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. 2022 Aug 25;17(8):e0272849. doi: 10.1371/journal.pone.0272849

Spatial variation and factors associated with home delivery after ANC visit in Ethiopia; spatial and multilevel analysis

Hiwotie Getaneh Ayalew 1,*, Alemneh Mekuriaw Liyew 2, Zemenu Tadesse Tessema 2, Misganaw Gebrie Worku 3, Getayeneh Antehunegn Tesema 2, Tesfa Sewunet Alamneh 2, Achamyeleh Birhanu Teshale 2, Yigizie Yeshaw 2,4, Adugnaw Zeleke Alem 2
Editor: Prafulla Kumar Swain5
PMCID: PMC9409554  PMID: 36007083

Abstract

Introduction

Institutional delivery is crucial to reduce maternal and neonatal mortality as well as serious morbidities. However, in Ethiopia, home delivery (attended by an unskilled birth attendant) after antenatal care (ANC) visit is highly in practice. Therefore, this study aimed to assess the spatial variation and determinants of home delivery after antenatal care visits in Ethiopia.

Method

A secondary data analysis was conducted using the 2019 mini Ethiopian demographic and health survey. A total of 2,923 women who had ANC visits were included. Spatial analysis was done by using GIS 10.7 and SaTscan 9.6. The risk areas for home delivery from GIS and spatial scan statistics results were reported. A multi-level logistic regression model was fitted using Stata14 to identify individual and community-level factors associated with home delivery after ANC visit. Finally, AOR with 95% CI and random effects were reported.

Result

Home delivery after ANC visit was spatially clustered in Ethiopia(Moran’s index = 0.52, p-value <0.01). The primary clusters were detected in Oromia and SNNP region (LLR = 37.48, p < 0.001 and RR = 2.30) and secondary clusters were located in Benishangul Gumuz, Amhara, Tigray and Afar (LLR = 29.45, p<0.001 and RR = 1.54). Being rural resident (AOR = 2.52; 95%CI 1.09–5.78), having no formal education (AOR = 3.19;95% CI 1.11–9.16), being in the poor (AOR = 2.20;95%CI 1.51–3.22) and middle wealth index (AOR = 2.07;95% CI 1.44–2.98), having one ANC visit (AOR = 2.64; 95% CI 1.41–4.94), and living in the agrarian region (AOR = 3.63; 95%CI 1.03–12.77) had increased the odds of home delivery after ANC visit.

Conclusion and recommendation

Home delivery after ANC visit was spatially clustered in Ethiopia. Factors like maternal education, wealth index, number of ANC visits, residency and region were significantly associated with home delivery after ANC visit. Therefore, it is better to increase the number of ANC contact by giving health education, especially for women with low levels of education and better to improve the wealth status of women. A special strategy is also vital to reduce home delivery after ANC visit in those high-risk regions.

Introduction

Home delivery is childbearing in women’s or other homes without unskilled health personnel and a non-equipped clinical setting. In Africa, home delivery is high and institutional deliveries attended by non-trained individuals are highly in practice [1]. In those countries, maternal mortalities are due to direct obstetric causes including postpartum hemorrhage, obstructed labor, pregnancy-induced hypertension and sepsis [2]. Maternal morbidity and mortality is a global health challenge and developing countries contributed to about 97% of maternal death [3]. It is also estimated that 810 women across the world die each day as a result of pregnancy and childbirth-related problems and a huge number of these deaths occur in developing countries. In Sub-Saharan Africa, childbearing women face a 1 in 39 risks of dying in childbirth [3, 4]. In Ethiopia, most maternal deaths are related to pregnancy and childbirth and the current maternal mortality ratio is 412 per 100,000 live births [5]. Place of delivery is crucial to reduce maternal morbidity and mortality as well as to reduce serious neonatal illness but in Ethiopia, home delivery attended by an unskilled birth attendant is highly practiced (4,6). However, a large number of these maternal deaths could be prevented through the presence and utilization of institutional delivery and skilled health care provider at delivery services. Currently, in Ethiopia, despite ANC coverage(74%), home delivery attended by unskilled birth attendants was 50% [6].

Even if, an ANC visit is a key factor for institutional delivery, many women who had ANC follow up, gave birth at home usually without skilled birth attendants [7, 8]. Mothers who had ANC visits also give birth at home [9]. In Ethiopia, the 2019 MEDHS report shows that mothers who had ANC visits didn’t give birth at health institutions, since ANC and institutional delivery coverage are 74% and 50% respectively [5].

Different studies identified factors that were significantly associated with home delivery after ANC visit. These were residence, inadequate knowledge of pregnancy-related problems, educational status of the mother (6, 8, 10), women’s income, number of ANC visits, parity, ANC follow up and distance from a health facility (6, 8, 9, 10, 11).

Home delivery after ANC visit is a major public health problem in Ethiopia, although limited studies have been conducted on the determinant factors and the spatial clustering of home delivery just after ANC visit. Therefore, this study aims to assess the spatial variation and determinants of home delivery just after ANC visits across regions in Ethiopia. Besides, the findings from the geostatistical analysis are helpful to locate the high-risk areas of home delivery after ANC visits which coud in turn has a policy implication for strategic intervention and allocation of resources [10].

