inequities in health and

coverage.

Health determinants

1. Determinants as intermediate factors can be mea-

sured at the individual-level and aggregated to the

country-level, but they can also be measured by

policy variables.

HEALTH (AND HEALTHCARE) DETERMINANTS HEALTH AND COVERAGE OUTCOMES

FIXED CONTEXT INTERMEDIATE FACTORS

[INSTRUMENTAL ROLE FOR HEALTH]

(i A)

(ii)

(iii)

(iv)

Society’s characteristics in relation to health

determinants

Health system characteristics:

service organization, governance and

financing

Population demographic

characteristics and prevalent diseases

/ill-health

Resources for health and financial protection coverage for direct medical

costs (Aggregate levels and measures of inequity)

Population health service

coverage (levels, equity)

Individuals’ interactions with health

services

Population health (levels, equity)

Responsiveness (Aggregate levels and measures of inequity)

Determinants: Environmental Quality, Accountability and Inclusion, Livelihoods

and Skills (Aggregate levels and measures of inequity)

(i B)

Fig. 1. Analytical model for tracing key pathways of influence determinants and responsiveness on population health and

population health service coverage.

Exploring models for the roles of health systems responsiveness

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329 3 (page number not for citation purpose)

2. Distributional measures of determinants measured

at the individual-level should be tested for model-

ling equity in health and health service coverage.

3. Health systems are key determinants of health and

health service coverage. Proxy measures of health

systems should be included in regression models

and should relate to levels of resources and financial

protection, given its importance as a determinant of

health service clinical quality and access (5).

The next sections focus on scoping recommended

dependent and independent variables outlined in the ana-

lytical model and on selecting the final data set for the

regression models.

Scope of variables

Health and coverage (dependent variables) Population health status can be represented by rates of

morbidity, mortality, the compound indicator life expec-

tancy, or self-reported health. Health service coverage

can be characterized by enrolment, utilization, or effective

service coverage rates (population in need receiving treat-

ment divided by the population in need). Coverage rates

can be measured comparably if morbidity�intervention combinations are standardized across countries (5). Similar

to other studies (16), the following additional criteria were

applied to select the final set of dependent variables:

1) completeness of the data for the time period and

countries; 2) a spectrum of health conditions or inter-

ventions covering reproductive, maternal and child health,

communicable diseases (the so-called ‘unfinished’ millen-

nium development goals), and non-communicable dis-

eases and injuries; and 3) variables for which country-level

inequality data were available. Inequalities in health out-

comes can be measured as gaps or concentration measures

describing between-group differences in aggregate health

outcome levels, where groups are defined by a ‘wealth’ or

‘income’ quintile (e.g. absolute or relative gap between

fifth and first quintiles), sex, geographic areas, and edu-

cational attainment (17).

Predictor and control variables (independent variables)

Health determinant variables (that are under the control

of policy sectors other than the health sector) can be

conveniently grouped into the following categories envir-

onmental quality, accountability and inclusion, and liveli-

hoods and skills (referred to as EQuAL). A variant of

these categories was discussed at an expert meeting held by

WHO (18). Based on these categories, a range of poten-

tially relevant country-level indicators were drawn from

a descriptive review of recent peer-review literature and

from key informant reports.

Environment quality indicators representing physical

exposures are: urban households living in ‘durable’ struc-

tures; population exposed to small/fine urban particulates

(PM10 or PM2.5) in concentrations exceeding WHO Air

Quality Guidelines; households using modern fuels/

technologies for all cooking, heating, and lighting activities;

health facilities with access to clean and reliable electricity;

population using a basic (improved) water source; the popu-

lation whose access to safe water sources and sanitation is

at risk from changing climate (19, 20); exposure to harmful

substances in the work environment, and broader physical

conditions in the work environment (e.g. night shifts,

length of working week). Social elements of housing are:

residential stability or affordability of neighbourhoods

(20); urban design or green space and safety, and for

products, enforceable and regulatory product quality

and labelling measures (21).

Accountability and inclusion indicators include: vio-

lence against women; ratios of female to male schooling

(attainment); social capital; self-reported gender inequal-

ity or discrimination; discrimination in laws and policies,

and related composite indices (e.g. World Bank Good

Governance database) (22).

Livelihoods and skills indicators include: child stunting

(23); caloric intake; household poverty; access to social

protection (e.g. cash transfers); value in work; associated

psychosocial exposures; employment relations (e.g. in-

formal or formal, own account/salaried � access to paid parental leave, old age pensions); maternal education and

birth spacing; child development; access to early child

development services, and social inequality (18, 23).

Responsiveness measurement is described in the litera-

ture (24, 25). The original eight responsiveness domains

can be regrouped by the EQuAL framework: basic

amenities and communication under Environmental qual-

ity; autonomy, confidentiality, dignity, and social sup-

port under Accountability and inclusion, and choice and

prompt attention under Livelihoods and skills.

Health systems pathways related to health system

availability and financing are characterized in terms of

levels of expenditure and financial protection coverage

(other factors less commonly considered are human re-

source levels) (4, 16). Out-of-pocket expenditure indica-

tors often represent financial protection coverage, with

higher levels representing higher copayments or low

financial protection coverage (16, 26), which are known

to be regressive (27).

Demographic and biological drivers of need are primarily

age and sex structure of the population. For our analysis,

such variables are controlled for as has been done

elsewhere (28).

Data sources and final data sets

Country-level indicators and data

In view of the scope of variables and indicators outlined

above, we scanned the range of potential data sources from

WHO (World Health Statistics; Global Health Observa-

tory) and World Bank (World Development Indicators

including the Worldwide Governance Indicators) databases.

