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Public Health Intelligence: PUB015-6Assessment 2: Epidemiological ReportRisk factors for neonatal mortality in UgandaDate: 7th December 2020Word Count: 2721PUB015-6: Assessment 2 Example Epidemiological Report2Table of ContentsIntroduction ………………………………………………………………………………………………………. 3Literature Review……………………………………………………………………………………………….. 4Maternal Education Level ………………………………………………………………………………………. 4Maternal Age………………………………………………………………………………………………………… 4Skilled Birth Attendants …………………………………………………………………………………………. 5Methods……………………………………………………………………………………………………………. 6Secondary Dataset ………………………………………………………………………………………………… 6Statistical Analysis …………………………………………………………………………………………………. 6Results ……………………………………………………………………………………………………………… 7Participants ………………………………………………………………………………………………………….. 7Risk Factors for Neonatal Mortality …………………………………………………………………………. 7Discussion ……………………………………………………………………………………………………….. 10Risk Factors for Neonatal Mortality ……………………………………………………………………….. 10Strengths and Limitations …………………………………………………………………………………….. 11Conclusion and Recommendations………………………………………………………………………. 13References ………………………………………………………………………………………………………. 14Appendix: SPSS Output Tables…………………………………………………………………………….. 18PUB015-6: Assessment 2 Example Epidemiological Report3IntroductionUganda is one of poorest developing countries in the World, with 19.7% of the populationreported to be living below the poverty line, while 34.6% of the population live on less than$1.90 US per day (World Bank, 2016). In response to the health problems caused by poverty,the United Nations (UN) developed eight Millennium Development Goals (MDG) with the aimof eradicating poverty in developing countries by 2015 (United Nations Millennium Summit,2000). The fourth of these MDG focused on reducing child mortality rate (CMR), which isdefined as death before a child reaches the age of five years old and is expressed as thenumber of deaths per 1000 live births (United Nations, 2011).One of the greatest risk periods for a child is during the first four weeks of their life, with thedeath rate in this period known as neonatal mortality rate (NMR) (United Nations, 2011).Between 1990 and 2019, the CMR in Uganda decreased from 182.1 to 45.8, which representsa reduction of 74.8% (UNICEF, 2020c). Over the same time period, NMR decreased in Ugandaat a slower rate, from 38.8 to 20.0, which represents a reduction of 48.5% (UNICEF, 2020b).When the NMR for Uganda is expressed as a percentage of CMR, the rate has more thandoubled from 21.3% in 1990 to 43.6% in 2020, which shows that NMR should increasinglybecome the focus of programmes designed to reduce CMR (Lawn, Cousens and Zupan, 2005).As a point of comparison, NMR in England and Wales was 2.8 in 2018, which represents 26.9%of CMR, with this percentage also increasing since 1990 when it was 22.0% of CMR (Office forNational Statistics, 2020).The main causes of neonatal mortality in Uganda were identified in an early hospital study asbirth asphyxia, respiratory distress and aspiration syndromes, very low birthweight, infection,anaemia and congenital malformations (Mukasa, 1992). However, although these are theacute causes of neonatal mortality, underlying risk factors lead to mothers giving birth inconditions without adequate medical care (Lawn, Cousens and Zupan, 2005). The aims of thisepidemiological report are twofold: the initial aim is to identify risk factors for neonatalmortality in Uganda based on an appraisal of the literature, which will enable key risk factorsto be identified. The second aim is to take the identified risk factors and evaluate theirimportance with respect to neonatal mortality in Uganda using an analysis of a suitablesecondary dataset.PUB015-6: Assessment 2 Example Epidemiological Report4Literature ReviewThis literature review section identifies the main risk factors for neonatal mortality in Uganda.The search was performed using MEDLINE and CINAHL databases, using a combination ofMeSH terms and keywords. There is no MeSH term for neonatal mortality, therefore theMeSH terms ‘Infant Mortality’ OR ‘Perinatal mortality’ were combined with the keywordneonatal using the Boolean operator AND. This search was combined with a second searchthat used the MeSH term Uganda to restrict the search to articles on neonatal mortality inUganda. The combined search yielded 