Method

Data source, study population and sampling technique

Secondary data analysis was done based on the mini Ethiopian demographic and health survey 2019 dataset, which was a crossectional survey conducted from March 21, 2019, to June 28, 2019. The source population was all pregnant women who had ANC visits within five years preceding the survey in Ethiopia. Whereas, the study population was all pregnant women aged 15–49 years who were pregnant or had last birth and who had ANC visits in the selected households five years preceding the survey. A two-stage stratified cluster sampling was used in the 2019 mini Ethiopian Demographic and Health survey by taking enumeration areas as primary sampling units and households as secondary sampling units. The sample included 305 enumeration areas, 93 in urban and 212 in rural areas. A total of 8,885 reproductive-age women were interviewed in the 2019 survey. For this study, participants with missing in the outcome variable were excluded. Therefore a total of 2,923(weighted) pregnant women who had ANC visits five years preceding the survey were included in the analysis [5].

Study variables

Dependent variable

The outcome variable of this study was home delivery after ANC visit, which was defined as mothers who gave birth at her or another’s home after ANC visits by unskilled birth attendants. For analysis, it was coded as “1” for women who had home delivery and coded as “0” for women who had institutional delivery.

Independent variables

In this study, both individual and community-level factors were considered. Individual-level factors included were the educational status of the mother (no formal education, primary, secondary, higher), marital status (married, unmarried), maternal age (15–24, 25–34 and 35–49 years), wealth status (poor, middle, rich), history contraceptive utilization (no, yes), media exposure (no, yes), the number of ANC follow-up (one visit, 2–4 visit, above 4 visits), time of ANC visit (first trimester, second trimester, third trimester), birth interval (short, long), whereas the community-level factors included were the place of residency (urban, rural), region,community-level poverty and community media exposure. The region was categorized into three as agrarian (Tigray, Amhara, Oromia, and Sothern Nations Nationalities and Peoples Region), pastoral (Afar, Somali, Benishangul, and Gambella), and urban (Harari, Dire Dawa and Addis Ababa). The community-level media exposure was obtained by aggregating the individual-level media exposure in each cluster by using the proportion of those who had media exposure and this community-level media exposure shows the overall media exposure in the community. Median values were used to categorize as high and low because the aggregated variable had a skewed distribution. Community-level poverty level was also obtained by an aggregated proportion of poor women which shows the overall poverty status within the cluster. It was categorized into two categories based on the median value a higher proportion of poor mothers and a lower proportion of mothers within a cluster.

Data management and analysis

The data was accessed from the DHS program’s official database after permission was granted through an online request [11]. The outcome variable with important predictors was extracted from MEDHS 2019, maternal individual records data set. Labeling, recoding and analysis were done using STATA 14. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the representativeness of the survey for unequal sample sizes across clusters and to tell the STATA to take into account the sampling design when calculating standard errors to get reliable statistical estimates.

Spatial analysis

The weighted frequency of the outcome variable with cluster number and geographic coordinate data was used. A total of 305 clusters having longitude and latitude were included in the spatial analysis. Spatial autocorrelation analysis (Global Moran’s I index) and hotspot analysis were conducted by using ArcGIS 10.7, while spatial scan statistics was done by SaTscan 9.6. Finally, a weighted proportion of home delivery after ANC visit per cluster was used for spatial autocorrelation analysis and hotspot analysis. For spatial scan statistics number of women who had home delivery after ANC visit and the number of women who had institutional delivery were considered as cases and controls respectively to fit the Bernoulli model [12].

Spatial autocorrelation analysis

The Global Moran’s I statistic test was used to measure whether home delivery after ANC visit patterns were randomly distributed, dispersed, or clustered in Ethiopia. The calculated Moran’s I values close to −1 indicate home delivery was dispersed, whereas Global Moran’s I close to +1 indicate home delivery after ANC visit was clustered and if the Global Moran’s I value zero home delivery were distributed randomly [13, 14].

Hot spot (Getis-Ord Gi*) analysis

The nature of the variation of spatial autocorrelation over the study setting was investigated by calculating Gi* statistic, for each area by using Gettis-Ord Gi* statistics. Z-score is computed to determine the statistical significance of clustering, and the p-value is computed for the significance [14]. If the z-score is between −1.96 and +1.96, the p-value would be larger than 0.05, and could not reject the null hypothesis; the pattern exhibited could very likely be due to random spatial processes. If the z-score falls outside the range, the observed spatial pattern is probably unlikely to be because of random chance, and the p-value would be small. In this condition, the null hypothesis is rejected and dealing with what might be causing the statistically significant spatial pattern in the data is considered. Statistical output with high Gi* indicates a “hotspot” whereas low Gi* indicates a “cold spot” [13, 15].

Spatial scan statistical analysis

Spatial scan statistical analysis or the Bernoulli-based model was employed to test for the presence of statistically significant spatial clusters of home delivery after ANC visit using Kuldorff’s SaTScan version 9.6software. The spatial Scan statistical method is commonly recommended that it is better than others in detecting local clusters and has higher power as compared to available spatial statistical methods [16]. The presence of statistically significant clusters of home delivery after ANC visit was tested by using spatial scan statistical analysis. It uses a scanning window that moves across the study area [15, 17]. Women who had home delivery after ANC visit were taken as cases and those who had institution delivery were taken as controls to fit the Bernoulli model. The number of cases in each location had a Bernoulli distribution and the model requires data with or without a disease. For each potential cluster, a likelihood ratio test statistic was used to determine the number of observed home delivery cases within the potential cluster was significantly higher than expected or not. The primary and secondary clusters were identified and assigned p-values and ranked based on their likelihood ratio test, based on 999 Monte Carlo replications [18, 19].

