Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A pooled analysis of nine East African countries.

BACKGROUND: In low-income nations, high-risk fertility behavior is a prevalent public health concern that can be ascribed to unmet family planning needs, child marriage, and a weak health system. As a result, this study aimed to determine the factors that influence high-risk fertility behavior and its impact on child stunting and anemia.
METHOD: This study relied on secondary data sources from recent demography and health surveys of nine east African countries. Relevant data were extracted from Kids Record (KR) files and appended for the final analysis; 31,873 mother-child pairs were included in the final analysis. The mixed-effect logistic regression model (fixed and random effects) was used to describe the determinants of high-risk fertility behavior (HRFB) and its correlation with child stunting and anemia. RESULT: According to the pooled study about 57.6% (95% CI: 57.7 to 58.2) of women had at least one high-risk fertility behavior, with major disparities found across countries and women's residences. Women who lived in rural areas, had healthcare access challenges, had a history of abortion, lived in better socio-economic conditions, and had antenatal care follow-up were more likely to engage in high-risk fertility practices. Consequently, Young maternal age at first birth (<18), narrow birth intervals, and high birth orders were HRFBs associated with an increased occurrences of child stunting and anemia.
CONCLUSION: This study revealed that the magnitude of high-risk fertility behavior was higher in east Africa region. The finding of this study underscores that interventions focused on health education and behavioral change of women, and improvement of maternal healthcare access would be helpful to avert risky fertility behaviors. In brief, encouraging contraceptive utilization and creating awareness about birth spacing among reproductive-age women would be more helpful. Meanwhile, frequent nutritional screening and early intervention of children born from women who had high-risk fertility characteristics are mandatory to reduce the burden of chronic malnutrition.

Introduction

Rapid population growth has been observed in developing countries including Sub-Saharan African countries, with an estimated population of 1.2 billion by 2025 [1]. The total fertility rate is declining globally, but it is decreasing more slowly in SSA, with a total fertility rate of 4.69 children per woman in 2018, down from 5.37 in 2008. Despite declining natural resources, a lack of infrastructures such as housing, schools, and health facilities, and increased unemployment, most African countries lack a demography and population policy to control or monitor fertility rates [2].

Women’s high-risk fertility habits, which are defined by narrow birth intervals, high birth order, and younger maternal age at birth, have been linked to negative health outcomes for both the mother and the child [3-5]. Due to increased family planning use, expansion of women’s education, and economic trends, pooled decomposition analysis revealed that high-risk fertility behavior was decreased over decades. High-risk fertility behavior (HRFB) is linked to an increased risk of infant mortality, chronic malnutrition, and adverse birth outcomes such as stillbirth, prematurity, and low birth weight, according to research findings [6-11]. The nations of the East African region share many of the same socio-demographic and cultural characteristics. Maternal and child mortality remains high in this area, owing to risky fertility behaviors, the cultural taboo against contraception use, and insufficient health infrastructures. HRFBs are also common in the area due to child marriage, rape, and harmful sexual behaviors in elementary school [12-15].

Furthermore, nutritional problems among children under the age of five are common, as evidenced by the magnitude of stunting (36.7%) and anemia (60%) [16, 17]. Maternal HRFBs was one of the major contributors to infant malnutrition. For example, children who were born from women with high-risk fertility behavior had 40 percent and 43 percent more likely to suffer from stunting and anemia, respectively [16, 17]. As a result, a better understanding of the factors linked to risky fertility behavior and its consequences for child malnutrition may aid in the development of interventions. HRFB is more prevalent in low-income countries due to widespread poverty, a lack of basic health services, and early child marriage. Whilst interventions are challenging due to a lack of information about the magnitude and determinants of high-risk fertility behavior in the East Africa region.

Thus, this study aimed to discover factors that influence high-risk fertility behaviors and associations with child stunting and anemia. As maternal and child health is at the top of the region’s agenda, the findings of this study may help to incorporate efforts at the Intergovernmental Authority for Development (IGAD) and African Union level.

Method

Data sources

This study was based on the secondary data from nine East African Demography and Health the most recent Survey (Burundi, Ethiopia, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zimbabwe, and Madagascar) with the analysis period ranged from July 1–30, 2020. The appended datasets of countries were used to estimate the magnitude of high-risk fertility behavior and its effects among reproductive-age women. We included women in this study who had given birth in the five years before the survey and had a child under the age of five.

We used Kids Record (KR) files, which contain information about women and children, for this specific research. In terms of data extraction, we took women who were married and had completed data for the main variables, as well as children’s anthropometric measurements. The data includes socioeconomic, reproductive health, and infant traits such as height for age and hemoglobin level. After data cleaning, the final sample size was 31,873 mothers-children pair who were included in the final analysis. To select study participants in each enumeration region, the DHS used a two-stage stratified sampling technique. We combined data from nine DHS surveys conducted in East African countries, yielding a weighted sample of 31,873 women and children. The strategy is described in detail in the DHS methodology section [16].

Variables of the study

Outcome variables

Maternal health outcome. For this study, maternal high-risk fertility behavior was the primary outcome variable which is defined based on several criteria’s as follow;

High-risk fertility behavior is the outcome of interest for women who gave birth, defined as women age at birth less than 18 or above 34 years or birth interval less than 24 months or high birth order were criteria used to define [16].

Single high-risk fertility behavior: when a woman reported to had one high-risk fertility behavior the is either younger age less than 18 years, or older age above 34 years, or birth interval less than 24 months, or high-birth order (four and above) [3, 17–19].

