Geographically vary determinants of High-Risk Fertility Behavior among Reproductive age women in Ethiopia. Geographically Weighted Regression Analysis

Maternal and child mortality is the main public health problem worldwide and it is the major health concern in developing countries such as Africa and Asia. Fertility behavior of women characterized in relation to maternal age, birth spacing, and order which has an impact on the health of women and children. The aim of this study was to assess the geographically vary Risk factors of High-Risk Fertility Behavior(HRFB) among reproductive-age women in Ethiopia. Methods A total of 11,022 reproductive-age women were included in this study. The data was cleaned and weighted by STATA 14.1 software. Bernoulli based spatial scan statistics were used to identify the presence of purely spatial clusters HRFB using Kulldorff’s SaTScan version 9.6 software. ArcGIS 10.7 was used to visualize spatial distribution for HRFB. Geographical weighted regression analysis was employed by Multiscale Geographical weighted regression version 2.0 software. A P-value of less than 0.05 was used to declare statistically significant predictors locally.


Abstract Background
Maternal and child mortality is the main public health problem worldwide and it is the major health concern in developing countries such as Africa and Asia. Fertility behavior of women characterized in relation to maternal age, birth spacing, and order which has an impact on the health of women and children. The aim of this study was to assess the geographically vary Risk factors of High-Risk Fertility Behavior(HRFB) among reproductive-age women in Ethiopia.

Methods
A total of 11,022 reproductive-age women were included in this study. The data was cleaned and weighted by STATA 14.1 software. Bernoulli based spatial scan statistics were used to identify the presence of purely spatial clusters HRFB using Kulldorff's SaTScan version 9.6 software. ArcGIS 10.7 was used to visualize spatial distribution for HRFB. Geographical weighted regression analysis was employed by Multiscale Geographical weighted regression version 2.0 software. A P-value of less than 0.05 was used to declare statistically significant predictors locally.

Results
Overall, 76% with 95% confidence interval of 75.60 to 77.20 of reproductive age women were faced with High-Risk Fertility problems in Ethiopia. High-Risk Fertility Behavior was highly clustered at the Somali, and Afar regions of Ethiopia. SaTScan identified 385 primary spatial clusters (RR= 1.13, P < 0.001) located at Somali, Afar, and some parts of Oromia Regional State of Ethiopia. Women live in primary clusters were 13% more likely venerable HRFB than outside the cluster. In geographically weighted regression not contraceptive use, and home delivery were statistically significant spatially vary risk factors affecting HRFB.

Conclusion
In Ethiopia, HRFB had to vary geographically across regions. Statistically, a significant-high hot spot of HRFB was identified at Somali and Afar. This study showed that predictor variables for HRFB were varied spatially in Ethiopia. Not use a contraceptive, and home delivery were statistically significant predictors locally in different regions of Ethiopia. Therefore, policymakers and health planners should design an effective intervention program at Somali, and Afar to reduce HRFB and Special attention needs about health education on the advantage of contraceptive utilization and health facility delivery to reduce HRFB.

