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International Internal Medicine Journal(IIMJ)

ISSN: 2837-4835 | DOI: 10.33140/IIMJ

Impact Factor: 1.02

Research Article - (2026) Volume 4, Issue 1

The Determinants of Postnatal Health Check Providers in Sierra Leone: Insight from the 2019 SLDHS

Philomene Nsengiyumva * and Palesa Patience Moloi
 
Department of Statistics & Population Studies, Faculty of Natural Sciences, University of the Western Cape, Robert Sobukwe Road, Cape Town, South Africa
 
*Corresponding Author: Philomene Nsengiyumva, Department of Statistics & Population Studies, Faculty of Natural Sciences, South Africa

Received Date: Apr 01, 2026 / Accepted Date: Apr 28, 2026 / Published Date: May 20, 2026

Copyright: ©2026 Philomene Nsengiyumva, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source

Citation: Nsengiyumva, P., Moloi, P. P. (2026). The Determinants of Postnatal Health Check Providers in Sierra Leone: Insight from the 2019 SLDHS. Int Internal Med J, 4(1), 01-11.

Abstract

Introduction: Maternal and neonatal outcomes are critically influenced by the quality of postnatal care. Despite efforts to improve maternal healthcare utilization in Sierra Leone, significant disparities persist in access to professional postnatal care. This study examines the factors associated with the type of healthcare providers performing postnatal check-ups among mothers.

Methods: This study used data from the 2019 Sierra Leone Demographic and Health Survey (SLDHS). Of the 7323 women who gave birth in the five years preceding the survey, 3403 women aged between 15 and 49 years provided information on whether their postnatal check-ups were conducted by a doctor, nurse/midwife, auxiliary midwife, traditional birth attendant, community/village health worker, or another provider. Descriptive statistics, Chi-square test and multinomial logistic regression techniques were used to analyse the data by using the IBM SPSS version 29 software platform.

Chi-square tests revealed that region (χ2=169.349, p=0.001); place of residence (χ2=90.723, p=0.001); level of education (χ2=29.253, p=0.0253); wealth index (χ2=107.884, p=0.001); working status (χ2=23.667, p=0.001); and birth order (χ2=19.022, p=0.04) were associated with postnatal health providers. Multinomial logistic regression analysis revealed that age (women aged 30-34, OR=1.491; 35-39, OR=1.566; 40-44, OR=1.585), region of residence (Eastern, OR=2.833; Northern, OR=3.512; North Western, OR=2.269; Southern, OR=2.842), place of residence (urban, OR=1.209), wealth index (poorest, OR=1.466; poorer, OR=1.527; middle, OR=1.788), place of delivery (government health centre, OR=1.371; private hospital/clinic, OR=3.952), marital status (living with a partner, OR=1.991) and birth order (0-4, OR=0.687; 5-9, OR=0.547) influenced postnatal health check providers in Sierra Leone. Conversely, determinants for non-professional care showed inverse trends.

Conclusion: These findings highlight significant socioeconomic and geographical inequities in postnatal care access, underscoring the need for targeted interventions to improve healthcare infrastructure, promote facility-based deliveries, and enhance maternal health education.

Keywords

Sierra Leone Demographic and Health Survey, Postnatal Health Check, Type of Postnatal Check Provider, Socioeconomic Determinant

Introduction

Postnatal healthcare is a critical component of maternal and neonatal well-being, particularly in low-resource settings where maternal mortality rates remain high. The postpartum period is widely recognized as one of the most vulnerable times for both mothers and newborns, yet access to skilled postnatal care remains inadequate in many African countries, including Sierra Leone. World Health Organization (WHO) (2014) recommends that all women receive a postnatal health check within the first 48 hours after delivery, as most maternal deaths occur in the immediate postpartum period due to preventable complications, such as infections, postpartum haemorrhage, and hypertensive disorders [1]. However, studies show that in many low-income countries, less than 50% of women receive any form of postnatal care from a skilled provider [2].

Moreover, Sierra Leone continues to face significant challenges in maternal healthcare, with one of the highest maternal mortality rates globally, estimated at 1.120 deaths per 100000 live births [3]. Hence, neonatal mortality remains a critical issue as well, with a rate of 31 deaths per 1000 live births, primarily due to complications arising from inadequate maternal healthcare [4]. Previous research highlights that sociodemographic factors such as educational attainment, wealth status, and place of residence play a significant role in determining whether a woman receives postnatal care [5]. Studies in sub-Saharan Africa have consistently found that women with higher education levels are two to three times more likely to seek professional maternal healthcare services than those with no formal education [6].

