4.1.1 Descriptive analysis of maternal mortality
The descriptive statistics served to present the census data in general and more specifically information related to maternal mortality. This section comprised of univariate and bivariate analysis. At univariate level of analysis, the data is presented with focus on the level of missing information, the proportion, distribution and the measurement of central tendency (mean, median) and dispersion (standard devia-tion, inter-quartile range). At bivariate level, variables are taken two by two to measure the association between each independent variable and the dependant variable. We also made use of statistics hypoth-esis tests to check the association or difference of means between each selected factor and maternal mortality status.
1called ”recensement g´en´eral de la population et de l’habitat (RGPH) in french
2called ”Soins obstetrical neonatal d’urgence (SONU)” in french
3called ”enqu`ete d´emographique et de sant´e (EDS)” in french
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4.1.2 Bivariate analysis of maternal causes of death
Bivariate analysis is the main aspect of the descriptive analysis. We have used it to describe the relationship between maternal mortality status and the age of women, including the characteristics of the heads of households. The bivariate analysis together with the hypotheses tests performed in this section helps to provide preliminary explanation of maternal mortality.
Age groups
The literature review presented the age of women as an important risk factor of obstetrical complications and death from maternal causes. The table 4.1 summarizes results of the differential analysis of maternal mortality according to women age groups.
Figure 4.1: Maternal mortality status according to mothers’ age groups
χ2 = 9662.630 (Pvalue= 0.000 < 0.05)
The figure 4.1 shows the prevalence of maternal mortality for each age group. The figure points out women aged 40-44 , 45 or more, less than 19 and 35-39 years old as being highly exposed to maternal mortality with proportions of 3.55 , 3.32 , 2.97 , 2.54 respectively. Women aged 20-24, 30-34 and 25-29 have the lower prevalence of maternal deaths with 1.84 , 1.84 and 1.89 respectively. The Chi-square test reveals a statistically significant association between age of women and maternal mortality. Indeed, the Pvaluerelated to the Chi-square test (Pvalue= 0.000 < 0.05) allows us to say with 99 % of precision that maternal mortality is differentiated by women age groups.
Gender of the HoH
The gender of the HoH is considered as a potential factor capable of making a difference in the occurrence of maternal mortality (figure 4.2). The probability of dying from maternal causes is 2.27 among women living under a male-led household and 1.33 for female-led household (figure 4.2). In other words, pregnant women staying in household under a male leadership could be more exposed to maternal death than those under a female leadership.
Figure 4.2: Maternal mortality status according to gender of the head of the household
χ2y = 11.679 (Pvalue= 0.001 < 0.05)
The chi-square test with continuity correction (χ2y = 11.679, Pvalue= 0.001 < 0.05) brought evidence of a statistical significant association between maternal mortality and the gender of the head of the household.
Age of the HoH
The age of the head of the household is among selected potential factors influencing maternal mortality.
The figure 4.3 presents the relationship between the age of the head of the household and maternal mortality. Results of the χ2 test related to the figure indicates a significant association at 99 % between HoH age groups and the risk of maternal mortality.
It appears that the probability of dying from maternal mortality increases with age except the age group 20-24, where the higher risk of maternal death is registered. Indeed, the occurrence of maternal deaths
among women aged 20-24 is estimated at 6.19, more than 2 times the proportion in other age groups (see figure 4.3). In fact, the proportion of maternal deaths increased from 0.68 for women less than 19 years old to 0.75, 1.04 , 1.11 , 1.57 , 2.17 , 2.87 and 3.8 for the 25-29, 30-34, 35-39, 40-44, 45-49, 50-54 and more than 54 years old respectively.
Figure 4.3: Maternal mortality status according to the age of the head of the household
χ2 = 719.586 (Pvalue= 0.000 < 0.05)
In total, the figure 4.3 highlighted the higher risk of maternal deaths in households led by the extreme age groups: very young and old people (20-24 years and 50 years or more). The higher risk of maternal deaths among young head of households (20-24) could be assigned to their lack of experiences in dealing with pregnancy related matters or the probable young ages of their female household members. The increasing of maternal risk of death could be the effect of the increase in parity with ages or the increase in number of exposed women to maternal mortality in the household.