Multiple high-risk fertility behavior: when a woman had a combination of at least two above-mentioned behaviors [3, 17–19]. Unavoidable high-risk fertility behavior is defined as women whose age was between 18 and 34 years and first birth order [16, 17].

Unavoidable HRFB: when first-order births between ages of 18 and 34 years in women not amenable to the interventions.

Not in any high-risk category: when women don’t have any risk fertility behavior

Children health outcomes. another objective of this study was to see the association between maternal risky fertility behaviors and chronic malnutrition and anemia in children.

Height-for-age is a measure of linear growth retardation and cumulative growth deficits. Children whose height-for-age Z-score is below minus two standard deviations (-2 SD) from the median of the reference population are considered short for their age (stunted), or chronically undernourished.

Children who are below minus three standard deviations (-3 SD) are considered severely stunted.

Anemia is a disease condition marked by low levels of hemoglobin, often below 10g/dl after correction for altitude [17].

Mildly anemia: when the level of levels of hemoglobin between 10.0 and 10.9 g/dl [17].

Moderately anemia: when the level of levels of hemoglobin between 7.0 and 9.9 g/dl [17].

Severe anemia: when the level levels of hemoglobin less than 7g/dl [17].

Independent variables

Socio-demographic and maternal health services like age group, sex of household headed, women’s educational status, husband’s educational status, maternal occupation status, marital status, media exposure, wealth status, sex of the child, birth order, antenatal care visits, sources of family planning, postnatal care visit, place of delivery, birth attendants, and healthcare access problems were independent variables.

Data management and analysis

After extracting the variables based on literature, data from the nine East African countries were combined. To restore the representativeness of the survey and take sampling design into account when calculating standard errors and reliable estimates, the data were weighted using sampling weight, main sampling unit, and strata before any statistical analysis. STATA version 14 was used to perform cross-tabulations and summary statistics.

Using a forest plot, the overall magnitude of high-risk fertility behavior, stunting, and anemia was estimated with the 95 percent Confidence Interval (CI). The DHS data had a hierarchical structure for the determinant factors, which contradicts the classical logistic regression model’s independence of observations and equal variance assumptions. As a result, children are nested within a cluster, and we anticipate that children in the same cluster will be more similar than children across the country. This means that advanced models should be used to account for the variability between clusters. As a result, a mixed effect logistic regression model was fitted (with both fixed and random effects). Standard logistic regression and Generalized Linear Mixed Models (GLMM) were used because the outcome variable was binary (presence or absence of high-risk fertility behavior in women, stunting, and anemia in children). Since the models were nested, the Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR) test, Median Odds Ratio (MOR), and deviance (-2LLR) values were used to compare and assess model fitness. It was decided to use the model with the lowest deviance. As a result, the mixed-effect logistic regression model fits the data the best. In the multivariable mixed-effect logistic regression model, variables with a p-value of less than 0.2 in the bivariable analysis were considered. The multivariable model used Adjusted Odds Ratios (AOR) with a 95 percent Confidence Interval (CI) and p-value 0.05 to declare major factors high-risk fertility behavior. A multivariable Generalized Linear Mixed Models (GLMM) model was also fitted to see the relationship between HRFB and infant stunting and anemia. The HRFB had a major impact on stunting and anemia, as measured by the AOR with 95 percent confidence intervals and variables with a p-value less than 0.05.

Ethical clearance and consent to participate

Measure DHS provided ethical clearance after filling out a request for data access form. The data used in this study is aggregated secondary data that is publicly accessible and does not contain any personal identifying information that can be related to study participants. The data was kept confidential in an anonymous manner.

Result

Socio-demographic characteristics

A total of 31,873 study participants were drawn from nine East African countries, with Ethiopia, Tanzania, Madagascar, Burundi, Malawi, and Zimbabwe accounting for 21.8%, 15.6%, 12 percent, 11.3%, 10.9%, and 10.3%, respectively. The median age of respondents was 29 years, with an IQR of 25 to 35, and half of them aged between 25 and 35 years. The majority (80.5%) of women came from rural areas, nearly one-third (32.2%) had no formal schooling, and 45.4 percent lived in poverty (Table 1).

Table 1

Socio-demographic characteristics of reproductive age women in east Africa region.

Characteristics	Frequency	Percentage
Country
Burundi	3,631	11.4
Ethiopia	6,935	21.8
Malawi	3,492	11
Mozambique	2,254	7.1
Rwanda	1,701	5.3
Tanzania	4,976	15.6
Uganda	1,760	5.5
Zimbabwe	3,313	10.4
Madagascar	3,811	12
Age of respondents
15–19	1,168	3.7
20–24	6,413	20.1
25–29	8,782	27.6
30–34	7,341	23
35–39	5,044	15.8
40–44	2,393	7.5
45–49	732	2.3
Residence
Urban	6,899	19.3
Rural	28,785	80.7
Women level of education
No formal education	10259	32.2
Primary school	14893	46.7
Secondary school	5930	18.6
Diploma and higher	791	2.5
Husband education
No formal education	8034	25.2
Primary school	15239	47.8
Secondary school	6977	21.9
Diploma and higher	1623	5.1
Wealth index
Poor	14465	45.4
Middle	5887	18.5
Rich	11521	36.1
Household head
Male	26892	84.4
Female	4981	15.6
Media exposure
Yes	19653	60.3
No	12653	39.7
Health insurance coverage
Yes	2309	7.6
No	27863	92.4
Women working condition
Yes	22055	69.2
No	9818	30.8
Husband working condition
Yes	30073	94.4
No	1800	5.6

Reproductive history of women

The majority of the participants (88.4%) were multiparous, almost two-thirds (65.6%) gave birth in the health facilities, and about 4.5 percent gave birth by cesarean section. The majority (62.2%) had ANC follow-up, 21.2% of women had also postnatal follow-up, 40.3% of women had family planning details from the media, and 35.4% had discontinued family planning in the five years preceding the survey. More than two-thirds (68.2%) of women had trouble accessing healthcare due to a lack of resources, distance, permission, or companionship (Table 2).

