Application of geographically weighted regression analysis to assess predictors of short birth interval hot spots in Ethiopia

PLoS ONE, Volume 15, No. 5, Year 2020

Ukuhunyushwa

Background Birth interval duration is an important and modifiable risk factor for adverse child and maternal health outcomes. Understanding the spatial distribution of short birth interval, an interbirth interval of less than 33 months, and its predictors are vital to prioritize and facilitate targeted interventions. However, the spatial variation of short birth interval and its underlying factors have not been investigated in Ethiopia. Objective This study aimed to assess the predictors of short birth interval hot spots in Ethiopia. Methods The study used data from the 2016 Ethiopia Demographic and Health Survey and included 8,448 women in the analysis. The spatial variation of short birth interval was first examined using hot spot analysis (Local Getis-Ord Gi∗ statistic). Ordinary least squares regression was used to identify factors explaining the geographic variation of short birth interval. Geographically weighted regression was used to explore the spatial variability of relationships between short birth interval and selected predictors. Results Statistically significant hot spots of short birth interval were found in Somali Region, Oromia Region, Southern Nations, Nationalities, and Peoples’ Region and some parts of Afar Region. Women with no education or with primary education, having a husband with higher education (above secondary education), and coming from a household with a poorer wealth quintile or middle wealth quintile were predictors of the spatial variation of short birth interval. The predictive strength of these factors varied across the study area. The geographically weighted regression model explained about 64% of the variation in short birth interval occurrence. Conclusion Residing in a geographic area where a high proportion of women had either no education or only primary education, had a husband with higher education, or were from a household in the poorer or middle wealth quintile increased the risk of experiencing short birth interval. Our detailed maps of short birth interval hot spots and its predictors will assist decision makers in implementing precision public health. The study was conducted in Ethiopia, which is located in the Horn of Africa (30–150 N latitude and 330–480 E longitude) [15]. The country occupies an area of 1.1 million square kilometres with an altitude that ranges from the highest peak at Ras Dashen (4,620 metres above sea level) down to the Dallol Depression, about 148 metres below sea level [32, 33]. Administratively, Ethiopia is divided into nine regions and two administrative cities [15]. This analysis was based on the 2016 Ethiopia Demographic and Health Survey (EDHS) data. The EDHS sample was derived using a stratified, two-stage cluster design where Enumeration Areas (EAs) were the sampling units for the first stage and households for the second stage. The detailed methodologies of the surveys are presented in the full EDHS report [15]. The current study included 8,448 women from 620 clusters, who had reported at least two live births during the five years preceding the 2016 survey. Women who had never been married (n = 12) were not included in the study since women who have multiple births out of wedlock are unlikely to plan their births in the same way as married women. When women had more than two births in the five years preceding the survey, birth interval of their most recent two births (i.e., the birth interval between the index child and the immediately preceding child) was uniformly considered for all the study participants. Global Positioning System (GPS) receivers were used to collect the location data (geographic coordinates) of each survey cluster. The GPS reading was made at the centre of each cluster. The GPS data collectors ensured the centre was relatively open, away from tall buildings, and out from under tree canopy in order to receive adequate satellite signal strength. To maintain respondents’ confidentiality, GPS latitude/longitude positions for all survey clusters were randomly displaced. The maximum displacement for urban clusters was two kilometres (km) and five km for 99% of rural clusters. The remaining 1% of the rural clusters were displaced a maximum of 10 km. The displacement was restricted to the country’s second administrative level (DHS survey region) so that the points stay within the country [34]. In addition, the administrative polygons of Ethiopia, which were obtained from the Natural Earth [35] has been used to develop the map of hot and/or cold spots of short birth interval. The country’s administrative polygons reflect administrative boundaries, such as regions, zones, and districts of Ethiopia. The outcome variable, short birth interval, was defined as an interval of less than 33 months between two successive live births [3]. Women’s birth interval data were collected through reviewing the date of birth of their biological children from children’s birth /immunization certificate and/or asking information regarding their children’s date of birth from the women. Birth interval data of women for all their children born live irrespective of their survival status at the time of the interview were collected. For children who had birth certificates, their mothers were asked to confirm the accuracy of the information prior to documenting children’s date of birth. This was done to avoid errors because in some cases the information on the document may be the date when the birth was recorded and not the date when the child was born. When children did not have a birth certificate, information regarding their date of birth were obtained from their mothers. Then, the length of birth interval was computed in months and the data were accessible for further analysis in this form. Further explanation about how birth interval data were collected can be found in the Demographic and Health Survey Interviewer’s Manual [36]. The candidate explanatory variables included in the Exploratory Regression of the current study are presented online (see S1 Table). These were maternal age at first marriage, maternal age at birth of the preceding child, polygyny status, maternal education level, husband’s/partner’s education level, maternal occupation, husband’s/partner’s occupation, wealth quintile, sex of the preceding child, survival status of the preceding child, total number of children born before the index child, exposure to mass media, and perceived distance to the health facility. Variables were selected based on reviewed literature [2, 14, 20–28]. An Exploratory Regression tool, discussed below under the spatial regression analysis section, was used to identify properly specified Ordinary Least Squares (OLS) models. Descriptive analyses were performed using Stata version 14 statistical software (StataCorp. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP. 2015). The spatial analysis was performed using ArcGIS 10.3.1(ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. 