DHS ANALYTICAL 

STUDIES 59

Levels and Determinants

of Out-of-pocket Health

Expenditures in The Democratic 

Republic of the Congo, Liberia, 

Namibia, and Rwanda 

August 2016

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Wenjuan Wang, Gheda Temsah, and Emily Carter.

DHS Analytical Studies No. 59 

Levels and Determinants of Out-of-Pocket Health 

Expenditures in the Democratic Republic of the Congo, 

Liberia, Namibia, and Rwanda 

Wenjuan Wang 

Gheda Temsah 

Emily Carter 

ICF International 

Rockville, Maryland, USA 

August 2016 

Corresponding author: Wenjuan Wang, International Health and Development, ICF International, 

530 Gaither Road, Suite 500, Rockville, MD 20850, USA; Telephone: +1 301-407-6500; Fax: 

+1 301-407-6501; E-mail: wenjuan.wang@icfi.com 
Acknowledgment: The authors would like to thank Sara Sulzbach for her review and helpful comments. 

Editor: Bryant Robey 

Document Production: Natalie La Roche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AIDOAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about the DHS Program, contact: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. 

Phone: +1 301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; Internet: 

www.dhsprogram.com. 

Recommended citation: 

Wang, Wenjuan, Gheda Temsah, and Emily Carter. 2016. Levels and Determinants of Out-of-Pocket Health 

Expenditures in the Democratic Republic of the Congo, Liberia, Namibia, and Rwanda. DHS Analytical 

Studies No. 59. Rockville, Maryland, USA: ICF International.
iii 

Contents 

Tables ............................................................................................................................................................ v 

Figures ........................................................................................................................................................ vi 

Preface ....................................................................................................................................................... vii 

Abstract ....................................................................................................................................................... ix 

Executive Summary ................................................................................................................................... xi 

1. Introduction .................................................................................................................................... 1 

1.1. Overview of Out-of-Pocket Health Expenditures ............................................................... 1 

1.2. Determinants of Out-of-Pocket Health Expenditures ......................................................... 2 

1.3. Overview of Health Service Delivery, Health Care Spending, and Financing 

in the Study Countries ......................................................................................................... 4 

2. Data and Methods .......................................................................................................................... 7 

2.1. Data ..................................................................................................................................... 7 

2.2. Definitions of Variables ...................................................................................................... 8 

2.3. Statistical Methods ............................................................................................................ 10 

3. Results ........................................................................................................................................... 13 

3.1. Description of Household Members Included in the Analysis ......................................... 13 

3.2. Use of Inpatient and Outpatient Care Services ................................................................. 16 

3.3. Out-of-Pocket Health Expenditures .................................................................................. 27 

4. Discussion and Conclusion .......................................................................................................... 39 

4.1. Health Care Utilization ..................................................................................................... 39 

4.2. Out-of-Pocket Health Expenditures .................................................................................. 40 

4.3. Conclusion ........................................................................................................................ 41 

References .................................................................................................................................................. 43 

v 

Tables 

 

 Table 1. Percent distribution of de jure household members by individual, household, 

and community background characteristics ............................................................... 14 

 Table 2. Percentage of household members who received health care services in the 

reference period preceding the survey by individual, household, and community 

background characteristics ......................................................................................... 20 

 Table 3. Results of logistic regression predicting the odds of health care utilization, 

DRC DHS 2013 .......................................................................................................... 23 

 Table 4. Results of logistic regression predicting the odds of health care utilization, 

Liberia DHS 2013 ...................................................................................................... 24 

 Table 5. Results of logistic regression predicting the odds of health care utilization, 

Namibia DHS 2013 .................................................................................................... 25 

 Table 6. Results of logistic regression predicting the odds of health care utilization, 

Rwanda DHS 2010 ..................................................................................................... 26 

 Table 7. Summary of out-of-pocket health expenditures for the most recent health care 

services received among household members who received health services ............. 27 

 Table 8. Average out-of-pocket health expenditures for the most recent health care 

services, by type of provider ...................................................................................... 29 

 Table 9. Average out-of-pocket health expenditures for the most recent health care 

services, by reasons for care ....................................................................................... 29 

 Table 10. Average out-of-pocket health expenditures for the most recent health care 

services received, by individual, household, and community background 

characteristics ............................................................................................................. 31 

 Table 11. Results of two-part models for out-of-pocket expenditures for the most recent 

health care services, DRC DHS 2013......................................................................... 34 

 Table 12. Results of two-part models for out-of-pocket expenditures for the most recent 

health care services, Liberia DHS 2013 .................................................................... 35 

 Table 13. Results of two-part models for out-of-pocket expenditures for the most recent 

health care services, Namibia DHS 2013 .................................................................. 36 

 Table 14. Results of two-part models for out-of-pocket expenditures for the most recent 

health care services, Rwanda DHS 2010 .................................................................... 37 

Appendix Table 1. Share of government and private sector as a percent of total health expenditure ...... 49 

Appendix Table 2. Total health expenditures (THE) and out-of-pocket (OOP) payments per capita, 

in million constant 2010 US dollars, and share of out-of-pocket expenditures as a 

percent of total health expenditure ............................................................................. 49 

 
vi 

Figures 

 

Figure 1. Percentage of household members who received inpatient and outpatient care 

services during the reference period ................................................................................. 16

Figure 2. Percent distribution of household members who received inpatient care by providers 

of the most recent care visit .............................................................................................. 17

Figure 3. Percent distribution of household members who sought outpatient care by providers 

of the most recent care visit .............................................................................................. 17

Figure 4. Percent distribution of household members who sought inpatient care by reasons 

for the most recent care ..................................................................................................... 18

Figure 5. Percent distribution of household members who sought outpatient care by reasons 

for the most recent care ..................................................................................................... 19

Figure 6. Percentage of household members who received health care services but reported 

zero expenditure for the most recent visit ......................................................................... 28
vii 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several 

countries, and applying multivariate statistical tools and models. These reports are also intended to illustrate 

research methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with the 

U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

ix 

Abstract 

This study uses data from the Demographic and Health Surveys to explore the levels and determinants of 

out-of-pocket health expenditures in four African countries—Democratic Republic of the Congo (DRC), 

Liberia, Namibia, and Rwanda. The analysis assesses the use of inpatient and outpatient services and 

estimated out-of-pocket expenditures for the care received in the most recent visit. The highest use of health 

care services was in Namibia (18% for inpatient care and 41% for outpatient care), and the lowest was in 

DRC (4% for inpatient care and 7% for outpatient care). Health care was provided predominantly by public 

health facilities, with private providers being used more for outpatient than inpatient care. Average out-ofpocket spending for health care was highest in Liberia and lowest in Rwanda. Health expenditures were 

highly skewed to large amounts, and many people received care but did not pay for the services. Individuals 

from poorer households generally had less out-of-pocket expenditure than wealthier individuals. Health 

insurance coverage stands out as an important factor affecting the magnitude of out-of-pocket health 

expenditure in all four countries, but the results are mixed. In DRC and Rwanda health insurance coverage 

was associated with lower out-of-pocket expenditures for both inpatient and outpatient care services, while 

in Liberia and Namibia it was associated with higher out-of-pocket expenditures. Our results provide 

evidence of a need to expand health insurance coverage, especially in countries with low use of health 

services and high out-of-pocket health expenditures. 

Key words: Out-of-pocket health expenditures, health insurance, socioeconomic determinants, Democratic 

Republic of the Congo (DRC), Liberia, Namibia, Rwanda 

xi 

Executive Summary 

Out-of-pocket spending on health care constitutes a significant portion of household expenditure in many 

low- and middle-income countries. This study explored levels and determinants of out-of-pocket health 

expenditures in four African countries—the Democratic Republic of the Congo (DRC), Liberia, Namibia, 

and Rwanda—using nationally representative data from the Demographic and Health Surveys (DHS), 

focusing on outpatient and inpatient care. 

Methods 

The DHS surveys collect health expenditure data in a separate module, but the method of collecting 

information on inpatient and outpatient health care services may vary from one country to another. The 

study countries were selected based on the similarity in approaches for collecting data on health care costs 

in the most recent survey. The main outcome of interest was out-of-pocket payments for health care services 

received (laboratory test, consultations, and drugs/medication) for the most recent visit. Because out-ofpocket health expenditures are directly linked to health care utilization, the study analyzed the use of 

inpatient and outpatient services among all household members. Explanatory variables at the individual, 

household, and community levels that may affect use of health care services and out-of-pocket spending 

were determined based on Anderson and Newman’s model of pre-disposing, enabling, and needing factors 

of health-care-seeking behavior. The correlates of health care utilization were modeled using logistic 

regression. Because many individuals who received health care reported zero expenditures, two-part models 

were used to model out-of-pocket health spending. 

Results 

Use of inpatient care ranged from 4% to 18% in the four study countries. Use of outpatient care was 

generally higher than inpatient care, ranging from 7% to 41%. DRC had the lowest levels of health care use 

and Namibia had the highest. Inpatient care was provided predominantly at public health facilities, while 

the use of private providers was more common for outpatient than inpatient services. 

In Liberia, Namibia, and Rwanda the most commonly reported reason for seeking inpatient care was for 

sexual and reproductive health, at 8%, 20%, and 35%, respectively. In DRC more than one-third of 

individuals sought inpatient care for fever and malaria. For outpatient care, a wider range of reasons was 

reported. Among those, malaria was commonly reported in DRC and Liberia; preventive care was more 

commonly reported in Namibia and Rwanda than in Liberia and DRC. 

Across all four study countries, the use of health care services was consistently associated with individuals’ 

sex and age, and with the educational attainment level of the household head. Health insurance coverage 

increased the likelihood of using both inpatient and outpatient care services in Rwanda, where data on 

health insurance were available for all household members. Regional variation in the use of health care 

services remained statistically significant even after controlling for other variables. 

Absolute levels of out-of-pocket spending on health care received during the last visit varied substantially 

across countries. Health expenditures were highly skewed to large amounts. The percentage of individuals 

receiving health care but not paying for it ranged from 3% in Rwanda to 44% in Liberia. Average cost of 

health care services was highest in Liberia followed by Namibia, DRC, and Rwanda. Spending on health 

services differed by type of provider. The average out-of-pocket expenditure for inpatient care in a public 

facility was half or less that of care from a private provider. Differences between public and private 

providers in health expenditures are less pronounced for outpatient than inpatient care. Individuals spent 

the most on inpatient care received due to an accident or injury. 
xii 

Health insurance coverage stands out as an important factor affecting the magnitude of out-of-pocket 

expenditures in all countries, but the results are mixed. In DRC and Rwanda health insurance coverage was 

associated with lower out-of-pocket expenditures for both inpatient and outpatient care services, while in 

Liberia and Namibia it was associated with higher out-of-pocket payments. Poorer households generally 

spent less in absolute terms on out-of-pocket payments. Among other factors, sex and age were significantly 

associated with the level of out-of-pocket expenditures. Women tended to spend less than men, and cost of 

care generally increased with age. 

Conclusion 

Country use of inpatient care ranges from 4% to 18%, and use of outpatient care, from 7% to 41%. Health 

care provision primarily relies on public health providers. Out-of-pocket health expenditures vary 

substantially across countries. While the limited use of health services relates to many factors, cost is an 

important barrier to access. Our results suggest that expanding health insurance coverage, especially in 

Liberia and DRC, where service use is low and out-of-pocket health expenditure is high, may be conducive 

to lower out-of-pocket payments depending on the type and level of coverage. Evidence from existing 

studies demonstrates that risk pooling through health insurance can be one effective way for increasing use 

of health care and reducing the likelihood of high out-of-pocket health expenditures. 
1 

1. Introduction 

Universal health coverage—access to affordable and good-quality health services—is essential to human 

welfare and economic and social development. Financial protection is a key goal of universal health 

coverage (World Health Organization 2010). Health financing can be achieved through a variety of 

channels, including government budgets (e.g., taxes), donor funding, health insurance, and direct payments. 

In many countries direct payments, such as over-the-counter payments for medications and fees for doctors 

and services, are the main form of health financing (World Health Organization 2010). In many low- and 

middle-income countries (LMICs), out-of-pocket health payments represent a significant portion of 

household income expenditures. The incidence of catastrophic out-of-pocket health payments, defined by 

the World Health Organization (WHO) as exceeding 40% of household income1

, is linked to a vicious cycle 

of impoverishment because households have to scale back spending on other necessities such as food and 

children’s education. Several studies have explored out-of-pocket health expenditures, and many have 

focused on catastrophic health expenditures. Documenting levels and determinants of out-of-pocket health 

expenditures can help inform public policies aimed at achieving universal health coverage. 

This study explores health care utilization and out-of-pocket expenditures in four African countries—

Democratic Republic of the Congo (DRC), Liberia, Namibia, and Rwanda—using nationally representative 

data from the Demographic and Health Surveys (DHS). We focus on outpatient and inpatient care. 

Outpatient care includes consultation or treatment sought from a health care provider, pharmacist, or 

traditional healer without hospitalization or an overnight stay. Inpatient care refers to hospitalization. The 

main outcome of interest is out-of-pocket health payments. Because out-of-pocket health expenditures are 

directly linked to the use of health care, the report provides up-to-date information on overall levels of 

health care utilization and out-of-pocket health expenditures by background characteristics of individuals 

and their households, as well as by type of provider and condition or illness. We model health care 

utilization using logistic regression and model out-of-pocket health expenditures using two-part models and 

generalized linear models. Existing studies frequently use sub-national or sub-district samples or address 

only one illness or condition. Additionally, the methods for defining and collecting expenditure data vary 

greatly across studies, prohibiting comparisons across settings or over time (McIntyre et al. 2006). This 

study contributes to the existing body of knowledge by using nationally representative data that are 

relatively comparable in their collection of health expenditure information and coverage of a range of 

illnesses and conditions. 

1.1. Overview of Out-of-Pocket Health Expenditures 

Several recent studies have explored levels and determinants of out-of-pocket health expenditures in lowand middle-income countries, with a focus on catastrophic health spending. Out-of-pocket health 

expenditures include payment of cash or goods for direct health care services, such as medicines, 

consultation fees, and laboratory diagnostic tests. Out-of-pocket spending on health care can constitute a 

substantial portion of household expenditure. An analysis of World Health Survey data from 51 LMICs 

found health care expenditures accounted for 13% to 32% of total monthly household expenditure (Wagner 

et al. 2011). Other studies document a lower share of health care expenditures. One study found Kenyan 

households on average spend 5% of their annual budget on outpatient services and 2% on inpatient services 

(Chuma and Maina 2012). A study in Agincourt, South Africa, found that households experiencing illness 

spend about 5% of their total household expenditure on direct health care costs (Goudge et al. 2009). A 

cohort study in Pelotas, Brazil, found a large proportion of families spent more than 15% of their income 

on health care for their children (da Silva et al. 2015). Data from a national survey in India found out-ofpocket expenditure on health care constituted about 5% of total household expenditure (Garg and 

 

1

 WHO glossary of technical terms 
2 

Karan 2009). In Rajshahi, Bangladesh, monthly out-of-pocket health care expenditure comprised 11% of 

total household expenditure (Rahman et al. 2013). 

Health spending is considered catastrophic when a household has to reduce other spending to compensate 

for health expenditure. Catastrophic health expenditures can be calculated as the proportion of total income 

or effective income spent out-of-pocket on health care. Although WHO uses a 40% threshold for defining 

catastrophic health expenditures, the defined threshold for proportion of health expenditure qualifying as 

catastrophic health expenditures varies by study, typically ranging from 5% to 40% of income (Xu et al. 

2003). The proportion of the population experiencing catastrophic health expenditures varies greatly by 

country, but also by survey and method of calculation (Raban, Dandona, and Dandona 2013; van Doorslaer 

et al. 2006; Xu et al. 2003). 

1.2. Determinants of Out-of-Pocket Health Expenditures 

Out-of-pocket payments for health services are related to a number of institutional and provider-level 

factors, including inefficiencies in the delivery of health care that range from over-prescription of antibiotics 

and injections, use of expensive medicines in place of cheaper alternatives, medical errors, and incorrect 

diagnosis or treatment (World Health Organization 2010). Individual and household characteristics also 

influence out-of-pocket health expenditure. The most commonly cited covariates include age, gender, 

education, household socioeconomic status, household location, type of illness or condition and its severity, 

type of health care provider (public versus private), and health insurance coverage. 

1.2.1. Age, gender, and education 

In general, existing literature indicates that out-of-pocket health expenditures are higher for older 

individuals, women, and the more educated. A cohort study in Pelotas, Brazil found average medical 

expenditure for children decreased from age 12 to 48 months (da Silva et al. 2015). An analysis of the 

determinants of out-of-pocket expenditures in China found that individuals age 65 and older were likely to 

have higher out-of-pocket expenditures compared to younger age groups (You and Kobayashi 2011). A 

study in eight provinces in China found that, excluding maternal health expenditures, women age 20-34 

had higher curative health expenditures than men in the same age range, and women’s expenditures were 

more sensitive to family income than men’s (Gao and Yao 2006). On the other hand, a study of the 

determinants of out-of-pocket and catastrophic health expenditures in a rural community in India found that 

men were more likely to incur out-of-pocket health expenses than women (Brinda et al. 2012). In Brazil, 

better-educated mothers are more likely to report higher out-of-pocket expenditures for medicines and 

private health insurance for their children (da Silva et al. 2015). In India, two studies found higher household 

educational attainment was associated with increased spending on maternal health (Leone, James, and 

Padmadas 2013; Mohanty and Srivastava 2013). A positive association between level of education and outof-pocket expenditure was found in a study of the determinations of out-of-pocket health payments for 

malaria among child under age 5 in Uganda (Orem et al. 2013). 

