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A multiscale geographically weighted regression analysis of teenage pregnancy and associated factors among adolescents aged 15 to 19 in Ethiopia using the 2019 mini-demographic and health survey
Determinants of teenage pregnancy, a multilevel and MGWR analysis
https://orcid.org/0000-0002-2911-2095
Tebeje Tsion Mulat Conceptualization Investigation Methodology Software Writing – original draft Writing – review & editing 1 *
Abebe Mesfin Investigation Writing – review & editing 2
Aragaw Fantu Mamo Investigation Writing – review & editing 3
https://orcid.org/0000-0002-9278-6826
Seifu Beminate Lemma Investigation Writing – review & editing 4
Mare Kusse Urmale Investigation Writing – review & editing 5
Shewarega Ever Siyoum Investigation Writing – review & editing 6
Sisay Gizaw Investigation Writing – review & editing 1
https://orcid.org/0000-0002-8309-5986
Seboka Binyam Tariku Investigation Writing – review & editing 1
1 School of Public Health, College of Health Science and Medicine, Dilla University, Dilla, Ethiopia
2 Department of Midwifery, College of Health Science and Medicine, Dilla University, Dilla, Ethiopia
3 Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
4 Department of Public Health, College of Medicine and Health Sciences, Samara University, Afar, Ethiopia
5 Department of Nursing, College of Medicine and Health Sciences, Samara University, Afar, Ethiopia
6 Department of Reproductive Health, School of Public Health, College of Health Science and Medicine, Dilla University, Dilla, Ethiopia
Fort Alfredo Luis Editor
Independent Consultant, UNITED STATES OF AMERICA
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: yemarina12@gmail.com
6 9 2024
2024
19 9 e031002523 8 2023
22 8 2024
© 2024 Tebeje et al
2024
Tebeje et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background

Teenage pregnancy remains one of the major reproductive health problems, especially in sub-Saharan African countries. It can lead to maternal and neonatal complications and social consequences. The proportion of teenage pregnancy differs across regions of Ethiopia. Thus, this study aimed to determine the spatial variation in determinants of teenage pregnancy among adolescents aged 15–19 years in Ethiopia using the 2019 Demographic and Health Survey (DHS).

Methods

This study included a total weighted sample of 2165 teenage girls aged 15 to 19 years. A mixed-effect binary logistic regression model was employed to consider the hierarchical nature of the DHS data using STATA version 17. Adjusted odds ratios with 95% confidence intervals are reported, and a p-value less than 0.05 was used to identify significant predictors. The spatial analysis was conducted with ArcGIS version 10.7 and Python 3. To identify factors associated with the hotspots of teenage pregnancy, a multiscale geographically weighted regression (MGWR) was performed. Spatial regression models were compared using adjusted R2, the corrected Akaike information criterion (AICc), and the residual sum of squares (RSS).

Results

The prevalence of teenage pregnancy among adolescents aged 15 to 19 years was 12.98% (95% CI: 11.6%, 14.5%). It was spatially clustered throughout the country with a significant Moran’s I value. Significant hotspot areas were detected in central and southern Afar; northern, central, and western Gambela; northeastern and southern central Oromia; and the eastern Somali region. The MGWR analysis revealed that the significant predictors of spatial variations in teenage pregnancy were being illiterate and being married. Based on the multivariable multilevel analysis, age 17 (AOR = 3.54; 95% CI: 1.60, 7.81), 18 (AOR = 8.21; 95% CI: 3.96, 17.0), 19 (AOR = 15.0; 95% CI: 6.84, 32.9), being literate (AOR = 0.57; 95% CI: 0.35, 0.92), being married (AOR = 22.8; 95% CI: 14.1, 37.0), age of household head (AOR = 0.98; 95% CI: 0.98, 0.99) and residing in the Gambela region (AOR = 3.27; 95% CI: 1.21, 8.86) were significantly associated with teenage pregnancy among adolescents aged 15 to 19.

Conclusion

Teenage pregnancy is a public health problem in Ethiopia. Policymakers should prioritize addressing early marriage and improving teenage literacy rates, with a focus on the Gambela region and other hotspot areas. It is crucial to implement policies aimed at transforming the traditional practice of early marriage and to take measures to enhance literacy levels and promote awareness about sexual and reproductive health at the family and school levels. This will help ensure that young people have the opportunity to pursue education and make informed decisions about their reproductive health.

The author(s) received no specific funding for this work. Data AvailabilityThe data used in this research are publicly available from the measure DHS program via an online request at https://dhsprogram.com/data/dataset_admin/login_main.cfm?CFID=10818526&CFTOKEN=c131014a480fe56-4E0C6B7F-F551-E6B2-50.
Data Availability

The data used in this research are publicly available from the measure DHS program via an online request at https://dhsprogram.com/data/dataset_admin/login_main.cfm?CFID=10818526&CFTOKEN=c131014a480fe56-4E0C6B7F-F551-E6B2-50.
==== Body
pmcIntroduction

The highest number of teenage childbirths occurs in developing countries, where many girls marry early in life due to poverty, limited educational opportunities, cultural and social norms, and weak enforcement against child marriage [1–3]. Approximately 20 countries in sub-Saharan Africa (SSA) and Asia are responsible for 82% of adolescent maternal mortality [4]. This high rate could have resulted from the lower utilization of maternal health services by adolescents, coupled with their physical immaturity and discrimination [5,6]. Girls aged 15 to 19 years have a 28% greater risk of maternal mortality compared to women aged 20 to 24 years, influenced by physiological and bio demographic factors [7,8]. Pregnancy-related conditions are a significant cause of death among adolescent girls aged 15–19 years worldwide [9]. Girls aged 15 to 19 years make up 11% of all births worldwide [10]. Although there has been a decline in the rate of adolescent births, the total number of births has not decreased as much due to the growing adolescent population [11]. Each year, adolescents from low- and middle-income countries experience an estimated 21 million pregnancies, resulting in 12 million births [12]. Half of these pregnancies are unintended, and 55% of the unintended pregnancies often end in unsafe abortions [13]. Annually, 3.2 million unsafe abortions take place among adolescents aged 15 to 19, accounting for 15% of the total global incidence [14].

Adolescents have a significant unmet need for sexual and reproductive health care. A total of 43% of girls aged 15 to 19 years have an unmet need for modern contraception, which is higher than that of women of reproductive age, who have a 24% unmet need [12]. Adolescent pregnancies lead to health consequences such as increased risks of eclampsia, puerperal endometritis, and systemic infections. There are also social consequences, including stigma, rejection, violence by partners or parents, and dropping out of school, which are commonly observed [15]. Additionally, adolescent pregnancy is related to lower child survival, which is attributable to biological and social risks [16].

