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Abstract

Background:

Child and maternal mortality continue as a major public health concern in East African countries. Optimal birth interval is a key strategy to curve the huge burden of maternal, neonatal, infant, and child mortality. To reduce the incidence of adverse pregnancy outcomes, the World Health Organization recommends a minimum of 33 months between two consecutive births. Even though short birth interval is most common in many East African countries, as to our search of literature there is limited study published on factors associated with short birth interval. Therefore, this study investigated factors associated with short birth intervals among women in East Africa.

Objective:

To identify factors associated with short birth intervals among reproductive-age women in East Africa based on the most recent demographic and health survey data

Design:

A community-based cross-sectional study was conducted based on the most recent demographic and health survey data of 12 East African countries. A two-stage stratified cluster sampling technique was employed to recruit the study participants.

Methods and analysis:

A total weighted sample of 105,782 reproductive-age women who had two or more births were included. A multilevel binary logistic regression model was fitted to identify factors associated with short birth interval. Four nested models were fitted and a model with the lowest deviance value (–2log-likelihood ratio) was chosen. In the multivariable multilevel binary logistic regression analysis, the adjusted odds ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association between short birth interval and independent variables.

Results:

The prevalence of short birth interval in East Africa was 16.99% (95% confidence interval: 16.76%, 17.21%). Women aged 25–34 years, who completed their primary education, and did not perceive the distance to the health facility as a major problem had lower odds of short birth interval. On the contrary, women who belonged to the poorest household, made their own decisions with their husbands/partners or by their husbands or parents alone, lived in households headed by men, had unmet family planning needs, and were multiparous had higher odds of having short birth interval.

Conclusion:

Nearly one-fifth of births in East Africa had short birth interval. Therefore, it is essential to promote family planning coverage, improve maternal education, and empower women to decrease the incidence of short birth intervals and their effects.

Keywords

demographic and health survey East Africa multilevel analysis short birth interval

Introduction

Adverse pregnancy outcomes remain a global public health problem specifically in East African countries.^1
–4 Globally, an estimated 13 million and 2.6 million pregnancies end as preterm birth and stillbirth annually, respectively.^5
–7 As advocated by the World Health Organization (WHO), optimal birth spacing is one of the most effective strategies to reduce adverse child, maternal, and pregnancy outcomes.^8,9 In low- and middle-income nations, about 200 million women of reproductive age need to space out or limit their pregnancies, yet there is little access to family planning, particularly in East African nations.¹⁰ If all births are spaced out by at least 33 months, an estimated 1.6 million deaths of children under five would be prevented each year.¹¹ A minimum of 33 months is needed for mothers to fully recuperate from macro- and micro-nutrient depletion that occurred during pregnancy and lactation.¹² According to the (WHO), short birth interval (SBI) is defined as when the duration of two successive live births is less than 33 months.^9,13 SBI is significantly associated with adverse maternal, perinatal, infant, and child health outcomes.^12,14
–20

The prevalence of SBI has varied across countries (Uganda:13.4%, Ethiopia: 58.5%, Tanzania: 48.4%).^13,21
–25 Evidence documented that limited availability and use of family planning services are the main cause of the high prevalence of SBI in East African countries.^26,27 Birth spacing has a significant effect on pregnancy outcomes.¹² SBI increases the risk of stillbirth,²⁸ small for gestational age,²⁹ lower birth weight,³⁰ neonatal mortality, preterm birth,³¹ maternal mortality.^32
–35 These associations stem from the biological factor commonly referred to as maternal depletion syndrome.³⁶

Promoting contraceptive use immediately after birth is a crucial family planning programmatic strategy for preventing SBI and its related consequences.^37,38 Studies revealed that maternal education,^39,40 household wealth status, maternal age,⁴¹ women’s health care decision-making autonomy,⁴² parity,⁴¹ sex of household head, media exposure,¹⁰ contraception,³⁷ husband education,^43,44 and health care access^10,45 were significantly associated with SBI.

