Summary
Background
Burkina Faso has undertaken decentralization reforms in the health care sector to improve the performance of the health system. This study aimed to analyze the differences in health outcomes by health district's demographic and economic status, and the distribution of health resources during the period of health care decentralization.
Methods
A bivariate correlation analysis was conducted using data at the health districts and regions level. Data from the health management information system (HMIS) and national households' surveys were used.
Results
The results indicate a strong correlation between district's population size and the availability of health resources (P ≤ .05). The health visits per capita and skilled birth attendance are correlated with the economic status of the health district (P ≤ .05). Malnutrition among under‐five and maternal mortality was associated with the availability of health personnel and health infrastructures (P ≤ .05). No correlation was found between financial resources and health outcomes.
Conclusion
The results indicated disparities in health among the health districts in Burkina Faso. The ways to address this inequality include more transparent resource allocation, as well as policies to address the socio‐economic disparities and financial barriers to health services. Further research is needed to collect relevant data and investigate the effects of decentralization, which was not possible in our study.
Keywords: Burkina Faso, socio‐economic difference, health care decentralization, health inequality, health outcomes
1. INTRODUCTION
Health inequalities are a major source of social inequity. Several studies and reports have looked at inequality in health care in Africa.1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 Attempts to address health inequalities include initiatives toward universal health coverage. In many developing countries, the aim to establish universal health coverage has led to the revitalization of the public sector and promotion of the private sector through health sector reforms, with the main corollary being the decentralization of health care provision and financing.
Regarding health care decentralization, it aims to bridge the gap between providers and users in order to increase health coverage and to achieve better health outcomes through a more equitable distribution of resources and an efficiency in health services provision.12, 13, 14
However, the rationale for health care decentralization varies across countries. A systematic review of the evidence on the impacts of decentralization on health‐related equity found that the main objective for this policy is the improvement of the overall health system performance. 15 This objective is based upon three key expectations. The first expectation is that decentralization provides the opportunity for health systems to attain both technical and allocative efficiency, empower local governments, increase accountability, and make gains in areas like quality, cost, and equity. The second expectation is imperative to make health services responsive to local population needs and to improve access and quality of health care. The third expectation is to minimize government expenditure, reduce the role of the state in the provision of health care and to introduce competition and cost‐consciousness in the public sector.
In 1992, Burkina Faso has undertaken a reform of its health system, which has led to the decentralization of health care. 16 This resulted in the establishment of health regions, divided into health districts that are the operational level for implementation of the national health policy. The district health services are organized into two levels, the first of which comprises the primary health care centers that provide communities with basic preventive and curative primary health care. The second level consists of district hospitals that represent the point of referral for primary health care centers. District hospitals are managed by a district health management team and are led by a medical officer. The health district funding is largely obtained from the central government.
As part of the decentralization process, a decision was taken in 2009 by the central government to transfer the health resources and skills to local governments. These local administrative entities (called communes) are run by elected mayors that are autonomous in the management and financing of health resources without any formal hierarchical relation with the health districts. This transfer first involved primary health care centers. An institutional agreement was made to define the roles and responsibilities of each party (central government, local governments, health districts, and primary health care centers). As a result of this agreement, the local governments' roles now include the management of health care centers, procurement and supply of drugs and medical commodities, disease prevention, and sanitation. The central government only plays a regulatory role, which includes defining the national health policy and orientation, setting norms and standards for the health infrastructure, equipment, health services functioning, and management. It also oversees health facilities and allocates financial resources through grants and subsidies to local governments. 17
It is expected that these reforms will help to improve the overall health system performance in terms of better availability and equitable distribution of resources, increased access to good quality health services, as well as better health outcomes across health regions and health districts. It should be noted that these expectations are not solely decentralization‐led as some studies 15 questioned whether decentralization leads to improvement in overall health system performance. A number of these studies report that the outcomes and benefits of decentralization are mixed, the effects of decentralization on health care are negative or ambiguous, and inequity is a major concern. 15
The aim of this study is to analyze the regional health disparities in Burkina Faso in terms of health districts' demographics, economic status, and distribution of health resources during the period of health care decentralization. The effects of decentralization are, however, out of the scope of our study due to the lack of suitable data. We, therefore, limit our investigation to the analysis of correlations between key regional indicators over time.
We apply quantitative analytical methods based on health districts and regions level data in order to explore associations between districts' demographics and economic profile, health resources, health outcomes, and health status. The analysis is of interest for policymakers in Burkina Faso who attempt to examine the above expectations. The study helps to gauge whether the health care decentralization in Burkina Faso is on a good track to achieve the equality objective and also to provide evidence to the debate surrounding the ultimate aim of health care decentralization, namely, the improvement of service quality and coverage. The analysis is also of interest to other Sub‐Saharan African countries that go through a decentralization process to revitalize the public sector while promoting the development of the private sector.
2. METHODS
2.1. Data collection
To assess the relationship between health districts' demographic and economic status and health outcomes, we created our own database using district and region level data in Burkina Faso. The data sources are the health management information systems and national households' surveys. These two sources provided the main representative, standardized and updated quantitative data on household's health, demographic and economic status in Burkina Faso. The data sources are described below.
2.1.1. Health Management Information System (HMIS) reports
The HMIS reports are published yearly by the Ministry of Health to release key information and data on the health system functioning and services organization, the health services resources available and the distribution, access, utilization and coverage of health care, as well as health outcomes and health status. These reports are used to evaluate health system performances and health planning. In this study, HMIS reports were reviewed to gather the secondary data by health districts, namely demographic, health sources, health use, and outcomes data.
2.1.2. National Household Survey reports
Two reports published by the National Statistics and Demographic Institute were used to gather secondary data on the economic profile of the 13 administrative regions which also correspond to the territorial breakdown of the 13 health regions covering the 63 health districts. These surveys are representative of the country and they involve large samples of households.
The 2010 Demographic and Health Survey (DHS) report. This survey included 15 000 households and data were collected using a quantitative method. A structured questionnaire was developed to collect data on household members' characteristics, education level, health services availability and access, use of iodized salt, household's welfare indicators.
The 2014 Households Living Conditions Survey report. The sample size of this survey was 10 411 households and data were collected using a quantitative survey method. A structured questionnaire was developed to collect data on household members' characteristics, education level, health, occupation, income, and housing.
The data collection points from all sources were 2010 and 2014 to allow for the changes in the socio‐economic status, health services access, and health outcomes in health districts during the decentralization period, which started in 2009. The number of health districts was 63 during the period covered in the study. Another rationale for selecting the years 2010 and 2014 was the availability of data on economic characteristics collected through the national household surveys during the same periods.