Background
Maternal and child mortality is the main public health problem worldwide and it is the major health concern in developing countries such as Africa and Asia (1). Globally, 830 women die from preventable causes related to pregnancy and childbirth, of which 99% of all deaths occur in developing countries (2). Ethiopia one of the countries with the highest maternal mortality ratio with 412 deaths per 100,000 live births according to 2016 EDHS reports, of which most of the deaths were attributed to high-risk fertility behavior (3). The maternal mortality issue is under a sustainable development goal (SDG) targeted to reduce below 70 deaths per 100,000 live births at the end of 2030(4).
The global population is rapidly increasing and according to the 2016 report, the total fertility was 2.5 and 4.8 per woman globally and Ethiopia, respectively (5). Fertility behavior of women characterized evidence maternal age, birth spacing, and order which has an impact on the health of women and children (6,7).High-risk fertility behavior associated with numerous unfavorable child and maternal health outcomes such as chronic undernutrition, anemia, and child mortality (8)(9)(10). Different studies showed that high-risk fertility behaviors are associated with chronic undernutrition and anemia among under-five children. In addition, these behaviors are associated with ith adverse birth outcomes such as stillbirth, low birth weight and prematurity (9,(11)(12)(13). As the birth interval got narrower (less than 24 months) the chance of child morality increased sharply compared to long spaced birth intervals (14). The risk of infant mortality from teenage mothers was increased by 30% compared to those women who gave birth between the age of 20 and 30 years. The problem is higher in developing countries where health care services are inaccessible, low socio-economic conditions and high unmet family planning needs (8,(11)(12)(13)15,16). In addition, early age marriage is also another problem for high-risk fertility problems in Ethiopia and other low and middle-income countries (15).
Different factors are associated with high-risk fertility behavior such as socio-demographic characteristics (residence, religion, level of education and marital status) and reproductive health characteristics such as history of child death, facility delivery, and family panning utilizations are factors associated with high-risk fertility behavior (10)(11)(12)(13)(15)(16)(17)(18). Government and stakeholders made tremendous efforts such as increasing health services accessibility and coverage, providing maternal health services free of charge and postnatal care follow up for mothers for halting high-risk fertilities (19). Although, different studies conducted to assess the magnitude and effects of high-risk fertility behaviors No national studies have accounted for geographical variability risk factors of High-Risk Fertility Behavior.
To our knowledge, we provide this first geographically weighted analysis on high-risk fertility behavior and geographically vary risk factors among reproductive-age women in Ethiopia. This study could help health care planners and policymakers for evidence-based interventions and appropriate allocation of resources in hot spot areas.

Study design, area and period:
This study is a community-based cross-sectional study was conducted using a nationally representative Ethiopian Demographic and Health Survey (EDHS) dataset 2016. Ethiopia is situated in the Horn of Africa from 3 0 to 14 0 and 33 0 to 48 0 E.

Sources and study populations:
The source population was all reproductive age group, women, five years preceding the survey.
A total of 15,683 women aged 15-49 years were interviewed and 11,023 women included in the analysis. In the 2016 EDHS, a total of 645 clusters (EAs) (202 urban and 443 rural) were selected with a probability proportional to each EAs size and independent selection in each sampling stratum(urban = 1,215 and rural = 9,807). Among a total selected clusters that coordinate data not obtained and missing data were excluded for the analysis. Finally, a total of (185 urban and 413 rural) clusters were used for this study. Among the selected clusters a total of 11,023 (urban = 1,215 and rural = 9,807) weighted women were included in this study. The recorded data were accessed at www.measuredhs.com on request with the help of ICF International, Inc.

Data collection tools and procedures:
Ethiopian Demographic and Health Survey data were collected by two-stage stratified sampling. Each region of the country was stratified into urban and rural areas, yielding 21 sampling strata. In the first stage, 645 EAs were selected with probability proportional to Enumeration Area size by independent selection in each sampling stratum. In the second stage of selection, a fixed number of 28 households per cluster were selected with an equal probability systematic sampling from the newly created household listing. The detail sampling procedure was available in the Ethiopian Demographic and Health Survey reports from Measure DHS website (www.dhsprogram.com).

Outcome Variable
For this study, we considered three parameters, maternal age at the time of delivery, birth order and birth interval, to define the high-risk fertility behaviors. Three exposure variables were defined for this analysis. Any high-risk fertility behavior versus non-risk coded as 1/0 respectively. The presence of any of the following four conditions was termed high-risk fertility behavior: (i) mothers aged less than 18 years at the time of delivery; (ii) mothers aged over 34 years at the time of delivery; (iii) latest child born less than 24 months after the previous birth; and (iv) latest child of order three or higher.
We applied the definition of ' high-risk fertility behaviors' adopted by the 2016 EDHS(3). The dependent variable in this analysis was high-risk fertility behavior (proportion in the cluster). Spatial autocorrelation and hot spot analysis: Spatial autocorrelation (Global Moran's I) statistic measure was used to assess whether HRFB among reproductive-age women were dispersed, clustered, or randomly distributed in Ethiopia. Moran's I values close to − 1 indicates the low proportion of HRFB and dispersed, close to + 1 indicates clustered, and if Moran's I value zero indicates randomly distributed (20). A statistically significant Moran's I value (p < 0.05) had a chance to rejection of the null hypothesis which indicates the presence of spatial autocorrelation. Hot Spot Analysis (the Getis-Ord Gi* statistic) of the z-scores and significant p-values tells the features with either hot spot or cold spot values for the clusters spatially.