Multi-level logistic regression analysis

The outcome variable was binary; home delivery and institution delivery. Because of the hierarchical nature of data and the binary outcome variable, the multi-level logistic regression model was used. Before fitting the multilevel logistic regression model for individual and community level variables the chi-square assumption was checked. In the Bivariable multilevel logistic regression model and Variables which were statistically significant at a p-value less than 0.2 in the Bivariable multilevel logistic regression analysis were considered for model adjustments for the multivariable multilevel logistic regression model. Four models were fitted. The first was the null model containing no exposure variables which was used to check variation in the community and provide evidence to assess random effects. The second model was the multivariable model adjustment for individual-level variables and model three was adjusted for community-level factors. In the fourth model, variables from both individual and community-level variables were fitted. Regarding the measures of variation (random effects) intracluster correlation coefficient (ICC) and Proportional Change in Community Variance (PCV) were used.

The ICC (Intracluster correlation coefficient) quantifies the degree of heterogeneity ofhome delivery after ANC visit between clusters (enumeration areas).

ICC=ϭ2/(ϭ2+π2/3)

Where:—ϭ 2-between cluster variance, π2 /3 -within-cluster variance

Whereas PCV measures the total variation attributed to individual-level factors and community-level factors in the multilevel model as compared to the null model which was computed by using the following formula;PCV = (variance in (null model)-variance in (full model))/ variance (null model) [20].

Since the models were hierarchical and nested, the model comparison was done using deviance and log likely hood ratio. Consequently, the best-fitted model for the data was used. Finally, the adjusted odds ratio with a 95% confidence interval was reported for statistically significant variables.

Ethical consideration

This study was a secondary analysis of the 2019 mini Ethiopian Demographic and Health Survey data.For conducting this study; we registered and requested the dataset from DHS online archive. The approval letter for the use of the data set was also gained from the Measure DHS. No information obtained from the data set was disclosed to any third person.

Result

Individual and community-level characteristics of the respondents

Among a total of 2,923 participants, the mean age of the participants was 28 years (SD±6.42), and 1,283 (43.87%) participants had no formal education. Most of the respondents 2,754 (94.22%) were married. Regarding media, 1,664 (56.94%) participants had no media exposure. A total of 1348 (46.12%) participants were rich and most of the respondents 2,581 (88.30%) had a history of contraceptive use. Looking at the timing of ANC follow up above half of the participants 1,563 (53.48%) were started in the second trimester and 2,014(68.91%) participants had 2–4 ANC visits.

A total of 305 communities (clusters) were included in the study. about 89% of study participants lived in agrarian regions. Around two-thirds (60.14%) of the participants lived in communities with low poverty levels and nearly half of them (51.31%) lived in communities with high community media exposure (Table 1).

Table 1. Individual and community-level characteristics of study participants who had home delivery after ANC visit in Ethiopia, MEDHS 2019 (N = 2,923).

Individual level variables Frequency (weighted) Percentage
Maternal age (years)
    15–24 759 25.95
    25–34 1,550 53.03
    35–49 615 21.02
Maternal education
    No formal education 1,283 43.87
    Primary education 1,153 39.46
    Secondary education 335 11.45
    Higher education 153 5.22
Marital status
    Married 2,754 94.22
    Unmarried 169 5.78
Wealth index
    Poor 986 33.74
    Middle 586 20.13
    Rich 1,348 46.12
Media exposure
    No 1,664 56.94
    Yes 1,259 43.06
Parity
    Primiparous 689 23.57
    Multiparous 1,662 56.87
    Grand multiparous 572 19.57
Sex of household head
    Male 2,538 86.82
    Female 385 13.18
Previous contraceptive use
    No 342 11.70
    Yes 2,581 88.30
Birth interval
    Short 419 18.90
    Long 2,504 81.10
Time of ANC visit
    First trimester 1,092 37.37
    Secondtrimester 1,563 53.48
    Third trimester 268 9.16
Number of ANC visit
    1 visit 130 4.46
    2–4 visit 2,014 68.91
    Above 4 visit 779 26.63
Community-level variables
Place of residency
    Urban 871 29.81
    Rural 2,052 70.19
Region
    Urban 150 5.11
    Agrarian 2,620 89.61
    Pastoral 154 5.28
Community poverty
    Low 1,758 60.14
    High 1,165 39.86
Community media exposure
    Low 1,423 48.69
    High 1,500 51.31
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Spatial analysis results

Spatial autocorrelation of home delivery after ANC visit in Ethiopia

This study identified thathome delivery was clustered in Ethiopia with Global Moran’s I = 0.52 and p-value = 0.000.A Z-score of 11.34 indicated that there is less than a 1% likelihood that this clustered pattern could be the result of random chance. The bright red and blue colors to the end tails indicate an increased significance level (Fig 1).

Fig 1. Spatial autocorrelation of home delivery in Ethiopia, MEDHS 2019.

Fig 1

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Hot spot analysis of home delivery after ANC visit in Ethiopia

Hot spot analysis was performed to identify high-risk areas for having home delivery after ANC visitin Ethiopia. The green color indicates less risky areas for home delivery in Ethiopia. The red color indicates significant high-risk areas that were detected in the Somalia,Amhara, Afar and Oromia and SNNP regions. However, cold spot areas were detected in Dire Dawa, Addis Abeba and Harari peoples regions which, were indicated by green colored dots (Fig 2).

Fig 2. Hot spot analysis of home delivery after ANC visit in Ethiopia, MEDHS 2019.