The following main macro indicators were extracted from the sources described above and were used in the study:
Demography: health district population estimated by the National Statistics and Demographic Institute
Availability of health resources:
Health personnel per capita: health personnel refers to the sum of the different categories of health personnel used in the HMIS report. These are physicians, pharmacists, midwives, nurses, and skilled birth attendants from the public sector. This ratio was expressed to 10 000 capita;
Health infrastructure per capita: health infrastructure refers to district hospitals and primary health care centers. This ratio was expressed to 10 000 capita;
Financial resources: these include funds allocated, funds spent, and the difference between these two;
Health care use and outcomes: the number of visits to health facilities per capita per year, the percentage of skilled birth attendance, the percentage of malnutrition among under 5, and the maternal mortality ratio.
Poverty index and GNI coefficient: Given that the poverty index and the GNI coefficient were not disaggregated to the district level, the profile of each region applied to all the districts in the respective region.
These indicators were selected because they are suitable for assessing health disparities/inequities. Previous studies have discussed the relevance of these indicators in measuring the geographical difference in health resources, health expenditures, utilization of health services, and health outcomes.18, 19, 20 Specifically, these indicators provide insight into various dimensions of health inequalities. These were also the only data available in Burkina Faso for a comparison across the regions.
2.2. Comparative quantitative analyses
Data on 1512 observations were compiled using Excel spreadsheets and analyzed with statistical package SPSS version 25 to assess the correlation between the following variables:
Demographic and economic characteristics: These characteristics refer to the geographical location (urban or rural), the size of the population, the poverty index, and the GNI coefficient.
Availability of resources: The resources refer to the health personnel and health infrastructures.
Health outcomes: The health outcomes refer to the visits to health facilities for care‐seeking, coverage of selected health services, morbidity, and mortality among vulnerable groups (malnutrition among under‐five and maternal mortality ratio).
Specifically, we carried out a bivariate correlation analysis to determine, on the one hand, the extent to which health districts' demographic and economic characteristics are associated with the availability of resources, as well as with health outcomes, and, on the other hand, to determine the association between the availability of resources and health outcomes. The Pearson Test was applied and the correlation between the variables investigated was found to be significant when P‐value was smaller than .05 (95% CI).
3. RESULTS
Table 1 presents the general demographic and economic characteristics in the 63 health districts in the 13 health regions.
TABLE 1.
General demographic and economic characteristics in health districts in health regions of Burkina Faso, 2010 and 2014
| 2010 | 2014 | |||||
|---|---|---|---|---|---|---|
| Health Regions/Number of Health Districts | Populationa | Poverty index (%)b | GINI coefficient (%)b | Populationa | Poverty index (%)b | GINI coefficient (%)b |
| Boucle du Mouhoun (N district = 6) | ||||||
| M | 264 458 | 12.70 | 8.80 | 295 316 | 20.40 | 25.40 |
| (SD) | 55 798 | 0.00 | 0.00 | 63 948 | 0.00 | 0.00 |
| Median | 268 854 | 12.70 | 8.80 | 299 650 | 20.40 | 25.40 |
| Cascades (N district = 3) | ||||||
| M | 219 082 | 6.70 | 14.70 | 237 686 | 3.20 | 30.10 |
| (SD) | 63 059 | 0.00 | 0.00 | 100 825 | 0.00 | 0.00 |
| Median | 182 675 | 6.70 | 14.70 | 205 740 | 3.20 | 30.10 |
| Centre (N district = 5) | ||||||
| M | 480 583 | 2.10 | 17.70 | 485 944 | 1.90 | 37.70 |
| (SD) | 225 293 | 0.00 | 0.00 | 264 298 | 0.00 | 0.00 |
| Median | 615 480 | 2.10 | 17.70 | 310 812 | 1.90 | 37.70 |
| Centre Est (N district = 7) | ||||||
| M | 180 397 | 14.20 | 14.70 | 203 903 | 13.90 | 31.70 |
| (SD) | 59 254 | 0.00 | 0.00 | 67 821 | 0.00 | 0.00 |
| Median | 176 246 | 14.20 | 14.70 | 199 773 | 13.90 | 31.70 |
| Centre Nord (N district = 4) | ||||||
| M | 333 715 | 22.10 | 12.70 | 375 749 | 11.80 | 27.90 |
| (SD) | 139 689 | 0.00 | 0.00 | 158 846 | 0.00 | 0.00 |
| Median | 335 666 | 22.10 | 12.70 | 378 222 | 11.80 | 27.90 |
| Centre Ouest (N district = 5) | ||||||
| M | 262 129 | 19.60 | 16.90 | 293 793 | 11.70 | 26.30 |
| (SD) | 106 335 | 0.00 | 0.00 | 116 475 | 0.00 | 0.00 |
| Median | 233 315 | 19.60 | 16.90 | 264 473 | 11.70 | 26.30 |
| Centre Sud (N district = 4) | ||||||
| M | 175 840 | 18.40 | 13.70 | 195 858 | 10.50 | 27.60 |
| (SD) | 73 582 | 0.00 | 0.00 | 82 982 | 0.00 | 0.00 |
| Median | 170 196 | 18.40 | 13.70 | 189 279 | 10.50 | 27.60 |
| Est (N district = 6) | ||||||
| M | 228 206 | 42.14 | 11.00 | 260 691 | 12.60 | 25.10 |
| (SD) | 133 578 | 0.00 | 0.00 | 151 082 | 0.00 | 0.00 |
| Median | 227 860 | 42.14 | 11.00 | 257 411 | 12.60 | 25.10 |
| Hauts Bassins (N district = 7) | ||||||
| M | 237 273 | 9.40 | 21.40 | 236 143 | 7.00 | 34.50 |
| (SD) | 136 008 | 0.00 | 0.00 | 111 953 | 0.00 | 0.00 |
| Median | 258 084 | 9.40 | 21.40 | 252 250 | 7.00 | 34.50 |
| Nord (N district = 5) | ||||||
| M | 261 324 | 17.60 | 15.20 | 324 592 | 25.20 | 23.80 |
| (SD) | 122 701 | 0.00 | 0.00 | 130 285 | 0.00 | 0.00 |
| Median | 185 199 | 17.60 | 15.20 | 370 272 | 25.20 | 23.80 |
| Plateau Central (N district = 3) | ||||||
| M | 254 858 | 13.80 | 12.40 | 284 179 | 12.60 | 24.30 |
| (SD) | 100 846 | 0.00 | 0.00 | 113 579 | 0.00 | 0.00 |
| Median | 261 892 | 13.80 | 12.40 | 291 759 | 12.60 | 24.30 |
| Sahel (N district = 4) | ||||||
| M | 271 563 | 49.80 | 9.70 | 308 390 | 4.00 | 24.20 |
| (SD) | 92 117 | 0.00 | 0.00 | 104 186 | 0.00 | 0.00 |
| Median | 258 296 | 49.80 | 9.70 | 293 106 | 4.00 | 24.20 |
| Sud Ouest (N district = 4) | ||||||
| M | 171 957 | 44.00 | 17.50 | 193 243 | 10.00 | 33.70 |
| (SD) | 94 254 | 0.00 | 0.00 | 105 618 | 0.00 | 0.00 |
| Median | 161 715 | 44.00 | 17.50 | 180 728 | 10.00 | 33.70 |
Sources of data: aHealth Management Information System (HMIS) reports. bNational Surveys on Households Leaving Conditions and Poverty.
