Menu
Menu
Menu
Menu
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.
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]. 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; 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. 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. 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. 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.