34 articles after duplicates were removed. Three mainrisk factors were identified for neonatal mortality in Uganda, with the key findings for theserisk factors presented in the following sections of the literature review.Maternal Education LevelSeveral of the studies reported that maternal education level had an effect on neonatalmortality. In one study it was reported that each additional year of schooling for a mother inUganda decreased the chances of a child dying by 16.6 % (Andriano and Monden, 2019).Likewise, in another cross-sectional study using multiple surveys in Uganda, the quality ofantenatal care was reduced in the lowest socioeconomic groups, including those with limitededucation (Benova et al., 2018). In another cross-sectional study, it was reported that inmothers with post-primary education, the adjusted odds ratio (aOR) for the likelihood of NMRwas lower than in those with none (aOR = 0.68, 95% CI: 0.46, 0.98) (Kananura et al., 2017).Based on these findings, maternal education level will be retained as a risk factor for neonatalmortality in this report.Maternal AgeThe age of the mother was also associated with increased NMR in several studies. Forinstance, in one study, mothers aged 40 and above had increased NMR when compared to25-29 year olds (OR=2.99, 95%CI: 1.31, 6.83), while those aged 15-19 years had lower NMR(OR=0.10, 95%CI: 0.01, 0.77) (Kananura et al., 2016). In another study, in-hospital neonatalmortality was greater in mothers aged 35 years and over (aOR=4.5; 95% CI: 1.35, 15.31) thanin mothers aged 25-34 years old (Egesa et al., 2020). Maternal age was also associated withincreased NMR in a study using 2011 DHS data, with increased NMR for mothers aged 35-49years (aOR 2.34, 95% CI 1.28, 4.25) when compared to mothers aged 18-34 years old (KujalaPUB015-6: Assessment 2 Example Epidemiological Report5et al., 2017). Based on these findings, maternal age will be retained as a risk factor forneonatal mortality in this report.Skilled Birth AttendantsThe WHO defines a skilled birth attendant (SBA) as including “accredited health professionalssuch as midwifes, doctors or nurses” (World Health Organisation, 2004, p. 1). Indeed, therecommended WHO model for antenatal care includes the presence of an SBA at the birth(Villar et al., 2001). However, it was reported in a literature review that only 45.2% of birthsin Uganda were in the presence of an SBA (Moyer, Dako-Gyeke and Adanu, 2013). In anothermulti-country study using 2011 data, including Uganda, the presence of an SBA decreasedNMR in Latin America, the Caribbean and Asia, but was reported to increase the risk in Africa(Singh, Brodish and Suchindran, 2014). In many studies of NMR in Uganda, rather than thepresence of an SBA at birth, it is the place of delivery that is used as surrogate measure, withdelivery in a healthcare facility implying the presence of an SBA (Kananura et al., 2016;Atusiimire et al., 2019). Given the doubt surrounding the presence of an SBA as a risk factorin Uganda for NMR, this variable will be included as a risk factor in this report.PUB015-6: Assessment 2 Example Epidemiological Report6MethodsSecondary DatasetThe secondary dataset used for this report was obtained from the Demographic and HealthSurveys (DHS), which is a programme funded by the United States Agency for InternationalDevelopment (USAID) to improve understanding of global health in developing countries(Corsi et al., 2012). The Ugandan dataset for the 2016 DHS survey was used for this report(Uganda Bureau of Statistics (UBOS) and ICF, 2018). Ethical approval for the DHS study wasobtained from the Institutional Review Board of the Inner City Fund (ICF) and the ethicsCommittee of the Ministry of Health in Uganda, with this approval extended to includesecondary analysis of the data approved by the DHS. Accordingly, permission to use theUgandan DHS dataset for this report was requested from the DHS Program (Authorisation145058). The 2016 DHS survey in Uganda was a nationally representative sample, withparticipants randomly chosen from all regions of Uganda between June and December 2016.The dataset can be downloaded from https://dhsprogram.com/data/available-datasets.cfm.Statistical AnalysisAll statistical analyses were performed using SPSS Statistics (v26, IBM Corporation, Armonk,New York, USA). Each of the three risk factors identified in the literature review wastransformed