Factors associated with unskilled birth attendance among women in sub-Saharan Africa: A multivariate-geospatial analysis of demographic and health surveys

Isaac Yeboah Addo; Evelyn Acquah; Samuel H Nyarko; Ebenezer N K Boateng; Kwamena Sekyi Dickson

doi:10.1371/journal.pone.0280992

. 2023 Feb 2;18(2):e0280992. doi: 10.1371/journal.pone.0280992

Factors associated with unskilled birth attendance among women in sub-Saharan Africa: A multivariate-geospatial analysis of demographic and health surveys

Isaac Yeboah Addo ^1,^*, Evelyn Acquah ², Samuel H Nyarko ³, Ebenezer N K Boateng ⁴, Kwamena Sekyi Dickson ^5,^*

Editor: Kannan Navaneetham⁶

PMCID: PMC9894461 PMID: 36730358

Abstract

Background

Several studies have shown that unskilled birth attendance is associated with maternal and neonatal morbidity, disability, and death in sub-Saharan Africa (SSA). However, little evidence exists on prevailing geospatial variations and the factors underscoring the patterns of unskilled birth attendance in the region. This study analysed the geospatial disparities and factors associated with unskilled birth attendance in SSA.

Methods

The study is based on data from thirty (30) SSA countries captured in the latest (2010–2019) demographic and health surveys (DHS). A total of 200,736 women aged between 15–49 years were included in the study. Geospatial methods including spatial autocorrelation and hot spot analysis as well as logistic regression models were used to analyse the data.

Results

There were random spatial variations in unskilled birth attendance in SSA, with the main hotspot located in Chad, whereas South Africa and the Democratic Republic of Congo showed coldspots. Residence (urban or rural), wealth status, education, maternal age at the time of the survey and age at birth, desire for birth, occupation, media exposure, distance to a health facility, antenatal care visits, and deaths of under-five children showed significant associations with unskilled birth attendance.

Conclusion

Random geospatial disparities in unskilled birth attendance exist in SSA, coupled with various associated socio-demographic determinants. Specific geospatial hotspots of unskilled birth attendance in SSA can be targeted for specialised interventions to alleviate the prevailing disparities.

Introduction

Historically, traditional or unskilled birth attendants defined as “persons who assist a mother during childbirth and who initially acquired their skills by delivering babies themselves or through apprenticeship to other traditional birth attendants” [1], have been the primary caregivers for women during pregnancy and childbirth in sub-Saharan Africa (SSA) [2]. In addition to their provision of care during pregnancy and child delivery, unskilled birth attendants play other roles, such as providing family planning information and advising pregnant women about their nutritional requirements [3]. Due to challenges associated with collecting maternal health data in some SSA countries [1], an aggregated estimate of unskilled birth attendance in the region is limited. However, current anecdotal reports show that more than one in two pregnant women (>50%) utilise the services of unskilled birth attendants in some SSA countries [4].

Employing the services of unskilled birth attendants in the management of pregnancy and child delivery is known to be associated with a high risk of maternal and neonatal morbidity, disability, and even death [5]. Unskilled birth attendants commonly lack the required knowledge, skills, or resources to balance risk with benefits during pregnancy and child delivery, and may have difficulty managing pregnancy or childbirth complications, such as haemorrhage, eclampsia, and obstructed labour [5]. It is therefore believed that women’s utilisation of services from unskilled birth attendants during pregnancy and childbirth plays a significant contribution to the level of maternal and neonatal mortality in SSA which is ranked as the highest in the world with estimates of more than two-thirds of maternal deaths per annum or 68% or 533 maternal deaths per 100,000 live births, or 200,000 maternal deaths [6].

Given the assumption that many women in SSA continue to utilise the services of unskilled birth attendants rather than professional or skilled birth attendants, it is important to understand the factors accounting for these utilisations. Understanding these factors is essential for developing context-specific interventions tailored toward more specific population characteristics which in turn can inform efforts to reduce maternal deaths. However, to date, no empirical study, to the best of our knowledge, has examined the geospatial distribution and factors associated with the utilisation of maternal and neonatal services from unskilled birth attendants in the whole of SSA. Also, there are limited studies that have explored the spatial distribution of the utilisation of unskilled birth services in SSA. The available studies on unskilled birth attendants were mostly focused on only single countries in Africa, e.g., Olakunde, Adeyinka (5) for Nigeria, or explored perceptions of women about unskilled birth attendants from a qualitative perspective, e.g.,- Gurara, Muyldermans (7) for Ethiopia, and Selepe and Thomas (8) for South Africa [7–12].

Previous studies focused on skilled birth attendance among women in SSA have suggested that availability of a healthcare facility, distance between home and the healthcare facility, socio-economic status of pregnant women, place of residence, birth parity, ethnicity, previous contact with a healthcare system, and levels of education are associated with the use of services from skilled birth attendants during pregnancy and delivery [5, 13–16]. However, a significant gap in current knowledge is the extent to which these factors apply to women who utilise the services of unskilled birth attendants. Exploring women’s use of services from unskilled birth attendants and the factors associated with the use of such services will provide important supporting evidence for the development and implementation of evidence-based interventions. Using representative Demographic and Health Survey (DHS) data for women aged 15–49 years from thirty (30) SSA countries, this study examined factors associated with unskilled birth attendance among women in SSA. The study further examined the levels of service use from unskilled birth attendants among women in SSA as well as the hot and cold spot countries associated with the use of such services. Examining these factors can be useful to maternal health promoters, health practitioners, policymakers, and global health advocates addressing the unacceptable level of maternal mortality in SSA.

Materials and methods

Data source

The study analysed data from a pool of the latest demographic and health surveys (DHS) of thirty (30) countries in sub-Saharan Africa (SSA), conducted between 2010 and 2019. The surveys comprised nationally representative samples of women in their reproductive age groups (15–49 years), and the participants were selected based on a two-stage stratified cluster sampling procedure. The DHS generates reliable data on fertility, family planning, infant and child mortality, maternal and child health, among others, and was therefore deemed suitable for this study. Both standard and continuous DHS data were obtained for countries such as Angola, Burkina Faso, Burundi, Cameroon, Chad, Comoros, Congo, Cote D’Ivoire, Democratic Republic of Congo, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mozambique, Namibia, Nigeria, Rwanda, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. The unit of analysis comprised 200,736 women aged from 15 to 49 years selected from various households in these countries. The sample was limited to the last births of women within the five years preceding the surveys.

Study variables and measurements

The outcome of interest was unskilled birth attendance. This was based on the person who assisted during the delivery of the last child. This comprised multiple categories of persons such as a doctor, nurse/midwife, community health officer/nurse, traditional birth attendant, traditional health volunteer, community/village health volunteer, relatives, and others. In this study, we created a binary outcome where deliveries assisted by traditional birth attendants, traditional health volunteers, community/village health volunteers, relatives, and others were measured as unskilled birth attendance while all others were considered skilled birth attendance [13, 17].