Fig 2

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Spatial SaTScan analysis of home delivery after ANC visit in Ethiopia (Bernoulli model)

Spatial scan statistics identified 133 significant most likely clusters, of which 14 primary, 119 secondary clusters were identified.The primary clusters’ spatial window was located in southwest Oromia and SNNP regions, which was centered at (6.362562 N, 38.759281 E) with a 111.22 kmradius, and Log-Likelihood ratio (LLR) of 37.48, at p < 0.001. This showed that women within the primary scanning window had 2.30 times higher risk of home delivery after ANC visit as compared to women outside the scanning window. The secondary clusters scanning window was located in the north part of Benishangul Gumuz, Amhara, Tigray and Afar regions, which was centered at (14.154958 N, 39.329700 E) with a 553.25 km radius, and LLR of 29.45 at p<0.001. This showed that women who live within the secondary scanning window had a 1.54 times higher risk of home delivery after ANC visit as compared to women outside the scanning window. In addition, an other secondary clusters’ scanning window was located in Somalia and some parts of the Oromia regions. It was centered at (6.639662 N, 44.465855 E) with a 390.28 km radius, with LLR 16.27 at a p-value<0.01. This showed that women who live within the tertiary scanning window had a 1.88 times higher risk of home delivery after ANC visit as compared to women outside the scanning window (Fig 3, Table 2).

Fig 3. Spatial scan analysis home delivery after ANC visit in Ethiopia, MEDHS 2019.

Fig 3

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Table 2. Most likely clusters of spatial scan statistics analysis of home delivery after ANC visit in Ethiopia, MEDHS 2019.
Most likely clusters Enumeration areas(clusters) identified Coordinate/radius Population Case RR LLR p-value
First most likely cluster 113, 183, 186, 182, 181, 117, 115, 185, 187, 172, 89, 188, 184, 114 (6.362562N, 38.759281) / 111.22 km 128 86 2.30 37.48 <0.01
Second most likely cluster 16, 10, 15, 11, 3, 17, 35, 14, 39, 12, 2, 5, 37, 25, 27, 13, 38, 36,7, 23, 1, 6, 24, 20, 9, 8, 19, 18, 22, 78, 56, 45, 29, 46, 21, 62,83, 44, 82, 34, 61, 58, 84, 4, 57, 30, 33, 60, 59, 65, 64, 63, 54,81, 31, 55, 74, 85, 26, 51, 47, 32, 66, 75, 48, 53, 70, 49, 71, 76, 68, 50, 67, 72, 52, 73, 79, 165, 80, 43, 100, 162, 77, 40, 126, 69,163, 42, 148, 119, 99, 166, 159, 164 (14.154958 N,39.32970E) / 553.25 km 981 384 1.54 29.45 <0.01
Third most likely cluster 135, 123, 140, 137, 138, 124, 131, 132, 122, 134, 136, 142, 133, 139,129, 121, 130, 107, 250, 128, 248, 249, 255, 254, 247 (6.639662 N,44.465855E) / 390.28 km 114 64 1.88 16.27 <0.01
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Random effect analysis results

Variance component analysis was performed to decompose the total variance of home delivery after ANC visit between clusters in the null model. The cluster-level variance which indicates the total variance of home delivery that can be attributed to the context of the community in which the mothers were living was estimated. The applicability of the multi-level logistic regression model in the analysis was justified by the significance of the community-level variance [community variance = 3.05; standard error (SE) = 0.46; P-value = 0.00], indicating the existence of significant differences between communities regarding home delivery after ANC visit incidence. The community variance was expressed as an intracluster correlation coefficient (ICC). The ICC was 0.4811 which revealed that 48.11% of the total variance of home delivery after ANC visitin Ethiopia could be attributed to the context of the communities (clusters) where the mothers were living. The PCV in this model was 32.45%. It suggested that 32.45% of community variance observed in the null model was explained by both community and individual-level variables.

Model comparison

A model with a high likelihood (-1107.13) and low (2214.26) deviance was the best model. The combined multilevel logistic regression model (model IV) was thebest-fitted model in this study (Table 3).

Table 3. Random effects and model fitness.

Random effects Model l Model ll Model lll Model lV
Community variance(SE) 3.05(0.46) 2.08(0.36) 2.09(0.32) 2.06(0.31)
    ICC (%) 48.11(0.40–0.55) 38.79(0.30–0 .47) 38.89(0.31–0.46) 38.60(0.31–0.45)
    PCV (%) Reference 31.8 31.47 32.45
Model fitness Model I Model ll Model lll Model lV
Log likelihood -1513.41 -1125.72 -1462.03 -1107.13
Deviance(-2LLR) 3026.82 2251.77 2924.06 2214.26
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ICC: intracluster correlation coefficient; PCV: proportional change in variance; SE: standard error; LLR: log-likelihood ratio.

Fixed effect analysis results

In bi-variable multilevel logistic regression analysis wealth index, maternal age, marital status, media exposure, maternal education, sex of household head, history of contraceptive use, birth interval,number of ANC visits,time of ANC visit, place of residence, region,community poverty level and community media exposure were significant at p-value<0.2 and fitted for Multi-variable multilevel analysis. Multivariable multilevel logistic regression analysis was fitted to identify factors associated with home delivery after ANC visit. Thus, in the final model (Model IV) Maternal Education, wealth index, number of ANC visits; residency and region were significantly associated with home deliveryafter ANC visit.