Table 2

Reproductive characteristics of child bearing women in East Africa region.

Characteristics	Frequency	Percentage
Parity
Primiparous	3699	11.6
Multiparous	28174	88.4
Age at first birth
Less than 18	1553	14.9
18–34 years	25314	79.4
Above 34	5006	15.7
Place of delivery
Home	10877	34.1
Health facility	20996	65.9
History of abortion
Yes	4336	13.6
No	27537	86.4
Current contraceptive use
Yes	18434	57.8
No	13439	42.2
The average size at birth
Small	5505	17.3
Average	15962	50.1
Large	10403	32.6
Delivered Cesarean section
Yes	1432	4.5
No	30391	95.5
A faced healthcare access problem
Yes	21748	68.2
No	10125	31.8
ANC follow up
Yes	19825	62.2
No	12048	37.8
Postnatal follow-up
Yes	6743	21.2
No	25130	78.8
Sex of child
Male	15980	50.1
Female	15893	49.9
Discontinued contraceptive methods
Yes	11,283	35.4
No	20,590	64.6
Know the source of family planning
Yes	14,184	55.5
No	17,689	44.5
Had information about family planning
Yes	12,853	40.3
No	19,020	59.7

High-risk fertility behavior

The pooled analysis of this study indicated that 57.6% (95 percent CI: 57.7 to 58.2) of women had at least one high-risk fertility behavior, while 21.6 percent had multiple risk factors. The most common single high-risk fertility activity was higher birth order (45%), older age at birth (over 34 years) (15.7%), and birth period shorter than 24 months (15.6%). A combination of older women’s age and higher birth order (age over 34 and birth order above 3) and a birth period less than 24 months and birth order above 3 accounted for 14.5 percent and 8.7% of women, respectively. Within the country, there was also variance, ranging from 66.59% in Uganda to 41% in Zimbabwe (Fig 1). Significant variations were also found between women from rural and urban areas, with risk differences of 17.68% and 7.21% for single and multiple high-risk fertility behaviors in the East Africa region, respectively (Fig 2) and (Table 3).

Fig 1

Forest plot of proportion of high-risk fertility behavior among reproductive-age women in East Africa countries.

Fig 2

Forest plot of risk differences of high-risk fertility behavior among between rural and urban reproductive-age women in East Africa countries.

Table 3

High-risk fertility behavior of reproductive age women in east Africa region.

Characteristics	Frequency	Percentage
Any high-risk behavior
Yes	18346	57.6
No	13527	42.4
Single high-risk fertility behavior
Age less than 18 years	1553	4.9
Age above 34 years	5006	15.7
Birth order above 3	14351	45
The birth interval of less than 24 months	4963	15.6
Multiple high-risk fertility behavior
Age less than 18 years and birth interval less than 24 months	121	0.4
Age above 34 years and birth interval less than 24 months	662	2.1
Age above 34 years and birth order above 3	4619	14.5
Birth interval less than 24 months and birth order above 3	2768	8.7
Age above 34 years and birth interval less than 24 months, and birth order above 3	643	2
Unavoidable risk category
First birth order and age of mother between 18 and 34 years	4630	14.5
No high-risk fertility behavior	10,928	34.3

Factors associated with high-risk fertility behavior

In the multivariable mixed-effect logistic regression model, mother and husband education levels, residence, country, wealth status, sex of household, place of delivery, delivered by CS, abortion, healthcare access problems, currently contraceptive utilization were variables correlated with high-risk fertility behaviors at a p-value of 0.05. In contrast to uneducated mothers, the chances of high-risk pregnancy activity were reduced by 41% (AOR = 0.59, 95% CI: 0.56 to 0.64), 68 percent (AOR = 0.32, 95% CI: 0.29 to 0.36), and 76% (AOR = 0.24, 95% CI: 0.19 to 0.29) for women who completed primary, secondary, and certificate and higher-level schooling. For those husbands who attended primary, secondary, diploma, and above level of education, the odds of high-risk fertility behaviors were reduced by 11% (AOR = 0.89, 95% CI: 0.83 to 0.95), 29% (AOR = 0.71, 95% CI: 0.65 to 0.78), and 25% (AOR: 0.75, 95% CI: 0.65 to 0.87) compared to low level of education, respectively.