Teenage pregnancy is a significant public health issue in Ethiopia, where there is limited knowledge and access to reproductive health services, restricted educational opportunities, and unwanted marriage at a young age [17]. The country ranks third in terms of adolescent maternal deaths [4]. Data from the 2000, 2005, 2011, and 2016 Ethiopian Demographic and Health Survey (EDHS) show that teenage pregnancy rates in Ethiopia were 16%, 17%, 12%, and 13%, respectively [18–21]. This overall decline demonstrates how public health interventions have effectively altered societal perceptions of early marriage and provided adolescents with access to reproductive health services [22]. However, the inconsistent trends indicate the need for continuous efforts. When broken down by region, the highest percentage was in the Afar region (23%), and the lowest percentage was in the capital, Addis Ababa (3%) [21].

According to previous research, factors related to adolescent pregnancy in LMICs include lower education and socioeconomic status, child marriage, not utilizing contraception, child sexual abuse and early sexual activity, age, religious beliefs, limited knowledge of reproductive and sexual health, gender inequality, physical or sexual violence, lower family income, being in the 18–19 age group, maternal history of teenage pregnancy, and inadequate communication with parents about reproductive health issues [23–28].

Fertility and childbearing among young people are among the most neglected issues in Ethiopia [29]. Ethiopian efforts for adolescents and youth are dispersed across ministries, lack coordination and meaningful youth involvement, have insufficient funding, focus on specific projects, and ineffectively implement policies [30]. Some similar studies have been conducted in the country [31–35], but they were based on the 2016 EDHS. Four of the aforementioned studies [31–34] investigated the trend and determinants of teenage pregnancy and did not take into account the geographical variation in teenage pregnancy in Ethiopia. However, one of them [35] examined regional disparities and studied the factors that influence regional disparities in teenage pregnancy using geographically weighted regression (GWR). Our study used the most recent data from the mini-EDHS 2019 to provide updated information. Additionally, this study was conducted to address the limitation of the previous study, which used GWR to assess the relationship between teenage pregnancy and its determinants. GWR assumes that the relationship between the outcome and its predictors operates at the same spatial scale. This disregards the fact that different relationships may occur at various scales due to heterogeneity or nonstationarity in geospatial relationships. To address this methodological gap, we used Multiscale Geographically Weighted Regression (MGWR), which considers multiple scales and relaxes the assumption of GWR by determining parameter-specific bandwidths (the spatial scale at which the underlying spatial processes operate). This allows the relationships between independent and dependent variables to vary at different spatial scales [36–38]. The purpose of this study was to better understand the spatial context of the determinants of teenage pregnancy using an explicit multiscale approach. Therefore, our study aimed to explore the spatial determinants of teenage pregnancy among adolescents aged 15 to 19 years using the most recent EDHS.

Materials and methods

The 2019 mini-EDHS was conducted in Ethiopia through a community-based cross-sectional study. After Nigeria, Ethiopia is the second most populous country in Africa, with a total population of almost 126.5 million people in 2023 and an area of 1.1 million square kilometers. Children under the age of 15, individuals aged 15 to 64, and those aged 65 or above represent 44%, 52%, and 4% of the total population, respectively [39,40]. The country is subdivided into nine regions and two city administrations.

The 2019 mini-EDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of enumeration areas (EAs) were selected independently in each stratum in the two stages. The first stage involved a selection of 305 EAs, with 93 in urban areas and 212 in rural areas. In the second stage, a fixed number of 30 households per cluster was selected with an equal probability of systematic selection from the newly created household listing [39]. We utilized the individual data (IR) set from the mini-EDHS 2019 for the analysis, and we obtained longitude and latitude coordinates at the cluster or EA level. The Ethiopian administrative boundaries shapefile was obtained from the Central Statistical Agency (CSA).

The outcome variable is teenage pregnancy, defined as women aged 15–19 years who had a live birth, who were pregnant with their first child, or who had begun childbearing at the time of the survey [41]. It is dichotomized as 0 = no (those who did not experience teenage pregnancy) and 1 = yes (those who experienced teenage pregnancy). The percentage of women who had begun childbearing was calculated by dividing the number of women who either had a birth or who were pregnant at the time of the interview by all women aged 15–19 years [41]. The independent variables included were age, marital status, religion, sex of the household head, education level, wealth index, media exposure, and contraceptive utilization. The community-level variables were residence and region; we also created aggregate community-level variables, which were generated by aggregating individual-level factors at the cluster level: community education, community poverty, community contraceptive utilization, community media exposure, and community percentage of marriage.

Data processing and analysis

Factors associated with teenage pregnancy

Due to the hierarchical nature of the EDHS data, teenagers within the same cluster exhibit similar characteristics compared to those in another cluster. Therefore, a multilevel binary logistic regression model that accounts for the heterogeneity between clusters was constructed. To assess the clustering effect, the intraclass correlation coefficient (ICC) was estimated. The ICC showed a significant clustering effect (ICC = 20.9%). The median odds ratio (MOR), which is the median value of the odds ratio between the highest risk area and lowest risk area when randomly picking out two areas, is the increased risk that would exist when moving to a different area with a higher level of risk [42]. The proportional change in variance (PCV) is the variation in the dependent variable explained by all individual- and community-level factors [43].

Four models were constructed for the multilevel logistic regression analysis. The first model was a null model without explanatory variables to determine the degree of cluster variation in teenage pregnancy. The second model was fitted with individual-level variables, the third model was fitted with community-level variables, and the fourth model was fitted with both individual- and community-level variables at the same time. Variables with p values < 0.25 in the bi-variable analysis were added to the multivariable model. The associations between the dependent and independent variables were assessed and are presented as adjusted odds ratios (AORs) with 95% confidence intervals (CIs) at p values < 0.05.

Spatial analysis

A spatial analysis was conducted by using ArcGIS version 10.7 and Python 3. To assess whether the spatial distribution of teenage pregnancy in Ethiopia is dispersed, clustered, or randomly distributed, global spatial autocorrelation (global Moran’s I) was utilized. A Moran’s I value < 0.05 indicated that the distribution of teenage pregnancy in Ethiopia was nonrandom [44]. Hotspot analysis was conducted to identify statistically significant clustering areas using Getis-ordGi* statistics, which resulted in z scores and p-values indicating where high and low values of teenage pregnancy clustered spatially [45].