Nicole Britt Valentine and Gouke J. Bonsel

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Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329

Country-level data were obtained, in most cases for the

years 2002�2003. For responsiveness, we needed to calculate country-level measures from individual-level

data sets from the WHS. The WHO WHS data are the

only large publicly available cross-country and region

source with information on a range of health system res-

ponsiveness domains. Implemented between 2002 and

2004, the WHS data, acquired through nationally repre-

sentative 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 public and private sector providers). The selection of

the remaining indicators was made for these 57 countries

classified by the United Nations Development Agency

in 2003 (30): 23 low-income countries; 13 lower middle-

income countries; 11 upper middle-income countries; and

10 high-income countries.

Table 1 lists the final indicator names, the number

of observations obtained, descriptive statistics, and data

sources (31�34). All data except for responsiveness were obtained as country-level indicators. The estimation of

country-level responsiveness indicators from the World

Health Survey individual-level data set (35) is described

in detail below.

Acquiring and linking data from different sources took

place between August and December 2014. Two consoli-

dated data sets were used for analyses. The final six health

and coverage average levels data set contained between

52 (coverage) and 57 (health) country-level records. The

final three data sets for health and coverage inequalities

consisted of 23 (country-level) records each.

Responsiveness indicators were derived from health

service user responses to the WHS for all 57 countries as

indicated earlier. Responsiveness level indicators were cal-

culated by averaging domain summations of individual-

level responses dichotomized from a five-point verbal

response scale (‘very good’, ‘good’ [0, no problem]

‘moderate’, ‘bad’, and ‘very bad’ [1, problem]). Dichot-

omizing the scale and standardizing by education and

self-reported health status make results less susceptible

to ‘reporting behaviour’ bias and more comparable across

countries (36). The final indicator calculated for the aver-

age level of responsiveness was: the frequency of report-

ing ‘a problem’ or ‘poor responsiveness’ in a particular

domain. The domains of prompt attention and dignity

were selected as they were among the two most important

domains across a wide range of countries (37), and illus-

trated two different faces of responsiveness as described

in the original WHO work (1): prompt attention, ‘client

orientation’ domain, and dignity, a ‘respect for persons’

domain. A composite responsiveness equity indicator was

used for outpatient services rather than having domain-

specific indicators. The responsiveness equity indicator

was the average percentage across domains of responsive-

ness problems reported in the bottom two wealth quintiles

for the less healthy in the population (those reporting

moderate, poor, or 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 avail-

able by WHO as part of the World Health Survey data

set (38). Like the poverty measure, this is not strictly an

inequality measure. However, it does measure the respon-

siveness experiences of disadvantaged groups, which could

explain inequities in health and coverage outcomes.

Neither the relative or absolute gap measures of inequality

for responsiveness showed any correlation with the

dependent variables.

Missing data procedures

Missing data were not extensive for the final analyses. The

missing data procedure followed used multiple imputa-

tion by chained equations as specified in the standard

Stata mi command routines and associated instructions

(39). Missing data for the dependent (health and cover-

age) country-level indicators were not filled and the

procedure was not necessary for the responsiveness and

health systems indicators. Missing data for the determi-

nants indicators were predicted from the country income

group (dummy) and total health expenditure per capita.

The following variables and observations were incomplete

before imputation: accountability and voice index (miss-

ing for Cote d’Ivoire, Congo, Sri Lanka); and mean years

of schooling for the population of 15 years or more in

2000 (which of the variables filled had the highest missing

rates, for 9 out of 57 countries: United Arab Emirates,

Bosnia and Herzegovina, Congo, Georgia, Israel, Lao

People’s Democratic Republic, Myanmar, Senegal, and

Tunisia). The percentage of the population below the

national poverty line in 2002 was only available for 35

countries out of 57 countries and therefore not filled.

Analyses

Standard univariate and bivariate descriptive analyses on

the dependent and independent variables preceded regres-

sion analysis (see Appendix 2). Normality of the distri-

bution was tested. With respect to dependent variables,

distributional characteristics required several transforma-

tions. The logarithmic transformation of the dependent

variables generally improved analytical properties. It was

necessary to log the mortality rates in order to normalize

the skewed data distribution (16, 40, 41). For predictor

variables, health expenditure per capita and the difference

in access to improved water sources also required log

transformation. These transformations do not affect the

principle relationships tested. Scatter plots were used to

assess the linearity of bivariate associations between pre-

dictor and outcome variables. Correlation matrices were

used to assess collinearity of predictors (the highest cor-

relation was a Pearson correlation coefficient of 0.75 for

Exploring models for the roles of health systems responsiveness

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329 5 (page number not for citation purpose)

Table 1. Variables used in regression models: descriptive statistics and data sources

Descriptive statistics Data source

Analytic model categories Variable or indicator names Mean Std dev Minimum Maximum Reference Year

Population health levels All (n �57)

Maternal mortality per 100,000 live births (2005) 308 368 1 1,500 (31) 2005

Under 5 child mortality per 1,000 live births (2005) 63 66 4 220 (32) 2005

TB cause of death per 100,000 (2004) 36 50 0.5 269 (33) 2004

Population health service All (n �52)

coverage levels Percentage of births attended by skilled health personnel (2000�2006) 76 28 6 100 (31) 2000�2006

Percentage of population covered with 1 dose of measles vaccination (2003) 84 15 42 99 (34) 2003

Percentage of women receiving a Pap smear (2000�2006) 31 29 0.1 82 (31) 2000�2006

Population health and All (n �23)

service coverage equitya Child mortality: absolute difference by wealth quintile [poor quintile

(I)/less wealthy quintile (V)]