Female-headed households had an 11% lower risk of high-risk fertility activity than male-headed households (AOR = 0.89, 95% CI: 0.83 to 0.95). Furthermore, rural mothers had a 1.26-fold higher probability of fertility activity than city mothers (AOR = 1.26, 95% CI: 1.17 to 1.36). Similarly, the chances of high-risk fertility activity were 1.10 times higher in wealthy women than in poor women (AOR = 1.10, 95%CI: 1.03 to 1.18). In addition, women with healthcare access challenges had a 10% higher risk of high-risk fertility activity than mothers who did not have such a history (AOR = 1.14, 95% CI: 1.08 to 1.20). Women who had terminated pregnancies had a 16 percent increased risk of high-risk fertility activity relative to those who had no such history (AOR = 1.16, 95% CI: 1.08 to 1.25). The chances of high-risk fertility activity were 1.51 times higher for mothers who gave birth at home (AOR = 1.51, 95% CI: 1.41 to1.61) than for mothers who gave birth in a hospital (AOR = 1.51, 95% CI: 1.41 to1.61). Furthermore, women who had antenatal care follow-up with their recent baby had a 16% higher risk of delivering a healthy baby than those who did not (AOR = 1.16, 95% CI: 1.10 to 1.23). In contrast, mothers who were aware of the sources of family planning had an 11% lower risk of high-risk fertility activity than those who were not aware of the sources of family planning (AOR = 0.89, 95% CI: 0.79 0.97). Women who gave birth by cesarean section had a 30% lower risk of high-risk fertility activity than women who gave birth vaginally (AOR = 0.70, 95% CI: 0.63 to 0.79). Similarly, women who were currently using contraception were reduced by 10% compared to those who were not currently using contraception (AOR = 0.90, 95% CI: 0.85 to 0.95). (Table 4).

Table 4

Factors associated with high-risk fertility behavior among women gave birth in east Africa region.

Characteristics	Odds ratio		Characteristics	Crude 95%CI	Adjusted 95%CI
	Crude 95%CI	Adjusted 95%CI	Current contraceptive utilization
Country			Yes	0.62(0.59 0.65)	0.90(0.85 0.95)*
Burundi	2.14(1.94 2.36)	0.93(0.83 1.06)	No	1	1
Ethiopia	2.32(2.13 2.53)	0.73(0.65 0.83)*	Sex of household
Malawi	1.40(1.27 1.55)	0.87(0.78 0.97)*	Male	1	1
Mozambique	2.23(2.0 2.49)	1.06(0.92 1.21)	Female	0.77(0.72 0.82)	0.89(0.83 0.95)*
Rwanda	1.35(1.20 1.52)	0.77(0.67 0.88)*	Faced healthcare access problem
Tanzania	2.20(2.01 2.41)	1.09(0.98 1.22)	Yes	1.56(1.48 1.63)	1.14(1.08 1.20)*
Uganda	2.85(2.52 3.23)	1.68(1.46 1.93)*	No	1	1
Madagascar	2.42(2.19 2.67)	1.06(0.94 1.19)	Delivered by CS
Zimbabwe	1	1	Yes	0.42(0.38 0.48)	0.70(0.63 0.79)*
Women level of education			No	1	1
No formal education	1	1	Residence
Primary	0.54(0.51 0.57)	0.59(0.56 0.64)*	Urban	1	1
Secondary	0.23(0.22 0.25)	0.32(0.29 0.36)*	Rural	2.20(2.07 2.33)	1.26(1.17 1.36)*
Higher	0.13(0.11 0.16)	0.24(0.19 0.29)*	Media exposure
Husband level of education			Yes	0.65(0.62 0.68)	0.97(0.92 1.03)
No formal education	1	1	No	1	1
Primary	0.68(0.64 0.72)	0.89(0.83 0.95)*	Postnatal care follow up
Secondary	0.34(0.32 0.36)	0.71(0.65 0.78)*	Yes	0.71(0.67 0.75)	0.98(0.92 1.05)
Higher	0.24(0.22 0.27)	0.75(0.65 0.87)*	No	1	1
Wealth status			ANC follow up
Poor	1	1	Yes	0.84(0.81 0.88)	1.16(1.10 1.23)*
Middle	0.86(0.80 0.91)	1.03(0.96 1.10)	No	1	1
Rich	0.54(0.51 0.57)	1.10(1.03 1.18)*	Know sources of family planning
Ever terminated pregnancy			Yes	0.71(0.68 0.74)	0.89(0.79 0.97)*
Yes	1.14(1.07 1.22)	1.16(1.08 1.25)*	No	1	1
No	1	1
Place of delivery
Home	2.04(1.94 2.14)	1.51(1.41 1.61)*
Health facility	1	1

Association between maternal high-risk fertility behaviors and stunting and anemia in children

To investigate the relationship between high-risk fertility activity and infant stunting and anemia, a mixed effect generalized linear mixed model (GLLM) was fitted. Thus, mothers under the age of 18 and over the age of 34, birth order greater than three, birth interval, and interactions of higher birth order and age greater than 34 years are associated with anemia stunting. Stunting was 1.55 (AOR = 1.55, 95% CI: 1.39 to 1.73), 1.33 (AOR = 1.33, 95% CI: 1.21 to 1.46) and 1.25 (AOR = 1.25, 95% CI: 1.18 to 1.32) times more likely in children born to mothers under the age of 18 at the time of birth, birth period less than 24 months, and birth order above three. Similarly, Similarly, an interactions of mother age over 34 and birth order greater than 3 was related to a 1.35 higher risk of infant stunting than those that did not have these characteristics (AOR = 1.35, 95 percent CI: 1.06 to 1.73). On other hand a mother’s age at birth for 34 years is associated with a 25% lower risk of child stunting (AOR = 0.75, 95 percent CI: 0.60 to 0.95), compared to other age groups.