To comprehend the association between the density of a certain event and other various sociodemographic and environmental aspects of the population, spatial regression models are important [46]. After the identification of the high-risk areas (hotspot areas) of teenage pregnancy, factors that contributed to the spatial clustering of teenage pregnancy in these areas were determined at the EA level by a global and local spatial regression model: ordinary least squares (OLS) and GWR and MGWR, respectively. The spatial nonstationarity of the proportion of teenage pregnancy was ascertained by using global spatial autocorrelation before fitting the OLS, GWR, and MGWR models.

First, OLS was fitted to choose variables that are appropriate for the spatial variation of teenage pregnancy. Before proceeding to the GWR model, we first checked the six assumptions of the OLS model using exploratory regression. Exploratory regression is a data mining tool that helps us determine which combinations of explanatory variables meet all necessary OLS diagnostics [47]. We checked for the expected relationship of the explanatory variables, the statistical significance of each explanatory variable, the randomness of residuals (which should be normally distributed with no spatial patterns), the statistical nonsignificance of the Jarque-Bera statistics, freedom from multicollinearity in the explanatory variables (VIF < 7.5), and the model performance (strength of the adjusted R-squared) [48]. The Koenker Breusch–Pagan (BP) test was significant, indicating nonstationarity or heterogeneity in the relationship between the dependent and independent variables. As a result, we utilized local spatial regression models (GWR and MGWR), which account for variations in the relationships between variables across different spatial locations, for this study [49].

A geographically weighted regression model was used to determine the predictor variables for teenage pregnancy among adolescents aged 15 to 19 years. The proportion of the outcome variable and all predictors was calculated for each cluster. Classical GWR assumes that all of the processes being modeled operate at the same spatial scale. However, the MGWR reduces this assumption by allowing different processes to operate at different spatial scales by deriving an optimal bandwidth vector in which each element indicates the spatial scale at which a particular process takes place. Hence, MGWR is preferable because covariate-specific bandwidths are obtained rather than a single average bandwidth and provides valuable information on the scale at which different processes operate. Model calibration and bandwidth vector selection in MGWR were conducted using a back-fitting algorithm. Model fitness was compared among the global and local models using the Akaike information criterion (AIC), residual sum of squares (RSS), and adjusted R-squared values [50,51].

Ethical approval and consent to participate

The data were obtained from the Demographic and Health Surveys (DHS) Program and can be freely accessed from the program website (www.dhsprogram.com). As the study was a secondary data analysis of publicly available data from the MEASURE DHS program, ethical approval and participant consent were not necessary for this particular study. We requested the use of the DHS Program, and permission was granted to download and use the data for this study. Approval for the use of the data was obtained from the Measure DHS program, and the dataset was downloaded from https://dhsprogram.com/data/available-datasets.cfm. The IRB-approved procedures for DHS public-use datasets do not allow the identification of respondents, households, or sample communities. Geographic identifiers only go down to regional levels, and each EA has a primary sampling unit (PSU) number without labels. Surveys collect GIS coordinates for the entire enumeration area, preventing specific enumeration areas from being identified.

Results

Descriptive results

A total of 2165 teenage girls aged 15 to 19 years were included in the study. The median age of the respondents was 17 years. One-fifth (20.4%) of the participants were married, and only 9.5% of the girls utilized contraceptives. One-third of the girls were urban residents, while a quarter of them (25.1%) were illiterate. Regarding regional states, the majority of the participants were from the Oromia region (851, 39.3%), and the minority of the participants were from the Harari region (6, 0.29%) (Table 1).

10.1371/journal.pone.0310025.t001 Table 1 Descriptive characteristics of the study participants with a prevalence of teenage pregnancy in Ethiopia, 2019 EDHS.

Variables	Weighted frequency (percentage)	Teenage pregnancy	
Yes (%)	No (%)	
Total	2165 (100)	281 (12.98)	1884 (87.02)	
Age	15	506 (23.4)	6 (1.07)	500 (98.9)	
16	451 (20.8)	21 (4.67)	430 (95.3)	
17	328 (15.2)	37 (11.4)	291 (88.6)	
18	608 (28.1)	139 (23.0)	468 (77.0)	
19	272 (12.6)	77 (28.5)	195 (71.5)	
Religion	Orthodox	928 (42.9)	86 (9.2)	842 (90.8)	
Muslim	589 (27.2)	79 (13.5)	510 (86.5)	
Protestant	614 (28.4)	114 (18.7)	499 (81.3)	
Others	34 (1.6)	2 (4.2)	33 (95.8)	
Literacy	Illiterate	543 (25.1)	161 (29.7)	382 (70.3)	
Literate	1622 (74.9)	120 (7.4)	1502 (92.6)	
Education level	No education	229 (10.6)	75 (32.7)	154 (67.3)	
Primary	1437 (66.4)	177 (12.3)	1259 (87.7)	
Secondary	432 (19.9)	19 (4.3)	413 (95.7)	
Higher	67 (3.1)	10 (14.9)	57 (85.1)	
Marital status	Single	1725 (79.7)	50 (2.9)	1675 (97.1)	
Married	440 (20.4)	231 (52.5)	209 (47.5)	
Contraceptive utilization	Yes	206 (9.5)	93 (45.3)	113 (54.7)	
No	1959 (90.5)	188 (9.6)	1772 (90.4)	
Wealth index	Poor	720 (33.2)	124 (17.2)	596 (82.8)	
Middle	441 (20.4)	71 (16.1)	370 (83.9)	
Rich	1004 (46.4)	87 (8.6)	917 (91.4)	
Media exposure	No	1361 (62.9)	193 (14.2)	1168 (85.8)	
Yes	804 (37.1)	88 (11.0)	716 (89.0)	
Sex of household head	Male	1658 (76.6)	240 (14.5)	1418 (85.5)	
Female	507 (23.4)	41 (8.1)	466 (91.9)	
Community-level variables	
Residence	Urban	720 (33.2)	71 (9.9)	648 (90.1)	
Rural	1445 (66.8)	210 (14.5)	1236 (85.5)	
Region	Tigray	138 (6.4)	16 (11.6)	122 (88.4)	
Afar	16 (0.75)	5 (28.3)	11 (71.7)	
Amhara	498 (23.0)	35 (7.0)	464 (93.0)	
Oromia	851 (39.3)	129 (15.2)	722 (84.8)	
Somali	111 (5.1)	20 (17.6)	91 (82.4)	
Benishangul	25 (1.2)	5 (17.8)	20 (82.2)	
SNNPR	402 (18.6)	63 (15.8)	339 (84.2)	
Gambella	9 (0.42)	2 (27.4)	7 (72.6)	
Harari	6 (0.29)	1 (14.3)	5 (85.7)	
Addis Ababa	93 (4.3)	4 (3.8)	89 (96.2)	
Dire Dawa	15 (0.7)	2 (12.9)	13 (87.1)	
Community media exposure	Low proportion	1115 (51.5)	141 (12.7)	974 (87.3)	
High proportion	1050 (48.5)	140 (13.3)	910 (86.7)	
Community education level	Low proportion	869 (40.1)	174 (20.0)	695 (80.0)	
High proportion	1296 (59.9)	107 (8.3)	1189 (91.7)	
Community wealth index	Low proportion	991 (45.8)	155 (15.6)	836 (84.4)	
High proportion	1174 (54.2)	126 (10.7)	1048 (89.3)	
Community contraceptive use	Low proportion	1142 (52.7)	119 (10.4)	1023 (89.6)	
High proportion	1023 (47.3)	163 (15.9)	861 (84.1)	
Community literacy	Low proportion	1014 (46.8)	203 (20.0)	811 (80.0)	
High proportion	1151 (53.2)	78 (6.8)	1073 (93.2)	
Community marital status	Low proportion	1083 (50.0)	55 (5.1)	1028 (94.9)	
High proportion	1082 (50.0)	226 (20.9)	856 (79.1)	

