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  • Research article
  • Open Access

Socio-demographic determinants of skilled birth attendant at delivery in rural southern Ghana

  • 1Email author,
  • 1, 3,
  • 1, 3,
  • 2, 3,
  • 1, 3,
  • 1, 2, 3 and
  • 1, 3
BMC Research Notes201710:268

https://doi.org/10.1186/s13104-017-2591-z

©  The Author(s) 2017

  • Received: 19 May 2016
  • Accepted: 30 June 2017
  • Published:

Abstract

Background

Maternal mortality is the subject of the United Nations’ fifth Millennium Development Goal, which is to reduce the maternal mortality ratio by three quarters from 1990 to 2015. The giant strides made by western countries in dropping of their maternal mortality ratio were due to the recognition given to skilled attendants at delivery. In Ghana, nine in ten mothers receive antenatal care from a health professional whereas only 59 and 68% of deliveries are assisted by skilled personnel in 2008 and 2010 respectively. This study therefore examines the determinants of skilled birth attendant at delivery in rural southern Ghana.

Methods

This study comprises of 1874 women of reproductive age who had given birth 2 years prior to the study whose information were extracted from the Dodowa Health and Demographic Surveillance System. The univariable and multivariable associations between exposure variables (risk factors) and skilled birth attendant at delivery were explored using logistic regression.

Results

Out of a total of 1874 study participants, 98.29% of them receive antenatal care services during pregnancy and only 68.89% were assisted by skilled person at their last delivery prior to the survey. The result shows a remarkable influence of maternal age, level of education, parity, socioeconomic status and antenatal care attendance on skilled attendants at delivery.

Conclusion

Although 69% of women in the study had skilled birth attendants at delivery, women from poorest households, higher parity, uneducated, and not attending antenatal care and younger women were more likely to deliver without a skilled birth attendants at delivery. Future intervention in the study area to bridge the gap between the poor and least poor women, improve maternal health and promote the use of skilled birth at delivery is recommended.

Keywords

  • Skilled attendants
  • Antenatal care
  • Pregnancy
  • Determinant
  • Dodowa
  • Ghana

Background

Annually, more than 200 million women become pregnant globally and 40% of them are estimated to experience pregnancy-related health problems with 15% experiencing serious or long-term complications [1]. This is underlined by the continuing occurrence of more than half a million deaths of women yearly due to pregnancy-related complications in the world, of which 99% are distributed by developing countries [2]. Lifetime risk of death due to pregnancy-related complications is 250-fold higher among women in developing countries in developed countries and it has been estimated that 88–98% of these deaths are avoidable [1].

The Millennium Development Goal (MDG) on maternal health is to reduce maternal mortality by three quarters between 1990 and 2015. One of the health care indicators identified to monitor the “process” of reducing maternal mortality is the proportion of births attended by skilled health personnel. A study revealed that the giant strides made by Western countries in dropping of their maternal mortality figures was due to much recognition given to skilled attendants at delivery in their countries [3].

While a number of studies [4–11] found that majority of women in developing countries utilize antenatal care (ANC) during their pregnancy, their deliveries often lack skilled birth attendants. The importance of skilled attendance at delivery has been recognised by a number of studies [4, 9, 12–24], and it is crucial for decreasing maternal and neonatal mortality, yet many women in low and middle income countries deliver outside of health facilities without skilled attendance at childbirth [3]. ANC visits serve to encourage women to have skilled attendants at birth at a facility, who can provide life-saving emergency obstetric care (EOC) interventions to women who develop serious complications.

In Ghana, many women do not seek skilled health care due to cost of service, the distance to the health facility, and quality of care thereby bringing about a low coverage of 59% skilled deliveries despite the various strategies being put in place. The 2008 Ghana Demographic Health Survey (GDHS) report shows that, over nine in ten mothers (95%) receive antenatal care from a health professional however only 59% of deliveries were assisted by skilled personnel [25].

Trend of skilled delivery services assessed in the Ga East Municipal area of Greater Accra Region of Ghana revealed that although antenatal services is at an appreciable level of 67% in 2010, skilled deliveries however is as low as 37.5%. This depicts a marginal increase of about 6% over the previous year‘s coverage which in 2010 was below the national and global targets of 60 and 85% respectively [26].