Although SBI is a common problem in East African countries, as to our search of literature there is limited study on the prevalence and associated factors of SBI among reproductive-age women in East Africa. Therefore, this study investigated the prevalence of SBI and associated factors among reproductive-age women in East Africa.

Methods

Data source and sampling procedures

We used the most recent demographic and health survey (DHS) data of 12 East African countries (Burundi (2016/2017), Ethiopia (2016), Comoros (2012), Uganda (2016), Rwanda (2014/2015), Tanzania (2015/2016), Mozambique (2011), Madagascar (2008/2009), Zimbabwe (2015), Kenya (2014), Zambia (2018), and Malawi (2015/2016)).⁴⁶ DHS is a nationally representative cross-sectional survey conducted every 5 years to generate updated health and health-related indicators. A multistage stratified cluster sampling technique was employed using enumeration areas (EAs) and households as primary sampling units and secondary sampling units, respectively.

Inclusion and exclusion criteria

To define the outcome variable, a woman should have two or more births. Therefore, reproductive-age women who had two or more births were eligible for this study. A total of 105,782 reproductive-age women who had data on the duration of preceding birth intervals were included in this study.

Study variables and measurements

The dependent variable was birth interval (SBI vs optimal birth interval (OBI)). We defined SBI (less than 33 months between consecutive births) and OBI (33 months and more between consecutive births).

Variables such as country, place of residence (rural/urban), maternal age (15–24, 25–34, and ⩾ 35 years), maternal educational status (no, primary, and secondary and above), husband’s educational status (no, primary, and secondary and above), wealth status (poorest, poorer, middle, richer, and richest), perceived distance to reach health facility (big problem/not a big problem), mother health care decision-making autonomy (respondent alone, jointly with husband/partner, and respondent/partner alone), parity (2–3, 4–5, and ⩾ 6), marital status (single, married, and divorced/widowed/separated), covered by health insurance (no/yes), unmet need for family planning (no/yes), media exposure (no/yes), and sex of household head (male/female) were the independent variables.

Statistical analysis

STATA version 17 statistical software was used for data management and analysis. The data were weighted for sampling weight, primary sampling unit, and strata. As the DHS data have a hierarchical nature, women in the same cluster are more likely to have similar characteristics compared to those in another cluster. Variables were extracted consistently in each country and appended together using the “append using” STATA command. We denormalized the sampling weight and created a new population-level weight by dividing the sampling weight by the denormalized weight.

We created the new weighting variable (V005) computed as

\begin{array}{l} V 005 = v 005 \times total number of \\ reproductive-age women \\ at the time of the survey / \\ number of reproductive-age \\ women interviewed in the survey . \end{array}

The intra-class correlation coefficient (ICC) and likelihood ratio (LR) tests were employed to assess the presence of a significant clustering effect. The ICC quantifies the degree of heterogeneity of SBI between clusters (the proportion of the total observed individual variation in SBI that is attributable to the between-cluster variations)⁴⁷ and is given as

I C C = \frac{σ^{2}}{σ^{2} + \frac{π^{2}}{3}}

where π²/3 is the individual-level variance that is approximated to 3.29 and $σ^{2}$ represents the community-level variance.

For complex survey data such as DHS, advanced statistical models such as marginal or conditional models should be considered. For this study, a mixed-effect binary logistic regression model was used to identify factors associated with SBI. Deviance, that is, –2log-likelihood ratio (–2LLR) was used for model comparison. Variables with a p-value less than 0.2 in the bivariable analysis were considered for the multivariable mixed effect binary logistic regression analysis. In the final model, the adjusted odds ratio (AOR) with the 95% confidence interval (CI) was reported to ascertain the strength and the presence of a significant association between SBI and independent variables.

Ethics consideration

Permission to get access to the data was obtained from the measure DHS program online request from http://www.dhsprogram.com.website and the data used were publicly available with no personal identifier.

Results

The socio-demographic and economic characteristics of respondents

A total of 105,782 reproductive-age women were included. The median age of the respondents was 30 years (interquartile range (IQR) ± 4.5) and more than three-fourth (80.5%) of women were rural residents. About 13.6% and 2.4% of the women were from Kenya and Comoros, respectively. More than half (53.9%) of women attained primary education (see Table 1).