The demographic data indicate a variation in population size across the regions. The regions with most inhabitants are the Centre, Centre Nord, Centre Ouest, Est, Haut Bassins, and Nord regions. The Centre region, which hosts the capital of the country, remains the most populated region.
We assumed that the region's economic characteristics (poverty index and GINI coefficient) pertain to all health districts within the region. Therefore, there is no variation in these characteristics within a region (SD = 0). Across the regions, the poverty index varied considerably. In 2010, the five regions with the highest poverty index were the Sahel (49.80%), Sud Ouest (44.00%), Est (42.14%), Centre Nord (22.10%), and Centre Ouest (19.60%). Compared to 2010, poverty was cut nearly in half in 2014 and the five regions with the highest poverty index were the Nord (25.20%), Boucle du Mouhoun (20.40%), Centre Est (13.90%), Est (12.60%), and Plateau Central (12.60%). The only region that remained in the five poorest regions in 2010 as well as in 2014 was the Est region. This region has also been known as the poorest region of the country where the less well‐off province is located. 21 With regard to the GINI coefficient, in 2010, it ranged from 8.80% (Boucle du Mouhoun region) to 21.40% (Hauts Bassins region), and in 2014, from 23.80% (Nord region) to 37.70% (Centre region). Thus, compared to 2010, the inequality in income indicated by the GINI coefficient was considerably higher in 2014, and it literally doubled and even tripled in some regions (Boucle Mouhoun). The two regions with the highest income inequality were the Centre and the Hauts Bassins which include the two major cities of the country.
Table 2 shows the data on the availability of health resources (financial resources, human resources, and health infrastructures).
TABLE 2.
Availability of health resources in health districts in regions of Burkina Faso, 2010 and 2014
| 2010 | 2014 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Health Regions/Number Health Districts | Financial resources allocated ($) | Financial resources spent ($) | Difference ($) | Ratio health personnel/10 000 capita | Ratio health infrastructure/10 000 capita | Financial resources allocated ($) | Financial resources spent ($) | Difference ($) | Ratio health personnel/10 000 capita | Ratio health infrastructure/10 000 capita |
| Boucle du Mouhoun (N district = 6) | ||||||||||
| M | 960 667 | 931 333 | 29 333 | 8.70 | 1.33 | 1 290 460 | 1 228 933 | 61 527 | 9.26 | 1.33 |
| (SD) | 172 049 | 160 111 | 12 737 | 1.81 | 0.14 | 205 659 | 242 184 | 67 725 | 1.43 | 0.13 |
| Median | 996 000 | 965 000 | 28 667 | 8.00 | 1.00 | 1 287 945 | 1 253 265 | 63 792 | 8.74 | 1.33 |
| Cascades (N district = 3) | ||||||||||
| M | 650 667 | 596 667 | 54 000 | 5.66 | 1.32 | 1 079 138 | 1 032 410 | 46 728 | 8.62 | 1.35 |
| (SD) | 199 342 | 228 843 | 75 020 | 2.91 | 0.58 | 229 209 | 264 467 | 46 945 | 2.37 | 0.57 |
| Median | 624 000 | 484 000 | 20 000 | 7.17 | 1.59 | 951 286 | 919 656 | 31 630 | 8.01 | 1.48 |
| Centre (N district = 5) | ||||||||||
| M | 1 005 200 | 970 400 | 34 800 | 9.60 | 2.02 | 1 295 463 | 1 254 791 | 40 672 | 10.44 | 2.03 |
| (SD) | 628 580 | 607 866 | 26 668 | 4.13 | 1.59 | 649 431 | 608 548 | 68 453 | 1.67 | 1.10 |
| Median | 626 000 | 582 000 | 30 000 | 10.33 | 1.74 | 937 174 | 932 678 | 4596 | 10.65 | 1.72 |
| Centre Est (N district = 7) | ||||||||||
| M | 570 286 | 537 143 | 33 143 | 6.21 | 1.14 | 837 974 | 780 180 | 69 726 | 7.57 | 1.21 |
| (SD) | 190 061 | 176 007 | 39 036 | 1.41 | 0.20 | 285 419 | 260 894 | 57 578 | 1.84 | 0.25 |
| Median | 608 000 | 592 000 | 18 000 | 6.01 | 1.12 | 951 622 | 850 860 | 69 902 | 7.69 | 1.14 |
| Centre Nord (N district = 4) | ||||||||||
| M | 897 500 | 771 500 | 126 000 | 5.48 | 0.98 | 941 202 | 858 188 | 83 014 | 5.27 | 0.97 |
| (SD) | 322 260 | 262 177 | 82 024 | 0.96 | 0.19 | 318 715 | 324 147 | 42 693 | 0.76 | 0.21 |
| Median | 907 000 | 822 000 | 92 000 | 5.32 | 1.01 | 918 029 | 822 996 | 71 137 | 5.28 | 1.06 |
| Centre Ouest (N district = 5) | ||||||||||
| M | 750 400 | 692 000 | 58 400 | 7.01 | 1.37 | 1 087 087 | 1 009 555 | 77 533 | 6.74 | 1.41 |
| (SD) | 332 149 | 303 397 | 29 846 | 1.80 | 0.33 | 546 669 | 515 895 | 66 767 | 1.38 | 0.38 |
| Median | 734 000 | 688 000 | 54 000 | 6.69 | 1.48 | 1 098 977 | 1 059 838 | 66 255 | 6.89 | 1.52 |
| Centre Sud (N district = 4) | ||||||||||
| M | 647 500 | 591 000 | 56 500 | 9.62 | 1.63 | 820 627 | 751 928 | 68 699 | 12.24 | 1.66 |
| (SD) | 83 096 | 82 793 | 43 186 | 3.43 | 0.33 | 223 605 | 218 511 | 18 021 | 6.69 | 0.38 |
| Median | 620 000 | 587 000 | 54 000 | 9.28 | 1.56 | 769 637 | 716 467 | 69 520 | 10.42 | 1.65 |
| Est (N district = 6) | ||||||||||