In 2003, Ghana introduced the free maternal delivery services to solve the cost of services. With prior establishment of the Community-based Health Planning and Services (CHPS) initiative to increase access, health education on benefits of utilization of maternity services and other activities towards improving maternal health care in the country. Such strategies were expected to show a corresponding increase in the coverage of skilled deliveries but this however, was not the case [27]. It is clear that, improving maternal health remains the most elusive of the MDG in Ghana. The only way this can be improved is if women receive supervised care from health professionals. The aim of this study is to examine factors contributing to this low trend of skilled deliveries in the Dodowa Health and Demographic Surveillance System.

Methods

Study area

Data for this study were extracted from Dodowa Health and Demographic Surveillance System (DHDSS) site database. The DHDSS is located in the south-eastern part of Ghana and operates within the boundaries of the Shai-Osudoku and Ningo-Prampram districts [28]. The DHDSS site lies between latitude 5°45′ south and 6°05′ north and longitude 0°05′ east and 0°20′ west with a land area of 1528.9 sq km. It is about 41 km from the national capital, Accra [28, 29]. The two districts cover a population of 115,754 people in 380 communities in 23,647 households covering a total land area of 1442 sq km. The inhabitants are predominantly subsistence farmers, fishermen and petty traders [29]. Road networks in the DHDSS are usually inaccessible during the wet seasons, making access to health and other services a challenge.

The DHDSS visits every household in the demographic surveillance area twice in a year to collect data on demographic, migration and other health indicators [29]. Health care service in the DHDSS is delivered by hospitals, health centres, CHPS zones, private facilities, clinics, maternity homes, mission clinics and quasi government clinics.

Study population

The study population comprised women of reproductive age (15–49 years) who were resident in the DHDSS from 1st January 2011 to 31st December 2011 who had given birth not more than 1 year prior to the study.

Outcome and exposure variables

The outcome variable for this study is skilled birth attendant (SBA) at delivery which is binary recorded as: 1 “Skilled person” and 0 “No Skilled person”. From the questionnaire and data available, we selected 8 exposure variables which were based on available literature and has the potential to influence the place of delivery. These exposure variables includes: maternal age, education, parity, first live birth or not, marital status, ANC attendance, and wealth index.

The wealth index is a proxy measure of a household’s long term standard of living; it’s based on social status, assets ownership, and availability of utilities, among others. The index measures were combined into a wealth index, using weights derived through principal component analysis (PCA) [30]. The proxies from the PCA were divided into five quintiles; poorest, very poor, poor, less poor and least poor. Maternal ages at delivery were calculated using the mothers’ and babies’ birthdates.

Statistical methods

To ensure the assumption of independence of observations, an initial assessment of clustering at household level was carried out since women from the same household may have same or similar health seeking behaviours. The assessment shows that the assumption of independence was upheld.

The univariable associations between each exposure variable (risk factor) and SBA were explored, and those significant at P < 0.05 were entered together into a multiple logistic regression model. Collinearity between all variables and models fitted with and without adjustment was checked using Pearson’s correlation matrix. All analyses were conducted in Stata version 12 and results were presented in the form of tables and summary statistics.

Results

Background characteristics

Table 1 presents the background characteristics of the research participants. The mean age of the participants (n = 1874) was 27.41 ± 6.87 years, with ages ranging from 15 to 49. Large proportion of the study participants (73.11%) were of the Ga-Dangme ethnic group. Christians account for the highest proportion in religious affiliation (92.16%) followed by Islamic (5.34%) and the remaining religious denominations contributed only small proportions of the participants. Majority (27.21%) of the respondents were petty traders while 25.45 and 15.96% were unemployed and farmers respectively and the remaining categories of occupation contributed smaller proportion of participants. While 34.47% of the study participants had junior high/middle school level of education, 30.26 and 27.32% had primary and no education respectively. It is well noting that only small proportion (7.95%) of the participants had senior high and above level of education. While 48.40% of the women had their marital status as cohabiting, 29.35 and 18.62% were single (never marry) and married respectively. Majority of the respondents (28.98%) had parity of 1, while 24.76, 17.56 and 16.81% had parity 2, 3, and 5+ respectively. Of a sample of 1873, 1291 (68.89%) were assisted by skilled person at their last delivery prior to the survey. The results show that 98.29% of the women receive ANC services during pregnancy and 71.02% had previous live birth.
Table 1

Socio-demographic characteristics of study population (women aged 15–49)

Characteristics

Frequencya

Proportion (%)

Age group

 15–19

223

11.9

 20–24

494

26.36

 25–29

498

26.57

 30–34

348

18.57

 35–39

207

11.05

 40+

104

5.55

Mean = 27.41 (SD = 6.87)

