Socioeconomic Conditions and Infant Mortality: The Recent Experience of Francophone Africa

Abstract

This chapter highlights the impact of socioeconomic conditions on infant mortality trends, based on the experience of 18 French-speaking African countries over the past 30 years. We use a mix of classification, decomposition, and regression methods to highlight convergences and divergences between countries. The analyses show steep declines in five countries (Congo, DRC, Burundi, Central African Republic, Niger, and Mali). For most (14 out of 18) countries, the analyses also show a narrowing of the rural-urban gap that is unfortunately due to slow declines or a rise in the risk of mortality in urban areas. Elsewhere, this gap remains steady. Decomposition analyses underscore the role of general improvements in health services and infrastructure, which appear as the main driver of change.

1. Introduction

Since 1990, child mortality has been nearly halved worldwide, but it is now concentrated in sub-Saharan Africa and South Asia, where the burden is greatest [1, 2]. Nearly, 56% of all child deaths occur in sub-Saharan Africa and 26% in South Asia [1, 2, 3]. At the same time, there are profound rural-urban differences in the chances of survival. Although sub-Saharan Africa still holds the record for the highest level of child mortality in the world, it also showed the fastest decline. Between 1997 and 2017, child mortality fell from 166 to 78 deaths per 1000 live births, a 53% decline concomitant with an 11-year gain in life expectancy [4].

Sub-Saharan Africa itself is not homogeneous, and its contextual diversity is likely to induce variability in child mortality. French-speaking sub-Saharan Africa exemplifies this diversity. This group of 23 countries¹ have French as their official language and share the French or Belgian colonial heritage that still affects their economic performance and governance².

Yet there are questions as to whether this group of countries is itself homogenous. To what extent do they show the same trends in infant mortality? What similarities and differences can be found? This is the central question in this chapter.

Its objective is to describe trends in levels and rural-urban differences in infant mortality over the past 30 years across a sample of 18 French-speaking African countries for which data are available, and to highlight the impact of socioeconomic status.

2. Socioeconomic status and infant mortality: the expected links

Models of child mortality point to several levels of influence involving policy, community, family resources, parental characteristics, and individual factors [10, 11]. Socioeconomic policies influence child mortality indirectly through literacy, health behaviors and standard of living, residence environment, etc. At the community level, the availability of basic social services and infrastructure (markets, drinking water, electricity, health and family planning services, etc.) has a direct or indirect influence on child mortality.

Thus, socioeconomic theories differ according to whether they emphasize selection or causal effects [12]. Selection means a systematic filtering during the process of social mobility: people with better health have a greater opportunity to move up the socioeconomic ladder. Selection can be direct, with healthy people moving upward and unhealthy people moving downward. It can also be indirect if the standard of living exposes or does not expose people to risk factors (the higher the standard of living, the less people are exposed to risk factors, and the lower the standard of living, the more people are exposed to risk factors). On the other hand, causal arguments assume that the socioeconomic status of individuals affects health through influences that may be structural (living conditions, neighborhoods, urban or rural residence, etc.) or cultural/behavioral (knowledge, attitudes, values, lifestyle, etc.) but also depending on the political and ecological context [10, 13, 14, 15].

This study explores the influence of economic conditions on aggregate levels of child mortality by distinguishing the effect related to the change in the distribution of the population between different socioeconomic categories (composition effect) from that related to the influence of the change in the risk of death within different groups (rate or behavior effect). Because these two types of effects call for different policies — redistribution of economic wealth versus general improvement of health services and infrastructure, or public sector investments and diffusion versus selective modernization and individual progress — their relative importance deserves attention. The methods used in this study make it possible to dissociate these complementary influences. Most French-speaking African countries have experienced remarkable economic growth over the last 30 years, but efforts to redistribute wealth have not led to a significant reduction in poverty. Therefore, we assume that progress in reducing child mortality is mainly driven by overall improvement in health services and infrastructure that has led to a significant reduction in the vulnerability of the poor.

3. Materials and methods

The study covers a sample of 18 French-speaking sub-Saharan African countries and draws on a database from two main sources:

• The demographic and health surveys (DHS) whose infant mortality quotient indicators are produced by standard of living and area of residence to produce rural-urban differences) are produced from the DHS program’s Statcompiler tool [16].