2011). Before performing spatial analysis, the weighted proportion (using sample weight) of short birth interval and candidate explanatory variables (see S1 Table) data were exported to ArcGIS. A detailed explanation of the weighting procedure can be found elsewhere [15]. Participant characteristics were described using frequency with percent. Pearson’s chi-squared tests were used to assess differences in short birth interval frequencies between place (urban/rural) and regions of residence. The global Moran’s I statistic was computed to test for the presence of spatial autocorrelation. This statistic indicates whether the pattern of short birth interval in the study area is clustered, dispersed, or random. When the z-score or p-value indicates statistical significance, a positive Moran’s I index value indicates a tendency toward clustering while a negative Moran’s I index value indicates a tendency toward dispersion. Based on this, a decision was made about whether to reject the null hypothesis that short birth intervals are randomly distributed across the study area [37]. The Getis-Ord General G statistic was used to measure the degree of clustering, which may be high or low. The higher (or lower) the z-score, the stronger the intensity of the clustering. A z-score near zero indicates no apparent clustering within the study area. A positive z-score indicates clustering of high values and a negative z-score indicates clustering of low values [38]. Subsequently, Incremental Spatial Autocorrelation was assessed to calculate an appropriate distance threshold for identifying spatial processes that promote clustering [39]. Hot spot analysis using local Getis-Ord Gi* statistics [40] was used to depict short birth interval variation in the study area. This statistic produces a hot and/or cold spot map using short birth interval rate as the input. It compares the local mean rate (the rates for a cluster and its nearest neighboring clusters) to the global mean rate (the rates for all clusters). A z-score and p-value are produced for each cluster, allowing assessment of the significance of differences between local and global means. A high positive z-score and a small p-value for a feature (cluster in this case) indicate a spatial clustering of high values (a hot spot). A low negative z-score and a small p-value indicate a spatial clustering of low values (a cold spot). A z-score near zero indicates no apparent spatial clustering [40–43]. Getis-Ord Gi* statistic is given as [42]: where xj is the attribute value for feature (cluster in the current study) j, wi,j is the spatial weight between feature i and j, n is equal to the total number of features and When estimating local Getis-Ord Gi* statistics, a False Discovery Rate (FDR) correction method was applied to account for multiple, dependent tests [44–46]. This helps to identify true clusters by estimating the number of false positives for a given confidence level and adjusting the critical p-value accordingly. Thus, statistically significant p-values are ranked from smallest (strongest) to largest (weakest), and based on the false positive estimate, the weakest are removed from the list [44, 46]. The importance of considering the FDR correction method in DHS data has been documented elsewhere [45]. The Ethiopian Polyconic Projected Coordinate System, based on the World Geodetic System 84 (WGS84) coordinate reference system (CRS), was used to produce a flattened map of the country. After identifying short birth interval hot spots, spatial regression modeling was performed to identify predictors of the observed spatial patterns of short birth interval. Findings from ordinary least squares (OLS) regression are only reliable if the regression model satisfies all of the assumptions that are required by this method [47]. The coefficients of explanatory variables in a properly specified OLS model should be statistically significant and have either a positive or negative sign. In addition, there should not be redundancy among explanatory variables (free from multicollinearity). The model should be unbiased (heteroscedasticity or non-stationarity). The residuals should be normally distributed and revealed no spatial patterns. The model should include key explanatory variables. The residuals must be free from spatial autocorrelation [47–49]. The OLS regression equation [50] is given as: where i = 1,2,…n; β0, β1, β2, …βp are the model parameters, yi is the outcome variable for observation i, xik are explanatory variables and ε1, ε2, … εn are the error term/residuals with zero mean and homogenous variance σ2. To identify a model that fulfills the assumption of the OLS method, Exploratory Regression, a data-mining tool, was used. Similar to Stepwise Regression, Exploratory Regression identifies models with high Adjusted R2 values. Moreover, unlike Stepwise Regression, Exploratory Regression identifies models that meet all of the assumptions of the OLS method [47, 51, 52]. The model was validated using internal cross-validation. Cross-validation provides an idea of how well a model built in the training dataset predicts unknown values in a validation dataset. For a model that provides accurate predictions, the mean error should be close to 0, the root-mean-square error and average standard error should be as small as possible (this is useful when comparing models), and the root-mean-squared standardized error (RMSE) should be close to 1 [53]. The model in the current study fulfilled the above statistical requirements. A variable that is a strong predictor in one cluster may not necessarily be a strong predictor in another cluster. This type of cluster variation (non-stationarity) can be identified through the use of GWR. In this context, GWR can help to answer the question: “Does the association vary across space?” Unlike OLS that fits a single linear regression equation to all of the data in the study area, GWR creates an equation for each DHS cluster. While the equation in OLS is calibrated using data from all features (cluster in this case), GWR uses data from nearby features. Thus, the GWR coefficient takes different values for each cluster [54, 55]. Maps of the coefficients associated with each explanatory variable, which are produced using the GWR, provide guidelines for targeted interventions. The GWR model [56] can be written as: where yi are observations of response y, (uivi) are geographical points (longitude, latitude), βk(uivi) (k = 0, 1, … p,) are p unknown functions of geographic locations (uivi), xik are explanatory variables at location (uivi), i = 1,2,…n and εi are error terms/residuals with zero mean and homogenous variance σ2. Fig 1 presents a summary of the model’s framework. OLS = Ordinary Least Squares; GWR = Geographically Weighted Regression. Ethical approval was obtained from the Human Research Ethics Committee (H-2018-0332), The University of Newcastle. The 2016 EDHS was approved by the National Research Ethics Review Committee of Ethiopia (NRERC) and ICF Macro International. Permission from The DHS Program was obtained to access the datasets.