  

Ethnicity

 Ga-Dangme

1370

73.11

 Akan

101

5.39

 Ewe

298

15.9

 Northern

97

5.18

 Others

8

0.43

Religion

 Christianity

1727

92.16

 Islamic

100

5.34

 Traditional

22

1.17

 Others

25

1.33

Occupation

 Unemployed

477

25.45

 Farmer

299

15.96

 Artisan

252

13.45

 Petty trader

510

27.21

 Civil servant

30

1.6

 Student

265

14.14

 Others

41

2.19

Level of education

 No education

512

27.32

 Primary

567

30.26

 Junior high/middle school

646

34.47

 Senior high and above

149

7.95

Marital status

 Single

550

29.35

 Married

349

18.62

 Separated/divorced

32

1.71

 Cohabiting

907

48.4

 Widowed

9

0.48

 Missing

27

1.44

Parity

 Parity 1

543

28.98

 Parity 2

464

24.76

 Parity 3

329

17.56

 Parity 4

223

11.9

 Parity 5+

315

16.81

Assisted delivery

 No skilled person

583

31.11

 Skilled person

1291

68.89

ANC attendance during last pregnancy

 Yes

1841

98.29

 No

32

1.71

Is this your first live birth

 Yes

543

28.98

 No

1331

71.02

n = 1873

SD standard deviation

aNumber of respondents across some variables categories may not add up to 1873 due to missing data

Crude model

Table 2 shows both the crude and adjusted models of determinants of SBA at delivery. The crude model revealed that maternal age is significantly associated with SBA at delivery. Participants aged 20–24, 25–29, 30–34 and 40+ were 34, 87, 74 and 45% more likely to be delivered by skilled person respectively compared with those aged 15–19 years (OR 1.34, 95% CI 0.97–1.85), (OR 1.87, 95% CI 1.34–2.60), (OR 1.74, 95% CI 1.22–2.48), (OR 1.45, 95% CI 0.89–2.36). While the odds of having SBA at delivery increased significantly by 69% for women who were married compared to those who were single (OR 1.69, 95% CI 1.25–2.29), women who were separated/divorced, cohabiting and widowed had an increased odds but it was not statistically significant. Level of education had significant influence on SBA at delivery such that, women who had primary education were 61% more likely to have SBA at delivery compared to those who had no education (OR 1.61, 95% CI 1.26–2.05) and those who had junior high/middle school level of education were three times more likely to have SBA at delivery (OR 3.09, 95% CI 2.40–4.00) and those who had senior high and above level of education were more than eight times more likely to have SBA at delivery (OR 8.88, 95% CI 4.90–16.10) compared to those who had no education. While the odds of having SBA at delivery for women who were farmers significantly reduced by 63% compared to those who were unemployed (OR 0.63, 95% CI 0.47–0.85), women who were petty traders had a significant increased odds of 43% of having SBA at delivery compared to those who were unemployed (OR 1.43, 95% CI 1.09–1.88). Women who were artisans and civil servants were two and seven times more like to have SBA at delivery respectively (OR 2.30, 95% CI 1.59–3.33), (OR 7.20, 95% CI 1.69–30.60) compared to those who had no employment.
Table 2

Crude and adjusted odd ratios of determinants of skilled birth attendants at delivery

Characteristics

Crude

Adjusted

OR

P values (95% CI)

OR

P values (95% CI)*

Age group

 15–19

1

 

1

 

 20–24

1.34

0.079 (0.97–1.85)

1.53

0.039 (1.02–2.30)

 25–29

1.87

<0.001 (1.34–2.60)†

2.12

0.002 (1.31–3.42)†

 30–34

1.74

0.002 (1.22–2.48)†

2.44

0.002 (1.40–4.24)†

 35–39

2.15

<0.001 (1.42–3.25)†

4

<0.001 (2.10–7.61)†

 40+

1.45

0.139 (0.89–2.36)

4.25

<0.001 (2.08–8.71)†

Marital status

 Single

1

 

1

 

 Married

1.69

<0.001 (1.25–2.29)†

1.32

0.192 (0.87–1.10)

 Separated/divorced

1.56

0.289 (0.687–3.54)

3.4

0.036 (1.08–10.68)†

 Cohabiting

1.09

0.460 (0.87–1.36)

1.2

0.246 (0.88–1.64)

 Widowed

1.82

0.459 (0.37–8.836)