• World Population Prospect (WPP)³ child mortality indicators [17]. To provide estimates of mortality over a long period from 1990 to 2022.

The analysis was based on decomposition methods. Decomposition methods allow us to determine the source of change of levels in infant mortality. First, a simple decomposition separates out the share of change due to variation in the distribution of the population across socioeconomic categories (demographic or compositional effect) from that due to behavioral change (performance or behavioral effect). When the behavioral effect is predominant, an advanced decomposition is used to dissociate the part due to an overall, baseline gain that reflects public policies from the part due to a change in the health returns to socioeconomic status (differential effect).

Note that decomposition methods best reveal the “sources” rather than the root “causes” of change. While it does not support specific causal claims, it can provide a detailed “an accounting of the proximate sources” of a change. In short, it does not reveal “why,” “from what,” or “from whom” the change occurred [18]. In our analysis, we aim to understand the sources of changes in infant mortality based on the type of residence (urban-rural) and wealth quintile.

The actual calculation follows the formula below, where $Y_t$ is a weighted average (by $w_{\mathit{jt}}$) of the values of individual subpopulations ($y_{\mathit{jt}}$) (socioeconomic categories). From this formula, the national change in infant mortality is decomposed as follows:

$$Y_t={\sum }_{j=1}^n{w_{\mathit{jt}}}^{\ast }{y}_{\mathit{jt}}$$E1

$$\mathrm{\Delta }{Y}_t=\sum \underset{\mathit{\text{Composition\hspace{0.17em}\hspace{0.17em}effect}}}{\underset{\uparrow }{{\overline{y}}_j\ast \Delta w_j}}+\sum \underset{\mathit{\text{Behavior\hspace{0.17em}\hspace{0.17em}effect}}}{\underset{\uparrow }{{\overline{w}}_j\ast \Delta y_j}}$$E2

The dependent variable here is the infant mortality rate, and the classificatory variables are wealth index.

The extension of the behavioral effect is based on the socioeconomic category function $y_j$, which represents the performance on infant mortality of socioeconomic groups:

$$y_j=\alpha +\beta x_j+{\epsilon }_j,$$

where

$\alpha$ represents the baseline performance, when $x$ = 0;

$\beta$ is the increase in mortality related to a unit increase in the variable, and

${\epsilon }_j$, the error, (relative outperformance/underperformance of the group, or as the residual effect of factors other than, or as the residual effect of factors other than, not considered in the analysis $x$_.

The change in the value of between two groups is obtained as follows: $y_j$.

$$\Delta y_j=\mathit{\Delta \alpha }+\overline{\beta }\Delta x_j+\Delta {\epsilon }_j+\overline{x}\mathit{\Delta \beta }+\Delta {\epsilon }_j,$$E3

Inserting (3) into (2), we have:

$$\Delta Y_t=\underset{\mathit{\text{Composition\hspace{0.17em}\hspace{0.17em}effect}}}{\underset{\uparrow }{\underset{A}{\sum _{n=1}^n{\overline{y}}_j\Delta w_{\mathit{jt}}+}}}\underset{\mathit{\text{Behavior\hspace{0.17em}\hspace{0.17em}effect}}}{\underbrace{\underset{B_1}{{\sum }_{n=1}^n{\overline{\mathrm{w}}}_j\mathit{\Delta \alpha }+}\underset{B_2}{\underbrace{{\sum }_{n=1}^n{\overline{\mathrm{w}}}_j\overline{\beta }\Delta x_j{+}_j{\sum }_{n=1}^n{\overline{\mathrm{w}}}_j\overline{x}\mathrm{\Delta \beta }+}}\underset{B_3}{{\sum }_{n=1}^n{\overline{\mathrm{w}}}_j\mathrm{\Delta }{\mathrm{\epsilon }}_j}}}$$E4

$A:$ The composition effect.

$B_1:$ The baseline performance effect, which reflects the decline in infant mortality due to improvements in the basic health conditions of populations, is attributable to public policies.