| M | 725 333 | 630 333 | 95 000 | 6.15 | 1.01 | 1 004 310 | 937 316 | 66 994 | 6.46 | 0.96 |
| (SD) | 363 533 | 378 959 | 137 042 | 1.84 | 0.32 | 481 948 | 427 867 | 84 170 | 2.76 | 0.26 |
| Median | 630 000 | 428 000 | 41 000 | 5.87 | 0.93 | 1 004 669 | 907 163 | 23 736 | 5.77 | 0.90 |
| Hauts Bassins (N district = 7) | ||||||||||
| M | 691 143 | 639 714 | 51 429 | 8.50 | 1.57 | 1 087 053 | 1 050 557 | 36 496 | 9.89 | 1.64 |
| (SD) | 321 490 | 319 742 | 53 587 | 2.26 | 0.57 | 526 779 | 510 333 | 40 384 | 4.86 | 0.90 |
| Median | 570 000 | 516 000 | 28 000 | 8.04 | 1.42 | 1 150 931 | 1 064 283 | 24 534 | 7.14 | 1.31 |
| Nord (N district = 5) | ||||||||||
| M | 744 800 | 715 200 | 29 600 | 7.04 | 1.42 | 1 083 190 | 1 053 545 | 29 645 | 6.80 | 1.36 |
| (SD) | 212 441 | 201 413 | 11 781 | 0.81 | 0.21 | 396 777 | 374 538 | 32 220 | 1.27 | 0.35 |
| Median | 768 000 | 734 000 | 34 000 | 7.31 | 1.39 | 1 260 936 | 1 253 018 | 25 674 | 6.85 | 1.35 |
| Plateau Central (N district = 3) | ||||||||||
| M | 832 667 | 792 667 | 40 000 | 8.77 | 1.72 | 1 267 649 | 1 227 723 | 39 926 | 10.70 | 1.79 |
| (SD) | 291 591 | 259 356 | 36 056 | 2.37 | 0.35 | 383 195 | 370 263 | 36 924 | 3.44 | 0.42 |
| Median | 954 000 | 884 000 | 50 000 | 8.96 | 1.73 | 1 464 523 | 1 430 534 | 25 674 | 12.34 | 1.74 |
| Sahel (N district = 4) | ||||||||||
| M | 696 000 | 650 500 | 45 500 | 4.95 | 0.73 | 1 117 672 | 1 099 938 | 32 378 | 4.96 | 0.84 |
| (SD) | 326 537 | 303 541 | 34 732 | 0.39 | 0.09 | 230 946 | 229 260 | 1420 | 0.32 | 0.11 |
| Median | 653 000 | 594 000 | 41 000 | 4.88 | 0.72 | 1 139 195 | 1 112 067 | 32 318 | 5.07 | 0.87 |
| Sud Ouest (N district = 4) | ||||||||||
| M | 616 500 | 574 500 | 42 000 | 9.68 | 1.50 | 885 316 | 826 954 | 21 649 | 10.00 | 1.58 |
| (SD) | 147 814 | 148 937 | 24 386 | 2.76 | 0.07 | 256 888 | 206 059 | 2353 | 3.29 | 0.15 |
| Median | 610 000 | 555 000 | 41 000 | 10.16 | 1.52 | 778 932 | 739 279 | 21 844 | 10.01 | 1.64 |
Sources of data: Health Management Information System (HMIS) Report.
Data on financial resources indicated some variations in financial resources allocated and financial resources spent, and particularly in the difference between these two amounts. There was an increase in health financial resources allocated to health districts between 2010 and 2014. In 2010, the mean of resources allocated ranged from $570 286 (Centre Est region) to $1 005 200 (Centre region) against $820 627 (Centre Sud region) to $1 295 463 (Centre region) in 2014. For resources spent, the means ranged from $537 143 (Centre Est region) to $970 400 (Centre region) in 2010 and from $751 928 (Centre Sud region) to $1 254 791 (Centre region) in 2014. The Centre Est and the Centre Sud regions, which received the least amount in 2010 and 2014, respectively, had the lowest expenditures. However, the Centre region with the highest amount allocated in 2010 and 2014, registered the highest expenditures in the same period. With regard to differences in resources allocated and resources spent, the region with the lowest difference in 2010 was the Boucle du Mouhoun ($29 333) and the region with the highest difference was the Centre Nord ($126 000). In 2014, the highest difference between resources allocate and spent, was the Centre Nord region ($83 014) and the lowest difference was in the Sud Ouest region ($21 469).
With regard to health personnel availability, the density of health personnel (ratio per 10 000 capita) remained far below the WHO norms set at 23 per 10 000 capita. In 2010, the mean of the health personnel ratio ranged from 4.95 in the Sahel region to 9.68 in the Sud Ouest region. In 2014, the region with the lowest health personnel ratio was still the Sahel (4.96) being one of the rearmost parts of the country, against the Centre Sud region which presented the highest health personnel ratio in 2014 (12.24).
The distribution of health infrastructure did not improve during the period. In 2010 and 2014, the Sahel region had the lowest health infrastructure ratio estimated, respectively, at 0.73 and 0.84, while the Centre region with infrastructure ratio of 2.02 and 2.03, ranked first in both years.
Overall, as indicated above, health resources were not equally distributed across the regions. The urban regions were privileged at the expense of the rural and more remoted regions, especially with regard to health personnel and health infrastructures.
Table 3 presents a range of selected indicators for health service utilization and health outcomes. The number of health visits per capita in 2010 was less than one across all regions, indicating potential underuse of health services. In 2010, the number of visits ranged from 0.49 in the Centre Nord region to 0.93 in the Centre region and from 0.65 in the Centre region to 1.01 in the Centre Est region in 2014.
TABLE 3.