After keeping another individual, community-level factors and random effect constant the odds of having home delivery after ANC visit among women who had no formal education was 3.19 (AOR = 3.19;95% CI 1.11–9.16) times higher as compared to women who had higher education. Women with poor and middle wealth index had 2.20(AOR = 2.20; 95%CI 1.51–3.22) and 2.07(AOR = 2.07;95% CI 1.44–2.98) respectively times higher odds ofhome delivery after ANC visitas compared to women with rich wealth index. Women who had one ANC visit had 2.64(AOR = 2.64; 95% CI 1.41–4.94) times higher odds of home delivery after ANC visit as compared to women who had four and above ANC visits.The odds of having home delivery after ANC visit among rural women was 2.51(AOR = 2.52; 95%CI 1.09–5.78) times higher as compared to urban women. Women who had lived in the agrarian regions had 3.63(AOR = 3.63; 95%CI 1.03–12.77) times higher odds of home delivery after ANC visit as compared to urban regions (Table 4).

Table 4. Multilevel logistic regression analysis of individual and community-level factors associated with home delivery after ANC visit in Ethiopia, MEDHS 2019.

Characteristics Fixed effect Model I Model II AOR(95%CI) Model lll AOR(95%CI) Model IV AOR(95%CI)
Maternal age (years)
    15–24 - 1.00 - 1.00
    25–34 - 1.03(0.70–1.52) - 1.10(0.75–1.62)
    35–49 - 0.84(0.52–1.34) - 0.91(0.57–1.46)
Maternal education
    No - 4.39(1.62–11.92) - 3.19(1.11–9.16)*
    Primary - 2.78(1.03–7.49) - 2.08(.73–5.90)
    Secondary - 1.42(0.49–4.12) - 1.10(0.36–3.38)
    Higher - 1.00 - 1.00
Marital status
    Married - 1.00 - 1.00
    Unmarried - 1.26(0.64–2.48) - 1.35(0.67–2.69)
Wealth index
    Poor - 2.91 (2.02–4.19) - 2.20(1.51–3.22)*
Middle - 2.58(1.80–3.69) - 2.07(1.44–2.98)*
    Rich - 1.00 - 1.00
Media exposure
    No - 1.20 (0.89–1.61) - 1.12(0.83–1.51)
    Yes - 1.00 - 1.00
Sex of household head
    Male - 1.00 - 1.00
    Female - 1.36 (0.85–2.16) - 1.48(0.92–2.40)
Previouscontraceptive utilization
    No - 1.38(0.97–1.98) - 1.38(0.96–1.97)
    Yes - 1.00 -
Time of ANC visit
    First trimester - 1.00 - 1.00
    Second trimester - 1.28(0.97–1.69) - 1.24(0.93–1.64)
    Third trimester 2.44(0.56–3.82) 2.33(0.49–3.66)
Number of ANC visit - -
    One visit - 2.82(1.51–5.25) - 2.64(1.41–4.94)*
    2–4 visit 1.08(0.80–1.46) 1.04(0.77–1.41)
    Above 4 visit 1.00 1.00
Birth interval -
    Long - 0.85(0.63–1.15) - 0.84(0.63–1.14)
    Short - 1.00 - 1.00
Residency
    Urban - - 1.00 1.00
    Rural - - 3.57(1.66–7.69) 2.51(1.09–5.78)*
Region
    Agrarian - - 3.34(1.16–9.61) 3.63(1.03–12.77)*
    Pastoral - - 2.97(0.92–9.53) 2.08(0.52–8.27)
    Urban - - 1.00 1.00
Community poverty
    Low - - 1.00 1.00
    High - - 2.88(1.64–5.07) 1.79(0.97–3.29)
Community media exposure
    Low - - 1.92(1.06–3.48) 1.57(0.84–2.94)
    High - - 1.00 1.00
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ANC: antenatal care

*significant; 1.00: reference.

Discussion

In this study among the spatial analysis methods hotspot analysis and SaTScan statisticsanalysis were used to demonstrate the clustering of home delivery after ANC visits in Ethiopia. In hotspot analysis, hot spot areas of home delivery after ANC visit were found in Somalia, Amhara, Afar, Oromia and SNNP region. This might be due to sudden onset of labor, lack of privacy, absence of transportation, long-distance and low accessibility of health service facilities resulting in-home delivery [9, 21, 22]. Another possible justification for this finding might be regions like Addis Ababa, Dire Dawa and Harari were urban areas where, most of the mothers had better education levels and could access media and health facilities compared to the other regions [23, 24]. Besides, maternal health care service-seeking behavior of women is low in those hotspot areas like Somalia and Afar which could lead to home birth [25–27].

Moreover, Spatial scan statistics identified 133 significant most likely clusters, of which 14 primary, 119 secondary clusters. These most likely clusters’ were located in Oromia, Amhara,and Tigray, Afar, Somalia, Benishangul Gumuzand SNNP regions. This could be due to the inequality in the distribution of maternal health services and the inaccessibility of infrastructure [28, 29]. Besides, the mobile lifestyle of nomads, negative Attitude toward skilled birth attendants and low perception of institutional delivery could be potential resons that might reduce maternal health care service utilization [30, 31].