$B_2$: The differentiation effect, which measures the decrease in infant mortality associated with the overall improvement in the standard of living of populations (improvement in the standard of living of different categories and change due to the socioeconomic effect).

$B_3$: The residual effect.

3.1 Distribution of the countries covered by the study according to the period of change

The study covers 18 countries in French-speaking sub-Saharan Africa, namely: Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Comoros, Congo, DRC, Ivory Coast, Gabon, Guinea, Madagascar, Mali, Mauritania, Niger, Senegal, and Togo. The DHS data from these countries have varying periodicity for studying changes in rural-urban levels and differences in child mortality. Table 1 below summarizes the periodicity of DHS data for each country (time between the first and last survey). It shows that the duration of available DHS surveys ranges from 7 years in Burundi, Congo, and DRC to 29 years in Madagascar, over a period from 1991 to 2021.

Countries	Period	Duration
Benin	1996–2017	21
Burkina Faso	1993–2017	24
Burundi	2010–2017	7
Cameroon	1991–2018	27
Central Africa	1994–20191	25
Chad	1997–2014	17
Comoros	1996–2012	16
Congo	2005–2012	7
DRC	2007–2014	7
Ivory Coast	1994–2012	18
Gabon	2000–2012	12
Guinea	1999–2021	22
Madagascar	1992–2021	29
Mali	1996–2021	25
Mauritania	2000–2020	20
Niger	1992–2012	20
Senegal	1993–2020	27
Togo	1998–2017	19

Table 1.

Presentation of the countries covered by the study according to the period of coverage of the DHS surveys.

¹

MICS 2019.

3.2 The infant mortality transition in francophone sub-Saharan Africa

Overall, child mortality in the 18 French-speaking sub-Saharan African countries studied has declined over the last three decades despite some reversals in some countries (Burkina Faso, Cameroon, Ivory Coast, and Rwanda) between 1990 and 2010 [10]. French-speaking sub-Saharan African countries have each gone through a significant transition phase in child mortality. According to United Nations estimates [17], child mortality has declined from a peak of 139 deaths to less than 68 deaths per 1000 live births. The transition of child mortality can be assessed by grouping these countries into four categories between the following groups of levels, from highest to lowest.

• The highest-level group with infant mortality between 115.1 and 139.6 deaths per 1000 live births.

• The second group whose infant mortality is between 96.6 and 115.1 deaths per 1000 live births.

• The third level group with infant mortality between 73.0 and 96.6 deaths per 1000 live births.

• The fourth group with the lowest infant mortality, ranging from 0.1 to 72.9 deaths per 1000 live births.

Figure 1 and Table 2 describe the mobility of countries between the four levels of child mortality over the four time periods: 1990, 2000, 2015, and 2020. Overall, there has been a shift from the highest-level group in the 1990s, represented by four countries (Chad, Niger, Mali, and Guinea) to the lowest-level group in 2022 with 17 countries (Chad, CAR, Mauritania, Gabon, Senegal, Cameroon, Burundi, Burkina Faso, Comoros, Congo, Togo, Chad, Guinea, Mali, Niger, Ivory Coast, and DRC).

Figure 1.

Health transition in francophone sub-Saharan Africa from 1990 to 2022.

Infant mortality level group	1990	2000	2015	2022
Groupe 1 (115,1‰ - 139,6‰)	Chad, Niger, Mali, Guinea.	N/A	N/A	N/A
Groupe 2 (96,6‰ - 115,0‰)	Ivory Coast, Benin, Burkina Faso, Central African Republic, DRC.	CAR, Chad, Guinea, Mali, Niger, Ivory Coast, DRC	N/A	N/A
Groupe 3 (73,0‰ - 96,5‰)	Cameroon, Madagascar, Togo, Comoros.	Cameroon, Burundi, Burkina Faso, Comoros, Congo, Togo, Benin	Chad, CAR	N/A
Groupe 4 (0,1‰ - 72,9‰)	Mauritania, Senegal, Gabon, Congo.	Mauritania, Gabon, Senegal, Madagascar	Mauritania, Gabon, Senegal, Cameroon, Burundi, Burkina Faso, Comoros, Congo, Togo, Chad, Guinea, Mali, Niger, Ivory Coast, DRC	Chad, CAR, Mauritania, Gabon, Senegal, Cameroon, Burundi, Burkina Faso, Comoros, Congo, Togo, Chad, Guinea, Mali, Niger, Ivory Coast, DRC

Table 2.