Additionally, various explanatory variables, of which most have been established by previous studies relating to the study outcome [17–19], were considered. These include age (Less than 20 years, 20–29 years, and 30–49 years), level of education (No education, Primary, Secondary, Higher), wealth status (Poorest, Poorer, Middle, Richer, Richest), marital status (Never in a union, Married, Cohabitation, Widowed, Divorced, Separated), occupation (Not working, Working), age at first birth (Less than 20 years, 20–29 years, 30–49 years), birth order (1, 2–3, 4 or more), mass media exposure (No, Yes), getting medical help for self: permission to go (Big problem, Not a big problem), getting medical help for self: getting the money needed for treatment (Big problem, Not a big problem), getting medical help for self: distance to health facility (Big problem, Not a big problem), desire for birth (Then, Later, No more), antenatal care visits (Less than 4, 4 or more), under five mortality (No, Yes), place of residence (Urban, Rural) and country.

Analytical procedure

We initially estimated the proportion of unskilled birth attendance by country and the socio-demographic characteristics of the respondents. We conducted a bivariate analysis of each explanatory variable and the outcome variable. All explanatory variables that had significant associations with the outcome variable were added to the multivariate model. Next, a multivariate analysis was performed using a logistic regression model to estimate associations between unskilled birth attendance and all the explanatory variables (age, level of education, wealth status, marital status, occupation, age at first birth, birth order, mass media exposure, getting medical help for self: permission to go, getting the money needed for treatment, distance to health facility, desire for birth, antenatal care visits, under-five mortality, place of residence and country variable). A multicollinearity test was performed for each variable which showed a mean-variance inflation factor (VIF) of 2.01 for the variables in the models. Unadjusted (Model 1) and adjusted odds ratios (Model 2) were then calculated for each variable at a 95% confidence interval. Stata (Version 14) was used to process and analyse the data. The results were sample-weighted to address any over-sampling and under-sampling in the total sample.

Geospatial analysis

Regarding the geospatial analysis, the proportions data on unskilled birth attendance were merged with the shapefiles for each country using ArcMap version 10.5. Spatial autocorrelation (Global Moran’s I) tool was used to explore the distribution of unskilled birth attendance in SSA after a hypothesis was set:

H₁: the distribution of unskilled birth attendance in SSA is not random

This spatial autocorrelation (Global Moran’s I) tool was used to assess whether the distribution of unskilled birth attendance in SSA is random, clustered, or dispersed across the selected countries. The results to determine the type of distribution is dependent on the Moran’s I index and the z-score or p-value. A positive and negative Moran’s I index value implies a likelihood of clustering and dispersion respectively. The z-score or p-value indicates statistical significance, and the null hypothesis is accepted if the z-score is -1.65–1.65 and if the p-value is not statistically significant. In addition, a hotspot analysis (Getis-Ord G) was conducted to identify areas with relatively higher (hotspot) and lower (coldspot) occurrence of unskilled birth attendance among the selected countries based on z-score and p-value. A higher z-score and a lower p-value show a higher probability of a phenomenon occurring at a place. The Anselin Local Moran’s I cluster, and outlier analysis were also conducted to ascertain statistically significant spatial outliers. This output showed outliers in the distribution using permutations such as high-high, low-low, high-low, low-high, and not significant.

Results

Unskilled birth attendance by country

Of the 200,736 women included in the study, the majority (8%) were from Burundi and the minority (1%) were from Lesotho. Chad had the highest proportion (69%) of women who utilised the services of unskilled birth attendants whereas the least proportion (3%) were from South Africa. Overall, about one in four women (27%) in SSA utilised the services of an unskilled birth attendant during pregnancy or delivery (Table 1).

Table 1. Proportion of unskilled birth attendance in sub-Saharan Africa.

Country	Frequency	Percentage	Proportion of unskilled birth attendance
1. Angola 2015–2016	8,577	4.3	50.0
2. Burkina Faso 2010	10,255	5.1	26.2
3. Burundi 2016–2017	16,316	8.1	7.9
4. Cameroon 2018	6,288	3.1	26.4
5. Chad 2014–2015	3,652	1.8	68.9
6. Comoros 2012	1,824	0.9	15.1
7. Congo 2011–2012	6,305	3.1	12.0
8. Congo DR 2013–2014	10,963	5.5	20.4
9. Cote d’Ivoire 2011–2012	5,304	2.6	40.8
10. Ethiopia 2016	7,052	3.5	60.4
11. Gabon 2012	3,986	2.0	15.9
12. Gambia 2013	5,677	2.8	16.8
13. Ghana 2014	4,201	2.1	25.9
14. Guinea 2018	5,362	2.7	44.2
15. Kenya 2014	6,896	3.4	40.1
16. Lesotho 2014–2015	995	0.5	18.5
17. Liberia 2013	4,186	2.1	16.0
18. Malawi 2015–2016	12,544	6.3	7.4
19. Mali 2018	6,157	3.1	29.5
20. Mozambique 2011	7,349	3.7	36.2
21. Namibia 2013	3,802	1.9	11.8
22. Nigeria 2018	15,091	7.5	46.1
23. Rwanda 2014–2015	5,796	2.9	7.5
24. Sierra Leone 2019	7,257	3.6	12.5
25. South Africa 2016	1,438	0.7	3.3
26. Tanzania 2015–2016	6,822	3.4	32.8
27. Togo 2013–2014	4,942	2.5	44.6
28. Uganda 2016	9,844	4.9	22.9
29. Zambia 2013–2014	7,188	3.6	18.3
30. Zimbabwe 2015	4,667	2.3	15.3
All Countries	200,736	100	26.7

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Unskilled birth attendance by socio-demographic characteristics of respondents

Over 40 percent of respondents without education, from the poorest households, and those who had less than four antenatal visits utilised the services of an unskilled birth attendant. More than one-third of respondents without media exposure (36.7%), with four or more children (34.3%), with permission (35.7%), and who were distanced from a health facility (36.5%) utilised services from an unskilled birth attendant. Additionally, about one-third of rural residents utilised the services of unskilled birth attendants (Table 2).

Table 2. Unskilled birth attendance by socio-demographic characteristics.

Variables	Frequency (n = 200,736)	Percentage	Proportion of unskilled birth attendance
*Place of residence*
Urban	63,953	31.9	12.1
Rural	136,783	68.1	33.6
*Wealth status*
Poorest	48,435	24.1	43.7
Poorer	42,777	21.3	33.9
Middle	39,756	19.8	26.2
Richer	36,506	18.2	15.6
Richest	33,262	16.6	5.7
*Age*
Less than 20	15,121	7.5	25.2
20–29	96,268	48.0	25.1
30–49	89,347	44.5	28.8
*Level of education*
No education	69,532	34.7	43.3
Primary	72,093	35.9	24.5
Secondary	52,465	26.1	10.9
Higher	6,646	3.3	3.0
*Marital status*
Never in union	17,100	8.5	16.3
Married	131,305	65.4	29.1
Cohabitation	36,431	18.1	24.1
Widowed	3,141	1.6	31.9
Divorced	3,591	1.8	26.4
Separated	9,168	4.6	21.1
*Occupation*
Not working	52,906	26.4	27.4
Working	147,830	73.6	26.5
*Age at first birth*
Less than 20	118,463	59.0	30.5
20–29	78,561	39.1	21.6
30–49	3,712	1.9	16.2
*Birth order*
1	42,414	21.1	16.4
2–3	70,022	34.9	23.4
4 or more	88,300	44.0	34.3
*Mass media exposure*
No	78,388	39.1	36.7
Yes	122,348	60.9	20.4
**Getting medical help for self: permission to go**
Big problem	37,391	18.6	35.7
Not a big problem	163,345	81.4	24.7
**Getting medical help for self: getting money needed for treatment**
Big problem	111,763	55.7	31.7
Not a big problem	88,973	44.3	20.6
**Getting medical help for self: distance to health facility**
Big problem	82,583	41.1	36.5
Not a big problem	118,153	58.9	19.9
*Desire for birth*
Then	129,671	64.6	27.3
Later	12,237	6.1	31.8
No more	58,828	29.3	24.5
*Antenatal care visits*
Less than 4	85,250	42.5	40.5
4 or more	115,486	57.5	16.6
Under-five mortality
No	192,492	95.9	26.5
Yes	8,244	4.1	32.3