Region of residence
Results of the bivariate analysis of the spatial differences of maternal risk of mortality are displayed in the figure 4.4. These results highlight an important difference in the risk of maternal mortality between regions of residence. Moreover, the probability of maternal deaths varies from a maximum of 3.64 in Sud-Ouest region to 1.36 in the region of Centre.
Regions with high exposure to maternal death (above the national average of 2.2 ) are by order of
Figure 4.4: Maternal mortality status according to the region of residence
χ2 = 86.450 (Pvalue= 0.000 < 0.05)
importance the regions of Cascades (3.14), Sahel (2.89 ), Boucle du Mouhoun (2.73 ), Centre-Ouest (2.64) and Hauts-Bassins (2.34 ). In opposite, women residing in the regions of Centre-Sud (2.11 ), Centre-Est (2.06 ), Nord (1.82 ), Est (1.81 ), Centre-Nord (1.8), Plateau Central (1.67 ) and Centre (1.36 ) are less exposed to maternal mortality than an average women in the country (national average is 2.2). The hypothesis test performed indicated a statistically significant association between the region of residence and maternal mortality (χ2 = 86.450 Pvalue = 0.000 <
0.05).
Area of residence
The figure 4.18 reveals a statistical significant association at 99 % between area of residence and maternal mortality (Pvalue= 0.002 < 0.05). In addition, it shows that 2.31 women in rural areas deceased from maternal causes while only 1.8 of women in urban areas experienced maternal deaths.
This result highlights the high exposure to maternal death in rural areas compared to urban areas.
Figure 4.5: Maternal mortality status according to the area of residence
χ2y = 9.709 (Pvalue= 0.002 < 0.05)
Disability status
Figure 4.6 indicates a difference in the exposure to maternal death between women staying in household led by a disabled person and the other women. In fact, results from the descriptive analysis of the relationship between maternal mortality and disability status of the head of the household shows a statistical significant association between them (Pvalue = 0.000 < 0.05). The probability of maternal death is two times higher for women under the lead of a disabled HoH (5.44 ) than those under a non disabled HoH (2.18).
Figure 4.6: Maternal mortality status according to the disability status of the head of the household
χ2y = 35.150 (Pvalue= 0.000 < 0.05)
Religion
The figure 4.7 describes the difference of exposure to maternal death between households headed by a traditional religion, Muslims or Christians. For this particular factor, we can consider approximately most of the women have the same religion as their HoH. Therefore, the analysis could be generalized.
Instead of concerning the religion of the head of the household, the results should reflect the religion of women. Findings from the figure 4.7 show that 2.79 women under the lead of a traditional religion person deceased from maternal mortality and 2.18 for Muslims HoH and 1.86 for Christians HoH. In brief, figure 4.7 pointed out the religion of the HoH as an important risk factor of maternal mortality.
Figure 4.7: Maternal mortality status according to religion of the head of the household.
χ2 = 21.589 (Pvalue= 0.000 < 0.05)
Furthermore, the chi-square test of independence showed a statistical significant relationship between the religion of the HoH and maternal mortality. Since the Pvalue associated to this hypothesis testing is Pvalue= 0.000 < 0.05, we have evidence at 99 % that religion is statistically associated to maternal mortality.
Nationality
The figure 4.8 shows that the proportion of maternal deaths among females from foreign countries (1.31
) is very small compared to female citizens in Burkina Faso (2.22 ). However, the relationship between maternal mortality and nationality is not confirmed by the chi-square test of association. The
Pvaluegreater than 0.05 indicates no statistical association between nationality and maternal mortality.
Figure 4.8: Maternal mortality status according to nationality of the head of the household
χ2 = 0.960 (Pvalue= 0.619 > 0.05)
Education
The figure 4.9 shows that non educated HoH are more exposed to an occurrence of maternal death in their HH than educated. Indeed, the proportion of maternal deaths among women with non educated HoH is 2.33 and 1.68 for those with educated HoH.
Figure 4.9: Maternal mortality status according to the education status of the HoH
χ2y = 14.792(Pvalue= 0.000 < 0.05)
The χ2test of association between maternal mortality and the education status of the head of households
led to interesting results. Indeed, the Pvalue= 0.000 < 0.05 indicates a significant association between maternal mortality and education status of the HoH. In other words, there is a statistical evidence at 99 % that the education level of the HoH could influence the occurrence of maternal mortality in the household. In brief, this result proves that the characteristics of the HoH influence the behaviours of adult females in the household.