Selected health outcomes in health districts in health regions of Burkina Faso, in 2010 and 2014
| 2010 | 2014 | |||||||
|---|---|---|---|---|---|---|---|---|
| Health Regions/Number of Health Districts | Number of health visits /capita/y | Skilled birth attendance (%) | Malnutrition under 5 (%) | Maternal mortality ratio | Number of health visits /capita/y | Skilled birth attendance (%) | Malnutrition under 5 (%) | Maternal mortality ratio |
| Boucle du Mouhoun (N district = 6) | ||||||||
| M | 0.59 | 83.02 | 4.68 | 97.93 | 0.74 | 91.33 | 14.75 | 68.12 |
| (SD) | 0.16 | 2.81 | 1.68 | 43.59 | 0.09 | 6.22 | 6.67 | 35.28 |
| Median | 0.52 | 82.55 | 4.84 | 101.77 | 0.73 | 91.42 | 14.63 | 71.11 |
| Cascades (N district = 3) | ||||||||
| M | 0.54 | 74.47 | 5.20 | 60.10 | 0.86 | 87.13 | 8.43 | 29.57 |
| (SD) | 0.08 | 20.26 | 1.95 | 46.09 | 0.14 | 13.26 | 0.40 | 18.43 |
| Median | 0.56 | 67.00 | 5.10 | 66.10 | 0.90 | 88.60 | 8.50 | 37.50 |
| Centre (N district = 5) | ||||||||
| M | 0.93 | 86.84 | 4.02 | 55.12 | 1.00 | 91.92 | 3.64 | 29.02 |
| (SD) | 0.17 | 16.95 | 1.73 | 39.80 | 0.19 | 30.99 | 2.03 | 26.71 |
| Median | 0.99 | 84.40 | 4.70 | 43.60 | 0.94 | 97.20 | 2.80 | 29.70 |
| Centre Est (N district = 7) | ||||||||
| M | 0.80 | 79.60 | 3.57 | 53.03 | 1.01 | 86.10 | 9.03 | 40.00 |
| (SD) | 0.08 | 7.74 | 1.57 | 41.63 | 0.07 | 11.91 | 4.50 | 12.94 |
| Median | 0.82 | 81.00 | 3.30 | 45.30 | 1.02 | 86.60 | 7.60 | 41.40 |
| Centre Nord (N district = 4) | ||||||||
| M | 0.49 | 66.00 | 8.80 | 57.15 | 0.65 | 83.30 | 18.15 | 36.93 |
| (SD) | 0.08 | 9.44 | 1.69 | 33.31 | 0.13 | 10.51 | 13.14 | 27.37 |
| Median | 0.50 | 62.85 | 9.30 | 45.55 | 0.64 | 85.60 | 17.05 | 42.90 |
| Centre Ouest (N district = 5) | ||||||||
| M | 0.54 | 66.80 | 5.30 | 90.12 | 0.72 | 74.80 | 21.54 | 26.78 |
| (SD) | 0.14 | 5.04 | 2.22 | 95.15 | 0.12 | 11.85 | 5.15 | 26.89 |
| Median | 0.55 | 67.50 | 4.60 | 76.80 | 0.79 | 75.60 | 21.80 | 27.70 |
| Centre Sud (N district = 4) | ||||||||
| M | 0.67 | 81.15 | 8.23 | 74.70 | 0.86 | 73.73 | 3.90 | 49.75 |
| (SD) | 0.04 | 10.62 | 3.67 | 18.44 | 0.13 | 5.96 | 3.41 | 45.72 |
| Median | 0.66 | 76.95 | 8.60 | 75.80 | 0.83 | 73.55 | 2.35 | 41.30 |
| Est (N district = 6) | ||||||||
| M | 0.54 | 65.32 | 6.68 | 85.58 | 0.81 | 79.27 | 13.50 | 64.15 |
| (SD) | 0.09 | 5.98 | 1.78 | 73.79 | 0.14 | 12.62 | 8.80 | 34.16 |
| Median | 0.53 | 62.75 | 6.05 | 79.95 | 0.83 | 73.00 | 10.70 | 75.80 |
| Hauts Bassins (N district = 7) | ||||||||
| M | 0.59 | 80.01 | 5.73 | 54.07 | 0.83 | 89.44 | 6.16 | 40.56 |
| (SD) | 0.12 | 11.09 | 4.95 | 48.49 | 0.16 | 14.64 | 6.00 | 34.78 |
| Median | 0.61 | 85.30 | 2.70 | 50.70 | 0.88 | 94.60 | 3.80 | 35.30 |
| Nord (N district = 5) | ||||||||
| M | 0.72 | 79.58 | 17.18 | 64.40 | 0.81 | 93.92 | 37.02 | 60.88 |
| (SD) | 0.21 | 15.43 | 8.47 | 71.16 | 0.18 | 12.40 | 15.48 | 26.12 |
| Median | 0.65 | 74.10 | 18.30 | 44.50 | 0.73 | 91.80 | 36.90 | 69.80 |
| Plateau Central (N district = 3) | ||||||||
| M | 0.72 | 83.10 | 3.10 | 50.53 | 0.92 | 81.97 | 9.23 | 68.27 |
| (SD) | 0.03 | 6.65 | 1.82 | 13.56 | 0.08 | 14.12 | 2.10 | 53.90 |
| Median | 0.72 | 84.70 | 2.20 | 57.60 | 0.94 | 80.40 | 8.50 | 37.50 |
| Sahel (N district = 4) | ||||||||
| M | 0.59 | 58.40 | 14.63 | 92.45 | 0.86 | 77.83 | 19.33 | 151.83 |
| (SD) | 0.29 | 24.93 | 4.56 | 59.05 | 0.34 | 26.43 | 11.99 | 72.64 |
| Median | 0.57 | 55.55 | 15.75 | 101.35 | 0.85 | 76.75 | 21.00 | 181.35 |
| Sud Ouest (N district = 4) | ||||||||
| M | 0.60 | 61.48 | 6.90 | 38.93 | 0.86 | 77.73 | 10.13 | 31.93 |
| (SD) | 0.05 | 16.09 | 3.10 | 27.58 | 0.13 | 14.46 | 10.31 | 20.71 |
| Median | 0.58 | 60.00 | 6.90 | 46.85 | 0.83 | 81.35 | 5.95 | 28.70 |
Sources of data: Health Management Information System (HMIS) report.
The coverage of skilled birth attendance ranged from 58.40% in the Sahel region to 86.10% in the Centre region in 2010. There was a slight increase in skilled birth attendance in 2014. The lowest coverage was recorded in the Centre Sud region (73.73%) and the highest coverage in the Nord region (93.92%).