�57.6 32.8 �157 �15 (31) 1996�2006

Child mortality: relative ratio [wealthy quintile(I)/poor quintile(V)] 0.5 0.2 0.3 0.8 (31) 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 (31) 1996�2006

Percent population with 1 dose measles vaccination: relative ratio (wealthy

quintile/poor quintile)

1.7 0.8 1 4.6 (31) 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 (31) 1996�2006

Percent live births with skilled personnel: relative ratio [wealthy quintile

(I)/poor quintile (V)]

6.3 8.2 1.1 38 (31) 1996�2006

Health and health care Fixed context (n �57)

determinants � fixed Accountability and voice ( �2.5 to �2.5) �0.07 0 .96 �2 1.6 (30) 2002

context Control in limited regressions: number of lower income countries (2002) (n) 23 n/a n/a n/a (30) 2002

Control in limited regressions: number of lower middle income countries (2002) (n) 13 n/a n/a n/a (30) 2002

Control in limited regressions: number of upper middle-income countries (2002) (n) 11 n/a n/a n/a (30) 2002

Control in limited regressions: number of high-income countries (2002) (n) 10 n/a n/a n/a (30) 2002

Health and health care Intermediate factors (n �57, except poverty)

determinants � Access to improved drinking water (%) 92 12 40 100 (31) 2000

intermediate Education (mean number of years) 7.1 3 1 12.4 (30) 2000

Percentage of the population below the national poverty line (%) (n �34)

(2000�2006)

37 15 6 69 (30) 2000�2006

Determinants equity measures (n �23)

Absolute difference in access to improved sources of drinking water (urban�rural) (n �23) 27 17 �6 70 (31) 2000

Gini coefficient [0�1 index (1 � highest income inequality)] 0.43 0.9 0.3 0.64 (30) 2000�2005

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Descriptive statistics Data source

Analytic model categories Variable or indicator names Mean Std dev Minimum Maximum Reference Year

Responsiveness Aggregate level � percentage of responsiveness problems

(%)

Dignity (n �57)

Prompt attention (n �57)

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Inequality in responsiveness: difference by wealth or levels

of responsiveness problems in poorest quintiles (IV, V)

(outpatient services) (%)

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problems in the poor quintile

(I, II) (n �25)

40 9 28 59 (35) 2002/2003

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(V)/less poor (I, II)] (n �25)

0 6 0 22 (35) 2002/2003

Relative ratio (wealthy/poor)

(n �25)

2 0 0 2 (35) 2002/2003

Healthcare resources and Health expenditure per capita (International Dollars) (n �57) 624 837 21 3,409 (34) 2002

financial protection (for

medical costs)

Out-of-pocket health expenditure as a percentage of total health expenditure

per cap (n �25)

47 18 3 71 (30) 2002

Population demographics

and prevalent diseases

Population more than 60 years (%) (2006) (N �57) 11 7 2 24 (31) 2006

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access to improved water and log health expenditure

per capita).

Different regression models were tested: ordinary least-

squares (OLS) linear regression, OLS log-linear regression,

and Poisson and negative binomial maximum likelihood

regressions. Although OLS regressions are more com-

mon than Poisson-based models, it was appropriate to

try different models based on assumptions regarding

the outcome variable (42). Judging the appropriate form

of the model of the outcome variables required assess-

ing model fit statistics, considering the underlying data

generation mechanisms assumption, as well as a priori

assumptions regarding the impact of predictors on out-

comes variables. Model comparisons were undertaken

for the domains of dignity, prompt attention, and basic

amenities as these variables have high importance and

variance across countries (24, 37). Model comparisons for

mortality outcome variables, including log-linear regres-

sions and Poisson-based negative binomial models. The

negative binomial is a form of the Poisson that recognizes

the original count, and integer (non-negative) nature of

data, while relaxing assumptions regarding the mean equal

to the variance (high mean dispersion). It is arguably

preferred for mortality regressions (42, 43). Whereas

OLS model fit statistic uses R 2 , which ranges from 0 to 1,

with numbers closer to 1 representing higher fit, the

log-likelihood becomes more positive as fit improves.

Comparing models using the log-likelihood statistic

requires calculation of the likelihood ratio chi-squared

test ( �2 times the difference in the log-likelihood ratios between the baseline and fitted models).

Only negative binomial regressions were used in regres-

sions on average levels of health � maternal mortality, child mortality, and Tuberculosis (TB) cause of death

(mortality). For the aggregate levels of health coverage� population coverage of births by skilled attendants,

coverage with measles vaccination, and receiving Pap

smears � linear log and linear regressions were used. Final regression models for health outcomes and coverage levels

contained a total of six predictor variables (after poverty

rate was tested initially) and were each run twice in order

to have separate predictions for dignity and prompt

attention. This was done to reduce variables in a single

regression, given the sample sizes of 57 and high correla-

tions between responsiveness domain scores (Pearson

correlation coefficient, 0.85).

For regressions on inequalities, final models had only

four predictors at a time (only 23 countries). To select

the four predictors, once again, the pretesting of several

models was performed. Regression results shown are

selected from the model with the highest R 2

or the most

positive log-likelihood ratios (best fit) from the three

combinations of independent variables tested, which were:

1) out-of-pocket expenditure, responsiveness inequality,

difference in access to drinking water between urban

and rural areas, accountability, and voice, Gini (largest

number of variables); 2) out-of-pocket expenditure, re-

sponsiveness inequity, years of education (smallest number

of variables); and 3) responsiveness inequity, the differ-

ence in access to drinking water between urban and rural

areas, years of education, Gini. In all regressions, a larger

number meant greater inequity (favouring wealthier). In

results, regressions were presented for difference and ratio

properties of the three dependent variables (six regression

results).