I-AI Digest

Study Justification:

– Birth interval duration is a significant factor affecting child and maternal health outcomes.
– Understanding the spatial distribution of short birth intervals and their predictors is crucial for targeted interventions.
– However, the spatial variation of short birth intervals and its underlying factors have not been studied in Ethiopia.

Study Highlights:

– The study used data from the 2016 Ethiopia Demographic and Health Survey.
– Hot spot analysis identified statistically significant hot spots of short birth intervals in certain regions of Ethiopia.
– Factors such as maternal education, husband’s education, and household wealth were found to be predictors of short birth interval variation.
– Geographically weighted regression analysis explained about 64% of the variation in short birth interval occurrence.

Study Recommendations:

– Residing in areas with a high proportion of women with low education, husbands with higher education, or poorer/middle wealth quintiles increases the risk of short birth intervals.
– The detailed maps of short birth interval hot spots and its predictors can assist decision makers in implementing precision public health interventions.

Key Role Players:

– Researchers and data analysts to conduct further studies and analysis.
– Policy makers and government officials to implement targeted interventions.
– Healthcare professionals and organizations to provide appropriate healthcare services and education.

Cost Items for Planning Recommendations:

– Research and data analysis costs.
– Implementation costs for targeted interventions.
– Costs for healthcare services and education programs.
Please note that the above information is a summary of the study and its findings. For more detailed information, please refer to the publication in PLoS ONE, Volume 15, No. 5, Year 2020.

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used a large sample size and employed rigorous statistical methods to analyze the data. The findings are supported by statistically significant results and the use of geographically weighted regression to explore spatial variability. However, the abstract could be improved by providing more specific details about the methodology, such as the specific variables included in the analysis and the criteria used to define short birth interval. Additionally, it would be helpful to include information about potential limitations of the study, such as any potential biases in the data collection process or the generalizability of the findings to other populations.