1.2.2. Socioeconomic status 

Household socioeconomic status is the household characteristic most commonly associated with variation 

in out-of-pocket health expenditure. However, the relationship between income or household 

socioeconomic status and out-of-pocket expenditure can be mixed. Studies in Kenya, Namibia, Nigeria, 

Albania, Bangladesh, and India found that poorer individuals and households had lower absolute out-ofpocket expenditures on health care than wealthier households, but the relative proportion of health care 

expenditure to total or non-food household expenditure was significantly higher in poorer households 

(Chuma and Maina 2012; Gustafsson-Wright, Janssens, and van der Gaag 2011; Hotchkiss et al. 2005; 

Karan, Selvaraj, and Mahal 2014; Onwujekwe et al. 2014; Rahman et al. 2013). Two studies in Brazil and 

India found that poorer households spent a greater proportion of their income on child health care (da Silva 

et al. 2015; Dongre, Deshmukh, and Garg 2010). In Kenya out-of-pocket costs for pediatric hospitalizations 
3 

were statistically higher for poorer households compared with wealthier households (Barasa et al. 2012). 

On the other hand, an analysis of out-of-pocket expenditures in Nepal found a positive association between 

income and out-of-pocket health expenditures; income had a direct effect on out-of-pocket spending and 

had an indirect effect through provider choice (Rous and Hotchkiss 2003). A study in South Delhi, India, 

found that women in high-income areas spent approximately 10 times more on delivery and newborn care 

than those in lower-income areas (Dhar et al. 2009). In some contexts OOP payments do not appear to differ 

by households’ income or socioeconomic status. An analysis of data from Burkina Faso, Kenya, and 

Tanzania showed no significant variation in delivery expenditures by wealth quintile (Perkins et al. 2009). 

A study in Cambodia suggested out-of-pocket spending was equitable in the country, largely due to 

widespread use of the private sector across socioeconomic groups (Dalal and Aremu 2013)(19). A 

systematic review of publications on out-of-pocket expenditure in China found no association between 

socioeconomic status and tuberculosis treatment costs (Long et al. 2011). A similar finding was recorded 

by another study of out-of-pocket payments in China, which found that except for the wealthiest 5%, 

spending on health care was the same across socioeconomic groups (You and Kobayashi 2011). 

1.2.3. Location 

Location—whether the household is in an urban area or a remote rural area—can influence out-of-pocket 

health care expenditure, although the direction of the association can vary. In Kenya a national study found 

that mean spending among those with illness was significantly higher in urban than rural areas, although 

rural households spent a larger proportion of their annual budgets on health care compared with urban 

households (Chuma and Maina 2012). In Nigeria mean out-of-pocket spending on health care was higher 

in urban than rural households (Onwujekwe et al. 2014). In Albania rural clients attending Primary Health 

Centers paid less in consultation fees than those attending similar facilities in urban areas, but rural clients 

paid more in consultation fees at polyclinics (Hotchkiss et al. 2005). In India rural households paid more 

than urban households for delivery and neonatal care, regardless of socioeconomic level or state of 

residence (Leone, James, and Padmadas 2013). 

1.2.4. Type of provider 

Health care expenditure also varies by type of provider. In general, seeking care from the private sector is 

associated with higher out-of-pocket health expenditures. A multi-country literature review found use of 

private facilities and hospitalization are both associated with high out-of-pocket health expenditures (Alam 

and Mahal 2014). An analysis of 39 World Health Surveys had similar findings (Saksena et al. 2012). One 

study in Bangladesh and two studies in India found that out-of-pocket expenditures for maternal health care 

were significantly greater in private facilities compared with public facilities (Bonu et al. 2009; Leone, 

James, and Padmadas 2013; Mohanty and Srivastava 2013; Rahman et al. 2012). A study in Kasulu, 

Tanzania found that delivery at a government facility was cheaper than at a mission facility due to higher 

out-of-pocket expenditures on consultation, drugs, and diagnostics at mission facilities (Kruk et al. 2008). 

In Bangladesh out-of-pocket expenditures for antenatal care (ANC), normal delivery, and cesarean section 

were much higher in private compared with public facilities due higher consultation fees and costs for 

medicines (Rahman et al. 2012). In India average out-of-pocket expenditures for antenatal care (ANC), 

delivery, and postnatal care were two to four times higher in private than public facilities (Leone, James, 

and Padmadas 2013). In Uttar Pradesh, India, mean out-of-pocket expenditures for neonatal illness were 

statistically greater in non-government clinics and dispensaries compared with government clinics, and 

mean expenditures were greater for those who were hospitalized (Srivastava, Awasthi, and Agarwal 2009). 

A study in Ethiopia on management of uncomplicated malaria found out-of-pocket expenditures were 

higher if more than one source of care was used, and average direct costs were higher for those using private 

versus public health facilities (Deressa, Hailemariam, and Ali 2007). In Ulanga District, Uganda individuals 

seeking malaria treatment in the private sector had 19% higher direct medial costs than those in the public 

sector, although the difference was not statistically significant (Mikkelsen-Lopez et al. 2013). 
4 

1.2.5. Type of illness and severity 

More severe or complicated illnesses or conditions often result in higher out-of-pocket health expenditures. 

As mentioned above, hospitalization is associated with high out-of-pocket expenditure (McIntyre et al. 

2006). In South Delhi, India cesarean sections resulted in higher out-of-pocket expenditures than normal 

deliveries, in both public and private facilities (Dhar et al. 2009). In Matlab, Bangladesh, households spent 

almost 10 times more on complicated deliveries relative to normal deliveries (Hoque et al. 2015). In Burkina 

Faso and Kenya, costs for complicated delivery were double the costs for normal delivery (Perkins et al. 

2009). In Ecuador mean out-of-pocket expenditure for treatment of multidrug-resistant tuberculosis was 

over four times higher than treatment for non-resistant tuberculosis (Rouzier et al. 2010). 

1.2.6. User fees and health insurance coverage 

Removal of user fees, introduction of insurance schemes, and other interventions have the potential to 

reduce out-of-pocket expenditure for health care in low- and middle-income countries. User fees refer to 

any point-of-service charges required and can include payment for registration, consultation, drugs and 

medical supplies, and outpatient and inpatient health care services (Dodd et al. 2006). In Burkina Faso 

removal of user fees for children under age 5 reduced average out-of-pocket expenditure for care by more 

than 80% (Illou et al. 2015). However, catastrophic health expenditures for tuberculosis remained high in 

Burkina Faso even after the removal of user fees (Laokri et al. 2013). Studies in Indonesia, Laos, India, and 

Vietnam found that health insurance schemes reduced out-of-pocket spending for health care (Aji et al. 

2013; Alkenbrack and Lindelow 2015; Fan, Karan, and Mahal 2012; Sepehri 2014)). In Mali, Ghana, and 

Senegal, participation in a mutual health organization (MHO) reduced the risk of catastrophic health 

expenditures for hospitalization, but not out-of-pocket payments for outpatient care (Chankova, Sulzbach, 

and Diop 2008). In India a community health insurance scheme halved catastrophic health expenditures 

due to hospitalization (Devadasan et al. 2007). However, in Vietnam an insurance scheme showed no 

significant effect on out-of-pocket spending, while in Zambia prepayment insurance schemes were 

associated with increased risk of catastrophic health expenditures (Ekman 2007; Nguyen 2012). A study in 

India illustrates that health insurance that only covered inpatient care provided illegible protection against 

poverty due to health spending (Shahrawat and Rao 2012). One study on the determinants of out-of-pocket 

payments in China found that health insurance increased out-of-pocket health spending (You and 

Kobayashi 2011). 

Interventions other than insurance schemes can also affect out-of-pocket expenditure for health care. In 

Cameroon decentralization was associated with reduced prevalence of catastrophic health expenditures 

related to HIV management (Boyer et al. 2011). In Tanzania out-of-pocket health expenditures were lower 

at government facilities implementing integrated management of childhood illness and at facilities with 

cost-sharing schemes (Manzi et al. 2005). 

1.3. Overview of Health Service Delivery, Health Care Spending, and Financing in the Study 

Countries 

Government health expenditure is a core indicator of health financing systems, reflecting the relative 

contribution of public entities to total spending on health. It refers to resources collected and pooled by 

public agencies regardless of the source, so includes any donor (external) funding passing through these 

agencies. It also comprises health expenditures made by parastatals, extra-budgetary entities, and 

compulsory health insurance payments (World Health Organization). Appendix Table 1 illustrates the 

contributions of the public and private sectors to total health expenditures in the study countries. In all of 

the study countries except Namibia, the private sector accounts for close to two-thirds of total health 

expenditures.2

 Appendix Table 2 illustrates total health expenditure and out-of-pocket payments per capita 

in millions of constant 2010 US dollars (USD). While per capita health spending and out-of-pocket 

 

2

 Private sector spending includes out-of-pocket payments. 
5 

payments are relatively high in Namibia, the share of out-of-pocket payments as a percentage of total health 

expenditures is the lowest among the four countries. Except in Namibia, where direct payments by users 

account for only 7% of total health expenditures, the share of out-of-pocket spending is high, at 39% in 

DRC, 31% in Liberia, and 28% in Rwanda in 2014. 

The level of health spending and the share of financing from the public and private sector and the burden 

of out-of-pocket payments vary by country, and this variation may be related to differences in the level of 

health care coverage and delivery of services. The following section provides an overview of health service 

delivery, mechanisms for health care financing, user fees, and insurance schemes in the four study countries. 

DRC 

About one-third of health facilities in the DRC are operated by faith-based organizations (FBOs) in 

partnership with the Ministry of Health (MoH). The lack of government support has resulted in some 

facilities becoming de facto privatized, with operating costs supported exclusively by user fees. Numerous 

NGOs support facilities at the local level. Parastatal companies once operated facilities, but few remain. 

Formal for-profit small clinics can be found in major cities. Informal providers, particularly drug sellers, 

are common in many urban and rural areas (Africa Region Human and Health 2005). Health care services 

are largely funded by external resources. In 2014, external donors covered about 37% of total health 

expenditure, with a focus on basic service delivery at the community and primary level, or on priority 

diseases. The level of government and donor funding for health care varies by health zone. For example, 

some zones receive no support or only salary support from the central government (Africa Region Human 

and Health 2005), and this can have implications on the quality and cost of care. The MoH enacted a policy 

to subsidiz,e and in some cases, remove fees for vulnerable groups such as sexual abuse survivors, indigents, 

and the elderly. MoH also supports waiving user fees during states-of-emergency in conflict zones, although 

this is not always enforced. Some NGOs have piloted health programs with subsidized or zero user fees for 

target groups, and in some cases, for the general population (Maini et al. 2014). Despite efforts to reduce 

direct payments by subsidizing or removing user fees, out-of-pocket spending accounts for 39% of total 

health expenditures. There are no major insurance schemes available in the DRC. 

Liberia 

In 2007 Liberia’s national health plan stressed multi-sectoral involvement in the provision of health 

services, including NGO, FBO, and private providers (Republic of Liberia Ministry of and Social 2007). 

NGOs and private agencies support 90% of health care services (WHO Health Action in Crises 2005). 

However, health care utilization is low. For example, only 25% of the population has access to referral 

services (WHO Health Action in Crises 2005). Rural areas have fewer skilled staff and mid-to-high level 

staff are still scarce nationally (WHO African Health Observatory 2014a). External funding supports a little 

less than half of total health expenditures, and out-of-pocket spending by households accounts for about 

one-third of the total (WHO African Health Observatory 2014a). Donor funds predominantly support 

primary care services. Referral hospitals account for the greatest proportion of government expenditure 

(WHO African Health Observatory 2014a). Since 2007, all primary care services are provided free-ofcharge in public health facilities (Republic of Liberia Ministry of and Social 2007; Yates 2009). The 

Liberian Health Equity Fund (National Insurance Scheme) was put on hold in the design phase due to the 

Ebola outbreak (ThinkWell 2016). The national insurance scheme was designed to cover free basic services 

with government funding and to offer a “mid-level” package of services for enrollees (Liberia Ministry of 

Health 2013). 

Namibia 

In Namibia the public sector provides services for the great majority of the population (85%), while 

facilities in the private sector cover the remainder. Since desegregation (following the abolition of 

apartheid), there has been a push toward increasing access to services in rural areas as part of a Primary 

Health Care (PHC) approach (WHO African Health Observatory 2014b). 
6 

Public health facilities are financed primarily through taxation. Most private health care is funded through 

employer/employee contributions—implying that it is paid for through private insurance or contracts 

between businesses and health care providers and therefore is funded through monthly premium payments 

from employees and employer contributions (WHO African Health Observatory 2014b). Mission facilities 

operate with government contracts. The level of out-of-pocket health expenditure is low and declining 

(WHO African Health Observatory 2014b). Donor funding has increased since 2000 and accounts for 50% 

of Namibia’s HIV/AIDS program (WHO African Health Observatory 2014b). Half of the country’s total 

health budget is allocated to public and mission hospitals, health centers, and clinics. Only 4% of the budget 

is directed to primary health care programs. In 2010 one-third of health expenditures targeted HIV. 

Reproductive health care accounts for 10% of total health expenditures. About 54% of total health 

expenditures are related to inpatient and outpatient care, and 15% to public health programs (WHO African 

Health Observatory 2014b). 

User fees vary by health facility and condition for receiving care (WHO African Health Observatory 

2014b). HIV prevention and treatment services are provided at no cost (WHO African Health Observatory 

2014b). Fee exemptions are provided for notifiable diseases, preventive and promotive services, and for 

children under age 5 and pregnant women (WHO African Health Observatory 2014b). A number of private 

and employer-contracted health insurance schemes operate in Namibia, including the Public Service 

Employees Medical Aid Scheme (PSEMAS) for civil servants. Insurance schemes are regulated through 

the Namibia Financial Institutions Supervisory Authority (Ministry of, Social, and Macro 2010). The 

government of Namibia has been evaluating the feasibility of moving to universal coverage through a 

national health insurance scheme (WHO African Health Observatory 2014b). 

Rwanda 

Rwanda has adopted a bottom-up approach for the delivery of public primary health care, with facilities 

financed and managed by communities (Sekabaraga et al. 2011). Faith-based organizations and NGO notfor-profit facilities are considered part of the public health system and are subject to government standards. 

About 40% of facilities are owned by FBOs or NGOs, and these are mostly health centers. The role of 

private-sector health facilities is small and is concentrated in urban areas, but recent policy states an interest 

in increasing their role in the provision of services (Republic of Rwanda: Ministry of Health 2008). 

Between 2002 and 2007 public health spending in Rwanda increased threefold (Sekabaraga et al. 2011). 

The country has implemented three strategies to improve health financing: community-based insurance—

“Mutuelle de Santé,” performance-based financing, and fiscal decentralization (Sekabaraga et al. 2011). 

The Mutuelle, which was initiated in 2004 and is heavily subsided by the government, offers low and sliding 

premiums, including no premium for the poorest 25%, and covers a wide range of preventive and curative 

care services as well as delivery care (Rosenberg 2012; Saksena et al. 2011). Mutuelle premiums cover 45% 

of public sector operating costs, with the remaining amount covered by external donor funding, which is 

mostly earmarked for health insurance (Rosenberg 2012). In 2008, about 85% of the population was 

covered by the Mutuelle (Sekabaraga et al. 2011). In 2012, 96% of the population was covered by health 

insurance. Increasing insurance coverage has reduced household out-of-pocket health expenditures 

(Sekabaraga et al. 2011). 
7 

2. Data and Methods 

This study used a logistic model to estimate the correlates of health care utilization. It also used two-part 

models and generalized linear models to assess the determinants of out-of-pocket heath expenditures for 

outpatient and inpatient care in the four African study countries. 

2.1. Data 

The data used in this study come from Demographic and Health Surveys (DHS). The DHS Program has 

been providing technical assistance in the implementation of more than 300 surveys spanning more than 90 

developing countries. DHS surveys are a key source of nationally representative and comparative data on 

population and health indicators. The DHS collects standard information on household and respondent 

characteristics; environmental health; child health and nutrition; infant and child mortality; maternal health 

and morality; family planning, fertility, and fertility preferences; knowledge of HIV/AIDS; and women’s 

empowerment. 

The selection of countries for this study was carried out in two stages. In the first stage we reviewed data 

collected from 125 DHS surveys in 60 countries spanning South and Southeast Asia, Latin America and 

the Caribbean, and sub-Saharan Africa conducted between 1989 and 2016 to determine if the survey 

collected information on outpatient and inpatient care, health expenditures, and health insurance coverage. 

A total of 12 countries were identified using these criteria.3

 Country selection was further refined after 

careful review of the health expenditures modules for each of the 12 countries. At this second stage of 

country selection, the criterion of comparability was applied. The health expenditures module is countryspecific and there is substantial variability in the way the data are collected and how questions are asked. 

For example, the Cambodia DHS combined inpatient and outpatient care; the Dominic Republic DHS 

collected total cost on inpatient care during the reference period without specifying whether these costs 

pertain to the most recent visit or all visits during the reference period, and collected outpatient care cost 

only for the most severe illness. 

From among the 12 countries, we selected countries with the most recent survey data on health seeking 

behavior and health expenditures, and with largely comparable approaches for the sample selection for the 

health module and the reference period, in order to have consistency in the data across countries. This 

resulted with a smaller sample of four study countries—DRC, Liberia, Namibia, and Rwanda. The Rwanda 

DHS 2010 and DRC DHS 2013-2014 collected information on health seeking behavior and health 

expenditures for all members of the household who received inpatient care in the six months preceding the 

survey, and collected information on outpatient care in the four weeks preceding the survey. Detailed 

information was recorded for up to three outpatient visits and three inpatient admissions that occurred 

during the reference period. The Namibia DHS 2013 collected information for the last admitted household 

member for their most recent outpatient care that occurred in the four weeks preceding the survey and their 

most recent inpatient admission that occurred during the six months preceding the survey. The Liberia DHS 

2013 identified all household members who reported receiving outpatient care, but collected detailed 

information for up to three outpatient visits for only one randomly selected household member from among 

those who reported receiving and paying for outpatient care in the four weeks preceding the survey. The 

Liberia DHS 2013 collected detailed information on inpatient care for up to three admissions for all 

members of the household who received inpatient care in the six months preceding the survey. In this study, 

to facilitate cross-country comparisons we limited analysis of outpatient and inpatient care and costs to the 

most recent visit or admissions. 