Coefficients were assessed for statistical significance at

the intervals: B0.10; B0.05; and B0.001.

The negative binomial regression coefficients were

interpreted as an increase of x in an explanatory variable

multiplying the fitted mean mortality rate by exp(bx) (42).

Results

Comparing regression models for the role of

predictors of health outcomes

Table 2 displays the regression test results using maternal

mortality with the dignity domain for responsiveness

as an example. Four regression formats are shown: OLSs

linear and log-linear models (models 1�3), Poisson and negative binomial models (models 4�6). Comparisons of this nature were made for all outcome variables.

Comparing the model fit statistics for OLSs regression

shows better fit for log linear regressions (model 2). The

Poisson regression log-likelihood statistics indicate poor

fit relative to the negative binomials (more negative). In

the negative binomial regressions, compared with base-

line models (containing only population over 60 years),

both regression models likelihood ratio tests are adequate

to warrant inclusion of more predictors (pB0.000).

Specific experimentation showed that for all models,

the percentage of the population older than 60 years is sig-

nificantly associated with the level of maternal mortality.

This obvious demographic�biological need pathway will receive no further comment. Other variables show less

uniform patterns.

Using OLS regression (regression 1), lower maternal

mortality, without log transformation, is predicted by res-

ponsiveness but not by health expenditure per capita. This

result is contrary to theory-driven expectations. In regres-

sion 2, with outcome variables log transformed, there is an

association of maternal mortality with total health ex-

penditure per capita (natural log), but the association for

responsiveness is small and non-existent for years of

schooling and access to drinking water. Model 3, which

also treats the outcome variable as logged, adds as an

independent variable, the percentage of the population

below the national poverty line, which was available for

34 mostly lower and lower middle income countries out of

57. National poverty rates are associated with maternal

mortality [coefficient 0.03 (p�0.00)] as would be expected.

Nicole Britt Valentine and Gouke J. Bonsel

8 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329

Table 2. Maternal mortality cross-country regression models using the responsiveness dignity domain only (percentage of problems reported by health service users) a

Regression no. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Model

Ordinary

least-squares

regression

Log-linear ordinary

least-squares

regression

Log-linear ordinary

least-squares regression

(with poverty)

Basic Poisson

regression

Negative binomial

maximum likelihood

regression

Negative binomial

maximum likelihood

regression

Comparing coefficient

in models 2 and 6

Fit

MSE 190.8 0.82 0.93

R2 (or pseudo) 0.76 0.86 0.5 0.93 0.17 0.14

Log-likelihood �6,776 �359 �373

Average total health expenditure per capita (log)

Coefficient �19.54 �0.4 �0.03 0.18 0.02 �0.26 �0.40; �0.26

Std error 38.36 0.16 0.14 0.01 0.09 0.12

T-statistics �0.51 �2.42 �0.21 35.14 0.22 �2.17

p 0.61 0.02 0.84 0 0.83 0.03

Percent population with responsiveness problems

Coefficient 110.39 0.33 0.34 �0.17 0.31 0.29 0.33; 0.29

Std error 46.84 0.2 0.16 0.01 0.11 0.14

T-statistics 2.36 1.63 2.09 �32.28 2.75 1.97

p 0.02 0.11 0.05 0 0.01 0.05

Percent population accessing drinking water

Coefficient �592.68 �0.81 �0.24 �0.42 �0.21 �0.48 �0.81; �0.48

Std error 133.13 0.57 0.44 0.01 0.32 0.46

T-statistics �4.45 �1.42 �0.55 �42.29 �0.66 �1.04

p 0 0.16 0.59 0 0.51 0.3

Accountability and voice

Coefficient 74.11 0.01 �0.09 0.25 �0.06 �0.04 0.01; �0.04

Std error 41.4 0.18 0.18 0 0.12 0.13

T-statistics 1.79 0.04 �0.51 92.95 �0.52 �0.3

p 0.08 0.97 0.61 0 0.6 0.77

Average years of schooling

Coefficient �28.53 �0.05 �0.1 �0.05 �0.11 �0.07 �0.05; �0.07

Std error 12.85 0.06 0.05 0 0.03 0.04

T-statistics �2.22 �0.86 �1.99 �34.89 �3.28 �1.73

p 0.03 0.4 0.06 0 0 0.08

Percentage population over 60 years

Coefficient �13.51 �0.15 �0.14 �0.25 �0.17 �0.18 �0.15 �0.18

Std error 6 0.03 0.03 0 0.02 0.02

E x p

lo rin

g m

o d

e ls

fo r

th e

ro le

s o f

h e a lth

s y s te

m s

re s p

o n s iv

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tio n :

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9 :

2 9 3 2 9

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Table 2 (Continued )

Regression no. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Model

Ordinary

least-squares

regression

Log-linear ordinary

least-squares

regression

Log-linear ordinary

least-squares regression

(with poverty)

Basic Poisson

regression

Negative binomial

maximum likelihood

regression

Negative binomial

maximum likelihood

regression

Comparing coefficient

in models 2 and 6

T-statistics �2.25 �5.84 �4.65 �151.75 �10.01 �8.67

p 0.03 0 0 0 0 0

Percent population below national poverty line coefficient

Coefficient 0.03

Std error 0.01

T-statistics 3.4

p 0

Income group (low-middle)

Coefficient �0.91 �0.95

Std error 0.01 0.25

T-statistics �89.84 �3.74

p 0 0

Income group (middle)

Coefficient �1.73 �1.97

Std error 0.02 0.34

T-statistics �107.24 �5.83

p 0 0

Income group (high)

Coefficient �2.71 �2.77

Std error 0.09 0.46

T-statistics �30.57 �6.05

p 0 0

Constant

Coefficient 3530.55 12.66 7.61 �2.4 �1.27 �0.48

Std error 515.92 2.21 1.91 0.03 1.23 1.78

T-statistics 6.84 5.72 3.98 �75.58 �1.03 �0.27

p 0 0 0 0 0.31 0.79

aN is 57 countries for all regressions except for Model 3, where the number of country observations is 34.