2.19

0.490 (0.24–20.38)

 Missing

0.88

0.760 (0.40–1.97)

0.97

0.956 (0.39–2.42)

Level of education

 No education

1

   

 Primary

1.61

<0.001 (1.26–2.05)†

1.52

0.006 (1.13–2.06)†

 Junior high/middle school

3.09

<0.001 (2.40–4.00)†

2.07

<0.001 (1.51–2.83)†

 Senior high and above

8.88

<0.001 (4.90–16.10)†

3.41

0.001 (1.65–7.04)†

Occupation

 Unemployed

1

   

 Farmer

0.63

0.003 (0.47–0.85)†

0.69

0.050 (0.48–1.00)

 Artisan

2.3

<0.001 (1.59–3.33)†

1.04

0.856 (0.67–1.63)

 Trader

1.43

0.010 (1.09–1.88)†

1.25

0.200 (0.89–1.75)

 Civil servant

7.2

0.007 (1.69–30.60)†

  

 Student

0.95

0.756 (0.69–1.30)

0.87

0.533 (0.56–1.34)

 Others

1.4

0.355 (0.69–2.87)

1.25

0.609 (0.53–2.91)

Parity

 Parity 1

1

 

1

 

 Parity 2

0.74

0.029 (0.56–0.97)†

0.46

<0.001 (0.32–0.66)†

 Parity 3

0.76

0.075 (0.56–1.03)

0.34

<0.001 (0.22–0.52)†

 Parity 4

0.84

0.331 (0.59–1.19)

0.5

0.008 (0.30–0.83)†

 Parity 5+

0.41

<0.001 (0.30–0.55)†

0.24

<0.001 (0.14–0.39)†

Socio economic status

 Poorest

1

 

1

 

 Poorer

1.39

0.034 (1.03–1.88)†

1.17

0.342 (0.84–1.63)

 Poor

1.7

0.001 (1.23–2.34)†

1.31

0.129 (0.92–1.86)

 Less poor

3.54

<0.001 (2.53–4.96)†

2.55

<0.001 (1.77–3.69)†

 Least poor

8.75

<0.001 (5.60–13.67)†

4.8

<0.001 (2.96–7.77)†

ANC attendance

 Attend ANC

1

 

1

 

 Don’t attend ANC

0.03

<0.001 (0.01–0.12)†

0.05

<0.001 (0.01–0.22)†

Type of live birth

 First live birth

1

 

1

 

 Not first live birth

0.65

<0.001 (0.52–0.82)†

  

OR odd ratio, CI confidence interval

†Statistically significant

* Correct classification rate of the model = 72.65%

Parity has shown significant effect on use of SBA at delivery. Parity 2 and 5 had significantly increased odds of 74 and 41% respectively compared to those with parity 1 (OR 0.74, 95% CI 0.56–0.97), (OR 0.41, 95% CI 0.30–0.55). The rest of the categories of the parity (three and four) were not significantly different (P > 0.05). Socioeconomic status was another variable which strongly predicted having SBA at delivery. While women with socioeconomic status of poorer and poor had significantly increased odds of 39 and 70% of having SBA at delivery respectively compared to those in the poorest category (OR 1.39, 95% CI 1.03–1.88), (OR 1.70, 95% CI 1.23–2.34), less poor and least poor women were more than three and eight times more likely respectively to have SBA at delivery compared to those who were poorest (OR 3.54, 95% CI 2.53–4.96), (OR 8.75, 95% CI 5.60–13.67). The results further showed that, women who do not attend ANC were 3% less like to have SBA at delivery compared to their counterparts who attend ANC (OR 0.03, 95% CI 0.01–0.12). Finally, women who ever had live birth were 65% less likely to have SBA at delivery compared to those who never had live birth (OR 0.65, 95% CI 0.52–0.82).

Adjusted model

The adjusted model assessed the independent effect of each determining variable on SBA at delivery. The model contain eight determinates (age group, marital status, level of education, occupational status, parity, socioeconomic status, ANC attendance and type of birth) of SBA at delivery with over all correct classification rate of 72.65%. The result shows a remarkable influence of maternal age, level of education, parity, socioeconomic status and ANC attendance on SBA at delivery.