Economic disparities further exacerbate the issue. Research by Langlois et al. (2015) found that women from wealthier households are significantly more likely to receive skilled postnatal care compared to those in lower wealth quintiles [7]. Similarly, geographic disparities persist, as rural women are much less likely to access healthcare facilities due to long travel distances, high transportation costs, and limited health infrastructure [8]. In Sierra Leone, only 55% of rural women receive postnatal health check¬ups compared to 75% of urban women, highlighting the urban-rural divide in healthcare access [9].

The availability of skilled healthcare professionals also remains a major barrier. WHO (2014) recommends a minimum of 44.5 doctors, nurses, and midwives per 10000 people [1]. However, Sierra Leone falls drastically short, with only 1.4 healthcare professionals per 10000 people [10]. This shortage forces many women, particularly in rural areas, to rely on Traditional Birth Attendants (TBAs) who may not be adequately trained to handle postnatal complications [11]. While TBAs are often trusted within communities, studies show that births attended by skilled healthcare professionals result in lower maternal and neonatal mortality rates [12].

Health crises, such as the 2014-2016 Ebola epidemic, further weakened maternal healthcare utilization in Sierra Leone, affecting not only antenatal care and facility-based deliveries but also postnatal health checks. Jones et al. (2016) reported a 22% decline in antenatal care visits and a 23% reduction in facility-based deliveries during the epidemic, which contributed to a reduction in the availability of skilled providers performing postnatal checks [13]. The epidemic resulted in a severe shortage of healthcare staff, disrupting the continuity of professional postnatal care and forcing many mothers, especially in rural areas, to rely on less trained providers such as TBAs. Even after the epidemic, maternal healthcare utilization remained low due to lingering distrust in health facilities, which extended to postnatal care services [14].

Given these disparities, it is crucial to understand the factors influencing postnatal healthcare utilization in Sierra Leone. This study builds on previous research by examining the association between mothers’ sociodemographic and socioeconomic characteristics and the type of healthcare providers performing postnatal health checks. By identifying key determinants, the study aims to provide insights that can inform targeted interventions to improve postnatal healthcare access and reduce maternal and neonatal mortality.

Data and Methods

This study utilized a cross-sectional research design and employed secondary data from the 2019 Sierra Leone Demographic and Health Survey (SLDHS). The SLDSH, conducted by Statistics Sierra Leone with technical assistance from the Demographic and Health Surveys (DHS) Program, provides nationally representative data on various health indicators, including maternal and child health. The 2019 SLDHS data set is widely used for analysing healthcare utilization patterns and serves as a critical source for examining the determinants of postnatal care access in Sierra Leone [4,15].

The study population comprises women in the reproductive age range from 15 to 49 years who had given birth within the five years preceding the survey. The SLDSH employed a stratified two-stage cluster sampling approach to ensure national representativeness. In the first stage, enumeration areas (EAs) were selected using probability proportional to size (PPS) to ensure adequate coverage across Sierra Leone’s administrative regions. In the second stage, households were systematically selected from these EAs, and eligible women were interviewed. The final sample consists of 7323 women, allowing for robust statistical analysis of postnatal healthcare utilization [1,8].

Variables of Interest

The primary outcome variable in this study is the type of healthcare professionals performing the postnatal health check, categorized as either a skilled healthcare provider, such as a doctor, nurse, or midwife – or a non-professional healthcare provider, which include TBAs, and community or village health workers.

The independent variables include maternal age, region of residence, place of residence (urban or rural), level of education, household wealth index, marital status, working status, place of delivery (home or health facility), and birth order, or total children ever born (TCEB). These factors have been widely recognized in prior research as key determinants of maternal healthcare utilization, particularly in low-resource settings [16,17].

Data Analysis

The data analysis followed a structured approach, beginning with descriptive statistics to summarize the characteristics of the study population. Bivariate analysis, which includes Chi-square tests, was conducted to assess associations between the independent variables and the likelihood of receiving postnatal care from a skilled provider. To further examine these relationships, multinomial logistic regression was used to determine the odds of receiving postnatal care from a skilled professional based on sociodemographic factors. Statistical significance level (alpha) was assessed with a p-value of equal to or less than 0.05, which is indicative of a meaningful association. All analyses were conducted using the IBM SPSS version 29.0 software platform to ensure statistical accuracy and reliability.

Ethical Consideration

Given that the study utilizes publicly available secondary data, no ethical clearance was required for this analysis [4]. Ethical approval for the SLDSH was granted by the Sierra Leone Ethics and Scientific Review Committee and the DHS Program. The Sierra Leone Demographic and Health Survey obtained informed consent from all participants before data collection, and all personal identifiers were removed to ensure confidentiality.