N ic

o le

B ritt

V a le

n tin

e a n d

G o u k e

J .

B o n s e l

1 0

(p a g

e n

u m

b e r

n o

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r c ita

tio n

p u

rp o

s e )

C ita

tio n :

G lo

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A c tio

n 2 0 1 6 ,

9 :

2 9 3 2 9

- h ttp

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9 .2

9 3 2 9

Again, the effect of national poverty rates swamps out

health expenditure per capita and accountability, which

may suggest that the log-transformation alone is insuffi-

cient to correct for the underlying distributional form.

Regression 5 introduces the country’s average level of

income (World Bank categories) as independent variables.

Income removes some effects of other variables, in parti-

cular for health expenditure per capita, responsiveness,

and years of schooling. Regression 6, on the other hand,

shows up these variables. Maternal mortality across coun-

tries is associated with health expenditure (coefficient:

0.26; p �0.03), with responsiveness barriers (coefficient: 0.29; p �0.05), and average years of schooling (coeffi- cients: �0.07; p �0.08). Although associations with accountability and access to water and sanitation are in-

significant, the clearer pathways related to health systems

and health service interactions make this model preferable

in our view, given that the health outcome maternal

mortality is usually associated with the lack of health

service attention at birth.

Regression models that explain aggregate

health levels

Six regressions of health outcomes and coverage rates

are shown in Tables 3 and 4 for two domains: 1) dignity,

and 2) prompt attention. Regressions otherwise contain

the same independent variables. The first regression column

shows the maternal mortality negative binomial regression

with dignity (repeated from Table 2, Model 6). The next

column in Table 3 shows the regression for child mortality,

then TB mortality rates, and so on.

Across Tables 3 and 4, one observes that statistically

significant coefficients for predictors are more com-

mon for mortality rate regressions than for coverage rate

regression. In Tables 3 and 4, column 3, there are a high

number of significant covariates, in the expected direction,

for child mortality on the one hand (all predictors except

adult education) and a low number for measles coverage

on the other hand (Tables 3 and 4, column 6). Respon-

siveness is statistically significant for all mortality regres-

sions but for only one of the service coverage (skilled

attendants). Higher percentages of responsiveness pro-

blems in countries are associated with increased maternal,

TB, and injuries mortality (Table 3, columns 2�4 and Table 4, columns 2 and 4), and reduced coverage of

the population with skilled birth attendants (Table 3,

column 5). On average, the effect sizes of responsiveness

on the dependent variable, measured in terms of numerical

percentages, are higher for service coverage than for mor-

Table 3. Cross-country regression models for health outcomes and health service coverage, using the responsiveness domain

dignity only a

Health outcomes Service coverage

Explanatory variables

Maternal

mortality (natural

log by negative

binomial model)

Child mortality

(natural log by

negative

binomial model)

TB mortality

(natural log by

negative

binomial model)

Percentage

coverage by

skilled

attendants at

birth (natural log)

Percentage

coverage of

measles

vaccination

(natural log)

Percentage

coverage of

Pap smear

Health expenditure per

capita (log)

�0.26** �0.21** �0.2 0.48** �0.15 9.24**

Users reporting

responsiveness problems

0.29** 0.21* 0.74*** �0.49** �0.25 �6.79

Access to improved drinking

sources/water

�0.48 �0.75* �0.69 2.53*** 1.22 �8.06

Accountability and voice

( �2.5 to �2.5) higher better

�0.04 �0.18* �0.19 �0.32 0.05 �0.97

Average years of schooling of

adults (�18 years)

�0.07* �0.04 0.07 0.11 0.16** 1.14

Percentage of population over

60 years of age

�0.18*** �0.07*** �0.11*** 0.1*** 0.05 1.12*

Constant �0.48 2.56* �2.87 �14.03*** �4.36 �14.83

Model fit Negative

binomial

Negative

binomial

Negative

binomial

Log-linear Log-linear Ordinary

least squares

MSE 1.01 1.13 18.53

R2 (pseudo for Poisson, NB) 0.16 0.09 0.07 0.81 0.37 0.59

Log likelihood �359 �563 �503

an �57 except for regression for skilled birth attendants (n �52). *pB0.10; **pB0.05; ***pB0.001.

Exploring models for the roles of health systems responsiveness

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329 11 (page number not for citation purpose)

tality rates. Using coefficient results in Table 2, and the

proportionate formula for interpreting negative binomial

regression coefficients, as described in the methodology

[exp b(x) �exp(0.29*0.10)], an increase in responsiveness problems by 10% increases mortality rates by 3% and

decreases service coverage rates by 5%.

Looking across indicators, access to improved drinking

sources is statistically significant in only one mortality

regression � child mortality � and one coverage regression � skilled attendants at birth. Accountability (and voice) is

statistically significant only in one mortality regression � child mortality. Average years of schooling is relevant

to one mortality outcome � maternal mortality � and to one coverage outcome � measles vaccination. Health expenditure per capita is statistically significant in four

out of six regressions (except TB mortality and measles

coverage).