Abstract

The study was conducted in Ethiopia, which is located in the Horn of Africa (30–150 N latitude and 330–480 E longitude) [15]. The country occupies an area of 1.1 million square kilometres with an altitude that ranges from the highest peak at Ras Dashen (4,620 metres above sea level) down to the Dallol Depression, about 148 metres below sea level [32, 33]. Administratively, Ethiopia is divided into nine regions and two administrative cities [15]. This analysis was based on the 2016 Ethiopia Demographic and Health Survey (EDHS) data. The EDHS sample was derived using a stratified, two-stage cluster design where Enumeration Areas (EAs) were the sampling units for the first stage and households for the second stage. The detailed methodologies of the surveys are presented in the full EDHS report [15]. The current study included 8,448 women from 620 clusters, who had reported at least two live births during the five years preceding the 2016 survey. Women who had never been married (n = 12) were not included in the study since women who have multiple births out of wedlock are unlikely to plan their births in the same way as married women. When women had more than two births in the five years preceding the survey, birth interval of their most recent two births (i.e., the birth interval between the index child and the immediately preceding child) was uniformly considered for all the study participants. Global Positioning System (GPS) receivers were used to collect the location data (geographic coordinates) of each survey cluster. The GPS reading was made at the centre of each cluster. The GPS data collectors ensured the centre was relatively open, away from tall buildings, and out from under tree canopy in order to receive adequate satellite signal strength. To maintain respondents’ confidentiality, GPS latitude/longitude positions for all survey clusters were randomly displaced. The maximum displacement for urban clusters was two kilometres (km) and five km for 99% of rural clusters. The remaining 1% of the rural clusters were displaced a maximum of 10 km. The displacement was restricted to the country’s second administrative level (DHS survey region) so that the points stay within the country [34]. In addition, the administrative polygons of Ethiopia, which were obtained from the Natural Earth [35] has been used to develop the map of hot and/or cold spots of short birth interval. The country’s administrative polygons reflect administrative boundaries, such as regions, zones, and districts of Ethiopia. The outcome variable, short birth interval, was defined as an interval of less than 33 months between two successive live births [3]. Women’s birth interval data were collected through reviewing the date of birth of their biological children from children’s birth /immunization certificate and/or asking information regarding their children’s date of birth from the women. Birth interval data of women for all their children born live irrespective of their survival status at the time of the interview were collected. For children who had birth certificates, their mothers were asked to confirm the accuracy of the information prior to documenting children’s date of birth. This was done to avoid errors because in some cases the information on the document may be the date when the birth was recorded and not the date when the child was born. When children did not have a birth certificate, information regarding their date of birth were obtained from their mothers. Then, the length of birth interval was computed in months and the data were accessible for further analysis in this form. Further explanation about how birth interval data were collected can be found in the Demographic and Health Survey Interviewer’s Manual [36]. The candidate explanatory variables included in the Exploratory Regression of the current study are presented online (see S1 Table). These were maternal age at first marriage, maternal age at birth of the preceding child, polygyny status, maternal education level, husband’s/partner’s education level, maternal occupation, husband’s/partner’s occupation, wealth quintile, sex of the preceding child, survival status of the preceding child, total number of children born before the index child, exposure to mass media, and perceived distance to the health facility. Variables were selected based on reviewed literature [2, 14, 20–28]. An Exploratory Regression tool, discussed below under the spatial regression analysis section, was used to identify properly specified Ordinary Least Squares (OLS) models. Descriptive analyses were performed using Stata version 14 statistical software (StataCorp. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP. 2015). The spatial analysis was performed using ArcGIS 10.3.1(ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. 2011). Before performing spatial analysis, the weighted proportion (using sample weight) of short birth interval and candidate explanatory variables (see S1 Table) data were exported to ArcGIS. A detailed explanation of the weighting procedure can be found elsewhere [15]. Participant characteristics were described using frequency with percent. Pearson’s chi-squared tests were used to assess differences in short birth interval frequencies between place (urban/rural) and regions of residence. The global Moran’s I statistic was computed to test for the presence of spatial autocorrelation. This statistic indicates whether the pattern of short birth interval in the study area is clustered, dispersed, or random. When the z-score or p-value indicates statistical significance, a positive Moran’s I index value indicates a tendency toward clustering while a negative Moran’s I index value indicates a tendency toward dispersion. Based on this, a decision was made about whether to reject the null hypothesis that short birth intervals are randomly distributed across the study area [37]. The Getis-Ord General G statistic was used to measure