Spatial interpolation:
The spatial interpolation technique is used to predict HRFB proportion among reproductive-age women for unsampled areas in the country based on sampled EAs. For the prediction of unsampled EAs, we used deterministic and geostatistical Empirical Bayesian Kriging spatial interpolation techniques. Ordinal Kriging method of Gaussian distribution was used (21).

Spatial scan statistics:
We employed Bernoulli based model spatial scan statistics to determine the geographical locations of statistically significant clusters for HRFB using Kuldorff's SaTScan version 9.6 software (22). The scanning window that moves across the study area in which HRFB was taken as cases and no HRFB were taken as controls to fit the Bernoulli model. The default maximum spatial cluster size of < 50% of the population was used as an upper limit, allowing both small and large clusters to be detected, and ignored clusters that contained more than the maximum limit with the circular shape of the window. Most likely clusters were identified using p-values and likelihood ratio tests on the basis of the 999 Monte Carlo replications.
Geographically weighted regression analysis: Ordinary Least Square regression (OLS) model is a global model, which estimates only one single coefficient per explanatory variable over the entire study area. Global models assume factors that affect HRFB were stationary geographically. The assumption of geographical independence may bias the parameter estimates. The assumption of geographical independence relaxes by geographically weighted regression analysis. A geographically weighted regression model is an extension of the OLS regression model and gives local parameter estimates to reflect changes over space in the association between an outcome and explanatory variables (23).
For the interest of geographically weighted regression analysis, the aggregated proportion of HRFB among reproductive-age women and all the predictor variables were calculated for each cluster. To determine the predictor variables for HRFB among reproductive-age women, we used a geographically weighted regression model.
To check the assumption of spatial dependency explanatory analysis was performed first by Arc GIS 10.7 software. Statistically significant (P < 0.01) Koenker (BP) statistic indicates that the relationships modeled are not consistent (either due to non-stationarity or heteroskedasticity). Multicollinearity (Variance Inflation Factor < 7.5) was checked to exclude redundancy among explanatory variables. In the case of spatial dependency, the coefficient of predictor variable varies locally as well the predictor variables may or may not significant locally. The model structure of geographically weighted regression written as,