When the mother was less than 18 years old at the time of birth, the birth span was less than 24 months, and the birth order was greater than 3, the odds of infant anemia were 1.19 (AOR = 1.19, 95 percent CI: 1.07 to 1.33), 1.12 (AOR = 1.12, 95 percent CI: 1.01 to 1.23), and 1.26 times higher than their counterparts. Women over 34 years old had a 28 percent lower risk of infant anemia than women of other ages (AOR = 0.72, 95 percent CI: 0.58 to 0.90) (Table 5).

Table 5

Effect of high-risk fertility behavior on child chronic malnutrition and Anemia.

High-risk fertility behaviors	Stunting		Crude OR	Adjusted OR	Anemia		Crude OR	Adjusted OR
	Yes	No			Yes	No
Age less than 18 years
Yes	711	842	1.35(1.21 1.50)	1.55(1.39 1.73)*	877	676	1.08(0.98 1.20)	1.19(1.07 1.33)*
No	18633	11687	1	1	16391	13929	1	1
Age above 34 years
Yes	2022	2984	1.07(1.01 1.14)	0.75(0.60 0.95)*	2660	2346	0.95(0.90 1.20)	0.72(0.58 0.90)*
No	10376	16491	1	1	14608	12259	1	1
Birth interval less than 24 months
Yes	2160	2803	1.26(1.18 1.34)	1.33(1.21 1.46)*	8161	2103	1.15(1.08 1.23)	1.12(1.01 1.23)*
No	10238	16672	1	1	9107	12502	1	1
Birth order 4 and above
Yes	5950	8401	1.21(1.16 1.27)	1.25(1.18 1.32)*	2860	6190	1.21(1.16 1.26)	1.26(1.19 1.34)*
No	6448	11074	1	1	14408	8415	1	1
Age >34 years and Birth order >3
Yes	1907	2712	1.12(1.05 1.19)	1.35(1.06 1.73)*	2493	2126	0.99(0.93 1.06)	1.19(0.95 1.50)
No	10491	16763	1	1	14775	12479	1	1
Age < 18 years & birth interval <24 months
Yes	58	63	1.44(1.00 2.07)	0.81(0.55 1.20)	72	49	1.24(0.86 1.80)	1.03(0.69 1.52)
No	12340	19412	1	1	17196	14556	1	1
Age >34 years & birth interval <24 months
Yes	278	384	1.15(0.98 1.35)	1.43(0.55 3.73)	363	299	1.02(0.87 1.19)	1.03(0.40 2.65)
No	19091	12120	1	1	16905	14306	1	1
Birth interval <24 months and birth order >3
Yes	1233	1535	1.30(1.20 1.41)	0.92 (0.80 1.05)	1658	1110	1.26(1.16 1.37)	1.03(0.90 1.18)
No	11165	17940	1	1	15610	13495	1	1
Age above 34, birth order >3, and birth interval <24 months
Yes	270	373	1.15(0.98 1.35)	0.59(0.22 1.59)	354	289	1.03(0.87 1.20)	0.86(0.33 2.26)
No	12128	19102	1	1	16914	14316	1	1

Discussion

This study intended to determine the pooled estimates of high-risk fertility behavior in East Africa countries. Thus, the pooled analysis revealed that 57.7% and 21.6% of women who gave birth had at least one and multiple high-risk fertility behavior. Of which, higher birth order, age above 34 at birth, and birth interval less than 24 months were the common single high-risk fertility behaviors. Moreover, significant variations were also observed among countries ranged from 41% in Zimbabwe to 66% in Uganda. Likewise, a significant difference was also observed between rural and urban mothers in terms of high-risk fertility behavior which accounted for 17% of risk differences (RD). The possible explanations for the observed variation might be child marriage practices, a high magnitude of unmet need for family planning, and bad cultural myths and beliefs to use family planning among women. In addition, most of the countries in Africa had no demography and population policy despite rapid population growth. In addition, these findings suggest that more interventions which focus on maternal health services like provision of family planning and counseling on reduction of risky fertility behaviors are very important.

Furthermore, there were also substantial risk variations in high-risk fertility activity between rural and urban areas. This may be explained by a lack of access to healthcare and family planning, suggesting that rural areas are the best place to participate to minimize maternal mortality, meet sustainable development goals, and achieve universal health coverage. This result was in line with results from Nepalese and Indian studies [20-22]. Women and husbands with some degree of schooling had lower risky-fertility behavior than women with no formal education, according to this report. This result was in line with the findings of other studies [20, 21, 23]. Women’s awareness about the benefits of birth spacing and reproductive health attributes expanded as their educational levels rose. The effects of school reproductive clubs and the inclusion of fertility biology in the educational curriculum may also explain this. In contrast to male-headed households, female-headed households have a lower risk of high-risk fertility activity. This may be because women are responsible for both earning a living and caring for their children

Rich women, on the other hand, are more likely than poor women to participate in high-risk fertility activity. This may be because wealthier women (households) could want more children, which could contribute to risky fertility activity. This result was in line with previous research. Significant regional differences in high-risk fertility behavior were also discovered in this research. Women from Uganda had 1.68 times more likely to had high-risk fertility behavior than women from Zimbabwe, while women from Rwanda, Malawi, and Ethiopia had 23%, 13%, and 27% lower chances of high-risk fertility behavior than Zimbabwe, respectively. Regarding the place of delivery, women who gave birth at home had a greater high-risk fertility behavior than women who gave birth at a health facility [6, 7, 24, 25]. This result was in line with those of previous Ethiopian studies [26]. Immediate post-partum family planning programs, such as ICUD, were often available via health facilities. Integration and strengthening of family planning services with obstetrics services like IUCD insertion immediately after delivery.