The odds of women aged 20–24 and 25–29 years having SBA at delivery is 2.12 and 2.44 times more likely compared to those aged 15–19 years respectively (OR 2.12, 95% CI 1.31–3.42), (OR 2.44, 95% CI 1.40–4.24). Women aged 35–39 and 40+ years were significantly four times and more than four times more likely to have SBA at delivery respectively compared to those aged 15–19 years (OR 4.00, 95% CI 2.10–7.61), (OR 4.25, 95% CI 2.08–8.71).

With respect to level of education, while women with primary education were 52% more likely to have SBA at delivery (OR 1.52, 95% CI 1.13–2.06), those with junior high/middle school education were more than two times more likely to have SBA at delivery compared to those with no education (OR 2.07, 95% CI 1.51–2.83). Women with senior high and above level of education were three times more likely to have SBA at delivery compared to those in the reference category. Parity was another variable that exerted significant influence on SBA at delivery. While women with parity 2 were 46% less likely to have SBA at delivery (OR 0.46, 95% CI 0.32–0.66), those with parity 3 were 34% less likely to have SBA delivery compared to those with parity of 1 (OR 0.34, 95% CI 0.22–0.52). Women with parity 4 and 5 had a reduced odds of 0.50 and 0.24 respectively compared to those with parity 1 (OR 0.50, 95% CI 0.30–0.83), (OR 0.24, 95% CI 0.14–0.39).

The results showed further that women in less poor socioeconomic quintile were more than twice more likely to have SBA at delivery (OR 2.55, 95% CI 1.77–3.69) and those in least poor quintile were more than four times more likely to have SBA at delivery compared to those in the poorest quintile (OR 4.80, 95% CI 2.96–7.77). Although other socioeconomic quintiles were not significantly associated with SBA at delivery (P > 0.05), the odds of SBA at delivery was increasing with improvement in socioeconomic status. Furthermore, women who do not attend ANC were 5% less likely to have SBA at delivery compared to those who attend ANC (OR 0.05, 95% CI 0.01–0.22).

Discussions

This study sought to explore determinants of skilled birth attendants at delivery using data from Dodowa Health and Demographic Surveillance System. Data was collected from Shai-Osudoku and Ningo-Prampram districts of southern rural Ghana in 2011. The results show that SBA at delivery was high in the study area, with 68.89% coverage. This proportion is higher than the national estimate of 52.2%, Greater Accra regional estimate of 56.0% from 2011 Ghana Health Services annual report [31] and DWD estimate of 37.0% for the same year [32]. Our finding is similar to the national estimates of 68% reported by Ghana Statistical Services in 2011 Ghana Multiple Indicator Cluster Survey (MICS) [27].

The result shows that socioeconomic status is a significant determinant of SBA at delivery in that, better socioeconomic status was significantly associated with SBA at delivery. This implies that the user-fee exemption policy for maternal and child health services may have had less impact on the use of SBA because there are other costs including transport, hence reducing user-fees alone may not be sufficient motivation to bring in the poorest women.

This finding is consistent with a qualitative study in Ghana [33], 2011 Ghana MICS report [27] and other studies elsewhere [34, 35] where women from poorest households were less likely to deliver using skilled personnel compared to women from the richest households.

Furthermore, the result on level of education and SBA at delivery is consistent with the finding of 2011 Ghana MICS report which revealed that the more educated a women is, the more likely she is to have SBA at delivery [27]. This finding is further supported by other studies [36, 37]. Educated women may have better jobs and higher socioeconomic status which also enhances their negotiation power and confidence in making informed decision on place of delivery and SBA utilization [36, 38].

The finding on parity and SBA utilization is consistent with similar studies elsewhere [36, 39, 40]. Women with higher parity have experienced childbirth and takes childbirth as a natural process hence may not seek SBA at delivery if there is no complication [39]. It has also been shown that, if a woman had a bad experience with a health worker during previous childbirth, she may prefer delivering subsequent babies at home instead [36].

It has been established that ANC is the first step of continuum of services provided during pregnancy [41] and mothers who attends ANC are likely to attend health facility for childbirth [36, 42, 43]. This study revealed significant positive association between ANC attendance and SBA at delivery such that women who do not attend ANC were less likely to have SBA at delivery. Several other studies also found positive association between ANC attendance and facility delivery [42–44].

Finally, the result from this study shows older women were more likely to have SBA at delivery. Other similar studies established that advance maternal age is a risk factor for a woman to undergo induction and caesarean delivery [45–47] hence older women were encouraged to deliver in health facility so as to have SBA at delivery.

The major strength of this study is use of data from Health and Demographic Surveillance System which follows the entire population of the study area prospectively [29] hence the generalizability of the finding to the whole population.