 

3

 These countries included: Afghanistan, Cambodia, Dominican Republic, DRC, Honduras, Liberia, Maldives, Mali, 

Namibia, Mozambique, Philippines, and Rwanda. 
8 

2.2. Definitions of Variables 

2.2.1. Dependent variables 

The study explored the health seeking behavior of individuals and the costs associated with seeking 

outpatient and inpatient care. The DHS collected data about inpatient care by asking the household survey 

respondent to list all household members who stayed overnight in a health facility during the past six 

months. The variable for inpatient care is dichotomous and coded 0 or 1. The DHS also asked the respondent 

to list all household members who received care in the last four weeks without being hospitalized or staying 

overnight, including family planning visits, prenatal or postnatal care, child health monitoring, from a health 

care provider, pharmacist, or traditional healer. The dependent variable for outpatient care is also 

dichotomous and coded 0 or 1. 

For each health care visit, the DHS asked the household respondent to report how much was spent for 

inpatient treatment and services, including all costs for laboratory tests, drugs/medications, consultations, 

or other items related to the overnight stay. To obtain information about spending for outpatient care, the 

DHS asked the household respondent how much was spent for treatment and services, including 

consultation fees, drugs/medication, examinations, and other services.4

 In three of the four study countries, 

the expenditure data were collected and recorded in local currency, except for DRC, where data were 

converted and recorded in current US dollars (USD) using the exchange rate at the time of the survey. 

We converted health expenditures from local currency to constant 2010 USD for consistency across 

countries. We used this approach rather than exchange rates because, while exchange rates convert to a 

common currency, they do not account for differences in the purchasing power of currencies in their 

national markets (World Bank 2016b). This conversion requires the purchasing power parity (PPP) factor, 

which is the number of units of a country’s currency required to buy the same amounts of goods and services 

in the domestic market as a US dollar would buy in the United States.5

 The PPP is based on 2011 prices 

because this is the most recent year for which the most comprehensive data on the price of consumer goods 

and services is available from 199 countries.6

 To account for inflation between the survey year and 2011, 

the year for which PPP is based, the consumer price index (CPI) of the local currency for the survey month 

and the average CPI of the local currency in 2011 is required. The CPI is the most common measure of 

inflation and reflects changes in the cost to the average consumer of acquiring a basket of goods and 

services. The steps to convert health expenditure data collected in the country’ local currency units (LCU) 

to constant 2010 USD (2011 PPP adjusted to 2010 US prices) are:7

1. Convert LCU at the time of the survey to LCU at 2011 prices, by dividing by the ratio of the CPI 

for the survey month to the average CPI for 2011.8

 

4

 The DRC DHS 2010 included the cost of transportation in the total health expenditure. We subtracted 

transportation cost from the total out-of-pocket expenditures based on the itemized cost information. 5

 http://data.worldbank.org/indicator/PA.NUS.PPP. 

6

 For more information on the International Comparison Program which compiles price and expenditure data and 

estimates of PPP, refer to the following: 

http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/ICPEXT/0,,contentMDK:22377119~menuP

K:6782100~pagePK:60002244~piPK:62002388~theSitePK:270065,00.html. 7

 Because health expenditure data were converted and recorded in current US dollars (USD) using the exchange rate 

at the time of the survey, we used the exchange rate to re-convert the cost data to local currency and followed the 

same steps outlined above to then convert the figures into 2010 constant US dollars. 

8

CPI available at http://data.worldbank.org/indicator/FP.CPI.TOTL.
9 

2. Convert 2011 LCU to 2011 USD by dividing by the 2011 PPP conversion rate for the country’s 

currency.9

3. Convert USD in 2011 prices to USD in 2010 prices by dividing by 1.032, which is the ratio of US 

CPI for 2011 to US CPI in 2010.10 

2.2.2. Explanatory variables 

We assessed the association between a number of individual, household, and community characteristics 

and whether or not individuals sought health care, and the out-of-pocket spending associated with this care. 

We based our selection of covariates for health care use on the model developed by Anderson and Newman 

(Andersen and Newman 1973). Anderson and Newman’s model distinguishes between predisposing factors 

(e.g. age, sex, past illness, education, occupation, attitudes about health services, and knowledge about 

disease); enabling factors (e.g., income, health insurance coverage, access to health care providers; cost of 

health services, and other community level factors); and needing factors (e.g., disability, illness, diagnosis). 

We controlled for a variety of variables including: sex, age and education of the individual who received 

care; whether the individual slept under a long-lasting insecticide-treated net (LLIN); whether the 

individual was covered by health insurance; sex, age, and education of the head of the household; the 

household wealth quintile; whether the household practiced correct water treatment; whether the household 

accessed an improved water source; whether the household used an improved, not shared sanitation facility; 

and the number of household members. We also included regional dummies and a measure of whether the 

household was located in an urban area (coded 0 or 1) based on country–specific definitions. The regional 

dummies are expected to capture variations in the availability and accessibility of care, doctors’ fees, and 

price differences in health services. Our selection of these covariates was guided by Anderson and 

Newman’s model, by empirical evidence from the literature (Brinda et al. 2012; Jung and Liu Streeter 2015; 

Orem et al. 2013; Rous and Hotchkiss 2003; Van Minh et al. 2013; You and Kobayashi 2011), as well as 

by the availability of data for the four study countries. 

The sex, age, and level of the individual are predisposing factors for use of health care and are correlated 

with out-of-pocket health expenditures. Women, especially women of childbearing age, may be more 

predisposed to using health care services compared with men, and their expenditures may be more sensitive 

to income than is the case of men. Infants and younger children, women of childbearing age, and the elderly 

may be more inclined to seek health care services and their health expenditures may be higher than those 

of adolescents and working-age men. Individuals who are more educated are more likely to understand the 

benefits of preventative care or capable of identifying symptoms and seeking health care compared with 

individuals who are illiterate or less educated, and they are also more likely to be able to afford health care. 

Household water and sanitation facilities are also predisposing factors and are associated with the 

prevalence of morbidity and mortality, especially diarrheal disease, and therefore influence the likelihood 

of needing health care services. Correct water treatment includes chlorination, filtration, solar disinfection, 

or boiling. An improved water source is protected from outside contamination, particularly contamination 

from fecal matter, and includes water piped into dwelling, plot, or yard; public tap or standpipe; tube well 

or borehole; protected dug well; protected spring; or rainwater collection. An improved sanitation facility 

is not shared with other households and can be any of the following: flush or pour-and-flush connected to 

a piped sewer system or septic system; pit latrine or pit latrine on a slab; composting toilet; or a ventilated 

improved pit latrine. 

 

9

 PPP conversion factor, private consumption (LCU per international$), 2011 International Comparison Program. 

Available at http://data.worldbank.org/indicator/PA.NUS.PRVT.PP 10 2011 CPI annual average for US dollar = 224.939 (http://www.bls.gov/cpi/cpid05av.pdf), 2010 CPI annual 

average for US dollar= 218.06 (http://www.bls.gov/cpi/cpid10av.pdf). 224.939/218.06= 1.032. 
10 

The sex, age, and socioeconomic level of the head of household are enabling factors for the use of health 

services and are correlated with out-of-pocket health expenditures. Individuals living in households headed 

by educated, working-age men are be more likely to know about and to have access to health care services 

and to afford health care compared with those living in female-headed households or households headed 

by individuals without formal education or employment. The effects of insurance coverage, an enabling 

factor, on the cost of care is mixed; depending on the nature of the insurance scheme, it may create 

incentives for providers to require more testing and may result in higher payments, or it may lower direct 

payments from clients. Individuals who live in an urban area may be more likely to use health care services 

than those living in remote rural areas, where accessing clinics, hospitals, and health care providers may 

require travelling long distances. The availability of health care services, their quality, efficiency, and cost 

vary by region. 

Malaria is a key disease in the study countries, and is needing factor. Individuals who sleep under a LLIN 

may be less likely to contract malaria and require health care. Individuals with health insurance may be 

more likely to seek health care than those without insurance coverage. 

Our analysis also explored health care utilization and cost of care by provider type and reason for care. 

Public providers include government hospital, government health center, government health clinic, health 

post, community health worker, and other public health care providers such as maternity, mobile clinic. 

Private providers include private hospitals or clinics, pharmacies, doctors, mobile clinics, Planned 

Parenthood Associations, or any other types of providers in the private sector. Other service providers 

include shops, traditional practitioners, mobile vendors, black baggers or drug peddlers, among others. For 

inpatient care, we grouped reason for seeking care into the following categories: Pregnancy/delivery and 

postnatal care services; fever and malaria; and other illnesses. For outpatient care, we grouped reason for 

seeking care into the following categories: sexual and reproductive health care11; malaria; fever; diarrhea; 

respiratory illness; intestinal worms; accident or injury; vaccination/regular check-up/preventive care; and 

other illnesses. 

2.3. Statistical Methods 

Analyzing health expenditures entails methodological challenges: data are heavily right-skewed due to the 

presence of outliers; there may be a large number of observations with zero costs; and data may be censored 

(Gregori et al. 2011). Different models are available to address skewness and excess zeroes, but no unique 

model is capable of dealing with all issues simultaneously (Gregori et al. 2011; Mihaylova et al. 2011). The 

choice of model depends on the data (Gregori et al. 2011), and simpler models are preferable to more 

complex models, as they have been proven effective (Mihaylova et al. 2011). 

Log models are often used to deal with skewed data, but can lead to bias of ordinary least squares (OLS) 

estimators (Manning and Mullahy 2001). In generalized linear models (GLM) log models can lead to loss 

of precision, and lack of precision is an issue with health data because of the high variance in use and 

expenditures (Manning and Mullahy 2001). Log models are not robust to outliers and require back 

transformations to the raw scale (Gregori et al. 2011; Mihaylova et al. 2011). Transformation cannot be 

used if there are zeros in the data, and it is not ideal to replace a zero by adding a constant; the choice of 

constant is arbitrary, and this approach assumes that people with zero costs are similar to those with positive 

costs (Gregori et al. 2011; Mihaylova et al. 2011). To avoid back transformations, a GLM approach can be 

adopted and the identity-link can be specified (Mihaylova et al. 2011). Where there is a large number of 

zeros, a two-part model can be used (Mihaylova et al. 2011). Two-part models (sequential decision models) 

are appropriate when zeros are genuine rather than unobserved and participation and spending occur in 

sequential order (the individual uses health services and then pays out-of-pocket) (Humphreys 2013). 

 

11 Sexual and reproductive health care refers to the following: family planning, prenatal care, delivery or postnatal 

care, and HIV/AIDS or STDs.
11 

Hurdle models (joint decision models) are similar to two-part models but require that participation and 

consumption occur simultaneously (Humphreys 2013). 

Because of the presence of cases with zero health costs, we adopted the approach of a two-part model to 

estimate the correlates of out-of-pocket expenditures for outpatient and inpatient care. In the first part we 

used a probit model to estimate the odds of having any health expenditures. In the second part we used a 

GLM model with the gamma distribution and log-link to estimate the magnitude of out-of-pocket 

expenditures because health expenditure data are highly skewed (Dodd et al. 2006). The two-part model 

does not address self-selection due to unobserved covariates. Therefore our results may be biased due to 

sample selection based on unobservable characteristics. Because the Liberia DHS 2013 collected 

information on health spending only for individuals who reported having any health expenditures, we use 

a GLM model rather than a two-part model to estimate out-of-pocket health expenditures. 

13 

3. Results 

This section starts with a description of the analysis sample by individual, household, and community 

background characteristics in each country. It then presents results on household members’ use of inpatient 

and outpatient health care services in the reference period preceding the survey (six months for inpatient 

care and four weeks for outpatient care), including overall level of use, use by type of provider, and reasons 

for the last visit, as well as by background characteristics. Lastly, it shows results of the multivariate analysis 

for use of health care services and out-of-pocket health spending for the most recent inpatient and outpatient 

visit. 

3.1. Description of Household Members Included in the Analysis 

In each country the DHS survey collected information for all household members on use of inpatient care 

services in the six months preceding the survey and use of outpatient care service in the four weeks 

preceding the survey. This analysis focused on de jure household members (usual residents of the 

household) and excluded de facto-only members (guests who stayed in the household last night) because 

the household interview respondent was expected to be less knowledgeable about de facto-only members’ 

care seeking behavior and associated expenditures. The total number of de jure household members for 

whom information was collected ranged from 41,665 in Namibia to 96,337 in DRC. Table 1 shows the 

relevant background characteristics of the sample in each country. 

The four countries shared a similar sex distribution among household members, a slightly higher percentage 

of males than females. Between 40% and 49% of household members were age 15-49. In all four countries 

younger groups (age 0-5 and 5-14) made up a substantial proportion of the household population, but only 

a small proportion of household members were age 50 or older. Educational attainment varied by country. 

In Liberia more than half of household members had no education, while in the other three countries 

individuals with no education accounted for one-third or less of the population. The highest percentage of 

household members with a secondary or higher education was in Namibia, at 39%, and the lowest was in 

Rwanda, at 9%. Use of treated mosquito nets the night before the survey also varied substantially across 

countries, from 4% in Namibia to 57% in Rwanda. 

Although all four countries included health insurance information in their DHS surveys, the approach to 

collecting this information varied by country. In Rwanda a question about insurance was asked for all 

household members, while in DRC and Liberia the surveys collected information only for household 

members who received inpatient or outpatient care services. In Namibia health insurance information was 

collected for women age 15-49 and men age 15-64 who participated in individual interviews. Table 1, 

therefore, presents health insurance coverage only for Rwanda. More than two-thirds of household members 

in Rwanda reported coverage by health insurance in 2010, of whom most were covered by communitybased health insurance. Recent experience of injury or illness was also collected for all household members 

in Rwanda but not in other countries. About 11% of household members in Rwanda reported experience of 

illness or injury in the four weeks before the survey. 

Table 1 also presents select household characteristics of the sample in each country that are theoretically 

and empirically correlated with health seeking behavior and out-of-of-pocket health spending. In all four 

countries most individuals live in households headed by men, but in Namibia the percentage of femaleheaded households is nearly as high as the percentage headed by men, at 47% and 53%, respectively. In 

Liberia and Namibia about half of individuals live in households headed by a person with either no formal 

education or a primary education, and in Rwanda, 88%. In DRC the level of education is higher; about 60% 

of individuals live in households where the household head has a secondary education or higher. In DRC, 

however, only half of household members have access to improved water compared with 70-84% in the 

other three countries. Correct treatment of water is uncommon in the study countries, with the highest 
14 

prevalence in Rwanda, where half of household members reside in households with correct treatment of 

drinking water. Similarly, in all four study countries access to an improved sanitation facility that is not 

shared is rare (DRC, 21%; Liberia, 25%; Namibia, 12%; and Rwanda 11%). 