Employment status
Figure 4.10 shows that the proportion of maternal deaths differs between employed and unemployed HoH. The hypothesis testing reveals a strong statistical relationship between HoH employment and maternal mortality. Indeed, the chi-square test presented a Pvalue less than 0.05.
Figure 4.10: Maternal mortality status according to employment status of the head of the household
χ2 = 10.481 (Pvalue= 0.005 < 0.05)
Comparison of the probability of maternal deaths regarding the employment status of the HoH reveals a higher exposure of maternal death in Household led by unemployed HoH than an employed HoH.
Indeed, the census recorded 3.15 maternal deaths among women led by an unemployed person and 2.18 among the women led by an employed person.
Marital status
The descriptive level of analysis has clearly shown that maternal mortality is significantly associated to marital status of the HoH (figure 4.11). Indeed, the Pvalue associated with the Chi-square test is
less than 0.05 (χ2 = 3225.79 Pvalue = 0.000 < 0.05). In addition, the figure 4.11 reveals a higher exposure to maternal mortality in household led by a widow or a single HoH compared to the national level. In fact, the proportion of maternal deaths for widows HoH is 26.45 wit singles being 7.76 .
On the other hand, the proportion of maternal deaths among women led by married HoH is 1.75 .
Figure 4.11: Maternal mortality status according to marital status of the head of the household
χ2 = 3225.794 (Pvalue= 0.000 < 0.05)
Size of the household
The figure 4.12 shows that the proportions of maternal deaths among women living in household hosting 1 to 3, 21 or more or 7 to 10 members are the highest in the country with 2.93, 2.57 and 2.2 respectively. In opposite, we registered under the national average proportion of maternal deaths, women living in household sized 11-15 (2.16 ), 4-6 (2.01 ) and 16-20 (1.88 ). The Chi-square test of independence (χ2 = 24.345 Pvalue= 0.000 < 0.05) established a statistically significant association between the size of the household and maternal mortality.
Figure 4.12: Maternal mortality status according to size of the household
χ2 = 24.345 (Pvalue= 0.000 < 0.05)
Wealth Index
The figure 4.13 displays the changes of the proportion of maternal deaths regarding the wealth index
4 of the household. It appears that the proportion of maternal deaths decreases from women living in very poor condition to those in very good wealth situation. In fact, the proportion of maternal deaths changes from 2.41 to 2.43 , 2.26 , 2.01 and 1.85 for women in very poor, poor, medium, good and very good wealth situation respectively.
Figure 4.13: Maternal mortality status according to wealth index
χ2 = 12.421 (Pvalue= 0.014 < 0.05)
4This variable is also known in the literature as living condition or living standard
The relationship between maternal mortality and living standard is passed through an inferential analysis under the Chi-square test. Result of the hypothesis test shows that poverty is statistically significantly associated with maternal cause of death (Pvalue= 0.014 < 0.05) at 95 %.
4.1.3 Multivariate analysis of maternal mortality
The descriptive analysis undertaken in the previous section reveals very interesting results. Most findings at this level of the analysis collaborated with existent knowledge on the issue. In recall, the hypothesis tests presented as statistical significant factors of maternal mortality: the living condition of the house-holds, the sizes of the household, the marital status of the HoH, the employment status of the HoH, the education status of the HoH, religion of HoH, disability status of the HoH, area of residence, region of residence, age of the HoH, gender of the HoH and age of women. The only non statistically significant factor was the nationality of the HoH.
In sum, findings from bivariate analyses are very interesting and relevant in describing the issue and hav-ing a first view of potential factors influenchav-ing maternal mortality. But they are limited since the impact of each predictor on maternal mortality is not controlled by effect of other variables. In other words, findings from bivariate analysis could be due to hidden factors. To avoid that, a multivariate analysis is performed aiming to put in competition the effect of each factor with the others. Therefore models are built to take into consideration all factors together. In this study, we chose the logistic regression model because the dependant variable is dichotomous (0-maternal survivals, 1-maternal deaths). In total, three logistic models were built: a general model, stepwise model and finally the regional model.
4.1.3.1 General model of logistic regression
The general logistic regression model includes all selected covariates in the model. As significant variable at descriptive level of analysis can become not significant at multivariate level of analysis or a non significant factor can become not significant even if this latter situation is less probable. In this model, we removed non significant variables for which the removal doesn’t make any significant change in the result in term of level of significance and odds ratios. The final model is presented in table 4.1 below.