Conclusion

Although 69% of women in the study area had skilled birth attendants at delivery, women from poorest households, higher parity, uneducated, and not attending ANC and younger women were more likely to deliver without a skilled birth attendants at delivery.

Most of the factors that predicted having a SBA in this rural area of Ghana have also been identified in previous studies [27, 33–47]. Future intervention in the study area to bridge the gap between the poor and least poor women, improve maternal health and promote the use of skilled birth at delivery is recommended.

Abbreviations

ANC: 

antenatal care

CHPS: 

Community-based Health Planning and Services

DHDSS: 

Dodowa Health and Demographic Surveillance System

DHRC: 

Dodowa Health Research Centre

EOC: 

emergency obstetric care

GDHS: 

Ghana Demographic Health Survey

MDG: 

Millennium Development Goal

PCA: 

principal component analysis

SBA: 

skilled birth attendant

OR: 

odd ratio

Declarations

Authors’ contributions

AKM participated in the conceptualisation, and design of the research idea, data analysis and writing the results and discussion. He also conducted the literature review. VK participated in conception and design, conducted the literature review and reviewed manuscript. GO and SN participated in data extraction, statistical analysis and interpretation. DEA provided statistical support and reviewed manuscript. AA involved in conception, design, data interpretation and reviewed manuscript. RAE conducted the literature review, writing the introductory section and reviewed the manuscript. MG participated in refining the initial research idea, writing the introductory section and critically reviewed the drafts. All authors read and approved the final manuscript.

Acknowledgements

We acknowledge the hard work and commitment of the field and the data team of the Dodowa Health and Demographic Surveillance System. We are thankful to the opinion leaders, residents in the surveillance area for offering their time for interviews and consistently sharing their valuable personal information over the years. We thank INDEPTH Network for providing technical support to the DHDSS. The authors are grateful to all those who read this manuscript for their helpful comments.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

We are unable to make the dataset publically available due to participant confidentiality and the ethical policies of the Dodowa Health Research Centre.

Consent to publish

This article does not include any individual persons data therefore consent to publish is not applicable.

Ethics approval and consent to participants

The DHDSS data collection procedures were approved by the Ethical Committee of Ghana Health Service, Institutional Review Board of Dodowa Health Research Centre (DHRC) and INDEPTH Network. Permission to use these data was obtained from management of DHRC. The researchers briefed each respondent as well as their household heads about the HDSS data collection. Participating individuals and households gave their consent prior to the interview and those who refused were excluded. To ensure confidentiality names of the study participants were not included in the extracted data.

Funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Dodowa Health Research Centre, P. O. Box. DD1, Dodowa, Accra, Ghana
(2)
Shai-Osudoku District Hospital, Dodowa, Ghana
(3)
Ghana Health Services, Accra, Ghana