into a categorical variable. Education classifications used the DHS categories ofno education, primary, secondary, and higher education. Three age groups were created bydetermining the cut-offs to create three groups with as close to equal numbers as possible,which were 15-25 years, 26-34 years, and 35-49 years. Birth attendants were classified intotwo groups, depending on whether a skilled birth attendant (nurse, midwife or doctor) waspresent. Chi-squared analysis was used to determine the association of each risk factor withneonatal mortality, while logistic regression was used for multivariate analysis of all riskfactors combined. Confidence intervals of the odds ratios obtained from chi-squared andlogistic regression analysis were used to determine significant differences between groups,with 95% confidence levels used (Gardner and Altman, 1986).PUB015-6: Assessment 2 Example Epidemiological Report7ResultsParticipantsThe number of women included in the 2016 Ugandan DHS survey was 18,506, of which 13,745had given birth to at least one live child (74.3%). Of these mothers, 1345 (9.8%) hadexperienced neonatal mortality. There were 4,256 mothers aged 15-25 years (31.0%), 4739(34.5%) aged 26-34 years, and 4750 (34.6%) aged 35-49 years. With respect to education,1933 (14.1%) had no education, 8,305 had primary education (60.4%), 2663 (19.4%) hadsecondary education, while 844 (6.1%) had higher education. In total, 9,919 mothers had anSBA present at the birth (72.2%), while 3,826 (27.8%) had an unskilled attendant or nobodypresent at the birth.Risk Factors for Neonatal MortalityMaternal Education LevelThere was a significant effect of education level on neonatal mortality ( c2= 111.6, df=3,p=0.000). When compared to those with no education, which was taken as the referencegroup, there was a significantly decreased risk of neonatal mortality in mothers who had noeducation, primary education, and secondary education, with each increase in education levelcorresponding to a lower risk (Table 1).Table 1: Effect of education level on neonatal mortality Education LevelNeonatalmortalityChi-squared testUnadjusted odds ratio and 95% CI No education 14.3% Reference groupPrimary 10.3% c2= 25.04 df=1, p=0.000 0.69 (0.60, 0.80)Secondary 6.6% c2= 71.9 df=1, p=0.000 0.42 (0.35, 0.52)Higher 3.9% c2= 55.9 df=1, p=0.000 0.24 (0.17, 0.35)Maternal AgeThere was a significant effect of age-group on neonatal mortality ( c2= 245.7, df=2, p=0.000).There was a significantly increased risk of neonatal mortality for older mothers, with thosePUB015-6: Assessment 2 Example Epidemiological Report8aged 26-34 years and 35-49 years having a greater risk of neonatal mortality when comparedto the reference group aged 15-25 years (Table 2).Table 2: Effect of age group on neonatal mortality Age group(years)Neonatalmortality (%)Chi-squared testUnadjusted odds ratio and 95% CI 15-25 5.5% Reference group26-34 8.5% c2= 30.6 df=1, p=0.000 1.60 (1.36, 1.90)35-49 15.0% c2= 201.2 df=1, p=0.000 3.06 (2.61, 3.57)Skilled Birth AttendantsWith respect to SBA, there was a significant effect on neonatal mortality ( c2= 30.1, df=1,p=0.000), with the presence of SBA associated with a significantly lower risk of neonatalmortality (Table 3).Table 3: Effect of the presence of a skilled birth attendant on neonatal mortality BirthattendantNeonatalmortality (%)Chi-squared testUnadjusted odds ratio and 95% CI Skilled 8.9% Reference groupNon-skilledor nobody12.0% c2= 29.9 df=1, p=0.000 1.40 (1.24, 1.57)Multivariate AnalysisWhen all risk factors were entered into a multivariate logistic regression analysis to adjust forcovariates, both age group and education remained significant predictors of neonatalmortality (Table 4). However the presence of an SBA was no longer significantly associatedwith neonatal mortality rate.PUB015-6: Assessment 2 Example Epidemiological Report9Table 4: Logistic regression analysis of age group, education level and the presence of a skilledbirth attendant for neonatal mortalityRisk factor Group Chi-squared test Unadjusted odds ratio and 95% CIEducationlevelNo education Reference groupPrimary c2= 4.6 df=1, p=0.033 0.85 (0.73, 0.99)Secondary c2= 24.9 df=1, p=0.000 0.59 (0.48, 0.73)Higher c2= 41.6 df=1, p=0.000 0.29 (0.20, 0.43)Age-group15-25 years Reference group26-34 years c2= 32.7 df=1, p=0.000 1.64 (1.38, 1.94)35-49 years c2= 159.4 df=1, p=0.000 2.99 (2.53, 