Model fit within health outcomes regressions, as judged

by log-likelihood statistics, is best for maternal mortality

(LL ��359), followed by TB mortality (LL � �503) and child mortality. In health service coverage regressions,

fit as judged by the R 2

statistic is better for skilled atten-

dants at birth (R 2 �0.81) and Pap smear (R2 �0.55) than

measles vaccination (R 2 �0.37).

Qualitative changes are observed in the effects of

dignity versus prompt attention. For child mortality,

prompt attention barriers are not significant, whereas

dignity barriers are significant (pB0.05). On the other

hand, both dignity and prompt attention barriers are

highly significant (pB0.001) for TB mortality rate regres-

sions. However, effect sizes for TB are larger for prompt

attention responsiveness barriers than for dignity.

Regression models that explain aggregate health

inequalities

Table 5 shows regression results for health outcome

inequalities and service coverage inequalities as depen-

dent variables. Child mortality favoured combinations of

variable sets 1 and 3, whereas coverage inequality models

were best fitted with the smaller set of predictor variables

from set 2.

Responsiveness problems experienced by the unhealthy

poor groups were statistically significant only for measles

vaccination inequalities between rich and poor. Out-of-

pocket expenditure was statistically significant in predict-

ing coverage gaps for measles immunization. With respect

to the broader determinants, the Gini coefficient mat-

tered most for inequalities in child mortality between the

Table 4. Cross-country regression models for health outcomes and health service coverage, using the responsiveness domain

prompt attention only a

Health outcomes Service coverage

Explanatory variables

Maternal

mortality (natural

log by negative

binomial model)

Child mortality

(natural log by

negative

binomial model)

TB mortality

(natural log by

negative

binomial model)

Percentage

coverage by

skilled

attendants at

birth (natural log)

Percentage

coverage of

measles

vaccination

(natural log)

Percentage

coverage of

Pap smear

Health expenditure per capita

(log)

�0.28** �0.22** �0.22 0.55*** �0.04 10.61***

Users reporting

responsiveness problems

0.31* 0.22 0.83*** �0.34 0.23 �2.52

Access to improved drinking

sources/water

�0.5 �0.76** �0.53 2.6*** 1.22 �7.28

Accountability and voice

( �2.5 to �2.5) higher better

�0.08 �0.21** �0.26 �0.25 0.05 �0.26

Average years of schooling of

adults (�18 years)

�0.07* �0.04 0.06 0.1 0.15** 1.03

Percentage of population over

60 years of age

�0.18*** �0.07*** �0.11*** 0.1*** 0.05 1.1*

Constant �0.52 2.5* �3.71 �14.14*** �4.48 �16.81

Model fit Negative

binomial

Negative

binomial

Negative

binomial

Log-linear Log-linear Ordinary least

squares

MSE 1.03 1.13 18.89

R2 (pseudo for Poisson, NB) 0.16 0.09 0.07 0.8 0.37 0.57

� 2 times the log likelihood �359 �563 �503

an �57 except for regression for skilled birth attendants (n �52). *pB0.10; **pB0.05; ***pB0.001.

Nicole Britt Valentine and Gouke J. Bonsel

12 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329

rich and poor, and education mattered more for inequal-

ities in births attended by skilled personnel.

Discussion This paper presents an exploration of different models

for understanding the linkages between health and ser-

vice coverage outcomes, and related health determinants,

including health systems’ responsiveness. We used a set of

cross-sectional analyses of different types of health status

and health service coverage rates to explore different sets

of determinants and health systems’ responsiveness indi-

cators across 57 countries. To our knowledge, this is the

first time that both health conditions and service coverage

rates are explained using determinants and ‘acceptability’

barriers of responsiveness.

The determinants’ indicators tested here were asso-

ciated with health in the expected directions as shown

elsewhere (17, 20, 21, 40, 41), which is reassuring with

respect to the findings for health systems’ responsiveness.

An interesting new finding is that responsiveness was

systematically associated with poorer health outcomes

and coverage in the areas of maternal mortality, child

mortality, TB mortality, skilled birth attendance coverage,

and Pap smears (not measles vaccination). The results

imply that both responsiveness barriers and health deter-

minants have quantifiable, separate associations with

health status and health service coverage. Responsiveness

complements the financial barriers indicators recom-

mended to be measured as part of UHC in the health

goal, SDG-Goal 3.

Our analyses also have implications for monitoring

health determinants in the SDGs. SDG-Goal 3 (health)

covers health outcomes and ‘UHC’ (6, 12). UHC in the

SDGs is defined as the degree to which health services

meet population healthcare needs without undue financial

hardship. Two metrics derived for its quantification are:

financial protection coverage of individuals, which is mea-

sured by the absence of so-called catastrophic direct

medical costs (5), and service or intervention coverage,

which is measured as the proportion of people, who need

particular well-accepted health interventions, receiving

them. Both metrics can also be expressed as coverage

inequality (by sex, education, income, and geographic

area) (17). Yet, these metrics do not explicitly track res-

ponsiveness barriers, or the wider panorama of social and

environmental determinants such as education of mothers

and income inequality, which are clearly important for

achieving good population health and effective health

service coverage.