the degree of clustering, which may be high or low. The higher (or lower) the z-score, the stronger the intensity of the clustering. A z-score near zero indicates no apparent clustering within the study area. A positive z-score indicates clustering of high values and a negative z-score indicates clustering of low values [38]. Subsequently, Incremental Spatial Autocorrelation was assessed to calculate an appropriate distance threshold for identifying spatial processes that promote clustering [39]. Hot spot analysis using local Getis-Ord Gi* statistics [40] was used to depict short birth interval variation in the study area. This statistic produces a hot and/or cold spot map using short birth interval rate as the input. It compares the local mean rate (the rates for a cluster and its nearest neighboring clusters) to the global mean rate (the rates for all clusters). A z-score and p-value are produced for each cluster, allowing assessment of the significance of differences between local and global means. A high positive z-score and a small p-value for a feature (cluster in this case) indicate a spatial clustering of high values (a hot spot). A low negative z-score and a small p-value indicate a spatial clustering of low values (a cold spot). A z-score near zero indicates no apparent spatial clustering [40–43]. Getis-Ord Gi* statistic is given as [42]: where xj is the attribute value for feature (cluster in the current study) j, wi,j is the spatial weight between feature i and j, n is equal to the total number of features and When estimating local Getis-Ord Gi* statistics, a False Discovery Rate (FDR) correction method was applied to account for multiple, dependent tests [44–46]. This helps to identify true clusters by estimating the number of false positives for a given confidence level and adjusting the critical p-value accordingly. Thus, statistically significant p-values are ranked from smallest (strongest) to largest (weakest), and based on the false positive estimate, the weakest are removed from the list [44, 46]. The importance of considering the FDR correction method in DHS data has been documented elsewhere [45]. The Ethiopian Polyconic Projected Coordinate System, based on the World Geodetic System 84 (WGS84) coordinate reference system (CRS), was used to produce a flattened map of the country. After identifying short birth interval hot spots, spatial regression modeling was performed to identify predictors of the observed spatial patterns of short birth interval. Findings from ordinary least squares (OLS) regression are only reliable if the regression model satisfies all of the assumptions that are required by this method [47]. The coefficients of explanatory variables in a properly specified OLS model should be statistically significant and have either a positive or negative sign. In addition, there should not be redundancy among explanatory variables (free from multicollinearity). The model should be unbiased (heteroscedasticity or non-stationarity). The residuals should be normally distributed and revealed no spatial patterns. The model should include key explanatory variables. The residuals must be free from spatial autocorrelation [47–49]. The OLS regression equation [50] is given as: where i = 1,2,…n; β0, β1, β2, …βp are the model parameters, yi is the outcome variable for observation i, xik are explanatory variables and ε1, ε2, … εn are the error term/residuals with zero mean and homogenous variance σ2. To identify a model that fulfills the assumption of the OLS method, Exploratory Regression, a data-mining tool, was used. Similar to Stepwise Regression, Exploratory Regression identifies models with high Adjusted R2 values. Moreover, unlike Stepwise Regression, Exploratory Regression identifies models that meet all of the assumptions of the OLS method [47, 51, 52]. The model was validated using internal cross-validation. Cross-validation provides an idea of how well a model built in the training dataset predicts unknown values in a validation dataset. For a model that provides accurate predictions, the mean error should be close to 0, the root-mean-square error and average standard error should be as small as possible (this is useful when comparing models), and the root-mean-squared standardized error (RMSE) should be close to 1 [53]. The model in the current study fulfilled the above statistical requirements. A variable that is a strong predictor in one cluster may not necessarily be a strong predictor in another cluster. This type of cluster variation (non-stationarity) can be identified through the use of GWR. In this context, GWR can help to answer the question: “Does the association vary across space?” Unlike OLS that fits a single linear regression equation to all of the data in the study area, GWR creates an equation for each DHS cluster. While the equation in OLS is calibrated using data from all features (cluster in this case), GWR uses data from nearby features. Thus, the GWR coefficient takes different values for each cluster [54, 55]. Maps of the coefficients associated with each explanatory variable, which are produced using the GWR, provide guidelines for targeted interventions. The GWR model [56] can be written as: where yi are observations of response y, (uivi) are geographical points (longitude, latitude), βk(uivi) (k = 0, 1, … p,) are p unknown functions of geographic locations (uivi), xik are explanatory variables at location (uivi), i = 1,2,…n and εi are error terms/residuals with zero mean and homogenous variance σ2. Fig 1 presents a summary of the model’s framework. OLS = Ordinary Least Squares; GWR = Geographically Weighted Regression. Ethical approval was obtained from the Human Research Ethics Committee (H-2018-0332), The University of Newcastle. The 2016 EDHS was approved by the National Research Ethics Review Committee of Ethiopia (NRERC) and ICF Macro International. Permission from The DHS Program was obtained to access the datasets.