Table 1. Percent distribution of de jure household members by individual, household, and 

community background characteristics 

 DRC Liberia Namibia Rwanda

 % Number % Number % Number % Number

Individual characteristics 

Sex 

Male 48.6 46,867 49.9 23,074 47.7 19,873 47.4 26,327

Female 51.4 49,470 50.1 23,141 52.3 21,791 52.6 29,259

Age 

0-4 19.7 18,936 15.9 7,343 13.6 5,678 16.1 8,971

5-14 31.2 30,084 29.4 13,590 24.4 10,186 28.4 15,792

15-49 39.6 38,134 43.4 20,081 49.0 20,432 45.3 25,199

50-64 6.9 6,688 7.6 3,519 7.5 3,143 7.0 3,869

65+ 2.6 2,493 3.5 1,635 5.3 2,196 3.1 1,742

Missing 0.0 1 0.1 47 0.1 30 0.0 12

Education 

None 32.7 31,463 51.3 23,731 26.7 11,137 34.2 19,029

Primary 38.4 36,951 24.7 11,418 33.3 13,888 56.8 31,590

Secondary or higher 28.7 27,697 23.8 11,016 39.0 16,262 8.7 4,848

Missing 0.2 226 0.1 51 0.9 378 0.2 118

Slept under a LLIN 

No 51.1 49,184 69.5 32,126 96.4 40,170 43.4 24,147

Yes 48.9 47,153 30.5 14,090 3.6 1,495 56.6 31,438

Health insurance coverage 

No -- -- -- -- -- -- 31.1 17,311

Yes -- -- -- -- -- -- 68.9 38,275

Experience of injury/ 

illness in last 4 weeks 

 

No -- -- -- -- -- -- 88.2 49,372

Yes -- -- -- -- -- -- 11.2 6,214

Household characteristics 

Sex of household head 

Male 79.6 76,654 64.7 29,893 53.2 22,162 71.9 39,980

Female 20.4 19,683 35.3 16,322 46.8 19,503 28.1 15,606

Age of household head 

<24 4.2 4,011 5.4 2,511 4.0 1,677 4.0 2,241

25-34 22.8 21,987 22.0 10,168 16.2 6,749 25.0 13,889

35-44 29.2 28,125 27.6 12,759 23.3 9,718 25.3 14,063

45-54 22.1 21,310 22.2 10,253 21.3 8,856 24.3 13,521

55-64 14.1 13,563 12.7 5,880 13.7 5,717 12.4 6,909

65+ 7.6 7,341 10.0 4,636 21.3 8,875 8.9 4,945

Missing 0.0 8 0.1 52 0.0 18

Education of household head 

None 11.3 10,874 34.8 16,097 19.7 8,199 28.4 15,768

Primary 29.1 28,050 19.0 8,783 31.3 13,060 60.1 33,399

Secondary or higher 59.3 57,155 46.2 21,336 47.3 19,716 11.3 6,297

Missing 0.3 258 1.7 690 0.2 121

Household wealth status 

1st quintile 20.0 19,268 20.0 9,245 20.0 8,339 20.0 11,116

2nd quintile 20.0 19,290 20.0 9,234 20.0 8,331 20.0 11,119

3rd quintile 20.0 19,244 20.0 9,244 20.0 8,343 20.0 11,119

4th quintile 20.0 19,266 20.0 9,248 20.0 8,325 20.0 11,114

5th quintile 20.0 19,269 20.0 9,244 20.0 8,327 20.0 11,117

Continues 
15 

Table 1—Continued 

 DRC Liberia Namibia Rwanda

 % Number % Number % Number % Number

Household characteristics 

Access to improved water 

No 49.6 47,823 29.7 13,739 16.0 6,658 26.4 14,685 

Yes 50.4 48,514 70.3 32,476 84.0 35,007 73.6 40,900 

Correct treatment of water 

No 96.3 92,741 88.4 40,851 91.0 37,907 49.9 27,736 

Yes 3.7 3,596 11.6 5,365 9.0 3,758 50.1 27,849 

Access to improved sanitation facility 

Non improved 58.9 56,713 57.4 26,520 54.1 22,531 24.3 13,488 

Improved but shared 20.5 19,728 17.2 7,954 34.3 14,276 62.3 34,632 

Improved not shared 20.7 19,896 25.4 11,741 11.7 4,858 13.4 7,465 

Community-level characteristics 

Residence 

Rural 65.8 63,389 56.2 20,234 53.3 22,207 86.6 48,142 

Urban 34.2 32,948 43.8 25,982 46.7 19,458 13.4 7,444 

Region 

Kinshasa 8.9 8,614 -- -- -- -- -- --

Bandundu 15.8 15,227 -- -- -- -- -- --

Bas-congo 4.4 4,243 -- -- -- -- -- --

Equateur 13.7 13,198 -- -- -- -- -- --

Kasai-occidental 6.7 6,436 -- -- -- -- -- --

Kasai-oriental 10.4 10,018 -- -- -- -- -- --

Katanga 10.4 10,020 -- -- -- -- -- --

Maniema 3.4 3,299 -- -- -- -- -- --

Nord-kivu 8.5 8,208 -- -- -- -- -- --

Orientale 10.1 9,692 -- -- -- -- -- --

Sud-kivu 7.7 7,381 -- -- -- -- -- --

North Western -- -- 9.7 4,505 -- -- -- -- 

South Central -- -- 47.3 21,857 -- -- -- -- 

South Eastern A -- -- 6.4 2,940 -- -- -- -- 

South Eastern B -- -- 7.1 3,291 -- -- -- -- 

North Central -- -- 29.5 13,622 -- -- -- -- 

Caprivi -- -- -- -- 5.2 2,181 -- -- 

Erongo -- -- -- -- 7.4 3,083 -- -- 

Hardap -- -- -- -- 3.5 1,451 -- -- 

Karas -- -- -- -- 3.6 1,482 -- -- 

Kavango -- -- -- -- 10.3 4,308 -- -- 

Khomas -- -- -- -- 18.5 7,697 -- -- 

Kunene -- -- -- -- 3.1 1,288 -- -- 

Ohangwena -- -- -- -- 11.7 4,861 -- -- 

Omaheke -- -- -- -- 2.7 1,144 -- -- 

Omusati -- -- -- -- 11.6 4,829 -- -- 

Oshana -- -- -- -- 7.9 3,306 -- -- 

Oshikoto -- -- -- -- 8.4 3,483 -- -- 

Otjozondjupa -- -- -- -- 6.1 2,553 -- -- 

Kigali city -- -- -- -- -- -- 9.8 5,459 

South -- -- -- -- -- -- 24.3 13,534 

West -- -- -- -- -- -- 24.5 13,624 

North -- -- -- -- -- -- 16.9 9,413 

East -- -- -- -- -- -- 24.4 13,555 

Total number of de jure household 

members 96,337 46,215 41,665 55,585 
16 

Table 1 also shows the distribution of household members by urban and rural residence and by region of 

the country. The majority of individuals live in rural areas in DRC (66%) and Rwanda (87%), while the 

samples are divided almost evenly between urban and rural areas in Liberia and Namibia. 

3.2. Use of Inpatient and Outpatient Care Services 

3.2.1. Use of inpatient and outpatient care service, providers, and reasons for care 

Figure 1 presents the percentage of household members in DRC, Liberia, Namibia, and Rwanda who 

received inpatient care in the six months preceding the interview, and outpatient care services in the four 

weeks preceding the interview. In general, health care utilization is highest in Namibia and lowest in DRC. 

In Namibia 18% of household members sought inpatient care during the reference period, while the level 

was lower in the other three countries, at 4% in DRC, 5% in Liberia, and 4% in Rwanda. In all four countries 

outpatient care was more commonly reported than inpatient care. The highest level of outpatient care was 

in Namibia, at 41%, and the lowest was in DRC, at 7%. About one in every five Liberians and one in every 

10 Rwandese received outpatient care in the four weeks before the survey. 

Figure 1. Percentage of household members who received inpatient and outpatient care services 

during the reference period 

Figures 2 and 3 show the percent distribution of household members who received outpatient and inpatient 

health care services, respectively, by type of provider for the most recent visit. As Figure 2 shows, in DRC, 

Namibia, and Rwanda inpatient care was predominantly sought at public health facilities. In Rwanda almost 

all household members (97%) received their most recent inpatient care from a public health facility. Private 

health facilities such as private hospitals or private clinics were more commonly reported as the source of 

the most recent inpatient care in Liberia (at 41%) than in the other three countries. 

In all four study countries the use of private providers for health care was more commonly reported for 

outpatient services. As Figure 3 shows, for example, 44% of household members in DRC and 65% in 

Liberia reported receiving the most recent outpatient care from a private provider. Although treatment in 

private sector facilities was more common for outpatient care than inpatient care in Namibia (20%) and 

Rwanda (15%), the public sector was still the primary provider of outpatient care. In some cases, individuals 

reported seeking care from other providers including shops, traditional practitioners, mobile vendors, black 

beggars or drug peddlers, and any other sources of care. Care from informal or traditional healers was more 

commonly sought for outpatient care in DRC (6%) and Liberia (14%) compared with the other two 

countries, where less than 1% of care was provided by this type of provider. 

4 5

18

4

7

22

41

11

0

10

20

30

40

50

DRC Liberia Namibia Rwanda

inpatient outpatient
17 

Figure 2. Percent distribution of household members who received inpatient care by providers of 

the most recent care visit 

Figure 3. Percent distribution of household members who sought outpatient care by providers of 

the most recent care visit 

75

58

89

97

25

41

11

DRC Liberia Namibia Rwanda

Public Private Other Missing

Note: Percent at 2% or lower not shown.

49

21

79 80

44

65

20 15

6

14

5

DRC Liberia Namibia Rwanda

Public Private Other Missing

Note: Percent at 2% or lower not shown.
18 

Reasons for the most recent inpatient admission and outpatient visit were collected for household members 

who received care. Response options provided to the respondent in the survey varied across countries. 

Figure 4 indicates that in all countries a large proportion of household members reported “other illness” 

without further disaggregation by type. In Liberia, Namibia, and Rwanda the second most commonly 

reported reason for inpatient care was sexual and reproductive health, at 8%, 20%, and 35%, respectively. 

In DRC more than one-third of individuals sought inpatient care because of fever and malaria. In all four 

countries a small percentage of individuals sought inpatient care because of accident or injury, ranging from 

4% in DRC to 7% in Namibia. 

Figure 4. Percent distribution of household members who sought inpatient care by reasons for the 

most recent care 

13 8

20

35

38

9

44

85

62

58

4 6 7 6

DRC Liberia Namibia Rwanda

Pregnancy/delivery/postnatal care Fever/malaria

Other illness Accident/injury/other

DK/missing

Note: Percent at 2% or lower not shown.
19 

In all four study countries a wider range of reasons for care was reported for outpatient care than inpatient 

care. Figure 5 illustrates that malaria was a commonly reported reason for outpatient care in DRC (31%) 

and Liberia (40%). A notable proportion of individuals (DRC, 25%; Liberia, 14%; and Namibia, 14%) 

reported “fever” as the reason for their most recent outpatient visit, of which some cases could be 

undiagnosed malaria. In Rwanda and Namibia more than half of individuals listed “other illness” as the 

reason for the most recent outpatient care. Additionally, in Rwanda about 12% reported respiratory illness, 

and 11% reported intestinal worms. Preventive care is uncommon but was more commonly reported in 

Namibia (10%) and Rwanda (9%) than the other two countries. In all four countries reproductive health 

reasons were least commonly reported, at 2-4%, followed by diarrhea, at 3-6%. 

Figure 5. Percent distribution of household members who sought outpatient care by reasons for 

the most recent care 

3.2.2. Use of inpatient and outpatient care services by background characteristics 

Table 2 reports the percentage of household members who sought health care services in the specified 

reference periods preceding the survey (six months for inpatient care and four weeks for outpatient care) 

by individual, household, and community background characteristics. 

In DRC differentials in use of health care services by background characteristics exist but are not substantial 

given the overall low level of use—4% for inpatient care services and 7% for outpatient care services. 

Female household members were more likely than men to receive both inpatient and outpatient care, 

although the gap is less than 2 percentage points. The youngest age group and oldest groups were more 

likely to receive both inpatient and outpatient care. For example, 10% of household members age 0-4 and 

65+ received outpatient care in the four weeks preceding the interview. Individuals age 5-14 were least 

likely to receive health care services. Use of health care services also varied by individual educational 

attainment. Use of inpatient and outpatient care services was highest among individuals with no education. 

This may be partially due to the fact that, as Table 2 shows, a relatively large proportion of those receiving 

care consisted of household members age 0-5, who were unlikely to have received any education. 

Household members who slept under a mosquito net the night before the survey were more likely to seek 

inpatient and outpatient care. 

3 3 4

31

40

3

18

25

14

14

5

6 6

6

3

3

5 4

10 9

30 33

58 57

DRC Liberia Namibia Rwanda

Sexual & reproductive health Malaria

Fever Diarrhea

Accident/injury Preventive care

Other illness DK/missing

Note: Percent at 2% or lower not shown.
20 

Table 2. Percentage of household members who received health care services in the reference period 

preceding the survey by individual, household, and community background characteristics 

 DRC Liberia Namibia Rwanda 

 Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient

Individual characteristics 

Sex 

Male 3.2 6.8 4.1 20.5 17.8 39.8 2.2 10.1 

Female 4.8 7.8 5.4 23.8 18.3 42.1 4.7 12.6 

Age 

0-4 5.5 10.1 5.9 31.1 22.8 42.8 3.6 19.5 

5-14 2.4 5.3 2.7 16.5 18.5 43.8 1.0 6.6 

15-49 4.4 7.0 5.3 21.7 16.7 38.6 5.0 10.7 

50-64 4.6 9.4 5.5 25.2 17.2 41.1 3.6 14.3 

65+ 5.5 9.5 7.8 27.6 17.9 46.1 5.0 17.6 

Education 

None 5.1 9.0 4.8 24.6 20.3 41.3 3.6 14.8 

Primary 3.4 6.0 3.9 18.4 18.2 43.3 3.4 9.3 

Secondary or higher 3.7 7.3 5.4 20.9 16.4 39.0 3.9 11.8 

Slept under a LLIN 

No 3.7 6.8 4.6 21.0 18.1 40.8 2.7 9.2 

Yes 4.4 7.9 5.0 24.7 17.7 48.3 4.1 13.1 

Health insurance 

coverage 

No -- -- -- -- -- -- 1.6 7.2 

Yes -- -- -- -- -- -- 4.4 13.3 

Experience of injury/ 

illness in last 4 weeks 

 

No -- -- -- -- -- -- 3.0 4.2 

Yes -- -- -- -- -- -- 7.3 69.0 

Household characteristics 

Sex of the head of the 

household 

Male 4.1 7.2 4.5 22.0 17.8 39.9 3.6 11.5 

Female 3.8 7.8 5.1 22.5 18.4 42.3 3.2 11.3 

Age of household head 

13-24 5.0 8.5 7.2 26.4 12.8 28.6 7.0 12.2 

25-34 4.9 8.3 5.3 23.8 17.4 36.0 4.8 13.5 

35-44 4.3 7.3 4.2 22.4 18.3 37.2 3.0 11.2 

45-54 3.2 7.2 4.5 20.1 15.4 39.4 2.7 9.4 

55-64 3.3 6.3 4.5 20.8 19.7 45.2 3.0 11.2 

65+ 3.6 6.3 4.4 22.0 20.7 50.3 2.9 11.7 

Education of household 

head 

None 3.5 7.2 4.3 21.4 19.7 43.5 3.1 9.6 

Primary 4.2 6.5 5.3 24.5 18.2 44.3 3.7 11.9 

Secondary or higher 4.0 7.8 4.9 21.7 17.1 37.8 3.7 13.6 

Household wealth status 

1st quintile 4.0 7.0 4.2 25.0 19.2 42.4 3.8 11.6 

2nd quintile 3.6 7.2 4.9 22.9 18.7 39.6 3.7 10.4 

3rd quintile 4.5 7.1 5.2 22.2 17.4 44.1 3.6 10.3 

4th quintile 4.6 7.6 4.5 21.7 19.7 39.4 3.3 11.0 

5th quintile 3.4 7.8 4.9 19.0 15.4 39.6 3.2 13.8 

Continues 
21 

Table 2—Continued 

 DRC Liberia Namibia Rwanda 

 Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient

Household characteristics 

Access to improved 

water 

No 3.7 7.1 4.9 22.1 21.4 48.5 3.4 10.0 

Yes 4.3 7.6 3.9 22.9 17.4 39.6 3.6 12.0 

Correct treatment of 

water 

 

 

No 4.0 7.3 4.9 22.1 17.7 40.5 3.3 10.5 

Yes 3.4 8.5 3.9 22.9 21.9 46.4 3.7 12.3 

Access to improved 

sanitation facility 

Non improved 4.1 7.4 4.8 23.9 18.9 43.6 3.7 11.5 

Improved but shared 3.8 6.8 4.5 17.8 17.4 39.0 3.5 11.2 

Improved not shared 4.0 7.8 4.8 21.1 15.9 35.0 3.5 12.4 

Community-level variables 

Residence 

Urban 3.6 8.4 4.6 20.6 17.6 36.6 3.3 13.5 

Rural 4.2 6.8 4.9 24.1 18.5 44.9 3.6 11.1 

Region 

Kinshasa 2.7 8.8 -- -- -- -- -- -- 

Bandundu 3.4 6.8 -- -- -- -- -- -- 

Bas-congo 3.8 8.2 -- -- -- -- -- -- 

Equateur 4.4 9.0 -- -- -- -- -- -- 

Kasai-occidental 3.9 11.2 -- -- -- -- -- -- 

Kasai-oriental 2.2 5.6 -- -- -- -- -- -- 

Katanga 3.1 6.2 -- -- -- -- -- -- 

Maniema 4.5 6.2 -- -- -- -- -- -- 

Nord-kivu 5.2 7.2 -- -- -- -- -- -- 

Orientale 4.8 5.1 -- -- -- -- -- -- 

Sud-kivu 7.8 7.3 -- -- -- -- -- -- 

North Western -- -- 3.8 22.7 -- -- -- -- 

South Central -- -- 4.7 20.9 -- -- -- -- 

South Eastern A -- -- 5.8 29.7 -- -- -- -- 

South Eastern B -- -- 6.6 29.8 -- -- -- -- 

North Central -- -- 4.5 20.5 -- -- -- -- 

Caprivi -- -- -- -- 18.8 47.8 -- -- 

Erongo -- -- -- -- 16.1 35.6 -- -- 

Hardap -- -- -- -- 17.2 25.1 -- -- 

Karas -- -- -- -- 17.0 40.6 -- -- 

Kavango -- -- -- -- 26.9 42.1 -- -- 

Khomas -- -- -- -- 14.8 32.7 -- -- 

Kunene -- -- -- -- 15.3 21.4 -- -- 

Ohangwena -- -- -- -- 16.5 51.4 -- -- 

Omaheke -- -- -- -- 14.5 36.9 -- -- 

Omusati -- -- -- -- 18.5 53.6 -- -- 

Oshana -- -- -- -- 18.6 42.4 -- -- 

Oshikoto -- -- -- -- 17.3 44.0 -- -- 

Otjozondjupa -- -- -- -- 21.4 36.9 -- -- 

Kigali city -- -- -- -- -- -- 3.4 13.9 

South -- -- -- -- -- -- 3.1 13.7 

West -- -- -- -- -- -- 4.5 10.4 

North -- -- -- -- -- -- 3.3 10.3 

East -- -- -- -- -- -- 3.1 10.0 

Total 4.0 7.3 4.7 22.2 18.1 41.0 3.5 11.4 
22 

In terms of household characteristics, individuals in households with younger household heads were more 

likely to receive inpatient and outpatient care. Higher education level of the head of the household was also 

associated with more use of inpatient care, although an association was not obvious for outpatient care. Use 

of health care services was not substantially different by household wealth status, access to improved water 

sources, and access to improved sanitation facility. Use of health care services varied by region, from 2% 

in the Kasai-oriental region to 8% in the Sud-kivu region for inpatient care, and from 5% in the Orientale 

region to 11% in the Kasai-occidental region for outpatient care. Individuals in urban areas were more likely 

to use outpatient care but less likely to use inpatient care compared with those in rural areas. 

