Prevalence and associated factors of anemia among women of childbearing age in the eastern region of Burkina Faso

doi:10.21203/rs.3.rs-4953525/v1

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Prevalence and associated factors of anemia among women of childbearing age in the eastern region of Burkina Faso

https://doi.org/10.21203/rs.3.rs-4953525/v1

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Background: In Burkina Faso, anemia is a serious public health problem, with a prevalence well above the 40% threshold. We need to identify the factors associated with it in order to combat this scourge effectively.

Objective: The objective of this study is to identify associated factors of anemia among women of childbearing age in the eastern region of Burkina Faso.

Method: This was a cross-sectional descriptive and analytical study. A validated and digitized questionnaire on the ODK data collection application was used to collect data from women of childbearing age in households by means of individual interviews. The ‘hemocue’ rapid diagnostic test was used to measure the level of hemoglobin in the blood of 614 women aged 15 to 49 years. The cutoff point for any anemia was a hemoglobin level of less than 11.0 g / dL in pregnant women and 12.0 g/dL in non-pregnant women. The chi-square test was used for bivariate analyzes and logistic regression for multivariate analyzes using the SPSS software.

Results:

The results show that 71.8% of women (pregnant or not) were anemic. The age and marital status of women and the use of iron/folic acid supplements had a statistically significant association with anemia in women of childbearing age. In fact, women living alone (single, divorced or widowed) are 5 times more likely (OR=5,155; CI=1,821-14,593; P=0,002) to be anemic than married women.

Individual and household characteristics must be taken into account when designing policies and plans to address anemia.

anemia

women of childbearing age

associated factors

eastern region

Burkina Faso

Iron deficiency is the main cause of anemia, which remains a public health problem in both industrialized and developing countries [1]. According to the WHO, anemia is a medical condition in which the number of red blood cells is not sufficient to meet the physiological needs of the body. Anemia is confirmed in women of childbearing age (WCA) and in pregnant women if the hemoglobin level in the blood is less than 12.0 g/dL and 10.0 g/dL, respectively [2]. It affects all age groups, predominating in children under 5 years of age and women of childbearing age. There are many causes of anemia. Although it can be caused by parasitosis, hemorrhage, congenital disorders, chronic or infectious diseases, it is most often due to a dietary deficiency with a lack of iron at the base. The main risk factors for anemia are inadequate micronutrient intake (iron, vitamin B9, vitamin B12, vitamin A, vitamin C), poor absorption of these micronutrients in diets rich in phenolic compounds, nutritional interactions that affect iron bioavailability, infectious diseases (malaria, ascariasis, schistosomiasis, hookworm disease), chronic diseases (tuberculosis, HIV/AIDS), and the period of life when iron needs are highest (growth and pregnancy).

Anemia has significant negative impacts on the health and economic well-being of nations and communities. It affects physical growth, cognitive development, reproduction, and physical work capacity [3]. Anemia in adults can cause fatigue, lethargy, decreased physical productivity, and poor performance at work [4]. Maternal anemia increases the risk of preterm birth and low birth weight, and iron deficiency anemia is the primary cause of 115,000 maternal deaths and 591,000 perinatal deaths each year [5].

The World Health Organization (WHO) has classified it as one of the ten most serious problems in the modern world and is the most common form of micronutrient deficiency worldwide [6]. In 2019, almost one in three women aged 15–49 years worldwide (571 million) had anemia, a situation that has not improved since 2012. Anemia is more prevalent among women livingin rural areas, in poor households, and who have not attended school. In Africa, prevalence is higher in rural areas, whereas in Latin America and the Caribbean, it is higher in urban areas. In most regions, women in the lowest wealth quintiles and those who did not attend school or complete primary school are more likely to be anemic [7].

In Burkina Faso, in 2014, 62.0% and 72.5% of non-pregnant women of reproductive age and pregnant women, respectively, were anemic, according to the National Iodine and Anemia Survey [8]. Most studies carried out in Burkina Faso are limited to presenting the prevalence of anemia without performing an analysis to identify the associated factors.

To effectively control anemia through the development of effective strategies, it is necessary to identify risk factors. This requires an approach that integrates the multifactorial and multisectoral dimensions of anemia to address this public health issue. Therefore, this study is therefore part of this framework and was carried out in the Eastern region to know the prevalence and factors associated with anemia in women of childbearing age.