Izinto zokwakha

Emisha

Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and emergency contact numbers. These applications can also include features such as tracking the baby’s growth and development, providing nutrition advice, and connecting women with healthcare providers through telemedicine.

2. Community Health Workers: Train and deploy community health workers in rural areas to provide maternal health education, support, and referrals. These workers can conduct home visits, organize community health events, and serve as a bridge between the community and healthcare facilities.

3. Telemedicine: Establish telemedicine services to enable pregnant women in remote areas to consult with healthcare providers through video calls or phone calls. This can help address the shortage of healthcare professionals in certain regions and provide timely advice and guidance to pregnant women.

4. Transportation Solutions: Develop innovative transportation solutions to overcome geographical barriers and improve access to healthcare facilities. This could include providing transportation vouchers or subsidies, setting up community transportation networks, or utilizing drones for medical supply delivery in hard-to-reach areas.

5. Maternal Health Information Hotlines: Set up toll-free hotlines staffed by trained healthcare professionals who can provide information, counseling, and support to pregnant women and new mothers. These hotlines can be accessible 24/7 and offer services in multiple languages.

6. Maternal Health Financing Programs: Implement innovative financing programs, such as microinsurance or conditional cash transfer schemes, to reduce financial barriers to accessing maternal healthcare services. These programs can provide financial assistance for prenatal care, delivery, and postnatal care, ensuring that cost is not a deterrent for women seeking care.

7. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources and expertise to enhance healthcare infrastructure, service delivery, and technology solutions.

8. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and promote healthy behaviors during pregnancy and childbirth. These campaigns can utilize various media channels, including radio, television, social media, and community outreach programs.