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Factors associated with unskilled birth attendance

The findings showed that place of residence, wealth status, age, level of education, marital status, occupation, age at first birth, birth order, mass media exposure, permission, getting the money needed for care, distance to a health facility, desire for birth, antenatal care visits, experience of death of an under-five child, and country of residence had a significant relationship with unskilled birth attendance (Table 3).

Table 3. Logistic regression of factors associated with unskilled birth attendance.

Variables	Model 1	Model 2
	Odds Ratio	Adjusted Odds Ratio
	(95% Confidence interval)	(95% Confidence interval)
*Place of residence*
Urban	Ref	Ref
Rural	3.67^***(3.58, 3.77)	1.90^***(1.83, 1.97)
*Wealth status*
Poorest	12.76^***(12.14, 13.40)	4.36^***(4.09, 4.65)
Poorer	8.42^***(8.01, 8.86)	3.17^***(2.98, 3.37)
Middle	5.83^***(5.54, 6.14)	2.49^***(2.34, 2.64)
Richer	3.03****(2.87, 3.19)	1.73^***(1.62, 1.84)
Richest	Ref	Ref
*Age*
Less than 20	1.01(0.97, 1.05)	1.11^***(1.05, 1.17)
20–29	Ref	Ref
30–49	1.21^***(1.18, 1.23)	0.92^***(0.89, 0.95)
*Level of education*
No education	2.36^***(2.31, 2.41)	1.57^***(1.52, 1.62)
Primary	Ref	Ref
Secondary	0.38^***(0.37, 0.39)	0.58^***(0.56, 0.61)
Higher	0.10^***(0.08, 0.11)	0.26^***(0.22, 0.30)
*Marital status*
Never in union	0.47^***(0.45, 0.49)	0.97(0.92, 1.03)
Married	Ref	Ref
Cohabitation	0.77^***(0.75, 0.79)	0.95^**(0.91, 0.98)
Widowed	1.14^**(1.06, 1.23)	1.14^**(1.04, 1.25)
Divorced	0.87^***(0.81, 0.94)	1.11^*(1.01, 1.22)
Separated	0.65^***(0.62, 0.68)	0.95(0.89, 1.01)
*Occupation*
Not working	1.04^***(1.02	1.03^*(1.00, 1.07)
Working	Ref	Ref
*Age at first birth*
Less than 20	Ref	Ref
20–29	0.63^***(0.62, 0.64)	0.90^***(0.87, 0.92)
30–49	0.44^***(0.40, 0.48)	0.81^***(0.72, 0.91)
*Birth order*
1	0.38^***(0.36, 0.39)	0.51^***(0.49, 0.54)
2–3	0.59^***(0.57, 0.60)	0.79^***(0.76, 0.82)
4 or more	Ref	Ref
*Mass media exposure*
No	2.26^***(2.21, 2.30)	1.25^***(1.22, 1.29)
Yes	Ref	Ref
**Getting medical help for self: permission to go**
Big problem	1.69^***(1.65, 1.73)	1.02(0.98, 1.05)
Not a big problem	Ref	Ref
**Getting medical help for self: getting money needed for treatment**
Big problem	1.79^***(1.76, 1.83)	1.05^***(1.02, 1.09)
Not a big problem	Ref	Ref
**Getting medical help for self: distance to health facility**
Big problem	2.31^***(2.26, 2.35)	1.49^***(1.45, 1.53)
Not a big problem	Ref	Ref
*Desire for birth*
Then	1.16^***(1.13, 1.18)	1,17^***(1.13, 1.20)
Later	1.44^***(1.38, 1.50)	1.11^***(1.06, 1.17)
No more	Ref	Ref
*Antenatal care visits*
Less than 4	3.41^***(3.34, 3.48)	2.61^***(2.54, 2.68)
4 or more	Ref	Ref
*Under five mortality*
No	Ref	Ref
Yes	1.32^***(1.26, 1.39)	1.10^***(1.04, 1.17)
*Country*
Angola 2015–2016	29.01^***(21.69, 38.80)	15.06^***(11.15, 20.34)
Burkina Faso 2010	9.8^***(7.36, 13.18)	2.00^***(1.48, 2.71)
Burundi 2016–2017	2.47^***(1.84, 3.32)	0.65^**(0.48, 0.88)
Cameroon 2018	10.36^***(7.73, 13.89)	4.77^***(3.52, 6.47)
Chad 2014–2015	64.14^***(47.70, 86.24)	15.62^***(11.47, 21.28)
Comoros 2012	5.14^***(3.75, 7.04)	1.68^***(1.21, 2.34)
Congo 2011–2012	3.96^***(2.94, 5.34)	1.37^***(1.01, 1.87)
Congo DR 2013–2014	7.41^***(5.53, 9.91)	2.02^***(1.50, 2.73)
Cote d’Ivoire 2011–2012	19.96^***(14.89, 26.75)	5.51^***(4.06, 7.46)
Ethiopia 2016	44.08^***(32.93, 59.01)	12.64^***(9.33, 17.11)
Gabon 2012	2.21^***(2.00, 2.45)	2.43^***(1.78, 3.29)
Gambia 2013	5.86^***(4.36, 7.38)	2.42^***(1.78, 3.29)
Ghana 2014	10.82^***(4.05, 7.38)	6.45^***(4.75, 8.76)
Guinea 2018	22.90^***(17.09, 30.69)	5.60^***(4.13, 7.59)
Kenya 2014	19.41^***(14.50, 25.98)	9.53^***(7.05, 12.88)
Lesotho 2014–2015	6.57^***(4.08, 9.13)	6.03^***(4.27, 8.52)
Liberia 2013	5.50^***(4.08, 7.42)	2.20^***(1.61, 2.99)
Malawi 2015–2016	2.32^***(1.73,3.12)	0.67^*(0.50, 0.91)
Mali 2018	12.10^***(9.02, 16.21)	3.18^***(2.34, 4.31)
Mozambique 2011	16.45^***(12.29, 22.02)	6.90^***(5.09, 9.33)
Namibia 2013	3.87^***(2.85, 5.24)	3.00^***(2.19, 4.10)
Nigeria 2018	24.74^***(18.52, 33.05)	14.48^***(10.73, 19.54)
Rwanda 2014–2015	2.36^***1.74, 3.20)	0.87(0.63, 1.19)
Sierra Leone 2019	4.15^***(3.08, 5.58)	1.49^***(1.10, 2.03)
South Africa 2016	Ref	Ref
Tanzania 2015–2016	14.11^***(10.53, 18.90)	5.56^***(4.11, 7.53)
Togo 2013–2014	23.35^***(17.42, 31.30)	8.69^***(6.41, 11.79)
Uganda 2016	8.61^***(6.44, 11.53)	3,41^***(2.52, 4.61)
Zambia 2013–2014	6.49^***(4.84, 8.71)	2.69^***(1.98, 3.64)
Zimbabwe 2015	5.22^***(3.87, 7.04)	3.84^***(2.82, 5.22)