Distribution of French-speaking African countries covered by the study according to the downward trend in infant mortality between 1990 and 2022.

In 1990, Group 3 included four countries whose mortality levels were already at an average level, but which stagnated in 2000, with the exception of Madagascar which migrated to Group 4.

Between these two periods, there were not only certainly rapid changes but also stagnation. But overall, the transition in infant mortality has been significant but at different rates and levels in different countries.

3.3 Uneven progress in reducing infant mortality

3.3.1 Overall rate of change by country

Over the last 30 years (1990–2022), infant mortality rates have declined overall in French-speaking sub-Saharan Africa. However, the pace of the decline has varied across countries and between rural and urban areas.

3.3.2 Cross-country comparison

The ranking of countries according to the level of average annual decline in infant mortality shows that Congo, DRC, Burundi, Central African Republic, Niger, and Mali are the top five countries with the highest decline over the last 30 years with respectively −4.86, −4.29, −4.14, −3.60, and −3.55. At the bottom of the scale are Togo, Burkina, Senegal, Cameroon, and Ivory Coast with respectively −1.42, −1.25, −1.22, −1.00, and −0.83.

At the global level, analysis of the estimated infant mortality rates of the WPP 2022 shows that Niger, Guinea, Mali, Burundi, Madagascar, and the DRC are the countries with the fastest decline in mortality since 1990, with respective variations of 94.15, 75.70, 68.53, 66.69, 62.94, and 61.31. On the other hand, the countries with the slowest decline are Comoros, Cameroon, Gabon, Mauritania, and Congo, with variations of 39.7, 30.5, 27.5, and 23.82, respectively. These results show that the countries with the fastest decline in infant mortality are those that initially had the highest mortality rates (Niger, Guinea, Mali, Burundi, and DRC) or average rate (Chad, Burkina Faso, Benin, and Ivory Coast) in 1990. These countries are all from West Africa, with the exception of Burundi and the DRC, and had infant mortality rates in excess of 100 deaths per 1000 live births in 1990. The geographic location of these countries suggests a climate that is not conducive to child survival (cold and dry tropical climate where certain germs and insects thrive), nutritional problems prevalent at that time, cholera and Ebola epidemics, and the incidence of other water-related diseases. The decline in infant mortality has been impacted by urbanization and efforts to provide access to health care, clean water/sanitation, and national programs to combat malaria, Ebola, and cholera. There are also numerous free vaccination and vitamin A programs for children.

3.3.3 Ranking of French-speaking sub-Saharan African countries by average annual rural-urban difference in infant mortality over the past 30 years

Analysis of changes in rural-urban differences in infant mortality over the past 30 years reveals two major trends (Table 3):

• The increase or stagnation of rural-urban inequalities in infant mortality (four countries).

	Infant mortality rate (IMR)
Countries	1990	2022	Overall decrease
Niger	134.24	40.09	94.15
Guinea	139.63	63.93	75.70
Mali	121.81	53.28	68.53
Burundi	103.04	36.35	66.69
Madagascar	95.52	32.58	62.94
Dem. Rep. of Congo	111.31	50.00	61.31
Chad	125.30	65.47	59.83
Burkina Faso	100.20	47.42	52.78
Senegal	72.92	21.03	51.89
Benin	104.58	54.40	50.19
Ivory Coast	104.99	56.76	48.23
Toga	92.08	43.89	48.19
Central African Republic	115.01	67.70	47.31
Comoros	87.86	45.52	42.34
Cameroon	85.49	45.75	39.74
Gabon	59.99	29.47	30.52
Mauritania	71.02	43.57	27.46
Congo	60.52	36.70	23.82

Table 3.

Distribution of French-speaking sub-Saharan African countries covered by the study according to the overall level of decline in infant mortality between 1990 and 2022.