Results

Characteristics of the Respondents

Respondents were women in the age range of 15 to 49 years, with majority of women being in the age group of 25-29 (25.4%, n=863), while women in 45-49 age group represented the smallest share (3.1%, n=105). The sample was evenly distributed across the various regions, but were the lowest in the Eastern (11.2%, n=380) and Western (14%, n=475) regions. The wealth status of women was skewed towards lower socioeconomic status: (21.9%, n=745) of respondents were classified as the poorest, (24%, n=818) as poorer, while only (11.6%, n=396) women were classified as the richest. In terms of education, majority of women in Sierra Leone (56.2%, n=1913) had no formal education, while a smaller proportion (2.7%, n=93) had higher education, illustrating a stark educational divide.

Most respondents lived in rural areas (69%, n=2349) compared to urban areas (31%, n=1054), a factor that is critical for understanding access to healthcare. Women in the sample were predominantly married (78.5%, n=2673). A high percentage of women (75.5%, n=2670) were employed at the time of the survey, reflecting the economic demands in the context of rural and informal work environments. The remaining (24.5%, n=733) of mothers were unemployed.

Regarding postnatal care, most of postnatal check-ups (83.8%, n=2853) were conducted by nurses and midwives, while only (3.4%, n=115) of mothers received care from a doctor, indicating limited access to highly trained providers. Traditional birth attendants (TBAs) attended to (9.0%, n=307) of the mothers, while auxiliary midwives and community health workers accounted for (1.9%, n=65) and (1.6%, n=55), respectively.

Age

n

%

Type of place residence

n

%

Currently working

n

%

15-19

278

8.2

Urban

1054

31

No

733

24.5

20-24

734

21.6

Rural

2349

69

Yes

2670

75.5

25-29

863

25.4

Place of delivery

n

%

Wealth index

30-34

604

17.7

Mother’s home

555

16.3

Poorest

745

21.9

35-39

603

17.7

Other home

213

6.3

Poorer

818

24.0

40-44

213

6.3

Government hospital

939

27.6

Middle

764

22.5

45-49

105

3.1

Government health centre

1218

35.8

Richer

680

20.0

Region

n

%

Government health post

410

12.0

Richest

396

11.6

Eastern

380

11.2

Other public sector

1

0.0

Birth order

n

%

Northern

891

26.2

Private hospital/clinic

58

1.7

0-4 children

2530

74.3

North Western

831

24.4

Other

9

0.3

5-9 children

844

24.8

Southern

826

24.3

 

 

 

10-14 children

29

0.9

Western

475

14.0

 

 

 

 

 

 

Educational attainment

n

%

 

 

 

Postnatal check provider

n

%

No education

1913

56.2

 

 

 

Doctor

115

3.4

Incomplete primary

339

10.0

 

 

 

Nurse, midwife

2853

83.8

Complete primary

112

3.3

 

 

 

Auxiliary midwife

65

1.9

Incomplete secondary

800

23.5

 

 

 

Traditional birth attendant

307

9.0

Complete secondary

146

4.3

 

 

 

Community/village health worker

55

1.6

Higher

93

2.7

 

 

 

Other

8

0.2

Source: Researcher’s own calculation using SPSS, based on 2019 SLDHS data set (n=3403)

                                                     Table 1: The Distribution of the Characteristics of Respondents

Bivariate Analysis

In the Northern region, nurses and midwives conducted 89.1% of the postnatal check-ups, while community or village health workers performed 2.8% and doctors only 1% of the check-ups, indicating a strong reliance on midwifery services. In the Western region, 85.9% of check-ups were performed by nurses or midwives; however, doctors contributed 6.5% of the cases, the highest percentage among all regions, while TBAs handled 6.3% of the cases. In the Southern region, nurses and midwives carried out 81.4% of the check-ups, doctors were involved in 4.6%, and TBAs performed 12.2%, reflecting a comparatively greater dependence on traditional providers. In the North Western region, 82.4% of postnatal check-ups were done by nurses or midwives, with doctors involved in 3.1%, TBAs in 10%, and auxiliary midwives in 2.8%. The Eastern region showed the lowest involvement of nurses and midwives at 77.4%, while auxiliary midwives contributed 7.1%, TBAs 10.5%, doctors 2.9%, and community health workers 2.1%. The Chi-square test for region was statistically significant: (χ² = 85.654, p= 0.001).

Comparing urban and rural areas, urban settings saw nurses and midwives performing 87.8% of postnatal check-ups, with doctors conducting 6.1%, and TBAs 4.6%, while community health workers and auxiliary midwives played minimal roles (0.4% and 1.1%, respectively). In rural areas, nurses and midwives performed 82.1% of the check-ups, TBAs accounted for 11.0%, auxiliary midwives 2.3%, doctors 2.2%, and community health workers also 2.2%. This distribution was statistically significant (χ² = 90.723, p = 0.001), highlighting notable differences in the types of health providers based on residence.

