SLIDE 1 Factors a associated wit ith i infant m mortalit ity i in S South A Africa: A A provin incia ial perspective Pris iscil illa B Bartus
Statistics South Africa: Population Statistics Division
Abstract
The sustainable development goal 3 adopted by countries in 2015 seeks to ensure healthy lives and promote well-being for all at all ages by 2030. For South Africa to achieve this there is a need to reduce infant mortality which will result in a decline in child mortality. Addressing specific challenges to the provinces will ensure that the national mortality rate is reduced. The aim of the study was to determine which factors are associated with infant mortality in South Africa from a provincial perspective by applying the Cox regression. Infant mortality rates were found to be high in Free State and North West
- provinces. The rates were estimated to be 44.2 in Free State and 41.9 in North West. The p-value was
(0.2733) greater than 0.05 indicating that the chi-square is not significant at the 5% level of significance. The findings suggest the fitted models can be used to assess provinces that are experiencing high infant
- mortality. By including covariates that are related to basic services the environmental factors that
contribute to mortality are highlighted for government interventions.
Key words: : Infant, mortality rate, factors 1.
ion
1.1 Backgro round nd
African children face higher risk of death as compared to European children. Child mortality was estimated to be 55 per 1000 live births in World Health Organisation (WHO) African countries while that of WHO European countries was 10 per 1000 live births (WHO, 2015). The reduction of child mortality is one problem many countries especially in Africa continue to battle with. Better health care services are not available to many and governments struggle to provide this basic service. Goal 4 of the millennium development goals (MDG 2015) aimed at reducing child mortality. In 2015 it was reported that globally under five mortality rate had declined between the period 1990 and 2015. Over the 25 year period child mortality decreased from 90 to 43 deaths per 1,000 live births (United Nations, 2015). Like many Sub-Saharan countries South Africa by 2010 was failing to reach the target to reduce
SLIDE 2 child mortality. The MDG Country Report 2010 revealed that under five mortality rates for South Africa were high and the country was far from achieving the international target of 20 per thousand deaths (MDG Country Report 2010). A comparison of provinces showed that Limpopo had the highest under five mortality rate of 110 per 1000 deaths with infant mortality rate (IMR) contributing 55% to the under five mortality (MDG Country Report 2010). Also, in South Africa, studies exist identifying the factors associated with infant mortality, but none has done so from a provincial perspective, using the census 2011 data. Thus, creating a gap which this study is aimed to address. Therefore, this paper seeks to examine the determinants of infant mortality in provinces.
1.2 Li Litera rature re Revie iew
The study of infant and child mortality is one that has been much researched about by demographers because for a population to grow it is important that children born survive and develop to reproduce children of their own. This forms the bases for the need to understand factors associated with mortality as to be able to decrease child mortality (Tymicki, 2009). However this does not mean that women should give birth too many children. Birth intervals is also an important factor to consider. A study by Nsejje et al 2015 found children born to women who give birth in shorter intervals were at a higher risk of death within the first five years of life indicating a need for education on family planning in Uganda. This case would be no different in rural provinces of South Africa like Eastern Cape, KwaZulu-Natal and Limpopo. Muriithi D. M and Murrithi D. K (2015) found that poor mothers were more likely to have their infant die than rich mothers and age of mother was found to be a contributing factor in Kenya. A study by Ntuli et al 2013 found that infant mortality accounted for 31% of the under five mortality and the leading causes of death were found to be diarrhoea, pneumonia and HIV/AIDS. South Africa faces similar challenges regarding health care services in certain provinces. The Western Cape Health Department (2005) reported that Khayelitsha had an IMR of 44 per 1000 live births which was the highest in the province. These results implies that even though the Western Cape is highly urbanised there are certain areas or communities that are experiencing challenges in terms of service delivery and health care services. In 2003, Eastern Cape was found to have an IMR of 55.1 per 1000 live births whilst the national figure was 41 per 1000 live births (SANRAL, 2007). Northern Cape is among the provinces that reported that 50 per 1000 children were underweight (The Triennial Report 2015). Underweight babies have a higher likelihood of death as compared to infants that are born
- healthier. Analysis of mortality in Free State found that the mortality rates were higher in children