3.55) Skilled birthattendantYesReference groupNoc2= 2.15 df=1, p=0.1430.91 (0.79, 1.03) PUB015-6: Assessment 2 Example Epidemiological Report10DiscussionRisk Factors for Neonatal MortalityThe education level of mothers was a significant factor in neonatal mortality, even afteradjusting for other risk factors, with each increase in education level corresponding to adecreased risk of experiencing neonatal mortality. Similar results have been reported in otherstudies, with lower neonatal mortality observed in mothers with at least secondary educationlevel when compared to those with primary education level or lower (Kananura et al., 2017).This could be related to other elements of socioeconomic status that were not included in theanalysis. According to the latest UNICEF report on Uganda, uneducated women are morelikely to live in poverty, while they are also more likely to have negative attitudes towardsmodern medical care during pregnancy, delivery and the postnatal period (UNICEF, 2020a).Given the role that maternal education appears to play in neonatal mortality, it will beworthwhile investigating the impact of the educational reforms in Uganda, which in 1997introduced universal free primary education, followed by universal free secondary educationin 2007 (Chapman, Burton and Werner, 2010).Age group was also a significant factor in neonatal mortality after adjusting for other riskfactors. These results were expected, with other studies in Uganda also reporting greaterneonatal mortality rates among older mothers (Kananura et al., 2016; Egesa et al., 2020).Similar results have been reported in other countries, such as Trinidad and Tobago (Cupen etal., 2017) and Bangladesh (Al Kibria et al., 2018). However, in a UNICEF report using data from2011, greater levels of neonatal mortality were reported in women aged under 20 whencompared to those aged over 20 (UNICEF, 2020a). Given that the difference in age remainedafter adjusting for education level, and that there was a change from the 2011 results, itPUB015-6: Assessment 2 Example Epidemiological Report11would be worthwhile investigating this relationship in the future once the educationalreforms in Uganda have had a chance to make an impact.The final risk factor evaluated in this report was the presence of an SBA at the birth. Althoughthe unadjusted analysis showed an increased risk of neonatal mortality when an SBA was notpresent, when age and education level were adjusted for in the logistic regression model,there was no longer any effect of having an SBA present at the birth. This could be partlyexplained by the relatively high rate of SBA presence observed in this study, with 72.2% ofbirths being attended by an SBA, which represents a steady increase from previous studies inUganda in which only 46.2% of mothers reported having an SBA present at birth in 2006(Moyer, Dako-Gyeke and Adanu, 2013). In a qualitative study, it was suggested that barriersto change in Uganda with respect to the attitudes to having an SBA present or giving birth ina healthcare facility were decreasing, with more acceptance of the need for modernhealthcare (Byaruhanga et al., 2011). One of key issues that needs to be resolved with respectto the presence of SBA is the rural/urban disparity in healthcare in Africa, which was notevaluated in the present study (Ameyaw and Dickson, 2020).Strengths and LimitationsThe use of a DHS dataset was a strength of this report. The DHS programme is internationallyrecognised to be of high quality and provides a nationally representative sample of mothersin Uganda. Furthermore, the dataset used was the most recent dataset available, with datacollection occurring in 2016. Given that the dataset was large, collected by a highly-skilledresearch team and representative of Ugandan mothers, the results can be consideredgeneralisable. However, despite these strengths, there were some limitations to this report.The methodology used was retrospective, meaning that mothers were asked about their pastPUB015-6: Assessment 2 Example Epidemiological Report12experiences, which could lead to recall bias, especially given the low education levels of someof the participants (Neal, Channon and Chintsanya, 2018). The method used to calculateneonatal mortality was also a limitation, with each mother classified as having had a child diewithin the first four weeks after birth, with no differentiation