Within this trend, there are countries where rural-urban differences are significantly increasing (Mauritania, Comoros, and Cameroon), mainly due to an increase in the risk of mortality in the countryside. In these countries, the vulnerability of urban areas is reinforced, probably because of the persistent inadequacy and precariousness of health care provision. There is also one country (Benin) where rural-urban differences in child mortality have not changed over time, with a prevalent rural excess mortality.

• Reducing rural-urban inequalities in infant mortality (14 countries).

In these countries (Gabon, Ivory Coast, Togo, Central African Republic, Chad, Senegal, Burkina Faso, Mali, Madagascar, Guinea, Niger, DR Congo, Congo, and Burundi). In these countries, the significant reduction in rural-urban differences in child mortality is due to a significant increase in the risk of mortality in urban areas probably inherent to the growing phenomenon of rural exodus associated with the development of slums. The five countries with the greatest reduction in rural-urban disparities in child mortality are Guinea, Niger, DR Congo, Congo, and Burundi (Table 4).

Countries	Rural-Urban differences in IMR		Total rural-urban differences in IMR	Average annual change in Rural-Urban differences in IMR	Period	Duration	Rank
	Base	Final
Mauritania	−9	11	20	1.00	2020–2000	20	1
Comoros	7	18	11	0.69	2012–1996	16	2
Cameroon	4	14	10	0.37	2018–1991	27	3
Benin	17	17	0	0.00	2017–1996	21	4
Gabon	4	3	−1	−0.08	2012–2000	12	5
Ivory Coast	16	11	−5	−0.28	2012–1994	18	6
Togo	18	12	−6	−0.32	2017–1998	19	7
Central Africa	28	14	−14	−0.56	2019–1994	25	8
Chad	7	−3	−10	−0.59	2014–1997	17	9
Senegal	30	10.8	−19.2	−0.71	2020–1993	27	10
Burkina	32	9.7	−22.3	−0.93	2017–1993	24	11
Mali	48	23	−25	−1.00	2021–1996	25	12
Madagascar	32	0	−32	−1.10	2021–1992	29	13
Guinea	38	4.4	−33.6	−1.53	2021–1999	22	14
Niger	51	20	−31	−1.55	2012–1992	20	15
DR Congo	32	8	−24	−3.43	2014–2007	7	16
Congo	38	−2	−40	−5.71	2012–2005	7	17
Burundi	32	−10	−42	−6.00	2017–2010	7	18

Table 4.

Distribution of 18 French-speaking sub-Saharan African countries covered by the study according to changes in rural-urban differences in infant mortality over the past 30 years.

3.3.4 Sources of change

Analysis of the sources of changes in infant mortality levels by socioeconomic status using decomposition methods in the 18 French-speaking sub-Saharan. African countries studied show the overall preponderance of behavioral rather than composition influences. In other words, the downward trends in mortality in the 18 countries are largely attributable to improvements in health care provision and behavior. The composition effect, that is, changes in the distribution of births across socioeconomic categories, had a more marginal influence on the decline in infant mortality in the countries studied over the past 30 years.

3.3.5 Ranking of French-speaking sub-Saharan African countries by type of behavioral effect

An advanced behavioral effect decomposition analysis was performed to assess whether it was due mostly to an overall improvement in the baseline mortality or instead to an improvement in the returns to socioeconomic status. The results (Table 5) show the dominance of the baseline effect, suggesting an improvement in public health that lifted everyone in all the study countries. It is therefore mainly thanks to the action of maternal and child health policies and programs (vaccinations, nutrition, vitamin supplementation, measures to prevent malaria and diarrhea, medical care for newborns, etc.) that infant mortality has fallen significantly in the countries studied. The differentiation effect inherent in the variation of the individual risk of mortality was less important but noticeable. In most cases, this premium to socioeconomic status was negative, meaning it worked to narrow the gaps in child mortality between the rich and poor, a finding that reinforces the importance of public health investments. Only in Burkina Faso, Mauritania, and Togo did the gap widen (Table 6).