and the elderly population. Sex disparity was evident in infant mortality with regards to infectious diseases, maternal and perinatal diseases and nutritional deficiencies (Bradshaw et al 2000). A study conducted in KwaZulu-Natal estimated infant mortality to be 92 per 1000 live births and under five
SLIDE 3
mortality of 132 per 1000 live birth. These rates were highest in rural districts as compared to EThekwini metropolitan district (Hoque, 2001). There has been a growth of informal settlements in North West from 12.3% in 2002 to 22.8% in 2011 (The Triennial Report 2015). Informal settlement do not have access to services and live under poor conditions which are associated with infant mortality. Tshwane metropolitan district is among the districts with the lowest infant mortality in the Gauteng, 17 per 1000 live births, however the growing levels of poverty account for children being malnutrition which is a contributing factor to infant mortality (The Triennial Report 2015). HIV/AIDS with diarrhoeal diseases, low birth weight, lower respiratory infections and protein-energy malnutrition was reported to be the leading causes of death in under-fives in Mpumalanga province (Bradshaw et al 2000). The Limpopo Provincial Department partnered with other stakeholders to form the Limpopo Initiative for New-born Care (LINC) in 2003 with aim of improving child care in hospitals. The program has resulted in neonatal deaths being reduced by 8.0%. Hospital staff and mothers were trained on infant care (UNICEF for South Africa, 2015). The Triennial Report 2015 indicated that 6.5% of children die from non-natural deaths in Limpopo. Most of these deaths do not happen in a health facility. Cultural beliefs leads to preference of traditional healer and parents delay consulting a medical doctor which may resulting in the death of the child or infant in the province (The Triennial Report 2015). The conceptual framework on childhood mortality is influenced by bio-genetic factors and environment factors (Tymicki, 2009). This paper focuses on environment factors by considering the social economic level of households and the demographic factors.
1.3 Pro robl blem Statement
The sustainable development goal 3 adopted by countries in 2015 seeks to ensure healthy lives and promote well-being for all at all ages by 2030. For South Africa to achieve this, there is a need to reduce infant which will result in a decline in child mortality. Addressing specific challenges to the province will ensure that the national mortality rate is reduced. The paper seeks to find the determinants of infant mortality in South Africa.
SLIDE 4 1.4 Obje jectives
The main objective of the study is to determine which factors are associated with infant mortality in South Africa by applying the Cox Hazard model. Specific objections To provide a comparison of provincial estimates of infant mortality rates in South Africa To determine factors that are associated with infant mortality in South Africa by fitting the Cox model
1.5 Rese searc rch qu questions a s and hypo pothe hesis
Which provinces in South Africa experience high infant mortality rates? Which factors contributes to infant mortality in these provinces? Therefore it is hypothesised that Western Cape has lower infant mortality as compared to other provinces and that the fitted model will fit the data well.
2.
logy
2.1 Th The data
Census 2011 10% sample dataset used in the study was obtained from Statistics South Africa. Census 2011 was the third census in the country conducted by Statistics South Africa since 1996. The census was conducted by dividing the country into manageable enumeration areas for data collection. Three questionnaires was used for data collection. Questionnaire A was used to collect information from households about all persons forming a household. Questionnaire B, a shorter questionnaire was used to capture transients and questionnaire C was used to collection information on institutionalised
- population. Interviews were conducted using the face-to-face method. This study is restricted to
information collected from households. Section G of the questionnaire A, asked women aged 12 - 50 about their birth history. See appendix for the snapshot of the questions asked. Using the question on children ever born 7.7 million women aged 12-50 that had given birth were selected for the study. Date of birth of last child born was used to filter for births that occurred in the 24 months (10 Oct 2009 to 9 Oct 2011) preceding the census. A total of 1.7 million births that occurred in the period was used for the analysis. The sex distribution of infants was 846 926 males and 827 625 females. Due to the incompleteness of the mortality data collected from surveys and censuses the definition of infant
SLIDE 5
mortality is redefined to include births that occurred in the 24 months to increase the deaths and reduce the bias of HIV related deaths of mothers (Blacker and Brass, 2005). The census is a de-factor census (Statistics South Africa, 2011). Individuals were counted where they were found on the reference night 9 October 2011. This was the reference night for data collection. Therefore the 10 October 2011 is considered the end date of the survey for this study.