Prevalence of teenage pregnancy in Ethiopia

The prevalence of teenage pregnancy among adolescents aged 15 to 19 years was 12.98% (95% CI: 11.63%, 14.46%). The highest percentage of teenage pregnancy was observed in the Afar region (28.3%), followed by the Gambella region (27.4%). The lowest prevalence was observed in Addis Ababa (3.8%) (Table 1).

Spatial analysis

Global spatial autocorrelation analysis revealed that the spatial distribution of teenage pregnancy in Ethiopia significantly varied across the country, with a global Moran’s index value of 0.04 and a p-value < 0.001. The Z score of 4.71 indicated that there was less than a 1% likelihood that the clustered pattern could be a result of random chance. (Fig 1).

10.1371/journal.pone.0310025.g001 Fig 1 Global spatial autocorrelation analysis of teenage pregnancy among adolescents aged 15 to 19 years in Ethiopia, 2019.

Significant hotspot areas (areas with a high proportion of teenage pregnancy) of teenage pregnancy were observed in central and southern Afar; northern, central, and western Gambela; northeastern and southern central Oromia; and the eastern Somali region. Significant cold spot areas (areas with a low proportion of teenage pregnancy) were identified in central and southern Amhara, central Oromia, Addis Ababa, and northern SNNPR. (Fig 2).

10.1371/journal.pone.0310025.g002 Fig 2 The Getis Ord Gi statistical analysis of hotspots of teenage pregnancy among adolescents aged 15 to 19 years in Ethiopia, 2019.

Shapefile source: (CSA, 2013; https://africaopendata.org/dataset/ethiopia-shapefiles); Map output: Own analysis using ArcGIS Software.

Spatial regression analysis

To explore the assumptions of spatial regression and to estimate the coefficients of the selected variables on teenage pregnancy, OLS regression was conducted. The OLS model identified predictors of each hotspot of teenage pregnancy. The joint F-statistics and Wald statistics were significant, which shows that the model was statistically significant. The Koenker statistics was statistically significant, which is indicative of nonstationarity or heterogeneity of the relationship between the dependent and the independent variables across the study areas. Therefore, GWR was applied because it assumes that the relationship between the independent and dependent variables has spatial heterogeneity (as confirmed by Koenker statistics). No multicollinearity was observed between the selected explanatory variables (Table 2).

10.1371/journal.pone.0310025.t002 Table 2 The ordinary least squares analysis results for the 2019 EDHS.

Variable	Coefficient	Robust SE	Robust t-statistics	Robust probability	VIF	
Intercept	0.015	0.01	1.48	0.14	---	
Proportion of poor wealth index	0.016	0.03	0.47	0.64	3.30	
Proportion of married teenagers	0.58	0.05	10.85	0.000000*	1.24	
Proportion of Muslim teenagers	-0.026	0.017	-1.54	0.13	1.21	
Proportion of illiterate teenagers	0.098	0.047	2.09	0.036839*	2.95	
Proportion of community poverty	0.025	0.02	1.05	0.29	2.96	
Proportion of community illiteracy	-0.040	0.02	-1.77	0.079	2.51	
Ordinary least square regression diagnostics	
Number of observations	303	Adjusted R-squared	0.547	
Joint F-statistics	61.7	Prob(>F), (6,296) degree of freedom	0.000000*	
Joint Wald statistics	258.7	Prob (> chi-squared), (6) degree of freedom	0.000000*	
Koenker (BP) statistics	49.9	Prob (> chi-squared), (6) degree of freedom	0.000000*	
Jarque–Bera	60.1	Prob (> chi-squared), (2) degree of freedom	0.000000*	
*p value < 0.05.

The local models, GWR and MGWR, were implemented to capture spatial heterogeneity. Both of the local models had better model fitness than the global model. The adjusted R2 improved from 0.547 (OLS) to 0.634 (GWR) and 0.691 (MGWR). The adjusted R2 indicates that the GWR and MGWR have 8.7% and 14.4% increased explanatory power, respectively, compared to the OLS. The residual sum of squares (RSS), which indicates unexplained variations, was very high in the OLS model (134.6) and was 93.7 (GWR) and 78.4 (MGWR). The local regression showed an AICc of 630.4, while the GWR reduced the AICc to 618.6, and the MGWR further decreased the AICc to 570.4. Relying on this information, it was confirmed that the local models improved the model performance (Table 3).

10.1371/journal.pone.0310025.t003 Table 3 Comparison of the goodness-of-fit measures between the global (OLS) and local (GWR and MGWR) models.

Model comparison parameter	Adj-R2	RSS	AICc	
Model	
OLS	0.547	134.573	630.444	
GWR	0.634	93.709	618.603	
MGWR	0.691	78.424	570.350	

The GWR model has a single bandwidth of 103; thus, 103 nearest neighbors are considered to inform the construction of parameter estimates at each local regression point. This implies that the GWR analysis focused on the 103 nearest neighboring data points to calculate parameter estimates. Using the 103 bandwidths for each variable, 63.4% of the observations were correctly classified. In the case of MGWR, the optimum bandwidth for each variable is computed. By allowing multiple bandwidths in MGWR, the model theoretically accounts for an optimal number of neighbors for each parameter estimate, thus allowing better predictions for the response variables. In MGWR, the bandwidths for each parameter are 111 for poor wealth index, 43 for being married, 301 for being Muslim, 301 for being illiterate, 292 for community poverty and 161 for community illiteracy. These specific bandwidths enable the model to adjust to the unique spatial characteristics of each variable (Table 4).