Framework and type of study

This study was carried out in the eastern region of the Gourma and Gnagna provinces.

This is a cross-sectional descriptive and analytical study that took place between September 23 and October 17, 2021.

Study population

The study population consisted of all women aged 15 to 49 years.

Women aged 15 to 49 years with disease were excluded from the study.

Sample and sampling

The survey was based on a two-degree stratified survey design, with primary units being villages or sectors, and households being secondary units. The main survey base was the administrative village file of the national institute of statistics and demography General Population and Housing Census of 2019. A statistically significant sample of households was drawn by province.

The minimum sample size was determined using the following formula:

$$\:n=deff\times\:\frac{Npq}{\frac{{d}^{2}}{{Z}^{2}}\:\left(N-1\right)+pq}$$

n = sample size, deff = calculation factor (1,5), N = total population, p = estimated proportion (72,5%), Z = 1,96, q = 1-p) and d is the accuracy error set at 5%.

With an estimated 192,867 population of women of childbearing age (INSD projection on RGPH 2019) in both provinces and a nonresponse rate of 10%, the sample size was estimated at 591 women of childbearing age to be sought in 600 households (proportion of women of childbearing age was estimated at 19% in the region by RGPH 2019). The size of the household was adjusted to 800 to account for the region’s food consumption score. In the selected households, only one woman of childbearing age was randomly selected from among the women in the households eligible for this study.

Data collection method and tools

Direct interviews were used to collect quantitative data using a tablet questionnaire via the Open Data Kit (ODK). This questionnaire was pretested and finalized prior to data collection. The information collected covers the following topics: demography, education, access to clean water and sanitation, household equipment, main activities, household expenditure, shock management strategy, and household food consumption.

A reminder of the exhaustion of assets during the 30 days was carried out in each household through questions. The depleted assets of the household reflect the stress on their livelihoods. The answers to the various questions were used to understand the stress and insecurity faced by households and describe their future productivity capacity [9].

For the food expenditure, share is simply constructed by dividing the total food expenditure by the total household expenditure. However, the denominator and numerator should include the value of foods consumed but not purchased.

To determine the share of food expenditure in the household, a 30-day reminder was carried out in each household on food expenditure in cash and credit and food consumed that was not purchased. For each household, the sum of total food expenses (cash and credit) for the 30 days of the recall period was added to the total value of food consumed in the last 30 days that was not purchased. This sum represents the total value of the household food basket over the past 30 days. The sum of short-term (30-day) and long-term non-food expenditure was realized for each household while excluding savings. Thus, the share of food expenditure is the relationship between the total value of the food basket of the household in the last 30 days and the sum of the total value of the food basket and the total expenditure on food and non-food of the household in the short and long term [9].

An open reminder of 7 days of household food consumption was carried out to assess household food consumption. This reminder was given to the person usually responsible for preparing meals for the household (assisted by others if necessary) by direct interview [9].

Hemoglobin testing was performed in households using the HemoCue™ rapid diagnostic test (Hb201+, HemoCue, Angelsborg, Sweden) by the investigators. It has been validated and recommended in population surveys [10]. It is not invasive and requires a small amount of blood. It is highly sensitive and specific with a positive predictive value for assessing the hemoglobin level in the blood. For the blood sample, a finger prick was made with a lancet, and a droplet is collected with a microcuvette. The hemocue quality control was carried out daily using low and medium control blood samples, which were kept cold throughout the collection period.

Dependent Variable

In this study, the dependent variable was the appearance of anemia in pregnant and nonpregnant women of childbearing age (WCA). In fact, the presence of anemia is detected when the hemoglobin level is ≤ 11.0 g/dL in pregnant women. First, when this level is between 10.0 and 10.9 g/dL, it is called mild anemia. Moderate anemia is defined as between 7.0 and 10.9 g/dL and severe anemia is defined as ≤ 7.0 g/dL (Table 1).