Table 4.1: General model of logistic regression using census data
Variables / Categories B Std. Error z value Exp(B) Pr(> |z|)
Age of Women (Less than 19 @)
20-24 -0.3833778 0.0886824 -4.3230431 0.6816*** 0.0000
25-29 -0.4800617 0.0926753 -5.1800384 0.6187*** 0.0000
30-34 -0.7096118 0.1041756 -6.8116927 0.4918*** 0.0000
35-39 -0.5400221 0.1093386 -4.9389906 0.5827*** 0.0000
40-44 -0.3581892 0.1286626 -2.7839422 0.6989*** 0.0054
45+ -0.5744019 0.1833874 -3.1321777 0.563*** 0.0017
Age of the HoH (Less than 24 @)
25-29 -1.8966834 0.190665 -9.9477252 0.1501*** 0.0000
30-34 -1.4907062 0.1227176 -12.147452 0.2252*** 0.0000
35-39 -1.3071595 0.1131899 -11.548377 0.2706*** 0.0000
40-44 -0.9271537 0.1028079 -9.018312 0.3957*** 0.0000
45-49 -0.6037025 0.0955922 -6.3153949 0.5468*** 0.0000
50-54 -0.4354949 0.0974561 -4.4686242 0.6469*** 0.0000
55+ -0.2093852 0.0991829 -2.111102 0.8111** 0.0348
Gender of HOH (Male @)
Female -3.1904927 0.1719481 -18.55498 0.0412*** 0.0000
Disability status of HoH (Not Disable @)
Disable 0.3838606 0.1700479 2.2573676 1.4679** 0.0240
Religion of HoH (Chirstian @)
Traditional/Others 0.0446861 0.0962652 0.464198 1.0457 0.6425
Muslim 0.1435515 0.0790649 1.8156161 1.1544* 0.0694
HoH Nationality (Burkinabe @)
Other Nationality -0.3075609 0.5841847 -0.5264789 0.7352 0.5986
HoH education status (No educated @)
Educated 0.0438076 0.0961351 0.4556881 1.0448 0.6486
HoH employment (Unemployed@)
Employed -0.5114544 0.1332185 -3.8392131 0.5996*** 0.0001
Area of residence (Urban @)
Rural 0.2763835 0.1058893 2.6101178 1.3184*** 0.0091
HH marital status (Single @)
Married -1.9906891 0.1796423 -11.081405 0.1366*** 0.0000
Widow 1.9922385 0.1918316 10.385352 7.3319*** 0.0000
Wealth index (Very Poor @)
Poor -0.031365 0.0847257 -0.3701947 0.9691 0.7112
Medium -0.1034701 0.0905747 -1.1423729 0.9017 0.2533
Good -0.2033252 0.0945113 -2.1513319 0.816** 0.0315
Very Good -0.1383102 0.1321511 -1.0466065 0.8708 0.2953
Null deviance: 18123 on 581451 degrees of freedom Residual deviance: 16133 on 581424 degrees of freedom AIC: 16189
Results of the general logistic regression model presented in tables 4.1 identified the following predictors as the statistically significant determinants of maternal mortality in Burkina Faso: women age groups, age of HoH, gender of HoH, disability status of HoH, HoH employment, area of residence, HoH marital status and the wealth index . These variables present a statistical significant influence on maternal mortality at 95 %. Other variables such as HoH education status, HoH nationality, religion did not show any statistical evidence of significant influence at 95 %.
After controlling the effect of other independent factors, the age of women appears to be a significant determinant of maternal mortality. In fact, women aged more than 20 years old are between 36 % and 55 % less at risk of maternal death than those aged 15-19 years old. Indeed, compared to female aged 15-19, the risk of maternal mortality is 32 %, 38 %, 51 %, 42 %, 30 % and 44 % less for women aged 20-24, 25-29, 30-34, 35-39, 40-44 and more than 45 respectively.
The regression model also indicated that the age of the head of the household is a determinant of maternal mortality. All HoH aged more than 24 years old are less at risk of having a maternal death in their household than the youngest HoH aged less than 24 years old. However, the net risk of maternal deaths increased with the age of the HoH from 25-29 age group to those aged more. The odds ratios increase from 0.15 to 0.22, 0.27, 0.39, 0.55, 0.65 and 0.81 for women living under the leadership of a HoH aged 25-29, 30-34, 35-39, 40-44, 45-49, 50-54 and more than 55 respectively.