10.1371/journal.pone.0310025.t004 Table 4 Summary of the GWR and MGWR model results for the predictors of teenage pregnancy in Ethiopia in the 2019 EDHS.

Variable	Mean	STD	Min	Median	Max	Bandwidth	
GWR model	
Intercept	-0.034	0.093	-0.231	-0.017	0.284	103	
Proportion of poor wealth index	0.056	0.160	-0.249	0.051	0.399	103	
Proportion of married teenagers	0.670	0.137	0.302	0.700	1.012	103	
Proportion of Muslim teenagers	-0.071	0.074	-0.259	-0.062	0.120	103	
Proportion of illiterate teenagers	0.135	0.106	-0.305	0.115	0.388	103	
Proportion of community poverty	0.037	0.178	-0.328	0.077	0.348	103	
Proportion of community illiteracy	-0.060	0.149	-0.330	-0.043	0.381	103	
MGWR model	
Intercept	-0.052	0.197	-0.373	-0.079	0.662	50	
Proportion of poor wealth index	-0.008	0.101	-0.222	-0.050	0.292	111	
Proportion of married teenagers	0.640	0.250	-0.087	0.678	1.128	43	
Proportion of Muslim teenagers	-0.067	0.014	-0.094	-0.065	-0.050	301	
Proportion of illiterate teenagers	0.187	0.007	0.175	0.190	0.200	301	
Proportion of community poverty	0.061	0.019	0.025	0.058	0.096	292	
Proportion of community illiteracy	-0.051	0.054	-0.141	-0.065	0.064	161	

The figures below show the mapped GWR and MGWR coefficient estimates for the intercept and the covariates. In Fig 3, the local intercept in the MGWR is interpreted as the value of the outcome that would be expected if every location had exactly the same average value of each covariate. Based on the mapping of the intercept coefficients, the local intercept in the MGWR model can be interpreted as showing the level of teenage pregnancy that would be expected in each region if each of the regions had an average population profile or held all covariates constant. There is a high rate of teenage pregnancy in Gambella and some parts of western and northeastern Oromia. In contrast, there was a lower rate of teenage pregnancy in central and western Amhara. The intercept in the GWR was not significantly different from zero, but there was an outlier with a positive relationship in southern Afar at the border with Oromia.

10.1371/journal.pone.0310025.g003 Fig 3 GWR (left) and MGWR (right) parameter estimates for the intercept showing local patterns of spatial heterogeneity. The gray dots are not significantly different from zero. Shapefile source: (CSA, 2013; https://africaopendata.org/dataset/ethiopia-shapefiles); Map output: Own analysis using Python Software.

The parameter estimates for the proportion of being married were positively associated with teenage pregnancy in Tigray, Amhara, Afar, Somali, eastern SNNPR, Benishangul, Gambela, Harari, Dire Dawa and eastern Oromia. The GWR model showed a positive relationship with the outcome variable across the study area with no spatial heterogeneity (Fig 4).

10.1371/journal.pone.0310025.g004 Fig 4 GWR (left) and MGWR (right) parameter estimates for the proportion of married women showing local patterns of spatial heterogeneity. The gray dots are not significantly different from zero. Shapefile source: (CSA, 2013; https://africaopendata.org/dataset/ethiopia-shapefiles); Map output: Own analysis using Python Software.

The proportion of being illiterate does not show any spatial heterogeneity, as it has a positive association with teenage pregnancy across the country. This implies that being illiterate is associated with teenage pregnancy throughout the country, with no spatial differences. In the GWR model, it was not significantly different from zero because it has no statistically nonzero parameter estimate (Fig 5).

10.1371/journal.pone.0310025.g005 Fig 5 GWR (left) and MGWR (right) parameter estimates for the proportion of illiterate women showing local patterns of spatial heterogeneity. The gray dots are not significantly different from zero. Shapefile source: (CSA, 2013; https://africaopendata.org/dataset/ethiopia-shapefiles); Map output: Own analysis using Python Software.

Multilevel analysis of factors associated with teenage pregnancy

Multilevel mixed-effect logistic regression analysis was conducted to identify individual- and community-level variables that were significantly associated with teenage pregnancy. The ICC in the null model was 0.209, which shows that 20.9% of the variation in teenage pregnancy is explained by community-level variables (between-cluster variability). According to the MOR in the null model (2.43), there was a variation in teenage pregnancy between the clusters. When a teenage girl is switched from a low-risk cluster to a high-risk cluster, she is 2.43 times more likely to have a teenage pregnancy. According to the final model (model 4), the PCV showed that 72% of the total variability in teenage pregnancy was explained by both individual- and community-level factors. (Model 4) was the best-fitting model because it attained the highest log likelihood (in negative numbers) and the lowest deviance (Table 5).

10.1371/journal.pone.0310025.t005 Table 5 Parameters and model fit statistics for multilevel models.

Parameters	Null model	Model 2	Model 3	Model 4	
Community level variance (SE)	0.87	0.46	0.015	0.24	
ICC	0.21	0.12	0.0046	0.069	
MOR	2.43	1.91	1.125	1.589	
PCV	Ref	0.47	0.98	0.72	
Model fitness	
Deviance	1715.1	946.82	1536.88	920.48	
Log-likelihood	-857.55	-473.41	-768.44	-460.24	

Based on the findings of Model 4, the factors that were found to have a significant association with teenage pregnancy were the age of the girls, literacy status, marital status, age of the household head and residence in the Gambela region (Table 6).

10.1371/journal.pone.0310025.t006 Table 6 Multilevel analysis of factors associated with teenage pregnancy among adolescents aged 15 to 19 years in Ethiopia, 2019 EDHS.