Table 1

Hemoglobin cut-off values
Target	Clinical thresholds and anemia classification [2]
Target	Adequate	Slight	Moderate	Severe
Woman of childbearing age	≥ 12.0 g/dL	11.0-11.9 g/dL	8.0-10.9 g/dL	< 8.0 g/dL
Pregnant woman	≥ 11.0 g/dL	10.0-10.9 g/dL	7.0-9.9 g/dL	< 7.0 g/dL

Independent Variables

These are factors that may explain the occurrence of anemia in nonpregnant or pregnant women of childbearing age (WCA). For this study, several variables were involved. In terms of context, the place of residence was chosen. At the household level, survival strategy, share of food expenditure, household food consumption, and wealth quintile.

All survival strategies have been classified into three general groups, including stress, crisis, and emergency strategies [9].

Based on the results of the food consumption score, households were grouped into poor, limited, and acceptable food consumption levels [9].

The wealth quintile was calculated using durable goods owned by each household and divided into quintiles on unweighted data.

At the individual level, the characteristics of pregnant and non-pregnant women were retained. These are the marital status of the woman, the level of schooling of the head of household, iron and/or folic acid supplementation in the woman, the age of the woman, and the age and sex of the head of household.

Processing and statistical analysis of data

The data entered on the tablets and sent directly to the kobotoolbox server have been exported, processed, and analyzed. Data were analyzed using SPSS v.25 software using an analysis plan developed for this purpose.

A univariate analysis was used using the Pearson chi-square test to investigate whether there was a correlation between anemia (dependent variable) and each of the independent variables. Variables with a p-value of less than 0.20 in bivariate analysis were introduced into the binary logistic regression model and then selected using the stepdown method, leaving only those variables with a p-value of less than 0.05 in the final model.

Household’s characteristics

Of the selected households, about 6 out of 10 households had acceptable food consumption and about 61% of their share of food expenditure was less than 50% of the total budget (Table 2).

Table 2

Household characteristics
Variables	Modalities	n	Percentage
Province	Gnagna	277	45.1
Province	Gourma	337	54.9
Place of residence	Rural	418	68.1
Place of residence	Urban	196	31.9
Type of household	100% de IDPs	36	5.9
	Aboriginal people	537	87.5
	Hosts (Indigenous and IDPs)	41	6.7
Household size	<= 6	455	74.1
Household size	≥ 7	159	25.9
Household food consumption	Poor	36	5.9
	Limit	210	34.2
	Acceptable	368	59.9
Household survival strategy	No strategy adopted	417	67.9
	Stress	88	14.3
	Crisis	77	12.5
	Emergency	32	5.2
Proportion of household food expenditure	Less than 50%	376	61.2
	Between 50% and 65%	135	22.0
	Between 65% and 75%	64	10.4
	Greater than 75%	39	6.4
Household poverty quintile	Very poor	228	37.1
	Poor	119	19.4
	Medium	99	16.1
	Rich	76	12.4
	Very rich	92	15.0
Existence of latrine in the household	Yes	405	66.0
Existence of latrine in the household	No	209	34.0
Type of fuel used mainly for cooking	Wood/charcoal	558	90.9
Type of fuel used mainly for cooking	Natural gas	56	9.1
Household source of drinking water	Unprotected dug well	6	1.0
Household source of drinking water	Tap/Drill or Fountain	608	99.0
Existence of garbage can	No	334	58.8
Existence of garbage can	Yes	234	41.2
IDPs: Internally displaced persons

Women socio-demographic characteristics

Of the selected women, approximately 9 out of 10 were married, 68% were between 20 and 34 years of age, 45% were breastfeeding and 44% had no level of education (Table 3).

Table 3

Sociodemographic characteristics
Variables	Modalities	n	Percentage
Sex of household heads	Female	46	7.5
Sex of household heads	Male	568	92.5
Level of education of household heads	None	254	41.4
	Primary or Quranic or Literate	189	30.8
	Secondary or higher	171	27.9
Marital status of household heads	Single	33	5.4
Marital status of household heads	Married	581	94.6
Age group of household heads	<= 35.0	271	44.1
	35.1–72.5	328	53.4
	≥ 72.6	15	2.4
Women age group	≤ 19 years	56	9.1
	20 to 34 years	416	67.8
	≥ 35 years	142	23.1
Educational attainment of women	None	269	43.8
	Primary or Quranic or Literate	114	18.6
	Secondary or higher	231	37.6
Marital status of women	Single	54	8.8
Marital status of women	Married	560	91.2
Current iron/folic acid supplement use	No	535	87.1
Current iron/folic acid supplement use	Yes	79	12.9
Women physiological status	Pregnant	93	15.1
	Lactating	279	45.4
	Neither pregnant nor nursing	242	39.4