Table 1.

The socio-demographic and economic characteristics of respondents.

Variable	Frequency (N = 105,782)	Percentage (%)
Country
Burundi	11,009	10.4
Ethiopia	8953	8.5
Kenya	14,388	13.6
Comoros	2506	2.4
Madagascar	9822	9.3
Malawi	12,848	12.2
Mozambique	9138	8.6
Rwanda	5619	5.3
Tanzania	7556	7.1
Uganda	11,875	11.2
Zambia	7321	6.9
Zimbabwe	4747	4.5
Residence
Rural	85,193	80.5
Urban	20,589	19.5
Maternal age (years)
15–24	17,359	16.4
25–34	57,827	54.7
⩾ 35	30,596	28.9
Maternal education
No	29,473	27.9
Primary	56,996	53.9
Secondary and above	19,313	18.2
Husband education
No	19,578	18.5
Primary	46,500	44.0
Secondary and above	39,704	37.5
Wealth status
Poorest	26,662	25.2
Poorer	23,426	22.2
Middle	20,872	19.7
Richer	19,036	18.0
Richest	15,786	14.9
Media exposure
No	38,982	36.9
Yes	66,800	63.1
Sex of household head
Wife	23,281	22.0
Husband	82,501	88.0
Marital status
Single	1576	1.5
Widowed	93,814	88.7
Divorced/widowed/separated	10,392	9.8

Maternal obstetric and health service-related characteristics

Of the total 105,782 reproductive-age women, about 42,204 (39.9%) of the women had two to three births and 66,468 (62.8%) had an unmet need for family planning. More than half (59.7%) of women perceived distance to access health-care as a big problem (see Table 2).

Table 2.

Maternal obstetric and health service-related characteristics of reproductive-age women.

Variable	Frequency (N = 105,782)	Percentage (%)
Parity
2–3	42,204	39.9
4–5	33,028	31.2
⩾ 6	30,550	28.9
Unmet need for family planning
No	39,314	37.2
Yes	66,468	62.8
Distance to health facility
Not a big problem	42,669	40.3
Big problem	63,113	59.7
Women health care decision-making autonomy
Respondent alone	20,685	19.6
Jointly with husband/partner	43,909	41.5
Respondent/partner alone	41,188	38.9
Covered by health insurance
No	81,781	77.3
Yes	24,001	22.7

Prevalence of SBI in East Africa

The prevalence of SBI in East Africa was 16.99% (95% CI: 16.76%, 17.21%). The prevalence has varied across countries ranging from 11.27% in Zimbabwe to 29.77% in Comoros.

Factors associated with SBI

Model comparison: The ICC value in the null model was 0.12 (95% CI: 0.10, 0.15), indicating that about 12% of the overall variability of the SBI was attributable to the between-cluster variability while the remaining 88% of the overall variability was explained by the between-individual variation. The mixed-effect binary logistic regression model was the best-fitted model as it had a lower deviance value. Besides, the likelihood ratio test (LR test vs logistic model: χ² (01) = 129.32, p < 0.001) was significant indicating that the mixed-effect binary logistic regression model was the best model for the data (see Table 3).

Table 3.

Model comparison parameters.

Models	Deviance	AIC	BIC
Standard logistic regression model	96655.84	96715.83	97002.94
Mixed-effect logistic regression model	96596.86	96658.86	96955.54

AIC: Akaike information criterion; BIC: Bayesian information criterion.