A very interesting result of this regression model concerns the gender of HoH. This variable appears to be a significant explanatory factor of maternal mortality in Burkina Faso. The odds ratio related to the gender of the HoH indicates that women living in a household led by a woman are 96 % less at risk of maternal mortality than those living under a male household leader (table 4.1). The latter result points out the importance of the head of the household characteristics in protecting women from maternal causes of deaths. Indeed, female HoH are disposed to have more ability to understand, share problems and propose adequate solution to others females in the household. In addition, at descriptive level, it appeared that the large majority of HoH are males and female HoH are generally widows, single mothers, etc. Therefore, this result appears critical for the issue of maternal mortality. Findings support the higher exposure of women whose HoH is disable compared to the others. The table 4.2 shows that women living under a household led by a disabled HoH have 1.5 times more risk of maternal death than the others.
Results from the logistic regression analysis highlighted HoH employment status as significant factor influencing the occurrence of maternal mortality in the household. Women in household headed by an
employed person have 41 % less risk of maternal death than those with an unemployed HoH (table 4.1).
This result confirms knowledge from the literature about the causes of maternal mortality. Also, financial repercussion of a prenatal visit, health care or good nutrition at pregnancy are among the reasons for the negative impact of HoH unemployment. The area of residence appeared in our analysis as a significant determinant of maternal mortality (pvalue < 0.05). Findings of multivariate analysis presented in 4.1 provided evidence that women living in rural areas are 1.3 times more at risk of maternal mortality than those living in urban areas.
The table 4.1 also shows that HoH marital status is among the significant factors influencing maternal mortality. After control of other predictors, it appears that women headed by a married HoH are 86.3
% less at risk of maternal mortality than those who have a single HoH (table 4.1). When the head of the household is a widow, women in the household have 7.3 times more risk of maternal death than women in household led by a single person.
The regression model indicates that women living under good living conditions are 18 % less at risk of maternal death than those living in very poor households (table 4.1). The general model presented in the table 4.1 helps to identify the determinants of maternal mortality. But, this method does not reflect the mechanisms through which their influence operates. That is why a forward logistic regression has been developed. Results of the forward logistic regression is not presented due to the fact that no significant changes are observed, even with the inclusion of predictors one after the others in the model.
4.1.3.2 Regional differences of maternal mortality determinants
Understanding the issue of maternal mortality at the regional level is among the key objectives of this study. To fulfil this aim, the current section is initiated in order to dissect the determinants of maternal mortality between and within regions. The section begins with the distribution of maternal deaths and survivals by region presented in table 4.2. The table 4.2 shows that the region of Plateau Central has the smallest number of maternal deaths (n=51, 3.9 %) and Boucle du Mouhoun region has the highest with 172 maternal deaths (13.2 %). Boucle du Mouhoun (n=172, 13.2 %), Hauts-Bassins (n=137, 10.5 %), Centre-Ouest (n=131, 10.0 %), Sahel (n=123, 9.4 %) and Est (n=115, 8.8 %) host the highest number of these deaths and accumulate more than half of the total maternal deaths cases in the country. On the other hand, regions hosting the smaller number of maternal deaths (less than 5 %) are the region of Plateau Central (n=51, 3.9 %) and Centre-Sud (n=56, 4.3 %).
Table 4.2: Distribution of maternal deaths and survivals by region
Region of residence Maternal survivals Maternal deaths
Total
N % N %
Boucle du Mouhoun 62849 10.7 172 13.2 63021
Cascades 21926 3.7 69 5.3 21995
Centre 52943 9 72 5.5 53015
Centre-Est 48853 8.3 101 7.7 48954
Centre-Nord 55006 9.4 99 7.6 55105
Centre-Ouest 49505 8.4 131 10 49636
Centre-Sud 26430 4.5 56 4.3 26486
Est 63557 10.8 115 8.8 63672
Hauts-Bassis 58483 10 137 10.5 58620
Nord 50420 8.6 92 7 50512
Plateau Central 30441 5.2 51 3.9 30492
Sahel 42467 7.2 123 9.4 42590
Sud-Ouest 24356 4.1 89 6.8 24445
Sud-Ouest 24356 4.1 89 6.8 24445