Educational attainment also revealed significant disparities in terms of postnatal health check-ups. The findings revealed that, whether mothers were educated or not, their check-ups were performed by nurses and midwives at over 85%. The association between educational attainment and the type of health providers was statistically significant (χ² = 29.253, p = 0.0253). Overall, while nurses and midwives were the primary providers across all regions and settings, there were marked variations. Regions such as the Northern and Western areas, as well as urban settings and mothers with higher education, exhibited higher engagement with formally trained providers (including doctors), whereas the Southern, North Western, and Eastern regions, rural areas, and lower educational groups showed a greater reliance on traditional and auxiliary providers.

Additionally, the Chi-square test results revealed a significant relationship between the type of healthcare provider performing postnatal care and mothers’ working status, place of delivery, and birth order. For working status, the data showed that nurses and midwives served as the primary providers for both working and non-working mothers. Specifically, 84.6% of working mothers received postnatal care from nurses and midwives, compared to 80.9% of non-working mothers. The relationship between employment status and type of healthcare provider was statistically significant (χ² = 75.453, p = 0.001). This means that the type of healthcare providers is associated with whether a woman is working or not.

In terms of place of delivery, the analysis indicated clear differences in terms of health provider utilization. In government hospitals, nurses and midwives conducted 89.2% of postnatal checks, with doctors performing 7% of them. Government health centres relied on nurses and midwives for 86.9% of check-ups, while doctors attended only 2.5% of cases. Government health posts showed an even higher dependence on nurses and midwives, with 91.5% of check-ups performed by this group, supplemented by auxiliary midwives in 2.7% of cases. Conversely, home births presented a stark contrast whereby 48.1% of postnatal check¬ups were conducted by TBAs and 6.6% by community health workers, underscoring a heavy reliance on traditional health providers. Private hospitals or clinics exhibited a relatively higher involvement of doctors at 13.8%, with nurses and midwives still providing the majority of care (84.5%). These differences were statistically significant (χ² = 478.486, p = 0.001).

Finally, when type of healthcare provider was examined by birth order, the results indicated that the type of healthcare providers varied with the birth order. Among mothers with 0-4 births, 84.8% of postnatal check-ups were performed by nurses and midwives, and TBAs accounted for 8.3%. For mothers with 5-9 births, the percentage of check-ups by nurses and midwives decreased slightly to 81.4%, while TBAs’ involvement increased to 10.7%. In mothers with 10-14 births, nurses and midwives conducted only 72.4% of the check-ups, but reliance on TBAs rose sharply to 24.1%. The involvement of doctors and auxiliary midwives remained low across all birth order groups. The Chi-square test was also statistically significant (χ² = 19.022, p = 0.04), indicating an association between type of healthcare provider and birth order.

Overall, the findings emphasize that while nurses and midwives are the predominant providers across different subgroups, working status, place of delivery, and birth order are significantly associated with variations in the type of postnatal healthcare received.

Variables

Person who performed postnatal health check up

Doctor

Nurse, midwife

Auxiliary midwife

Traditional birth attendant

Community/ village health worker

Other

Total count

Total %

p-value

Age

%

%

%

%

%

%

 

 

 

15-19

2.5

80.9

3.2

11.2

1.8

0.4

278

100

0.213

20-24

3.3

82.6

1.4

10.5

2.0

0.3

734

100

 

25-29

2.5

86.4

0.9

8.2

1.7

0.1

863

100

 

30-34

4.5

81.6

2.3

9.6

1.7

0.3

604

100

 

35-39

3.5

85.6

2.2

7.5

1.2

0.2

603

100

 

40-44

4.2

84.5

3.8

6.1

1.4

0.0

213

100

 

45-49

4.6

80.6

2.8

11.1

0.0

0.9

105

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Region

Eastern

2.9

77.4

7.1

10.5

2.1

0.0

380

100

0.001

Northern

1.0

89.1

1.1

5.9

2.8

0.0

891

100

 

North Western

3.1

82.4

2.8

10.0

1.0

0.7

831

100

 

Southern

4.6

81.4

0.0

12.2

1.6

0.2

826

100

 

Western

6.5

85.9

1.1

6.3

0.2

0.0

475

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Type of place of residence

Urban

6.1

87.8

1.1

4.6

0.4

0.0

1054

100

0.001

Rural

2.2

82.1

2.3

11.0

2.2

0.3

2349

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Education level

No education

2.9

83.3

2.1

9.8

1.7

0.2

1913

100

0.0253

Incomplete primary

2.9

84.4

1.5

8.8

2.1

0.3

339

100

 

Complete primary

4.5

77.7

1.8

15.2

0.9

0.0

112

100

 

Incomplete secondary

3.8

84.9

1.8

7.8

1.5

0.4

800

100

 

