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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predictors of skilled attendance at delivery among antenatal clinic attendants in Ghana: a cross-sectional study of population data</title>
<author><name sortKey="Amoakoh Coleman, Mary" sort="Amoakoh Coleman, Mary" uniqKey="Amoakoh Coleman M" first="Mary" last="Amoakoh-Coleman">Mary Amoakoh-Coleman</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af2"><institution>School of Public Health, University of Ghana</institution>
,<addr-line>Legon</addr-line>
,<country>Ghana</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Ansah, Evelyn K" sort="Ansah, Evelyn K" uniqKey="Ansah E" first="Evelyn K" last="Ansah">Evelyn K. Ansah</name>
<affiliation><nlm:aff id="af3"><addr-line>Research and Development Division</addr-line>
,<institution>Ghana Health Service</institution>
,<addr-line>Accra</addr-line>
,<country>Ghana</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Agyepong, Irene Akua" sort="Agyepong, Irene Akua" uniqKey="Agyepong I" first="Irene Akua" last="Agyepong">Irene Akua Agyepong</name>
<affiliation><nlm:aff id="af4"><addr-line>Department of Health Policy Planning and Management</addr-line>
,<institution>School of Public Health, University of Ghana</institution>
,<addr-line>Legon</addr-line>
,<country>Ghana</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Grobbee, Diederick E" sort="Grobbee, Diederick E" uniqKey="Grobbee D" first="Diederick E" last="Grobbee">Diederick E. Grobbee</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Kayode, Gbenga A" sort="Kayode, Gbenga A" uniqKey="Kayode G" first="Gbenga A" last="Kayode">Gbenga A. Kayode</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Klipstein Grobusch, Kerstin" sort="Klipstein Grobusch, Kerstin" uniqKey="Klipstein Grobusch K" first="Kerstin" last="Klipstein-Grobusch">Kerstin Klipstein-Grobusch</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af5"><addr-line>Faculty of Health Sciences, Division of Epidemiology & Biostatistics</addr-line>
,<institution>School of Public Health, University of the Witwatersrand</institution>
,<addr-line>Johannesburg</addr-line>
,<country>South Africa</country>
</nlm:aff>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">25991459</idno>
<idno type="pmc">4442247</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4442247</idno>
<idno type="RBID">PMC:4442247</idno>
<idno type="doi">10.1136/bmjopen-2015-007810</idno>
<date when="2015">2015</date>
<idno type="wicri:Area/Pmc/Corpus">000009</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Predictors of skilled attendance at delivery among antenatal clinic attendants in Ghana: a cross-sectional study of population data</title>
<author><name sortKey="Amoakoh Coleman, Mary" sort="Amoakoh Coleman, Mary" uniqKey="Amoakoh Coleman M" first="Mary" last="Amoakoh-Coleman">Mary Amoakoh-Coleman</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af2"><institution>School of Public Health, University of Ghana</institution>
,<addr-line>Legon</addr-line>
,<country>Ghana</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Ansah, Evelyn K" sort="Ansah, Evelyn K" uniqKey="Ansah E" first="Evelyn K" last="Ansah">Evelyn K. Ansah</name>
<affiliation><nlm:aff id="af3"><addr-line>Research and Development Division</addr-line>
,<institution>Ghana Health Service</institution>
,<addr-line>Accra</addr-line>
,<country>Ghana</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Agyepong, Irene Akua" sort="Agyepong, Irene Akua" uniqKey="Agyepong I" first="Irene Akua" last="Agyepong">Irene Akua Agyepong</name>
<affiliation><nlm:aff id="af4"><addr-line>Department of Health Policy Planning and Management</addr-line>
,<institution>School of Public Health, University of Ghana</institution>
,<addr-line>Legon</addr-line>
,<country>Ghana</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Grobbee, Diederick E" sort="Grobbee, Diederick E" uniqKey="Grobbee D" first="Diederick E" last="Grobbee">Diederick E. Grobbee</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Kayode, Gbenga A" sort="Kayode, Gbenga A" uniqKey="Kayode G" first="Gbenga A" last="Kayode">Gbenga A. Kayode</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Klipstein Grobusch, Kerstin" sort="Klipstein Grobusch, Kerstin" uniqKey="Klipstein Grobusch K" first="Kerstin" last="Klipstein-Grobusch">Kerstin Klipstein-Grobusch</name>
<affiliation><nlm:aff id="af1"><addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af5"><addr-line>Faculty of Health Sciences, Division of Epidemiology & Biostatistics</addr-line>
,<institution>School of Public Health, University of the Witwatersrand</institution>
,<addr-line>Johannesburg</addr-line>
,<country>South Africa</country>
</nlm:aff>
</affiliation>
</author>
</analytic>
<series><title level="j">BMJ Open</title>
<idno type="eISSN">2044-6055</idno>
<imprint><date when="2015">2015</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><sec><title>Objective</title>
<p>To identify demographic, maternal and community predictors of skilled attendance at delivery among women who attend antenatal clinic at least once during their pregnancy in Ghana.</p>
</sec>
<sec><title>Design</title>
<p>A cross-sectional study using the 2008 Ghana Demographic and Health Survey (DHS) data. We used frequencies for descriptive analysis, χ<sup>2</sup>
test for associations and logistic regression to identify significant predictors. Predictive models were built with estimation of area under the receiver operating characteristic curves (AUC).</p>
</sec>
<sec><title>Setting</title>
<p>Ghana.</p>
</sec>
<sec><title>Participants</title>
<p>A total of 2041 women who had a live birth in the 5 years preceding the survey, and attended an antenatal clinic having a skilled provider, at least once, during the pregnancy.</p>
</sec>
<sec><title>Outcome</title>
<p>Skilled attendance at delivery.</p>
</sec>
<sec><title>Results</title>
<p>Overall, 60.5% (1235/2041) of women in our study sample reported skilled attendance at delivery. Significant positive associations existed between skilled attendance at delivery and the variables such as maternal educational level, wealth status class, ever use of contraception, previous pregnancy complications and health insurance coverage (p<0.001). Significant predictors of skilled attendance were wealth status class, residency, previous delivery complication, health insurance coverage and religion in a model with AUC (95% CI) of 0.85 (0.83 to 0.88).</p>
</sec>
<sec><title>Conclusions</title>
<p>Women less likely to have skilled attendance at delivery can be identified during antenatal care by using data on wealth status class, health insurance coverage, residence, history of previous birth complications and religion, and targeted with interventions to improve skilled attendance at delivery.</p>
</sec>
</div>
</front>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMJ Open</journal-id>
<journal-id journal-id-type="iso-abbrev">BMJ Open</journal-id>
<journal-id journal-id-type="hwp">bmjopen</journal-id>
<journal-id journal-id-type="publisher-id">bmjopen</journal-id>
<journal-title-group><journal-title>BMJ Open</journal-title>
</journal-title-group>
<issn pub-type="epub">2044-6055</issn>
<publisher><publisher-name>BMJ Publishing Group</publisher-name>
<publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25991459</article-id>
<article-id pub-id-type="pmc">4442247</article-id>
<article-id pub-id-type="publisher-id">bmjopen-2015-007810</article-id>
<article-id pub-id-type="doi">10.1136/bmjopen-2015-007810</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Public Health</subject>
<subj-group><subject>Research</subject>
</subj-group>
</subj-group>
<subj-group subj-group-type="hwp-journal-coll"><subject>1506</subject>
<subject>1724</subject>
<subject>1692</subject>
<subject>1845</subject>
</subj-group>
</article-categories>
<title-group><article-title>Predictors of skilled attendance at delivery among antenatal clinic attendants in Ghana: a cross-sectional study of population data</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Amoakoh-Coleman</surname>
<given-names>Mary</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ansah</surname>
<given-names>Evelyn K</given-names>
</name>
<xref ref-type="aff" rid="af3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Agyepong</surname>
<given-names>Irene Akua</given-names>
</name>
<xref ref-type="aff" rid="af4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Grobbee</surname>
<given-names>Diederick E</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kayode</surname>
<given-names>Gbenga A</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Klipstein-Grobusch</surname>
<given-names>Kerstin</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af5">5</xref>
</contrib>
</contrib-group>