Maternal education was significantly associated with home delivery after ANC visit. Women with no formal education had more than three times higher odds of having home delivery after ANC visit as compared to women who had higher education. This finding was supported by studies done in Ethiopia [9, 32, 33], Kenya [34] and Tanzania [22, 35]. This mightbe due to that educated women had a better level of understanding about skilled delivery service utilization, healthcare-seeking behavior and maternal survival [36]. Another possible reason might be that educated women can easily be exposed to media and can easily access information about the health benefits of institutional delivery [37]. In addition to this most women who had higher education levels lived in urban areas, which help them to easily access health institutions for delivery care services [38].

The wealth status of women was another factor associated with home delivery after ANC visit. The odds of having home delivery after ANC visit was about two times higher for women within a community with the poor wealth status as compared to those from communities with rich wealth status.This finding was in line with studies done in Ethiopia [33, 39], Tanzania [22] and Nepal [40]. This might be due to the demand for different costs related to transportation and food that might not be afforded by the mother’s in low level of income [31, 41]. Possibly it might be due to that mostly poor women live in rural areas in Ethiopia in the absence of transportation and long-distance travel and access to health facilities which may lead to home delivery [42–45].

The number of ANC visits was also significantly associated with home delivery after ANC visits. Women who had one ANC visit had about 64% increased odds of home delivery after ANC visit as compared to women who had four and above ANC visits. This finding was consistent with studies done in Ethiopia [2, 9, 32] and Nepal [40].The possible reason might be due to the increased number of ANC contact with health professionals enhancing the chance to advise about birth preparedness and place of delivery [46]. In addition,women who had fewer than four ANC visits didn’t have a better number of contact with skilled health care providers but, an increased number of ANC visits gave chance for the mothers to be aware of the importance of institutional delivery by skilled birth attendants.

The Place of residency was another factor which strongly associate with home delivery after ANC visit. The odds of having home delivery after ANC visit among rural women were more than two times higher as compared to urban women. This finding was supported by different studies done in Ethiopia [33, 39, 47, 48] and Kenya [49]. The possible explanation might be due to rural residents usually have little access to health facilities as compared to urban residents [33]. This may also be due to distance to the health facilities, ambulance delays due to distance and difficult roads, inaccessibility of information about the advantage of institutional delivery and low educational status of women from rural areas [50].

Finally, the region woman lived in was also a significant factor. Women who had lived in the agrarian regions had a 34% increased odds of home delivery as compared to the urban regions. This finding was concordant with studies done in Ethiopia [51, 52], Kenya [53], Ghana [54] and Nigeria [55]. The possible explanation might be due to scarcity of resources and poor clinical settings,in which most of the services are concentrated in urban areas or city administrates [56, 57]. In addition, those women who live in agrarian regions are farmers commonly had sudden labor onset and short duration labor due to hard work and exercise, which lead to home delivery [58, 59]. It might also be due to regions being attributable to the differences in access to health services, information and social and cultural attributes [55].

The strength of this study was it used a large data set and applied sampling weight to make it nationally representative and to give reliable estimates and also used an advanced model for analysis. Whereas, the limitation of this study could be the possibility of committing social desirability since it was a community-based national survey.

Conclusion

Home delivery after ANC visit was spatially clustered in Ethiopia. Women from Somalia, Amhara, Afar, Oromia, Benishangul Gumuz,Gambella, Tigrayand SNNP regions had a higher risk of home delivery after ANC visit. Factors like maternal education, wealth index, number of ANC visits, residency and region were significantly associated with home delivery after ANC visit in Ethiopia. Therefore, it is better to increase the number of ANC contact by giving health education, particularly for women with low levels of education and better to improve women’s income by setting different strategies and job opportunities at a national level to support women in those high-risk regions.

Acknowledgments

We would like to greatly acknowledge MEASUREDHS for granting us to access the Demographic and Health Surveys data set.

Acronym and abbreviation

ANC

Antenatal Care

AOR

Adjusted odds Ratio

EDHS

Ethiopian Demographic and Health Survey

ICC

Intra-cluster Correlation Coefficient

LLR

log-likelihood Ratio

MEDHS

Mini Ethiopian Demographic and Health Survey

PCV

Proportional Change in Variance

RR

Relative Risk

SNNP

South Nation Nationality and Peoples Region

Data Availability

One can get data at the link: www.measuredhs.com after being authorized user of demographic and health survey data.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Prafulla Kumar Swain

4 Apr 2022

PONE-D-21-33331

SPATIAL VARIATION AND FACTORS ASSOCIATED WITH HOME DELIVERY AFTER ANC VISIT IN ETHIOPIA; SPATIAL AND MULTILEVEL ANALYSIS

PLOS ONE

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Reviewer #2: Yes

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I am happy reading the version where authors addressed an important topic, however I have some observations.

Please re-edit the conclusion of abstract. Is is liking odd to read. It is better.. starting of two sentences. It is large and the repetition of the conclusion of the main manuscript. I wonder authors are working with Ethiopia data but in the conclusion they wrote “It is better to give special attention for women who lived in Somalia, Amhara, Afar, Oromia, Benishangul Gumuz, Tigray and SNNP regions” do you have any clarification for this?

In the introduction section I didn’t found the strong rationality of the study, that is completely missing.

I would suggest to re-work on this to establish a strong rationality for the work.

In the first line of the introduction they told “skilled health care providers” I am fear for this line. Would it be “skilled birth attendants” please recheck.

“Studies have shown that mothers who have ANC visit to be more likely to give birth at health institutions” references missing for this line.

I think the Study setting is irrelevant, If I were you I would delete this section.

Authors used in some line “unskilled personal” or “unskilled personnel” please solve this issue.