References

  1. Yanagisawa S, Oum S, Wakai S. Determinants of skilled birth attendance in rural Cambodia. Trop Med Int Health. 2006;11:238–51.View ArticlePubMedGoogle Scholar
  2. Hadi A, Rahman T, Khuram D, Ahmed J, Alam A. Raising institutional delivery in war-torn communities: experience of BRAC in Afghanistan. Asia Pac J Fam Med. 2007;6:51.Google Scholar
  3. De Browere V, Van Lerberghe W. Safe motherhood strategies: a review of the evidence studies in health services organization and policy. 2001.Google Scholar
  4. Bryce J, Daelmans B, Dwivedi A, Fauveau V, Lawn JE, Mason E, Newby H, Shankar A, Starrs A, Wardlaw T. Countdown to 2015 for maternal, new-born, and child survival: the 2008 report on tracking coverage of interventions. Lancet. 2008;371:1247–58.View ArticlePubMedGoogle Scholar
  5. Mpembeni RN, Killewo JZ, Leshabari MT, Mas-sawe SN, Jahn A, Mushi D, Mwakipa H. Use pat-tern of maternal health services and determinants of skilled care during delivery in southern Tanzania: implications for achievement of MDG-5 targets. BMC Pregnancy Childbirth. 2007;7:29.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Magoma M, Requejo J, Campbell OMR, Cou-sens S, Filippi V. High ANC coverage and low skilled attendance in a rural Tanzanian district: a case for implementing a birth plan intervention. BMC Pregnancy Childbirth. 2010;10:13.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Waiswa P, Peterson S, Tomson G, Pariyo GW. Poor new born practices—a population based survey in eastern Uganda. BMC Pregnancy Childbirth. 2010;10:9.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Osubor KM, Fatusi AO, Chiwuzie JC. Maternal health-seeking behavior and associated factors in a rural Nigerian community. Mat Child Health J. 2006;10:159–69.View ArticleGoogle Scholar
  9. Adekunle C, Filippi V, Graham W, Onyemunwa P, Udjo E. Patterns of maternity care among women in Ondo States, Nigeria. In: Determinants of health and mortality in Africa. In: Hill AG editor. Demographic and health survey further analysis series. New York; 1990: 1–45.Google Scholar
  10. Mekonnen Y, Mekonnen A. Factors influencing the use of maternal healthcare services in Ethiopia. J Health Popul Nutr. 2003;21:374–82.PubMedGoogle Scholar
  11. Stewart MK, Stanton CK, Ahmed O. Maternal health care. DHS comparative studies. Maryland: Macro International Inc; 1997.Google Scholar
  12. Abera M, Mariam AG, Belachew T. Predictors of safe delivery service utilization in Arsi Zone, South-East Ethiopia. Ethiop J Health Sci. 2011;21(3):101–13.Google Scholar
  13. Adegoke AA, van den Broek N. Skilled birth attendance—lessons learnt. BJOG. 2009;116(Suppl 1):33–40. doi:10.1111/j1471-0528200902336x.View ArticlePubMedGoogle Scholar
  14. Adegoke AA, van den Broek N. Skilled birth attendance—lessons learnt. BJOG Int J Obstet Gynaecol. 2009;116:33–40.View ArticleGoogle Scholar
  15. Albrecht J, Dar ML, Shah U, Diesfeld HJ. Maternity care in rural Nepal: a health service analysis. Trop Med Int Health. 2000;5:657–65.View ArticleGoogle Scholar
  16. Anwar I, Sami M, Akhtar N, Chowdhury ME, Salma U, Rahman M, Koblinsky M. Inequity in maternal health-care services: evidence from home based skilled birth attendant programmes in Bangladesh. Bull World Health Organ. 2008;86:252–9.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Anya S, Hydara A, Jaiteh L. Antenatal care in the Gambia: missed opportunity for information, education and communication. BMC Pregnancy Childbirth. 2008;8(1):9.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Baral YR, Lyons K, Skinner J, van Teijlingen ER. Determinants of skilled birth attendants for delivery in Nepal. Kathmandu Univ Med J. 2010;8(31):325–32.Google Scholar
  19. Bazzano AN, Kirkwood B, Tawiah-Agyemang C, Owusu-Agyei S, Adongo P. Social costs of skilled attendance at birth in rural Ghana. Int J Gynaecol Obstet. 2008;102(1):91–4.View ArticlePubMedGoogle Scholar
  20. Bhutta ZA, Lassi ZS, Mansoor N. Systematic review on human resources for health interventions to improve maternal health outcomes: evidence from developing countries. Karachi, Pakistan: Aga Khan University; 2010. p. 1–89.Google Scholar
  21. Blum LS, Sharmin T, Ronsmans C. Attending home vs. clinic-based deliveries: perspectives of skilled birth attendants in Matlab, Bangladesh. Reprod Health Matters. 2006;14(27):51–60.View ArticlePubMedGoogle Scholar
  22. Central Statistics D. 2003 Population and housing census—provisional results. Banjul. 2004.Google Scholar
  23. Choudhury N, Ahmed SM. Maternal care practices among the ultra poor households in rural Bangladesh: a qualitative exploratory study. BMC Pregnancy Childbirth. 2011;11(1):15.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Graham WJ, Bell JS, Bullough CH. Can skilled attendance at delivery reduce maternal mortality in developing countries? Studies in health services organisation and policy, vol. 17. 2001. p. 97–130.Google Scholar