Indicators of women's nutritional status by hemoglobin

About one in 10 nonpregnant women of childbearing age had some form of anemia (71.5%). Among pregnant women, this proportion was 73.3% (Table 4). In general, 71.8% of women (pregnant or not) were anemic (Fig. 1).

Table 4

Indicators of women's nutritional status by hemoglobin
Indicators	n (%)
Proportion of non-pregnant WCA with all forms of anemia	372 (71.5)
Proportion of non-pregnant WCA with mild anemia (11.0-11.9 g/dL)	123 (23.7)
Proportion of non-pregnant WCA with moderate anemia (8.0-10.9 g/dL)	231 (44.4)
Proportion of non-pregnant WCA with severe anemia (< 8.0 g/dL)	18 (3.5)
Proportion of pregnant women with all forms of anemia	69 (73.3)
Proportion of pregnant women with mild anemia (10.0-10.9 g/dL)	21 (22.3)
Proportion of pregnant women with moderate anemia (7.0-9.9 g/dL)	43 (45.7)
Proportion of pregnant women with severe anemia (< 7.0 g/dL)	5 (5.3)
WCA: women of childbearing

Household characteristics and anemia in women of childbearing age

Household variables are not significantly associated with anemia in women of childbearing age (Table 5).

Table 5

Household characteristics and anemia among women of childbearing age
Variables	Modalities	No anemia n (%)	Anemia n (%)	P-value
Province	Gnagna	68 (24.5)	209 (75.5)	0.070*
Province	Gourma	105 (31.2)	232 (68.8)	0.070*
Place of residence	Rural	116 (27.8)	302 (72.2)	0.733
Place of residence	Urban	57 (29.1)	139 (70.9)	0.733
Type of household	100% de IDPs	11 (30.6)	25 (69.4)	0.634
	Aboriginal people	153 (28.5)	384 (71.5)
	Hosts (Indigenous and IDPs)	9 (22.0)	32 (78.0)
Household size	<= 6	131 (28.8)	324 (71.2)	0.566
Household size	≥ 7	42 (26.4)	117 (73.6)	0.566
Household food consumption	Poor	8 (22.2)	28 (77.8)	0.662
	Limit	62 (29.5)	148 (70.5)
	Acceptable	103 (28.0)	265 (72.0)
Household survival strategy	No strategy adopted	121 (29.0)	296 (71.0)	0.811
	Stress	25 (28.4)	63 (71.6)
	Crisis	20 (26.0)	57 (74.0)
	Emergency	7 (21.9)	25 (78.1)
Proportion of household food expenditure	Less than 50%	103 (27.4)	273 (72.6)
	Between 50% and 65%	40 (29.6)	95 (70.4)	0.955
	Between 65% and 75%	19 (29.7)	45 (70.3)
	Greater than 75%	11 (28.2)	28 (71.8)
Household poverty quintile	Very poor	64 (28.1)	164 (71.9)	0.705
	Poor	33 (27.7)	86 (72.3)
	Medium	24 (24.2)	75 (75.8)
	Rich	21 (27.6)	55 (72.4)
	Very rich	31 (33.7)	61 (66.3)
Existence of latrine in the household	Yes	109 (26.9)	296 (73.1)	0.330
Existence of latrine in the household	No	64 (30.6)	145 (69.4)	0.330
Type of fuel used mainly for cooking	Wood/charcoal	161 (28.9)	397 (71.1)	0.239
Type of fuel used mainly for cooking	Natural gas	12 (21.4)	44 (78.6)	0.239
Household source of drinking water	Unprotected dug well	2 (33.3)	4 (66.7)	0.778
Household source of drinking water	Tap/Drill or Fountain	171 (28.1)	437 (71.9)	0.778
Existence of garbage can	No	85 (25.4)	249 (74.6)	0.132
Existence of garbage can	Yes	73 (31.2)	161 (68.8)	0.132
*= significant (p ≤ 0.05) IDPs: Internally displaced persons

Sociodemographic characteristics and anemia in women of childbearing age

The age of the woman and the marital status, as well as iron/folic acid supplementation, were statistically significantly associated with anemia in women of childbearing age (Table 6).