Complete secondary

6.2

86.3

2.1

3.4

2.1

0.0

146

100

 

Higher

6.5

87.1

0.0

6.5

0.0

0.0

93

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Wealth index

Poorest

1.9

78.8

1.7

14.4

2.8

0.4

745

100

0.001

Poorer

3.4

80.8

3.1

10.4

2.2

0.1

818

100

 

Middle

2.4

85.2

2.4

8.1

1.6

0.4

764

100

 

Richer

4.6

88.2

0.7

5.7

0.6

0.1

680

100

 

Richest

6.1

89.4

1.0

3.5

0.0

0.0

396

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Respondent working

No

5.2

80.9

3.0

10.1

0.5

0.3

733

100

0.001

Yes

2.9

84.6

1.6

8.7

1.9

0.2

2670

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Place of delivery

Mother’s home

1.6

68.3

2.5

21.8

4.7

1.1

555

100

0.001

Other home

0.0

68.1

2.8

26.3

1.9

0.9

213

100

 

Government hospital

7.0

89.2

0.9

2.7

0.2

0.0

939

100

 

Government health centre

2.5

86.9

2.0

7.1

1.5

0.0

1218

100

 

Government health post

0.5

91.5

2.7

4.1

1.2

0.0

410

100

 

Other public sector

0.0

0.0

100.0

0.0

0.0

0.0

1

100

 

Private hospital/ clinic

13.8

84.5

1.7

0.0

0.0

0.0

58

100

 

Other

0.0

88.9

0.0

11.1

0.0

0.0

9

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

 

 

Birth Order/TCEB

0-4

3.3

84.8

1.7

8.3

1.7

0.2

2530

100

0.04

5-9

3.6

81.4

2.7

10.7

31.3

0.4

844

100

 

10-14

3.4

72.4

0.0

24.1

0.0

0.0

29

100

 

Total

3.4

83.8

1.9

9.0

1.6

0.2

3403

100

 

Source: Researcher’s own calculation using SPSS, based on 2019 SLDHS data set (n=3403)

                                    Table 2: Determinants Associated with the Type of Postnatal Care Provider

Multivariate Analysis

The multivariate analysis revealed several key factors influencing whether a professional healthcare provider conducted the postnatal check-up, and age emerged as an important determinant. The findings revealed that women in the age groups of 30-34 years, 35-39 years and 40-44 years had higher odds (OR= 1.941, OR= 1.566 and OR= 1.585, respectively) of being checked by professional healthcare providers compared to mothers in the age group of 45-49 years. These findings indicate that as women grow older, particularly beyond the age of 30 years, they are more likely to have a check-up by professional healthcare providers.

The findings indicate further that region variable was also significant. Women residing in the Eastern, Northern, North Western, and Southern regions had substantially higher odds of being attended by professional healthcare providers compared to women residing in the Western region. Furthermore, staying in urban areas increased the likelihood of receiving postnatal health check-up from professional healthcare providers (OR= 1.209) compared to their rural counterparts.

The wealth index also proved to be a significant predictor. Women in the poorest, poorer, and middle wealth categories were more likely to receive postnatal health check-ups from professional healthcare providers (OR=1.466, OR=1.527, and OR= 1.788, respectively) compared to those in the richest category. Additionally, place of delivery was highly influential. The data show that delivering in a government health centre (OR=1.371) or private hospital/ clinic (OR=3.952) was associated with higher odds of receiving postnatal health check-ups from healthcare professional, whereas delivery in a government hospital (OR=0.788) or other public sectors (OR=0.657) reduced the odds of being checked by healthcare professionals. Marital status was another significant determinant. Women living with a partner had higher odds (OR = 1.991) of being checked by healthcare professionals compared to women who were no longer living with their partners. Birth order was also significant. The data show that women with 0-4 births had lower chances of receiving postnatal health check-ups (OR=0.687) compared to women with 10-14 births, suggesting that the likelihood of receiving professional postnatal care increases with the increase in the number of children.

When examining factors contributing to the use of non-professional healthcare providers, the patterns were largely the inverse of those observed for professional care. Older women, particularly those in the age groups of 30-34 years, 35-39 years, and 40-44 years, were significantly less likely to receive care from non-professional healthcare providers (OR= 0.671, OR= 0.638, and OR= 0.631, respectively) compared to women in the 45-49 age group. Regional disparities were similarly pronounced – being women staying in the Eastern, Northern, North Western, and Southern regions decrease the likelihood of receiving postnatal health check-ups from non-professional healthcare providers compared to women staying in Western region of Sierra Leone. Furthermore, staying in urban areas reduces the odds of using non-professional healthcare providers compared to women staying in rural areas.