<aff id="af1"><label>1</label>
<addr-line>Julius Global Health</addr-line>
,<institution>Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht</institution>
,<addr-line>Utrecht</addr-line>
,<country>The Netherlands</country>
</aff>
<aff id="af2"><label>2</label>
<institution>School of Public Health, University of Ghana</institution>
,<addr-line>Legon</addr-line>
,<country>Ghana</country>
</aff>
<aff id="af3"><label>3</label>
<addr-line>Research and Development Division</addr-line>
,<institution>Ghana Health Service</institution>
,<addr-line>Accra</addr-line>
,<country>Ghana</country>
</aff>
<aff id="af4"><label>4</label>
<addr-line>Department of Health Policy Planning and Management</addr-line>
,<institution>School of Public Health, University of Ghana</institution>
,<addr-line>Legon</addr-line>
,<country>Ghana</country>
</aff>
<aff id="af5"><label>5</label>
<addr-line>Faculty of Health Sciences, Division of Epidemiology & Biostatistics</addr-line>
,<institution>School of Public Health, University of the Witwatersrand</institution>
,<addr-line>Johannesburg</addr-line>
,<country>South Africa</country>
</aff>
<author-notes><corresp><label>Correspondence to</label>
Mary Amoakoh-Coleman; <email>menba19@yahoo.com</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>19</day>
<month>5</month>
<year>2015</year>
</pub-date>
<volume>5</volume>
<issue>5</issue>
<elocation-id>e007810</elocation-id>
<history><date date-type="received"><day>29</day>
<month>1</month>
<year>2015</year>
</date>
<date date-type="rev-recd"><day>9</day>
<month>3</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>18</day>
<month>3</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-statement>Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="open-access"><license-p>This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</ext-link>
</license-p>
</license>
</permissions>
<self-uri xlink:title="pdf" xlink:href="bmjopen-2015-007810.pdf"></self-uri>
<abstract><sec><title>Objective</title>
<p>To identify demographic, maternal and community predictors of skilled attendance at delivery among women who attend antenatal clinic at least once during their pregnancy in Ghana.</p>
</sec>
<sec><title>Design</title>
<p>A cross-sectional study using the 2008 Ghana Demographic and Health Survey (DHS) data. We used frequencies for descriptive analysis, χ<sup>2</sup>
test for associations and logistic regression to identify significant predictors. Predictive models were built with estimation of area under the receiver operating characteristic curves (AUC).</p>
</sec>
<sec><title>Setting</title>
<p>Ghana.</p>
</sec>
<sec><title>Participants</title>
<p>A total of 2041 women who had a live birth in the 5 years preceding the survey, and attended an antenatal clinic having a skilled provider, at least once, during the pregnancy.</p>
</sec>
<sec><title>Outcome</title>
<p>Skilled attendance at delivery.</p>
</sec>
<sec><title>Results</title>
<p>Overall, 60.5% (1235/2041) of women in our study sample reported skilled attendance at delivery. Significant positive associations existed between skilled attendance at delivery and the variables such as maternal educational level, wealth status class, ever use of contraception, previous pregnancy complications and health insurance coverage (p<0.001). Significant predictors of skilled attendance were wealth status class, residency, previous delivery complication, health insurance coverage and religion in a model with AUC (95% CI) of 0.85 (0.83 to 0.88).</p>
</sec>
<sec><title>Conclusions</title>
<p>Women less likely to have skilled attendance at delivery can be identified during antenatal care by using data on wealth status class, health insurance coverage, residence, history of previous birth complications and religion, and targeted with interventions to improve skilled attendance at delivery.</p>
</sec>
</abstract>
<kwd-group><kwd>EPIDEMIOLOGY</kwd>
</kwd-group>
</article-meta>
</front>
<body><boxed-text position="float" orientation="portrait"><caption><title>Strengths and limitations of this study</title>
</caption>
<list list-type="bullet"><list-item><p>The use of nationally representative data that facilitates generalisability of results to pregnant women in Ghana.</p>
</list-item>
<list-item><p>An assessment of a combination of factors that significantly predict skilled attendance at delivery which has not been done for the study setting.</p>
</list-item>
<list-item><p>The Demographic and Health Survey (DHS) data are retrospectively collected data, and therefore has the chance of recall bias potentially affecting results.</p>
</list-item>
<list-item><p>Only surviving mothers were interviewed and this could have affected the prevalence of the outcome.</p>
</list-item>
<list-item><p>Our results are not applicable to the women who do not access antenatal care during pregnancy.</p>
</list-item>
</list>
</boxed-text>
<sec sec-type="intro" id="s1"><title>Introduction</title>
<p>Risk of maternal death continues to be high in sub-Saharan Africa (SSA) and Southeast Asia compared with the more developed parts of the world. In 2008, maternal deaths in SSA and Southern Asia accounted for 87% of global maternal deaths and progress to reverse this, especially in SSA, has been slow.<xref rid="R1" ref-type="bibr">1–7</xref>
Efforts to encourage utilisation of healthcare facilities should also be vigorously pursued for optimal results.</p>
<p>A skilled attendant is defined as “an accredited health professional—such as a midwife, doctor or nurse—who has been educated and trained to proficiency in the skills needed to manage normal (uncomplicated) pregnancies, childbirth and the immediate postnatal period, and in the identification, management and referral of complications in women and newborns”.<xref rid="R8" ref-type="bibr">8</xref>
<xref rid="R9" ref-type="bibr">9</xref>
Antenatal care and delivery by a skilled attendant both contribute to reduction in maternal deaths.<xref rid="R10" ref-type="bibr">10–17</xref>
The issue of who is a skilled attendant has been debated for some time, with respect to which category of health workers is ‘skilled’.<xref rid="R18" ref-type="bibr">18</xref>
In Ghana, these are obstetricians, general practitioners, midwives, auxiliary midwives and nurses with midwifery training, including community health nurses/officers.<xref rid="R19" ref-type="bibr">19</xref>
There is also evidence that there are variations in these categories, with respect to the extent of training and roles in different countries across the world.<xref rid="R20" ref-type="bibr">20–22</xref>
Delivery by any other person (health worker or non-health worker) is termed ‘unskilled attendance’ at delivery.</p>
<p>Despite the body of evidence of the importance of skilled attendance at delivery, there are records of high antenatal attendance, but low skilled attendance at delivery, especially in developing countries.<xref rid="R23" ref-type="bibr">23–26</xref>
In Tanzania, over 90% of pregnant women are known to attend antenatal clinic (ANC) at least once, with 62% attending four times. However, less than 50% of these ANC attendants have their deliveries attended to by skilled personnel.<xref rid="R3" ref-type="bibr">3</xref>
In a study in Uganda, although ANC attendance was found to be as high as 94%, this was not reflected in care at delivery, with about 25% of women being assisted during labour by a relative or friend.<xref rid="R13" ref-type="bibr">13</xref>
</p>
<p>In Ghana, according to the 2008 Ghana Demographic and Health Survey (DHS), about 97% of pregnant women received antenatal care during pregnancy, with as many as 78.2% attending at least four times. However, only 57.1% delivered in health facilities, with a total of 59% being assisted by skilled personnel.<xref rid="R19" ref-type="bibr">19</xref>
Several reasons have been attributed to this pattern, including access to health facilities, health worker attitude towards women during delivery and cultural issues, among others.<xref rid="R27" ref-type="bibr">27</xref>
</p>
<p>Some factors have also been quantitatively shown to be associated with skilled attendance at delivery. Maternal age is believed to influence the decision to take such an action, with younger women preferring skilled attendance at delivery due to perception of risk.<xref rid="R28" ref-type="bibr">28</xref>
This is closely linked with parity, where multiparous women with experience in labour tend to opt for other unskilled support during delivery due to the perception that they are experienced. Other factors such as marital status, wealth index, employment status and high educational background, especially secondary education, of both the woman and the husband has also been positively associated with the outcome.<xref rid="R3" ref-type="bibr">3</xref>