Table 6

Sociodemographic characteristics and anemia in women of childbearing age
Variables	Modalities	No anemia n (%)	Anemia n (%)	P-value
Sex of household heads	Female	9 (19.6)	37 (80.4)	0.177
Sex of household heads	Male	164 (28.9)	404 (71.1)	0.177
Level of education of household heads	None	69 (27.2)	185 (72.8)	0.501
	Primary or Quranic or Literate	50 (26.5)	139 (73.5)
	Secondary or higher	54 (31.6)	117 (68.4)
Marital status of household heads	Single	5 (15.2)	28 (84.8)	0.087
Marital status of household heads	Married	168 (28.9)	413 (71.1)	0.087
Age group of household heads	<= 35.0	77 (28.4)	194 (71.6)	0.889
	35.1–72.5	91 (27.7)	237 (72.3)
	≥ 72.6	5 (33.3)	10 (66.7)
Women age group	≤ 19 years	6 (10.7)	50 (89.3)	0.010*
	20 to 34 years	124 (29.8)	292 (70.2)
	≥ 35 years	43 (30.3)	99 (69.7)
Educational attainment of women	None	78 (29.0)	191 (71.0)	0.741
	Primary or Quranic or Literate	34 (29.8)	80 (70.2)
	Secondary or higher	61 (26.4)	170 (73.6)
Women's marital status	Single	6 (11.1)	48 (88.9)	0.004*
Women's marital status	Married	167 (29.8)	393 (70.2)	0.004*
Current iron/folic acid supplement use	No	159 (29.7)	376 (70.3)	0.027*
Current iron/folic acid supplement use	Yes	14 (17.7)	65 (82.3)	0.027*
Women physiological status	Pregnant	25 (26.6)	69 (73.4)	0.407
	Lactating	86 (30.8)	193 (69.2)
	Neither pregnant nor nursing	62 (25.7)	179 (74.3)
*= significant (p ≤ 0.05)

Determinants of anemia in women of childbearing age

According to binary logistic regression, marital status of women and current intake of iron/folic acid supplements were significant explanatory factors for anemia in women aged 15 to 49 in the Eastern region. In fact, women who lived alone (single, divorced, or widowed) were 5 times more likely (OR = 5.155; CI = 1.821–14.593; P = 0.002) to be anemic than married women (Table 7).

Table 7

Determinants of anemia in women of childbearing age
Variables	Modalities	B	Odd ratio (OR)	IC at 95%	P-value
Current intake of iron/folic acid supplements	No Yes®	-0.783 -	0.457 1	(0.232–0.901) -	0.024* -
Women's marital status	Single Married®	1.640 -	5.155 1	(1.821–14.593) -	0.002* -
®= reference; *= significant (p ≤ 0.05)

In the Eastern region, the prevalence of anemia was 73.3% and 71.5% among pregnant and nonpregnant women respectively. This reflects the persistence of a serious public health problem. Anemia is a major concern when it affects pregnant women, as it can lead to increased maternal mortality and have serious consequences on the outcome of pregnancy [11]. The high proportion of pregnant women with anemia could be explained by low iron/folic acid intake during pregnancy, infections, low use of mosquito nets, and intermittent preventive treatment (IPT) against malaria.

The Demographic and Health Survey revealed that 60.6% of women of childbearing age suffered from anemia in the Eastern region. Among pregnant women, this proportion was 72.5% [12]. In 2014, Burkina Faso's national iodine and anemia survey found that 61.9% of women of childbearing age suffer from anemia in the same region [8].

Prevalences lower than the results of this study have been recorded in other countries. The results of a hospital study in Morocco also show that the overall prevalence of anemia in pregnant women was 35.7%.[13]. The prevalence of anemia among married women in sub-Saharan Africa was 49.7% [14]. In Ethiopia, 39% of teenage girls were anemic [15], 36.1% of pregnant women were anemic [16]. In Bangladesh, 62.5% of pregnant women were anemic [17].