Similarly, women who had ever terminated a pregnancy (abortion history) were more likely to engage in high-risk fertility activity than those who had not. This result was in line with previous results in Sub-Saharan Africa [9, 27]. Abortion was commonly associated with unwanted pregnancies with shorter birth periods and pregnancies at a young age, and it represented a lack of contraception use, which affected high-risk fertility. Women who had trouble accessing health services were often more likely to participate in high-risk fertility behaviors. This finding was consistent with previous research [28]. This may be because women who had trouble accessing health services used less family planning and received less ANC and postnatal care, resulting in shorter birth periods, births at an older age, and high birth orders. In addition, this study showed that mothers who received ANC during pregnancy had an increased risk of high-risk fertility activities relative to those who did not. This result contradicted previous research. This may be because women with shorter birth periods and conceptions at a later age are frequently high-risk and need regular monitoring and follow-up.

Another result of this study was that cesarean section delivery is associated with lower risky fertility activity as compared to vaginal delivery. This may be because frequent Cesarean section deliveries reduced the number of pregnancies due to the possibility of negative effects of repeated CS [29]. Similarly, during data collection, women who understood the origins of modern contraception and existing users were associated with lower risk fertility activity. This result was consistent with previous research [3, 5]. A strong understanding of contraceptive strategies and their use decreased unintended pregnancy and improved birth intervals.

This research, on the other hand, discovered a correlation between high-risk fertility activity and childhood chronic stunting and anemia. Thus, in the East African region, the prevalence of stunting and anemia was 38.9 percent and 54.2 percent, respectively, among those who gave birth in the five years preceding the study. In this report, the prevalence of chronic malnutrition (stunting) was lower than in India (45.1 percent) and Nepal (39.7 percent) [30]. This finding, however, was higher than that of Bangladesh (36%) and three disadvantaged east African countries (36.7%) [19, 31]. Furthermore, the incidence of infant anemia was lower than a study finding of 43.7 percent of Bangladeshis. This finding reflects that maternal fertility behaviors are also contributors to nutritional problems among children. Socio-cultural disparities, such as cultural taboos against some food products in Southeast Asia, maybe one reason. Another point to remember is the correlation between high-risk fertility and chronic malnutrition in children. As a result, women under the age of 18 at the time of birth were related to a higher risk of stunting and anemia. This result was in line with previous research. Birth age is often linked to social and health disadvantages and inequality. In comparison to other age classes, women over 34 years old at the time of birth have a lower risk of stunting and anemia.

Furthermore, children with a birth order greater than 3 and a birth interval of less than 24 months have a higher risk of stunting and anemia. This result was in line with previous research. This may be attributed to a short birth period and a high birth order, which is related to intrauterine growth retardation in infants, maternal anemia, and maternal stress, both of which contribute to prematurity. Similarly, women with multiple high-risk fertility behaviors, such as age over 34 and high birth order of three or more, had a higher occurrence of high-risk fertility activity than those who did not. This finding was consistent with previous research. In general, this study found that high-risk fertility activity is widespread among East African reproductive-age women. Material high-risk fertility activity is linked to chronic malnutrition and anemia in children. This indicates that growing contraceptive use by women of childbearing age would help both the mother and the child’s health.

For evidence-based approaches, this research has implications for reproductive-age women, healthcare planners, and policymakers. Furthermore, the results of this study revealed that amenable variables such as home delivery, educational status, wealth status, and contraceptive usage could be the target area for resolving the issues. In addition, factors such as schooling, residency, and family planning source have been described as strategies for reducing maternal and child mortality. However, there are some drawbacks to this research. First, the study’s cross-sectional nature influenced the cause-effect relationship; second, health system characteristics were not assessed; and finally, the data in this study had recall bias issues, such as the number of months between births.

Conclusion

This study revealed that the magnitude of high-risk fertility behavior was higher in the region. The finding of this study underscores that interventions focused on health education and behavioral change of women, and improvement of maternal healthcare access would be helpful to avert risky fertility behaviors. In brief, encouraging contraceptive utilization and creating awareness about birth spacing among reproductive-age women would be more helpful. Meanwhile, frequent nutritional screening and early intervention of children born from women who had high-risk fertility characteristics are mandatory to reduce the burden of chronic malnutrition.

22 Feb 2021

PONE-D-20-27151

Determinants of maternal high-risk fertility behavior and its effects on stunting and anemia in the East Africa region: Pooled analysis of nine East African countries

PLOS ONE

Dear Dr. Tamirat,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Academic Editor

PLOS ONE

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Please see below my comments.

1. Define abbreviations at first mention (e.g HRFB).

2. In the abstract, what do you mean by "Women and husband education"? please define the direction. Are you saying poor/low women and husband education?

3. The abstract's conclusion is not properly written and hard to follow. Please do not repeat the study result in the conclusion section.

4. The following statement in the methods section is confusing: "Whereas to see the relationship of risky behaviors with child chronic malnutrition and anemia, child hemoglobin level, and height for age measurements as dependent variables."

Which are/is the dependent variable(s)? High risk fertility behaviour? or chronic malnutrition? or anemia? or child hemoglobin level?

How were they defined? How were they expressed in the analysis? What is the diference between chronic malnutrition and height for age measurements in your study? I thought height for age measurements are used to measure chronic malnutrition.