Explanatory variable	Null model	Model 2
AOR (95%CI)	Model 3
AOR (95%CI)	Model 4
AOR (95%CI)	
Age	
15		1		1	
16		1.68 (0.70, 3.99)		1.77 (0.75, 4.18)	
17		3.63 (1.64, 8.04)		3.54(1.60,7.81)**	
18		8.38 (4.03, 17.4)		8.21(3.96,17.0)**	
19		15.3 (7.02, 33.7)		15.0(6.84,32.9)**	
Religion	
Orthodox		1		1	
Muslim		1.13 (0.69, 1.81)		0.89 (0.47, 1.68)	
Protestant		2.14 (1.26, 3.64)		1.62 (0.85, 3.10)	
Others		1.46 (0.35, 6.07)		1.08 (0.24, 4.83)	
Literacy	
Illiterate		1		1	
Literate		0.65 (0.41, 1.02)		0.57 (0.35, 0.92)*	
Education level	
No education		1		1	
Primary		0.77 (0.46, 1.29)		0.84 (0.48, 1.45)	
Secondary		0.59 (0.28, 1.26)		0.71 (0.32, 1.54)	
Higher		0.37 (0.11, 1.30)		0.39 (0.11, 1.46)	
Marital status	
Single		1		1	
Married		24.03(15.3,37.6)		22.8(14.1,37.0)**	
Contraceptive utilization	
Yes		1		1	
No		0.97 (0.58, 1.63)		1.04 (0.59, 1.81)	
Wealth index	
Poor		1		1	
Middle		0.97 (0.57, 1.67)		1.11 (0.63, 1.93)	
Rich		0.49 (0.29, 0.80)		0.59 (0.32, 1.11)	
Media exposure	
No		1		1	
Yes		1.25 (0.80, 1.93)		1.19 (0.74, 1.91)	
Age of HHH		0.98 (0.97, 0.99)		0.98 (0.98, 0.99)*	
Residence	
Urban			1	1	
Rural			1.03 (0.68, 1.58)	0.92 (0.50, 1.68)	
Region	
Tigray			1	1	
Afar			1.31 (0.68, 2.51)	1.31 (0.44, 3.92)	
Amhara			0.50 (0.25, 1.01)	0.45 (0.17, 1.18)	
Oromia			1.07 (0.59, 1.95)	1.03 (0.39, 2.70)	
Somali			1.16 (0.59, 2.28)	1.04 (0.34, 3.26)	
Benishangul			1.36 (0.72, 2.59)	1.34 (0.48, 3.71)	
SNNPR			1.04 (0.55, 1.95)	0.77 (0.28, 2.13)	
Gambella			2.04 (1.09, 3.81)	3.27 (1.21, 8.86)*	
Harari			1.42 (0.70, 2.86)	1.26 (0.39, 4.07)	
Addis Ababa			0.80 (0.29, 2.25)	0.86 (0.23, 3.21)	
Dire Dawa			1.02 (0.52, 1.99)	1.19 (0.39, 3.57)	
Community media exposure	
Low proportion			1	1	
High proportion			0.89 (0.65, 1.23)	0.93 (0.50, 1.68)	
Community education level	
Low proportion			1	1	
High proportion			0.81 (0.58, 1.12)	0.81 (0.49, 1.32)	
Community wealth index	
Low proportion			1	1	
High proportion			0.77 (0.53, 1.12)	0.83 (0.45, 1.54)	
Community contraceptive use	
Low proportion			1	1	
High proportion			1.25 (0.91, 1.71)	1.00 (0.61, 1.63)	
Community literacy	
Low proportion			1	1	
High proportion			0.89 (0.63, 1.25)	1.28 (0.76, 2.15)	
Community marital status	
Low proportion			1	1	
High proportion			4.84 (3.35, 7.00)	1.34 (0.79, 2.25)	
Intercept	0.14 (0.11, 0.17)	0.03(0.009, 0.08)	0.07(0.03, 0.15)	0.02(0.005, 0.12)	
*p value < 0.05;

** p value < 0.01;

AOR, adjusted odds ratio; HHH, household head.

The odds of having teenage pregnancy among girls aged 17, 18 and 19 years were 3.5 times greater (AOR = 3.54; 95% CI: 1.60, 7.81), 8.2 times greater (AOR = 8.21; 95% CI: 3.96, 17.0) and 15 times greater (AOR = 15.0; 95% CI: 6.84, 32.9), respectively, than teenagers aged 15 years. Literate teenagers had a 43% (AOR = 0.57; 95% CI: 0.35, 0.92) lower risk of teenage pregnancy than did those who were illiterate. Compared with single teenagers, married teenagers had 22.8 (AOR = 22.8; 95% CI: 14.1, 37.0) times greater odds of having a teenage pregnancy. With a one-year increase in the age of the household head, the odds of experiencing teenage pregnancy decreased by 2% (AOR = 0.98; 95% CI: 0.98, 0.99). Teenagers residing in the Gambela region had 3.27 (AOR = 3.27; 95% CI: 1.21, 8.86) times greater odds of having a teenage pregnancy than did those residing in the Tigray region. However, religion, education level, contraceptive use, wealth index, media exposure, and community variables, except region, were not statistically significant (Table 6).

Discussion

The spatial statistics showed that the spatial distribution of teenage pregnancy among adolescents aged 15 to 19 years had significant variation in Ethiopia. The hotspot areas of teenage pregnancy were identified in central and southern Afar, northern, central and western Gambela, northeastern and southern central Oromia and the eastern Somali region. A possible explanation might be that most of these areas are pastoralists and semi pastoralists, characterized by seasonal mobility and lower utilization of contraceptives [52]. They also live in traditional settings and strongly adhere to traditional cultural values and beliefs, which leads to poor sexual and reproductive health outcomes and early marriage [53].

According to the spatial regression analysis of the global model, the significant predictors of teenage pregnancy hotspots among adolescents aged 15 to 19 years were marital status and illiteracy. Local models were applied to the same predictors used in OLS to explore the local spatial variation in the relationship with the proportion of teenage pregnancy. As the MGWR model had the smallest AICc and RSS values and the highest adjusted R2 value, it is a statistically preferable local model, which is in line with previous studies [54].

The MGWR analysis revealed a significant positive relationship between the proportion of being a married women and teenage pregnancy in central and southern Afar, most parts of Amhara, Tigray and Benishangul, eastern SNNPR, Gambela, Harari, Dire Dawa, and eastern Oromia. This is because adolescents who are married are exposed to early sexual debut, which results in teenage pregnancy [31]. In most parts of Ethiopia, specifically rural areas, it is common for married girls to become pregnant soon after marriage to prove their fertility. Additionally, family planning utilizations are usually disapproved by the partner, in-laws and the community as a whole as a result of social norms, and little awareness and negative perception also play a role [29,55].

The proportion of being illiterate was significantly positively associated with teenage pregnancy throughout the country according to the MGWR model. This is because literacy increases the understanding of reproductive and sexual health through reading printed materials and contraceptive utilization [26]. Teenagers who are illiterate will have lower self-esteem and want to be accepted and validated by society through early childbearing [56].