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*P<0.05

**p<0.01

***p<0.001

Ref = Reference category; Mean vif = 2.01

A higher likelihood of utilising the services of unskilled birth attendants was observed among rural women (AOR = 1.90, CI = 1.83, 1.97) compared to urban women. Those with the poorest wealth status were more likely (AOR = 4.36, CI = 4.09, 4.65) to utilise the services of unskilled birth attendants compared to those with the richest wealth status. Women who had desired to have children later (OR = 1.17, CI = 1.13, 1.20) and those who had a big problem getting money needed for care (OR = 1.05, CI = 0.1.02, 1.09) had a higher likelihood of utilising the services of unskilled birth attendants compared to those who desired to have children then and those who did not have a big problem getting money needed for care (Table 3).

Our findings also showed that teenagers (less than 20 years) had a higher likelihood (AOR = 1.11, CI = 1.05, 1.17) of utilising the services of unskilled birth attendants during delivery compared to women aged 20–29 years. Women who had their first birth at age 30–49 years (AOR = 0.81, CI = 0.72, 0.91) were less likely to utilise the services of unskilled birth attendants compared to those who had their first birth when they were less than 20 years of age. Women with no formal education had higher odds (AOR = 1.57, CI = 1.52, 1.62) of utilising the services of unskilled birth attendants during delivery compared with those with primary education. Women who were not exposed to mass media had a higher likelihood (AOR = 1.25, CI = 1.22, 1.29) of utilising the services of unskilled birth attendants during delivery compared to those who were exposed to mass media (Table 3).

Furthermore, the women who saw distance to a healthcare facility as a big problem had a higher likelihood (AOR = 1.49, CI = 1.45, 1.53) of utilising the services of unskilled birth attendants during delivery compared to women who stated that distance to healthcare facilities was not a big problem. Women who had less than 4 antenatal care visits had higher odds (AOR = 1.10, CI = 1.04, 1.17) of utilising the services of unskilled birth attendants during delivery compared to women who had 4 or more antenatal care visits. Women who had experienced the demise of their under-five child were more likely (AOR = 1.10, CI = 1.04, 1.17) to utilise the services of unskilled birth attendants during delivery compared to those who had not experienced the death of their under-five child (Table 3).

Spatial distribution

Results from the spatial autocorrelation analysis (Fig 1) revealed that the spatial variations in unskilled birth attendance in sub-Saharan Africa (SSA) were random. Therefore, the alternate hypothesis which states that the spatial distribution of unskilled birth attendance is not randomised was not accepted. Thus, the occurrence of unskilled birth attendance in SSA is by chance.

Although the occurrence of unskilled birth attendance was found to be random, the Hotspot Analysis (Fig 2) showed that at a 90% confidence level, the occurrence of unskilled birth attendance was likely to be found in Chad compared to the other selected countries. This implies a higher tendency for women in Chad to deliver without a skilled birth attendant than in the other selected countries. Also, two countries were found to have coldspots at a 90% confidence level (Fig 2). These countries were South Africa and the Democratic Republic of Congo. Concerning all the selected countries for this study, the occurrence of unskilled birth attendance in South Africa and the Democratic Republic of Congo was found to be less likely.

Results from the cluster and outlier analysis (Fig 3) showed no high-high, high-low and low-high clusters. The cluster and outlier analysis showed that the Democratic Republic of Congo and Lesotho had low unskilled birth attendance and were surrounded by countries with low unskilled birth attendance. This is evident from the hotspot analysis presented in Fig 3.

Discussion

This study examined levels of unskilled birth attendance among women in sub-Saharan Africa (SSA) and the factors associated with the utilisation of such services. The study further examined hot and cold spot countries in SSA where women utilised pregnancy and child delivery services from unskilled birth attendants. Consistent with a previous study [17], we found that women in rural areas had a higher likelihood of utilising services of unskilled birth attendants compared to women in urban areas. This is probably because rural areas often have a relatively large presence of unskilled birth attendants and have a higher likelihood of supporting customary child delivery practices compared to urban areas, such as keeping or disposing of a mother’s placenta at a preferred location after delivery [20, 21].

Findings that women with the poorest wealth status as well as those who had a big problem getting money needed for care were more likely to utilise the services of unskilled birth attendants compared to those with the richest wealth status, as well as those who had no big problem getting money needed for care, demonstrate the important role played by financial capital in promoting the use of skilled birth attendants in SSA. In line with findings in a survey involving 80 low and middle-income countries across the world [22], our findings imply that some women utilised the services of unskilled birth attendants due to a lack of adequate capital to afford pregnancy and child delivery services from a skilled birth attendant. These findings show the need to establish or strengthen stimulus packages, such as free or low service costs in government facilities, particularly, for pregnant women in low-income settings [23]. However, measures should be taken to prevent such stimulus packages from becoming a motivation for proliferation in childbirth.

The findings further showed that teenage pregnancy (pregnancy among women below 20 years old) is a risk factor for the utilisation of services from unskilled birth attendants and this confirms a similar study indicating that early age at first childbirth is associated with low-skilled assistance during delivery [24]. Multiple reasons may account for this result including shame or stigma associated with teenage pregnancy in many African settings, financial constraints as many teenagers are in school and thus have no or low income to afford the services of a skilled birth attendant, and absence of partner support during pregnancy as teenagers are likely to be unmarried in their pregnancy [25–27].

Moreover, we found that women with low levels of education as well as those who were less exposed to media messages were more likely to utilise the services of unskilled birth attendants compared to highly educated women and those exposed to mass media. A possible reason for these findings is that educated women are likely to be more knowledgeable about the risks associated with utilising services of unskilled birth attendants and the mass media, particularly, the print media, is an important source of maternal health information for educated women compared with women who are less educated [28]. Additionally, educated women are more likely to be financially empowered than less educated women [29], which can enable them to afford skilled maternal healthcare services compared to less educated women. Our findings correspond with a study conducted in Nigeria [30] and indicate that promoting formal education among women in SSA is an important indirect approach to reducing the risk of child delivery supervised by an unskilled birth attendant in the region.

Concerning the issue of distance and clinical attendance, the findings showed that women who saw distance to a healthcare facility as a big problem as well as those who had less than 4 antenatal care visits had a higher likelihood of utilising the services of unskilled birth attendants during delivery compared to women who stated that distance to healthcare facilities was not a big problem as well as those who had more than 4 antenatal care visits. These findings are consistent with previous country-specific studies in SSA [17, 31–33]. It is important to be reminded that embedded in the problem of long distance to healthcare facilities are other possible challenges such as time and financial costs associated with utilising the services of skilled birth attendants in formal healthcare settings as well as lack of available transport to a healthcare facility, especially, in rural settings and during late hours of the night [31]. Together, these distance-related factors may have influenced the women to utilise the services of unskilled birth attendants in the region.

Our findings indicate that women who had ever experienced the demise of their under-five children and those who had desired to have children later were likely to utilise services from unskilled birth attendants. These results are quite difficult to explain due to a lack of adequate published papers on these specific variables. Nevertheless, the findings may imply that many women who had lost their under-five children in previous deliveries as well as those who were not desiring an immediate child had no alternatives to services from unskilled birth attendants due to some preceding factors highlighted in this study, such as long distance to healthcare facilities, low-income status, poor maternal education, among others.

Overall, the findings indicate the need to amplify education for women in SSA on the benefits of seeking professional care and suggest a call for improvements in the psychosocial interactions between healthcare professionals and women in SSA. They also show a reflection of outcomes from previous studies focused on skilled birth attendance among women in SSA [5, 13–16]. The gross disparities in unskilled birth attendance among the various characteristics of women in this present study suggest that socio-economic inequalities among women are strong determinants of their health. Many SSA countries have fragile healthcare systems with a lack of skilled birth attendants and aggravated by an increased cost of medical care, geographic inaccessibility of healthcare facilities, among others [34]. Hence, many socially disadvantaged women, such as those with no or low exposure to media advocacies on maternal health, low levels of education, low incomes, and living in rural areas, are likely to continue using the relatively cheaper and easily accessible services of unskilled birth attendants.

The outcomes from our geospatial analysis further suggest significant spatial variations in unskilled birth attendance among women in the thirty (30) SSA countries, but those variations did not occur by chance. This means that although the use of services from unskilled birth attendants during pregnancy and delivery in SSA is a holistic problem, the situation varies by country of residence. Notably, with an outstanding level of unskilled birth attendance (69%), Chad can be classified as a hotspot and a high-risk country for unskilled birth attendance compared to its neighbouring countries. A possible reason is that the country has a relatively high incidence of unskilled birth attendance compared to its neighbouring countries. On the other hand, South Africa (3%) can also be classified as a coldspot country where women predominantly seek maternal health care from skilled birth attendants. The findings indicate that even though the geospatial distributions of unskilled birth attendance were random, there appeared to be some notable geospatial variations in unskilled birth attendance among women in some parts of SSA ranging from hotspots to coldspots as observed by [17]. The government of Chad and concerned healthcare institutions in the country need to seriously consider developing urgent interventions to address the high rate of unskilled birth attendance among women in the country.

Strengths and limitations

This study is characterised by a few strengths. The geospatial aspect of the analysis provided a novel and deeper insight into the context of unskilled birth attendance in sub-Saharan Africa (SSA). To the best of our knowledge, this is also the first study on unskilled birth attendance that has employed nationally representative samples drawn from thirty (30) SSA countries. We used a large dataset comprising 200,736 women aged between 15–49 years which increased the statistical power of our analysis. Nevertheless, the study is constrained by some shortcomings. First, it is worth noting that many of the surveys were collected in different periods which may impact the findings due to possible changes in outcomes over time as many sub-Saharan African countries do not collect their DHS data in the same year and at the same period. Second, some sub-Saharan African countries were excluded from the study because they lacked data on the outcome variable. Third, the outcome was self-reported and there is a possibility of social desirability and recall bias in the responses as women from cultural orientations where utilisation of unskilled birth services is stigmatised may provide favourable responses to conform to the ethos of their cultural orientations. Lastly, contextual factors such as cultural norms and attitudes of health service providers would have been interesting to examine in this study, but such variables were not captured in the DHS dataset.

Implications for policy and practice

The multiple factors associated with unskilled birth attendance among the women in this study imply that there is a need for multi-faceted and local-specific policies and programmes in addressing the problem of unskilled birth attendance in SSA. Thus, a “one size fits all” approach may not be efficient in addressing unskilled birth attendance among women in the region. Some possibly useful strategies may include the provision of routine localised or rural health education for pregnant women about the importance of utilising the services of skilled birth attendants. Governments in SSA need to also make it a priority to extend healthcare facilities to rural areas to bridge the distance gap and improve the accessibility of skilled birth services for pregnant women in disadvantaged settings.

It is also important to recognise that although there is a general advocate for skilled birth attendance, there are some situations where service use from unskilled birth attendants may be non-negotiable. For instance, access to healthcare can be challenging in many conflict-affected countries or areas in protracted armed conflicts where healthcare workers have vacated their posts due to fear of persecution [3]. Some regions in SSA also have significant scarcities in skilled birth attendants making access to professional care limited [9]. As revealed in this study, long-distance to healthcare facilities, rural residency, and low-income levels can also deter women from accessing professional care during pregnancy and child delivery. In such circumstances, health policy advocates, local governments, and concerned healthcare institutions can collaborate to develop a system whereby unskilled birth attendants are well-trained and equipped with the needed modern equipment to facilitate optimal care for pregnant women. In recent times where telehealth and telemedicinal approaches are emerging as useful resources, the role played by unskilled birth attendants can be integrated into local healthcare systems and adequately monitored. A practical example is a situation in Timor-Leste where unskilled birth attendants were incorporated into a family health promoter programme which played a crucial role in delivering and increasing access to reproductive health services in rural communities of the nation. Although it required long-term dedication and effective collaborations, the current reduction in maternal mortality ratio in Timor-Leste is encouraging and serves to illustrate how such approaches can be useful [35].

Conclusions

This study has confirmed that many women in sub-Saharan Africa (SSA) continue to utilise the services of unskilled birth attendants during pregnancy and delivery. The study also concludes that women in rural areas, with low income, aged below 20 years, with no or low levels of education, less exposed to mass media information, distanced away from healthcare facilities, and with low levels of antennal attendance (<4 hospitals or clinic attendance) are likely to utilise the services of unskilled birth attendants. These findings suggest that multiple interventions focused on these contextual factors are needed in SSA. We propose an integrated approach where skilled birth attendants are prioritised, but in situations where there are shortages of skilled birth attendants, unskilled birth attendants can be trained, licensed, and monitored to save the lives of pregnant women. Extending maternal education and healthcare facilities or centres to rural areas should be a priority for managers of healthcare systems in SSA. Intensifying education for pregnant women about the importance of using skilled or professional healthcare during pregnancy, delivery, and post-delivery periods is also needed. Future studies can enhance a deeper understanding of these results by exploring our findings from qualitative perspectives.

Acknowledgments

We acknowledge Measure DHS for providing us with the data upon which the findings of this study were based.

Data Availability

Al relevant data are located at https://dhsprogram.com/data/available-datasets.cfm.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0280992.r001

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PONE-D-21-36273Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveysPLOS ONE

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Reviewer #1: The manuscript entitled “Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveys” was reviewed carefully. There are some major and minor issues that should be considered.

Abstract: please consider to limit the abstract to 250 words. Background section is longer than other parts. The result section of abstract is not supported by Effect size. Please revise this section.

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Line 199-206: It is suggested to re-analysis the variables (making variables reverse) to have risk factor instead of preventive factor for better interpretation and understanding of readers.

Please indicate the confounders which were adjusted in this model.

Line236: unlike other analysis , authors selected 90% confidence interval for this analysis. Please give some reasons for this discrepancy.

Tables:

Please indicate percentage with 95% CI.

Discussion:

Line 2: 254-262 : the structure of scientific writing is not suitable for this part and is much more related to introduction. Please consider that the first paragraph of discussion should answer the research question of the introduction based on the eye-catching results of this study. Please revise.

The main key home massage of this study which makes this distinguish from other local mentioned published articles that authors had mentioned in introduction is not crystal clear in this manuscript. Please discuss it with more prominent evidence.

Reviewer #2: I found the paper good. The finding is supported by the analysis technique. It covers large geographical areas and it generates valuable information for public health authorities in the study areas. There are some grammatical errors and editorial issues that are highlighted yellow in the document. However, there is similar topic done in Ghana by similar authors and I have raised a question regarding this issue in my comments and suggestions that I will attach to you.

**********

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Reviewer #2: No

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PLoS One. 2023 Feb 2;18(2):e0280992. doi: 10.1371/journal.pone.0280992.r002

Author response to Decision Letter 0

12 Sep 2022

PONE-D-21-36273

Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveys

PLOS ONE

Dear Prof. Tsegaye Lolaso Lenjebo,

We are grateful to you and the reviewers for your comments on our paper entitled " Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveys". We would also take this opportunity to thank the reviewers for finding merit in this paper and suggesting that it can be suitable for publication in your renowned journal if we incorporate some major revisions. We have taken notice of all the comments raised by the reviewers and have responded accordingly as follows. Please be informed that the reviewers' comments are in black texts whereas our responses are in red texts.

Responses to the Editor’s Comments

3. Please include a separate caption for each figure in your manuscript.

Response: Thank you. We have included a separate caption for each figure as recommended.

Response: Thank you. We have included the tables as part of the main manuscript and removed the individual files as recommended

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:

a. You may seek permission from the original copyright holder of Figures 2 & 3 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

Response: We can confirm that Figures 1-3 were created or constructed by the authors based on the study data using ArcMap version 10.5. Permission from a copyright holder is not applicable in this context. We have, therefore, provided a source in the manuscript showing that the Figures were self-created by the authors.

Responses to the Reviewers' comments:

Response: Thank you. We have addressed the concerns as follows.

Abstract: please consider to limit the abstract to 250 words. Background section is longer than other parts.

Response: Thank you. We have reduced the abstract to 227 words and have cut down the background section as recommended.

The result section of abstract is not supported by Effect size. Please revise this section.

Response: Thank you. This section has been revised.

Method :

Please indicate confounders that the model was adjusted for.

Response: Thank you. The section under the analytical procedure has been revised as recommended.

Line 153-154: both lines are not essential. H1 is enough if authors insist in mentioning it in this section.

Response

Thank you for the comment. H0 has been deleted accordingly.

Result:

Line 171: It is not common to start the results with tables at first. It is suggested to revise this line.

Response

The entire sentence has been deleted. Thank you for your observation

Line 192-193: same as above.

Response

The entire sentence has been deleted. Thank you for your observation

Line 192-193: same as above.

Response The entire sentence has been deleted. Thank you for your observation

Line 199-206: It is suggested to re-analysis the variables (making variables reverse) to have risk factor instead of preventive factor for better interpretation and understanding of readers.

Response: Thank you. This section has been re-analysed and revised as suggested.

Please indicate the confounders which were adjusted in this model.

Response Thank you. This has been added. Please see the “Analytical procedure” section under the Methods.

Line 236: unlike other analysis, authors selected 90% confidence interval for this analysis. Please give some reasons for this discrepancy.

Response: Thank you for this observation. The tool used for running the Getis Ord G spatial analysis generates a seven-level categorised output based on confidence level. These seven-level categorised outputs are segmented into three, coldspot (99%, 95% and 90%), random and hotspot (99%, 95% and 90%). Our output automatically revealed that the distribution of unskilled birth attendance is spatially and significantly clustered at 90% confidence level.

Unlike the other models where we had the liberty to select 95% confidence interval levels, we did not have control over the confidence interval for the geospatial analysis as the software automatically selected the 90% confidence interval based on the level of significance within the spatial distribution clusters.

Tables:

Please indicate percentage with 95% CI.

Response: Please be informed that the Footnotes in Table 3 show the meaning of the * in the table. Thus, *p<0.05 equals 95% CI

Discussion:

Response Thank you. The structure has been revised as recommended

Response Thank you. The Discussion section of the manuscript has been revised to show the key variables and the take-home message.

Response: Thank you for this positive feedback.

There are some grammatical errors and editorial issues that are highlighted yellow in the document.

Response: Thank you for the highlights. The issues have been resolved.

However, there is similar topic done in Ghana by similar authors and I have raised a question regarding this issue in my comments and suggestions that I will attach to you.

Response: Thank you. We have explained this in the main comment.

Comments and suggestions

Line-38: “older current age and age at birth”

• The association direction for age at birth is not specified.

• Does the word “older” applies for both or not? If it applies for both then better if you write separately as older current age, older age at birth …

Response Thank you for the observation. The results section of the abstract has been revised.

Line 38 and 39:

• The two variables; rich wealth status Vs not having money problems for healthcare

• Both are complementary to each other. So, I recommend to select and use one. Wealth status is better.

• Response Thank you for the observation. The results section of the abstract has been revised.

Line 43:

• Why Burundi?

• Lowest proportion is from South Africa (SA) (less exposed to the outcome; meaning less proportion of the outcome is indicated from the descriptive analysis. So, I recommend to make SA as reference)

• Then what is the implication behind the result? All counties have higher odds?

Response: Thank you. The analysis has been redone and South Africa has been made the reference category. The section has been revised.

Line 78-79:

• Add something about the spatial pattern of the issue in addition to what you stated.

Response: Thank you. An additional sentence has been included to show that there are limited studies utilising spatial analysis to explore the distribution of unskilled birth attendance in SSA.

Line 93:

• The word “Explored” is not the appropriate action verb for such type of quantitative analysis. Better to use “identified” or “examined” …

Response: Thank you for this observation. The word “explored” has been replaced with “examined”

Line 95:

• The same comment as line 93 (Exploring???)

Response. Thank you. The word “Exploring” has been replaced with “Examining”

• Line 101:

• Could it be different for countries? If it the year is different how you handle the analysis? How do you control the effect of time? So, it is good if you make DHS data sets which are produced in the same year across different countries. Otherwise, the result is biased due to time effect or make it time series analysis?

• Response: Thank you for this important observation. We agree with the reviewer that the differences in survey years may affect the results due to possible effect of time. However, DHS data for sub-Saharan African countries are commonly collected in different years and at different periods. This has been a notable limitation of most studies using pooled DHS data of various countries. As a result, we have added this as one of the limitations of the study.

Line 180-187:

• The description is not as such important as far as these variables are included in the regression model. The proportion (percentage) here is crude and it doesn’t add any value. So, delete it and try to include all variables into the model and report their adjusted effect on the outcome.

Response: Thank you for this comment. While we appreciate the reviewer’s suggestion, in this instance, we plead to disagree. The description of participants is commonly provided in this type of public health research to give readers information on the number of respondents in each variable category. It also sets the basis for interpreting the results (particularly, in the model) and helps in identifying possible limitations induced by small samples in the variable categories.

Line 194:

• Make is showed i.e.) add “ed”

Response: This has been rectified. Thank you

Line 202:

• Delete (Table 3).; as it is reported at the end of the paragraph.

Response The expression “Table 3” has been deleted in the line as suggested. Thank you.

Line 213:

• Delete the word “see” as it is not important (be consistent).

Response. Thank you. The word “see” has been deleted as recommended.

Line 222:

• A dd (Table 3) immediately after the word child.

Response: Thank you. The expression “Table 3” has been added as suggested.

Line 254-258:

• The paragraph you stated as a starting point and discussion is not directly addressed through this analysis. So, I recommend to write based on the study objectives. This study didn’t address the effect of unskilled birth attendance on maternal death though it is addressed by other studies. So, try to focus on the study objectives. This study has nothing related to this issue.

• Start with your main finding and then try to compare to others studies and policy implications

• The proportion of the outcome is not discussed well. so, discuss the percentage in relation to expected proportion based on global initiatives

Response: Thank you. We acknowledge this, have deleted the part not related to the analysis and have revised the Discussion section.

Line 263-265

• Delete it; what is the important? Or make it short (paraphrase it).

Response: Thank you. This has been done.

• Line 266-280:

• It is good you have stated some important statement for discussion. But these significant variables must be discussed separately with their corresponding justification. E.g.) why younger women higher odds of utilizing the service? Labor and child birth experience Vs Woman’s age and then Vs unskilled birth attendance?

• So, the same is true for other variables found to be significant in the final model need to be discussed.

Response: Thank you. The discussion has been revised as recommended.

Line 283:

• The use of conjunction (“Interestingly,”) is not correct for under five demise Vs Unskilled birth attendance.

• The reasoning stated in the document is not convincing. So, try to make it informative and convincing? Imogie (it is single study) and it is difficult to compare this study where it is the analysis many countries with a single study in Nigeria?

Response

● The word interestingly has been deleted. The discussion has been revised as recommended. Thank you for the observation.

• Line 281-296:

• The reasoning or justification need to be modified.

Response: Thank you. The discussion has been revised as recommended.

Line 297-307:

• What could be the reason for hotspot for some countries and coldspot for some countries? Justification if you can?

Response: The probable cause of hotspot could be due to the high incidence of unskilled birth attendance among neighbouring countries and vice versa. A further explanation has now been included in the manuscript.

• Line 309-316: regarding the strength

• Statements about strength of the study need to be paraphrased. Please come to the point in short (the only strength could be using large sample size; nothing else). Using logistic model can not be a strength. The spatial can be a strength.

Response: The strength subsection has been revised as recommended. Thank you

Line 316-327:

• Please try to state your own study or analysis limitation.

• And then what are the potential limitation that you will share with DHS protocol.

• Being cross-sectional can’t be a limitation for your analysis. (Causal analysis is not your plan/objective)

• Still this subsection needs to paraphrased.

Response: This subsection has been revised as recommended by the reviewer.

Line 357: delete the “.” Before the reference

Response: Thank you. This has been done.

� The points stated under “Implications for policy and practice” can be written under discussion and conclusion based on the variables you discussed. Under discussion you can finalize with its public health policy and implementation implications

� Response Thank you for this suggestion. While we appreciate the reviewer’s suggestion, on this occasion, we plead to maintain the section on “Implications for policy and practice” as that section deals with high-level implications that are not specific to a particular variable.

🡺 Similar topic is done in Ghana by similar author; so, why you include Ghana in this analysis? What makes different from that study? What is new?

🡺 Response: While we acknowledge that a similar topic has been done for Ghana please be informed that only one out of the five current authors was part of the previous study conducted in Ghana. More importantly, we included Ghana in the analysis to enable us to directly compare the previous findings on Ghana (particularly the geospatial elements) to the remaining 29 Sub-Saharan African countries included in this study. In addition, the previous study on Ghana did not include the following important variables captured in our study: age at first birth, marital status, getting medical help for self: permission to go, getting medical help for self: getting money needed for treatment, desire for children, antenatal care visits, and under-five mortality.

� The key legends for figure 2 and 3 Vs the colours in the figure are not comparable and this hide the information that can be captured from the figures.

� Response: Thank you. Although the two figures may be comparing the distribution of unskilled birth attendance, the colours can be different since they are analysed by two different tools. Also, using the same colours for the two would not be possible because the colour grading used in figure 2 shows magnitude whereas that of figure 3 does not.

Thank you.

Attachment

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.^{(32.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0280992.r003

Decision Letter 1

Kannan Navaneetham

31 Oct 2022

PONE-D-21-36273R1Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveysPLOS ONE

Dear Dr. Dickson,

Please submit your revised manuscript by Dec 15 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Kannan Navaneetham, PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

PLoS One. 2023 Feb 2;18(2):e0280992. doi: 10.1371/journal.pone.0280992.r004

Author response to Decision Letter 1

3 Dec 2022

PONE-D-21-36273

Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveys

PLOS ONE

Dear Prof. Tsegaye Lolaso Lenjebo,

Responses to the Editor’s Comments

3. Please include a separate caption for each figure in your manuscript.

Response: Thank you. We have included a separate caption for each figure as recommended.

Response: Thank you. We have included the tables as part of the main manuscript and removed the individual files as recommended

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:

a. You may seek permission from the original copyright holder of Figures 2 & 3 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

Responses to the Reviewers' comments:

Response: Thank you. We have addressed the concerns as follows.

Abstract: please consider to limit the abstract to 250 words. Background section is longer than other parts.

Response: Thank you. We have reduced the abstract to 227 words and have cut down the background section as recommended.

The result section of abstract is not supported by Effect size. Please revise this section.

Response: Thank you. This section has been revised.

Method :

Please indicate confounders that the model was adjusted for.

Response: Thank you. The section under the analytical procedure has been revised as recommended.

Line 153-154: both lines are not essential. H1 is enough if authors insist in mentioning it in this section.

Response

Thank you for the comment. H0 has been deleted accordingly.

Result:

Line 171: It is not common to start the results with tables at first. It is suggested to revise this line.

Response

The entire sentence has been deleted. Thank you for your observation

Line 192-193: same as above.

Response

The entire sentence has been deleted. Thank you for your observation

Line 192-193: same as above.

Response The entire sentence has been deleted. Thank you for your observation

Line 199-206: It is suggested to re-analysis the variables (making variables reverse) to have risk factor instead of preventive factor for better interpretation and understanding of readers.

Response: Thank you. This section has been re-analysed and revised as suggested.

Please indicate the confounders which were adjusted in this model.