<xref rid="R23" ref-type="bibr">23</xref>
Living in rural areas where poverty is more prevalent compared with urban areas has been shown to be negatively associated with skilled delivery.<xref rid="R23" ref-type="bibr">23–26</xref>
<xref rid="R29" ref-type="bibr">29</xref>
Other notable factors that have positive influence on the outcome are short distance to health facility and availability of a birth-preparedness plan designed together with the woman during ANC.<xref rid="R3" ref-type="bibr">3</xref>
</p>
<p>Many interventions have been implemented to improve maternal health in Ghana, including but not limited to the Safe Motherhood Initiative (SMI), free delivery policy, High Impact Rapid Delivery (HIRD) and the Emergency Obstetric and Neonatal Care programme.<xref rid="R30" ref-type="bibr">30–32</xref>
The free delivery fee policy was first introduced in four regions in 2003, and subsequently extended to the entire country in 2005. Some studies have so far been conducted in Ghana to identify factors that influence skilled attendance at delivery.<xref rid="R33" ref-type="bibr">33</xref>
<xref rid="R34" ref-type="bibr">34</xref>
Secondary data analysis of Ghana DHS offers the opportunity to evaluate a larger sample across the country in order to identify demographic, maternal and community predictors of skilled attendance at delivery among women who attend ANC at least once during their pregnancy in Ghana. The question we seek to answer is whether there are possible significant predictors that will enable providers to identify women who are less likely to have skilled attendance at delivery from the antenatal care attendants, so that these women can be supported to have this desirable outcome. The aim of the study is to identify demographic, maternal, community and contextual predictors of skilled attendance at delivery among women who attend ANC at least once during their pregnancy.</p>
</sec>
<sec sec-type="methods" id="s2"><title>Methods</title>
<sec id="s2a"><title>Study design</title>
<p>Secondary data analysis of the data set of the 2008 Ghana DHS, a nationally representative population-based survey of 4305 women aged 15–49 years was conducted.</p>
</sec>
<sec id="s2b"><title>Data collection</title>
<p>Comprehensive information on the sampling techniques and survey procedures applied for data collection in the Ghana DHS have been published in detail elsewhere.<xref rid="R35" ref-type="bibr">35</xref>
In summary, the 10 regions of Ghana are each administratively subdivided into districts, while each district is divided into localities. Each locality is then further divided into enumeration areas based on the 2000 population census and these form the primary sampling units (PSUs). The PSU, defined for the purposes of this study as the cluster, was provided by the Ghana Statistical Service. A stratified two-stage cluster randomised sampling technique was applied. The first stage involved probability proportional sampling of a total of 412 PSUs (clusters) from all the regions, comprising of 182 clusters from the urban areas and 230 from rural areas. During the second stage, an average of 15 households was randomly sampled from each of the PSU using their household sampling frame. Finally, for half of the surveyed households, all eligible women aged 15–49 years were interviewed with a women's questionnaire. This had questions with socioeconomic, demographic and health indicators. Questionnaires were translated into three major local Ghanaian languages (Akan, Ga and Ewe) and were pretested on the field by trained personnel before finalisation for use. All respondents gave informed consent to participate in the survey. A total of 4916 women aged between 15 and 49 years were interviewed, with a 96.5% (4305) response rate. Out of these, the subset of women who had a live birth in the 5 years preceding the survey numbered 2099; 95.4% (2041) of them did receive antenatal care from a skilled provider (doctor, nurse, midwife, auxiliary midwife or community health officer) in the health system.<xref rid="R19" ref-type="bibr">19</xref>
Characteristics of these 2041 women were assessed in this study.</p>
<p>The index delivery studied is the latest delivery within the 5 years preceding the survey.</p>
<p>The 2008 DHS data are the latest available population-based data on health indicators that address the objectives of this study. Another survey was due in 2013, but is yet to be conducted.</p>
</sec>
<sec id="s2c"><title>Ethical approval</title>
<p>Ethical approval to conduct DHS in Ghana was approved by the Ethics Committee of ICF Macro in Calverton, USA, and the Ethics Committee, Ghana Health Service, Accra, Ghana. We obtained ethics approval for analysis of this data from the Ethics Committee of ICF Macro in Calverton, USA through an online request.</p>
</sec>
</sec>
<sec id="s3"><title>Variables</title>
<sec id="s3a"><title>Outcome</title>
<p>Skilled attendance at delivery was the outcome. This in Ghana, and as used in the DHS data collection and analysis, is defined as delivery by a doctor, nurse, midwife, auxiliary midwife or community health officer,<xref rid="R19" ref-type="bibr">19</xref>
with a response of either yes (1) or no (0).</p>
</sec>
<sec id="s3b"><title>Determinants</title>
<p>The factors included in this study are based on findings from other studies in published literature and availability in the 2008 DHS data set. These were categorised as demographic, maternal, community and contextual factors. All the variables were put into building the models to avoid preselection. <xref ref-type="table" rid="BMJOPEN2015007810TB1">Table 1</xref>
shows all the variables and their definitions.</p>
<table-wrap id="BMJOPEN2015007810TB1" orientation="portrait" position="float"><label>Table 1</label>
<caption><p>Definition of variables used in the study</p>
</caption>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="left" span="1"></col>
</colgroup>
<thead valign="bottom"><tr><th align="left" rowspan="1" colspan="1">Variables</th>
<th align="left" rowspan="1" colspan="1">Description</th>
</tr>
</thead>
<tbody><tr><td colspan="2" rowspan="1">Demographic</td>
</tr>
<tr><td rowspan="1" colspan="1"> Age</td>
<td rowspan="1" colspan="1">Age (years and categories) at last delivery within 5 years preceding survey (<20; 20–34; ≥35)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Highest educational level</td>
<td rowspan="1" colspan="1">Highest educational attainment (no education; primary; secondary and higher)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Marital status</td>
<td rowspan="1" colspan="1">Marital status as at interview date (never married; currently married; formerly married)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Ethnicity</td>
<td rowspan="1" colspan="1">Local ethnic group (Akan; Ga/Dangme; Ewe; Guan; Mole-Dagbani; Grussi, Gruma, other)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Religion</td>
<td rowspan="1" colspan="1">Christian; Muslim; traditional/spiritualist; no religion and other</td>
</tr>
<tr><td rowspan="1" colspan="1"> Employment</td>
<td rowspan="1" colspan="1">Employment status as at interview date (currently employed (yes or no))</td>
</tr>
<tr><td rowspan="1" colspan="1"> Wealth class status</td>
<td rowspan="1" colspan="1">Wealth quintiles categorised into three classes (low; middle; high)*</td>
</tr>
<tr><td rowspan="1" colspan="1"> Covered by health insurance</td>
<td rowspan="1" colspan="1">Having a viable health insurance registration (yes or no)</td>
</tr>
<tr><td colspan="2" rowspan="1">Maternal</td>
</tr>
<tr><td rowspan="1" colspan="1"> Ever use of contraception</td>
<td rowspan="1" colspan="1">Use of any method of contraception at any point in time by woman (yes or no)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Previous birth interval</td>
<td rowspan="1" colspan="1">Number of years between last delivery and the preceding delivery (<2; 2–5; 5+ years)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Complication during previous delivery</td>
<td rowspan="1" colspan="1">Whether woman had any complication during previous deliveries before last delivery (DHS uses caesarian section as proxy) (yes or no)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Birth order</td>
<td rowspan="1" colspan="1">The order in which the index child, whose delivery data are used for our study, was born: 1; 2–3; 4–5; 6+</td>
</tr>
<tr><td rowspan="1" colspan="1"> Gestational age at first ANC</td>
<td rowspan="1" colspan="1">Months pregnant at first ANC visit categorised into three trimesters of pregnancy</td>
</tr>
<tr><td rowspan="1" colspan="1"> Number of times attended ANC</td>
<td rowspan="1" colspan="1">Number of times woman attended ANC during last pregnancy (1; 2–3; 4+; don't know)</td>
</tr>
<tr><td colspan="2" rowspan="1">Community and contextual</td>
</tr>
<tr><td rowspan="1" colspan="1"> Region</td>
<td rowspan="1" colspan="1">Where in the 10 regions of Ghana woman lives (Western; Central; Greater Accra; Volta; Eastern ;Ashanti; Brong Ahafo; Northern; Upper East and West)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Residence</td>
<td rowspan="1" colspan="1">Whether woman's residence is rural or urban</td>
</tr>
<tr><td rowspan="1" colspan="1"> Type of city</td>
<td rowspan="1" colspan="1">Woman's residence described as capital or large city, small city; a town or country side</td>
</tr>
<tr><td rowspan="1" colspan="1"> Spouse's highest educational level</td>
<td rowspan="1" colspan="1">Highest educational level of spouse (no education; primary; secondary and higher)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Who has final say on woman's health</td>
<td rowspan="1" colspan="1">Whose decision on woman's health is paramount (respondent alone; respondent and partner; husband/partner/someone else alone)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Period of delivery</td>
<td rowspan="1" colspan="1">Whether last delivery was before or during the period of free cost of delivery, that is, before or after 2005</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>*Wealth measured using information on household ownership of consumer items, ranging from a television to a bicycle or car, as well as dwelling characteristics, such as source of drinking water, sanitation facilities and type of flooring material to produce an asset index.</p>
</fn>
<fn><p>ANC, antenatal clinic; DHS, Demographic and Health Survey.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3c"><title>Statistical analyses</title>
<p>Total missing data were more than 5% and this level of missing data is rarely random. Thus, multiple imputations of missing data were conducted and analysis was based on the data set with imputed data. We carried out descriptive univariate analysis to evaluate the prevalence of delivery by a skilled provider (outcome variable) across the categories of each of the determinants. We regrouped wealth quintiles into low (poorest and poorer quintiles), middle (middle quintile) and high (richer and richest quintiles) wealth status classes due to small numbers in some of the quintile groups.</p>
<p>Bivariate analysis using χ<sup>2</sup>
test was used to investigate the relationship between the independent variables and the categorical outcome variable, with detection of significant differences at p<0.05. We explored possible correlations between the variables region and birth order; region and religious groups; region and wealth status class; residence and wealth status class; and wealth status class and educational level.</p>
<p>Three predictive models were built using a backward stepwise elimination approach and all correlated variables were built into the models as interaction terms. Model 1 included the demographic and maternal characteristics; model 2 included the community and contextual factors; and model 3 combined demographics, maternal, community and contextual factors. Age was explored both as a categorical and continuous variable in building the models. Associations were estimated by ORs and their corresponding 95% CIs. Each OR is adjusted for the other covariates in the model. The area under the curve (AUC), that is, under the receiver operating characteristic curves, <italic>for</italic>
all the models were estimated.</p>
</sec>
</sec>
<sec sec-type="results" id="s4"><title>Results</title>
<sec id="s4a"><title>Background characteristics of the women</title>
<p><xref ref-type="table" rid="BMJOPEN2015007810TB2">Table 2</xref>
shows a summary of the characteristics of the 2041 women who for their latest delivery within the period of 2003 and 2008 had at least one ANC visit which was attended to by a health professional. The mean age (SD) of the women was 30 (7.24) years and this was the same for both groups of women who had skilled and unskilled attendance at delivery. As many as 89.0% of women were currently married during the survey and 86.8% of them were currently working. Only 41.4% of the women had secondary or higher education, and 35.3% had no education. Most of them were Christians (68%) and 20.1% were Muslims. Wealth distribution among this population was 50.2%, 17.7% and 32.1% for low, middle and high wealth status classes, respectively. Only 42.8% of the women had health insurance coverage at the time of the survey. Prevalence of ever use of any contraceptive method among the women was 60.0%. As many as 80.0% of women attended ANC at least four times during the pregnancy. Rural dwellers were 63.4%.</p>
<table-wrap id="BMJOPEN2015007810TB2" orientation="portrait" position="float"><label>Table 2</label>
<caption><p>Baseline characteristics of the study population and univariable associations of predictor variables with outcome</p>
</caption>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="char" char="(" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead valign="bottom"><tr><th align="left" rowspan="1" colspan="1">Variable</th>
<th align="left" rowspan="1" colspan="1">Frequency (%) (N=2041)</th>
<th align="left" rowspan="1" colspan="1">Per cent of unskilled delivery (N=807)</th>
<th align="left" rowspan="1" colspan="1">Per cent of skilled delivery (N=1234)</th>
<th align="left" rowspan="1" colspan="1">p Value for χ<sup>2</sup>
test</th>
</tr>
</thead>
<tbody><tr><td colspan="5" rowspan="1"><italic>Demographic and maternal factors</italic>
</td>
</tr>
<tr><td rowspan="1" colspan="1">Age (years)</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.05</td>
</tr>
<tr><td rowspan="1" colspan="1"> <20</td>
<td rowspan="1" colspan="1">102 (5.0)</td>
<td rowspan="1" colspan="1">44.1</td>
<td rowspan="1" colspan="1">55.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 20–34</td>
<td rowspan="1" colspan="1">1331 (65.2)</td>
<td rowspan="1" colspan="1">37.6</td>
<td rowspan="1" colspan="1">62.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> ≥35</td>
<td rowspan="1" colspan="1">608 (29.8)</td>
<td rowspan="1" colspan="1">42.9</td>
<td rowspan="1" colspan="1">57.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Highest educational level</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> No education</td>
<td rowspan="1" colspan="1">720 (35.3)</td>
<td rowspan="1" colspan="1">60.8</td>
<td rowspan="1" colspan="1">39.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Primary</td>
<td rowspan="1" colspan="1">476 (23.3)</td>
<td rowspan="1" colspan="1">41.4</td>
<td rowspan="1" colspan="1">58.6</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Secondary and higher</td>
<td rowspan="1" colspan="1">845 (41.4)</td>
<td rowspan="1" colspan="1">21.6</td>
<td rowspan="1" colspan="1">78.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Marital status</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.04</td>
</tr>
<tr><td rowspan="1" colspan="1"> Never married</td>
<td rowspan="1" colspan="1">112 (5.5)</td>
<td rowspan="1" colspan="1">28.6</td>
<td rowspan="1" colspan="1">71.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Currently married</td>
<td rowspan="1" colspan="1">1816 (89.0)</td>
<td rowspan="1" colspan="1">40.4</td>
<td rowspan="1" colspan="1">59.6</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Formerly married</td>
<td rowspan="1" colspan="1">113 (5.5)</td>
<td rowspan="1" colspan="1">37.2</td>
<td rowspan="1" colspan="1">62.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Ethnicity</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> Akan</td>
<td rowspan="1" colspan="1">806 (39.5)</td>
<td rowspan="1" colspan="1">29.2</td>
<td rowspan="1" colspan="1">70.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Ga/Dangme</td>
<td rowspan="1" colspan="1">96 (4.7)</td>
<td rowspan="1" colspan="1">38.5</td>
<td rowspan="1" colspan="1">61.5</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Ewe</td>
<td rowspan="1" colspan="1">256 (12.6)</td>
<td rowspan="1" colspan="1">32.8</td>
<td rowspan="1" colspan="1">67.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Guan</td>
<td rowspan="1" colspan="1">52 (2.6)</td>
<td rowspan="1" colspan="1">46.2</td>
<td rowspan="1" colspan="1">53.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Mole-Dagbani</td>
<td rowspan="1" colspan="1">524 (25.7)</td>
<td rowspan="1" colspan="1">52.1</td>
<td rowspan="1" colspan="1">47.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Grussi</td>
<td rowspan="1" colspan="1">111 (5.4)</td>
<td rowspan="1" colspan="1">43.2</td>
<td rowspan="1" colspan="1">56.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Gruma</td>
<td rowspan="1" colspan="1">108 (5.3)</td>
<td rowspan="1" colspan="1">75.9</td>
<td rowspan="1" colspan="1">24.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Other</td>
<td rowspan="1" colspan="1">86 (4.2)</td>
<td rowspan="1" colspan="1">25.6</td>
<td rowspan="1" colspan="1">74.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Religion</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> Christian</td>
<td rowspan="1" colspan="1">1389 (68.0)</td>
<td rowspan="1" colspan="1">33.1</td>
<td rowspan="1" colspan="1">66.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Muslim</td>
<td rowspan="1" colspan="1">410 (20.1)</td>
<td rowspan="1" colspan="1">42.8</td>
<td rowspan="1" colspan="1">57.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Traditionalist/spiritualist</td>
<td rowspan="1" colspan="1">149 (7.3)</td>
<td rowspan="1" colspan="1">80.4</td>
<td rowspan="1" colspan="1">19.6</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> No religion and other</td>
<td rowspan="1" colspan="1">89 (4.4)</td>
<td rowspan="1" colspan="1">54.8</td>
<td rowspan="1" colspan="1">45.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Employment (currently working)</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> No</td>
<td rowspan="1" colspan="1">269 (13.2)</td>
<td rowspan="1" colspan="1">33.3</td>
<td rowspan="1" colspan="1">66.7</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Yes</td>
<td rowspan="1" colspan="1">1772 (86.8)</td>
<td rowspan="1" colspan="1">40.5</td>
<td rowspan="1" colspan="1">59.5</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Wealth status class</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> Low</td>
<td rowspan="1" colspan="1">1024 (50.2)</td>
<td rowspan="1" colspan="1">60.7</td>
<td rowspan="1" colspan="1">39.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Middle</td>
<td rowspan="1" colspan="1">361 (17.7)</td>
<td rowspan="1" colspan="1">30.7</td>
<td rowspan="1" colspan="1">69.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> High</td>
<td rowspan="1" colspan="1">656 (32.1)</td>
<td rowspan="1" colspan="1">11.3</td>
<td rowspan="1" colspan="1">88.7</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Ever use of contraception</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> No</td>
<td rowspan="1" colspan="1">817 (40.0)</td>
<td rowspan="1" colspan="1">51.9</td>
<td rowspan="1" colspan="1">48.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Yes</td>
<td rowspan="1" colspan="1">1224 (60.0)</td>
<td rowspan="1" colspan="1">30.1</td>
<td rowspan="1" colspan="1">69.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Previous birth interval (years)</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> <2</td>
<td rowspan="1" colspan="1">990 (62.4)</td>
<td rowspan="1" colspan="1">42.4</td>
<td rowspan="1" colspan="1">57.6</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 2–5</td>
<td rowspan="1" colspan="1">236 (14.9)</td>
<td rowspan="1" colspan="1">48.3</td>
<td rowspan="1" colspan="1">51.7</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 5+</td>
<td rowspan="1" colspan="1">360 (22.7)</td>
<td rowspan="1" colspan="1">27.2</td>
<td rowspan="1" colspan="1">72.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Complication during previous delivery</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> No</td>
<td rowspan="1" colspan="1">1920 (94.1)</td>
<td rowspan="1" colspan="1">51.3</td>
<td rowspan="1" colspan="1">48.7</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Yes</td>
<td rowspan="1" colspan="1"> 121 (5.9)</td>
<td rowspan="1" colspan="1"> 4.9</td>
<td rowspan="1" colspan="1">95.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Birth order</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> 1</td>
<td rowspan="1" colspan="1">450 (22.0)</td>
<td rowspan="1" colspan="1">28.0</td>
<td rowspan="1" colspan="1">72.0</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 2–3</td>
<td rowspan="1" colspan="1">753 (36.9)</td>
<td rowspan="1" colspan="1">35.7</td>
<td rowspan="1" colspan="1">64.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 4–5</td>
<td rowspan="1" colspan="1">482 (23.6)</td>
<td rowspan="1" colspan="1">42.9</td>
<td rowspan="1" colspan="1">57.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 6+</td>
<td rowspan="1" colspan="1">356 (17.4)</td>
<td rowspan="1" colspan="1">57.6</td>
<td rowspan="1" colspan="1">42.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Gestational age at first ANC</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> 1st trimester</td>
<td rowspan="1" colspan="1">1179 (57.8)</td>
<td rowspan="1" colspan="1">34.2</td>
<td rowspan="1" colspan="1">65.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 2nd trimester</td>
<td rowspan="1" colspan="1">807 (39.5)</td>
<td rowspan="1" colspan="1">45.4</td>
<td rowspan="1" colspan="1">54.6</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 3rd trimester</td>
<td rowspan="1" colspan="1">55 (2.7)</td>
<td rowspan="1" colspan="1">64.2</td>
<td rowspan="1" colspan="1">35.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Number of times ANC attended</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> 1</td>
<td rowspan="1" colspan="1">60 (2.9)</td>
<td rowspan="1" colspan="1">75.0</td>
<td rowspan="1" colspan="1">25.0</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 2–3</td>
<td rowspan="1" colspan="1">293 (14.3)</td>
<td rowspan="1" colspan="1">65.9</td>
<td rowspan="1" colspan="1">34.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> 4+</td>
<td rowspan="1" colspan="1">1633 (80.0)</td>
<td rowspan="1" colspan="1">33.1</td>
<td rowspan="1" colspan="1">66.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Don't know</td>
<td rowspan="1" colspan="1">55 (2.7)</td>
<td rowspan="1" colspan="1">52.7</td>
<td rowspan="1" colspan="1">47.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Covered by health insurance</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> No</td>
<td rowspan="1" colspan="1">1167 (57.2)</td>
<td rowspan="1" colspan="1">49.4</td>
<td rowspan="1" colspan="1">50.6</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Yes</td>
<td rowspan="1" colspan="1"> 874 (42.8)</td>
<td rowspan="1" colspan="1">26.1</td>
<td rowspan="1" colspan="1">73.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td colspan="5" rowspan="1"><italic>Community and contextual factors</italic>
</td>
</tr>
<tr><td rowspan="1" colspan="1">Region</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> Western</td>
<td rowspan="1" colspan="1">179 (8.8)</td>
<td rowspan="1" colspan="1">43.0</td>
<td rowspan="1" colspan="1">57.0</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Central</td>
<td rowspan="1" colspan="1">145 (7.1)</td>
<td rowspan="1" colspan="1">37.2</td>
<td rowspan="1" colspan="1">62.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Greater Accra</td>
<td rowspan="1" colspan="1">201 (9.8)</td>
<td rowspan="1" colspan="1">11.9</td>
<td rowspan="1" colspan="1">88.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Volta</td>
<td rowspan="1" colspan="1">166 (8.1)</td>
<td rowspan="1" colspan="1">39.8</td>
<td rowspan="1" colspan="1">60.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Eastern</td>
<td rowspan="1" colspan="1">178 (8.7)</td>
<td rowspan="1" colspan="1">33.7</td>
<td rowspan="1" colspan="1">66.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Ashanti</td>
<td rowspan="1" colspan="1">310 (15.2)</td>
<td rowspan="1" colspan="1">23.9</td>
<td rowspan="1" colspan="1">76.1</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Brong Ahafo</td>
<td rowspan="1" colspan="1">199 (9.7)</td>
<td rowspan="1" colspan="1">33.7</td>
<td rowspan="1" colspan="1">66.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Northern</td>
<td rowspan="1" colspan="1">287 (14.0)</td>
<td rowspan="1" colspan="1">67.6</td>
<td rowspan="1" colspan="1">32.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Upper East</td>
<td rowspan="1" colspan="1">172 (8.5)</td>
<td rowspan="1" colspan="1">52.3</td>
<td rowspan="1" colspan="1">47.7</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Upper West</td>
<td rowspan="1" colspan="1">204 (10.0)</td>
<td rowspan="1" colspan="1">49.5</td>
<td rowspan="1" colspan="1">50.5</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Residence</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> Urban</td>
<td rowspan="1" colspan="1">748 (36.6)</td>
<td rowspan="1" colspan="1">13.1</td>
<td rowspan="1" colspan="1">86.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Rural</td>
<td rowspan="1" colspan="1">1293 (63.4)</td>
<td rowspan="1" colspan="1">54.8</td>
<td rowspan="1" colspan="1">45.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Type of city</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> Capital/large</td>
<td rowspan="1" colspan="1">315 (15.4)</td>
<td rowspan="1" colspan="1">10.2</td>
<td rowspan="1" colspan="1">89.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Small</td>
<td rowspan="1" colspan="1">263 (12.9)</td>
<td rowspan="1" colspan="1">16.0</td>
<td rowspan="1" colspan="1">84.0</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Town</td>
<td rowspan="1" colspan="1">468 (23.0)</td>
<td rowspan="1" colspan="1">29.5</td>
<td rowspan="1" colspan="1">70.5</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Country side</td>
<td rowspan="1" colspan="1">995 (48.8)</td>
<td rowspan="1" colspan="1">59.8</td>
<td rowspan="1" colspan="1">40.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Spouse's highest educational attainment</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.00</td>
</tr>
<tr><td rowspan="1" colspan="1"> No education</td>
<td rowspan="1" colspan="1">569 (27.9)</td>
<td rowspan="1" colspan="1">66.8</td>
<td rowspan="1" colspan="1">33.2</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Primary</td>
<td rowspan="1" colspan="1">179 (8.8)</td>
<td rowspan="1" colspan="1">43.6</td>
<td rowspan="1" colspan="1">56.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Secondary and higher</td>
<td rowspan="1" colspan="1">1293 (63.3)</td>
<td rowspan="1" colspan="1">26.0</td>
<td rowspan="1" colspan="1">74.0</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Who has final say on woman's health</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.02</td>
</tr>
<tr><td rowspan="1" colspan="1"> Respondent alone</td>
<td rowspan="1" colspan="1">446 (21.9)</td>
<td rowspan="1" colspan="1">36.2</td>
<td rowspan="1" colspan="1">63.8</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Respondent and partner</td>
<td rowspan="1" colspan="1">915 (44.8)</td>
<td rowspan="1" colspan="1">38.7</td>
<td rowspan="1" colspan="1">61.3</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Husband/partner/someone alone</td>
<td rowspan="1" colspan="1">680 (33.3)</td>
<td rowspan="1" colspan="1">45.1</td>
<td rowspan="1" colspan="1">54.9</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Period of delivery</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0.11</td>
</tr>
<tr><td rowspan="1" colspan="1"> Before free delivery policy</td>
<td rowspan="1" colspan="1">535 (26.2)</td>
<td rowspan="1" colspan="1">36.6</td>
<td rowspan="1" colspan="1">63.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> During free delivery policy</td>
<td rowspan="1" colspan="1">1506 (73.8)</td>
<td rowspan="1" colspan="1">40.6</td>
<td rowspan="1" colspan="1">59.4</td>
<td rowspan="1" colspan="1"></td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>ANC, antenatal clinic.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Prevalence of skilled attendance at delivery was 60.5%; most of the pregnant women were attended to by midwives and nurses (45.4%). Doctors conducted only 9% of all deliveries.</p>
</sec>
<sec id="s4b"><title>Comparability of the two outcome groups</title>
<p><xref ref-type="table" rid="BMJOPEN2015007810TB2">Table 2</xref>
also summarises the comparison between women who had skilled or unskilled attendance at delivery. There were significant differences (p<0.05) between those having skilled and unskilled attendance among all the categories of the characteristics studied, except for period of delivery as per the free delivery policy (p=0.11)<italic>.</italic>
Women with secondary and higher education had more skilled attendance at delivery compared with those without any education. About 67.0% of Christians had skilled attendance at delivery compared with 57.2% and 45.2% of Muslims and non-religious women, respectively. Women who had ever used a contraceptive method were more likely to have skilled attendance at delivery (69.9%). The proportion of skilled attendance at delivery decreased with increasing birth order; 95.1% of women who had a caesarean section during their previous pregnancy had skilled attendance at delivery compared with 48.7% of those who did not have that complication. The outcome was more prevalent (73.9%) among women with health insurance coverage compared with those without (50.6%). There were regional variations in the proportion of women who had skilled attendance at delivery. Only 45.0% of women in rural areas had skilled attendance at delivery compared with 86.7% in urban areas. We checked for correlation among variables, and birth order and timing of first ANC attendance were significantly correlated (p<0.001). Wealth status class was significantly different among the categories of region, rural or urban settlements, and educational levels (p<0.001)<italic>.</italic>
Birth order and religious group categories also varied across the various regions (p<0.001)<italic>.</italic>
These correlations were explored as interaction terms in building the predictive models.</p>
</sec>
<sec id="s4c"><title>Predictors of skilled delivery</title>
<p>Three models for predicting skilled delivery among the women in the survey, using a backward stepwise approach, were built. These are shown in <xref ref-type="table" rid="BMJOPEN2015007810TB3">table 3</xref>
. <italic>Model 1</italic>
consists of maternal factors and significant predictors were wealth quintile, history of previous delivery complication, having health insurance, birth order, number of times ANC was attended, the exact maternal age in years, religious group and ethnicity. Interaction terms explored were not significant predictors. <italic>Model 2</italic>
was built with community or cluster factors, and the significant predictors were residence (rural/urban), type of residence (capital/large city, small city and town or country side), spouse's highest educational level and the geographical region. In <italic>Model 3</italic>
, demographic, maternal and community factors were combined. Significant predictors were wealth status class, history of previous birth complication, health insurance coverage, residence and religious group. Again in this model, interaction terms included were not significant. The predictive probabilities of all three models are (c-statistics (95% CI)) 0.85 (0.82 to 0.88); 0.80 (0.78 to 0.82) and 0.85 (0.83 to 0.88), respectively, for models 1, 2 and 3. Model 3 combines both groups of variables, has the least number of variables and also the best fit, making it the best among the three for use in practice.</p>
<table-wrap id="BMJOPEN2015007810TB3" orientation="portrait" position="float"><label>Table 3</label>
<caption><p>Predictors of skilled attendance at delivery among pregnant women who attended ANC at least once during the pregnancy, Ghana Health and Demographic Survey, 2008</p>
</caption>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
</colgroup>
<thead valign="bottom"><tr><th align="left" rowspan="1" colspan="1">Predictor</th>
<th align="left" rowspan="1" colspan="1">β</th>
<th align="left" rowspan="1" colspan="1">p Value</th>
<th align="left" rowspan="1" colspan="1">OR* (95% CI)</th>
</tr>
</thead>
<tbody><tr><td colspan="4" rowspan="1"><italic>Demographic and maternal factors (model 1)</italic>
<break></break>
<italic>AUC (95% CI) 0.85 (0.82 to 0.88)</italic>
</td>
</tr>
<tr><td colspan="4" rowspan="1">Wealth status class</td>
</tr>
<tr><td rowspan="1" colspan="1"> Low</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Middle</td>
<td rowspan="1" colspan="1">1.34</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">3.82 (2.25 to 6.49)</td>
</tr>
<tr><td rowspan="1" colspan="1"> High</td>
<td rowspan="1" colspan="1">2.10</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">2.14 (1.09 to 4.20)</td>
</tr>
<tr><td rowspan="1" colspan="1">Complication during previous delivery (yes)</td>
<td rowspan="1" colspan="1">2.16</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">13.53 (2.63 to 69.47)</td>
</tr>
<tr><td rowspan="1" colspan="1">Covered by health insurance (yes)</td>
<td rowspan="1" colspan="1">1.10</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">3.00 (1.98 to 4.54)</td>
</tr>
<tr><td colspan="4" rowspan="1">Birth order</td>
</tr>
<tr><td rowspan="1" colspan="1"> 1</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> 2–3</td>
<td rowspan="1" colspan="1">−0.30</td>
<td rowspan="1" colspan="1">0.24</td>
<td rowspan="1" colspan="1">0.74 (0.45 to 1.22)</td>
</tr>
<tr><td rowspan="1" colspan="1"> 4–5</td>
<td rowspan="1" colspan="1">−0.88</td>
<td rowspan="1" colspan="1">0.02</td>
<td rowspan="1" colspan="1">0.42 (0.21 to 0.84)</td>
</tr>
<tr><td rowspan="1" colspan="1">Maternal age in years</td>
<td rowspan="1" colspan="1">0.05</td>
<td rowspan="1" colspan="1">0.04</td>
<td rowspan="1" colspan="1">1.05 (1.00 to 1.09)</td>
</tr>
<tr><td colspan="4" rowspan="1">Religious group</td>
</tr>
<tr><td rowspan="1" colspan="1"> Christian</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Muslim</td>
<td rowspan="1" colspan="1">−0.12</td>
<td rowspan="1" colspan="1">0.40</td>
<td rowspan="1" colspan="1">0.77 (0.43 to 1.40)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Traditionalist</td>
<td rowspan="1" colspan="1">−1.34</td>
<td rowspan="1" colspan="1">0.01</td>
<td rowspan="1" colspan="1">0.26 (0.10 to 0.67)</td>
</tr>
<tr><td rowspan="1" colspan="1"> No religion/other</td>
<td rowspan="1" colspan="1">−0.52</td>
<td rowspan="1" colspan="1">0.28</td>
<td rowspan="1" colspan="1">0.60 (0.23 to 1.54)</td>
</tr>
<tr><td colspan="4" rowspan="1">Ethnicity</td>
</tr>
<tr><td rowspan="1" colspan="1"> Akan</td>
<td rowspan="1" colspan="1">1.00</td>
<td rowspan="1" colspan="1">0.02</td>
<td rowspan="1" colspan="1">2.72 (1.14 to 6.46)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Ga/Dangme</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Ewe</td>
<td rowspan="1" colspan="1">1.53</td>
<td rowspan="1" colspan="1">0.03</td>
<td rowspan="1" colspan="1">4.62 (1.71 to 12.53)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Guan</td>
<td rowspan="1" colspan="1">0.43</td>
<td rowspan="1" colspan="1">0.60</td>
<td rowspan="1" colspan="1">1.55 (0.30 to 7.90)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Mole-Dagbani</td>
<td rowspan="1" colspan="1">0.62</td>
<td rowspan="1" colspan="1">0.21</td>
<td rowspan="1" colspan="1">1.87 (0.71 to 4.94)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Grussi</td>
<td rowspan="1" colspan="1">0.84</td>
<td rowspan="1" colspan="1">0.15</td>
<td rowspan="1" colspan="1">2.32 (0.74 to 7.30)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Gruma</td>
<td rowspan="1" colspan="1">0.77</td>
<td rowspan="1" colspan="1">0.20</td>
<td rowspan="1" colspan="1">2.15 (0.68 to 6.86)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Other</td>
<td rowspan="1" colspan="1">1.56</td>
<td rowspan="1" colspan="1">0.02</td>
<td rowspan="1" colspan="1">4.78 (1.30 to 17.56)</td>
</tr>
<tr><td colspan="4" rowspan="1"><italic>Community and contextual factors (model 2)</italic>
<break></break>
<italic>AUC (95% CI) 0.80 (0.78 to 0.82)</italic>
</td>
</tr>
<tr><td rowspan="1" colspan="1">Residence (rural)</td>
<td rowspan="1" colspan="1">−1.31</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">0.27 (0.18 to 0.41)</td>
</tr>
<tr><td rowspan="1" colspan="1">Type of city</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Capital/large</td>
<td rowspan="1" colspan="1">0.49</td>
<td rowspan="1" colspan="1">0.14</td>
<td rowspan="1" colspan="1">1.63 (0.85 to 3.10)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Small</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Town</td>
<td rowspan="1" colspan="1">0.03</td>
<td rowspan="1" colspan="1">0.92</td>
<td rowspan="1" colspan="1">1.03 (0.62 to 1.70)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Country side</td>
<td rowspan="1" colspan="1">−0.66</td>
<td rowspan="1" colspan="1">0.01</td>
<td rowspan="1" colspan="1">0.52 (0.31 to 0.88)</td>
</tr>
<tr><td colspan="4" rowspan="1">Spouse's highest educational level</td>
</tr>
<tr><td rowspan="1" colspan="1"> No education</td>
<td rowspan="1" colspan="1">−0.87</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">0.42 (0.28 to 0.63)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Primary</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Secondary and higher</td>
<td rowspan="1" colspan="1">0.30</td>
<td rowspan="1" colspan="1">0.12</td>
<td rowspan="1" colspan="1">1.36 (0.92 to 1.99)</td>
</tr>
<tr><td colspan="4" rowspan="1">Region</td>
</tr>
<tr><td rowspan="1" colspan="1"> Western</td>
<td rowspan="1" colspan="1">−1.53</td>
<td rowspan="1" colspan="1">0.60</td>
<td rowspan="1" colspan="1">0.86 (0.49 to 1.52)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Central</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Greater Accra</td>
<td rowspan="1" colspan="1">0.24</td>
<td rowspan="1" colspan="1">0.53</td>
<td rowspan="1" colspan="1">1.27 (0.60 to 2.70)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Volta</td>
<td rowspan="1" colspan="1">0.22</td>
<td rowspan="1" colspan="1">0.43</td>
<td rowspan="1" colspan="1">1.25 (0.72 to 2.19)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Eastern</td>
<td rowspan="1" colspan="1">0.32</td>
<td rowspan="1" colspan="1">0.26</td>
<td rowspan="1" colspan="1">1.38 (0.79 to 2.42)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Ashanti</td>
<td rowspan="1" colspan="1">0.50</td>
<td rowspan="1" colspan="1">0.06</td>
<td rowspan="1" colspan="1">1.65 (0.97 to 2.78)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Brong Ahafo</td>
<td rowspan="1" colspan="1">0.47</td>
<td rowspan="1" colspan="1">0.09</td>
<td rowspan="1" colspan="1">1.61 (0.93 to 2.79)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Northern</td>
<td rowspan="1" colspan="1">−0.47</td>
<td rowspan="1" colspan="1">0.09</td>
<td rowspan="1" colspan="1">0.63 (0.37 to 1.08)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Upper East</td>
<td rowspan="1" colspan="1">0.31</td>
<td rowspan="1" colspan="1">0.28</td>
<td rowspan="1" colspan="1">1.37 (0.78 to 2.41)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Upper West</td>
<td rowspan="1" colspan="1">0.60</td>
<td rowspan="1" colspan="1">0.03</td>
<td rowspan="1" colspan="1">1.83 (1.05 to 3.17)</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>Demographic, maternal, community and contextual factors (model 3)</italic>
<break></break>
<italic>AUC (95% CI) 0.85 (0.83 to 0.88)</italic>
<break></break>
<italic>(Interactions not significant in model)</italic>
</td>
<td rowspan="1" colspan="1"> </td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1">Wealth status class</td>
<td rowspan="1" colspan="1"> </td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"> Low</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
<tr><td rowspan="1" colspan="1"> Middle</td>
<td rowspan="1" colspan="1">0.63</td>
<td rowspan="1" colspan="1">0.04</td>
<td rowspan="1" colspan="1">1.88 (1.02 to 3.47)</td>
</tr>
<tr><td rowspan="1" colspan="1"> High</td>
<td rowspan="1" colspan="1">1.18</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">3.27 (1.60 to 6.68)</td>
</tr>
<tr><td rowspan="1" colspan="1">Residence (rural)</td>
<td rowspan="1" colspan="1">−1.31</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">0.27 (0.14 to 0.52)</td>
</tr>
<tr><td rowspan="1" colspan="1">Complication during previous delivery (yes)</td>
<td rowspan="1" colspan="1">3.09</td>
<td rowspan="1" colspan="1">0.04</td>
<td rowspan="1" colspan="1">21.93 (2.61 to 184.04)</td>
</tr>
<tr><td rowspan="1" colspan="1">Covered by health insurance (yes)</td>
<td rowspan="1" colspan="1">1.03</td>
<td rowspan="1" colspan="1">0.00</td>
<td rowspan="1" colspan="1">2.74 (1.83 to 4.30)</td>
</tr>
<tr><td colspan="4" rowspan="1">Religious group</td>
</tr>
<tr><td rowspan="1" colspan="1"> Christian</td>
<td rowspan="1" colspan="1">0.34</td>
<td rowspan="1" colspan="1">0.51</td>
<td rowspan="1" colspan="1">1.40 (0.52 to 3.76)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Muslim</td>
<td rowspan="1" colspan="1">−0.08</td>
<td rowspan="1" colspan="1">0.99</td>
<td rowspan="1" colspan="1">0.99 (0.34 to 2.87)</td>
</tr>
<tr><td rowspan="1" colspan="1"> Traditionalist/spiritualist</td>
<td rowspan="1" colspan="1">−0.78</td>
<td rowspan="1" colspan="1">0.24</td>
<td rowspan="1" colspan="1">0.46 (0.12 to 1.70)</td>
</tr>
<tr><td rowspan="1" colspan="1"> No religion/other</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
<td rowspan="1" colspan="1">†</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>*Each OR is adjusted for the other covariates in the model.</p>
</fn>
<fn><p>†Reference category.</p>
</fn>
<fn><p>ANC, antenatal clinic; AUC, area under the curve.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s4d"><title>Model fit statistics</title>
<p>There was a significant increase in the c-statistic (AUC) estimates for model 3 compared with models 1 and 2. More importantly, there was a progressive increase in R<sup>2</sup>
value observed in model 1 when we fitted models 2 and 3. This implies that model 3 explains the predictors better and can, thus, be considered to be the most accurate model for application in clinical practice.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s5"><title>Discussion</title>
<sec id="s5a"><title>Main findings</title>
<p>The role of skilled attendance at delivery in averting maternal morbidity and mortality cannot be overemphasised as being the single most important intervention for safe motherhood.<xref rid="R22" ref-type="bibr">22</xref>
<xref rid="R36" ref-type="bibr">36</xref>
</p>
<p>Highly educated women are significantly more likely to have skilled attendance at delivery compared with women with no education and previous studies have highlighted this finding.<xref rid="R33" ref-type="bibr">33</xref>
<xref rid="R37" ref-type="bibr">37–39</xref>
This emphasises the importance of female education for achievement of Millennium Development Goal (MDG) 5. The relationship between cost and access to healthcare, specifically, maternal health services, has been explored in various studies and the summary of findings agrees with what we also found.<xref rid="R28" ref-type="bibr">28</xref>
<xref rid="R40" ref-type="bibr">40</xref>
<xref rid="R41" ref-type="bibr">41</xref>
Wealthier women in Ghana, as well as those with health insurance coverage, are more likely to have skilled delivery compared with poor women. Family planning services uptake is an important indicator for utilisation of maternal health services,<xref rid="R42" ref-type="bibr">42</xref>
<xref rid="R43" ref-type="bibr">43</xref>
which leads to improved outcomes.<xref rid="R44" ref-type="bibr">44</xref>
In our study, ever use of contraception increases the chance of having skilled attendance at delivery. Utilisation of skilled attendants is also preventive and that is possibly why women who have had previous pregnancy complications prefer to have a skilled attendant for subsequent deliveries, and thus reduce their vulnerability.</p>
<p>On the community level, we observed previously noted rural–urban disparities.<xref rid="R28" ref-type="bibr">28</xref>
<xref rid="R40" ref-type="bibr">40</xref>
<xref rid="R45" ref-type="bibr">45</xref>
The opportunities for higher education and improved wealth status, among other factors, are undeniably few in rural areas<xref rid="R46" ref-type="bibr">46</xref>
<xref rid="R47" ref-type="bibr">47</xref>
and when these were adjusted for in the prediction model, women from rural areas still had less prevalence in the outcome. Perhaps other factors such as geographical access to healthcare are at play here, but the data we used did not allow us to explore this in any meaningful way. Apart from rural–urban disparities, we also noted marked regional disparities for skilled delivery. The three Northern regions, which have the lowest prevalence of the outcome, are mostly rural, with low wealth class status, lower levels of education and are predominantly Muslim compared with the rest of the population. These factors are among our significant predictors of the outcome.</p>
<p>Contextual issues also come into play. The observed influence of spousal education emphasises the role of an environment of high literacy on maternal outcomes. Women who have other people, including their partners, participating in the final decision on their health have a reduced chance of having skilled attendance at delivery from our results. ANC attendance and counselling for couples could be explored as a strategy to improve knowledge of the significant others and eventually improve outcomes as has been shown for HIV/AIDS prevention interventions.<xref rid="R48" ref-type="bibr">48</xref>
Deliveries after the introduction of free delivery policy were observed to be less attended to by skilled professionals. This is likely to be due to the gradual regional roll out of the policy across the country, the lack of knowledge about the policy and general implementation challenges, as previously observed in studies that evaluated use of free health services.<xref rid="R49" ref-type="bibr">49</xref>
<xref rid="R50" ref-type="bibr">50</xref>
</p>
<p>Among ANC attendants, providers of care can adequately identify women who are likely to have skilled delivery (and therefore, those who are unlikely to) using the information on their wealth status class, history of previous birth complication, health insurance coverage, rural or urban residence, and religious group. This provides an opportunity to use routinely collected data to enhance service delivery and improve health outcomes.</p>
</sec>
<sec id="s5b"><title>Application of this prediction</title>
<p>This prediction ability is only useful when situated within the availability of effective interventions that encourage skilled attendance at delivery. Birth preparedness is an important component of the counselling at ANC that women are expected to receive. Key components of the birth plan include recognition of danger signs, a plan for a skilled birth attendant, a plan for the place of delivery, and saving money for transport or other costs in case the need arises.<xref rid="R51" ref-type="bibr">51</xref>
Applying the results of our study, providers will be able to sort out ANC attendants into those who are likely to use skilled attendance at delivery and those who are unlikely to do so. Those who are unlikely can be supported during the period of pregnancy to access skilled delivery.</p>
<p>For example, uninsured pregnant women can be encouraged to register for health insurance at the onset of the pregnancy or whenever they sign up for ANC, so that by the time of delivery the cost of care will be covered by insurance. If need be, discussions can take place with the spouse or any significant other so that money can be set aside for any eventualities. At the community level, where home visits by community health workers are possible, women who are less likely to have the outcome can have more purposeful visits by health workers that will ensure that they improve their chances of having skilled attendance at delivery.</p>
<p>These predictors could form the basis of a very useful clinical decision-making tool for providers. We advocate for future validation of these predictors in a prospective study and in a health facility since all the demographic and maternal data of pregnant women attending ANC in this study are captured at ANC registration. On validation, the model can be incorporated into health facility antenatal protocols and other job aids that ensure that health workers identify and practically support pregnant women to opt for skilled attendance at delivery.</p>
</sec>
<sec id="s5c"><title>Strengths and limitations of the study</title>
<p>Strength of this study is the national representative sample that facilitates generalisability of the study results to pregnant women in Ghana. It also not only assesses possible associations but also considers a combination of factors that significantly predict the outcome. However, the DHS data are retrospectively collected data, and therefore has some limitations. There is the chance of recall bias potentially affecting results. Only surviving mothers were interviewed and this could have affected the prevalence of the outcome. Also, we were unable to study other variables that possibly influence the outcome, for which data are not available in the 2008 DHS database. Last but not the least, our results will not apply to the women who do not access antenatal care during pregnancy.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="s6"><title>Conclusion</title>
<p>Women less likely to have skilled attendance at delivery can be identified during antenatal care using data on wealth status class, health insurance coverage, residence, history of previous birth complication and religion, and can be targeted with interventions to improve skilled attendance at delivery.</p>
</sec>
</body>
<back><ack><p>The authors gratefully acknowledge technical support from the Julius Center for Health Sciences and Primary Care.</p>
</ack>
<fn-group><fn><p><bold>Contributors:</bold>
MA-C designed and wrote up the study protocol, acquired permission from Measure DHS to use data, carried out data analysis, wrote the report and drafted this manuscript for publication. GAK, IAA, DEG, EKA and KK-G provided scientific guidance and did review of the study design, data analysis, and were also actively involved in the preparation and review of the manuscript and approved it.</p>
</fn>
<fn><p><bold>Funding:</bold>
Funding for the conduct of the study was from the Netherlands Organization for Scientific Research (NWO) Global Health Policy and 396 Health Systems Research Program, the Netherlands (Grant number: 07.45.102.00). They supported authors MA-C and GAK as PhD candidates.</p>
</fn>
<fn><p><bold>Competing interests:</bold>
Authors MA-C and GAK had financial support from the Netherlands Organization for Scientific Research (NWO) Global Health Policy and 396 Health Systems Research Program, the Netherlands, for the submitted work.</p>
</fn>
<fn><p><bold>Provenance and peer review:</bold>
Not commissioned; externally peer reviewed.</p>
</fn>
<fn><p><bold>Data sharing statement:</bold>
Secondary data were used for this study. These data are public and freely available to anyone from MEASURE DHS, on request. The website for MEASURE DHS is <ext-link ext-link-type="uri" xlink:href="http://dhsprogram.com/data/available-dataset.cfm">http://dhsprogram.com/data/available-dataset.cfm</ext-link>
.</p>
</fn>
</fn-group>
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