The wealth index further influenced the use of non-professional healthcare. The study revealed that women in the poorest, poorer, and middle wealth categories were less likely to be checked by non-healthcare professionals compare to women in the richest wealth index. Place of delivery was also significant. The findings show that, delivering at government health centres, in private hospitals, or at the clinics significantly reduced the likelihood of receiving postnatal check-ups from non-professional healthcare providers. Higher birth orders were associated with increased use of non- professional healthcare providers, where mothers with 0-4 and 5-9 births had higher odds (OR= 1.455 and OR= 1.827, respectively) of using non-professional healthcare providers.

In summary, the analysis indicates that factors, such as older age, specific regional locations, urban residence, lower wealth index, facility-based deliveries, and living with a partner significantly increase the odds of receiving postnatal health check-ups from professional healthcare providers. Conversely, these same factors tend to decrease the likelihood of relying on non-professional healthcare, while a higher birth order is associated with an increased reliance on non-professional healthcare providers. These results underscore substantial socioeconomic and geographic disparities in postnatal healthcare access among women in Sierra Leone.

Variables

Professional healthcare provider

Non-professional healthcare provider

B

Std. error

Wald

Sig.

Exp(B)

B

Std. error

Wald

Sig.

Exp(B)

Age in 5-year groups

 

 

21.889

.001

 

 

 

21.889

.001

 

15-19

.021

.103

.043

.835

1.022

-.021

.103

.043

.835

.979

20-24

.135

.106

1.642

.200

1.145

-.135

.106

1.642

.200

.873

25-29

.175

.115

2.318

.128

1.191

-.175

.115

2.318

.128

.839

30-34

.400

.122

10.698

.001

1.491

-.400

.122

10.698

.001

.671

35-39

.449

.149

9.070

.003

1.566

-.449

.149

9.070

.003

.638

40-44

.460

.182

6.367

.012

1.585

-.460

.182

6.367

.012

.631

45-49 (reference category)

Region

 

 

243.308

<.001

 

 

 

243.308

<.001

 

Eastern

1.041

.082

159.389

<.001

2.833

-1.041

.082

159.389

<.001

.353

Northern

1.256

.086

212.315

<.001

3.512

-1.256

.086

212.315

<.001

.285

North Western

.819

.081

101.394

<.001

2.269

-.819

.081

101.394

<.001

.441

Southern

1.045

.105

99.263

<.001

2.842

-1.045

.105

99.263

<.001

.352

Western (reference category)

Type of place of residence

Urban

.190

.089

4.541

.033

1.209

-.190

.089

4.541

.033

.827

Rural (reference category)

Educational attainment

 

 

5.752

.331

 

 

 

5.752

.331

 

No education

.066

.087

.566

.452

1.068

-.066

.087

.566

.452

.937

Incomplete primary

-.252

.138

3.341

.068

.777

.252

.138

3.341

.068

1.287

Complete primary

.049

.071

.479

.489

1.050

-.049

.071

.479

.489

.952

Incomplete secondary

.123

.132

.873

.350

1.131

-.123

.132

.873

.350

.884

Complete secondary

.060

.160

.141

.707

1.062

-.060

.160

.141

.707

.942

Higher (reference category)

Wealth index

 

 

62.418

<.001

 

 

 

62.418

<.001

 

Poorest

.382

.074

26.502

<.001

1.466

-.382

.074

26.502

<.001

.682

Poorer

.423

.077

30.228

<.001

1.527

-.423

.077

30.228

<.001

.655

Middle

.581

.106

30.259

<.001

1.788

-.581

.106

30.259

<.001

.559

Richer

.166

.131

1.615

.204

1.181

-.166

.131

1.615

.204

.847

Richest (reference category)

Place of delivery

 

 

58.640

<.001

 

 

 

58.640

<.001

 

Respondent's home

.168

.137

1.502

.220

1.183

-.168

.137

1.502

.220

.845

Other home

-.021

.089

.058

.810

.979

.021

.089

.058

.810

1.022

Government hospital

-.239

.082

8.481

.004

.788

.239

.082

8.481

.004

1.270

Government health centre

.315

.104

9.198

.002

1.371

-.315

.104

9.198

.002

.730

Government health post

.788

1.484

.282

.596

2.198

-.788

1.484

.282

.596

.455

Other public sector

-.420

.185

5.161

.023

.657

.420

.185

5.161

.023

1.521

Private hospital/ clinic

1.374

.697

3.886

.049

3.952

-1.374

.697

3.886

.049

.253

Other (reference category)

Current working status

No

.030

.064

.225

.635

1.031

-.030

.064

.225

.635

.970

Yes (reference category)

Birth order number & total children ever born

 

 

28.273

<.001

 

 

 

28.273

<.001

 