5. Which are the exploratory variables? How were they defined? How were they expressed in the analysis?

6. How was statistical bias avoided?

In general, the sub-section: "Variables of the study" in the methods section should be extensively revised.

I would strongly recommend extensive grammar and punctuation editing.

**********

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Reviewer #1: No

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9 Apr 2021

Point by point response

Manuscript title: Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A Pooled Analysis of nine East African countries

Manuscript ID: PONE-D-20-27151

Journal – PLOS ONE

Dear editor/reviewer

Dear all,

We would like to thank you for this constructive, building, and improvable comments on this manuscript that would improve the substance and content of the paper. We considered each comment and clarification questions of editors and reviewers on the document thoroughly. Our point-by-point responses for each comment and issues are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Koku Sisay Tamirat

On behalf of all authors

Editor comments

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

• Author response: Thanks editors for your constructive comments, based on your comments we made all corrections according to Submission guidelines.

2. Please include in your Methods section the date ranges of the DHS database analysed in the current study.

• Author response: Thanks editor for your comments for the purpose of this study we used secondary data sources from measure Demography and Health Survey (DHS) website with after fill the request form. The date of analysis for this study was from July 1-30, 2020. Mentioned in the method section, page 4, Line 88-89.

We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

• Author response: Thanks reviewer for your constructive comments which are highly important to improve the quality of manuscript quality.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

• Author response: Thank you editor for your constructive comments. This study is further analysis of publicly available secondary data sources from measure DHS. Ethical clearance was obtained after filling an online data acquisition form of measure DHS. The request form available at www.measuredhs.com.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

• Author response: Corrected in the main document of the manuscript. Mentioned in the declaration section of the manuscript, page 17, line 357-358.

We will update your Data Availability statement on your behalf to reflect the information you provide.

We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 3 in your text; if accepted, production will need this reference to link the reader to the Table.

• Author response: Corrected in the main document. Thank you editors for your insightful comments. it is already mentioned in the main document, page 9, line 188-189.

Reviewer 1: Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

• Author response: The conclusion section corrected based on the results and objectives of the study.

Reviewer comments: Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

• Author response: The language proofread by all authors and other language exerts. Language errors edited and corrected in the main document of the manuscript.

Reviewer comments: Define abbreviations at first mention (e.g HRFB).

• Author response: Corrected in the main document of the manuscript HRFB stands to High-risk fertility behavior among reproductive age women. The detail mentioned in the introduction section and variables of the study section. Mentioned in the Page 3, line 63-64 and page 5, 103-113.

2. In the abstract, what do you mean by "Women and husband education"? please define the direction. Are you saying poor/low women and husband education?

• Author response: Corrected as “In contrast to uneducated mothers, the chances of high-risk pregnancy activity were reduced by 41 % (AOR=0.59, 95 % CI: 0.56 to 0.64), 68 percent (AOR=0.32, 95 % CI: 0.29 to 0.36), and 76 % (AOR= 0.24, 95 % CI: 0.19 to 0.29) for women who completed primary, secondary, and certificate and higher level schooling. Those husband who attended primary, secondary, diploma and above level of education, the odds of high-risk fertility behaviors were reduced by 11 % (AOR=0.89, 95 % CI: 0.83 to 0.95), 29 % (AOR=0.71, 95 % CI: 0.65 to 0.78), and 25 % (AOR: 0.75, 95 % CI: 0.65 to 0.87) compared to low level of education, respectively. Corrected in the main document of the manuscript, page 9-10, Line 195-202.

Reviewer’s comments: The abstract's conclusion is not properly written and hard to follow. Please do not repeat the study result in the conclusion section.

• Author response: Thank you reviewer for your constructive comments. The abstract section revised and rephrased. Written as “Background: Low contraceptive utilization, child marriage, and a poor health system contributed to a high-risk fertility behavior in the East African region. As a result, this study aimed to establish determinants of high-risk fertility activity and their effect on child stunting and anemia. Method: This study relied on secondary data sources from recent demography and health surveys of nine east African countries. Relevant data were extracted from Kids Record (KR) files and appended for the final analysis; 31,873 mother-child pairs were included in the final analysis. The mixed-effect logistic regression model (fixed and random effects) was used to describe the determinants of high-risk fertility behavior (HRFB) and its correlation with child stunting and anemia. Result: According to the pooled study, 57.6% (95 % CI: 57.7 to 58.2) of women had at least one high-risk fertility behavior, with major a disparities found across countries and women's residences. High-risk fertility behaviors were more common among women of rural dwellers, faced healthcare access problems, history of abortion, better economic conditions, and had antenatal care follow-up. Consequently, younger women at first birth, narrow birth intervals, and high birth orders were HRFBs associated with an increased occurrence of child stunting and anemia. Conclusion: This study revealed that the magnitude of high-risk fertility behavior was higher in the region. The finding of this study underscores that interventions focused on health education and behavioral change of women, and improvement of maternal healthcare access would be helpful to avert risky fertility behaviors. In brief, encouraging contraceptive utilization and creating awareness about birth spacing among reproductive-age women would be more helpful. Meanwhile, frequent nutritional screening and early intervention of children born from women who had high risk fertility characteristics are mandatory to reduce the burden of chronic malnutrition.

4. The following statement in the methods section is confusing: "Whereas to see the relationship of risky behaviors with child chronic malnutrition and anemia, child hemoglobin level, and height for age measurements as dependent variables."