In Liberia 5% of household members received inpatient care and 22% receive outpatient care. Differentials 

in the use of inpatient care by background characteristics were small. Similar to DRC, female household 

members and both the youngest and oldest age groups were more likely to receive inpatient care. Age and 

sex patterns for outpatient and inpatient care were similar. Regional variation in the use of outpatient care 

was considerable, ranging from 20% in North Central to 30% in both South Eastern regions. 

Namibia had the highest use of inpatient and outpatient services, at 18% and 41%, respectively. The patterns 

in DRC were similar to those in Namibia in use of health service by age, sex, and education. In Namibia 

the insured were more likely to use outpatient services but less likely to use inpatient services. Unlike the 

other countries, Namibians in households headed by younger members were less likely to seek care 

compared with those in households headed by older members. Access to an improved water source and 

access to an improved sanitation facility that is not shared was associated with less use of inpatient and 

outpatient services in Namibia. In Namibia urban and rural differences were more prominent in the use of 

outpatient than inpatient care: 45% of rural residents compared with 37% of urban residents reported 

receiving outpatient care during the reference period. 

In Rwanda differentials in use of health services were more notable for outpatient than inpatient care. 

Household members with health insurance were more likely than those without insurance to receive both 

inpatient care (4% versus 2%) and outpatient care (13% versus 7%). Recent experience of illness and injury 

was also associated with higher level of use of health care services. Variations associated with household 

and community characteristics were less remarkable in Rwanda than in the other three countries. 

Tables 3-6 present the results of the logistic regression predicting the odds of using inpatient and outpatient 

health care in each of the study countries. In most cases, after adjusting for other explanatory variables the 

associations between the use of health care services and background characteristics are similar to those of 

the unadjusted (bivariate) associations described above. In general, female household members were more 

likely than men to seek both inpatient and outpatient care services. Children under age 5 and household 

members age 65 or older were more likely to receive both inpatient and outpatient care compared with other 

age groups. Generally, there was not a significant association between an individual’s education level and 

use of health care services; however, the household head’s higher educational attainment was associated 

with more use of inpatient care in DRC and more use of outpatient care in Liberia and Rwanda. In Rwanda, 

where data on health insurance were available for all household members, the analysis found that health 

insurance coverage increased the likelihood of using both inpatient and outpatient care services. A positive 

significant association between household wealth status and use of health care services was found only for 

inpatient care in Liberia, and for outpatient care in Namibia and Rwanda. After controlling for other 

covariates, urban-rural differences in use of health services were not significant except for outpatient care 

in DRC, where the odds of seeking outpatient care services among urban residents were 40% higher than 

for rural residents. In all countries regional variations in use of health care remained the same after 

controlling for other variables. 
23 

Table 3. Results of logistic regression predicting the odds of health care utilization, 

DRC DHS 2013 

 Inpatient care Outpatient care 

 OR 95% CI OR 95% CI 

Sex (ref.: male) 

Female 1.47*** 1.34 - 1.63 1.15*** 1.09 - 1.22 

Age (ref.: 0-4) 

5-14 0.51*** 0.42 - 0.61 0.57*** 0.49 - 0.65 

15-49 1.00 0.83 - 1.20 0.71*** 0.62 - 0.82 

50-64 1.28 0.99 - 1.66 1.11 0.94 - 1.31 

65+ 1.42* 1.07 - 1.90 1.19 0.92 - 1.56 

Education (ref.: none) 

Primary 0.87 0.73 - 1.02 0.92 0.82 - 1.03 

Secondary or higher 0.77* 0.63 - 0.94 0.90 0.79 - 1.03 

Slept under a LLIN (ref.: did not sleep under 

an LLIN) 1.10 0.98 - 1.23 1.09 0.99 - 1.20 

Female headed-household (ref.: male-headed) 1.01 0.84 - 1.22 1.05 0.94 - 1.19 

Age of household head (ref.: <24) 

25-34 1.11 0.88 - 1.40 1.03 0.84 - 1.26 

35-44 1.05 0.82 - 1.35 1.00 0.79 - 1.26 

45-54 0.78 0.59 - 1.03 0.99 0.76 - 1.28 

55-64 0.72* 0.54 - 0.95 0.78 0.61 - 1.01 

65+ 0.82 0.58 - 1.15 0.73* 0.53 - 0.99 

Education of household head (ref.: none) 

Primary 1.41* 1.01 - 1.97 0.93 0.78 - 1.12 

Secondary or higher 1.51* 1.07 - 2.14 1.10 0.92 - 1.33 

Household wealth status (ref.: 1st quintile) 

2nd quintile 0.88 0.72 - 1.09 1.11 0.95 - 1.30 

3rd quintile 1.05 0.79 - 1.41 1.09 0.93 - 1.27 

4th quintile 1.01 0.77 - 1.32 1.03 0.85 - 1.26 

5th quintile 0.95 0.68 - 1.31 0.86 0.63 - 1.16 

Household practices correct water treatment 

(ref.: incorrect water treatment) 0.91 0.64 - 1.29 1.08 0.84 - 1.39 

Household accesses an improved water source 

(ref.: unimproved water source) 1.13 0.96 - 1.33 1.01 0.85 - 1.21 

Household sanitation facility (ref.: unimproved 

sanitation facility) 

Improved but shared 0.99 0.83 - 1.17 1.04 0.90 - 1.21 

Improved not shared 1.08 0.92 - 1.27 1.08 0.94 - 1.24 

Number of household members 1.00 0.98 - 1.02 0.96** 0.93 - 0.98 

Urban (ref.: rural) 1.01 0.82 - 1.24 1.41** 1.13 - 1.75 

Region (ref.: Kinshasa) 

Bandundu 1.33 0.91 - 1.96 0.77 0.55 - 1.07 

Bas-congo 1.44* 1.00 - 2.06 0.98 0.74 - 1.30 

Equateur 1.73** 1.16 - 2.56 1.10 0.82 - 1.47 

Kasai-occidental 1.56* 1.09 - 2.23 1.38* 1.01 - 1.90 

Kasai-oriental 0.83 0.54 - 1.28 0.63** 0.47 - 0.84 

Katanga 1.18 0.81 - 1.73 0.72* 0.52 - 0.99 

Maniema 1.69* 1.13 - 2.53 0.67* 0.47 - 0.95 

Nord-kivu 2.07*** 1.45 - 2.95 0.86 0.59 - 1.26 

Orientale 1.88*** 1.32 - 2.68 0.60** 0.43 - 0.85 

Sud-kivu 3.22*** 2.04 - 5.07 0.92 0.54 - 1.58 

Total number of household members 96,337 96,337 

* p<0.05, ** p<0.01, *** p<0.001 
24 

Table 4. Results of logistic regression predicting the odds of health care utilization, 

Liberia DHS 2013 

 Inpatient care Outpatient care 

 OR 95% CI OR 95% CI 

Sex (ref.: male) 

Female 1.34*** 1.17 - 1.52 1.20*** 1.13 - 1.29 

Age (ref.: 0-4) 

5-14 0.47*** 0.36 - 0.60 0.49*** 0.44 - 0.55 

15-49 0.89 0.75 - 1.07 0.70*** 0.62 - 0.78 

50-64 0.98 0.73 - 1.31 0.86* 0.75 - 0.98 

65+ 1.58** 1.15 - 2.17 0.94 0.78 - 1.14 

Missing 1.39 0.44 - 4.35 1.08 0.49 - 2.38 

Education (ref.: none) 

Primary 0.91 0.78 - 1.08 0.83*** 0.75 - 0.92 

Secondary or higher 1.03 0.81 - 1.30 0.88 0.78 - 1.00 

Missing 0.89 0.20 - 3.86 0.47 0.20 - 1.11 

Slept under a LLIN (ref.: did not sleep under 

an LLIN) 1.05 0.88 - 1.25 1.20*** 1.10 - 1.30 

Female headed-household (ref.: male-headed) 1.11 0.95 - 1.28 1.08 0.96 - 1.21 

Age of household head (ref.: <24) 

25-34 0.79 0.59 - 1.07 0.92 0.77 - 1.09 

35-44 0.64** 0.47 - 0.88 0.9 0.75 - 1.09 

45-54 0.70* 0.49 - 1.00 0.79* 0.66 - 0.95 

55-64 0.68 0.46 - 1.01 0.81 0.64 - 1.03 

65+ 0.59** 0.41 - 0.85 0.84 0.67 - 1.06 

Missing 1 1.00 - 1.00 1 1.00 - 1.00 

Education of household head (ref.: none) 

Primary 1.23 1.00 - 1.51 1.22** 1.08 - 1.38 

Secondary or higher 1.12 0.89 - 1.41 1.18* 1.03 - 1.34 

Household wealth status (ref.: 1st quintile) 

2nd quintile 1.22* 1.04 - 1.43 0.99 0.86 - 1.14 

3rd quintile 1.38** 1.12 - 1.70 1.07 0.91 - 1.26 

4th quintile 1.18 0.85 - 1.66 1.12 0.87 - 1.43 

5th quintile 1.36 0.95 - 1.95 1.07 0.79 - 1.44 

Household practices correct water treatment 

(ref.: incorrect water treatment) 0.76* 0.60 - 0.95 1.09 0.95 - 1.26 

Household accesses an improved water source 

(ref.: unimproved water source) 0.99 0.82 - 1.19 0.87 0.75 - 1.01 

Household sanitation facility (ref.: unimproved 

sanitation facility) 

Improved but shared 0.88 0.71 - 1.10 0.73** 0.59 - 0.91 

Improved not shared 0.96 0.79 - 1.16 0.85* 0.75 - 0.97 

Urban 1.04 0.84 - 1.28 0.94 0.78 - 1.13 

Region (ref.: North Western) 

South Central 1.19 0.89 - 1.59 1.02 0.80 - 1.29 

South Eastern A 1.68** 1.23 - 2.31 1.48** 1.16 - 1.89 

South Eastern B 1.88*** 1.30 - 2.72 1.58*** 1.23 - 2.02 

North Central 1.15 0.86 - 1.55 0.9 0.71 - 1.13 

Total number of household members 46,215 46,215 

* p<0.05, ** p<0.01, *** p<0.001 
25 

Table 5. Results of logistic regression predicting the odds of health care utilization, 

Namibia DHS 2013 

 Inpatient care Outpatient care 

 OR 95% CI OR 95% CI 

Sex (ref.: male) 

Female 1.01 0.95 - 1.08 1.05* 1.00 - 1.11

Age (ref.: 0-4) 

5-14 0.73*** 0.65 - 0.82 0.94 0.86 - 1.03

15-49 0.79** 0.68 - 0.92 0.87* 0.76 - 0.99

50-64 0.92 0.79 - 1.07 0.92 0.81 - 1.03

65+ 0.83** 0.72 - 0.95 0.94 0.84 - 1.05

Education (ref.: none) 

Primary 1.04 0.94 - 1.14 1.09* 1.01 - 1.18

Secondary or higher 1.02 0.89 - 1.16 1.09 0.98 - 1.21

Slept under a LLIN (ref.: did not sleep under an 

LLIN) 0.88 0.65 - 1.19 1.24 0.97 - 1.58

Covered by health insurance(ref.: uninsured) 1.04 0.88 - 1.22 1.19** 1.05 - 1.35

Female headed-household (ref.: male-headed) 1.02 0.87 - 1.19 1.02 0.91 - 1.14 

Age of household head (ref.: <24) 

25-34 1.31 0.89 - 1.92 1.44* 1.08 - 1.91

35-44 1.23 0.83 - 1.82 1.34* 1.01 - 1.76

45-54 0.92 0.60 - 1.39 1.34* 1.00 - 1.78

55-64 1.11 0.72 - 1.72 1.59** 1.15 - 2.18

65+ 1.24 0.80 - 1.90 1.72*** 1.27 - 2.34

Education of household head (ref.: none)

Primary 1.01 0.81 - 1.27 1.04 0.88 - 1.24

Secondary or higher 1.16 0.89 - 1.52 0.95 0.78 - 1.16

Household wealth status (ref.: 1st quintile) 

2nd quintile 0.97 0.75 - 1.25 1.09 0.90 - 1.32

3rd quintile 0.91 0.69 - 1.21 1.56*** 1.25 - 1.94

4th quintile 1.06 0.73 - 1.54 1.83*** 1.39 - 2.40

5th quintile 0.84 0.54 - 1.30 2.44*** 1.72 - 3.45

Household practices correct water treatment 

(ref.: incorrect water treatment) 1.47** 1.17 - 1.86 1.25* 1.02 - 1.54 

Household accesses an improved water source 

(ref.: incorrect water treatment) 0.88 0.69 - 1.11 0.85 0.69 - 1.06

Household sanitation facility (ref.: unimproved 

sanitation facility) 

Improved but shared 0.95 0.72 - 1.26 0.75* 0.60 - 0.94

Improved not shared 0.97 0.69 - 1.36 0.93 0.71 - 1.21

Number of household members 1.13*** 1.10 - 1.16 1.06*** 1.04 - 1.09 

Urban (ref.: rural) 1.23 0.98 - 1.54 0.98 0.79 - 1.20

Region (ref.: Caprivi) 

Erongo 0.86 0.59 - 1.24 0.50*** 0.34 - 0.73

Hardap 0.86 0.61 - 1.22 0.30*** 0.21 - 0.44

Karas 0.85 0.59 - 1.22 0.63* 0.44 - 0.89

Kavango 1.21 0.87 - 1.68 0.69 0.47 - 1.01

Khomas 0.70 0.48 - 1.01 0.43*** 0.30 - 0.62

Kunene 0.80 0.56 - 1.15 0.28*** 0.19 - 0.41

Ohangwena 0.62* 0.43 - 0.90 1.00 0.70 - 1.43

Omaheke 0.73 0.51 - 1.06 0.61** 0.43 - 0.85

Omusati 0.86 0.63 - 1.18 1.01 0.71 - 1.44

Oshana 0.92 0.66 - 1.29 0.69* 0.50 - 0.95

Oshikoto 0.85 0.60 - 1.19 0.77 0.54 - 1.09

Otjozondjupa 1.09 0.75 - 1.58 0.55*** 0.39 - 0.77

Total number of household members 41,665 41,665 

* p<0.05, ** p<0.01, *** p<0.001 
26 

Table 6. Results of logistic regression predicting the odds of health care utilization, 

Rwanda DHS 2010 

 Inpatient care Outpatient care 

 OR 95% CI OR 95% CI 

Sex (ref.: male) 

Female 2.12*** 1.91 - 2.36 1.30*** 1.20 - 1.40 

Age (ref.: 0-4) 

5-14 0.36*** 0.29 - 0.46 0.38*** 0.32 - 0.46 

15-49 1.62*** 1.35 - 1.94 0.45*** 0.38 - 0.53 

50-64 1.34* 1.04 - 1.73 0.45*** 0.37 - 0.55 

65+ 2.41*** 1.68 - 3.46 0.39*** 0.29 - 0.52 

Education (ref.: none) 

Primary 0.97 0.83 - 1.12 0.96 0.86 - 1.08 

Secondary or higher 0.87 0.69 - 1.09 0.92 0.79 - 1.08 

Slept under a LLIN (ref.: did not sleep under 

an LLIN) 1.18** 1.07 - 1.30 1.32*** 1.19 - 1.46 

Covered by health insurance (ref.: uninsured) 2.67*** 2.28 - 3.13 2.02*** 1.78 - 2.28 

Ill or injured in last 4 weeks (ref.: not ill or injured) 2.09*** 1.87 - 2.34 53.56*** 45.86 - 62.57

Female headed-household (ref.: male-headed) 0.88* 0.78 - 1.00 0.96 0.86 - 1.08 

Age of household head (ref.: <24) 

25-34 0.72** 0.59 - 0.88 1.08 0.86 - 1.36 

35-44 0.52*** 0.42 - 0.64 1.00 0.78 - 1.28 

45-54 0.44*** 0.35 - 0.56 0.92 0.72 - 1.16 

55-64 0.44*** 0.35 - 0.57 1.10 0.85 - 1.44 

65+ 0.31*** 0.22 - 0.43 0.98 0.74 - 1.29 

Education of household head (ref.: none) 

Primary 1.04 0.92 - 1.19 1.19* 1.03 - 1.37 

Secondary or higher 1.09 0.87 - 1.36 1.27* 1.05 - 1.53 

Household wealth status (ref.: 1st quintile) 

2nd quintile 0.89 0.76 - 1.04 0.98 0.83 - 1.15 

3rd quintile 0.83* 0.71 - 0.98 0.98 0.82 - 1.17 

4th quintile 0.74** 0.62 - 0.89 1.03 0.85 - 1.24 

5th quintile 0.62*** 0.50 - 0.78 1.29* 1.05 - 1.58 

Household practices correct water treatment 

(ref.: incorrect water treatment) 1.08 0.97 - 1.21 1.09 0.98 - 1.22 

Household accesses an improved water source 

(ref.: unimproved water source) 1.08 0.96 - 1.22 1.09 0.95 - 1.24 

Household sanitation facility (ref.: unimproved 

sanitation facility) 

Improved but shared 1.01 0.88 - 1.15 0.90 0.77 - 1.06 

Improved not shared 0.94 0.78 - 1.14 0.84* 0.70 - 1.00 

Number of household members 1.00 0.98 - 1.03 0.98 0.96 - 1.00 

Urban (ref.: rural) 0.89 0.70 - 1.12 0.97 0.73 - 1.28 

Region (ref.: Kigali) 

South 0.77 0.57 - 1.05 1.56* 1.11 - 2.20 

West 1.08 0.78 - 1.48 1.12 0.80 - 1.56 

North 0.76 0.55 - 1.04 0.93 0.66 - 1.31 

East 0.83 0.60 - 1.14 1.26 0.90 - 1.76 

Total number of household members 55,585 55,585 

* p<0.05, ** p<0.01, *** p<0.001 
27 

3.3. Out-of-Pocket Health Expenditures 

3.3.1. Levels of out-of-pocket health expenditures 

Information on out-of-pocket health expenditures was collected for all household members who received 

health care services in DRC, Namibia, and Rwanda. As discussed earlier, in Liberia information on 

expenditures for outpatient care was collected from a household member randomly selected from among 

those who received and paid for care in the four weeks preceding the survey. In all four study countries 

expenditure data on inpatient care were collected for all household members who received care in the six 

months preceding the survey, regardless of whether they paid or not. Expenditures are reported in constant 

2010 US dollars (USD for consistency across countries. 

Table 7 summarizes out-of-pocket expenditures in constant 2010 USD for the most recent health care visit 

in each of the study countries. Spending on the most recent inpatient care varied enormously among the 

four countries. Out-of-pocket health expenditures were highest in Liberia. Devaluation of the local currency 

against the US dollar and high inflation have contributed to the low purchasing power of the Liberian dollar 

relative to the national currencies of the other study countries. The average out-of-pocket expenditure was 

23 USD in Rwanda, 60 USD in DRC, 123 USD in Namibia, and 3,119 USD in Liberia. The distribution of 

expenditures had a wide range and was highly skewed to large amounts in all four countries. Half the 

household members who received inpatient care spent 3 USD in Rwanda, 5 USD in Namibia, 26 USD in 

DRC, and 1,480 USD in Liberia, which is less than half of the average spending in the respective countries. 