0-4

-.375

.072

26.984

<.001

.687

.375

.072

26.984

<.001

1.455

5-9

-.602

.294

4.188

.041

.547

.602

.294

4.188

.041

1.827

10-14 (reference category)

Constant

-1.702

.173

97.256

<.001

.182

1.702

.173

97.256

<.001

5.485

Source: Researcher’s own calculation using SPSS, based on 2019 SLDHS data set (n=3403)

                         Table 3: Multinomial Logistic Regression on the Type of Postnatal Health Check Provider

Discussion

The aim of this study was to examine the influence of sociodemographic and socioeconomic factors on the type of healthcare providers performing postnatal health checks among mothers in Sierra Leone. Specifically, the objective was to determine how variables, such as maternal age, region, residence (urban versus rural), education level, wealth index, marital status, employment status, place of delivery, and birth order contribute to disparities in accessing professional versus non-professional maternal healthcare. By exploring these determinants, the study sought to provide a nuanced understanding of the factors associated with postnatal healthcare utilization in Sierra Leone, thereby offering evidence to guide interventions aimed at improving maternal and neonatal outcomes in Sierra Leone.

The univariate analysis revealed a predominantly young population of mothers in their prime reproductive years, with most respondents clustered in the 20-24 and 25-29 age groups. The majority resided in rural areas, and a considerable portion of the sample had little or no formal education. These baseline characteristics are important because they set the stage for understanding how demographic and socioeconomic factors might influence access to postnatal care. In this context, nurses and midwives emerged as the primary providers of postnatal health checks, aligning with global trends that highlight the critical role of mid-level healthcare professionals in maternal care [1].

The analysis of a person performing postnatal health checks in relation to sociodemographic and socioeconomic characteristics revealed several key trends. Across age groups, nurses and midwives dominated the provision of postnatal care, with particularly high percentages observed among mothers in the age groups of 25-29 years and 35-39 years, while the involvement of doctors was minimal, peaking at 4.6% among mothers in the age group of 45-49 years. In contrast, younger mothers (15-19 years) showed a greater reliance on traditional birth attendants which may reflect socioeconomic constraints or limited access to formal healthcare, a finding that aligns with the Health Belief Model’s emphasis on perceived barriers. However, age had no significant relationship with the type of healthcare provider conducting the postnatal health check.

Regional disparities were also apparent. In the Northern region, most of the postnatal check-ups were conducted by nurses and midwives, with community health workers contributing less, highlighting the impact of strategic healthcare resource allocation in rural and underserved areas. This pattern is supported by the Social Ecological Model and the Cultural Health Capital Theory, which emphasizes the role of community-level factors and cultural trust in shaping healthcare behaviour [8,9]. The type of place of residence further influenced health provider choice, with urban residents more likely to receive care from professional health providers, including a higher involvement of doctors, likely due to the concentration of government hospitals, private clinics, and comprehensive healthcare centres in urban areas [19].

Educational attainment significantly impacted postnatal care provider selection. Mothers with higher education levels were more likely to access professional healthcare, particularly doctor-assisted care, attributable to greater health literacy, awareness, and the ability to navigate healthcare systems [20]. In contrast, lower educational attainment was associated with a higher reliance on traditional or community-based birth attendants, reflecting socioeconomic and accessibility constraints [21,22]. Wealth index status also played a critical role. Mothers in higher wealth quintiles were more likely to rely on doctors, nurses, or midwives, whereas those in lower wealth quintiles depended more on auxiliary midwives or traditional birth attendants, indicating that economic barriers significantly affect healthcare choices.

Employment status was a critical factor with employed women benefiting from greater financial resources and employer-provided health benefits, tended to access professional care, while those who were unemployed were more likely to rely on traditional birth attendants or community health workers [23].

The place of delivery emerged as one of the strongest predictors of the type of healthcare provider that women use when seeking postnatal healthcare. Mothers delivering in healthcare facilities such as hospitals or health centres were much more likely to receive postnatal care from professional health providers, whereas home deliveries were associated with a higher reliance on traditional birth attendants and auxiliary midwives. This finding is consistent with global evidence indicating that facility-based deliveries enhance access to skilled postnatal care [24]. Finally, the analysis indicated that birth order significantly influenced the type of healthcare provider for postnatal healthcare. First-time mothers received additional care from professional healthcare providers, while as birth order increased, particularly among mothers with 10-14 births, there was a marked shift towards traditional birth attendants. This trend may be due to increased financial and time constraints as family size grows, which in turn reduces the likelihood of accessing formal healthcare services [7,8].

The multinomial logistic regression analysis provided further insights by controlling for the interplay of various factors simultaneously. The findings revealed that older women, particularly those over 30 years, were significantly more likely to receive postnatal care from professional healthcare providers, with higher odds compared to younger women. This trend may reflect increased health awareness or greater resource availability among older mothers, who are often more experienced and cautious about maternal health risks [6]. Regional differences remained robust in the multivariate model. Women from the Eastern, Northern, North Western, and Southern regions exhibited higher odds of receiving professional care compared to the Western region, underscoring substantial disparities in healthcare infrastructure across regions.

Urban residence further contributed to increased access to professional healthcare providers, as urban women had higher odds of receiving services from skilled providers compared to rural women. Additionally, the place of delivery emerged as one of the strongest predictors [25]. Mothers delivering in government or private facilities were significantly more likely to receive professional care, especially in private hospitals or clinics, where the odds were higher. Marital status also influenced care-seeking behaviour. Women living with a partner were almost twice as likely to receive postnatal care from professional care which may be attributed to additional financial and emotional support facilitating access to quality services. Conversely, birth order was inversely associated with the use of professional care. Women with fewer children were more likely to access professional services, while those with higher birth orders showed a marked reduction, suggesting that multiparous women might rely more on their previous experiences or on traditional birth attendants as an alternative.

In contrast, the multinomial logistic regression analysis for non-professional healthcare revealed an inverse relationship with many of these factors. Older age, favourable regional characteristics, urban residence, and facility-based deliveries were all associated with a significant reduction in the likelihood of receiving postnatal health care from non-professional health providers. For instance, older women and those in regions with robust healthcare services were less likely to rely on non-professional healthcare providers, reinforcing the idea that improved access and awareness drive the use of professional healthcare [26]. Similarly, lower odds of being checked by non-professional healthcare among urban residents and facility-based births indicate that geographic and institutional factors play a critical role in shaping maternal health-seeking behaviour. Furthermore, while higher birth order increased the likelihood of non-professional care, potentially due to cumulative financial constraints or a reliance on traditional practices, women living with a partner were significantly less likely to use non-professional healthcare providers, possibly because partner support helps overcome barriers to accessing formal healthcare.

These findings collectively highlight substantial disparities in postnatal healthcare access that are influenced by a complex interplay of age, region, residence, wealth, place of delivery, marital status, and birth order. The trends observed in this study mirror broader global patterns, where socioeconomic and geographic factors critically determine the quality and type of maternal healthcare services accessed [1,9]. Addressing these disparities will require targeted interventions that improve healthcare infrastructure in underserved regions, promote facility-based deliveries, and enhance educational and financial support for vulnerable groups [27]. Ultimately, ensuring equitable access to professional healthcare providers for postnatal care is essential for reducing maternal and neonatal morbidity and mortality in Sierra Leone.

These findings provide essential context for understanding the disparities in postnatal healthcare utilization. The sociodemographic characteristics of respondents play a pivotal role in determining their access to and use of postnatal healthcare services.

Limitations

Certain limitations need to be considered when interpreting the findings of this study. The first one is that the data from the Sierra Leone Demographic and Health Survey of 2019 is cross-sectional and represents only a snapshot of what influence these factors had on postnatal healthcare experiences at that point in time. As it has been a couple of years since then, the influence of these factors might have changed with time. The SLDHS is a survey that relies on self-report, which can be marred with recall bias and conformity bias, as respondents may not be able to recall certain aspects of their birth due to various factors, or the need to give answers that they think the interviewers want to hear.

Conclusion

The key factors, such as age, region, residence type, wealth index, place of delivery, marital status, and birth order shape postnatal health care experiences. Specifically, older mothers, urban residents, and those delivering in formal healthcare facilities were more likely to receive care from professional healthcare providers, predominantly nurses and midwives, while higher birth order and limited healthcare infrastructure increased reliance on non-professional healthcare providers.

Policy Recommendations

Targeted interventions such as expanding healthcare infrastructure in underserved regions, promoting facility-based deliveries, and enhancing maternal health education are essential for reducing maternal and neonatal morbidity and mortality. The World Health Organization’s 2022-2025 Country Cooperation Strategy supports these priorities by focusing on maternal and newborn healthcare improvements. Hence, further longitudinal research is needed to explore the underlying causes of these disparities and inform effective policy responses.

Authors’ Contributions

Conceptualization: Philomene Nsengiyumva

Investigation: Palesa Patience Moloi

Writing Review and Editing: Philomene Nsengiyumva

Funding: No funding received from anyone

Data Availability:

The datasets generated and/or analysed during the current study are available from the co-author on reasonable request.

Declarations

Ethics Approval: This study did not need formal approval because it used secondary data obtained from South African Demographic and Health Survey. Hence, the SADHS covered all formalities related to ethical clearance

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Conflict of Interest

The authors have no relevant financial or non-financial interests to disclose.

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