  25. Ghana statistical service (GSS), Ghana health service (GHS), ICF Macro: Ghana demographic and health survey. Accra Ghana; 2008.Google Scholar
  26. Esena RK, Sappor MM. Factors associated with the utilization of skilled delivery services in the ga east municipality of Ghana. Part 1: demographic characteristics. Int J Sci Technol Res. 2013;2(8):184–94.Google Scholar
  27. Ghana statistical service. Ghana multiple indicator cluster survey with an enhanced malaria module and biomarker with enhanced module biomarker. Ghana; 2011.Google Scholar
  28. Awini E, Sarpong D, Adjei A, Manyeh KA, Amu A, Akweongo P, Adongo P, Kukula V, Odonkor G, Narh S, et al. Estimating cause of adult (15+ years) death using InterVA-4 in rural districts of southern Ghana. Global Health Action. 2014;7:25543.View ArticleGoogle Scholar
  29. Gyapong M, Sarpong D, Awini E, Manyeh KA, Tei D, Odonkor G, Agyepong IA, Mattah P, Wontuo P, Attaa-Pomaa M, et al. Health and demographic surveillance system profile: the Dodowa HDSS. Int J Epidemiol. 2013;42:1686–96.View ArticlePubMedGoogle Scholar
  30. Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006;21(6):459–68.View ArticlePubMedGoogle Scholar
  31. Ghana Health Service. Annual report (Unpublished Annual Report), vol. 63. 2011.Google Scholar
  32. Dangme West District Health Directorate. Annual report (Unpublished Annual Report). 2011.Google Scholar
  33. Sophie W, Bertha G, Valery R. An exploratory study of the policy process and early implementation of the free NHIS coverage for pregnant women in Ghana. Int J Equity Health. 2013;12:16.View ArticleGoogle Scholar
  34. Wagle RR, Sabroe S, Nielsen BB. Socioeconomic and physical distance to the maternity hospital as predictors for place of delivery: an observation study from Nepal. BMC Pregnancy Childbirth. 2004;4:8.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Mrisho M, Schellenberg JA, Mushi AK, Obrist B, Mshinda H, Tanner M, Schellenberg D. Factors affecting home delivery in rural Tanzania. Trop Med Int Health. 2007;12:862–72.View ArticlePubMedGoogle Scholar
  36. Hazarika I. Factors that determine the use of skilled care during delivery in India: implications for achievement of MDG-5 Targets. Matern Child Health J. 2011;15:1381–8.View ArticlePubMedGoogle Scholar
  37. Doku D, Neupane S, Doku PN. Factors associated with reproductive health care utilization among Ghanaian women. BMC Int Health Hum Rights. 2012;12:29.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Singh PK, Rai RK, Alagarajan M, Singh L. Determinants of maternity care services utilization among married adolescents in rural India. PLoS ONE. 2012;7:e31666.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Kitui J, Lewis S, Davey G. Factors influencing place of delivery for women in Kenya: an analysis of the Kenya demographic and health survey, 2008/2009. BMC Pregnancy Childbirth. 2013;13:40.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Vishnu K, Andy HL, Jonia DC, Lourenca NB, Rajendra K. Factors associated with non-utilisation of health service for childbirth in Timor-Leste: evidence from the 2009–2010 demographic and health survey. BMC Int Health Hum Rights. 2014;14:14.View ArticleGoogle Scholar
  41. Kerber KJ, de Graft-Johnson JE, Bhutta ZA, Okong P, Starrs A, Lawn JE. Continuum of care for maternal, newborn, and child health: from slogan to service delivery. Lancet. 2007;370:1358–69.View ArticlePubMedGoogle Scholar
  42. Rockers PC, Wilson ML, Mbaruku G, Kruk ME. Source of antenatal care influences facility delivery in rural Tanzania: a population-based study. Matern Child Health J. 2009;13:879–85.View ArticlePubMedGoogle Scholar
  43. Anyait A, Mukanga D, Oundo G, Nuwaha F. Predictors for health facility delivery in Busia district of Uganda: a cross sectional study. BMC Pregnancy Childbirth. 2012;12:132.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Akazili J, Doctor HV, Abokyi L, Hodgson A, Phillips JF. Is there any relationship between antenatal care and place of delivery? Findings from rural northern Ghana. Afr J Health Sci. 2011;18:62–73.Google Scholar
  45. Jean DKN, Anny-Nadege N, Julius SD, Celestine N, Jean MK. Pregnancy outcome at advanced maternal age in a group of African women in two teaching hospitals at Yaounde, Cameroon. Pan Afr Med J. 2013;13:134.Google Scholar
  46. Ezra Y, McParland P, Farine D. High delivery intervention rates in nulliparous women over age 35. Eur J Obstetr Gynecol Reprod Biol. 1995;62:203–7.View ArticleGoogle Scholar
  47. Gordon D, Milberg J, Daling J. Advanced maternal age as a risk factor for cesarean delivery. Obstet Gynecol. 1991;77:493–7.PubMedGoogle Scholar

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