made between those who hadsuffered multiple neonatal mortalities. The analysis was also limited to three risk factors, withother potential risk factors such as geography or religion not included in the analysis.PUB015-6: Assessment 2 Example Epidemiological Report13Conclusion and RecommendationsThe biggest risk factors for neonatal mortality in Uganda that were identified in this studywere education level and age. Of these two factors, education level is the risk factor that couldbe modified most easily. The adoption of universal free education in Uganda is a step in theright direction in this regard. However, it would be worthwhile investigating whether thereare any barriers to attaining universal free primary and secondary education, particularly inareas of high poverty, such as rural Uganda. The improvement of infrastructure in remoterural areas is also vital to reduce NMR, as timely critical care is needed, such as resuscitationequipment for new-borns with breathing difficulties. In addition, the impact of the presenceof skilled nurses, midwives and doctors requires further study given the equivocal findings inthe present study.PUB015-6: Assessment 2 Example Epidemiological Report14ReferencesAl Kibria, G. M., Khanam, R., Mitre, D. K., Mahmud, A., Begum, N., Moin, S. M. I., Saha, S. K.,Baqui, A. and Projahnmo Study Grp, B. (2018) ‘Rates and determinants of neonatal mortalityin two rural sub-districts of Sylhet, Bangladesh’, PLoS One, 13(11), pp. e0206795.Ameyaw, E. K. and Dickson, K. S. (2020) ‘Skilled birth attendance in Sierra Leone, Niger, andMali: analysis of demographic and health surveys’, BMC Public Health, 20(1), pp. 164.Andriano, L. and Monden, C. W. S. (2019) ‘The Causal Effect of Maternal Education on ChildMortality: Evidence From a Quasi-Experiment in Malawi and Uganda’, Demography, 56(5),pp. 1765-1790.Atusiimire, L. B., Waiswa, P., Atuyambe, L., Nankabirwa, V. and Okuga, M. (2019)‘Determinants of facility based-deliveries among urban slum dwellers of Kampala, Uganda’,PLoS One, 14(4), pp. e0214995.Benova, L., Dennis, M. L., Lange, I. L., Campbell, O. M. R., Waiswa, P., Haemmerli, M.,Fernandez, Y., Kerber, K., Lawn, J. E., Santos, A. C., Matovu, F., Macleod, D., Goodman, C.,Penn-Kekana, L., Ssengooba, F. and Lynch, C. A. (2018) ‘Two decades of antenatal anddelivery care in Uganda: a cross-sectional study using Demographic and Health Surveys’,BMC Health Services Research, 18(1), pp. 758.Byaruhanga, R. N., Nsungwa-Sabiiti, J., Kiguli, J., Balyeku, A., Nsabagasani, X. and Peterson,S. (2011) ‘Hurdles and opportunities for newborn care in rural Uganda’, Midwifery, 27(6),pp. 775-780.Chapman, D. W., Burton, L. and Werner, J. (2010) ‘Universal secondary education in Uganda:The head teachers’ dilemma’, International Journal of Educational Development, 30(1), pp.77-82.Corsi, D. J., Neuman, M., Finlay, J. E. and Subramanian, S. V. (2012) ‘Demographic and healthsurveys: a profile’, International Journal of Epidemiology, 41(6), pp. 1602-1613.PUB015-6: Assessment 2 Example Epidemiological Report15Cupen, K., Barran, A., Singh, V. and Dialsingh, I. (2017) ‘Risk Factors Associated with PretermNeonatal Mortality: A Case Study Using Data from Mt. Hope Women’s Hospital in Trinidadand Tobago’, Children-Basel, 4(12), pp. 108.Egesa, W. I., Odong, R. J., Kalubi, P., Ortiz Yamile, E. A., Atwine, D., Turyasiima, M., Kiconco,G., Maren, M. B., Nduwimana, M. and Ssebuufu, R. (2020) ‘Preterm Neonatal Mortality andIts Determinants at a Tertiary Hospital in Western Uganda: A Prospective Cohort Study’,Pediatric Health, Medicine and Therapeutics, 11, pp. 409-420.Gardner, M. J. and Altman, D. G. (1986) ‘Confidence-intervals rather than P values:estimation rather hypothesis testing’, British Medical Journal, 292(6522), pp. 746-750.Kananura, R. M., Tetui, M., Mutebi, A., Bua, J. N., Waiswa, P., Kiwanuka, S. N., EkirapaKiracho, E. and Makumbi, F. (2016) ‘The neonatal mortality and its determinants in ruralcommunities of Eastern Uganda’, Reproductive Health, 13, pp. 1-9.Kananura, R. M., Wamala, R., Ekirapa-Kiracho, E., Tetui, M., Kiwanuka, S. N., Waiswa, P. andAtuhaire, L. K. (2017) ‘A structural equation analysis on the relationship between maternalhealth services utilization and newborn health outcomes: a cross-sectional study in EasternUganda’, BMC Pregnancy & Childbirth, 17, pp. 1-12.Kujala, S., Waiswa, P., Kadobera, D., Akuze, J., Pariyo, G. and