Based on the multivariable multilevel logistic regression, literacy, marital status, age of the girls, age of the household head and residence in the Gambela region were significantly associated with teenage pregnancy among adolescents aged 15 to 19 years. Literate teenagers have a lower risk of pregnancy during adolescence than do illiterate teenagers. This finding is in line with studies conducted in Ethiopia [26], Zambia [57], Philadelphia, the USA [58] and Africa [59]. Giving an emphasis on female literacy will reduce teenage pregnancy. Teenagers who are married are more likely to experience teenage pregnancy than those who are single. This is supported by previously conducted similar studies in Ethiopia [26,31,33], Zambia [57], Uganda [60] and Nigeria [61]. As the frequency of sexual activity is greater among married teenagers, there is a greater likelihood of pregnancy in the absence of contraception than among those who are not married [62].

With respect to age, teenagers aged 17, 18 and 19 years had a greater risk of experiencing teenage pregnancy than did those aged 15 years. This finding is similar to those of studies conducted in Ethiopia [26] and Kenya [63]. This is because as age increases, the probability of engaging in sexual intercourse and marriage, which are proximal determinants of fertility, increases. Consequently, the chances of pregnancy and childbearing will also increase [64]. As the age of the household head increases, the chance of a teenage pregnancy decreases. This finding is consistent with a study from Rwanda [65]. This is because when household heads are older and more mature, they support teens under their household by giving them good parental support, including advising their teen girls on risky sex and fulfilling financial needs [66,67]. In terms of region, the odds of experiencing teenage pregnancy among adolescents aged 15 to 19 years who lived in the Gambela region were greater than those among adolescents in the Tigray region. This is attributable to the region’s high unmet need for family planning, greater use of polygamous practices, and early sexual initiation among adolescents, which exacerbates this condition [68].

The key strength of this study was the use of the most recent weighted nationally representative data with a relatively large sample size. To generalize findings at the national level by adjusting the data’s hierarchical nature, a multilevel model analysis was performed. The use of spatial and multiscale geographically weighted regression analysis assists in identifying factors that contribute to spatial variation in teenage pregnancy. This research, however, has certain limitations that must be considered when interpreting the findings. Important variables such as age at first sex and marriage and demand for family planning were not included since the mini-EDHS of 2019. In addition, because of the cross-sectional nature of the EDHS data, the cause–effect relationship between the dependent and independent variables cannot be shown.

Conclusion

There is significant spatial variation in teenage pregnancy among adolescents aged 15 to 19 years. The proportion of being illiterate and the proportion of being married were significantly positively associated with teenage pregnancy throughout the country according to the MGWR model. In the multivariable multilevel logistic regression, literacy, marital status, age of the girls, age of the household head and residence in the Gambela region were significantly associated with the outcome variable. To address this issue, stronger actions must be taken to implement the law, raising the minimum age for marriage to 18 years. Teenage marriage, which is the main cause of teenage pregnancy in Ethiopia, should be changed by addressing cultural norms that prioritize maintaining family status in the community.

Priority should also be given to enhancing levels of literacy and sexual and reproductive health education. In addition to increasing girls’ enrollment in formal schools, the government of Ethiopia must increase adult education programs for young women outside of school by incorporating reproductive health issues into the program. The population must also become aware of the consequences and adverse outcomes of teenage pregnancy. Generally, policies and implementations that focus on preventing early marriage, educating teenage girls, and creating awareness of reproductive health issues need to be strengthened.

We would like to acknowledge the measure DHS program for providing the dataset to use the mini-EDHS 2019.

Abbreviations

AICc corrected Akaike information criterion

AOR adjusted odds ratio

CI Confidence interval

CSA central statistical agency

DHS Demographic and Health Survey

EAs Enumeration areas

EDHS Ethiopian Demographic and Health Survey

GWR Geographically weighted regression

HHH Household Head

ICC Intraclass correlation coefficient

LMICs low- and middle-income countries

MOR median odds ratio

MGWR Multiscale Geographically Weighted Regression

PCV Proportional change in variance

RSS Residual sum of squares

OLS Ordinary least squares

SSA Sub-Saharan Africa

SNNPR Southern Nations Nationalities and Peoples Region

10.1371/journal.pone.0310025.r001
Decision Letter 0
Fort Alfredo Luis Academic Editor
© 2024 Alfredo Luis Fort
2024
Alfredo Luis Fort
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
24 Jun 2024

PONE-D-23-27072A multi-scale geographically weighted regression analysis of teenage pregnancy and associated factors among adolescents aged 15 to 19 in Ethiopia using the 2019 mini- demographic and health surveyPLOS ONE

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Attachment Submitted filename: PONE-D-23-27072-AF.pdf

10.1371/journal.pone.0310025.r002
Author response to Decision Letter 0
Submission Version1
16 Jul 2024

Subject: Responses to academic editor's comments

Dear Dr. Alfredo Luis Fort,

Thank you for taking the time to consider our manuscript titled “A multiscale geographically weighted regression analysis of teenage pregnancy and associated factors among adolescents aged 15 to 19 in Ethiopia using the 2019 mini-demographic and health survey” for the Plos One Journal original research article. We appreciate the time and effort you have dedicated to providing valuable feedback on our manuscript.

We have taken the comments and concerns into account and made every effort to address them. We agree with all the comments and have incorporated the corresponding revisions into the revised manuscript. We believe that our manuscript has been significantly improved as a result of these revisions, and we hope that our revised manuscript is acceptable for publication in the PLOS One journal.

We would like to thank you once again for your consideration of our work and for inviting us to submit the revised manuscript. We look forward to hearing from you. Our detailed, point-by-point responses to the comments are given below.

Best regards,

Tsion Mulat Tebeje

School of Public Health, Dilla University, Dilla, Ethiopia.

Email: yemarina12@gmail.com (corresponding author)

 

Response to journal’s requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: We prepared the manuscript according to the journal's requirements and double-checked its compliance before submitting our revised manuscript.

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Response: Thank you for your suggestion. We have included it in the revised manuscript and highlighted it in the 'Data Availability' section, specifically on lines 417-418, page 25.

3. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

Response: This study involved a secondary data analysis using publicly available data from the MEASURE DHS program and ethical approval and participant consent was not required. This is highlighted in the ethical approval and consent to participate section (lines 194-196, page 9). The data files do not contain any names of individuals or household addresses. We obtained approval from the DHS program to use the dataset and have attached the proof of granted permission as an “other” file with our submission.

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Response: Thank you for the suggestion. We have moved the ethics statement to the methods section in the revised manuscript.