With regard to the pregnancy outcomes, the situation did not improve between 2010 and 2014 with significant variations between regions. The maternal mortality ratio ranged from 38.93 in the Sud Ouest region to 97.93 in the Boucle Mouhoun region in 2010. In 2014, the Centre Ouest region has registered the lowest maternal mortality ratio (26.78) against the Sahel region with an alarming maternal mortality ratio (151.83). The most important variation in maternal mortality ratio in 2010 was in the Centre Ouest and Nord regions and in the Centre Ouest region once again in 2014.
The proportion of under‐five undernourished ranged from 3.10% in the Plateau Central region to 17.18% in the Nord region in 2010. This proportion ranged from 3.64% in the Centre region to 37.02% in the Nord region in 2014. The most important variation was observed in the Sud Ouest region in 2014.
Tables 4, 5, 6 present the results of the statistical analysis using bivariate correlations to investigate the relationship between the district demographic, socio‐economic characteristics, and health outcomes.
TABLE 4.
Health districts demographic and economic characteristics and availability of resources in Burkina Faso, 2010 and 2014
| 2010 | 2014 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Financial resources allocated ($) | Financial resources spent ($) | Difference ($) | Ratio health personnel/10 000 capita | Ratio health infrastructure/10 000 capita | Financial resources allocated ($) | Financial resources spent ($) | Difference ($) | Ratio health personnel/10 000 capita | Ratio health infrastructure/10 000 capita | ||
| Population size | Correlation Pearson Test | .778** | .754** | .239 | −.282* | −.072 | .742** | .723** | .324** | −.280* | −.064 |
| Poverty index | Correlation Pearson Test. | −.142 | −.177 | .160 | −.226 | −.355** | −.013 | −.021 | .061 | −.120 | −.152 |
| GINI coefficient | Correlation Pearson Test | −.107 | −.097 | −.069 | .228 | .318* | −.027 | −.022 | −.041 | .296* | .347** |
P < .05.
P < .1.
TABLE 5.
Health districts demographic and economic characteristics and health outcomes in Burkina Faso, 2010 and 2014
| 2010 | 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Number of visit per capita/year | Skilled birth attendance (%) | Malnutrition among under 5 (%) | Maternal mortality | Number of visits per capita/y | Skilled birth attendance (%) | Malnutrition among under 5 (%) | Maternal mortality | ||
| Populationsize | Correlation Pearson Test | −.006 | .040 | .035 | −.165 | −.099 | −.115 | .068 | .054 |
| Poverty index | Correlation Pearson Test | −.329** | −.544** | .326** | .097 | −.196 | .113 | .509** | .030 |
| GINI coefficient | Correlation Pearson Test | .155 | .134 | ‐.130 | −.226 | .307* | .120 | −.512** | −.389** |
P < .05.
P < .1.
TABLE 6.
Availability of resources and health outcomes in districts in Burkina Faso, 2010 and 2014 (n = 63)
| 2010 | 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Number of visits per capita/y | Skilled birth attendance (%) | Malnutrition among under 5 (%) | Maternal mortality | Number of visits per capita/y | Skilled birth attendance (%) | Malnutrition among under 5 (%) | Maternal mortality | ||
| Financial resources allocated (US $) | Correlation Pearson Test | −.041 | .056 | .045 | −.019 | .105 | −.055 | −.059 | .128 |
| Financial resources spent ($) | Correlation Pearson Test | .011 | .089 | .029 | −.013 | .129 | −.029 | −.049 | .131 |
| Difference ($) | Correlation Pearson Test | −.267* | −.159 | .092 | −.034 | −.146 | −.200 | −.091 | .017 |
| Ratio health personnel/10 000 capita | Correlation Pearson Test | .367** | .230 | −.117 | .037 | ,297* | −.001 | −.282* | −.133 |
| Ratio health infrastructure/10 000 capita | Correlation Pearson Test | .411** | .108 | −.160 | −.119 | .301* | −.234 | −.205 | −.308* |
P < .05.
P < .1.
Table 4 presents the correlation analysis of the health district's demographic and economic characteristics, and the availability of resources. The results indicate a strong correlation between district's population size and the availability of human resources for health, as well as health infrastructures (P < .05). We also found that a decrease in health personnel and health infrastructures ratio per capita is correlated with an increase in the population size and the poverty index (P < .05). A moderate correlation was found between the GINI coefficient and the health personnel, and health infrastructures ratio per capita (P < .10).
Table 5 shows the correlation between the health district's demographic and economic characteristics and health outcomes. The results indicate that the key health outcomes were correlated (P < .05) with the economic status of health districts. The poverty index increases, the number of health services visits per capita, and the percentage of skilled birth attendance decline, while the malnutrition among under‐five recorded an increase. No correlation was found between the population size and health outcomes.
Table 6 provides the results of the correlation analysis of health resources and health outcomes. It can be inferred that health services visits are correlated with the availability of health infrastructures and health personnel. The number of visits per capita increases with the increase in the ratio of health personnel and health infrastructure per capita (P < .05). The results also suggest that malnutrition among under‐five and maternal mortality decline when the ratio of health personnel and health infrastructure per capita increase (P < .05). No correlation is found between the financial resources and health outcomes.
4. DISCUSSION
In this study, we used secondary regional‐level data to analyze the relationship between health districts' demographic and economic status, and health outcomes, using a bivariate correlation analysis. We first determined the correlation between the health district's demographic and economic characteristics, on the one side, and the availability of resources and health outcomes, on the other side. Secondly, we determined the correlation between the availability of resources and health outcomes.
The results indicate sizeable variation in the population and poverty index across the regions and a variation in health resources and health outcomes across health districts. While the poverty index declined between 2010 and 2014, the inequality in income between regions has significantly increased during the same period. With regard to health resources, the data suggest inequality in their distribution, that is, urban regions are more privileged than rural regions, which also record poorer health outcomes.
The findings show an increase in health financing which is a favorable trend. However, this progress is unequally distributed across health districts highlighting the unfairness in the allocation of health financial resources. Another major finding of our study is the relationship between health resources (personnel and health infrastructures), the population size and the poverty index. This suggests that an increase in poverty and population size is associated with a decrease in health personnel and health infrastructure ratio, thus showing that the poorest and the most populated health districts are less likely to have an optimal level of health personnel and health infrastructure that are key for ensuring adequate health services coverage.
This reflects the failure of the health resources allocation policy to distribute health resources in a fair and equal manner across the country, thereby contributing to deepened inequality between urban and rural regions, as well as between well‐off and poorest regions.
Regional and district disparities were observed before the start of the health care decentralization as well. A study on the health sector reform in Burkina Faso conducted about two decades ago, 22 found that there was a strong urban bias to public spending on health, with 70% of funds being directed to the 16% of the population that was urban, leaving 30% for the remaining 84% rural population. Part of the bias in the distribution of financial resources was reflected by the inequitable distribution of human resources, with 57% of physicians, 65% of pharmacists, 55% of midwives, and 47% of nurses being deployed in the two main cities of the country. 22
Ten years later, another study showed that this situation remained unchanged. Specifically, in a policy note, Zida et al 23 identified the key issues in resource allocation in Burkina Faso, which were the failure to account for problems encountered by health facilities, the health status of the population, and the existing health system problems. The resources were allocated using a historical allocation key, which did not reflect the programs' performance and the population health's needs, resulting in poor performance of programs, multiplicity of resources allocation methods, poor access to health services, high morbidity, and mortality. These findings were similar to the results of a study for Ghana, which suggested that equity in resources allocation should consider the difference between health regions and the difference within the region as well. 24
Regarding the relationship between health outcomes and the economic performance of the health districts, this study found that a higher poverty rate was associated with less access to health services (health visits, skilled birth attendance decline) and poorer health status (more malnutrition among under five). It was also found that health services visits, malnutrition among under five, and maternal mortality were associated with the availability of health personnel and health infrastructures. The correlation between poverty and health has been widely explored in developing countries, including Burkina Faso, and the economic status was found to be associated with health care access.10, 25, 26 A review on inequalities in health care use and expenditures in eight developing countries and countries in transition revealed that richer groups had a higher probability of obtaining care when sick, to be more likely to be seen by a medical doctor, and to have a higher probability of receiving medicines when ill, than the poorer groups. 5 The inequity in malnutrition among under five 27 and inequalities in infant and under‐five mortality 28 had to do with households' income.
There is broad agreement that inequality is unfair, avoidable, and concerns everyone.1, 29 The causes or drivers of inequality are manifold and its resolution suggests a more in‐depth understanding toward equitable access to health services. Previous studies on health sector reform in Burkina Faso showed that the main causes of the unfair distribution of health resources are the over‐centralization of resources leading to allocative and technical inefficiencies. 22 The over‐centralization of resources, particularly for investments and staff recruitment, was found to be a barrier to equitable resource allocation at the health district level although the district's managers could have the option to decide over district's health priorities and to readjust resources allocation. 30 A study for Ghana indicated that equity in the allocation of resources within regions had been virtually ignored, creating a vacuum in knowledge regarding how resources were allocated intra‐regionally and their potential influence on promoting health equity. 24 The resource allocation method was found to be the main driver of inequity in resource allocation across geographic areas. The equity of funding allocation was greatly influenced by the availability of human resources for health, local capacity to utilize funds, donor involvement in the health sector, and commitment to promote equity.
As mentioned in the introduction, the decentralization of health care was aimed to increase health services provision and access in order to improve equitably the services coverage and population's health status across the health districts and health regions. The study findings show that the equality objective in health was not achieved in the period of health care decentralization in Burkina Faso. In relation to the issue of decentralization and health‐related equity, a systematic review found that depending on the context, decentralization could either lead to equity gains or exacerbate inequities. 15 It can lead to inequities in health financing between sub‐national jurisdictions. However, the impact of decentralization on inequities in health and health care depends on pre‐existing socio‐economic disparities and financial barriers to access. This suggests that addressing inequality between health districts and health regions would require the reduction of the gap between poor and rich, and the removal of financial barriers that impede equitable access to health services.
The selected indicators, although relevant to assess the health disparities, present some limitations because each indicator quantifies a different perspective of inequity. It is therefore imperative to consider how an indicator operationalizes inequities. In particular, it will be advantageous to determine and use a comprehensive set of indicators for a robust assessment of health inequities. 20 This was not possible in our study because the availability of data comparable across the regions is limited in Burkina Faso.
5. STUDY LIMITATIONS
The study used secondary data from the health management information system (HMIS) and national households' survey to carry out a bivariate correlation analysis, which presents four main limitations. First, the health management information system reports are official statistics published by the Ministry of Health that mainly focus on health facilities. They are not able to capture individual‐level details relevant to the wider population. Second, the measurement instruments used for the preparation of these reports have changed over time, making it difficult to perform a period‐to‐period comparison, as well as comparison across health regions and health districts. Third, some potential misinterpretation and samples representativeness issues might have occurred given our inability to control data quality and data interpretation. This could interfere with the statistical analysis. Fourth, the aim of the study was not to assess the effects of decentralization on the health system or on health. Therefore, the results should not be referenced as an evaluation of the decentralization impact on health inequality in Burkina Faso.
6. CONCLUSION
Through this study, we explored the relationship between health districts' demographics, economic status, and health outcomes in Burkina Faso. The results indicated demographic and economic differences in health outcomes between health districts, thus showing an inequality in health among health districts. The main underlying causes or drivers of inequality are the economic difference among the population, the health resources allocation policy, and methods that should promote equity among and within the different geographical entities.
The study has taken place in the context of decentralization in Burkina Faso, but it did not have the necessary data to link the results with the direct effect of the decentralization reform. However, it can be noted that the health and health care outcomes changed during the decentralization. Therefore, from a decentralization policy perspective, the findings suggest challenges in addressing the inequality between regions and districts to eliminate the inequality related factors and therefore enhance the equity in health services access, coverage and outcomes. This requires a more transparent resource allocation system based on regions and districts' needs considering the decentralization system, policies to address the socio‐economic disparities and financial barriers to health services. A consideration should be given to a health inequality monitoring system to be able to determine the root causes of inequity and identify feasible approaches to address these causes. 19
Further research is needed to collect relevant data and investigate the effects of decentralization, which was not possible in our study.
CONFLICT OF INTEREST
The authors whose names are listed above certify that they have NO conflicts of interests to report and are not affiliated with or involved in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent‐licensing arrangements), or non‐financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
COMPLIANCE WITH ANIMAL/HUMAN ETHICS GUIDELINES
Our research does not require any human/animal subjects to acquire a research ethics board approval.
ACKNOWLEDGEMENTS
We are grateful to the Ministry of Health and the Ministry of Finances for the different reports published. The views expressed in this paper are those of the authors and do not necessarily represent those of their institutions.
Zon H, Pavlova M, Groot W. Regional health disparities in Burkina Faso during the period of health care decentralization. Results of a macro‐level analysis. Int J Health Plann Mgmt. 2020;35:939–959. 10.1002/hpm.2979
REFERENCES
- 1. WHO . The World Health Report: Health Systems Financing: The Path to Universal Coverage. The World Health Organization 2010. ISBN 978 92 4 068480 5. [DOI] [PMC free article] [PubMed]
- 2. Ataguba JE, James AJ, McIntyre D. Socioeconomic‐related health inequality in South Africa: evidence from General Household Surveys. Int J Equity Health. 2011;10:48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Heaton T, Crookston B, Hayley P, Acheampong YA. Social inequality and children's health in Africa: a cross sectional study. Int J Equity Health. 2016;15:92 10.1186/s12939-016-0372-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Pillay‐van V, Bradshaw D. Mortality and socioeconomic status: the vicious cycle between poverty and ill health. Lancet. 2017;5(9):pe851‐e852. [DOI] [PubMed] [Google Scholar]
- 5. Makinen M, Waters H, Rauch M, et al. Inequalities in health care use and expenditures: empirical data from eight developing countries and countries in transition. Bull World Health Organ. 2000;78(1). [PMC free article] [PubMed] [Google Scholar]
- 6. Mwase T, Brenner S, Mazalale J, et al. Inequities and their determinants in coverage of maternal health services in Burkina Faso. Int J Equity Health. 2018;17(58). 10.1186/s12939-018-0770-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Okolo CO, Reidpath DD, Allotey P. Socioeconomic inequalities in access to health care: examining the case of Burkina Faso. J Health Care Poor Underserved. 2011;22(2):663‐682. [DOI] [PubMed] [Google Scholar]
- 8. Gwatkin DR. Health inequalities and the health of the poor: what do we know? What can we do? Bull World Health Organ. 2000;78(1). [PMC free article] [PubMed] [Google Scholar]
- 9. Su T, Kouyaté B, Steffen F. Catastrophic household expenditure for health care in a low‐income society: a study from Nouna District, Burkina Faso. Bull World Health Organ. 2006;84(1):21‐27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Bado AR, Susuman S. Decomposing wealth‐based inequalities in under‐five mortality in West Africa. Iran J Public Health. 2015;44(7):920‐930. [PMC free article] [PubMed] [Google Scholar]
- 11. Shankar P, Kumar R. Reducing health inequities in a generation: a dream or reality? Bull World Health Organ. 2009;87:84‐84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Litvack J, Junaid A, Bird R. Rethinking decentralization in Developing Countries. World Bank sector studies series 1998, ISBN0‐8213‐4350‐5.
- 13. Saltman R, Bankauskaite V, Vrangbæk K. Decentralization in Health Care, Strategies and Outcomes. [Google Scholar]
- 14. Rondinelli A, John R, Nellis G, Cheema S. Decentralization in developping countries. World Bank Staff Working Papers number 581, Management and Development Series number 8. 1983.
- 15. Sumah AM, Baatiema L, Abimbola S. The impacts of decentralization on health‐related equity: a systematic review of the evidence. Health Policy. 2016. Oct;120(10):1183‐1192. 10.1016/j.healthpol.2016.09.003. [DOI] [PubMed] [Google Scholar]
- 16. Haddad S, Nougtara A, Fournier P. Learning from health system reforms: lessons from Burkina Faso. Trop Med Int Health. 2006. Dec;11(12):1889‐1897. [DOI] [PubMed] [Google Scholar]
- 17.Ministère de l'Aménagement du Territoire et de la Décentralisation (MATD) Burkina Faso. Evaluation Globale des Compétences et des Ressources Transférées de l'Etat aux Communes [in French] 2014; Rapport d'études.
- 18. Dlouhy M. Measuring geographic inequalities: dealing with multiple health resources by data envelopment analysis. Front Public Health. 6(53). 10.3389/fpubh.2018.00053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Hosseinpoor AR, Bergen N, Schlotheubera A, Grovea J. Measuring health inequalities in the context of sustainable development goals. Bull World Health Organ. 2018;96:654‐659. 10.2471/BLT.18.210401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Olakunle A, David P. Utility and limitations of measures of health inequities: a theoretical perspective. Glob Health Action. 2015;8:27591 10.3402/gha.v8.27591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Programme des Nations Unies pour Développement (PNUD/UNDP), Burkina Faso. Cartographie de la pauvreté et des inégalités au Burkina Faso, 2014.
- 22. Bodart C, Servais G, Yansané M, Schmidt B. The influence of health sector reform and external assistance in Burkina Faso. Health Policy Plan. 16(1):74‐86. [DOI] [PubMed] [Google Scholar]
- 23. Zida A, Ouédraogo S, Kouyaté B. Stratégies d'amélioration des allocations budgétaires pour la santé au Burkina Faso (Note de politique SURE). Ouagadougou, Burkina Faso: Ministère de la santé; 2010. www.evipnet.org/sure. [Google Scholar]
- 24. Danso AA, Zwi AB, Ho MT. Equity in resource allocation for health: a comparative study of the Ashanti and Northern Regions of Ghana. Health Policy. 2006;78:135‐148. [DOI] [PubMed] [Google Scholar]
- 25. Bado AR, Susuman S. Determinants of under‐5 mortality in Burkina Faso. J Public Health. 41 10.1093/pubmed/fdy168. [DOI] [PubMed] [Google Scholar]
- 26.WHO. The state of inequality: reproductive, maternal, newborn and child health. Global Health Expenditures database. World Health Organization (WHO). 2015. Accessed October 5, 2018. https://apps.who.int/nha/database/Select/Indicators/en
- 27. Zere E, McIntyre D. Inequities in under‐five child malnutrition in South Africa. Int J Equity Health. 2003;2:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Wagstaff A. Socioeconomic inequalities in child mortality: comparisons across nine developing countries. Bull World Health Organ. 2000;78(1). [PMC free article] [PubMed] [Google Scholar]
- 29. Woodward A, Kawachi I. Why reduce health inequalities? J Epidemiol Community Health. 10.1136/jech.54.12.923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Von E, Savadogo B. Choix des priorités et allocation des ressources dans le système de santé du Burkina Faso : centres et pouvoirs de décision. Medicus Mundis Scheweiz Bulletin #91. December 2003.