Which are/is the dependent variable(s)? High risk fertility behaviour? or chronic malnutrition? or anemia? or child hemoglobin level?

How were they defined? How were they expressed in the analysis? What is the diference between chronic malnutrition and height for age measurements in your study? I thought height for age measurements are used to measure chronic malnutrition.

• Author response: For this study there were more than one dependent variables. Thus, High-risk fertility behavior among reproductive women were the outcome variables for women. Mentioned as “High-risk fertility behavior is the outcome of interest for women who gave birth, defined as women age at birth less than 18 or above 34 years or birth interval less than 24 months or high birth order were criteria used to define the outcome of the interest.”

• Secondary outcomes of the study : Chronic malnutrition like Stunting and Anemia were also outcome variables for children to see any association between chronic malnutrition and High-risk fertility behavior among reproductive age women. Please note that for decision of HFRB and nutritional assessment used was for the recent child and birth. The details about the variables of the study mentioned in the method sections of the study. Page 5-6, Line 101-132.

Which are the exploratory variables? How were they defined? How were they expressed in the analysis?

• Author response: Corrected in the main document of the manuscript. Mentioned page 5-6, Line 101-132.

Reviewer comments: How was statistical bias avoided? In general, the sub-section: "Variables of the study" in the methods section should be extensively revised. I would strongly recommend extensive grammar and punctuation editing.

• Author response: Some of the strategies used to reduce bias in this study was using standardized definitions for classification of outcomes like HRFB, nutritional status like Anemia, stunting. Regarding the variables of the study we describe in detail in the method section of the manuscript. In addition we tried to address the language error through proof read by all authors.

Submitted filename: Point by point response.docx

Click here for additional data file.

28 May 2021

PONE-D-20-27151R1

Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A Pooled Analysis of nine East African countries

PLOS ONE

Dear Dr. Tamirat,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 12 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Frank T. Spradley

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Well-done on your revised manuscript. Your submission has greatly improved. However, there are still some grammar errors that makes some portions hard to understand. Here are some but I would recommend you further proofread your manuscript before final submission.

Revise your grammar:

Line 28, .......child marriage, and a poor health system contributes to high-risk fertility behaviour in the East African region.

Line 39, ......were common among women who live in rural areas, are unable to access healthcare, have history of abortion, have better economic conditions and had antenatal care follow-up.

I would recommend you use 'Young maternal age at first birth (<18)" rather than "younger women at first birth".

Line 115 - 116, what do you mean by ".....to see the relationship between risky behaviors with the child chronic malnutrition and anemia defined as follow," - this is hard to understand. Please revise the grammar.

What is the difference between "Unavoidable risk category" and "No high-risk fertility behavior"?.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

4 Jun 2021

Point by point response

Manuscript title: Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A Pooled Analysis of nine East African countries

Manuscript ID: PONE-D-20-27151R1

Journal – PLOS ONE

Dear editor/reviewer

Dear all,

We would like to thank you for this constructive, building, and improvable comments on this manuscript that would improve the substance and content of the paper. We considered each comment and clarification questions of editors and reviewers on the document thoroughly. Our point-by-point responses for each comment and issues are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Koku Sisay Tamirat

On behalf of all authors

Revise your grammar:

Line 28, .......child marriage, and a poor health system contributes to high-risk fertility behavior in the East African region.

Author response: Thanks reviewer for your constructive comments based on your suggestion grammatical errors corrected in the main document. Mentioned in Page 2, Line 28

Line 39, ……….were common among women who live in rural areas, are unable to access healthcare, have history of abortion, have better economic conditions and had antenatal care follow-up.

Author response: Thanks for your constructive comments it is already corrected in the main document abstract section. Page 2

I would recommend you use 'Young maternal age at first birth (<18)" rather than "younger women at first birth".

Author response: Thanks for your constructive comments it is already corrected in the main document abstract section. Page 2

Line 115 - 116, what do you mean by ".....to see the relationship between risky behaviors with the child chronic malnutrition and anemia defined as follow," - this is hard to understand. Please revise the grammar.

Author response: uthor response: Thanks for your constructive comments it is already corrected in the main document as “Children health outcomes: another objective of this study was to see the association between maternal risky fertility behaviors and chronic malnutrition and anemia in children”.

What is the difference between "Unavoidable risk category" and "No high-risk fertility behavior"?.

Author response: Maternal health outcome: For this study, maternal high-risk fertility behavior was the primary outcome variable which is defined based on several criteria’s as follow;

• High-risk fertility behavior is the outcome of interest for women who gave birth, defined as women age at birth less than 18 or above 34 years or birth interval less than 24 months or high birth order were criteria used to define [16].

• Single high-risk fertility behavior: when a woman reported to had one high-risk fertility behavior the is either younger age less than 18 years, or older age above 34 years, or birth interval less than 24 months, or high-birth order (four and above) [3, 17-19].

• Multiple high-risk fertility behavior: when a woman had a combination of at least two above-mentioned behaviors [3, 17-19]. Unavoidable high-risk fertility behavior is defined as women whose age was between 18 and 34 years and first birth order[16, 17].

• Unavoidable HRFB: when first-order births between ages of 18 and

34 years in women not amenable to the interventions.

• Not in any high-risk category: when women don’t have any risk fertility behavior

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14 Jun 2021

Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A Pooled Analysis of nine East African countries

PONE-D-20-27151R2

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16 Jun 2021

PONE-D-20-27151R2

Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A Pooled Analysis of nine East African countries

Dear Dr. Tamirat:

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