In the multivariable mixed-effect binary logistic regression model; country, maternal age, wealth status, parity, distance to the health facility, health care decision-making autonomy, unmet need for family planning, and sex of household head were significantly associated with SBI. Women in Burundi, Ethiopia, Kenya, Comoros, Madagascar, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe were 1.90 (AOR = 1.90, 95% CI: 1.75, 2.06), 2.43 (AOR = 2.43, 95% CI: 2.24, 2.63), 1.84 (AOR = 1.84, 95% CI: 1.69, 2.00), 3.07 (AOR = 3.07, 95% CI: 2.75, 3.43), 2.34 (AOR = 2.34, 95% CI: 2.14, 2.56), 1.25 (AOR = 1.25, 95% CI: 1.14, 1.36), 1.78 (AOR = 1.78, 95% CI: 1.57, 2.02), 2.03 (AOR = 2.03, 95% CI: 1.87, 2.20), 2.38 (AOR = 2.38, 95% CI: 2.21, 2.55), 1.31 (AOR = 1.31, 95% CI:1.20, 1.44), and 1.12 (AOR = 1.12, 95% CI: 1.00, 1.25) times higher odds of experiencing SBI than women in Malawi, respectively. The odds of experiencing SBI among women aged 25–34 years and ⩾ 35 years were decreased by 59% (AOR = 0.41, 95% CI: 0.39, 0.43) and 78% (AOR = 0.22, 95% CI: 0.21, 0.24) than women aged 15–24 years, respectively. The odds of having SBI among women who attained primary education were decreased by 11% (AOR = 0.89, 95% CI: 0.85, 0.94) than women who did not have formal education. Women who belonged to the poorest household had 1.18 times (AOR = 1.18, 95% CI: 1.09, 1.27) higher odds of having SBI than women who belonged to the richest household.

The odds of having SBI among women who had less than or equal to three births and four to five births were 1.36 (AOR = 1.36, 95% CI: 1.30, 1.43) and 2.30 (AOR = 2.30, 95% CI: 2.17, 2.43) times higher compared to women who had greater than or equal to six births, respectively. The odds of having SBI among women who perceived the distance to the health facility as not a big problem was decreased by 5% (AOR = 0.95, 95% CI: 0.92, 0.98) than women who perceived the distance to the health facility as a big problem. The odds of having SBI among women who made their own health care decision jointly with their husband, and parent/husband only were 1.11 (AOR = 1.11, 95% CI: 1.05, 1.16), and 1.12 (AOR = 1.12, 95% CI: 1.07, 1.19) times higher than women who made their own health care decision alone, respectively. Women who had an unmet need for family planning had 1.06 times (AOR = 1.06, 95% CI: 1.02, 1.11) higher odds of SBI than women who did not have an unmet need. Women in male-headed households had 1.09 times (AOR = 1.09, 95% CI: 1.03, 1.14) higher odds of having SBI than those belonging to female-headed households (see Table 4).

Table 4.

Bi-variable and multivariable mixed effect logistic regression analysis of short birth interval among reproductive-age women in East Africa.