As Figure 6 shows, many individuals who received inpatient care did not pay for these services, ranging 

from 3% in Rwanda to 44% in Liberia. 

Table 7. Summary of out-of-pocket health expenditures for the most recent health care services 

received among household members who received health services 

Inpatient services 

(constant 2010 USD) 

Outpatient services 

(constant 2010 USD) 

 Mean Std. dev Median Minimum Maximum Mean Std. dev Median Minimum Maximum

DRC 59.5 186.1 26.2 0.0 8001 16.9 54.9 7.3 0.0 3636.8 

Liberia 3118.8 6439.8 1479.8 0.0 106542.8 1519.6 2678.5 739.9 0.0 43800.9 

Namibia 122.6 693.2 5.0 0.0 13418.0 18.0 260.0 0.7 0.0 11740.9 

Rwanda 22.7 131.9 2.9 0.0 3321.0 3.6 15.5 0.8 0.0 830.2 

*Std. dev= Standard deviation 
28 

Figure 6. Percentage of household members who received health care services but reported zero 

expenditure for the most recent visit 

As expected, out-of-pocket spending on outpatient care was lower than spending on inpatient care in all 

countries. On average, household members who received outpatient care spent 4 USD in Rwanda, 17 USD 

in DRC, 18 USD in Namibia, and 1520 USD in Liberia for the most recent outpatient care visit in the four 

weeks before the survey. Outpatient expenditures also had a wide range and were highly right skewed 

(skewed to large amounts). A considerable percentage of household members received outpatient care but 

reported zero out-of-pocket payments for the most recent visit—highest in Liberia (44%), followed by 

Namibia (24%), Rwanda (17%), and DRC (8%). 

3.3.2. Out-of-pocket health expenditures by providers and reasons 

Type of provider (public versus private) and reasons for care can influence the magnitude of out-of-pocket 

health expenditures. Table 8 indicates that for both inpatient and outpatient care services out-of-pocket 

health expenditures were lowest when care was received from a public provider. For inpatient care, the 

average out-of-pocket expenditure for care received in a public facility was half or less that of a private 

provider. In Rwanda for example, average spending was 21 USD for the most recent inpatient care received 

from a public provider, while it was 100 USD for the care received from a private provider. Public-private 

differences in health expenditures are less pronounced for outpatient care than inpatient care. In DRC, 

where 44% of household member who received outpatient care reported a private provider, household 

members, on average, spent 19 USD for their most recent visit with a private provider compared with an 

average 16 USD for care received from a public provider. High use of private providers was also reported 

in Namibia, and average out-of-pocket expenditure associated with health care received in the private sector 

(8 USD) was substantially higher than the average cost of care in the public sector (3 USD). Similar 

differences were observed in Liberia. Average out-of-pocket spending for inpatient care at private-sector 

facilities (4,684 USD) was more than double that for care by public providers (2,024.8 USD). Average outof-pocket spending for outpatient care at private sector facilities (1,717 USD) was nearly 1.5 times that of 

public providers (1,118 USD). 

7

42

18

3

8

51

24

17

DRC Liberia Namibia Rwanda

Inpatient Outpatient
29 

Table 8. Average out-of-pocket health expenditures for the most recent health care services, by 

type of provider 

 DRC Liberia Namibia Rwanda 

 Constant 2010 

USD 

Constant 2010 

USD 

Constant 2010 

USD 

Constant 2010 

USD 

Provider for inpatient care 

Public 47.9 2024.8 32.6 20.7 

Private 95.1 4684.0 950.9 100.9 

Other 60.4 2340.7 2012.7 51.6 

Average 59.5 3118.8 122.6 22.7 

Provider for outpatient care 

Public 15.9 1117.7 2.5 2.0 

Private 19.1 1716.8 88.0 10.0 

Other 10.4 1165.8 90.4 9.2 

Average 16.9 1519.6 18.0 3.6 

Out-of-pocket expenditures varied by reasons for seeking care (Table 9). For inpatient care, individuals 

spent the most on care received due to an accident or injury, ranging from 93 USD in Rwanda to 4,932 

USD in Liberia. Table 9 also shows that average spending for outpatient care was highest for accidents and 

injuries in DRC, Namibia, and Rwanda. In DRC, household members spent 18 USD on malaria-related 

services and preventive services, which was higher than the national average for outpatient care (17 USD). 

In DRC, Namibia, and Rwanda spending on outpatient costs for sexual and reproductive health related 

services was below average outpatient out-of-pocket costs at 13 USD in DRC, 16 USD in Namibia, and 0.9 

USD in Rwanda. Out-of-pocket expenditures for malaria-related inpatient and outpatient care were lower 

than the average costs of care in all study countries except DRC, where out-of-pocket spending on 

outpatient care for malaria treatment was higher than the average cost of outpatient care in the country. 

Table 9. Average out-of-pocket health expenditures for the most recent health care services, by 

reasons for care 

 DRC Liberia Namibia Rwanda 

 Constant 2010 

USD 

Constant 2010 

USD 

Constant 2010 

USD 

Constant 2010 

USD 

Reasons for inpatient care 

Sexual and reproductive health 58.5 4530.9 132.4 13.7 

Fever/malaria 39.3 -- 48.0 -- 

Other illness 71.4 2716.8 112.5 20.7 

Accident/injury 121.1 4931.9 271.4 92.8 

Average 59.5 3118.8 122.6 22.7 

Reasons for outpatient care 

Sexual and reproductive health 13.3 2029.1 15.7 0.9 

Malaria 18.0 1297.4 2.5 2.9 

Fever 10.8 923.2 8.2 1.8 

Diarrhea 10.0 1110.6 4.4 2.2 

Respiratory illness -- -- -- 3.1 

intestinal worms -- -- -- 3.1 

Other illness/other reason 21.9 3293.4 23.5 5.6 

Preventative care 18.2 1958.2 5.1 0.1 

Accident/injury 22.0 1196.7 39.2 5.4 

Average 16.9 1519.6 18.0 3.6 
30 

3.3.3. Out-of-pocket health expenditures by individual, household, and community background 

characteristics 

Table 10 indicates that out-of-pocket health expenditures differ by individual, household, and community 

background characteristics. In DRC men spent more than women on inpatient care but about the same for 

outpatient care. For both inpatient and outpatient care, out-of-pocket expenditure increased with age. 

Average out-of-pocket spending for children under age 5 was 43 USD for inpatient care and 12 USD for 

outpatient care, but more than double for those age 65 or older. Individuals with a secondary education or 

higher spent more than twice as much out-of-pocket compared with those without any education. Insurance 

coverage was associated with lower out-of-pocket health expenditures; household members with health 

insurance spent an average of 40 USD on the most recent inpatient care compared with 61 USD spent by 

those without health insurance. Similarly, individuals with health insurance spent an average of 8 USD for 

the most recent outpatient care compared with 17 USD spent by those without health insurance. 

Individuals living in households headed by a member who was female, older, or more educated reported 

higher out-of-pocket expenditures than their counterparts, for both inpatient and outpatient care. Out-ofpocket spending for health care was positively associated with household wealth status. Members of the 

wealthiest households spent an average of 147 USD for the most recent inpatient care, almost triple the 

national average (60 USD), while members of the poorest group spent 29 USD, half the national average. 

Similarly, those in the top wealth quintile spent 35 USD for the most recent outpatient care, double the 

national average (17 USD), while the poorest group spent only 9 USD. Urban residents reported higher outof-pocket spending on inpatient care than rural residents. Regional variation was noteworthy, ranging from 

27 USD in the Kasai-occidental region to 251 USD in the Kinshasa region. 

In Liberia differentials in out-of-pocket spending by age and educational attainment were similar to DRC. 

Health insurance coverage was associated with lower out-of-pocket spending for inpatient care but higher 

out-of-pocket spending for outpatient care. Average out-of-pocket expenditure for inpatient care among 

individuals with health insurance was 1,721 USD compared with 4,178 USD among those with no insurance 

coverage. On the other hand, individuals without health insurance coverage on average spent 35% less 

(1,510 USD) for the most recent outpatient care than those without insurance (2,331 USD). There was no 

clear pattern between out-of-pocket health expenditure and age of the head of the household or educational 

attainment of the household head. Individuals from the wealthiest households spent the most compared with 

the other wealth quintiles. Living in urban areas was associated with higher out-of-pocket expenditure for 

both inpatient and outpatient care. Regional variation was more prominent for inpatient care than outpatient 

care; the former ranged from 1,575 to 3,540 USD, while the latter ranged from 1,115 to 1,266 USD. 

Table 10 also indicates a few noteworthy findings in Namibia. Although men’s out-of-pocket expenditures 

were higher than women’s for inpatient care, women paid more than men out-of-pocket for outpatient care. 

Out-of-pocket health expenditures increase with age for both inpatient and outpatient care. Individuals with 

a secondary education or higher reported considerably higher out-of-pocket health expenditures—more 

than triple—compared with those with no education. Differences in health expenditures between the 

wealthiest and poorest groups are also substantial. Average out-of-pocket spending on the most recent 

inpatient care was 12 USD among the poorest group compared with 616 USD among the richest. For 

outpatient care, the range was from 2 USD for the poorest group compared with 50 USD for the wealthiest. 

Health insurance status was associated with higher health expenditures. Individuals covered by health 

insurance spent an average of 67 USD on inpatient care and 7 USD on outpatient care compared with 670 

USD and 79 USD, respectively, among those not covered by health insurance. 
31 

Table 10. Average out-of-pocket health expenditures for the most recent health care services received, 

by individual, household, and community background characteristics 

DRC Liberia Namibia Rwanda 

 Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient 

Background 

characteristics 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

Individual characteristics 

Sex 

Male 64.4 16.1 3013.9 1421.7 125.5 16.7 31.9 4.3

Female 56.4 17.6 3197.3 1605.3 120.0 19.0 18.9 3.1

Age 

0-4 42.8 11.8 1832.0 1042.2 93.5 9.5 12.5 2.5

5-14 52.8 13.0 2274.6 1354.6 59.6 17.0 25.5 2.5

15-49 67.4 18.9 3605.9 1649.4 143.8 18.7 20.0 4.1

50-64 68.6 30.7 3295.8 2304.5 214.2 33.3 66.3 5.5

65+ 105.3 25.8 5805.3 1464.8 220.9 18.6 27.7 5.3

Education 

None 43.3 12.4 2632.2 1263.5 73.1 8.0 12.4 2.5

Primary 53.0 13.7 3014.2 1538.0 46.9 13.6 21.7 3.7

Secondary or higher 92.7 26.5 4146.6 1991.8 244.5 30.1 66.2 8.2

Insurance status 

No 60.5 17.3 3178.3 1509.8 66.5 6.8 23.3 5.0

Yes 40.1 8.3 1721.1 2330.7 669.9 78.8 22.6 3.2

Household characteristics 

Female-headed household

No 58.4 16.8 3311.6 1561.3 168.7 27.9 22.3 3.7

Yes 64.0 17.6 2801.0 1444.4 72.8 7.5 23.9 3.2

Age of household head

<24 28.6 8.8 2008.0 951.1 107.1 8.0 15.9 3.1

25-34 48.9 13.5 2934.9 1453.2 134.6 13.3 18.1 3.0

35-44 66.0 15.0 3582.5 1418.6 176.9 16.9 22.4 3.3

45-54 65.0 17.7 2903.3 1771.6 153.6 35.7 25.9 4.9

55-64 61.4 25.8 2418.8 1838.6 108.0 19.2 33.1 3.7

65+ 79.2 26.4 4808.1 1637.0 53.9 8.5 30.4 3.6

Education of household head

None 39.8 12.0 3238.5 1238.8 15.0 3.7 14.1 2.3

Primary 38.3 11.9 2402.5 1465.1 29.7 5.7 19.0 3.3

Secondary or higher 73.6 19.7 3361.3 1708.4 252.9 36.3 62.0 7.3

Household wealth status

1st quintile 28.5 9.2 2434.4 1167.3 12.3 1.9 11.6 1.9

2nd quintile 36.6 10.3 3109.8 1256.7 17.8 25.8 10.7 2.3

3rd quintile 47.4 11.7 2444.8 1200.6 18.6 6.4 12.8 2.2

4th quintile 52.8 15.7 3018.5 1588.8 69.0 11.9 15.0 2.8

5th quintile 146.5 35.3 4556.1 2246.4 615.5 50.4 69.9 7.7

Continues 
32 

Table 10—Continued 

DRC Liberia Namibia Rwanda 

 Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient Inpatient Outpatient 

Background 

characteristics 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

(Constant 

2010 USD) 

Community-level variables

Residence 

Rural 37.6 10.1 2577.8 1290.2 50.4 6.1 16.8 2.7

Urban 108.6 27.4 3526.5 1670.6 214.3 35.8 64.7 8.5

Region 

Kinshasa 251.2 44.3 -- -- -- -- -- --

Bandundu 49.6 13.6 -- -- -- -- -- --

Bas-congo 86.7 17.1 -- -- -- -- -- --

Equateur 45.6 12.1 -- -- -- -- -- --

Kasai-occidental 27.3 8.9 -- -- -- -- -- --

Kasai-oriental 54.3 13.9 -- -- -- -- -- --

Katanga 60.9 21.3 -- -- -- -- -- --

Maniema 54.9 17.9 -- -- -- -- -- --

Nord-kivu 47.7 11.1 -- -- -- -- -- --

Orientale 44.5 13.6 -- -- -- -- -- --

Sud-kivu 36.5 9.2 -- -- -- -- -- --

North Western -- -- 1575.4 1114.5 -- -- -- --

South Central -- -- 3575.8 1750.0 -- -- -- --

South Eastern A -- -- 2014.1 1226.5 -- -- -- --

South Eastern B -- -- 1893.8 1534.2 -- -- -- --

North Central -- -- 3540.1 1265.6 -- -- -- --

Caprivi -- -- -- -- 14.6 9.8 -- --

Erongo -- -- -- -- 213.4 14.8 -- --

Hardap -- -- -- -- 133.7 30.0 -- --

Karas -- -- -- -- 213.6 13.3 -- --

Kavango -- -- -- -- 29.0 4.5 -- --

Khomas -- -- -- -- 456.4 69.3 -- --

Kunene -- -- -- -- 28.5 17.3 -- --

Ohangwena -- -- -- -- 9.5 2.0 -- --

Omaheke -- -- -- -- 30.3 24.2 -- --

Omusati -- -- -- -- 49.0 2.1 -- --

Oshana -- -- -- -- 166.0 9.6 -- --

Oshikoto -- -- -- -- 45.1 10.2 -- --

Otjozondjupa -- -- -- -- 68.3 43.1 -- --

Kigali city -- -- -- -- -- -- 95.6 10.3

South -- -- -- -- -- -- 15.3 2.0

West -- -- -- -- -- -- 12.7 2.6

North -- -- -- -- -- -- 11.1 1.8

East -- -- -- -- -- -- 21.8 4.4

Total 59.5 16.9 3118.8 1519.6 122.6 18.0 22.7 3.6 

In Rwanda women’s out-of-pocket health expenditures were lower than men’s for both inpatient and 

outpatient care. Out-of-pocket health expenditures for both inpatient and outpatient services increase with 

individuals’ age and level of education and with household wealth quintile. Health insurance does not 

appear to make a substantial difference on individual expenditures for inpatient care, but it is associated 

with slightly lower expenditures for outpatient care. Despite the overall low level of spending on health 

care in Rwanda, the wealthiest group had much higher out-of-pocket spending than other wealth quintiles 

for both inpatient and outpatient care. Urban residents reported spending more than rural residents, on 

average. Individuals living in Kigali spent the most on both inpatient and outpatient care, at 96 USD and 

10 USD, respectively, which is 3-4 times the national average. 
33 

3.3.4. Multivariable analysis results of out-of-pocket health expenditures 

Two-part models were fitted for each country to assess the correlates of out-of-pocket expenditures for 

outpatient and inpatient care. The first part was a probit model to estimate the probability of having positive 

health expenditures (that is, any health expenditures at all). The second part was a generalized linear model 

(GLM) to estimate the magnitude of out-of-pocket expenditures. In Liberia, since information on health 

expenditure for outpatient care was available only for household members who had a positive expenditure, 

only the generalized linear model was used to model magnitude of expenditures on outpatient care. A twopart model was used for health expenditures for inpatient care in Liberia. 

Table 11 shows the results of the two-part model for DRC. The results of the probit model indicate that 

only gender and health insurance status predict whether the household has positive health expenditures in 

the DRC. Net of other factors, women are more likely to have positive out-of-pocket payments for inpatient 

services than men (p<0.05), but gender differences for outpatient care are statistically nonsignificant. Net 

of other factors, those with health insurance are less likely to have positive out-of-pocket payments than 

those with no coverage (p<0.0001). The GLM results indicate that age of the individual, age of the 

household head, educational attainment of the household head, wealth status of the household, and urban 

residence are associated with higher out-of-pocket spending for both inpatient and outpatient care services, 

and these associations are statistically significant. Although women’s out-of-pocket payments for inpatient 

care are lower than those of men (p<0.001), gender differences in out-of-pocket spending for outpatient 

care are statistically nonsignificant. Regional differences in the probability of having positive out-of-pocket 

health expenditures are statistically nonsignificant but, conditional on having positive expenditures, living 

in any other region than Kinshasa is associated with lower out-of-pocket health payments for both inpatient 

and outpatient care, and these differences are statistically significant. 

Results for Liberia (Table 12) show that individual educational attainment increases the probability of 

positive expenditures for inpatient care, and this association is statistically significant. In Liberia as in DRC, 

having health insurance coverage decreases the likelihood of having positive expenditures for inpatient 

care, but unlike DRC, when there are expenditures individuals with health insurance spend more than those 

not covered by health insurance (p<0.05). Household wealth status is not significant in predicting either the 

probability of having positive expenditures or the level of the expenditure for the most recent inpatient care. 

Whereas the regional differences in the odds of having positive expenditures for inpatient care are 

statistically significant, conditional on positive expenditure, the magnitude of the out-of-pocket payments 

do not differ. 

We used a GLM model to analyze outpatient out-of-pocket health expenditure in Liberia. Age of the 

individual and age of the household head are significantly associated with higher out-of-pocket spending. 

The association between health insurance and the magnitude of out-of-pocket payments for outpatient 

services is statistically nonsignificant. Compared with individuals from the poorest households, those from 

the fourth and fifth wealth quintiles spend more on outpatient care. 

Table 13 shows the results of the two-part model for out-of-pocket expenditures in Namibia. Older 

household members are less likely to have positive expenditures for both inpatient and outpatient care. 

When they do have direct payments, however, they spend more out-of-pocket than younger individuals. 

Health insurance coverage is not associated with the probability of having positive expenditures. When 

spending does occur, people with insurance spend more than those without health insurance, for both 

inpatient and outpatient care. Conditional on incurring positive health expenditures, individuals from 

wealthier households tend to spend more. 

Results for Rwanda (Table 14) indicate that region of residence is the main determinant of whether an 

individual has positive out-of-pocket expenditures for inpatient care. Individual characteristics also 

influence the chances of spending for outpatient care; women are less likely to have positive payments, and 
34 

the chances of positive expenditures increase with age. When out-of-pocket payments are made, they tend 

to be lower for individuals who are insured than those without insurance, both for inpatient and outpatient 

care. For both inpatient and outpatient care the magnitude of out-of-pocket spending does not differ by 

wealth status of the household, except among individuals in the richest quintile, who tend to pay more than 

those in the poorest quintile. 

Table 11. Results of two-part models for out-of-pocket expenditures for the most recent health care 

services, DRC DHS 2013 

 Inpatient Outpatient 

 Probit GLM Probit GLM 

VARIABLES coef 95% CI coef 95% CI coef 95% CI coef 95% CI 

Sex (ref.: male) 

Female 0.18* 0.03 - 0.33 -0.15*** -0.24 - -0.06 0.08 -0.02 - 0.18 0.01 -0.06 - 0.08

Age (ref.: 0-4) 

5-14 0.00 -0.25 - 0.25 0.12 -0.07 - 0.31 0.05 -0.14 - 0.24 0.03 -0.09 - 0.15

5-49 0.17 -0.10 - 0.44 0.38*** 0.21 - 0.55 0.01 -0.21 - 0.22 0.30*** 0.16 - 0.45

50-64 -0.09 -0.42 - 0.25 0.65*** 0.40 - 0.91 0.13 -0.14 - 0.40 0.59*** 0.39 - 0.78

65+ 0.31 -0.28 - 0.89 0.64*** 0.30 - 0.99 -0.04 -0.43 - 0.34 0.38** 0.12 - 0.64

Education (ref.: none) 

Primary 0.09 -0.14 - 0.31 0.04 -0.12 - 0.20 0.02 -0.15 - 0.19 -0.00 -0.12 - 0.12

Secondary or higher -0.03 -0.34 - 0.28 0.12 -0.05 - 0.29 -0.07 -0.25 - 0.12 0.11 -0.06 - 0.27

Covered by health insurance(ref.: uninsured) -2.16*** -2.59 - -1.72 -0.02 -0.45 - 0.41 -1.76*** -2.12 - -1.40 -0.35** -0.58 - -0.12 

Female headed-household (ref.: male-headed) -0.16 -0.43 - 0.10 0.03 -0.18 - 0.24 -0.15 -0.33 - 0.02 -0.05 -0.16 - 0.05 

Age of household head (ref.: <24) 

25-34 -0.14 -0.66 - 0.38 0.25** 0.09 - 0.41 -0.20 -0.51 - 0.12 0.24** 0.09 - 0.39

35-44 -0.14 -0.67 - 0.39 0.45*** 0.27 - 0.62 0.10 -0.23 - 0.43 0.16* 0.02 - 0.31

45-54 -0.35 -0.89 - 0.20 0.46*** 0.27 - 0.66 0.11 -0.23 - 0.45 0.18* 0.03 - 0.34

55-64 -0.20 -0.75 - 0.35 0.46*** 0.21 - 0.72 -0.05 -0.38 - 0.29 0.35*** 0.15 - 0.55

65+ -0.07 -0.69 - 0.55 0.43** 0.16 - 0.70 -0.14 -0.58 - 0.29 0.44** 0.17 - 0.70

Education of household head (ref.: none) 

Primary -0.29 -0.63 - 0.06 0.09 -0.07 - 0.25 0.16 -0.06 - 0.38 0.07 -0.05 - 0.20

Secondary or higher -0.19 -0.58 - 0.19 0.27** 0.09 - 0.45 -0.05 -0.28 - 0.18 0.26*** 0.11 - 0.40

Household wealth status (ref.: 1st quintile) 

2nd quintile 0.15 -0.10 - 0.41 0.13 -0.05 - 0.30 -0.02 -0.23 - 0.18 0.02 -0.09 - 0.14

3rd quintile 0.34* 0.00 - 0.68 0.17* 0.00 - 0.33 -0.19 -0.41 - 0.04 0.06 -0.07 - 0.19

4th quintile 0.34 -0.00 - 0.69 0.24** 0.07 - 0.41 -0.12 -0.35 - 0.11 0.18* 0.02 - 0.35

5th quintile 0.30 -0.14 - 0.73 0.73*** 0.46 - 1.00 -0.03 -0.35 - 0.28 0.53*** 0.23 - 0.82

Urban (ref.: rural) 0.10 -0.22 - 0.42 0.33*** 0.18 - 0.48 0.25* 0.05 - 0.45 0.23** 0.09 - 0.38 

Region (ref.: Kinshasa) 

Bandundu 0.42 -0.17 - 1.01 -0.65** -1.12 - -0.18 -0.17 -0.59 - 0.24 -0.58*** -0.90 - -0.25

Bas-congo 0.28 -0.33 - 0.89 -0.31 -0.75 - 0.12 0.02 -0.44 - 0.49 -0.39* -0.72 - -0.06

Equateur 0.55* 0.04 - 1.07 -0.88*** -1.34 - -0.43 -0.19 -0.60 - 0.22 -0.70*** -1.03 - -0.37

Kasai-occidental 0.62 -0.09 - 1.33 -1.28*** -1.78 - -0.78 0.05 -0.41 - 0.52 -1.09*** -1.42 - -0.76

Kasai-oriental 0.13 -0.39 - 0.65 -1.06*** -1.53 - -0.60 -0.03 -0.44 - 0.38 -0.71*** -1.03 - -0.39

Katanga 0.27 -0.21 - 0.74 -0.54* -0.99 - -0.09 -0.03 -0.45 - 0.39 -0.27 -0.70 - 0.16

Maniema 0.15 -0.47 - 0.77 -0.70** -1.16 - -0.24 0.28 -0.18 - 0.74 -0.30 -0.65 - 0.05

Nord-kivu -0.33 -0.87 - 0.21 -1.03*** -1.48 - -0.59 -0.39 -0.91 - 0.13 -0.80*** -1.16 - -0.43

Orientale 0.24 -0.27 - 0.74 -0.84*** -1.29 - -0.38 -0.19 -0.60 - 0.23 -0.56** -0.90 - -0.21

Sud-kivu -0.54 -1.18 - 0.11 -0.87*** -1.33 - -0.42 -0.58 -1.19 - 0.02 -0.75*** -1.11 - -0.39

Total number of household members 3,797 6,167 

* p<0.05, ** p<0.01, *** p<0.001 

 
35 

Table 12. Results of two-part models for out-of-pocket expenditures for the most recent health 

care services, Liberia DHS 2013 

 Inpatient Outpatient 

 Probit GLM GLM 

VARIABLES coef 95% CI coef 95% CI Coef 95% CI 

Sex (ref.: male) 

Female 0.03 -0.08 - 0.15 -0.17* -0.31 - -0.03 0.07 -0.09 - 0.22

Age (ref.: 0-4) 

5-14 0.04 -0.15 - 0.23 0.17 -0.10 - 0.44 0.06 -0.14 - 0.27

15-49 -0.04 -0.24 - 0.15 0.65*** 0.44 - 0.86 0.35*** 0.15 - 0.55

50-64 0.04 -0.20 - 0.29 0.72*** 0.45 - 1.00 0.55*** 0.23 - 0.87

65+ 0.25 -0.02 - 0.53 0.91*** 0.60 - 1.22 0.37 -0.04 - 0.77

Education (ref.: none) 

Primary 0.28*** 0.12 - 0.44 -0.14 -0.34 - 0.06 0.02 -0.18 - 0.22

Secondary or higher 0.31** 0.12 - 0.50 0.05 -0.18 - 0.28 0.06 -0.16 - 0.28

Covered by health insurance (ref.: uninsured) -1.14*** -1.53 - -0.76 0.63* 0.11 - 1.15 0.07 -0.38 - 0.53 

Female headed-household (ref.: male-headed) 0.09 -0.08 - 0.27 -0.06 -0.25 - 0.13 -0.04 -0.21 - 0.12 

Age of household head (ref.: <24) 

25-34 0.02 -0.26 - 0.29 0.14 -0.13 - 0.40 0.44*** 0.19 - 0.69

35-44 -0.02 -0.30 - 0.27 0.40** 0.12 - 0.68 0.38** 0.15 - 0.61

45-54 -0.02 -0.31 - 0.27 0.35* 0.08 - 0.62 0.58*** 0.28 - 0.88

55-64 -0.06 -0.39 - 0.26 0.06 -0.25 - 0.36 0.55** 0.21 - 0.90

65+ -0.21 -0.56 - 0.14 0.45* 0.06 - 0.84 0.47* 0.06 - 0.87

Education of household head (ref.: none) 

Primary 0.02 -0.20 - 0.24 -0.06 -0.28 - 0.17 0.20 -0.01 - 0.42

Secondary or higher -0.10 -0.29 - 0.09 0.01 -0.21 - 0.23 0.14 -0.06 - 0.34

Household wealth status (ref: 1st quintile) 

2nd quintile 0.12 -0.05 - 0.29 -0.04 -0.27 - 0.18 0.12 -0.08 - 0.32

3rd quintile 0.14 -0.08 - 0.35 0.03 -0.20 - 0.26 0.06 -0.18 - 0.31

4th quintile 0.10 -0.16 - 0.35 0.08 -0.23 - 0.40 0.37** 0.12 - 0.62

5th quintile 0.29 -0.01 - 0.60 0.37 -0.04 - 0.78 0.63*** 0.32 - 0.93

Urban (ref.: rural) -0.08 -0.27 - 0.11 -0.18 -0.38 - 0.02 -0.12 -0.32 - 0.09 

Region (ref.: North Western) 

South Central 0.90*** 0.60 - 1.19 0.28 -0.05 - 0.61 0.15 -0.10 - 0.41

South Eastern A 0.19 -0.13 - 0.50 0.07 -0.25 - 0.39 0.07 -0.20 - 0.34

South Eastern B 0.11 -0.23 - 0.45 -0.13 -0.48 - 0.22 0.39* 0.01 - 0.77

North Central 0.69*** 0.40 - 0.99 0.28 -0.03 - 0.60 0.14 -0.12 - 0.40

Total number of household members 2,106 2,982 

* p<0.05, ** p<0.01, *** p<0.001 

 
36 

Table 13. Results of two-part models for out-of-pocket expenditures for the most recent health care 

services, Namibia DHS 2013 

 Inpatient Outpatient 

 Probit GLM Probit GLM 

VARIABLES coef 95% CI coef 95% CI coef 95% CI coef 95% CI 

Sex (ref.: male) 0.00 -0.07 - 0.07 -0.12 -0.24 - 0.01 0.02 -0.03 - 0.07 0.06 -0.03 - 0.16 

Female 

Age (ref.: 0-4) 

5-14 -0.08 -0.21 - 0.05 0.01 -0.24 - 0.25 -0.03 -0.13 - 0.06 0.14 -0.03 - 0.31

15-49 -0.16 -0.34 - 0.02 0.04 -0.26 - 0.34 -0.15* -0.27 - -0.03 0.10 -0.14 - 0.34

50-64 -0.17* -0.34 - -0.01 0.40* 0.09 - 0.71 -0.13* -0.25 - -0.02 0.19 -0.07 - 0.46

65+ -0.29*** -0.44 - -0.13 0.52*** 0.22 - 0.82 -0.31*** -0.42 - -0.20 0.76*** 0.32 - 1.21

Education (ref.: none) 

Primary 0.09 -0.03 - 0.20 0.04 -0.18 - 0.25 0.04 -0.04 - 0.11 0.05 -0.12 - 0.22

Secondary or higher 0.11 -0.04 - 0.26 0.30* 0.06 - 0.54 0.08 -0.02 - 0.18 0.11 -0.09 - 0.32

Covered by health insurance (ref.: 

uninsured) -0.20 -0.44 - 0.04 0.64*** 0.33 - 0.94 0.06 -0.07 - 0.19 0.87*** 0.52 - 1.23 

Female headed-household (ref.: maleheaded) -0.03 -0.22 - 0.16 -0.36* -0.69 - -0.03 -0.02 -0.14 - 0.10 -0.15 -0.41 - 0.11 

Age of household head (ref.: <24) 

25-34 0.04 -0.48 - 0.57 -0.32 -1.65 - 1.00 -0.11 -0.44 - 0.22 -0.07 -0.79 - 0.65

35-44 -0.01 -0.51 - 0.50 -0.15 -1.52 - 1.22 -0.21 -0.54 - 0.13 -0.29 -0.98 - 0.40

45-54 -0.02 -0.56 - 0.51 -0.31 -1.66 - 1.05 -0.12 -0.46 - 0.22 0.42 -0.39 - 1.23

55-64 -0.32 -0.87 - 0.22 0.27 -1.12 - 1.67 -0.58*** -0.92 - -0.25 0.50 -0.27 - 1.28

65+ -0.82** -1.38 - -0.27 0.56 -0.83 - 1.95 -1.17*** -1.51 - -0.84 0.92* 0.08 - 1.75

Education of household head (ref.: none) 

Primary 0.13 -0.14 - 0.40 0.31 -0.19 - 0.80 -0.02 -0.18 - 0.15 0.08 -0.25 - 0.40

Secondary or higher 0.07 -0.26 - 0.40 0.50 -0.00 - 1.01 0.05 -0.16 - 0.25 0.69*** 0.30 - 1.07

Household wealth status (ref.: 1st quintile) 

2nd quintile 0.37* 0.06 - 0.68 0.04 -0.46 - 0.54 -0.04 -0.24 - 0.15 0.71*** 0.32 - 1.09

3rd quintile 0.16 -0.18 - 0.50 -0.14 -0.72 - 0.43 0.08 -0.13 - 0.29 0.36 -0.04 - 0.75

4th quintile 0.14 -0.19 - 0.48 1.21*** 0.59 - 1.83 0.24 -0.01 - 0.48 0.73** 0.26 - 1.21

5th quintile 0.12 -0.33 - 0.56 3.16*** 2.35 - 3.97 0.47** 0.17 - 0.77 1.62*** 0.98 - 2.26

Urban (ref.: rural) 0.21 -0.08 - 0.50 -0.45* -0.90 - -0.01 -0.04 -0.22 - 0.15 0.02 -0.33 - 0.37 

Region (ref.: Caprivi) 

Erongo -0.20 -0.66 - 0.26 0.95* 0.08 - 1.82 -0.46** -0.81 - -0.11 0.14 -0.51 - 0.79

Hardap -1.04*** -1.51 - -0.56 -0.54 -1.22 - 0.13 -0.81*** -1.16 - -0.45 0.47 -0.35 - 1.30

Karas -1.17*** -1.65 - -0.69 0.44 -0.44 - 1.33 -0.58*** -0.87 - -0.29 0.09 -0.55 - 0.73

Kavango -0.28 -0.73 - 0.18 0.88** 0.29 - 1.47 -0.18 -0.47 - 0.11 0.08 -0.53 - 0.69

Khomas -0.47 -1.00 - 0.06 1.38*** 0.62 - 2.13 0.23 -0.14 - 0.60 1.69*** 0.75 - 2.63

Kunene -0.35 -0.89 - 0.20 0.76 -0.07 - 1.59 0.10 -0.27 - 0.47 1.22* 0.27 - 2.16

Ohangwena -0.51* -0.96 - -0.06 -0.01 -0.56 - 0.54 -0.33* -0.59 - -0.07 -0.31 -0.97 - 0.34

Omaheke -0.81** -1.34 - -0.28 0.63 -0.26 - 1.52 -0.53*** -0.81 - -0.24 1.03** 0.35 - 1.71

Omusati -0.56* -1.02 - -0.09 0.77 -0.22 - 1.77 -0.30* -0.58 - -0.01 -0.68* -1.27 - -0.08

Oshana -0.35 -1.11 - 0.41 0.61 -0.06 - 1.27 -0.20 -0.48 - 0.07 0.45 -0.25 - 1.15

Oshikoto -0.41 -0.90 - 0.09 0.10 -0.62 - 0.82 -0.37* -0.68 - -0.06 0.75 -0.28 - 1.77

Otjozondjupa -0.78** -1.26 - -0.29 0.42 -0.35 - 1.18 -0.40** -0.70 - -0.10 0.73* 0.02 - 1.44

Total number of household members 6,494 15,991 

* p<0.05, ** p<0.01, *** p<0.001 

 
37 

Table 14. Results of two-part models for out-of-pocket expenditures for the most recent health 

care services, Rwanda DHS 2010 

 Inpatient Outpatient 

 Probit GLM Probit GLM 

VARIABLES coef 95% CI coef 95% CI coef 95% CI coef 95% CI 

Sex (ref.: male) 

Female 0.05 -0.21 - 0.31 -0.43*** -0.65 - -0.21 -0.14** -0.23 - -0.06 -0.37*** -0.50 - -0.25

Age (ref.: 0-4) 

5-14 -0.18 -0.81 - 0.45 0.05 -0.47 - 0.58 -0.02 -0.20 - 0.16 -0.03 -0.25 - 0.19

15-49 -0.22 -0.74 - 0.29 -0.21 -0.61 - 0.19 0.39*** 0.21 - 0.56 0.31** 0.11 - 0.52

50-64 -0.15 -0.81 - 0.51 0.41 -0.24 - 1.06 0.68*** 0.41 - 0.95 0.56*** 0.31 - 0.81

65+ 0.10 -0.78 - 0.98 -0.36 -1.30 - 0.59 0.88*** 0.44 - 1.33 0.63** 0.24 - 1.02

Education (ref.: none) 

Primary 0.14 -0.20 - 0.48 0.67*** 0.39 - 0.94 -0.07 -0.21 - 0.06 0.10 -0.07 - 0.28

Secondary or higher -0.16 -0.72 - 0.40 0.83*** 0.35 - 1.31 -0.22 -0.45 - 0.01 0.21 -0.14 - 0.55

Covered by health insurance (ref.: 

uninsured) 0.23 -0.10 - 0.56 -0.51*** -0.78 - -0.24 0.44*** 0.32 - 0.56 -0.79*** -0.95 - -0.64

Injured in the last 4 weeks (ref.: not 

injured) -0.04 -0.35 - 0.27 0.24 -0.03 - 0.51 1.05*** 0.87 - 1.22 0.00 -0.15 - 0.16

Female headed-household (ref.: maleheaded) -0.25 -0.52 - 0.01 -0.13 -0.38 - 0.12 0.00 -0.12 - 0.12 0.01 -0.12 - 0.14

Age of household head (ref.: <24) 

25-34 0.41* 0.01 - 0.82 -0.21 -0.58 - 0.16 0.03 -0.20 - 0.26 0.03 -0.19 - 0.26

35-44 0.30 -0.13 - 0.74 0.09 -0.29 - 0.46 0.13 -0.11 - 0.37 0.02 -0.20 - 0.24

45-54 0.35 -0.07 - 0.77 0.16 -0.27 - 0.58 0.06 -0.19 - 0.30 0.31* 0.03 - 0.58

55-64 0.39 -0.18 - 0.96 0.14 -0.38 - 0.66 0.05 -0.21 - 0.31 0.04 -0.23 - 0.32

65+ 0.02 -0.67 - 0.71 0.75 -0.09 - 1.60 0.13 -0.20 - 0.46 0.09 -0.20 - 0.39

Education of household head (ref.: 

none) 

Primary -0.06 -0.35 - 0.23 -0.24 -0.50 - 0.03 -0.02 -0.16 - 0.12 0.12 -0.06 - 0.29

Secondary or higher -0.10 -0.55 - 0.35 0.17 -0.34 - 0.68 0.04 -0.17 - 0.25 0.19 -0.13 - 0.50

Household wealth status (ref.:1st 

quintile) 

2nd quintile 0.13 -0.21 - 0.47 0.02 -0.28 - 0.31 0.07 -0.08 - 0.22 0.03 -0.17 - 0.24

3rd quintile 0.07 -0.27 - 0.41 0.06 -0.25 - 0.37 0.05 -0.12 - 0.22 0.06 -0.15 - 0.27

4th quintile 0.39 -0.07 - 0.85 0.16 -0.15 - 0.48 0.16 -0.03 - 0.34 0.02 -0.22 - 0.26

5th quintile -0.14 -0.56 - 0.29 0.91*** 0.47 - 1.36 0.28** 0.07 - 0.49 0.68*** 0.41 - 0.95

Urban (ref) -0.19 -0.55 - 0.16 0.13 -0.20 - 0.46 0.22 -0.13 - 0.57 0.36* 0.07 - 0.66 

Region (ref.: Kigali) 

South -1.21*** -1.79 - -0.63 -0.94*** -1.45 - -0.44 -0.11 -0.47 - 0.25 -0.69*** -1.04 - -0.34

West -0.75* -1.38 - -0.12 -1.10*** -1.62 - -0.58 0.44* 0.07 - 0.81 -0.58** -0.95 - -0.21

North -1.00** -1.63 - -0.38 -1.15*** -1.67 - -0.62 0.24 -0.13 - 0.62 -0.76*** -1.14 - -0.38

East -0.99** -1.60 - -0.38 -0.78** -1.34 - -0.23 0.18 -0.19 - 0.55 -0.10 -0.54 - 0.33 

Total number of household members 1,908 6,264 

* p<0.05, ** p<0.01, *** p<0.001 

39 

4. Discussion and Conclusion 

The main focus of this study was to examine out-of-pocket health expenditures and associated factors in 

four African countries—The Democratic Republic of the Congo, Liberia, Namibia, and Rwanda—using 

nationally representative data from the Demographic and health Surveys. Because the level of out-of-pocket 

spending is related to health care utilization, we also assessed use of inpatient and outpatient care services 

among all household members. 

4.1. Health Care Utilization 

In the study countries use of inpatient care ranged from 4% to 18% and use of outpatient care from 7% to 

41%. The differences in the use of health care services reflect the countries’ respective economic 

development status. The lowest level of health care use was in DRC, where GDP per capita is 385 in 

constant 2010 USD (World Bank 2016a). Namibia had relatively higher levels of health care use than the 

other three countries, which may reflect the broad reach of public sector facilities and providers, and also 

because Namibia is a relatively wealthier country with a GDP per capita of 6,014 constant 2010 USD 

(World Bank 2016a). 

As would be expected, health care utilization rates are higher when confined to those who need health care, 

rather than calculated for all individuals. For example, in Rwanda, when we restricted the analysis to 

household members who reported experience of illness or injury in last four weeks, the estimate for 

outpatient care utilization was substantially higher (70%) compared with our estimate of 11% that included 

all individuals regardless of their need for health care. 

In analyzing individual, household, and community characteristics associated with using health services, 

we found that in most of the study countries age and sex play a role in seeking care. Women were more 

likely to seek health care services than men. Children under age 5 and household members age 65 or over 

were more likely to seek care compared with other age groups. While the education level of the head of the 

household was positively associated with use of the health care in some study countries, there was no clear 

association between health care utilization and individuals’ educational attainment, or an association 

between health care utilization and household wealth status. This non-finding may reflect the composition 

of the study samples. As mentioned, the analytical samples included all individuals rather than only 

individuals with a need for care. If educational attainment and household wealth status are associated with 

reduced likelihood of illness or injury but increased likelihood of seeking care, their association with use 

of health services would be cancelled out when the sample includes both individuals who needed and sought 

care and those who did not need care (or were not aware they needed to seek care). In addition, a large 

proportion of the sample (14-20%) was under age 5, which could also contribute to the nonsignificant 

association between individual educational attainment and health care utilization. 

We were able to assess the effect of health insurance on use of health services only in Rwanda because it 

was the only study country where health insurance data were collected for all household members regardless 

of whether they reported any use of health care. Health insurance coverage in Rwanda is common and the 

majority of the insured are covered by the community-based mutual health insurance, which is designed to 

increase the use of health care, especially for the poor. Our findings indicate that Rwandans with health 

insurance coverage were more than twice as likely to receive both inpatient and outpatient care services 

compared with the uninsured. A study based on a national survey in 2005-2006 in Rwanda also found that 

individuals in households covered by mutual health insurance used health services twice as much as those 

in households without insurance coverage (Saksena et al. 2011). The positive impact of health insurance on 

general service utilization has also been found in India, Vietnam, and China (Aggarwal 2010; Saksena et 

al. 2011; Wagstaff 2007; Wang et al. 2009). 
40 

Across all four countries, individuals were more likely to use public than private facilities and providers for 

treatment. Public providers may be a more attractive source of care for health seekers because they generally 

charge less for services than in the private sector. In Liberia the private sector plays a relatively larger role 

in the provision of health care compared with the other three study countries. This may reflect the structure 

of the health system, which is based on a multi-sectoral approach and partnership with the private sector. 

NGOs and private agencies provide most health care services in Liberia (WHO Health Action in Crises 

2005). The reasons for receiving health care differ by country. In DRC and Liberia, where the burden of 

malaria is among the highest in the world (WHO Global Malaria Programme 2015), the most common 

reason given for seeking care is malaria and/or fever. Reasons related to sexual and reproductive health are 

more commonly reported for inpatient than outpatient care. 

4.2. Out-of-Pocket Health Expenditures 

We found a wide range in absolute levels of out-of-pocket spending across the four study countries. The 

average cost of health care services was highest in Liberia, and Liberia also has the highest proportion of 

health care users with zero out-of-pocket payments. In all four countries cost of care was generally lower 

through public providers than private providers. The biggest differential in cost of care between public and 

private providers was observed in Namibia. 

We did not estimate the share of out-of-pocket health expenditures from total income or catastrophic health 

spending because the DHS does not collect information on household income or household expenditures 

or consumption. The results of our multivariate analysis indicate that the magnitude of out-of-pocket 

expenditures is associated with sex and age. When out-of-pocket spending does occur, women tend to spend 

less than men. Cost of care also generally increases with age. These findings are consistent with other 

studies (Brinda et al. 2012; You and Kobayashi 2011). While other studies have documented the effects of 

sex and education of the household head on higher household health expenditures (Leone, James, and 

Padmadas 2013; Mohanty and Srivastava 2013), we did not find a consistent association in the study 

countries. 

The relationship between health insurance coverage and out-of-pocket health spending varied across the 

study countries. In DRC and Rwanda, health insurance coverage was associated with lower out-of-pocket 

expenditures for both inpatient and outpatient care services, while in Liberia and Namibia it was associated 

with higher out-of-pocket payments. In Rwanda 95% of the insured are covered by the mutual health 

insurance scheme, which covers outpatient and inpatient services, essential drugs, medical imagery, and 

laboratory tests (Ministry of Health of Rwanda 2010). Sakesena and colleagues (2011) found that mutual 

health insurance in Rwanda provided financial risk protection and substantially reduced the incidence of 

catastrophic health expenditure. Studies in other countries have also shown that health insurance protected 

enrollees by reducing out-of-pocket spending (Acharya et al. 2013; Aggarwal 2010; Bauhoff, Hotchkiss, 

and Smith 2011; Nguyen and Wang 2013; Nguyen, Rajkotia, and Wang 2011). Our finding of an association 

between health insurance coverage and higher out-of-pocket spending in Namibia and Liberia is also 

documented in other studies (Shahrawat and Rao 2012; Wagstaff and Lindelow 2008; You and Kobayashi 

2011). One explanation for this counterintuitive relationship between health insurance coverage and outof-pocket health spending is adverse selection; that is, individuals at higher risk of illness or with chronic 

diseases are more likely to enroll in insurance and also more likely to incur higher health care costs. 

Alternatively, the benefit packages offered by insurance schemes may lead to higher out-of-pocket 

payments, for example, if they offer only limited coverage (for example, cover only hospitalization) 

(Shahrawat and Rao 2012). 

Although access to health services for poorer households did not differ from that of more affluent 

households, our results indicate that poorer households spent less in absolute terms on out-of-pocket 

payments. This finding agrees with other studies from a number of countries (Chuma and Maina 2012; 

Hotchkiss et al. 2005; Rahman et al. 2013; Rous and Hotchkiss 2003), which also indicated that the relative 
41 

share of health expenditures to total or non-food household expenditures was higher in poorer households. 

Unfortunately, our data did not allow us to test this association. 

The results of this study should be considered in light of several limitations. Ideally, we would have liked 

to examine access to health care among individuals who needed care—for example, household members 

who reported a condition or illness that required health care or treatment with medicines, or those with a 

chronic illness. However, identification of such household members was only possible with the Rwanda 

DHS, in which information for all household members was collected on whether they had an illness or 

injury in the four weeks before the survey. In the absence of such information in the other three study 

countries, we examined use of health services among all household members. The use of health services 

would be expected to be higher among those who needed care than the estimated use based on all household 

members regardless their need for care. Including all household members in the analysis, rather than only 

those who needed care could also affect the results on the associations between service utilization and 

individual, household, and community characteristics. If an individual characteristic (for example, 

socioeconomic status) is negatively associated with the probability of having an illness or injury, but 

positively associated with propensity of seeking care, then the estimated association between this 

characteristic and use of health services will be diluted or cancelled out if the analysis includes individuals 

who do not need care. This may partially explain why we did not find a consistent association between use 

of health care services and individual educational attainment or household wealth status. 

Another limitation of the study is our inability to assess the burden of out-of-pocket health expenditures 

relative to income, because income data were unavailable in DHS surveys. Universal health coverage 

requires health policies that protect against catastrophic payment. But catastrophic payment is not always 

synonymous with high health expenditures (Xu et al. 2003). In the absence of income data in DHS surveys, 

we were unable to calculate catastrophic health spending but could only estimate absolute out-of-pocket 

health expenditures. 

Finally, the results of our analysis of the determinants of out-of-pocket spending may be biased due to 

endogeneity. For example, the positive relationship between health insurance coverage and out-of-pocket 

health expenditures could be due to adverse selection, in that individuals with high risk of health problems 

are also more likely to enroll in insurance. 

4.3. Conclusion 

In the study countries the use of health care is generally low, and service provision primarily relies on public 

health providers. Out-of-pocket health spending in absolute terms varies substantially across countries, with 

individuals in poorer households spending less than those in wealthier households. While the use of health 

care services is related to many factors, cost is an important barrier to access. Our results provide evidence 

that expanding health insurance coverage, especially in Liberia and DRC, where service use is low and outof-pocket health expenditure is high, may contribute to lower out-of-pocket payments. Existing evidence 

suggests that risk pooling through health insurance has been effective in increasing health care utilization 

and reducing the likelihood of catastrophic expenditures (Xu et al. 2003). Community health insurance in 

Rwanda has been successful in increasing health care use and reducing out-of-pocket health expenditures. 

Enrollees in community health insurance in Rwanda are over twice as likely to seek care and spend 

significantly lower out-of-pocket compared with those without insurance. Namibia has higher coverage of 

private health insurance and employer-based insurance. While this may also contribute to the high level of 

health care utilization, out-of-pocket expenditures are also higher among the insured than those uninsured. 

No single insurance “prototype” or scheme fits all. Countries should explore various options, including 

social health insurance, community-based health insurance, private insurance, and employer-based 

insurance depending on the country context, such as the level of economic development, social stability, 

and disease burdens. 
42 

The results of this study also have implication for data collection. The DHS Program has collected data on 

out-of-pocket health expenditure in 12 countries since 2010 but the module is country-specific and differs 

substantially across these surveys. A standard out-of-pocket health expenditure module was developed in 

2012 and intended for use in countries that requested data on health expenditures. However, the standard 

module has not been adopted in subsequent surveys. For example, Namibia 2013 DHS and DRC 2013 DHS 

used a different module than the standard one. While modifications to the questionnaire may be necessary 

in light of a country’s request, maintaining consistency of the module is essential for comparisons over time 

and across countries. 
43 

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Appendix Table 1. Share of government and private sector as a percent of total health 

expenditure 

 DRC Liberia Namibia Rwanda 

 Government Private Government Private Government Private Government Private 

2010 26 74 23 77 58 42 37 63 

2011 36 64 35 65 61 39 36 64 

2012 45 55 33 67 59 41 37 63 

2013 34 66 31 69 59 41 38 62 

2014 37 63 31 69 60 40 38 62 

Source: WHO NHA indicators accessed at: http://apps.who.int/nha/database/Select/Indicators/en 

Appendix Table 2. Total health expenditures (THE) and out-of-pocket (OOP) payments per capita, in 

million constant 2010 US dollars, and share of out-of-pocket expenditure as a percent of total 

health expenditure 

Countries DRC Liberia Namibia Rwanda 

 THE OOP % THE OOP % THE OOP % THE OOP % 

2010 13 5 37% 39 10 27% 406 30 7% 44 12 28% 

2011 12 5 39% 42 12 29% 486 34 7% 47 14 29% 

2012 15 6 38% 42 13 30% 468 34 7% 51 15 29% 

2013 16 6 39% 42 13 31% 470 35 7% 52 14 28% 

2014 19 7 39% 46 14 31% 499 36 7% 52 15 28% 

Source: WHO NHA indicators accessed at: http://apps.who.int/nha/database/Select/Indicators/en