Countries	Total change	Composition effect	Behavioral effect
Benin	−41.85	−1.2%	101.2%
Burkina	−30.15	−2.4%	102.4%
Burundi	−29.12	−0.4%	100.4%
Cameroon	−26.96	−6.5%	106.6%
Central Africa R	−77.96	−4.9%	104.9%
Chad	−25.96	−2.5%	102.5%
Comores	−44.01	−1.0%	101.0%
Congo	−32.94	0.1%	99.9%
DR Congo	−28.50	−0.3%	100.3%
Ivory Coast	−14.44	−4.8%	104.8%
Gabon	−17.08	−1.2%	101.2%
Guinea	−50.62	−0.3%	100.3%
Madagascar	−52.05	−1.2%	101.2%
Mali	−72.48	−1.1%	101.1%
Mauritania	−33.53	−0.2%	100.2%
Niger	−68.96	−0.8%	100.8%
Senegal	−32.80	−2.8%	102.8%
Togo	−26.92	−1.2%	101.2%

Table 5.

Decomposition of the total change in infant mortality according to the contributions of the composition effect and the behavior effect in the 18 French-speaking sub-Saharan African countries studied.

	BASE %	Differentiation %	Error %
Benin	145.87	−46.02	0.15
Burkina	87.12	12.37	0.50
Burundi	129.93	−30.51	0.58
Cameroon	179.55	−79.61	0.07
Central Africa	168.20	−68.27	0.07
Chad	80.79	−21.08	40.29
Ivory Coast	214.74	−114.65	−0.08
Comoros	150.42	−48.59	−0.28
Congo	142.94	−44.14	1.20
Gabon	36.03	−45.32	109.29
Guinea	169.34	−69.39	0.05
Madagascar	172.70	−72.62	−0.08
Mali	110.63	−10.57	−0.06
Mauritania	42.24	32.15	25.61
Niger	131.91	−31.72	−0.19
DR Congo	202.50	−102.54	0.04
Senegal	135.24	−35.60	0.36
Togo	85.13	14.75	0.12

Table 6.

Decomposition of the predominant behavior effect into base and differentiation effects in the 18 sub-Saharan African countries studied.

4. Discussion, conclusion, and policy implications

The purpose of this chapter was to describe trends of levels and rural-urban differences in infant mortality over the past 30 years (1990 to 2022) in 18 French-speaking African countries and to highlight the impact of socioeconomic status.

The decline in infant mortality levels was substantial overall, but its rate varied by country and period. The ranking of countries according to the level of average annual decline in child mortality shows that Congo, DRC, Burundi, Central African Republic, Niger, and Mali are the top five countries with the highest. At the bottom of the scale are Togo, Burkina, Senegal, Cameroon, and Ivory Coast.

Analysis of the evolution of rural-urban differences in child mortality over the past 30 years has revealed two major trends. In some countries, rural-urban inequalities have grown or stagnated (Mauritania, Comoros, Cameroon, and Benin) due to an increase in the risk of mortality in the rural areas. On the other hand, these rural-urban inequalities have decreased (Gabon, Ivory Coast, Togo, Central African Republic, Chad, Senegal, Burkina Faso, Mali, Madagascar, Guinea, Niger, DR Congo, Congo, and Burundi) due to a significant increase in the risk of mortality in urban areas probably inherent to the growing phenomenon of urban slums. Analysis of the sources of changes in infant mortality levels by socioeconomic status using decomposition methods confirms the preponderant influence of general improvement in health services and infrastructure on the reduction of infant mortality, which has led to a significant reduction in the vulnerability of the poor. One of the challenges for future studies is to identify the programs or areas of intervention that have had the greatest impact on this decline in infant mortality. But infant mortality remains high in francophone Africa and is associated with very high levels of out-of-pocket health expenses with a proportion of 50% in Congo, Guinea, and Togo, 51% in Mauritania, 52% in Senegal, 59% in Niger, 61% in Chad, and 70% in Cameroon against 8% in English-speaking countries such as South Africa. Namibia and Mozambique [19].

Ultimately, the main policy implications of the findings of this study are to continuously improve the supply of maternal and child health services, achieve universal health coverage (UHC), and to educate the target populations on their optimal use and the adoption of healthy behaviors.