“being in the poor” or “being poor”.

First two lines of the discussion is unnecessary.

“Possibly it might be most poor women in Ethiopia live in rural areas, in the absence of transportation and long-distance travel may lead to home birth.” References missing for this line.

Need “compared to urban women This finding..” full stop or anything else.

“This may also be due to distance to the health facilities, ambulance delays, inaccessibility of information, and low educational status of women from rural areas” does it means rural areas of Ethiopia are underprivileged, please extend this line for further clarification.

“The possible explanation might be due to scarcity of resources and poor clinical settings, in which most of the services are concentrated in urban areas or city administrates“ references missing.

In addition, those most agrarian women’s are farmers mostly had sudden labor onset and short duration which… I really fear of this line. Do you have any evidence or supporting literature.

Reviewer #2: The present paper has many shortcomings that are listed below:-

1. Why the marital status (married/unmarried) variable included in individual level variables.

2. Why wealth status is coded in 3 categories? The DHS data has 5 categories.

3. Media exposure can be coded as no, partial, full.

4. What does it mean birth interval (short/long)?

5. Why both the residency and region variables included in the models? It could lead to the problem of multicollinearity.

6. Home delivery after ANC visit was spatially clustered in Ethiopia. What is the role of independent variables in this phenomena?

7. The manuscript has many grammatical and typing errors.

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PLoS One. 2022 Aug 25;17(8):e0272849. doi: 10.1371/journal.pone.0272849.r002

Author response to Decision Letter 0

7 May 2022

Rebuttal letter Date April 20,2022

Subject; submission of revised manuscript (PONE-D-21-33331)

SPATIAL VARIATION AND FACTORS ASSOCIATED WITH HOME DELIVERY AFTER ANC VISIT IN ETHIOPIA; SPATIAL AND MULTILEVEL ANALYSIS

Hiwotie Getaneh Ayalew

To PLOS ONE

Dear all,

We would like to thank you for these constructive, building, and improvable comments on this manuscript that would improve the substance and content of the manuscript. We considered each comment and clarification question of editors and reviewers on the manuscript thoroughly. Our point-by-point responses for each comment and question are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached. The manuscript language was further improved in the revised manuscript. and we follow journal guidelines. I have attached recent comments in a point-by-point response.

Version 1; editor’s comments

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

Authors’ response; thank you dear editor we have prepared the documents based on PLOS ONE requirements.

2. Thank you for stating the following in your Competing Interests section.

Authors’ response; thanks, dear editor.

3. Thank you for submitting the above manuscript to PLOS ONE. During our internal evaluation of the manuscript, we found significant text overlap between your submission and the following previously published work.

-https://www.researchsquare.com/article/rs-55326/v1

Authors’ response; Thank you, dear editor, we have included the reference and we have rephrased the duplicated texts and words in the revised manuscript.

Version 2; reviewers’ comments

1. 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

Authors’ response; thank you, dear reviewers

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

Reviewer #1: Yes

Reviewer #2: No

Authors’ response; thank you, dear reviewers. We have improved the revised version of the manuscript.

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Authors’ response; Thank you, dear reviewers. We have stated that the data was fully available without restriction in the revised manuscript at the declaration session.

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: No

Authors’ response; thanks a lot dear reviewer. we have critically improved the readability of the manuscript. Please see the revised version.

Reviewer 1 Comments

After having carefully read the manuscript and taking notes on overall strengths and weaknesses, I share my comments below.

General comments and suggestions 1; I am happy and glad to the editor for sending me the paper for reviewing.I am happy reading the version where authors addressed an important topic, however I have some observations. Please re-edit the conclusion of abstract. Is is liking odd to read. It is better.. starting of two sentences. It is large and the repetition of the conclusion of the main manuscript. I wonder authors are working with Ethiopia data but in the conclusion they wrote “It is better to give special attention for women who lived in Somalia, Amhara, Afar, Oromia, Benishangul Gumuz, Tigray and SNNP regions” do you have any clarification for this?

Authors’ response; thanks a lot dear reviewer for your critical comment to improve the manuscript. we have fully accepted your comment we have to avoid and reduce the repetition of the conclusion of the abstract.

These regions Somalia, Amhara, Afar, Oromia, Benishangul Gumuz, Tigray and SNNP regions were detected when we conduct spatial analysis. we find hotspot regions in hot spot analysis and high-risk areas of home delivery after ANC visit with spatial scan statistics scanning window in these regions. Therefore based on the finding it is important to recommend those regions. We kindly request to see the revised manuscript at the spatial analysis result session.

Comment 2; In the introduction section I didn’t found the strong rationality of the study, that is completely missing. I would suggest to re-work on this to establish a strong rationality for the work.

Authors’ response; thanks a lot dear reviewer for your unlimited effort to improve the manuscript. We accepted your comment and included sound justification in the revised manuscript.

Comment 3; In the first line of the introduction they told “skilled health care providers” I am fear for this line. Would it be “skilled birth attendants” please recheck.

Authors’ response; thanks, dear reviewer for your detailed revision. We have accepted the comment and we correct the revised manuscript.

Comment 4; Studies have shown that mothers who have ANC visits are more likely to give birth at health institutions” references missing from this line.

Authors’ response; Thanks a lot dear reviewer. We have added the reference in the revised manuscript.

Comment 5; I think the Study setting is irrelevant, If I were you I would delete this section. Authors used in some line “unskilled personal” or “unskilled personnel” please solve this issue.