2.2 Estim imating infa fant mort rtal ality ra rates
Infant mortality rate is defined as 𝐽𝑁𝑆 = 𝑂𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝑑ℎ𝑗𝑚𝑒𝑠𝑓𝑜 𝑒𝑓𝑏𝑢ℎ𝑡 𝑐𝑓𝑔𝑝𝑠𝑓 𝑏𝑓 1 𝑈𝑝𝑢𝑏𝑚 𝑜𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝑑ℎ𝑗𝑚𝑒𝑠𝑓𝑜 𝑐𝑝𝑠𝑜 ∗ 1000
2.3 Th The Co Cox-pro ropo port rtional al hazard rd mode del
The Cox-proportional hazard model is best suited when the objective of the study is to identify how certain variables called covariates will affect the outcome of the dependent variable by assessing how survival times and these covariates relate (Klein and Moeschberger, 2003). Letting ℎ(𝑢|𝑎) to represent the hazard rate at time t for an individual (infant) with risk vector X. According to Klein and Moeschberger (2003) the basic model given by Cox (1972) is as follows: ℎ(𝑢, 𝑦) = ℎ0(𝑢, 𝑦)𝑓𝑦𝑞(𝛾′𝑦) Where: ℎ0(𝑢) is the arbitrary baseline hazard rate 𝑦𝑘(𝑢) = (𝑦𝑘1(𝑢)………………..𝑦𝑘𝑞(𝑢)) denotes the covariate vector for the jth individual at time t. 𝛾 = (𝛾1,𝛾2,…………………………𝛾𝑞)
′ is a parameter vector
𝐼𝑆 = ℎ(𝑢, 𝑌) ̂ ℎ(𝑢, 𝑌∗) ̂ = 𝑓𝛾𝑗
SLIDE 6 The interpretation of the results is If HR is greater than zero implies that 𝑓 is greater than one, then the risk to death is increasing. If HR is less than zero implies that 𝑓 is also less than one, then the risk to death is decreasing. The estimated coefficients are denoted by 𝑓𝛾𝑗 = ln(𝐼𝑆) The null hypothesis for the estimated regression coefficients 𝛾𝑗 = 0. The model assumes constant hazard ratios over time. The Schoenfeld residuals are used to test the assumption of the proportional-
- hazard. Plotting Schoenfeld residuals against time shows that if the slope is not equal to zero then the
log hazard ratio function is proportional. The goodness of fit is used to test the overall fit of the model. The event variable is a derived variable used as the censoring variable. Infants that were alive when the survey ended (10 October 2011) are censored. That time to death is unknown as infants are still
- alive. Time is a variable measuring the time to death of an infant in days. The variable is derived by
subtracting date of death from date of birth. Infants that were alive at the end of the survey had missing time because they did not have a date of death. To overcome this problem the time to death
- f infants that were still alive was calculated as their current age in days by subtracting end of census
date and the date of birth. This ensures a random allocation of time to death of infants that are alive and these times are right censored. The time variable is the outcome variable which is the survival time of an infant in days.
SLIDE 7 3.
lts a and d dis iscussio ion
3.1 Estim imate infa fant mort rtal ality rates
Figure 3.1: Estimated infant mortality rates. Figure 3.1 above shows the estimated IMR per province. The IMR were lowest in Western Cape and highest in Free State and North West. The national IMR was estimated to be 28.1 per 1000 live births. This suggest that South Africa still has a long way to go in improving health care for newly born. The rural provinces still lack better health care facilities and education for young mothers. However, South Africa managed to be below the African continent IMR of 55.5 per 1000 lives. The districts findings are presented in the appendix.