Variable	Short birth interval		Crude odds ratio (COR) with 95% CI	Adjusted odds ratio (AOR) with 95% CI
Variable	No	Yes	Crude odds ratio (COR) with 95% CI	Adjusted odds ratio (AOR) with 95% CI
Residence
Urban	17,390	3199	1	1
Rural	69,429	15,764	1.22 (1.17, 1.27)	0.99 (0.94, 1.05)
Country
Malawi	11,373	1475	1	1
Burundi	9016	1993	1.77 (1.65, 1.91)	1.90 (1.75, 2.06)*
Ethiopia	7011	1942	2.66 (2.47, 2.86)	2.43 (2.24, 2.63)*
Kenya	11,818	2570	1.86 (1.74, 1.99)	1.84 (1.69, 2.00)*
Comoros	1760	746	3.33 (2.99, 3.69)	3.07 (2.75, 3.43)**
Madagascar	7571	2251	2.33 (2.17, 3.69)	2.34 (2.14, 2.56)*
Mozambique	7822	1316	1.28 (1.17, 1.39)	1.25 (1.14, 1.36)*
Rwanda	4811	808	1.33 (1.21, 1.46)	1.78 (1.57, 2.02)**
Tanzania	6132	1424	2.02 (1.87, 2.19)	2.03 (1.87, 2.20)*
Uganda	8995	2880	2.57 (2.40, 2.76)	2.38 (2.21, 2.55)**
Zambia	6297	1023	1.34 (1.23, 1.46)	1.31 (1.20, 1.44)*
Zimbabwe	4212	535	1.01 (0.90, 1.13)	1.12 (1.00, 1.25)*
Maternal age (years)
15–24	12,548	4812	1	1
25–34	47,984	9842	0.54 (0.52, 0.57)	0.41 (0.39, 0.43)*
⩾ 35	26,287	4309	0.43 (0.41, 0.45)	0.22 (0.21, 0.24)*
Maternal education status
No	23,555	5918	1	1
Primary	46,980	10,015	0.77 (0.75, 0.80)	0.89 (0.85, 0.94)**
Secondary and above	16,284	3029	0.69 (0.65, 0.72)	1.06 (0.98, 1.13)
Husband education status
No	15,790	3788	1	1
Primary	37,758	8742	0.85 (0.82, 0.89)	1.01 (0.96, 1.06)
Secondary and above	33,272	6432	0.74 (0.71, 0.77)	0.99 (0.93, 1.06)
Wealth status
Poorest	21,197	5464	1.56 (1.48, 1.64)	1.18 (1.09, 1.27)*
Poorer	19,016	4410	1.28 (1.22, 1.36)	1.05 (0.98, 1.14)
Middle	17,277	3596	1.15 (1.09, 1.22)	0.98 (0.91, 1.06)
Richer	15,930	3106	1.08 (1.02, 1.15)	0.98 (0.91, 1.05)
Richest	13,399	2387	1	1
Parity
2–3	34,969	7235	0.99 (0.95, 1.02)	1.36 (1.30, 1.43)*
4–5	27,536	5493	1.24 (1.20, 1.29)	2.30 (2.17, 2.43)*
⩾ 6	24,314	6235	1	1
Distance to health facility
Big problem	52,107	11,007	1	1
Not a big problem	34,712	7956	1.08 (1.04, 1.11)	0.95 (0.92, 0.98)**
Health care decision-making autonomy
Respondent alone	17,194	3491	1	1
Jointly with husband/partner	35,878	8031	1.12 (1.08, 1.17)	1.11 (1.05, 1.16)**
Husband/parent alone	33,747	7441	1.09 (1.04, 1.13)	1.12 (1.07, 1.19)**
Covered by health insurance
No	66,978	14,803	1	1
Yes	19,841	4160	0.94 (0.91, 0.98)	0.93 (0.86, 1.01)
Unmet need for family planning
No	32,412	6902	1	1
Yes	34,407	12,061	0.96 (0.93, 0.99)	1.06 (1.02, 1.11)**
Media exposure
No	31,721	7261	1.14 (1.10, 1.17)	1.01 (0.97, 1.05)
Yes	55,099	11,701	1	1
Sex of household head
Female	19,728	3552	1	1
Male	67,091	15,411	1.25 (1.20, 1.29)	1.09 (1.03, 1.14)*

CI: confidence interval.

p < 0.05; **p < 0.01.

Discussion

In this study, the prevalence of SBI among reproductive-age women in East Africa was 16.99% (95% CI: 16.76%, 17.21%). It was consistent with studies reported in Bangladesh⁴⁸ and Uganda.²¹ Despite the WHO recommending 33-month intervals between two consecutive births, still nearly one-fifth of women in East Africa did not adhere to the recommendation. It can be a result of the underutilization and unmet need for family planning in East African countries. Furthermore, it might be caused by a lack of access to modern methods of contraception and health information, which would raise the likelihood of experiencing SBI.