Hanson, C. (2017) ‘Trends andrisk factors of stillbirths and neonatal deaths in Eastern Uganda (1982-2011): a crosssectional, population-based study’, Tropical Medicine & International Health, 22(1), pp. 63-73.Lawn, J. E., Cousens, S. and Zupan, J. (2005) ‘4 million neonatal deaths: When? Where?Why?’, The Lancet, 365(9462), pp. 891-900.Moyer, C. A., Dako-Gyeke, P. and Adanu, R. M. (2013) ‘Facility-based delivery and maternaland early neonatal mortality in sub-Saharan Africa: a regional review of the literature’,African Journal of Reproductive Health, 17(3), pp. 30-43.Mukasa, G. K. (1992) ‘Morbidity and mortality in the Special Care Baby Unit of New MulagoHospital, Kampala’, Annals of Tropical Paediatrics, 12(3), pp. 289-95.PUB015-6: Assessment 2 Example Epidemiological Report16Neal, S., Channon, A. A. and Chintsanya, J. (2018) ‘The impact of young maternal age at birthon neonatal mortality: Evidence from 45 low and middle income countries’, PLoS One, 13(5),pp. e0195731.Office for National Statistics (2020) Child and Infant Mortality in England and Wales: 2018.Stillbirths, Infant and Childhood Deaths Occurring Annually in England and Wales, andAssociated Risk Factors. Available at:https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/childhoodinfantandperinatalmortalityinenglandandwales/2018#infant-mortalityrates-increase-in-england-and-wales-in-2018 (Accessed: 4th December 2020).Singh, K., Brodish, P. and Suchindran, C. (2014) ‘A Regional Multilevel Analysis: Can SkilledBirth Attendants Uniformly Decrease Neonatal Mortality?’, Maternal and Child HealthJournal, 18(1), pp. 242-249.Uganda Bureau of Statistics (UBOS) and ICF (2018) Uganda Demographic and Health Survey2016, Kampala, Uganda: Uganda Bureau of Statistics and the Demographic and HealthSurvey Program. Available at: http://dhsprogram.com/pubs/pdf/FR333/FR333.pdf.UNICEF (2020a) Maternal and Newborn Health Disparities: Uganda. Available at:https://data.unicef.org/wpcontent/uploads/country_profiles/Uganda/country%20profile_UGA.pdf (Accessed: 5thDecember, 2020).UNICEF (2020b) Neonatal mortality data. Available at: https://data.unicef.org/topic/childsurvival/neonatal-mortality/ (Accessed: 5th December 2020).UNICEF (2020c) Under-five mortality data. Available at:https://data.unicef.org/resources/dataset/under-five-mortality-data/ (Accessed: 5thDecember 2020).United Nations (2011) Mortality estimates from major sample surveys: towards the designof a database for the monitoring of mortality levels and trends, New York, USA: UnitedNations (Population Division Technical Paper No. 2011/2. Available at:PUB015-6: Assessment 2 Example Epidemiological Report17http://www.un.org/en/development/desa/population/publications/pdf/technical/TP2011-2_MortEstMajorSampSurv.pdf (Accessed: 5th December, 2020).United Nations Millennium Summit (2000) United Nations Millennium Declaration, NewYork, USA: United Nations (A/RES/55/2).Villar, J., Ba’aqeel, H., Piaggio, G., Lumbiganon, P., Belizán, J. M., Farnot, U., Al-Mazrou, Y.,Carroli, G., Pinol, A., Donner, A., Langer, A., Nigenda, G., Mugford, M., Fox-Rushby, J.,Hutton, G., Bergsjø, P., Bakketeig, L. and Berendes, H. (2001) ‘WHO antenatal carerandomised trial for the evaluation of a new model of routine antenatal care’, The Lancet,357(9268), pp. 1551-1564.World Bank (2016) The Uganda Poverty Assessment Report 2016: Farms, Cities and GoodFortune-Assessing Poverty Reduction in Uganda from 2006 to 2013: World Bank. Availableat: http://pubdocs.worldbank.org/en/381951474255092375/Uganda-Poverty-AssessmentReport-2016.World Health Organisation (2004) Making pregnancy safer: the critical role of the skilledattendant: a joint statement by WHO, ICM and FIGO: World Health Organisation(9241591692, WQ 240 2004WO). Available at:https://www.who.int/maternal_child_adolescent/documents/9241591692/en/ (Accessed:5th December, 2020).PUB015-6: Assessment 2 Example Epidemiological Report18Appendix: SPSS Output TablesEducation LevelPUB015-6: Assessment 2 Example Epidemiological Report19PUB015-6: Assessment 2 Example Epidemiological Report20Age GroupPUB015-6: Assessment 2 Example Epidemiological Report21PUB015-6: Assessment 2 Example Epidemiological Report22Skilled Birth AttendantPUB015-6: Assessment 2 Example Epidemiological Report23PUB015-6: Assessment 2 Example Epidemiological Report24Multivariate Analysis

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