5. We note that Figures 2,3,4 and 5 in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth).

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

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Response: We appreciate your concern regarding the ethical issues. However, the figures (2, 3, 4, and 5) mentioned in our manuscript are not copyrighted; rather, they are the results of spatial analysis conducted using ArcGIS and Python software. The DHS geographic information system, which contains shapefiles with coordinates (latitude and longitude), were obtained from the DHS office by explaining the objective of the study through online requests. To obtain these figures, we imported relevant data extracted from the 2019 Ethiopian mini Demographic and Health Survey. We have attached the permission letter obtained from MEASURE DHS to use the EDHS dataset and geographical location as an “other” file labeled 'AuthLetter_188507'. The Ethiopian administrative boundaries shapefile was obtained from the Ethiopian Central Statistical Agency (CSA). The shape file used to construct the figures can be accessed publicly at https://africaopendata.org/dataset/ethiopia-shapefiles. We incorporated and highlighted this information in captions of figures 2, 3, 4, and 5.

As a result, the maps presented in our study are not copyrighted. Instead, they are the findings of our spatial analysis, which we performed using ArcGIS and Python on shapefiles and projected CSV files of coordinates. This is the exact process that we followed when conducting our study. Therefore, we can confirm that the figures presented in our study represent our spatial analysis results and are not copyrighted.

Edits requested on the submission

1. We note your current data availability statement:

"The data used in this research are publicly available from the measure DHS program

via an online request at http://www.dhsprogram.com"

As the URL presented in your Data Availability Statement and cover letter links to the DHS Program general website, please provide any direct URL(s), accession numbers, instructions, and additional information (DOI, data set title, etc.) required for researchers to access the specific data underlying the results presented in your study.

Response: Thank you for the suggestion. We have edited the data availability statement by providing the direct URL which links to the login or register for datasets. By registering and logging in, anyone can directly access the dataset.

2. Please clarify if the data used for the maps are copyrighted/what copyright information or restrictions are attached to it. If the data is copyrighted then please send the below permission form to the owners of the data.

Response: Thank you for asking for clarification. The maps included in our study are not copyrighted from somewhere. They were the output of our work created using ArcGIS version 10.7 and Python 3 software based on the maps on the Ethiopian shapefile obtained from the Ethiopian Central Statistical Agency (CSA), which is publicly available on Open-Africa https://africaopendata.org/dataset/ethiopia-shapefiles, and the GPS data obtained from the measure DHS program after submitting a request along with the study's rationale. Therefore, we assure you that the maps presented in this paper are the result of our work, utilizing appropriate analytical methods and procedures. Furthermore, we have acknowledged the source of the data, and we, as authors, have previously published studies containing similar maps.

Response to editor’s comments

1. I have seen a similar study like this, with the same problems. The topic and method plus results are of extreme importance. However, we cannot invite Reviewers because they will probably reject the manuscript because, like me, they will find lots of errors, misspellings, and inadequate descriptions. Please take the manuscript to someone who can write proper English, and use my first suggestions (in attached file) to continue improving the wording and descriptions for the rest of the file.

Response: Thank you for giving us your valuable time and for sharing your valuable input. We fully agree with your suggestion. We have thoroughly reviewed existing similar studies and have appropriately cited them. In the final paragraph of the introduction section, we elucidated how our study differs. The revised manuscript has been meticulously edited and proofread to rectify quality issues, such as misspellings, grammatical errors, and unclear sentences.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0310025.r003
Decision Letter 1
Fort Alfredo Luis Academic Editor
© 2024 Alfredo Luis Fort
2024
Alfredo Luis Fort
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
23 Aug 2024

A multiscale geographically weighted regression analysis of teenage pregnancy and associated factors among adolescents aged 15 to 19 in Ethiopia using the 2019 mini-demographic and health survey

PONE-D-23-27072R1

Dear Dr. Tebeje,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Academic Editor

PLOS ONE

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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

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

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

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

Reviewer #1: The study’s purpose and aim are clear: to explore the spatial factors that contribute to teenage pregnancy in Ethiopia. But beyond just identifying these factors, we’re diving deeper into what they truly mean for the lives of young girls across different regions. We’re examining a range of determinant like socio-economic conditions, access to education, and cultural practices, that influence whether a teenage girl might become pregnant. By understanding these specific factors, we hope to shed light on the unique challenges faced by adolescents in various parts of the country. This approach isn’t just about numbers; it’s about understanding the real-world implications for these young girls and their futures. The study topic is comprehensive and well researched, but to make it resonate with a wider audience, it could benefit from simplifying some of the more technical parts. For instance, when discussing complex methodological terms, consider breaking them down into more relatable language or using analogies that can bridge the gap between expert knowledge and general understanding. This way, readers who might not be familiar with spatial analysis or statistics can still grasp the importance and impact of your work. Making these adjustments will help ensure that your findings are accessible and meaningful to a broader audience. Good job

Reviewer #2: Dear Author,

Thank you for submitting your manuscript for publication. I have some suggestions to enhance the quality and clarity of the study.

Firstly, I noticed that several similar studies have been using the same data, and methods have been published. To strengthen your manuscript, please highlight the new knowledge your study provides.

2. In the background section, you mention the decline in teenage pregnancy rates since 2000. It would be valuable to explore and discuss potential interventions that implemented during this period that may have contributed to this decline (If any). e.g. "The younglives study".

3. Regarding the results section, I found some discrepancies in the categorization of certain variables, including marital status, wealth index and community exposure to media, which do not add up to the total sample size of teenagers (n=2165). Please review and correct these errors.

4. Additionally, I had difficulty understanding the denominator used to calculate region-specif prevalence rates of teenage pregnancy. To ensure clarity, I suggest including the denominator in square brackets within the results, such as [n=XX/XX]. This will ensure transparency and enable readers to accurately follow the calculations.

For example, when I used the formular (no.teens pregnant in region/no.teen population in region X100) I obtained different rates to what is on Table 1. Providing the denominator will solve the problem.

Overall, your study has potential and addressing these concerns will enhance its quality and contribution to the field.

Regards

P. Nongena

**********

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Reviewer #1: No

Reviewer #2: Yes: Pelisa Nongena

**********

Attachment Submitted filename: PONE-D-23-27072_R1.docx - Review.docx

Attachment Submitted filename: PONE-D-23-27072_R1-AF.pdf

10.1371/journal.pone.0310025.r004
Acceptance letter
Fort Alfredo Luis Academic Editor
© 2024 Alfredo Luis Fort
2024
Alfredo Luis Fort
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
29 Aug 2024

PONE-D-23-27072R1

PLOS ONE

Dear Dr. Tebeje,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

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on behalf of

Dr. Alfredo Luis Fort

Academic Editor

PLOS ONE
==== Refs
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