Response Thank you. This has been added. Please see the “Analytical procedure” section under the Methods.

Line 236: unlike other analysis, authors selected 90% confidence interval for this analysis. Please give some reasons for this discrepancy.

Tables:

Please indicate percentage with 95% CI.

Response: Please be informed that the Footnotes in Table 3 show the meaning of the * in the table. Thus, *p<0.05 equals 95% CI

Discussion:

Response Thank you. The structure has been revised as recommended

Response Thank you. The Discussion section of the manuscript has been revised to show the key variables and the take-home message.

Response: Thank you for this positive feedback.

There are some grammatical errors and editorial issues that are highlighted yellow in the document.

Response: Thank you for the highlights. The issues have been resolved.

However, there is similar topic done in Ghana by similar authors and I have raised a question regarding this issue in my comments and suggestions that I will attach to you.

Response: Thank you. We have explained this in the main comment.

Comments and suggestions

Line-38: “older current age and age at birth”

• The association direction for age at birth is not specified.

• Does the word “older” applies for both or not? If it applies for both then better if you write separately as older current age, older age at birth …

Response Thank you for the observation. The results section of the abstract has been revised.

Line 38 and 39:

• The two variables; rich wealth status Vs not having money problems for healthcare

• Both are complementary to each other. So, I recommend to select and use one. Wealth status is better.

• Response Thank you for the observation. The results section of the abstract has been revised.

Line 43:

• Why Burundi?

• Then what is the implication behind the result? All counties have higher odds?

Response: Thank you. The analysis has been redone and South Africa has been made the reference category. The section has been revised.

Line 78-79:

• Add something about the spatial pattern of the issue in addition to what you stated.

Response: Thank you. An additional sentence has been included to show that there are limited studies utilising spatial analysis to explore the distribution of unskilled birth attendance in SSA.

Line 93:

• The word “Explored” is not the appropriate action verb for such type of quantitative analysis. Better to use “identified” or “examined” …

Response: Thank you for this observation. The word “explored” has been replaced with “examined”

Line 95:

• The same comment as line 93 (Exploring???)

Response. Thank you. The word “Exploring” has been replaced with “Examining”

• Line 101:

Line 180-187:

Line 194:

• Make is showed i.e.) add “ed”

Response: This has been rectified. Thank you

Line 202:

• Delete (Table 3).; as it is reported at the end of the paragraph.

Response The expression “Table 3” has been deleted in the line as suggested. Thank you.

Line 213:

• Delete the word “see” as it is not important (be consistent).

Response. Thank you. The word “see” has been deleted as recommended.

Line 222:

• A dd (Table 3) immediately after the word child.

Response: Thank you. The expression “Table 3” has been added as suggested.

Line 254-258:

• Start with your main finding and then try to compare to others studies and policy implications

• The proportion of the outcome is not discussed well. so, discuss the percentage in relation to expected proportion based on global initiatives

Response: Thank you. We acknowledge this, have deleted the part not related to the analysis and have revised the Discussion section.

Line 263-265

• Delete it; what is the important? Or make it short (paraphrase it).

Response: Thank you. This has been done.

• Line 266-280:

• So, the same is true for other variables found to be significant in the final model need to be discussed.

Response: Thank you. The discussion has been revised as recommended.

Line 283:

• The use of conjunction (“Interestingly,”) is not correct for under five demise Vs Unskilled birth attendance.

Response

● The word interestingly has been deleted. The discussion has been revised as recommended. Thank you for the observation.

• Line 281-296:

• The reasoning or justification need to be modified.

Response: Thank you. The discussion has been revised as recommended.

Line 297-307:

• What could be the reason for hotspot for some countries and coldspot for some countries? Justification if you can?

• Line 309-316: regarding the strength

Response: The strength subsection has been revised as recommended. Thank you

Line 316-327:

• Please try to state your own study or analysis limitation.

• And then what are the potential limitation that you will share with DHS protocol.

• Being cross-sectional can’t be a limitation for your analysis. (Causal analysis is not your plan/objective)

• Still this subsection needs to paraphrased.

Response: This subsection has been revised as recommended by the reviewer.

Line 357: delete the “.” Before the reference

Response: Thank you. This has been done.

🡺 Similar topic is done in Ghana by similar author; so, why you include Ghana in this analysis? What makes different from that study? What is new?

� The key legends for figure 2 and 3 Vs the colours in the figure are not comparable and this hide the information that can be captured from the figures.

Thank you.

Attachment

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.^{(32.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0280992.r005

Decision Letter 2

Kannan Navaneetham

13 Jan 2023

Factors associated with unskilled birth attendance among women in Sub-Saharan Africa: a multilevel geospatial analysis of demographic and health surveys

PONE-D-21-36273R2

Dear Dr. Dickson,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Kannan Navaneetham, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0280992.r006

Acceptance letter

Kannan Navaneetham

24 Jan 2023

PONE-D-21-36273R2

Factors associated with unskilled birth attendance among women in sub-Saharan Africa: a multivariate-geospatial analysis of demographic and health surveys

Dear Dr. Dickson:

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

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Kannan Navaneetham

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: PONE-D-21-36273 #Comments and suggestions.docx

Click here for additional data file.^{(20.7KB, docx)}

Attachment

Submitted filename: PONE-D-21-36273.pdf

Click here for additional data file.^{(1.2MB, pdf)}

Attachment

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.^{(32.3KB, docx)}

Attachment

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.^{(32.3KB, docx)}

Data Availability Statement

Al relevant data are located at https://dhsprogram.com/data/available-datasets.cfm.

[pone.0280992.ref001] 1.World Health O. Traditional birth attendants: a joint WHO/UNFPA/UNICEF statement: World Health Organization; 1992. [Google Scholar]

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PERMALINK

Factors associated with unskilled birth attendance among women in sub-Saharan Africa: A multivariate-geospatial analysis of demographic and health surveys

Isaac Yeboah Addo

Evelyn Acquah

Samuel H Nyarko

Ebenezer N K Boateng

Kwamena Sekyi Dickson

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Data source

Study variables and measurements

Analytical procedure

Geospatial analysis

Results

Unskilled birth attendance by country

Table 1. Proportion of unskilled birth attendance in sub-Saharan Africa.

Unskilled birth attendance by socio-demographic characteristics of respondents

Table 2. Unskilled birth attendance by socio-demographic characteristics.

Factors associated with unskilled birth attendance

Table 3. Logistic regression of factors associated with unskilled birth attendance.

Spatial distribution

Fig 1. Spatial autocorrelation analysis of unskilled birth attendance in sub-Saharan Africa.

Fig 2. Hotspot analysis of unskilled birth attendance in sub-Saharan Africa.

Fig 3. Cluster and outlier analysis of unskilled birth attendance in sub-Saharan Africa.

Discussion

Strengths and limitations

Implications for policy and practice

Conclusions

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Tsegaye Lolaso Lenjebo

Roles

Author response to Decision Letter 0

Decision Letter 1

Kannan Navaneetham

Roles

Author response to Decision Letter 1

Decision Letter 2

Kannan Navaneetham

Roles

Acceptance letter

Kannan Navaneetham

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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