The systematic testing of regression models, variables,

and indicators as illustrated in this paper, is useful for

determining which national comparable health determinants

indicators to track. Our findings show that several deter-

minant indicators are candidates. These include drinking

Table 5. Cross-country regressions explaining inequalities in health status and health service coverage by contextual and

instrumental factors including responsiveness (n �23)

Child mortality rates

Births attended by skilled

personnel Measles vaccination coverage

Difference ABS

(poor-rich),

larger worse

Ratio (poor/rich),

larger worse

Difference

(rich � poor),

larger worse

Ratio (rich/poor),

larger better

Difference

(rich - poor),

larger worse

Ratio (rich/poor),

larger worse

Model 3 1 1 2 2 2

Out-of-pocket health

expenditure

0.01 0.04 0.01 0.27** 0.01**

Responsiveness problems

(% unhealthy, poor)

0.02 �0.49 0.81 0.11 0.31 0.01**

Difference in percent

population accessing

drinking water

0.01 0 �0.1

Accountability index 0.50 (0.15) 14.98 (0.12)

Average years of schooling 0.11 �1.54** �2.72** �0.07**

GINI (0�1, 1 unequal) 0.05** 0.02 �0.9

Model fit Negative

binomial

Ordinary least

squares

Ordinary least

squares

Ordinary least

squares

Ordinary least

squares

Log linear

MSE 0.74 20.43 7.46 9.53 0.24

R2 (pseudo) 0.05 0.11 0.03 0.15 0.5 0.55

Log likelihood �118.09

*pB0.10; **pB0.05; ***pB0.001.

Exploring models for the roles of health systems responsiveness

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329 13 (page number not for citation purpose)

water coverage and coverage inequalities, poverty, mean

years of schooling, and income inequality. These are

candidates for both international and national use in

intersectoral monitoring frameworks that track health

determinants. Except for poverty, the data series are rela-

tively complete (poverty is more complete now relative to

2002�2004) and they complement SDG-Goal 3 (health). These indicators are also likely to be used by sectors

beyond health in monitoring other SDG goals (e.g. Goal

1 � poverty, Goal 4 � education, Goal 6 � water and sanitation, and Goal 10 � income inequality). Having the health sector in national contexts tracking a set of

determinant indicators is vital, as described in the Health

in All Policies approach (44). Tracking determinants

is statistically simple as well as efficient and provides a

rational for policy coherence if the same indicators are

already being used by another sector to monitor their

strategic performance. These data can also be used as

a bridge to build better information systems for health

impact assessments, thereby enabling anticipation of

health changes before they emerge as behavioural changes

in the population.

There are several limitations to our study. It consists of

data that are 12�13 years old. It is possible that, as health systems and development contexts have changed, other

patterns would have emerged if the study had been con-

ducted on current data (e.g. governance accountability

concepts can have altered). We also used a limited number

of variables. More recommendations for the use of vari-

ables in future research are discussed below. A further

limitation is that we only conducted relatively simple

cross-sectional analyses, which yielded information on

associations but specific longitudinal analyses should be

investigated in the future for more causative tracking of

health determinants. One example of a recent study that

used more sophisticated mathematical underpinnings is

Mondal and Shitan (45), which used path analysis and

found a significant association for low and lower-middle

income countries between life expectancy and mean years

of schooling. In more complex methodological studies,

there is a tendency for fewer health outcomes, predictors,

and countries to be analysed due to data availability

problems. Other typical enhancements to the analytical

approach are time-series analyses (46) and multi-level

analyses (20).

We were struck by the cross-country equity regression

results. Although much is known about measuring and

monitoring health and coverage inequalities (47), far less

is known about the predictors of the aggregate levels

of health inequalities. This is very important for under-

standing actions to improve health equity and which

determinants to monitor. Currently, there is little empiri-

cal literature using country-level health inequalities me-

trics (48, 49) as dependent variables. Our paper used gap

measures as dependent variables. Predictors were the Gini

index, differences in drinking water access, and health

systems responsiveness to poorer populations, which were

all relevant, but not for all health and service conditions.

For global monitoring of SDG-Goal 10, covering the

reduction of inequalities within countries, it would be

useful to know which determinants indicators are most

closely linked to health inequalities.

In the future, a wider range of indicators could be

tested for use in tracking health determinants as part

of the SDGs. We selected what appeared to be feasible

indicators, for some of which had available distribu-

tional information (i.e. for access to water). But several

additional examples of theory-driven indicators were

mentioned earlier. Variables already considered in the

cross-country literature are female education (45). In our

analysis, we used education overall, but further work

would explore female education. Kolves et al. (50) used

the Gini indices, unemployment rates, female participa-

tion in the labour force, GDP per capita, and divorce rates

to predict suicide rates. Fritzell et al. (41) found child

poverty rates and social spending were associated with

child mortality. For our data set of 57 countries, poverty

rates were too incomplete to use for all regressions. Another

study showed that paid maternity leave was also asso-

ciated with improved immunization coverage (48). These

studies illustrate the more specific indicators that require

further testing, including the importance of policy in-

dicators. Data sets on policy indicators related to the

labour market conditions for health may be of specific

interest in this regard (see the World Policy Analysis

Database: www.worldpolicycenter.org/).

A major obstacle to advancing empirical testing of

determinants and barriers or facilitators of health services

access, like responsiveness, is having both a holistic vision

of health, and the available data (3). As part of the

SDGs, the United Nations Secretary-General’s Indepen-

dent Expert Advisory Group on a Data Revolution for

Sustainable Development encourages the collection of

disaggregated data for monitoring equity across goals � most of which include important health determinants

(51). The follow-up of these recommendations will be very

important for any initiatives to track health determinants

and their population health and health equity impacts.

Investments need to be made to obtain better, disaggre-

gated data about the real sector of the economy, societal

well-being, and the environment (52). Health policy-

makers should advocate for better data collection and

disaggregation in other sectoral indicators in order to

identify common causes across sectors.

Conclusions In promoting monitoring of health determinants and

related barriers to health service coverage like responsive-

ness, the health sector will enhance public health pro-

motion, which is necessary for SDG-Goal 3, ‘attaining

Nicole Britt Valentine and Gouke J. Bonsel

14 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329

healthy life for all at all ages’ (5). It is only when national

health monitoring by the health sector reflects the true

intersectoral scope of health, that accountability across

sectors for actions affecting health will be demanded by

the whole-of-society.