Maternal age, wealth status, parity, distance to the health facility, health care decision-making autonomy, unmet need for family planning, and sex of household head were significantly associated with SBI. The odds of experiencing SBI decreased as the age of women increased. It was supported by studies reported in South Asia⁴⁹ and the United States.⁵⁰ This might be because as women get older, they have more opportunities to access health services related to reproduction and health education, which strengthens favorable attitudes and leads to increases in the use of family planning.^51,52

Maternal education was significantly associated with lower odds of SBI. This finding is in line with studies reported in Tanzania,¹³ Iran,⁵³ Bangladesh,⁵⁴ and low- and middle-income countries.¹⁰ The possible justification might be because education could raise mothers’ consciousness about the maternal and child health implications of SBI.^55,56

The odds of experiencing SBI among women who belonged to the poorest household were higher compared to women from the richest households. It was supported by previous studies;^57,58 this might be due to women in the poorest households having limited access to maternal healthcare services, which could contribute to the increased odds of SBI among women belonging to the poorest household.^59,60

Women who participated in making their own health care decisions had lower odds of experiencing SBI than those who did not, and the odds of experiencing SBI among women who resided in male-headed households were higher than female-headed households. This was consistent with the study reported in the Philippines.⁶¹ It can be explained as women who are autonomous in making their own health care decisions are more likely to visit maternal healthcare services, and have control over their fertility behavior.^59,62 In addition, autonomous women could control household assets and have good access to maternal healthcare services.⁶³ Women who had an unmet need for family planning had higher odds of experiencing SBI. This is consistent with previous study findings;⁶⁴ the possible explanation could be due to the reason that family planning plays a vital role in prolonging interpregnancy interval.^65,66

Another significant predictor of SBI was parity. Grand multiparous women had lower odds of SBI than their counterparts. This was consistent with previous studies;^10,67 this could be due to grand multiparous women not needing any more children and therefore more likely to use contraception.⁶⁸ In addition, the ovarian function of multiparous women might not recover quickly and get pregnant compared to those who had limited parity.⁶⁹ In low-income countries such as East African countries, having many children impose a huge economic burden on the family and therefore multiparous women may not wish for more children. Women who perceived distance to reach health facilities as a big problem had higher odds of experiencing SBI than those who perceived it as not a big problem. This was in line with prior study findings;^70,71 it can be explained that women who had poor healthcare access might not use family planning services.⁷²

The strength of this study was using weighted data to make it representative at national and regional levels; therefore, it can be generalized to all reproductive-age women in East Africa. Moreover, the use of mixed-effect binary logistic regression modeling considered the nested nature of the DHS data to get reliable standard error and estimate. This study had limitations. First, the DHS survey did not incorporate clinically confirmed data, rather it relied on mothers’ or caregivers’ reports and might have the possibility of social desirability and recall bias. Variables like maternal obstetric complications such as the previous history of antepartum hemorrhage, preeclampsia, eclampsia, premature rupture of membrane, infection, and congenital anomalies were not considered in this study since these variables were not collected in DHS. In addition, we did not calculate the sample size for this study as this was a secondary data analysis based on publicly available DHS data of the 12 East African countries. Furthermore, this study is prone to chicken egg dilemma because of its cross-sectional nature.

Conclusion

Although the WHO recommended having an OBI to reduce the incidence of neonatal and maternal mortality, the prevalence of SBIs in East Africa remains high. Maternal age, maternal education, unmet need for family planning, perceived distance to the health facility, parity, women’s health care decision-making autonomy, household wealth status, and sex of household status were significantly associated with SBIs. Therefore, East African countries should scale up maternal health care service access such as family planning services, and maternal education, and should empower women to decide on their health to reduce the prevalence of SBIs and their consequences.

Footnotes

Acknowledgements

We greatly acknowledge MEASURE DHS for granting access to the East African DHS data sets.

Declarations

ORCID iD

Getayeneh Antehunegn Tesema

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Factors associated with short birth interval among reproductive-age women in East Africa

Abstract

Background:

Objective:

Design:

Methods and analysis:

Results:

Conclusion:

Keywords

Introduction

Methods

Data source and sampling procedures

Inclusion and exclusion criteria

Study variables and measurements

Statistical analysis

Ethics consideration

Results

The socio-demographic and economic characteristics of respondents

Maternal obstetric and health service-related characteristics

Prevalence of SBI in East Africa

Factors associated with SBI

Discussion

Conclusion

Footnotes

Acknowledgements

Declarations

ORCID iD

References