3.2 Te Testin ing the pro roporti rtion
rd assum umptio ion
Figure 3.2: Kaplan-Meier survival estimates by age
15,2 32,8 39,3 44,2 30,6 41,9 23,1 30,8 20,9 28,1 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0 IMR (per 1000 live births) Province
0.98 1.00 200 400 600 800 analysis time age = 12-19 age = 20-29 age = 30-39 age = 40+
SLIDE 8 Figure 3.2 above shows the graph of the Kaplan-Meier survival function stratified by age. As expected the probability of survival decreases with time. Infants born to women in the age group 30-39 have a better chance of survival. Figure 3.3: Kaplan-Meier survival estimates by provinces Figure 3.3 above shows the graph of the Kaplan-Meier survival function stratified by provinces. Western Cape has a higher probability of survival as compared to other provinces. Free State and North West had a higher risk of infant mortality. Figure 3.4: Test of PH assumption Figure 3.4 shows a plot of the scaled Schoenfeld residuals for population group against survival time
- ranking. Horizontal lines implies that the PH assumption was satisfied for population group. This
0.98 1.00 200 400 600 800 analysis time
Western Cape Eastern Cape Northern Cape Free State KwaZulu-Natal North West Gauteng Mpumalanga Limpopo
10 20 30 5000 10000 15000 Rank(t)
bandwidth = .8
SLIDE 9 further suggests that the scaled Schoenfeld residuals were independent of survival time. The p-values reported in table 3.1 are not significant implying no violation of PH assumption for Black Africans, coloured, Asian\Indian and other population group. Table 3.1: Assessing the proportional-hazard assumption for the South African model
rho chi2 df Prob>chi2 Black African 0.01567 3.63 1 0.0568 Coloured
0.36 1 0.5475 Indian\Asian 0.00337 0.17 1 0.6808 Other
1.39 1 0.2387 Two children 0.01787 4.76 1 0.0292 Three children 0.00750 0.84 1 0.3602 Four children 0.01186 2.10 1 0.1468 More than five children
0.02 1 0.8875 Unemployed 0.00143 0.03 1 0.8598 Not economically active 0.01157 2.01 1 0.1565 No education 0.01486 3.30 1 0.0692 Primary education 0.01940 5.65 1 0.0174 Secondary education 0.01943 5.62 1 0.0178 No access to piped water 0.00191 0.05 1 0.8162 Gas
0.14 1 0.7063 Paraffin 0.00638 0.63 1 0.4262 Wood 0.00641 0.63 1 0.4256 Coal
0.73 1 0.3939 Animal dung 0.00187 0.05 1 0.8189 Other energy sources 0.01487 3.29 1 0.0699 Pit toilet with ventilation 0.00023 0.00 1 0.9766 Pit toilet without ventilation
0.03 1 0.8673 Bucket\ other type of toilet 0.02013 6.38 1 0.0115 No toilet facility 0.00478 0.36 1 0.5502 Traditional dwelling 0.01163 2.03 1 0.1537 Informal dwelling 0.01581 3.89 1 0.0486 Other dwelling 0.00936 1.31 1 0.2525 12-19 0.01425 3.04 1 0.0813 30-39
4.69 1 0.0304 40+
0.24 1 0.6218 Traditional areas
2.07 1 0.1503 Farms areas
0.05 1 0.8246 global test 116.08 32 0.0000
The global tests results for the models South Africa and Western Cape shows that the proportional hazard assumption was violated with p-value less than 0.05. The covariates two children, primary education, secondary education, bucket\other type of toilet and age group 30-39 violated the assumption whilst other covariates and provinces showed no evidence of violation of the proportional-hazard assumption. The results for the South African model are presented in table 3.1 above.