Authors’ contributions The first author (NV) provided the primary hypothesis

of the paper, and performed the statistical work. Both

authors (NV, GB) were responsible for the principal

design of the multi-level approach and its execution, the

interpretation of its findings, and the writing of the paper.

Conflict of interest and funding

The paper was partially funded by the World Health

Organization. The authors have not received any funding or

benefits from industry to conduct this study. Some of the

authors are staff members of the World Health Organization.

The authors alone are responsible for the views expressed in

this publication and they do not necessarily represent the

views, decisions or policies of the World Health Organization.

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Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329

Appendix 1. Countries in regression analyses: outcome variables for health status and coverage

Country

Regression 1:

maternal mortality

per 100,000 lbs

Regression 2: child

mortality per 1,000

live births*

Regression 3: TB

cause of death

per 100,000

Regression 4: percentage of

births attended by skilled

health personnel � 2000�

2006 (2008 WHS)*

Regression 5: percentage of

population with coverage with one

dose of measles vaccination in the

first year of life � 2003 (WHS 2005)*

Regression 6: percentage of

women receiving a Pap

smear (2000�2006) (WHS

2008, 58 countries)

Bangladesh 570 69 4 20 77 0

Bosnia and

Herzegovina

3 17 6 100 84 40

Brazil 110 35 7 97 99 72

Burkina Faso 700 207 54 54 76 5

Chad 1,500 200 82 14 61 6

China 45 37 16 98 84 21

Comoros 400 73 7 62 63 8

Congo 740 108 70 83 50 23

Cote d’Ivoire 810 193 104 57 56 7

Croatia 7 7 6 100 95 65

Czech Republic 4 5 1 100 99 73

Democratic Republic 660 91 25 19 42 3

Dominican Republic 150 35 16 96 79 66

Ecuador 210 27 25 80 99 45

Estonia 25 8 6 100 95 53

Ethiopia 720 169 79 6 52 1

Finland 7 4 1 100 97 67

France 8 5 1 Not available 86 75

Georgia 66 45 13 92 73 13

Ghana 560 95 50 50 80 3

Hungary 6 9 3 100 99 65

India 450 87 30 47 67 3

Ireland 1 6 1 100 78 39

Israel 4 6 1 Not available 95 45

Kazakhstan 140 73 20 100 99 79

Kenya 560 123 133 42 72 4

Latvia 10 13 10 100 99 3

Luxembourg 12 4 1 100 91 82

Malawi 1,100 178 97 54 77 3

Malaysia 62 7 16 100 92 30

Mali 970 220 73 41 68 5

Mauritania 820 184 60 53 71 4

Mauritius 15 17 11 99 94 13

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Appendix 1. (Continued )

Country

Regression 1:

maternal mortality

per 100,000 lbs

Regression 2: child

mortality per 1,000

live births*

Regression 3: TB

cause of death

per 100,000

Regression 4: percentage of

births attended by skilled

health personnel � 2000�

2006 (2008 WHS)*

Regression 5: percentage of

population with coverage with one

dose of measles vaccination in the

first year of life � 2003 (WHS 2005)*

Regression 6: percentage of

women receiving a Pap

smear (2000�2006) (WHS

2008, 58 countries)

Mexico 60 28 4 94 96 64

Myanmar 380 106 20 57 75 1

Namibia 210 65 81 76 70 13

Nepal 830 82 23 19 75 3

Norway 7 4 1 Not available 84 73

Pakistan 320 103 40 54 61 3

Paraguay 150 29 12 100 91 53

Philippines 230 36 48 60 80 10

Portugal 11 6 4 100 96 59

Russian Federation 28 16 21 100 96 78

Senegal 980 137 52 52 96 11

Slovakia 6 8 3 100 99 59

South Africa 400 66 134 92 83 6

Spain 4 5 2 Not available 97 60

Sri Lanka 58 15 9 97 99 2

Swaziland 390 153 269 74 94 62

Sweden 3 4 0.5 Not available 94 70

Tunisia 100 24 2 90 90 10

Ukraine 18 20 16 100 99 34

United Arab Emirates 37 8 2 100 94 12

Uruguay 20 15 3 100 95 62

Viet Nam 150 23 22 88 93 7

Zambia 830 182 138 43 84 3

Zimbabwe 880 126 131 69 80 9

Average 308 63 36 76 84 31

Std dev. 368 66 50 28 15 29

Minimum 1 4 1 6 42 0

Maximum 1,500 220 269 100 99 82

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Appendix 2. Comparing univariate distributions for skewness and Kurtosis statistics: example for health outcomes

Distribution

Variables Variable form logged Skewness Kurtosis

Maternal mortality rate per 100,000 live births (2005) No 1.15 3.48

Natural log of maternal mortality rate Yes �0.35 1.78

Maternal mortality counts No 4.79 25.24

Natural log of maternal mortality counts Yes �0.17 1.81

Under 5 child mortality per 1,000 live births (2005) No 0.98 2.65

Natural log of child mortality rate Yes �0.12 1.64

Child mortality counts No 4.77 27.11

Natural log of child mortality counts Yes �0.04 2.07

TB cause of death per 100,000 (2004) No 2.32 9.66

Natural log of TB cause Yes �0.19 1.99

Exploring models for the roles of health systems responsiveness

Citation: Glob Health Action 2016, 9: 29329 - http://dx.doi.org/10.3402/gha.v9.29329 19 (page number not for citation purpose)

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