According to the results of this study, in bivariate analysis, the factors significantly associated with anemia in women of childbearing age in the Eastern region were the woman's age, iron/folic acid supplementation, and the woman's marital status. These results are similar to others found by several authors. Regarding the age of the woman, studies on anemia in women of childbearing age carried out in Morocco [18], in Bangladesh [17], in the DRC [19], in China [20] and Brazil [21] show a significant association between the age of the woman and the anemia.

Furthermore, other studies had shown that there was no significant association between the onset of anemia and different age groups. This is the case of studies carried out in Turkey [22] and in Tanzania [23].

In multivariate analysis, only iron/folic acid supplementation and marital status showed significant odds ratios. In fact, women who were single (single, widowed, or divorced) were 5 times more likely to be anemic than those who were married. A study carried out in Morocco showed that marital status was significantly related to the onset of anemia in women [24]. Other authors have found similar results [25]. Another study conducted in Douala, Cameroon, showed that the frequency of anemia was not statistically higher in single women than in married women [26].

In northern Nigeria, the authors reported that single women were significantly more likely to be anaemic than married women. Married women are said to receive moral and financial support from their spouses, which may explain this difference [27].

Despite the results obtained, this study has several limitations that must be highlighted. In terms of limitations, it should be emphasized that we cannot have all the information likely to explain in women of childbearing age. It is possible that missing community or individual variables affect the explanatory variables and even the variable being explained.

The results of the present study show that there is a significant difference in the prevalence of anemia between women who reported taking iron/folic acid supplements and women who reported not taking them; however, the small number of women of childbearing age taking iron/folic acid supplements limits this comparison. Some authors have found that iron supplementation in pregnant women is a protective factor against anemia [28, 29]. Other authors have shown that iron supplementation is a predictor of anemia in pregnant women [22, 30].

In view of these results, initiatives aimed at strengthening anemia prevention are important for the population at risk.

The present study showed that anemia remains a public health problem among women of childbearing age (pregnant or not) in the provinces of Gourma and Gnagna of Burkina Faso. Factors significantly associated with anemia were female age, marital status, and iron/folic acid supplementation.

Therefore, it is necessary to reinforce preventive actions by encouraging sleeping under a mosquito net and following intermittent preventive treatment against malaria, as well as taking deworming tablets and iron/folic acid supplements.

Availability of data and materials

The database used in this study can be supplied by the corresponding author upon request.

Conflict of interest

The authors declares that there is no conflict of interest.

Funding

The authors disclosed the receipt of the following financial support for data collection. This work was supported by the joint program to combat chronic malnutrition in the eastern region of Burkina Faso.

Authors’ contributions

Conceptualization and methodology O. O., E. W. R. C, O. O.; data collection: O. O., E. W. R. C, M. H. D ; data analysis and interpretation O. O., E. W. R. C, O. O., drafting of article O. O. O., E. W. R. C ; critical revisions of article O. O., M. K. The final article is approved by M. H. D. All authors have read and approved the final manuscript.

Ethical approval

The study protocol was validated by the Burkina Faso Health Research Ethics Committee. Free written consent was obtained from the children's parents before the interview and blood sampling. Cases of severe anemia were referred to the nearest health center for appropriate treatment. Data confidentiality was ensured in accordance with ethical and moral standards.

Acknowledgements

Many thanks to the Health Research Ethics Committee, which authorized the study after reviewing the protocol.

Our thanks go to the entire population of the Eastern Region for their participation in this study, and to the data collection team for their efforts during this period of high security challenges.

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No competing interests reported.

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Prevalence and associated factors of anemia among women of childbearing age in the eastern region of Burkina Faso

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Abstract

Figures

Background

Methods

Framework and type of study

Study population

Sample and sampling

Data collection method and tools

Dependent Variable

Independent Variables

Processing and statistical analysis of data

Results

Household’s characteristics

Women socio-demographic characteristics

Indicators of women's nutritional status by hemoglobin

Household characteristics and anemia in women of childbearing age

Sociodemographic characteristics and anemia in women of childbearing age

Determinants of anemia in women of childbearing age

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1