SLIDE 10 3.3 Cox
regre ressi sion
Table 3.2: Cox Regression model results
Characteristics Hazard ratios South Africa Western Cape Eastern Cape Northern Cape Free State KwaZulu-Natal North West Gauteng Mpumalanga Limpopo Background characteristics Maternal age at first birth 12-19 1,268** 1,283** 1,195** 1,157 1,289** 1,320** 1,211** 1,350** 1,261** 1,152 30-39 0,933 1,028 0,832 1,103 1,082 0,785 1,057 0,911 0,924 1,231 40+ 1,279 1,211 0,882 1,625 0,894 2,224 1,896 0,880 2,582 Maternal Population group Black African 3,651** 3,777** 7,085** 2,003 5,779** 3,305** 5,724** 2,629** 4,590** 2,802* Coloured 2,306** 3,551** 4,517** 1,951 4,750** 1,283 4,894** 1,893** 2,308 2,297 Indian\Asian 1,312** 3,732* 1,506 1,031 7,416** 0,892 1,229 1,343 1,047 Other 1,747** 2,445 1,469 5,290* 1,289 4,912 1,285 4,605 1,272 Total children ever born Two children 0,984 0,823* 0,996 0,957 1,001 0,944 1,038 1,032 0,948 1,004 Three children 1,079** 1,092 1,161* 0,829 1,098 1,081 1,173 1,083 0,976 1,028 Four children 1,147** 0,998 1,154 0,913 1,296* 1,068 1,222 1,208 0,978 1,308* More than five children 1,427** 1,290 1,468** 1,044 1,215 1,504** 1,458** 1,446** 1,349* 1,507** Economic and education characteristics Employment status Unemployed 0,963 1,060 0,936 0,963 0,956 0,824** 1,079 1,031 0,804* 0,784* Not economically active 0,818** 0,880 0,762** 0,662** 0,678** 0,714** 0,875 0,921 0,705** 0,691** Education level of mother No education 1,254** 1,983 1,326 1,742 2,206* 0,820 1,515 1,259 1,168 1,241 Primary education 2,050** 2,172** 2,100** 2,189* 2,630** 1,506** 2,232** 1,723** 1,809** 2,299** Secondary education 1,522** 1,709* 1,653** 1,434 2,143** 1,210 1,743* 1,269* 1,723** 1,836**
SLIDE 11
Table 3.2: Cox Regression model results (cont.)
Characteristics Hazard ratio South Africa Western Cape Eastern Cape Northern Cape Free State KwaZulu-Natal North West Gauteng Mpumalanga Limpopo Contextual characteristics Access to piped water No access to piped water 1,002 0,498 0,999 0,942 1,273 0,956 0,935 0,869 1,034 0,922 Source of energy for cooking Gas 1,133* 1,375* 1,415** 0,933 0,849 0,832 1,316 1,397 1,344 0,957 Paraffin 1,337** 1,965** 1,138 0,815 1,324* 1,516** 1,423** 1,581** 0,922 1,409* Wood 0,962 1,575 1,090 0,876 1,066 1,000 1,086 1,315 1,210 0,978 Coal 1,306** 1,222 0,000 1,240 1,014 1,276 1,881* 1,280 0,955 Animal dung 1,392** 3,611 1,179 0,620 1,552 1,567 0,709 5,423* 2,172 0,316 Other energy sources 0,825 1,236 1,058 1,395 0,375 0,758 0,242 1,043 0,831 0,746 Toilet facility Pit toilet with ventilation 1,362** 1,598 1,513** 1,502* 1,257* 1,272** 1,292 0,797 1,369* 1,161 Pit toilet without ventilation 1,175** 0,459 1,391** 1,141 1,167 1,166** 1,149 0,918 1,106 1,054 Bucket\ other type of toilet 1,262** 0,771 1,345** 0,928 1,505** 1,267** 1,305 0,851 1,179 1,038 No toilet facility 1,470** 1,116 1,548** 1,750** 1,352 1,313** 1,471* 1,311 1,487* 1,262 Type of dwelling Traditional dwelling 1,193** 0,986 1,111* 1,678 1,207 1,001 0,847 0,579 1,370* 0,986 Informal dwelling 1,158** 1,480** 0,923 1,470 1,166 1,175 0,962 1,348** 1,192 1,452** Other dwelling 1,066 1,550 1,027 1,384 1,409 1,110 0,890 1,029 1,129 1,015 Geography type Traditional areas 0,982 1,035 1,124 0,858 0,980 0,860 1,097 0,752** 1,154 Farms areas 1,395** 2,338** 0,994* 1,468 1,225 1,295** 0,913 1,161 1,170 1,662*
** Significant at 1% level of confidence, * significant at 5% level of confidence
SLIDE 12
The results presented in table 3.2 shows the results of the cox regression models for the provinces. The chi-square test was significant with p-value 0.0000 for all the models. Maternal age at first birth Infants born to young women aged 12-19 were 1.268 times more likely to die as compared to infants born to women aged 20-29 in South Africa. Western Cape, Eastern Cape, Free State, KwaZulu-Natal, North West, Gauteng and Mpumalanga had highly significant hazard ratios. Population group Population group or race was found to be significantly associated with infant mortality. Black Africans were at higher risk of death as compared to whites. Indians\Asians were 7.416 times more likely to die as compared to whites in Free State. Coloureds in Western Cape were 3.551 times more at risk of death as compared to whites. Population group or race was not significantly associated with infant mortality in Northern Cape. Total children ever born Infants that were born to a women that give birth to more