Results
Prevalence of High-risk fertility behavior A total of 11,022 women were included, with 643 of clusters nested in 11 regions. This study reveals that the magnitude of HRFB among women was 76.3% with 95% CI: (75.6, 77.2). The prevalence of HRFB among an urban and rural place of residence of women was 66.51% and 77.59% respectively (Table 1). This study revealed that the spatial distribution of HRFB was found to be non-random in Ethiopia with Global Moran's I 0.113 (p < 0.001) (Fig. 1 ) The clustered patterns (on the right sides) show high rates of HRFB occurred over the study area. The outputs have automatically generated keys on the right and left sides of each panel. Given the zscore of 3.78 indicated that there is less than 1% likelihood that this clustered pattern could be the result of random chance. The bright red and blue colors to the end tails indicate an increased significance level. The table shows that the observed value is greater than the expected value and Pvalue is < 0.05, it is statistically significant Incrementa Spatial l Autocorrelation among reproductive-age women in Ethiopia.
To determine spatial clustering for HRFB, global spatial statistics were estimated using Moran's I value. As shown in the figure below a statistically significant z-scores indicate at 166 Km distances where spatial processes promoting clustering are most pronounced. The incremental spatial Autocorrelation indicates that a total of 10 distance bands were detected with a beginning distance of 121813 meters. The spatial distribution of HRFB among reproductive-age women in Ethiopia was found non-random with a Global Maran's I was 0.11 and p-value 0.0001. The z-score of 3.77, there is a less than 1% likelihood that this high-clustered pattern could be the result of random chance.
( Fig. 2) Hot spot (Getis-Ord Gi) analysis: As shown in the figure below, the red color indicates the more intense clustering of high (hot spot) proportion HRFB preceding the survey period. A high proportion of HRFB was clustered at the Somali and Afar region of Ethiopia. Whereas, Amhara, SNNPR and Addis Ababa regions of Ethiopia were less risk area. (Fig. 3) Spatial Sat Scan analysis of High-risk fertility behavior among women across regions of Ethiopia, 2016 Most likely (primary clusters) and secondary clusters of HRFB were identified. A total of 383 significant clusters were identified. Of these, 181 of them were most likely (primary) clusters and 102 were secondary clusters. The primary clusters' spatial window was located in the Somali, Eastern Oromia, Dire Dawa and Harari region which was centered at 5.848373 N, 43.527981 E with 569.73 km radius, and Log-Likelihood ratio (LLR) of 65.24, at p < 0.001. It showed that women within the spatial window had 1.13 times higher risk of HRFB than women outside the window. The secondary clusters' spatial window was typically located in the central part of the Amhara region. Which was centered at 11.287790 N, 38.406887 E with 71.42 km radius, and LLR of 9.46 at p-value 0.032? It showed that women within the spatial window had a 1.16 times higher risk of HRFB than women outside the window ( Fig. 4 and Table 2). Interpolation of high-risk fertility behavior The predicted high-risk fertility behavior over the area increases from green to red-colored areas. The compared with 1655 least AICc best approach. As well, the GWR model best explained by the predictor variables log-likelihood also supports this. (Table 3). Table 3 Model comparison between the OLS model with the GWR model.

Ordinary Least Square (OLS) model Result
From the OLS model, we found two spatially vary risk factors that affect HRFB among reproductive age group women in Ethiopia. The Global beta coefficients for the Proportion of home delivery and not use family planning were statistically (home delivery beta coefficient = 0.08 p-value < 0.001, not use family planning beta coefficient 0.10 p-vale < 0.001 ). When the Koeker test is statistically significant, it indicates relationships between some or all of your explanatory variables and your dependent variable are non -stationary (Koenker (BP)Statistics = 47.8 p-vale < 0.001). Breusch-Pagan statistic is used to test for heteroskedasticity in a linear regression model (75.5; p-value < 0.001) since the test statistic has a p-value below an appropriate threshold ( p < 0.05) then the null hypothesis of homoskedasticity is rejected and heteroskedasticity assumed (Table 4). Table 4 Global beta coefficients of the GWR model Summary results for best non-spatial linear regression model for the proportion of HRFB behavior among reproductive-age women in Ethiopia, 2016

Discussion
This study revealed that 76% of women had high-risk fertility behavior with a 95% confidence interval of 75.6-77.20%. This finding was lower than a study conducted in the Afar region of Ethiopia (86.3%) (25). However, this finding was higher than of the 2011 EDHS report 58% (3), 34% in Bangladesh DHS, 38.3% in Nepal, and 44.9% in India (26). The possible explanation for the observed discrepancies might be due to the fact that sociodemographic characteristics changes and increased intention of fertility in society. Specifically, when compared with Asian countries such as Nepal the socio-demographic characteristics are quite different and also the health system variations could be the reason. In addition, in Ethiopia, child marriage is higher which might be responsible for the increased magnitude of high risky fertility behavior (27).
This study revealed that the spatial distribution of HRFB was nonrandom in Ethiopia. Significant HRFB highly clustered at Somali and Afar. In line with this high proportion clustering, spatial scan statistics analysis revealed that 385 significant clusters were identified. A high proportion of HRFB observed in Somali and Afar and a low proportion of HRFB observed at Amhara, Addis Ababa, Oromia, and SNNP. The observed geographical variation of HRFB across regions of Ethiopia might be due to the regional variation health system infrastructure and this result is supported Ethiopian demographic survey report(3).
Geographically weighted regression gave local parameter estimates of the predictor variables of the model fit vary spatially in Ethiopia. Home delivery and not use contraceptives were local statistically significant predictor variables for HRFB among reproductive-age women in Ethiopia.
One of the obstacles to tackle maternal and child mortality is High-risk fertility. This high-risk fertility is indirectly associated with home delivery due to the reason that women who deliver at home with high-risk fertility had low service utilization of counseling about the benefit of optimal birth spacing (14).
Across regions of Ethiopia, the estimates of high-risk fertility behavior for women who deliver at home varied between 0.221 and 0.228 and this variation in coefficients of high-risk fertility for those women who are delivering at home varied from region to region. Home delivery is a relatively stronger significant factor for highrisk fertility behavior in Amhara, Tigray, Afar, and Oromia regions than other regions. The statistically significant variation in estimates of high-risk fertility behavior across regions in Ethiopia is might be the reflection of the diverse socio-cultural setting differently responding to factors affecting fertility and child survival in the country than the perception of given community members to the issue in their own settings.
It should also be noted that there is a considerable variation in actual fertility level estimates across the different regions in the country (3). Therefore, the likelihood of getting exposed to high-risk fertility behavior is observed among regions experiencing high fertility and vice-versa indicating that the desire for more children is a trigger of high-risk fertility (13).
Women who had no ever used contraceptive was associated with an increased occurrence of high-risk fertility behavior compared to those who had used. This finding is supported by other studies and evidence (13,15) and DHS analytical studies (28). One of the purposes of contraceptive use is spacing birth and decreasing unintended pregnancies which might affect the health of mother and child. One of the basic postnatal intervention is family planning service provision for mothers with the aim of spacing birth intervals (18).
The study has some strengths. As Tobler's first law of geography states that "Everything is related to everything else, but near things are more related than distant things" (29). Based on Tobler's first law of geography, HRFB was spatially autocorrelated. In the presence of spatial dependence and heterogeneity, the estimates obtained from the global model would be biased. Therefore, fitting the GWR model and knowing the spatial distribution of HRFB in regions of Ethiopia provides important insight to policymakers and health planners and valuable hot spot maps used to more effective and cost-efficient nutrition intervention.
The study hs also limitations: Since the data used in this study was cross-sectional data, which limits the conclusions about the causality of the factors on the dependent variable and Since 21 clusters did not have coordinate data and we did not include in the analysis this may affect the estimated result.

Conclusions
In Ethiopia, High-Risk Fertility Behavior had to vary geographically across regions. Statistically, a significant-high hot spot of High-Risk Fertility Behavior was identified at Somali and Afar. Whereas, Amhara, Addis Ababa, Oromia and SNNPR regions of Ethiopia were less risk area. This study showed that predictor variables for High-Risk Fertility Behavior were varied spatially in Ethiopia. Not use a contraceptive, and home delivery were statistically significant predictors locally in different regions of Ethiopia. Therefore, policymakers and health planners should design an effective intervention program at Somali, and Afar to reduce High-Risk Fertility Behavior among reproductive-age women and Special attention needs about health education on the advantage of contraceptive utilization and health facility delivery to reduce High-risk fertility Behavior. The spatial autocorrelation of HRFB among reproductive age group women in Ethiopia by a function of distance Figure 3 Hot spot analysis of high-risk fertility behavior among women within 5 years preceding the survey in Ethiopia, 2016 Interpolation of high-risk fertility behavior among reproductive-age women in Ethiopia, 2016 Figure 6 Geographically varying values of significance and coefficients per cluster for predictor variable not use contraceptive Figure 7 Geographically varying values of significance and coefficients of home delivery per cluster for HRFB