Authors’ response; thank you in advance dear reviewer. We have delated the study setting and we also have taken an edition on the issue of “unskilled personal” in the revised manuscript. Comment 6; “being in the poor” or “being poor”. First two lines of the discussion is unnecessary.

Authors’ response; thank you dear reviewer we have written the interpretation of the variable wealth index as “being in the poor” and we have also removed the first line of the discussion session in the revised manuscript.

Comment 7; “Possibly it might be most poor women in Ethiopia live in rural areas, in the absence of transportation and long-distance travel may lead to home birth.” References missing for this line

Authors’ response; thanks dear reviewer we have to appreciate your comments. We have added some references and we kindly request you to see them in the revised manuscript.

Comment 8; Need “compared to urban women This finding.” full stop or anything else.

“This may also be due to distance to the health facilities, ambulance delays, inaccessibility of information, and low educational status of women from rural areas” does it means rural areas of Ethiopia are underprivileged, please extend this line for further clarification.

Authors’ response; thanks a lot dear reviewer for your deep comments. We have added a full stop at the end of “compared to urban women” Additionally for the next comment we have further extended our clarification for the explanation. Since it does not mean that rural women are underprivileged rather it means due to the topographic difficulty of the country and scattered living distribution of households and health institutions, there is long distance and difficult roads so this leads to home delivery. We also added a reference for this comment in the revised manuscript.

Comment 9; The possible explanation might be due to scarcity of resources and poor clinical settings, in which most of the services are concentrated in urban areas or city administrates “references missing.

Authors’ response; thank you, dear reviewer. We have added a reference and we kindly asked you to see the revised manuscript.

Comment10; In addition, those most agrarian women’s are farmers mostly had sudden labor onset and short duration which… I really fear of this line. Do you have any evidence or supporting literature?

Authors’ response; thanks dear reviewer for your constructive comments. We have added evidence of supporting kinds of literature in the revised manuscript. We kindly ask you to see the revised manuscript.

Reviewer #2 comments

Comment 1. Why the marital status (married/unmarried) variable included in individual level variables.

Authors’ response; thanks dear reviewer we accepted your comment. The analytic model of this study was the multilevel model and it includes individual and community level factors.so factors that can be directly found in the data set for each study participants were considered as individual-level factors like marital status. Whereas community-level factors were generated by aggregating individual-level factors.

Comment 2; why wealth status is coded in 3 categories? The DHS data has 5 categories.

Authors’ response; thank your dear reviewer for your comment. DHS has five categories for wealth index, but for our analysis, we have recoded wealth index into three categories, due to that it did not fulfill the chi-square assumption as we used it as five categories so we changed it to three categories. Some kinds of literature also use wealth index as three categories for analysis

comment 3; Media exposure can be coded as no, partial, full

Authors’ response; thank you, dear reviewer. we have used media exposure for analysis as exposed (yes)and unexposed (no) for media.

. Comment 4; what does it mean birth interval (short/long)?

Authors’ response; thank you, dear reviewer. We strongly appreciate your comment. Birth interval is the duration which refers to the time interval from delivery to the next subsequent pregnancy of the fetus. It is classified as short if it is less than 15 months and longer if it is greater than or equal to 15 months and it also has an effect on place of delivery as delivered in Textbook of Williams Obstetrics 25th edition.

Comment 5; why both the residency and region variables included in the models? It could lead to the problem of multicollinearity.

Authors’ response; Thanks a lot dear reviewer for your interesting and constructive comments. We initially checked the VIF (variance inflation factor) of all individual and community level variables before fitting the model and there was no multicollinearity in between variables.

comment 6; Home delivery after ANC visit was spatially clustered in Ethiopia. What is the role of independent variables in this phenomenon?

Authors’ response; thanks dear reviewer we appreciate your comments. In this study, we used two models spatial and multilevel analysis. In the case of spatial analysis, we only used the GPS data set and the outcome variable to identify high-risk areas of home delivery after ANC visit in Ethiopia, whereas in the multilevel model to identify determinants of home delivery after ANC visit we used independent variables (individual level and community level factors). therefore independent variables for spatial analysis to determine whether home delivery was spatially clustered or not have no role.

Comment 7; the manuscript has many grammatical and typing errors.

Authors’ response; thanks a lot dear reviewer we have critically improved the grammatical error and readability of the manuscript. Please see the revised version.

Thanks, a lot!!!

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (39.2KB, docx)

Decision Letter 1

Prafulla Kumar Swain

28 Jul 2022

SPATIAL VARIATION AND FACTORS ASSOCIATED WITH HOME DELIVERY AFTER ANC VISIT IN ETHIOPIA; SPATIAL AND MULTILEVEL ANALYSIS

PONE-D-21-33331R1

Dear Dr. Hiwotie Getaneh Ayalew,

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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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Thanks to the all authors addressing my previous comments, I am happy reading this version, I don’t have further comments. Congratulations to all.

Reviewer #2: All the comments have been addressed and MS looks greater now. In its current form, MS may be published.

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Reviewer #1: No

Reviewer #2: No

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Acceptance letter

Prafulla Kumar Swain

16 Aug 2022

PONE-D-21-33331R1

SPATIAL VARIATION AND FACTORS ASSOCIATED WITH HOME DELIVERY AFTER ANC VISIT IN ETHIOPIA; SPATIAL AND MULTILEVEL ANALYSIS

Dear Dr. Ayalew:

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on behalf of

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Academic Editor

PLOS ONE

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