than five children were more likely to die in the 24 months of birth as compared to those infants born to women with less number of births. The results showed that the higher the number of children a women has had the higher the risk of infant mortality. Maternal employment status The expanded definition employment was used. Infants born to women that were not economically active was significantly associated with infant mortality in South Africa. Infant mortality is more likely to occur for unemployed and not economically active as compared to employed. In Gauteng the risk to infant mortality is 1.031 times more for unemployed than employed. Employment is an indirect measure of poverty and therefore the findings indicate that unemployed women are not in position to seek better health care for their infants or seek antenatal care service whilst pregnant. Maternal highest level of education Mother’s highest level of education is a significant factor associated with infant mortality. The results showed that in South Africa infants born to women with primary education were 2.050 times more likely to die than infants born to mothers with a tertiary education. Whilst in Free State women with
SLIDE 13 no education were 2.206 times at risk of death than tertiary educated. Educated women are better informed and therefore seek health care for their infants. Access to piped water Access to piped water is an indicator that households have safe drinking water and household use. The results show that access to piped water was not significantly associated with infant mortality. In Free State an infant born in a household with no access to piped water was at risk of mortality 1.273 times more than an infant born in household with access to piped water. Source of energy for cooking Source of energy for cooking was found not to be significant associated with infant mortality in Northern Cape and Mpumalanga at 1% and 5% level of significance. The results show that households using other source of energy were at a higher risk of infant mortality as compared to households using electricity for cooking. Use of animal dung was 1.392 time more likely to result in death of infant as compared to electricity. This implies that the other energy sources emit harmful fuels that are easily inhaled by the infant. Toilet facility Toilet facility was not significantly associated with infant mortality in Western Cape, Gauteng and
- Limpopo. Not having a toilet facility lead to infants being 1.470 times more at risk of death compared
to infants in households with flush toilets in South Africa. Households not having a toilet facility is an indicator of poor living conditions. Hence likelihood of death is higher. Type of main dwelling Infants living in traditional dwelling or informal dwelling were more likely to die as compared to those in formal dwelling. Type of main dwelling was not significant with infant mortality in Northern Cape and North West. Infants in Limpopo living in informal dwelling were 1.452 times more at risk as compared to infants in Limpopo in formal dwelling. Geographical area Infants born in farm areas were more likely to die as compared to those born in urban areas. In the Western Cape infants in farm areas were 2.338 times more likely to die compared to those in urban
- areas. In Eastern Cape infants in traditional areas were 1.035 times more likely to die as compared to
those in urban areas.
SLIDE 14
Conclu lusio ion a and r recommendatio ions
Further work is to be done on the Western Cape and South African model using the extend cox model since the PH assumption was not satisfied. Studies have shown that education, toilet facilities and age are significantly associated with infant mortality. Provincial differentials showed that contextual factors, with the exception of access to piped water, were statistically significant with infant mortality in rural provinces. There is a need for provincial government to develop policies regarding service delivery that will better the basic services in the provinces. Improving services will improve the standard of living for the households and therefore reduce the risk of infant mortality. Educational programs should be established to educate mothers on child care. Job creation should also be prioritised at it was found that infants born to unemployed women were at a higher risk of infant mortality.