9. Maternal Health Monitoring Systems: Develop robust data collection and monitoring systems to track maternal health indicators, identify areas with high maternal health risks, and inform targeted interventions. This can involve using digital health tools, electronic medical records, and data analytics to improve decision-making and resource allocation.

10. Maternal Health Quality Improvement Initiatives: Implement quality improvement programs in healthcare facilities to enhance the quality of maternal healthcare services. This can involve training healthcare providers, improving infrastructure and equipment, and implementing evidence-based clinical guidelines.

These innovations can help address the challenges identified in the study and improve access to maternal health services in Ethiopia.

AI Innovations Description

The recommendation to improve access to maternal health based on the study is to implement precision public health interventions in the identified hot spots of short birth interval in Ethiopia. These interventions should focus on addressing the predictors of short birth interval, such as women’s education level, husband’s education level, and household wealth quintile.

Specifically, efforts should be made to improve access to education for women, especially in areas where a high proportion of women have no education or only primary education. This can be done through initiatives that promote girls’ education and provide opportunities for adult education.

Additionally, interventions should aim to increase the education level of husbands, as it was found to be a predictor of short birth interval. This can be achieved through targeted programs that provide educational opportunities for men, such as vocational training and adult education programs.

Furthermore, addressing wealth disparities is crucial in reducing the risk of short birth interval. Programs that focus on poverty alleviation and income generation can help improve the economic status of households in poorer and middle wealth quintiles, thereby reducing the likelihood of short birth intervals.

By implementing these recommendations, decision makers can prioritize and facilitate targeted interventions in the identified hot spots, ultimately improving access to maternal health in Ethiopia.

AI Innovations Methodology

Based on the provided description, the study conducted in Ethiopia aimed to assess the predictors of short birth interval hot spots in order to prioritize and facilitate targeted interventions to improve maternal and child health outcomes. The methodology used in the study included the following steps:

1. Data Collection: The study used data from the 2016 Ethiopia Demographic and Health Survey (EDHS), which included 8,448 women who had reported at least two live births during the five years preceding the survey. GPS receivers were used to collect the location data (geographic coordinates) of each survey cluster.

2. Spatial Analysis: The spatial variation of short birth interval was examined using hot spot analysis (Local Getis-Ord Gi* statistic) to identify statistically significant hot spots in different regions of Ethiopia. This analysis helps to determine whether the pattern of short birth interval is clustered, dispersed, or random.

3. Ordinary Least Squares (OLS) Regression: Ordinary least squares regression was used to identify factors explaining the geographic variation of short birth interval. This analysis helps to determine the relationship between short birth interval and selected predictors, such as maternal education, husband’s education, wealth quintile, etc.

4. Geographically Weighted Regression (GWR): Geographically weighted regression was used to explore the spatial variability of relationships between short birth interval and selected predictors. This analysis helps to identify whether the association between predictors and short birth interval varies across different geographic areas.

5. Hot Spot Analysis using GWR: Maps of the coefficients associated with each explanatory variable were produced using the GWR model. These maps provide guidelines for targeted interventions by identifying areas where specific predictors have a stronger influence on short birth interval.

6. Statistical Validation: The OLS and GWR models were validated using internal cross-validation to assess the accuracy of the models in predicting unknown values.

The study’s findings revealed statistically significant hot spots of short birth interval in certain regions of Ethiopia, and identified predictors such as maternal education, husband’s education, and wealth quintile that contribute to the spatial variation of short birth interval. The detailed maps of short birth interval hot spots and its predictors can assist decision makers in implementing precision public health interventions to improve access to maternal health.

Please note that this is a summary of the methodology described in the provided text. For a more detailed understanding, it is recommended to refer to the original study.

Ababhali & Co-Ababhali

Statistics:

Citations: 10

Authors: 4

Identifiers:

DOI: 10.1371/journal.pone.0233790

Research Areas:

Community Interventions, Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study, Exploratory Study

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: