University of Ghana http://ugspace.ug.edu.gh REGIONAL INSTITUTE FOR POPULATION STUDIES (RIPS) UNIVERSITY OF GHANA, LEGON HOUSEHOLD CHARACTERISTICS AND MATERNAL MORTALITY IN GHANA BY MICHAEL FOLI (10700995) THIS DISSERTATION IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF MASTER OF ARTS (MA) POPULATION STUDIES DEGREE JULY, 2019 i University of Ghana http://ugspace.ug.edu.gh ACCEPTANCE Accepted by the College of Humanities, University of Ghana, Legon, in partial fulfillment of the requirement for Master of Arts in Population Studies. Supervisor: Dr. Ayaga Agula Bawah …………………………… ……………………….. Signature Date ii University of Ghana http://ugspace.ug.edu.gh DECLARATION I MICHAEL FOLI hereby declare that, except for references to other people’s work which have been duly acknowledged, this thesis is the result of my own research and that it has neither in part nor in whole been presented for another degree elsewhere. Student…………………………………… Date………………………………… Michael Foli (10700995) iii University of Ghana http://ugspace.ug.edu.gh DEDICATION I dedicate this work to the best mother in the world; Madam Gertrude Ama Amedzi, and the best wife in the world; Jennifer Ama Dogbe. iv University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENT The unconditional love of God, how can I repay? I am grateful to all who contributed in diverse ways to this success especially, my siblings Eugene Sapathy and Emmanuel Agbesi Foli. I am particularly grateful to my supervisor Dr. Ayaga Agula Bawah for his guidance and support. Also, to all my colleagues at the Births and Deaths Registry, Kpando and RIPS, especially to Ruth Oluwatobi Sawyerr, I say thank you. v University of Ghana http://ugspace.ug.edu.gh ABSTRACT Pregnancy is a pre-condition to reproduction and childbearing. However, the life-threatening risks associated with pregnancy and childbirth have turned the whole process into one full of anxiety and fear as some women never make it alive. Upon several efforts by governments and international organizations, it is sad to still see families losing their loved ones during pregnancy or childbirth especially in our part of the world. Numerous studies tend to focus on the provision and utilization of health care services for women or government efforts targeted at reducing the maternal mortality canker. This study takes it a step further by looking at the relationship between maternal mortality and the household characteristics of the deceased women taking into account the family system in Africa in which decisions are usually made at the household level, and such decisions being influenced by the characteristics of the household. The study made use of the 2017 Ghana Maternal Health Survey in which women within the reproductive age (15-49) answered several questions concerning their reproductive health, utilization of health facilities, and their socio-demographic characteristics. A verbal autopsy was also conducted in households in which a female aged 12-49 has died in the five years prior to the survey. The study used binary logistic regression to assess the relationship between household characteristics, individual characteristics and maternal mortality. The results showed that the household characteristics were not significant predictors of maternal mortality but rather the individual characteristics like age and marital status were found to be significantly associated with maternal mortality. Based on the findings, it was recommended that the government put more effort into educating adolescents on maternal health issues since they are the most vulnerable group. vi University of Ghana http://ugspace.ug.edu.gh LIST OF ABBREVIATIONS AND ACRONYMS WHO - World Health Organization MCH - Maternal and Child Health U.N - United Nations UNICEF - United Nations Children’s Fund MDG - Millennium Development Goal NHIS - National Health Insurance Scheme LEAP - Livelihood Empowerment Against Poverty CHPS - Community-based Health Planning and Services MMR - Maternal Mortality Ratio HIV - Human Immune Virus AIDS - Acquired Immune Deficiency Virus MEP - Maternal Exemption Policy ANC - Antenatal Care DHS - Demographic and Health Survey GMHS - Ghana Maternal Health Survey GPHC - Ghana Population and Housing Census EA - Enumeration Area vii University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS ACCEPTANCE ............................................................................................................................. ii DECLARATION.......................................................................................................................... iii DEDICATION ............................................................................................................................... iv ACKNOWLEDGEMENT .............................................................................................................. v ABSTRACT ................................................................................................................................... vi LIST OF ABBREVIATIONS AND ACRONYMS ..................................................................... vii TABLE OF CONTENTS ............................................................................................................. viii LIST OF FIGURES ....................................................................................................................... xi LIST OF TABLES ........................................................................................................................ xii CHAPTER ONE ......................................................................................................................... 1 INTRODUCTION ....................................................................................................................... 1 1.0 Background to the study ........................................................................................................ 1 1.1 Problem statement ................................................................................................................. 3 1.2 Research Questions ............................................................................................................... 5 1.3 Objectives of the study .......................................................................................................... 6 1.4 Rational of the study ............................................................................................................. 6 1.5 Organization of the Study ..................................................................................................... 7 CHAPTER TWO ............................................................................................................................ 8 LITERATURE REVIEW ............................................................................................................ 8 2.0 Introduction ........................................................................................................................... 8 2.1 Causes of maternal mortality................................................................................................. 8 2.2 Measurement of maternal mortality .................................................................................... 11 2.3 Efforts to reduce Maternal Mortality .................................................................................. 12 2.4 Household characteristics .................................................................................................... 15 2.4.1 Sex of household head and maternal mortality ............................................................ 17 2.4.2 Educational level of the head of household and maternal mortality ............................ 18 2.4.3 Place of residence and mortality ................................................................................... 20 viii University of Ghana http://ugspace.ug.edu.gh 2.4.4 Region of residence and mortality ................................................................................ 21 2.4.5 Household Wealth index and maternal mortality ......................................................... 22 2.5 Theoretical framework ........................................................................................................ 23 2.6 Conceptual framework ........................................................................................................ 23 2.7 Hypothesis ........................................................................................................................... 26 CHAPTER THREE ...................................................................................................................... 27 METHODOLOGY .................................................................................................................... 27 3.1 Introduction ......................................................................................................................... 27 3.2 Data source .......................................................................................................................... 27 3.3 Sampling design .................................................................................................................. 28 3.4 Measurement of variables ................................................................................................... 29 3.4.1 Independent Variables ............................................................................................... 29 3.4.2 Dependent Variable .................................................................................................... 31 3.5 Methods of data analysis ..................................................................................................... 31 3.5.1 Univariate Analysis..................................................................................................... 32 3.5.2 Bivariate Analysis ....................................................................................................... 32 3.5.3 Multivariate Analysis ................................................................................................. 32 3.6 Data limitation ..................................................................................................................... 33 CHAPTER FOUR ......................................................................................................................... 34 4.0 Introduction ......................................................................................................................... 34 4.1 Sex of Household Head ....................................................................................................... 34 4.2 Age of household heads ...................................................................................................... 35 4.3 Level of education of household heads ............................................................................... 36 4.4 Place of residence ................................................................................................................ 37 4.5 Region of residence ............................................................................................................. 38 4.6 Household wealth Quintile .................................................................................................. 39 4.7 Household Size .................................................................................................................... 40 ix University of Ghana http://ugspace.ug.edu.gh 4.8 Age of individual ................................................................................................................. 41 4.9 Educational level of individual ........................................................................................... 42 4.10 Marital status of individual ............................................................................................... 43 4.11 Health facility utilization ................................................................................................... 44 4.7 Maternal mortality ............................................................................................................... 46 CHAPTER FIVE .......................................................................................................................... 47 BIVARIATE AND MULTIVARIATE ANALYSIS ................................................................... 47 5.0 Introduction ......................................................................................................................... 47 5.1 BIVARIATE ANALYSIS .................................................................................................. 48 5.1.1 Sex of household head and maternal mortality ............................................................ 48 5.1.2 Age of household head and maternal mortality ............................................................ 48 5.1.3 Educational level of Household head and maternal mortality ...................................... 49 5.1.4 Place of residence and maternal mortality .................................................................... 50 5.1.5 Region of residence and maternal mortality ................................................................. 51 5.1.6 Household size and maternal mortality ........................................................................ 53 5.1.7 Household wealth quintile and maternal mortality....................................................... 54 5.1.8 Age of individual and maternal death ........................................................................... 55 5.1.9 Educational level of individual and maternal mortality ............................................... 56 5.1.10 Marital status of individual and maternal death ......................................................... 57 5.1.11 Health facility utilization and maternal mortality ....................................................... 58 5.2 MULTIVARIATE ANALYSIS .......................................................................................... 59 CHAPTER SIX ............................................................................................................................. 66 SUMMARY, CONCLUSION, AND RECOMMENDATIONS .............................................. 66 6.1 Introduction ......................................................................................................................... 66 6.2 Summary of findings ........................................................................................................... 66 6.3 Conclusion ........................................................................................................................... 67 6.4 Recommendations ............................................................................................................... 68 REFERENCES ............................................................................................................................. 69 x University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Figure 2.1: Percentage distribution of the causes of maternal death globally…………………10 Figure 2.2: Conceptual Framework……………………………………………………………26 Figure 4.1: Percentage distribution of household heads by sex…...…………………………...37 Figure 4.2: Percentage distribution by wealth quintile………………………………………...43 Figure 4.3: Percentage distribution by household size………………………………………...44 Figure 4.4: Percentage distribution by educational level of individual…..……………………48 Figure 4.5: Percentage distribution of by marital status of individual…………...…………….49 Figure 4.6: Percentage distribution by health facility utilization………………..…………….50 Figure 4.7: Percentage distribution by maternal mortality...…………..………………………51 xi University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Table 4.1: Percentage distribution of household heads by age...………………………………...38 Table 4.2: Percentage distribution of household head by level of education ...…………………39 Table 4.3: Percentage distribution by place of residence………………………………………..40 Table 4.4: Percentage distribution by of region of residence……………………………………41 Table 4.5: Percentage distribution of individuals by age………………………………………..45 Table 5.1: Percentage distribution of sex of household head and maternal mortality…………..50 Table 5.2: Percentage distribution of age of Household head and maternal mortality……….…51 Table 5.3: Percentage distribution of educational level of HH and maternal mortality………...52 Table 5.4: Percentage distribution of Place of residence and maternal mortality………………53 Table 5.5: Percentage distribution of region of residence and maternal mortality……………...54 Table 5.6: Percentage distribution of household size and maternal mortality…………………..55 Table 5.7: Percentage distribution of Wealth Quintile and maternal mortality………………....56 Table 5.8: Percentage distribution of age of individual and maternal mortality………..………57 Table 5.9: Percentage distribution of Educational level of individual and maternal death……..58 Table 5.10 Percentage distribution of marital status of individual and maternal mortality……..59 Table 5.11 Percentage distribution of health facility utilization and maternal mortality…….….60 Table 5.12: Variations in maternal death by selected household characteristics…………………………………………………………………………................64 Table 5.13: Variations in maternal mortality by household characteristics, selected individual demographics, and the intermediate variable……………………………………………………65 xii University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.0 Background to the study Pregnancy and childbirth are supposed to be a source of joy to mankind and to the African whom childbearing is a way of extending the family lineage, being infertile or deciding not to have a child is a source of worry to most families. However, the journey from conception to delivery is a tedious one and many women end up losing their lives. Even in this contemporary time, maternal death still remains a major stumbling stone in regards to development in the public health domain notwithstanding the various measures put in place by the international community and governments around the world to halt it. Maternal mortality is defined as the death of a woman while pregnant or within 42 days of termination of the pregnancy (or giving birth) regardless of the gestational age and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes (WHO, 1992). Maternal mortality tops the list of causes of deaths among females within the reproductive age (U.N, 2009). According to the WHO, about 830 female deaths occur every day due to pregnancy and childbirth-related complications which are avoidable (WHO, 2018). Almost all (99%) maternal deaths occur in economically poor regions and globally, Sub-Saharan Africa is home to a little over half of these deaths; Sub-Saharan Africa has the highest maternal mortality ratio as 546 maternal deaths are witnessed for every 100,000 live births. Accordingly, this translates to 201,000 maternal deaths per annum (WHO, 2006). The maternal mortality problem started getting serious international attention when a thought-provoking article titled Maternal Mortality a 1 University of Ghana http://ugspace.ug.edu.gh neglected tragedy; where is the M in the MCH? was published in The Lancet in 1985 by Rosenfield and Maine. This article put the spotlight on how majority of the third world countries were neglecting the serious problems faced by pregnant women and critique the existing intervention policies which the writers perceived would not be sufficient to bring the high prevalence of maternal deaths under control. The WHO then followed in 1986 with an article titled ‘Maternal Mortality: helping women off the road to death’ which drew more light on the maternal mortality canker. This gave birth to a number of international conferences on issues of health, population and development. Some of the conferences include the 1990 World Summit for Children which was organized by the U.N and chaired by UNICEF, the 1994 International Conference on Population and Development also organized by the U.N and held in Cairo, Egypt and the 1995 Fourth World Conference on Women which was held in Beijing, China. The main theme of these conferences was halving the 1990 maternal mortality rates by the year 2000. The attention given to maternal mortality by various governments then culminated in the Millennium Summit in 2000, with the addition of maternal health improvement as a fifth target (MDG 5) to the Millennium Development Goals. In 2015, world leaders further incorporated maternal well-being into the Sustainable Development Goals (SDGs). Goal 3; ‘Ensure healthy lives and promote well-being for all at all ages’’ seeks to reduce maternal mortality to less than 70 deaths per 100,000 live births by 2030. Maternal mortality is widely viewed as a litmus test of the quality of a health care delivery system and as the main measure of health and socio-economic development (Wilmoth et al., 2012). It not only provides information on the risk of pregnancy and childbirth but also the status of women, quality of health systems and health delivery services. Therefore the international community 2 University of Ghana http://ugspace.ug.edu.gh setting the reduction of maternal mortality as a priority only reflects its importance as a measure of human and social development (Mojekwu, 2005). Reducing maternal mortality has been a global priority for more than two decades now. From 1990 to 2015, the maternal mortality ratio worldwide was dropping by only 2.3 percent per year. But it’s worth noting that, the introduction of the MDG 5 in 2000, accelerated declines in maternal mortality and as high as 5.5 percent decline per year was witnessed in some countries between 2000 and 2010. This had a worldwide ramification where global maternal mortality dropped by about 44 percent (down from 385 to 216 deaths) between 1990 and 2015 according to U.N Inter- Agency Estimates (WHO, 2015). Although this is impressive, the 2.3 percent annual decrease still falls short of the three-quarters decline in maternal mortality targeted for 2015 in MDG 5. 1.1 Problem statement Maternal mortality, in general, reduces the life expectancy of a country. In a country where the population is exposed to maternal death, the human resource base is reduced and this may consequently affect productivity. Even though every country at least made efforts in terms of reducing maternal mortality, levels are still embarrassingly high in Sub-Saharan Africa. In the developed world for instance, maternal deaths decreased from 69 to 25 deaths per 100,000 live births between 1990 and 2015 while in Sub-Saharan Africa, it only managed to drop from 987 deaths to 546 deaths between 1990 and 2015. As of 2015, the 25 countries on top of the list of the highest maternal mortality ratio are all from Sub-Saharan Africa with Sierra Leon leading the pack with 1360 deaths per 100,000 live 3 University of Ghana http://ugspace.ug.edu.gh births compared to Poland at the very bottom of the list with 3 deaths per 100,000 live births. (CIA World Factbook, 2015). Various governmental programmes and policies were initiated to improve maternal and child health as well as elevate household economic status in Ghana. Some of these policies include the promulgation of the National Health Insurance Scheme (NHIS) law in 2003, free maternal healthcare between 2005 and 2008, the commencement of the Livelihood Empowerment Against Poverty (LEAP) in 2012, and the introduction of Community-based Health Planning and Services (CHPS). The NHIS was established to abolish user fees and ensure more equitable access to health care. Through the NHIS also, the government’s introduction of the free maternal health care was to reduce maternal deaths and reduce financial costs associated with healthcare service. Furthermore, the LEAP project acts as a community cash transfer programme that provides money and health insurance to the very poor households within the country to reduce poverty and improve long term human capital development. In addition, the CHPS compound health zones are to reduce the indirect cost of health, related to travelling to the nearest health facility. Although these interventions have led Ghana to make significant progress during the past 10 years, challenges of inequalities, geographical disparities and sustaining progress still remain (ISSER, 2013). Between 1990 and 2005, MMR came down from 740 to 503 per 100,000 live births and then to 451 deaths per 100,000 live births in 2008 (GSS, 2009). According to a report by Maternal Mortality Estimation Inter-Agency Group (MMEIG) of the U. N, Ghana’s MMR increased from 201 in 2008 to 380 deaths per 100 000 live births in 2014 (U.N-MMEIG, 2015). The report further estimated that about 3,100 females died from pregnancy-related complications in the year 2013 alone. The MMR then slightly dropped to 310 deaths per 100,000 live births in 2017 (GSS et al., 4 University of Ghana http://ugspace.ug.edu.gh 2018). Thus, even though Ghana seems to be doing well in the Sub-Saharan region in terms of maternal health by halving maternal mortality ratio in two decades, MMR is still very high compared to the rest of the world. A Ghanaian woman's risk of dying from pregnancy-related causes over the course of her lifetime is about 1 in 36, compared to 1 in every 7,300 women in the developed world (Hatt et al., 2009). In 2015, the lifetime risk of maternal death in low-income countries as a whole was 1 in 41 compared to 1 in 3300 in high-income countries. (UNICEF, 2017). This means that despite the various interventions to reduce maternal mortality not much progress has been made. Evidence of the association of various measures of socioeconomic status and maternal health outcomes exists across and within countries (Agyemang et al., 2009). Unfortunately, most studies on maternal mortality tend to focus mainly on provision and utilization of health care and government actions and not much attention has been paid to the household conditions or characteristics which have been known to impact on maternal health and survival. This study seeks to fill that gap. 1.2 Research Questions I. Do characteristics of the household impact on the health of women and thus maternal mortality? II. If so, what is the relationship between household characteristics and maternal mortality? III. Are there other factors that impact on maternal mortality other than characteristics of the household? 5 University of Ghana http://ugspace.ug.edu.gh 1.3 Objectives of the study The general objective of the study is to examine the relationship between household characteristics and maternal death vis-à-vis death from other causes. The specific objectives are to; I. Examine the relationship between household characteristics and maternal mortality II. Examine if other factors such as demographic and other socioeconomic factors also affect maternal mortality 1.4 Rationale of the study Maternal mortality ratio selected as a basic measure of Millennium Development Goal 5 on enhancing maternal health has yielded interest in programmes to improve maternal health and having reliable sources of data on the prevalence of maternal mortality within countries. Moreover, the International Conference on Population and Development (ICPD)’s programme of action called for all people to have access to comprehensive reproductive health care, including safe pregnancy and childbirth services. Yet, due to certain socio-economic factors, quite a significant number of women still die from pregnancy and childbearing complications. Thus, it is important that we explore all factors that impact maternal death (directly or indirectly). There is, therefore, the need to examine the socio-demographic differentials among women (15- 49 years) that influence maternal mortality (GSS et al, 2018). It is also important to acknowledge the impact of such social and cultural factors on maternal health. 6 University of Ghana http://ugspace.ug.edu.gh This study will highlight the characteristics of households and how these characteristics expose women to maternal health risks. A study that seeks to explore this context is extremely important to guide policymakers in formulating policies and in which form to implement them to reduce the exposure to maternal mortality. 1.5 Organization of the Study This study is organized into six chapters. Chapter one consists of the background of the study, the statement of the problem, the research questions and objectives, the rationale for the study and the organization of the study. Chapter two is made up of the review of relevant literature on the relationships between household characteristics and maternal death, the theoretical base of the study, the conceptual framework, and the hypotheses. Chapter three outlined the methodology employed in the study. This basically includes the sources of data, sample size and the methods of data analysis. Chapter four presents a descriptive analysis of the variables and the bivariate association between the dependent and independent variables. Chapter five presents the regression analysis of the study and the final chapter, six, presents the summary of the entire findings, draws conclusions and suggests policy recommendations. 7 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO LITERATURE REVIEW 2.0 Introduction This chapter reviews the findings of other researchers and discusses the literature available on maternal mortality and household characteristics as well as summary of abstracts on various corpuses of literature and the general working title. 2.1 Causes of maternal mortality Several factors account for maternal death and they differ from one habitation to another subject to prevailing conditions. Numerous reasons at the individual and societal level are linked directly or indirectly with maternal mortality. Direct factors include pregnancy (a precondition for maternal mortality) and problems such as hemorrhage, eclampsia, sepsis, abortions, and clogged labor. Indirect factors include situations where a woman’s pre-existing conditions such as malaria, anemia, and nutrition are aggravated by pregnancy, which in turn may impact a pregnancy outcome (Alvarez et al., 2009). Moreover, women’s reproductive status, in light of elements such as age and parity, are associated with maternal death. Furthermore, access to medical services, females' health-seeking behavior coupled with the use of health care services can indirectly affect the outcome of maternal death. Socioeconomic, environmental, and cultural factors also impact maternal mortality (Illah et al., 2013) A study conducted in Argentina by Ramos et al. (2007) listed abortion complications, hemorrhage, sepsis and hypertensive disorders as the most prevalent causes of maternal death. These causes differ from what was found in the southern part of Africa where a study by Kongnyuy et al. (2009) 8 University of Ghana http://ugspace.ug.edu.gh revealed postpartum hemorrhage, postpartum sepsis, and HIV/AIDS as being the primary causes of maternal death in Malawi. Studies conducted in Senegal, Guinea Bissau, and Nigeria outlined puerperal sepsis, hemorrhage, eclampsia and abortion complications as the leading causes of maternal death (Illah et al., 2013). Say et al, (2014) undertook a study from 2003 to 2009 to determine the leading causes of maternal mortality around the globe. 417 datasets from 115 countries were included in their analysis. The study found that, between 2003 and 2009, about seventy-three percent of all maternal deaths were as a result of direct obstetric causes. Deaths due to indirect causes made up slightly over twenty- seven percent of the total deaths. Hemorrhage accounted for 27·1%, hypertensive disorders (14·0%), and sepsis (10·7%) of maternal deaths. The rest of the deaths were due to abortion (7·9%), embolism (3·2%), and all other direct causes of death (9·6%). According to the World Health Organisation, hemorrhage is still responsible for about twenty- seven percent of all maternal deaths making it the number one direct cause of maternal deaths worldwide. Indirectly, twenty-eight percent of deaths were due to pre-existing medical conditions aggravated by the pregnancy. Hypertensive disorders of pregnancy, sepsis, embolism, and complications of unsafe abortion made up the remaining percentage of maternal deaths (UNICEF, 2017). 9 University of Ghana http://ugspace.ug.edu.gh Figure 2.1, Percentage distribution of the causes of maternal death globally Source: Say et al., 2014 Direct causes of maternal death in Ghana include hemorrhage, abortion, miscarriages, sepsis, obstructed labor, ectopic pregnancy and hypertensive disorders (Asamoah, et al, 2011). According to the Ghana Statistical Service (2009), the indirect causes of maternal mortality are mostly communicable and non-infectious diseases and other various causes. These indirect causes mostly include malaria, HIV/AIDS, hepatitis, respiratory infections, anemia, sickle cell disease, meningitis, cerebrovascular diseases, and others. According to WHO (2004), socio-demographic factors such as age, access to resources and wealth are other determinants of maternal outcomes. Adolescents are more likely to die from maternal mortality than older women (WHO, 2018). Girls at younger ages have a much greater chance of complications that can lead to death. However, the risk of women in the last stages of their reproductive lives (late 40s to early 50s) is also very high (Murray, 2019). 10 University of Ghana http://ugspace.ug.edu.gh 2.2 Measurement of maternal mortality Maternal mortality is a rare event which is supposed to be measured per 100,000. This therefore makes it tough to measure as it requires a large sample size, so there is a scarcity of epidemiological data in regards to maternal deaths. Even though the above statement might be true to some extent, it is not an isolated case as similar difficulties are faced in gathering and evaluating other cause- specific mortality data (AbouZahr, 1998). Maternal death prevalence can be measured using maternal mortality rate, maternal mortality ratio, the lifetime risk of maternal death or percentage of maternal deaths among women within the reproductive years. While maternal mortality ratio (MMR) is calculated by dividing the number of maternal deaths which occurred within a specific time period by the number of live births during the same period and multiplying the answer by 100,000, maternal mortality rate is calculated by dividing the number of maternal deaths among a given population by the number of women aged (15-49) within the same population and usually multiplying the answer by 1000 (U.N-MMEIG, 2015). In the view of Mills et al. (2011), estimating maternal mortality ratio require three factors; first a complete account of all mortality cases, second, for every death the specific cause of death should be assigned, and thirdly, data on the pregnancy status of deceased women aged 15-49. Precisely quantifying maternal mortality is problematic due to underreporting or misclassifications of maternal deaths in vital registries (Kassebaum et al., 2014). Thus, low and middle-income countries with unreliable health systems, obtain vital records data from household surveys (Rogo et al., 2006). However, these surveys remain limited in number, making the existing measurements of maternal mortality, at best, underestimates of the situation. Problems in identifying maternal death ooze from the need to ascertain the procreative age of a female, gestation status (at the time 11 University of Ghana http://ugspace.ug.edu.gh of death), and the underlying cause of death before a death is classified in this manner (WHO, 2004). In other words, a precise and thorough civil registration system is based on the identification of the specific cause of maternal deaths that happen at medical institutions, those outlined by postmortem pathological examinations, and those reported in verbal autopsy (in cases when ladies pass on outside medical facilities). Studies have shown that countries with adequate and efficient civil registration systems are not even left out, as approximately 50 percent of maternal deaths in such countries are not reported because they were misclassified (WHO, 2010). Notwithstanding all the challenges, measuring maternal mortality remains important in evaluating the health of females in a given country. Indicators obtained from measuring maternal mortality are essential for planning, implementing and monitoring initiatives to improve maternal health (Meh, 2017). In addition, measures of maternal mortality examine the degree of success of programs in terms of reductions in maternal deaths, and complications during pregnancy and childbirth. They shed light on progress or constraints on the efforts of individual countries to improve maternal health such as an increase in maternal health services utilization by women or an increase in home births (Graham, et al, 2008). 2.3 Efforts to reduce Maternal Mortality Due to the magnitude of maternal death and its devastating effects on the social and economic fiber of society, numerous efforts have been made worldwide to reduce the incidence. During the 1987 International Safe Motherhood Conference in Kenya, there was an awareness creation about the devastating maternal mortality prevalence in the developing nations as the Safe Motherhood Initiative was formally launched (Nawal, 2008). The Conference aimed at reducing maternal 12 University of Ghana http://ugspace.ug.edu.gh mortality by fifty percent by the turn of the 21st Century and put the spotlight on the plight of the pregnant woman before the international community. Around this period, the United Nations (UN), some donor agencies, and governments focused on two strategies in the fight against maternal mortality; increasing antenatal care and training for traditional birth attendants. However, this objective was not achieved by the year 2000. The global community therefore reaffirmed its commitment when the U. N proclaimed the 8 Millennium Development Goals (MDG) in 2000. The fifth goal (MDG-5) targeted a three-quarters reduction in the maternal mortality rate by 2015 (U.N, 2008). According to the United Nations Population Fund, four elements are necessary in order to prevent maternal mortality. First is antenatal care which is the care given from conception to termination of pregnancy. The second is skilled birth attendance at birth. 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” (WHO, 2004). Moreover, emergency obstetric cares take care of complications during pregnancy and childbirth. Last but not the least, postnatal care which is the care given up to six weeks after delivery. During this time, complications which put both mother and child at risk can occur. Therefore, subsequent visitations by a health professional during this period are strongly recommended (UNFPA, 2017). Increase in access to facility-based delivery and emergency obstetric care have been seen as a key approach in putting the fore mentioned factors in place for fighting maternal deaths. Numerous barriers like inadequate health professionals and facilities, especially in the rural communities, poor delivery of service, access to health facilities and cultural inhibiting factors like low status of 13 University of Ghana http://ugspace.ug.edu.gh women, preferences for home-based births, and financial constraints like high out-of-pocket costs that come with facility-based deliveries among others, however, hinder this access (Hatt et al., 2009). To overcome the financial barriers to skilled delivery care, various governments introduced social intervention programs. For instance, Ghana introduced a National Health Insurance Scheme (NHIS) to provide free antenatal and facility-based delivery care to women covered under it. The government of Ghana further introduced a Maternal Exemption Policy (MEP) that abolished delivery fees (including more expensive procedures such as caesarean sections) in all health facilities in the country by 2005 (Witter, et al, 2007). Under this policy, pregnant women were automatically insured under the NHIS (free of charge) for a limited period (WHO, 2010). An analysis of the above policy in two regions show that facility-based deliveries shot up, there were high turn ups for antenatal care, and a decrease in out-of-pocket expenditures for delivery; while the quality of service was still maintained (Witter et al, 2008). Increase in antenatal visits due to the NHIS played no small role in reducing maternal death in the country. Apart from helping to identify complications and potential risks during pregnancy, antennal care also enables women to plan for safe delivery and is therefore, an essential part of maternal health. The primary outcome has been a decline in severe anemia, cases of obstructed labour, and treatment of medical conditions (Ameyaw, 2011). According to the United Nations Economic and Social Commission for Asia and the Pacific, the importance of antenatal visits goes beyond the pregnancy period because women who seek antenatal care are more likely to seek assistance from a health professional during childbirth. According to WHO (2015), maternal morbidity and mortality continue to remain an issue of global concern due to inadequate antenatal care (ANC). To ensure safe and effective delivery with fewer 14 University of Ghana http://ugspace.ug.edu.gh complications, the WHO recommended that, pregnant women attend a minimum of four (4) ANC visits during pregnancy beginning from the first three months. Antenatal care services have been used as an indicator of access to health care during pregnancy by all women of reproductive age. Several literatures have corroborated the positive relationship between ANC visits and a decline in maternal mortality rates within and amongst countries. Globally, several measures and implementation strategies concerning maternal and newborn health have been outlined. This includes “‘The Global Strategy for Women’s, Children’s and Adolescents’ Health’, ‘Strategies Toward Ending Preventable Maternal Mortality’” etc. (WHO, 2015). 2.4 Household characteristics A household is a person or a group of people (whether they are the same family or not) who live together in the same house, cook together, and identify one member as the head of the household (GSS et al., 2018). In demography, household characteristics usually refer to the characteristics of the household head, relationship structure (single adults, nuclear or extended) and household size (Ibisomi & De Wet, 2014). Every individual grows up in a household in which decisions are taken regarding the everyday lives of the household member(s). These decisions taken for and by individual members of a household are not arrived at out of a vacuum but rather after an inter-play between the household characteristics (which include resources at the disposal of the household) and individual characteristics. The household thus, become the primary unit that is responsible for the wellbeing of both children and adults and this, therefore, makes the characteristics of a 15 University of Ghana http://ugspace.ug.edu.gh household very critical for the health and wellbeing of the household members (Charmarbagwala, et al, 2004). It has been argued that the characteristics of household impacts on various demographic events including maternal mortality and the utilization of maternal health services (Wickrama & Keith, 1990). This is because in low-income countries, the decision for women to seek care is not an individual one but rather one that is made at the household level (Pandey & Singh, 2017). In many parts of Africa, autonomous decision making by females is not allowed especially in matters regarding reproduction and sexuality. For instance, in some parts of Nigeria, a woman has to seek the consent of the partner before visiting a health facility (Adegoke, et al, 2007). In this case, husbands and other family members often become the people who make final decisions concerning the use of maternal health care (WHO, 1998). It has already been pointed out that improved maternal health services are needed in improving female’s reproductive health in developing countries (Magadi et al, 2000). However, even though maternal health care utilization may be determined by distinctive features of the health care system such as the accessibility, quality, and cost of the services, this does not always mean that where there is a good supply of service, demand is created in and of itself, which will then improve utilization. It can happen that even under the same circumstances, some women would be more likely to use maternal health services than others. When this happens, then we cannot explain the utilization of maternal health services based on the features of the health delivery system alone. Other factors such as the household characteristics should come into play as has already been pointed out by Wickram and Keith (1990). 16 University of Ghana http://ugspace.ug.edu.gh 2.4.1 Sex of household head and maternal mortality Since the 1960s, the number of households headed by females has been on the increase in Ghana. In the 1960 population census, 25.7% of households were headed by females. This rose to 28.7% in the 1970 population census. The same percentage was maintained in the 1984 population census but shoot up to 31.3% in 2000 as shown by figures from the population and housing census of Ghana conducted in 2000. By 2017, the proportion of households headed by females increased to 34%, almost the same as in 2007 (GSS et al, 2018). Sex of the household head is considered an important determinant of mortality at the household level as mortality rates in male and female headed households sometimes differ significantly. For instance, a study on household headship and infant mortality in India by Gupta et, al. (2015) has shown that children from female headed households have higher survival probability at each age compared to children from male headed households irrespective of place of residence. Llyod and Gage-Brandon (1993) cited in Kishor and Neitzel (1996) postulated that looking at households in terms of sex of the household head is usually based on three assumptions taking into consideration existing studies in regards to gender differences in access to resources. The assumptions are that;  The economic wellbeing of the household members is to a greater degree the responsibility of the household head  Compared to males, females have limited economic opportunities and resources.  The gender of the head of the household influences the means by which resources are allocated and used within the household and the means by which the household interact with other households. 17 University of Ghana http://ugspace.ug.edu.gh It can be deduced from the first two assumptions that even though it is the responsibility of the household head to see to the wellbeing of the household, the means to do so are gender-biased. The role of the sex of the household head has been a topic for debate; females who are heads have more control over the household’s resources and are in a better position as final decision makers to invest such resources in seeking health (Kishor & Neitzel, 1997). This was earlier outlined by Wickram and Keith (1990) who were of the view that female heads and members in their households have better health-seeking behavior owing to greater autonomy and decision making power that their position confers on them. However, female headed households usually tend to be poorer than male headed households. Female heads are more often than not single parents who are the sole providers for their households and thus, their lower economic status might serve as a barrier in terms of seeking health at facilities (Kishor & Neitzel, 1997). 2.4.2 Educational level of the head of household and maternal mortality It has been noted that health outcomes are to a large extent determined by various social factors outside of health care. The variations in morbidity, mortality, and risk factors that studies have found among countries are structured according to classic social determinants of health, such as education and wealth, as well as the peculiar attributes of the physical and socio-political environment in which people live (WHO, 2008). Attending ANC, seeking health during pregnancy, and assistance during delivery by a skilled attendant are recommended as a means to reduce maternal mortality. However, given that heads of households tend to be the decision makers regarding accessing healthcare, some pregnant women may find themselves prevented from accessing healthcare. In a study which examined the 18 University of Ghana http://ugspace.ug.edu.gh association between head of household education level and health-seeking behaviors at delivery in Uganda, Vallières et al. (2013) found that education or rather limited or a lack of education for the head of household may be a barrier to women's use of health care in Uganda. The educational level of the household head is also a significant determinant of the wealth of the household which in turn influences the health outcomes of the household members. A major incentive for higher educational attainment is its expected economic returns; lucrative jobs with high salaries and an increase of value in the labour market. It has been pointed out that the role of education in labour market earnings and household welfare, especially in third world countries is an essential one (Schultz, 1961). A study which analyzed the association between the highest educational attainment of household heads and the poverty risk of households among female and male headed households in different geographical regions in Turkey found that, the higher the education level of the household head, the less likely that household was to be poor. In other words, education of the household head was inversely associated with household poverty (Bilenkisi et al, 2015). This means that the educational level of the household head was positively associated with the wealth index and welfare of the household. The wealth index of a household, in turn, has been shown by previous studies like the Ghana Demographic Health Survey and Ghana Maternal Health Survey to impact on health-seeking behavior of household members. Moreover, various demographic and health surveys have shown educational level to be inversely associated with mortality in general. For instance, the 1993 DHS in Turkey indicated that even though other socio- economic factors influence health care behavior and utilization of maternal health care services, education is the single most important determinant of the level of health care utilization by women. A relationship is found to persist even when other socio-economic influences have been controlled for. 19 University of Ghana http://ugspace.ug.edu.gh Lack of formal education of household heads may therefore be associated with increased risk of maternal death. This is because the educational status of the household head has been found to determine the quality of life the household members would enjoy; since education increases one’s access to better economic opportunities which usually translates into higher income and reflects in the health-seeking behavior of the household members (Piane, 2019). 2.4.3 Place of residence and mortality Place of residence is assumed to have a direct effect on access to health services since it serves as an indicator of physical proxy to health services. Rural and urban areas differ in respect to demographic makeup, distribution of socioeconomic resources and allocation of health care facilities. In a study of the factors influencing the utilization of maternal health services in Ghana, it was noticed that place of residence is a major determinant of the use of maternal health services. According to the study, “urban women tend to benefit from increased knowledge and access to maternal health services compared to their rural counterparts because of the urban concentration of government and private hospitals” (Addai, 1998). The role of place of residence was already highlighted in a 1993 study in Tunisia and Morocco in which place of residence came out as the most significant predictor of maternal health care utilization (Obermeyer, 1993). It was found that urban Moroccan women were 2 to 3 times more likely to use maternal health services than their rural counterparts. This is in line with another study of trends in delivery care in Bangladesh, Bolivia, Ghana, Indonesia, Malawi, and Philippines by Bells et al (2003) where they found that the number of women giving birth in the presence of a skilled birth attendant is consistently higher in the urban areas than rural areas. 20 University of Ghana http://ugspace.ug.edu.gh 2.4.4 Region of residence and mortality Variations in maternal mortality (levels and determinants) exist between and within countries (WHO et al., 2010). Disparities often exist among regions within the same country in terms of geography and disease environment. A number of demographic and health surveys have shown variations in maternal mortality between regions even after controlling for other factors. This may be as a result of the uneven apportionment of health facilities across a country or because of the socio-cultural practices of a particular area. In a study in India by Bhatia et al (1995), estimates of maternal mortality from the Human Development Profile Survey (HDPS), 1994 were derived for five zones. It was shown that the MMR was above six hundred in the east and north-central regions, while it was between three hundred and four hundred in north-western and southern regions. The association between MMR and region of residence was statistically significant. This was corroborated by a similar study in France which found that the risk of postpartum maternal death was significantly associated with region of residence (Saucedo et al, 2011). In addition, in most countries, the different regions are inhabited by specific ethnic groups whose behavioral taboos and superstition also contribute to maternal mortality. For instance, some tribes in Nigeria believe that witchcraft, infidelity, or bad manners are the underlying causes of maternal mortality (Muoghalu, 2010). Families that believe in such supernatural origin are more likely to seek care from faith or traditional healers other than health facilities and hence increasing maternal health risks (Piane, 2019). 21 University of Ghana http://ugspace.ug.edu.gh 2.4.5 Household Wealth index and maternal mortality Poverty and low economic status of households were noted as barriers to the effective utilization of ANC, medically assisted delivery, and postnatal care (Zere, et al, 2007). For instance, where there is a high out-of-pocket payment for health care services, a household's income may influence the health-seeking behaviour of its members so that those who are better placed financially would be at an advantage. A 2015 study in Ghana found that respondents who made up the richest wealth quintile group were more likely to visit a health facility during their last illness than respondents who made up the poor and poorest wealth quintile groups (Kuuire et. al., 2015). Wealth overcomes the financial barriers to good maternal health hence, wealth index has been used in many DHS reports to measure inequalities in household characteristics, in the use of health and other services, and in health outcomes” (GSS et al., 2015). For instance, in the 2017 Ghana Maternal Health Survey, females within the reproductive age who had ever been pregnant in the period 2012-2017 and did not receive antenatal care were asked why they did not seek antennal care. The most recurring answer was financial constraints (42%), and unsurprisingly, most of the women who cited this problem were from the rural areas (47%) compared 31% from the urban areas (GSS et al, 2018). Wealth and health have long been considered significant factors that affect the development of human capital, hence, the improvement of the standard of living of an individual. Woolf et al., (2015) establishes that “though it is easy to imagine how health is tied to wealth for the very poor or the very rich, the relationship between wealth and health is a gradient such that they are connected step-wise at every level of the economic ladder”. And this relationship is also seen as a vicious cycle: across countries; those with better health are generally richer, and those that improve their citizens’ health grow faster (The Economist, 2008). 22 University of Ghana http://ugspace.ug.edu.gh 2.5 Theoretical framework This study uses the household production theory to explain the relationship between household characteristics and health of the household members. The household production theory was developed by Gary Becker out of two theories, namely the human capital theory and the theory of allocation of time. Although both parent theories also treat health as an investment rather than consumption, Gary Becker's household theory takes on a narrower viewpoint on investments dealing solely with the household. It directly links household resources and investments to the wellbeing of household members (Becker, 1993). The household production theory is based on the assumption that a combined household utility function is maximized and decisions concerning the allocation of resources are made based on the “benevolent dictatorship” of the household head (Becker, 1981). The decision to seek care for a health condition therefore becomes dependent on the household head, all things being equal. The characteristics of the household head such as age, sex, and educational level, will therefore play a huge role in determining the wellbeing of the household members in regards to health. However, this may not always be the case as evidence suggests that such decisions may and can also be influenced by the bargaining power of the individual and other members of the household. 2.6 Conceptual framework Based on the household production theory and a review of the effect of household characteristics on mortality above, this study conceptualizes that maternal mortality can and is determined by certain household characteristics. Some of the household characteristics considered to have an influence on maternal mortality are related to the household head (HH) only such as his/her age, sex, and educational level. Aside these, other household characteristics such as household size, 23 University of Ghana http://ugspace.ug.edu.gh place of residence, region of residence, and household wealth quintile are also considered. These form the independent variables which previous studies and the literature reviewed in relation to this study have shown to influence maternal outcomes through the seeking of health at a health facility (which serves as the intermediate variable between each independent variable and maternal death). This study also controls for some demographic characteristics of the individual woman. Literature has shown that age, education, and marital status of women are significant factors in maternal health. For instance, a multi-country cross-sectional survey by Tuncalp et, al. (2014) on Education and severe maternal outcomes in developing countries show a significant association between low education and severe maternal outcomes. Fawole et al., (2012) were of the view that lack of education for females increases the risk of maternal mortality. Education boosts a woman’s bargaining power and enhances her ability to make decisions in the home. While illiterate women usually lack the capacity in taking the initiative to plan their reproductive lives and health, educated women, on the other hand, tend to have lower fertility rates, high contraception use, and are in a better position to make decisions concerning their health, thus reducing the risk of maternal death (Okeke et al, 2012) Similarly, several bodies of research have shown that age is a significant determinant of maternal mortality as women at the beginning and the end of the reproductive age are at a higher risk (WHO, 2018). Also, marital status has been shown to be an important factor in regards to maternal mortality. Asamoah et, al. (2011) in their study on the distribution of causes of maternal mortality among different socio-demographic groups in Ghana found that married women had a lower risk of dying from maternal related causes compared to single women as single women were more likely to induce abortion than married women. Age, educational level, and marital status of the individual women have therefore been included as control variables. 24 University of Ghana http://ugspace.ug.edu.gh This is presented below. Figure 2.2, Conceptual Framework INDEPENDENT VARIABLES Household Characteristics Sex of Household Head Age of Household Head DEPENDENT VARIABLE Educational level of HH Place of Residence Maternal mortality Region of Residence INTERMEDIATE VARIABLE Yes Wealth Index Used health facility before No Household Size death Yes Control Variables No Age of individual Level of education Marital Status Source; Author’s own construct 25 University of Ghana http://ugspace.ug.edu.gh 2.7 Hypotheses I. Women from poor households are more likely to suffer maternal mortality than women from wealthy households. II. Women from rural areas are more likely to suffer maternal death than women from urban areas. 26 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE METHODOLOGY 3.1 Introduction Chapter three gives information about the source of data for the study, the unit of analysis, the methods of data analyses, measurement, and variable categories, and the data limitation. 3.2 Data source The Ghana Maternal Health Survey (GMHS) which was conducted in 2017 is the main source of data for this study. It is the second GMHS after the one conducted in 2007. The GMHS is a nationally representative survey that gathered information on households and females within the reproductive age (15-49) from 1,900 enumeration areas (EAs) (466 in urban areas and 434 in rural areas). The main objective of the GMHS was to gather data that would provide up to date information on female reproductive health, maternal health, the utilization of health facilities, and the level of maternal mortality in Ghana. In addition, the 2017 GMHS creates room for further studies that can bring about reductions in both maternal and abortion-related mortality. The intention of this survey is to help policymakers in formulating and implementing policies that would promote maternal health in Ghana. The 2017 GMHS was implemented by the Ghana Statistical Service (GSS) with financial support from the Government of Ghana, and some international partners. 27 University of Ghana http://ugspace.ug.edu.gh 3.3 Sampling design The sample for the 2017 GMHS was designed to be representative at both the national and regional levels while taking the rural-urban population distribution of the country into consideration. The survey made use of the updated version of the sampling frame used for the 2010 Ghana Population and Housing Census (GPHC). The frame is made up of all enumeration areas (EAs) created for the GPHC. A stratified sampling method was then used and samples were selected in two stages; at the first stage, probability sampling was used to select nine hundred enumeration areas (466 in urban areas and 434 in rural areas) proportional to EA size. A household listing project was then conducted within the selected EAs and the result then became the sampling frame for the selection of households at the second stage. At the second stage, the listed households in stage one were used as a sampling frame to select households for the main survey and verbal autopsy survey. In selection for the main survey, 30 households were randomly selected from each EA summing up to a sample size of 27,000 households. Out of this, 26,324 households were successfully interviewed and in these households 25,062 eligible women aged 15-49 were asked questions about a wide range of maternal health related issues pertaining to pregnancies, live births, abortions and miscarriages, and utilization of health services in relation to these events. In selection for the verbal autopsy, all households in which a female aged 10-54 died in the five years preceding the survey were purposively selected for interview. It should however be noted that in the end, only the deaths of women aged 12-49 were included in the survey. The wider age gap was intentionally provided initially to safeguard against errors in the recollection of age during the household listing project in stage one. In all, the verbal autopsy questionnaire was used to 28 University of Ghana http://ugspace.ug.edu.gh collect information on 1,240 women aged 12-49 who died from 2012-2017 using the sibling history. For the purpose of this study, the 1240 verbal autopsy data collected were linked to the households in the main survey using the unique household identification numbers. This yielded a sample size of 1147 deaths since it was not every household which was purposively selected for verbal autopsy that was randomly selected for the main survey. 3.4 Measurement of variables This section deals with how the independent variables and the dependent variables were measured. 3.4.1 Independent Variables Sex of household head was measured using these options [0 = female] [1 = male] Age of household head was coded as 29 and below [0] 30-49 [1] 50 and above [2]. Those aged sixteen to twenty-nine years were categorized as 29 and below. Those aged from thirty to forty- nine years were categorized as 30-49 and the 50 and above age group is made of those aged fifty to ninety-five years. The ages were grouped as such due to the small number of respondents at the various single ages. Level of education of the household head was categorized into [0] No Education [1] Primary [2] Middle/JSS [3] Secondary [4] Higher. Those with Middle School education and JSS were combined. In the data set, there are two categories for those with secondary, vocational, and technical education. The two categories were also merged into one SSS/SHS/Tech/Voc category. 29 University of Ghana http://ugspace.ug.edu.gh Ghana is a third world country with high illiteracy. Ironically, there was no ‘no education’ among the categories of the variable measuring the level of education of the household head. The 359 missing values in this category were therefore categorized separately and labelled ‘no education’ for the purpose of this study. Household wealth quintile was categorized into very poor [0] poor [1] standard [2] wealthy [3] very wealthy [4]. Here, the original categories of wealth quintile in the data set were maintained. Place of residence was categorized into rural [0] and urban [1] Region of residence was categorized into the ten regions at the time of the survey; [0] Western [1] Central [2] Greater Accra [3] Volta [4] Eastern [5] Ashanti [6] Brong Ahafo [7] Nothern [8] Upper East [9] Upper West. Household Size comprises all the members of a household and was categorized into [0 = l member households] [1= 2 member households] [2 = 3-4 member households] [3 = 5-10 member households] [4 = more than 10 member households]. In the data set, the least household size was the 1 member households and the maximum size comprises 20 members. For the purpose of this study however, the household size was recoded into the above categories to reflect women who were living alone, who were possibly living with their partner, with their partner and/or children (nuclear family), with small extended family, or with large extended family. Age of the individual (deceased) was categorized into [0] = 19 and below, [1] = 20-29, [2] = 30- 39, [3] = 40-49. The educational level of the individual (deceased) was categorized into [0] = none, [1] = primary, [2] = JSS/JHS/Middle, [3] = SSS/SHS/Tech/Voc, [4] = Higher. Those in the ‘don’t know’ education category were added to the “none” category for the purpose of this study. There were 30 University of Ghana http://ugspace.ug.edu.gh two categories for secondary, technical, and vocational which were merged to form the SSS/SHS/Tech/Voc category. Marital status of individual (deceased) was categorized into [0] = never married, [1] = married/living with a partner, [2] = separated/divorced/widowed The health care utilization variable in this study refers to whether the deceased individual sought care at a health facility in the final days before death and was coded as [0 = yes] [1 = no]. There were 3 “don’t know” responses which were added to the no category. There were several questions in the data set concerning health-seeking behavior but this study used the specific question on “if the deceased sought care at a health facility in the final days before death” because it emerged from the literature review that there is an inverse association between care-seeking at a health facility and maternal mortality. 3.4.2 Dependent Variable Maternal mortality was categorized into [0 = yes (maternal death)] and [1 = No (not maternal death)]. So deaths that occurred during pregnancy, childbirth, and within 42 days of end of pregnancy but not due to accident were categorized as maternal death, while deaths that did not occur during pregnancy, childbirth, but rather occurred outside the 42 days period of end of pregnancy or from accidental causes were categorized as not maternal death. 3.5 Methods of data analysis The data for this study was analyzed using the Statistical Package for Social Sciences (SPSS). The analysis was performed at three levels; univariate, bivariate and multivariate. 31 University of Ghana http://ugspace.ug.edu.gh 3.5.1 Univariate Analysis At the univariate level, frequencies and descriptive statistics were used to describe the characteristics of the household which include age of the household head, sex of the household head, and educational level of the household head, type of place of residence, region of residence, wealth quintile, and size of the household. Frequencies and descriptive statistics were also ran for some controlled variables which are; age of the individual, level of education of the individual, and marital status of the individual. 3.5.2 Bivariate Analysis At the bivariate level, cross tabulations and chi-square tests were used to test for the association between maternal death and household characteristics. Each independent variable namely age of household head, sex, level of education, wealth quintile, place of residence, region of residence, and household size was ran with the dependent variable, thus, maternal death to find out if there is any association. At 95% confidence level, the Pearson test was conducted to indicate the nature of the relationship between each independent variable and the dependent variable. 3.5.3 Multivariate Analysis Binary logistic models were used to find out the relationship between the dependent variable and the independent variables. This model was used because it shows the likelihood of an event occurring or not occurring. Thus, it would show the likelihood of a woman dying due to maternal causes or other causes. 32 University of Ghana http://ugspace.ug.edu.gh The study is looking at the relationship between household characteristics and maternal death in Ghana. It is therefore limited to household characteristics like region of residence, place of residence, sex of household head, age of household head, educational level of household head, household wealth index, and household size. The dependent variable, maternal death was measured using the sisterhood method as an indicator in which the following questions were asked; was (THE DECEASED) pregnant when she died? Did (THE DECEASED) die during childbirth? Did (THE DECEASED) die within two months after the end of a pregnancy or childbirth? Did (THE DECEASED) die due to accident? Did (THE DECEASED) die due to a violent cause? It is an indirect estimate of maternal death and this study sought to find the maternal deaths that occurred in the various households taking into consideration the characteristics of the households. 3.6 Data limitation As outlined in the GMHS 2017 report, there are several limitations to the verbal autopsy data set. First of all, the respondent may not have understood all questions asked or may have withheld some information, made recollection errors in regards to date of death and age at death, or simply have inadequate information on the household and/or the deceased members in the years under review; the household might have relocated or if in case it was a single member household, might have dissolved upon the death of a member. Moreover, deaths that occurred after the household listing were not captured. 33 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR RESULTS AND DISCUSSION: UNIVARIATE ANALYSIS 4.0 Introduction This section examines the socio-demographic characteristics of females between the ages of 15 and 49 years who died five years prior to the survey and their households. Individual characteristics considered were age, educational level, and marital status. The household characteristics included are age, sex, and educational level of household head, place of residence, region of residence, household size, and wealth quintile. Information on these socio-demographic characteristics was presented in percentages and frequencies using charts, graphs, and tables. 4.1 Sex of Household Head Sex of the household head is an important variable in demographic analysis and in this regard important in making decisions concerning the health-seeking behavior of household members. Households with female heads usually have fewer assets and income capacity to effectively see to the wellbeing of their members (Javed and Asif, 2011). Studies have also shown that being a male provides an advantage to preserve a position above the poverty line as female-headed households are subjected to some disadvantages (Awan et al. 2011). Figure 4.1 shows that majority of the household heads were males (69.3%) while 3 out of every 10 respondents were females. Empirical evidence shows that there are more male household heads than females (GSS et al., 2018). 34 University of Ghana http://ugspace.ug.edu.gh Figure 4.1 Percentage distribution of household heads by sex 31% female male 69% Source: Computed from GMHS (2017) data 4.2 Age of household heads Age of the household head plays a role in his/her employment status and overall lifetime experiences which in turn determines the decision making process in the household. Nearly 46% of household heads were middle aged (30-49 years). This group of individuals also forms the majority. A good number (37.2%) were 50 years and over while just a handful (16.8%) were 29 years and below. 35 University of Ghana http://ugspace.ug.edu.gh Table 4.1. Percentage distribution of household heads by age Age groups Frequency Percent 29 and below 193 16.8 30-49 527 45.9 50 and above 427 37.2 Total 1147 100.0 Source: Computed from GMHS (2017) data 4.3 Level of education of household heads The educational level of the household head affects the general wellbeing of the household; there is a negative relationship between the probability of a household being poor and education levels of household heads (Bilenkisi et al. 2015). This is because educational status of the household head has been found to determine the quality of life the household members would enjoy; since education increases one’s access to better economic opportunities which usually translates into higher income and this reflects in the health-seeking behavior of the household members (Piane, 2019). Table 4.2 shows that most of the respondents were educated beyond the middle and junior secondary levels. Respondents who had attained middle/Junior secondary levels make up 34.1% of respondents while 31.4% of respondents had no formal education. The least represented respondents were those who had attained higher education (10.5%) followed by those who had attained primary education (11.8%) and finally those who had attained senior secondary education (12.3%). 36 University of Ghana http://ugspace.ug.edu.gh Table 4.2. Percentage distribution of household heads by level of education Level of Education Frequency Percent None 360 31.4 Primary 135 11.8 Middle/Junior Secondary School 391 34.1 Senior Secondary School/Technical/Vocational 141 12.3 Higher 120 10.5 Total 1147 100.0 Source: Computed from GMHS (2017) data 4.4 Place of residence Empirical evidence shows that urban dwellers have an advantage over rural dwellers with regards to access to health care facilities and thus are more likely to access healthcare facilities (Marrone et al., 2014). This is evident in Table 4.3 as majority of females aged 15-49 years who died five years prior to the survey reside in rural areas (53.1%) while the other 46.9% live in urban areas. 37 University of Ghana http://ugspace.ug.edu.gh Table 4.3 Percentage distribution by place of residence Place of residence Frequency Percent Rural 609 53.1 Urban 538 46.9 Total 1147 100.0 Source: Computed from GMHS (2017) data 4.5 Region of residence Every Ghanaian identifies with an ethnic group and each ethnic group is uniquely located within a particular region in the country. Some studies have shown that the cultural practices of these ethnic groups and regional distribution of health facilities have an impact on health outcomes including maternal health. Majority of deceased females aged 15-49 years belong to households located in the Northern Region (19.4%), followed by Eastern Region (12%), Volta Region (10%) and Brong Ahafo Region (9.5%). Western Region, Central Region, Greater Accra Region, Upper East Region and Upper West Region recorded 9.4%, 8.5%,7.8% and 7.7% representation respectively. Households located in the Ashanti Region recorded the least number of maternal deaths. 38 University of Ghana http://ugspace.ug.edu.gh Table 4.4. Percentage distribution by of region of residence Region Frequency Percent Western 108 9.4 Central 98 8.5 Greater Accra 98 8.5 Volta 115 10.0 Eastern 138 12.0 Ashanti 80 7.0 Brong Ahafo 109 9.5 Northern 223 19.4 Upper East 90 7.8 Upper West 88 7.7 Total 1147 100.0 Source: Computed from GMHS (2017) data 4.6 Household Wealth Quintile Wealth is a measure of the socio-economic status of a household as it provides opportunity for better education, more employment opportunities, and subsequently access to quality health care. Figure 4.2 shows that majority representing one-fourth of deceased females within their reproductive ages (25%), were from very poor households while the very wealthy households had the least representation with 12.7%. Poor, standard and wealthy households had 22.1%, 22.7%, and 17.2% representation respectively. 39 University of Ghana http://ugspace.ug.edu.gh Figure 4.2. Percentage distribution of household by wealth quintile 30.0 25.0 20.0 15.0 10.0 5.0 0.0 very poor poor standard wealthy very wealthy Wealth status Source: Computed from GMHS (2017) data 4.7 Household Size Household size determines the amount of resources allocated to each member of the household. Members of smaller households have been shown to benefit more from the household resources because there are enough resources to go around while those from larger households are a more disadvantaged group since available resources must be shared judiciously (Al-Samarrai and Peasgood, 1998). Table 4.8 shows that three out of every ten 5-10 member households (36.7%) recorded one maternal death. This is followed by 3-4 member households with 30.1% 40 Percentage University of Ghana http://ugspace.ug.edu.gh representation. 1 member households had 17% representation while 2 member households had 13.8% representation. Ten member households had the least representation (2.4%). Figure 4.3. Percentage distribution by household size 40 35 30 25 20 15 10 5 0 1 member 2 member 3-4 member 5-10 member 10+ member Household size Source: Computed from GMHS (2017) data 4.8 Age of individual The risk of maternal death varies with a woman’s age. According to the World Health Organization (WHO, 2018), adolescents face a higher risk of complications and death as a result of pregnancy than older women. However, results presented in Table 4.5 show that individuals 15-19 years recorded the least representation (10.4%) indicating that 1 out of every 10 deceased females within 41 Percentage University of Ghana http://ugspace.ug.edu.gh the ages of 15-49 years is between the ages of 15-19 years. Individuals between the ages of 30-39 years recorded the highest representation (31.9%) followed by those aged 40-49 years (31.9%) and 20-29 years (23.4%). Table 4.5. Percentage distribution of Individuals by Age Age group Frequency Percent 15-19 119 10.4 20-29 268 23.4 30-39 394 34.4 40-49 366 31.9 Total 1147 100.0 Source: Computed from GMHS (2017) data 4.9 Educational level of individual Education is inversely related to the risk of maternal mortality (Alvarez et al., 2009) as better educated women have a lower risk. Majority of maternal deaths happen in women with little or no education (Chowdhury et al., 2007). Figure 4.4 shows that majority of the females (33.2%) who died had attained JSS or middle school education. This is followed closely by those with no education with 31.4% representation and finally those with primary education (21.9%). As expected, those with higher education had the least representation (2.9%) followed by those who had attained secondary, tertiary or vocational levels of education (10.6%). 42 University of Ghana http://ugspace.ug.edu.gh Figure 4.4. Percentage distribution of Individuals by Educational Level 35 30 25 20 15 10 5 0 none primary JSS/JHS/Middle SSS/SHS/Tech/Voc Higher Level of education Source: Computed from GMHS (2017) data 4.10 Marital status of individual Majority of the deceased women between the ages of 15-49 years were either married or living with a partner indicating that 6 out of every 10 deceased females between the ages of 15-49 years (59.7%) were either married or living with a partner. One-fourth of the deceased women were never married (25.2%) while one out of every ten were either separated, divorced or widowed (15.1%). 43 Percentage University of Ghana http://ugspace.ug.edu.gh Figure 4.5 Percentage distribution of individuals by marital status 70 60 50 40 30 20 10 0 never married married/living with a partner separated/divorced/widowed Marital status Source: Computed from GMHS (2017) data 4.11 Health facility utilization According to the United Nations Population Fund, visiting a healthcare facility to access antenatal and postnatal care are prerequisites for preventing maternal mortality. Some studies have shown that unavailability and low utilization of maternal healthcare services have resulted in maternal deaths during pregnancy and childbirth especially in developing countries (Nuamah et al., 2019). Figure 4.6 shows that 67.3% of the deceased women sought care at a health facility prior to their demise while 3 out of every 10 deceased women did not seek care. 44 Percentage University of Ghana http://ugspace.ug.edu.gh Figure 4.6 Percentage distribution of Individuals by Health Facility Utilization. 32.7 Yes No 67.3 Source: Computed from GMHS (2017) data 45 University of Ghana http://ugspace.ug.edu.gh 4.7 Maternal mortality Figure 4.7 shows that 8 out of every 10 deceased females aged 15-49 years died from other causes while 12.6% died from maternal causes. Figure 4.7 Percentage distribution of individuals by maternal death Yes 12.6% No 87.4% Source: Computed from GMHS (2017) data 46 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE RESULTS AND DISCUSSION: BIVARIATE AND MULTIVARIATE ANALYSIS 5.0 Introduction This section presents the bivariate and multivariate analysis of the study. At the bivariate level, each of the independent variables was tested against the dependent variable and a chi-square test was conducted at 95% confidence level to examine the significance of the association between each socio-demographic characteristic, health facility utilization and maternal mortality. The chi- square test was used since all the variables were categorical. At the multivariate level, a binary logistic regression model was used to determine the extent to which the selected independent variables (household characteristics) predict the outcome variable (maternal mortality) while controlling for other factors (the selected individual demographics and health care utilization). Binary logistic regression was used because the outcome variable is dichotomous, that is whether a female death that occurred in the five years prior to the survey is due to maternal causes or not. Death which occurred as a result of other causes was coded as 0 while those that occurred as a result of maternal causes was coded as 1. For each of the variables, a reference category was created which all other categories were interpreted in reference to using the odds ratio exponential. The regression results were interpreted in two ways; first, if the relationship between a specific independent variable and the outcome variable is statistically significant at 95% confidence level. Second, using the odds ratio to determine the probability of an event occurring when compared to the reference category. 47 University of Ghana http://ugspace.ug.edu.gh 5.1 BIVARIATE ANALYSIS 5.1.1 Sex of household head and maternal mortality The results in table 5.1 show that there was no significant association between the sex of the household heads and maternal mortality. Also, there was no significant difference between prevalence of maternal mortality in female headed households (12.2%) and male headed households (12.8%). This is in line with multi-country DHS-based study by Heaton et al. (2005) which found no significant association between sex of household head and mortality. Table 5.1 Sex of household head and maternal mortality Maternal Mortality sex of household head % Number Female 12.2% 352 Male 12.8% 795 Total 12.6% 1147 x2 = 0.083 df = 1 significance = 0.773 Source: Computed from GMHS (2017) data 5.1.2 Age of household head and maternal mortality With a chi-square test of 0.822, the results in Table 5.2 show that there was no significant association between the age of the household head and maternal mortality. However, females in households whose head is aged 30-49 recorded the highest percentage of maternal deaths (13.3%) and the least percentage was recorded by females in households headed by persons who are 50 years and above. 48 University of Ghana http://ugspace.ug.edu.gh Table 5.2 Age of household head and maternal mortality Maternal Mortality Age of household head % Number 29 and below 12.4% 193 30-49 13.3% 527 50 and above 11.9% 427 Total 12.6% 1147 x2 = 0.392 df = 2 Significance = 0.822 Source: Computed from GMHS (2017) data 5.1.3 Educational level of Household head and maternal mortality It has been identified that educational level of the household head influences the decision making process and the lifestyles of the household members. For instance, people from educated households are more likely to seek and are in a better position to pay for health care as studies have also shown that educational attainment of the household head is tied to household wealth (Rolleston, 2011). Results from Table 5.3 show that there is no association between the level of education of a household head and maternal mortality (p value = 0.725). This means that the educational level of the household head does not have any significant relationship with maternal mortality. 49 University of Ghana http://ugspace.ug.edu.gh Table 5.3 Educational level of household head and maternal mortality Maternal Mortality Level of Education of Household Head % Number None 13.1% 360 Primary 11.9% 135 middle/JSS 12.3% 391 SSS/SHS/Tech/Voc 15.6% 141 Higher 10.0% 120 Total 12.6% 1147 x2 = 2.057 df = 4 Significance = 0.725 Source: Computed from GMHS (2017) data 5.1.4 Place of residence and maternal mortality Studies have shown that where an individual lives and the proximity to a health facility can serve as a barrier to maternal health care (Choe et al., 2016). Results from Table 5.4 show that, 14.1% of all the maternal deaths occurred in rural areas whilst 11% occurred in urban areas. A p-value of 0.109 indicates that no significant association exists between place of residence and maternal mortality. This result may be explained by the vigorous efforts of the Government of Ghana and the Ghana Health Service in introducing the CHPS programme and the home visitation of pregnant women by health workers in rural areas to bridge the gap between urban and rural areas in terms of health care accessibility. This result, therefore, rejects the second hypothesis that women from 50 University of Ghana http://ugspace.ug.edu.gh households in rural areas are more likely to suffer maternal death than women from households in urban areas. Table 5.4 Place of residence and maternal mortality Maternal Mortality Place of residence % Number Rural 14.1% 609 Urban 11% 538 Total 12.6% 1147 x2 = 2.575 df = 1 Significance 0.109 Source: Computed from GMHS (2017) data 5.1.5 Region of residence and maternal mortality There are regional differences in the distribution of health care facilities. Also, the socio-cultural practices of the different ethnic groups within these regions have been known to have an impact on pregnancy outcomes. Results from Table 5.5 show that there is no association between region of residence and maternal mortality (p-value = 0.270). This means that region of residence does not have any significant relationship with maternal mortality. 51 University of Ghana http://ugspace.ug.edu.gh Table 5.5 Region of residence and maternal mortality Maternal Mortality Region of residence % Number Western 15.7% 108 Central 21.4% 98 Greater Accra 11.2% 98 Volta 8.7% 115 Eastern 13.0% 138 Ashanti 12.5% 80 Brong Ahafo 12.8% 109 Northern 11.7% 223 Upper East 8.9% 90 Upper West 11.4% 88 Total 12.6% 1147 x2 = 11.089 df = 9 Significance = 0.270 Source: Computed from GMHS (2017) data 52 University of Ghana http://ugspace.ug.edu.gh 5.1.6 Household size and maternal mortality According to Bongaarts (2001), members of large households are exposed to the risk of death including maternal mortality due to the economic constraints of these households. The capacity of the household to adequately meet the needs of all its members is influenced mainly by the household size and the number of children ever born. The result in Table 5.6, however, shows that no significant difference exists between household size and maternal mortality (p-value = 0.114). Table 5.6 Household size and maternal mortality Maternal Mortality Household size % Number 1 member 10.3% 195 2 member 13.9% 158 3-4 member 10.7% 345 5-10 member 15.4% 421 10+ member 3.6% 28 Total 12.6% 1147 x2 = 7.458 df = 4 Significance = 0.114 Source: Computed from GMHS (2017) data 53 University of Ghana http://ugspace.ug.edu.gh 5.1.7 Household wealth quintile and maternal mortality According to Matthews (2002), wealth is linked to health-seeking behavior in that, the lack of money or insufficient funds pose a barrier to access to maternal health care as fees and cost of services limit demand and use of these services (McCarthy & Maine, 1992) and this may adversely affect maternal health outcomes. Contradictory evidence from this study (Table 5.7) indicates that household wealth is not significantly associated with maternal mortality (p-value=0.716). This may be due to improved access to maternal health care through the National Health Insurance Scheme (NHIS) and the Free Maternal Care Policy. The first hypothesis of this study which stipulates that women from poor households are more likely to suffer maternal deaths than women from wealthy households is also rejected based on this result. Table 5.7 Wealth Quintile and maternal mortality Maternal Death Wealth Quintile % Number very poor 14.1% 290 Poor 11.4% 254 Standard 10.8% 260 Wealthy 13.7% 197 very wealthy 13.7% 146 Total 12.6% 1147 x2 = 2.108 df = 4 Significance = 0.716 Source: Computed from GMHS (2017) data 54 University of Ghana http://ugspace.ug.edu.gh 5.1.8 Age of individual and maternal death Age has been identified as a major determinant of maternal health (Abou-Zahr & Royston, 1991). Pregnancy related deaths are highest among girls aged 15 years and younger and also among women 30 years and over (Wall, 1998). Adolescents are at a higher risk of maternal mortality mainly because their reproductive systems are not fully developed and thus may develop complications during pregnancy or childbirth (Abou-Zahr & Royston, 1991). Table 5.8 shows that the association between age and maternal mortality is statistically significant (p-value <0.001). Majority of the deceased females were between the ages of 20-29 years. Out of the 268 deceased females aged 20-29 years, 19.4% died from maternal causes. This is followed by those aged 30-39 years (15.5%) and those aged 19 years and below (9.2%). The last age group (40-49) had the least proportion of deceased females who died from maternal causes (5.7%). These findings confirm studies which indicate that age is significantly associated with maternal mortality (Hoj et al., 2002; McCarthy & Maine, 1992). The results from this study, however, contradict findings of other studies which show that maternal deaths are a more frequent phenomenon within the extreme age groups (Wall, 1998). 55 University of Ghana http://ugspace.ug.edu.gh Table 5.8 Age of individual and maternal mortality Maternal Death Age of individual % Number 19 and below 9.2% 119 20-29 19.4% 268 30-39 15.5% 394 40-49 5.7% 366 Total 12.6% 1147 x2 = 31.014 df = 3 Significance = 0.001 Source: Computed from GMHS (2017) data 5.1.9 Educational level of individual and maternal mortality Education empowers women with the ability to contribute in matters of their fertility, family planning and marital life (Abou-Zahr & Royston, 1991). It is also positively associated with use of antenatal and other health services which lower the risk of maternal mortality (Buor & Bream, 2004). The results in Table 5.9 show that the educational level of an individual is not significantly associated with maternal mortality (p-value =0.570) . 56 University of Ghana http://ugspace.ug.edu.gh Table 5.9 Educational level of individual and maternal mortality Maternal Death Educational level of individual % Number None 11.4% 360 Primary 10.8% 251 JSS/JHS/Midde 14.4% 381 SSS/Tech/Voc 14.8% 122 Higher 12.1% 33 Total 12.6% 1147 x2 = 2.930 df = 4 Significance = 0.570 Source: Computed from GMHS (2017) data 5.1.10 Marital status of individual and maternal death Marital status has been identified as having a significant association with health, including maternal health. Many studies have shown that individuals in marital unions demonstrate higher levels of psychological well-being and physical health as well as lower levels of mortality as compared to those who are single, separated, divorced and widowed (Idler et al, 2012). The relationship between marital status and maternal mortality is presented in Table 5.10 as follows; majority of the deceased females were either married or living with a partner (18.1%). Those who were never married follow with 5.5% while those who were widowed, separated, or divorced had the least proportion (2.9%). Evidence from the GMHS (2018) indicates that fertility rate is higher among married couples or those living with a partner and since pregnancy outcomes are a pre- 57 University of Ghana http://ugspace.ug.edu.gh condition to fertility, it is normal to expect more pregnancy related deaths from this group of individuals. Table 5.10 Marital status of individual and maternal mortality Maternal Death Marital status of individual % Number never married 289 5.5% married/living with a 685 partner 18.1% separated/divorced/wi 173 dowed 2.9% Total 1147 12.6% x2 = 46.603 df = 2 Significance < 0.001 Source: Computed from GMHS (2017) data 5.1.11 Health facility utilization and maternal mortality Evidence from GMHS (2017) indicates that, seeking care at health facilities lower the risk of maternal mortality. Results in Table 5.11 show that there is a statistically significant association between health facility utilization and maternal mortality. 16.3% of the deceased females who died from maternal causes sought care at a health facility while 5.1% did not visit any health facility. 58 University of Ghana http://ugspace.ug.edu.gh Table 5.11 Health facility utilization and maternal mortality Maternal Death Seek care at a health facility % Number Yes 16.3% 772 No 5.1% 375 Total 12.6% 1147 x2 = 28.949 df = 1 Significance <0.001 Source: Computed from GMHS (2017) data 5.2 MULTIVARIATE ANALYSIS 5.2.1 Relationship between household characteristics and maternal mortality Logistic regression was conducted to determine the relationship between maternal mortality and household characteristics. The results displayed in model 1 yielded a chi-square value of 28.936 with 25 degrees of freedom and is significant at 0.267. This means that the model had reliably distinguished between female deaths that were due to maternal causes and those that were not. Model 1 predicts about 87% of the response correctly and the selected variables explain 47% of the variation in female deaths, with 87.4 of correct prediction and Nagelkerke R2 of 0.047 respectively. The result shows that the selected household characteristics are not statistically significant in influencing maternal mortality. 59 University of Ghana http://ugspace.ug.edu.gh Table 5.12 Variations in maternal death by selected household characteristics in Ghana (model 1) Exp(B) 95% C.I. for EXP(B) Variables Lower Upper Sig. SEX OF HH Female (ref) 1.000 Male 1.025 0.671 1.565 0.910 AGE OF HH 29 and below (ref) 1.000 30-49 0.879 0.497 1.554 0.658 50 and above 0.938 0.621 1.416 0.760 EDUCATIONAL LEVEL OF HH None (ref) 1.000 Primary 0.770 0.343 1.728 0.526 JSS/Middle 0.895 0.372 2.155 0.805 SSS/SHS/Tech/Voc 0.960 0.459 2.012 0.915 Tertiary 0.685 0.312 1.504 0.346 TYPE OF PLACE OF RESIDENCE Rural (ref) 1.000 Urban 0.714 0.463 1.101 0.127 REGION OF RESIDENCE Western (ref) 1.000 Central 0.500 0.204 1.228 0.131 Greater Accra 0.338 0.138 0.827 0.018 Volta 0.775 0.283 2.120 0.620 Eastern 1.082 0.413 2.835 0.872 Ashanti 0.618 0.252 1.514 0.292 Brong Ahafo 0.673 0.246 1.842 0.441 Northern 0.613 0.246 1.528 0.294 Upper East 0.906 0.411 1.995 0.806 60 University of Ghana http://ugspace.ug.edu.gh Upper West 1.297 0.479 3.508 0.609 HOUSEHOLD SIZE 1 member (ref) 1.000 2 member 0.376 0.046 3.056 0.360 3-4 member 0.255 0.032 2.046 0.198 5-10 member 0.358 0.046 2.798 0.327 More than 10 member 0.227 0.030 1.736 0.153 HOUSEHOLD WEALTH QUINTILE Very Poor (ref) 1.000 Poor 1.121 0.486 2.587 0.789 Standard 1.593 0.766 3.311 0.212 Wealthy 1.693 0.842 3.404 0.140 Very Wealthy 1.182 0.610 2.291 0.621 Constant 3.631 Correct % prediction 87.4 Nagelkerke R2 47% Model X2 (df) 28.936 (25) Source: Computed from GMHS (2017) data In Model 2, selected background characteristics of the individual, and household characteristics were put together in finding out what variables are determinants of maternal mortality in Ghana. The overall model yielded a chi-square value of 142.423 with 35 degrees of freedom and a significance of 0.000. It predicts about 88% of the response correctly and the selected variables explain 22% of the variation in female deaths, with 87.5 correct predictions and Nagelkerke R2 of 0.22 respectively. The results suggest that the main household characteristics were not significant predictors of maternal mortality but rather the individual’s own characteristics like age, and marital 61 University of Ghana http://ugspace.ug.edu.gh status. Usage of health facilities within the final days prior to death was also found to be significantly associated with maternal death. Table 5.13. Variations in maternal mortality by household characteristics, selected individual demographics, and the intermediate variable.(model 2) 95% C.I.for EXP(B) Variable Exp(B) Lower Upper Sig. SEX OF HOUSEHOLD HEAD Female (ref) 1.000 Male 1.004 0.637 1.581 .988 AGE OF HOUSEHOLD HEAD 29 and below (ref) 1.000 .992 30-49 0.981 0.547 1.758 .947 50 and above 1.007 0.544 1.866 .981 EDUCATIONAL LEVEL OF HH None (ref) 1.000 .967 Primary 0.990 0.488 2.011 .978 JSS/Middle 0.868 0.498 1.514 .618 SSS/SHS/Tech/Voc 1.066 0.520 2.183 .862 Tertiary 0.975 0.407 2.338 .955 TYPE OF PLACE OF RESIDENCE Rural (ref) 1.000 Urban 0.738 0.464 1.174 .200 REGION OF RESIDENCE Western (ref) 1.000 .183 Central 1.905 0.855 4.242 .115 Greater Accra 0.833 0.334 2.072 .694 Volta 0.766 0.307 1.912 .568 Eastern 0.975 0.434 2.190 .951 Ashanti 1.043 0.410 2.656 .929 Brong Ahafo 1.161 0.496 2.717 .731 Northern 0.550 0.248 1.220 .141 Upper East 0.426 0.152 1.191 .104 Upper West 0.582 0.223 1.520 .269 62 University of Ghana http://ugspace.ug.edu.gh HOUSEHOLD WEALTH QUINTILE Very Poor (ref) .398 Poor 0.640 0.336 1.219 .174 Standard 0.616 0.310 1.224 .167 Wealthy 0.957 0.440 2.082 .912 Very Wealthy 0.859 0.346 2.128 .742 HOUSEHOLD SIZE 1 member (ref) 1.000 .149 2 member 1.406 0.688 2.874 .351 3-4 member 1.211 0.640 2.291 .556 5-10 member 1.817 0.964 3.428 .065 More than 10 member 0.314 0.037 2.701 .292 AGE OF INDIVIDUAL 19 and below (ref) 1.000 .000 20-29 0.909 0.385 2.141 .827 30-39 0.500 0.202 1.232 .132 40-49 0.196 0.074 0.521 .001 EDUCATIONAL LEVEL OF INDIVIDUAL None (ref) 1.000 .730 Primary 0.757 0.414 1.384 .365 JSS/Middle 1.083 0.633 1.853 .772 SSS/SHS/Tech/Voc 1.116 0.550 2.264 .760 Tertiary 1.265 0.355 4.508 .717 MARITAL STATUS OF INDIVIDUAL Never Married (ref) 1.000 .000 Married/Living with a partner 5.869 2.945 11.696 .000 Ever Married 1.078 0.346 3.357 .897 HEALTH FACILY UTILISATION Yes (ref) 1.000 No 0.279 0.164 0.476 .000 Constant -2.000 Correct % prediction 87.5 63 University of Ghana http://ugspace.ug.edu.gh Nagelkerke R2 22% Model X2 (df) 142.423 (35) Source: computed from GMHS, 2017 data Discussions Age of individual has been shown to be a significant predictor of maternal death. The older the individual, the less likely she is to suffer from maternal death compared to individuals aged 19 and below. This is supported by several studies which found that teenagers are more prone to maternal death than older women (WHO, 2018). With a p-value < 0.001, individuals who are married or living with a partner are six times as likely (OR=6.356) to suffer maternal death compared to individuals who are never married. Individuals who are divorced, separated or widowed are 1.129 times as likely to suffer from maternal related deaths compared to the reference category. This is normal and can be explained by the fact that several studies have found fertility to be highest among people in unions or living with partners (GSS et al. 2008; GSS et al. 2018). And since pregnancy is a pre-condition for maternal mortality, we should expect more maternal deaths from the people in this category. Another explanation may be that the women who are not in marriage unions or staying with a partner were probably making their own autonomous decisions concerning their reproductive lives hence, lowering the risk of maternal mortality. This explanation is supported by the findings of the World Health Organization which asserts that in low-income countries, a woman cannot visit a clinic or hospital without the permission of her husband, mother-in-law or the head of the household (WHO, 1989). The study also found a significant association between health facility utilization and maternal death with a p-value <0.000. But surprisingly, it is those women who did not visit a health facility in the final days prior to their death that were less likely to die from maternal mortality compared to those 64 University of Ghana http://ugspace.ug.edu.gh who visited a health facility in their final days. This can be explained by studies which show that most of the health facilities are far from communities and therefore people tend to delay until their conditions become severe before they seek help at the health facility (Amzat, 2015). In other words, by the time some of these women decide to seek care at a health facility, their condition may have already spiral out of control. The diminishing authority of household heads (especially male heads) over household members due to modernization may be used to explain why none of the characteristics of the household heads were found to be significant in this study. This is backed by Takwa (2011) who in a study on the differences between socio-economic characteristics of male and female households in Cameroon noted that men are often considered as household heads irrespective of their ages or economic situation or ability to make decisions on behalf of all or some members of the household. Boys as young as 10 are sometimes declared as household heads just because by tradition they represent their departed father. These boys often exercise no authority over other household members. In other situations, very old men who may not exercise any real power over a household are declared as household heads. 65 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX SUMMARY, CONCLUSION, AND RECOMMENDATIONS 6.1 Introduction This chapter provides a summary of the key findings and conclusion from this study. The chapter finally ends with recommendations to reduce the incidence of maternal mortality in Ghana. 6.2 Summary of findings The main objective of this study was to examine the influence of household characteristics on maternal mortality in Ghana. The household characteristics considered were sex of household head, age of household head, educational level of household head, type of place of residence, region of residence, household size, and household wealth quintile. It also aims at providing policy makers and government agencies with information that will aid effective formulation and implementation of policies that will reduce the incidence of maternal death in Ghana. Data from the Ghana Maternal Health Survey (2017) was used. The study hypothesized that; first women from poor households are more likely to suffer from maternal mortality than those from wealthy households. Secondly, women from rural areas are more likely to suffer maternal death compared to those from urban areas. At the univariate level of analysis, percentages were computed to determine the characteristics of the household. The results showed that majority of the household heads were males. In regards to educational level of the household head, one in every ten household heads has a tertiary education while over sixty percent have at least a primary education. 66 University of Ghana http://ugspace.ug.edu.gh The highest number of deceased females was from the Northern Region while the least proportion was from the Ashanti Region. Moreover, the percentage distribution of households by wealth quintile shows that the highest percentage of deceased females were from very poor homes. Furthermore, majority of the female deaths were not maternal mortality rather due to other causes. Just 12.6% were due to maternal causes. The bivariate analyses showed that household characteristics like sex of household head, age of household head, educational level of household head, household wealth quintile, and household size were not statistically significant in influencing maternal death. But characteristics of the individual women like age and marital status were found to be significant determinants of mortality as was health facility utilization. At the multivariate stage, the binary regression model controlled for other demographics of the deceased individual whilst examining the extent of influence of the household characteristics on predicting maternal death. The results suggest that the age of individual, marital status of the individual, and health facility utilization were significant predictors of maternal death among the sampled female population. 6.3 Conclusion Generally, this study has explored the relationship between household characteristics and maternal mortality. The results show that there is no statistically significant association between household characteristics and maternal mortality. It also shows that 8 out of every 10 deceased females aged 15-49 years died from other causes which are not maternal related causes. Household wealth was 67 University of Ghana http://ugspace.ug.edu.gh found not to be a significant determinant of maternal mortality at both the bivariate and multivariate levels. Thus the first hypothesis that women from poor households are more likely to suffer more from maternal death compared to women from wealthy households is rejected based on the findings of this study. Place of residence was also not significant at both the bivariate and multivariate levels of analysis thus, the second hypothesis of this study that women from rural areas are more likely to suffer more from maternal mortality than women from urban areas is also rejected. 6.4 Recommendations Based on the findings of this study, the following are made; I. Women in general and especially teenage girls should be targeted for education on maternal health risks since they are the most vulnerable group. II. 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