SLIDE 15 References
Blacker J. and Brass W, The estimation of infant mortality from proportions dying among births in the past 24 months. Available online journals.co.za/docserver/fulltext/sajdem/10/12/36.pdfexpires=1480159301id=idaccname=guestche cksum=49B775101805AB344651A28EFF1CF[Accessed on 01 November 2016] Bradshaw D, Nannan N, Laubscher R, Groenewald P, Joubert J, Nojilana B, Norman R, Pieterse D, Schneider M. South African National Burden of Disease Study, 2000, Estimates of Provincial
- Mortality. Available online http://www.mrc.ac.za/bod/freestate.pdf Accessed [11 November 2016]
HOQUE MONJURUL AKM Department of Health, CHILDHOOD MORTALITY IN KWAZULUNATAL. 2001 Available online http://www.kznhealth.gov.za/childmortality.pdf Accessed [11 November 2016] Klein J.P and Moeschberger M.L. Survival analysis: Techniques for censored and truncated data. Springer-Verlag,New York, 2003. Chapter 8 pages 243-290. Muriithi D. M and Murrithi D. K. Determination of Infant Mortality and Child Mortality in Kenya Using Cox-Proportional Hazard Model. American Journal of Theoretical and Applied Statistics. Vol 4, No.5, 2015, pp 404-413. doi:10.11648/j.ajtas.20150405.21 Available
- nline:http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.21.pdf Accessed
[11 November 2016] Nasejje J. B, Mwambi H.G and Achia T.N.O. Understanding the determinants of under five child mortality in Uganda including the estimation of unobserved household and community effects using both frequentist and Bayesian survival analysis approaches. BioMed Central. 2015 10:1005. doi10.1186/s12889-015-2332-y Accessed[11 April 2016] Rural Health Advocacy Project, The rural factsheet 2015. Avaiable online at http://www.rhap.org.za/wp content/uploads/2015/09/RHAP-Rural-Health-Fact-Sheet-2015-web.pdf Accessed [10 August 2016] Statistics South Africa, 2011. Census Statistical Release. Pretoria: Statistics South Africa. Available
- nline. www.statssa.gov.za
Statististics South Africa, MDG Country report 2015. Pretoria: Statistics South Africa. Available online http:www.statssa.gov.za/MDG/MDG Country%20Report Final30Sep2015.pdf [Accessed on 01 November 2016] The second Triennial Report of the Committee on Morbidity and Mortality in Children Under 5 Years (COMMIC), 2014 Availiable online http://www.kznhealth.gov.za/mcwh/2ndCoMMiC-Triennial Report-Abridged.pdf Accessed [09 August 2016] The South African National Road Agency Limited (SANRAL): Community Empowerment Assessment Report, Phase 1, 2007. Available online http://www.nra.co.za/content/Pondo1.pd Accessed [11 November 2016] Tymicki K, Correlates of infant and childhood mortality: A theoretical overview and new evidence from the analysis of longitudinal data of the Bejsce (Poland) parish register reconstitution study of the 18th-20th centuries. Demographic Research Vol.20, article 23, pages 559-559. Published 26 MAY 2009 http://www.demographic-research.org/Volumes/Vol20/23/ DOI: 10.4054/DemRes.2009.20.23 Accessed [09 August 2016]
SLIDE 16
UNICEF for South Africa. Improving Newborn Care in South Africa: Lessons learned from Limpopo Initiative for Newborn Care (LINC).Pretoria.2011. Available online http://www.unicef.org/southafrica/SAF resources newborncare.pdf Accessed [09 August 2016] Western Cape Department of Health, performance plan 2005/2006. March 2005 World Health Organisation 2015, website: http://www.who.int/gho/child health/mortality/neonatal infant text/en/ Accessed [11 November 2016]
SLIDE 17
Appendix ix A
Figure 1: Snapshot of questionnaire Figure 2: Estimated infant mortality rate by district municipalities in Western Cape, Census 2011
19,5 19,7 18,4 17,7 39,2 13,0 15,2 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 West Coast Cape Winelands Overberg Eden Central Karoo City of Cape Town Western Cape IMR (per 1000 live births) District municipalities
SLIDE 18
Figure 3: Estimated infant mortality rate by district municipalities in Eastern Cape, Census 2011 Figure 4: Estimated infant mortality rate by district municipalities in Northern Cape, Census 2011
27,9 34,9 38,6 44,7 38,2 41,3 19,7 17,5 32,8 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0 Cacadu Amathole Chris Hani Joe Gqabi O.R.TamboAlfred Nzo Buffalo City Nelson Mandela Bay Eastern Cape IMR (per 1000 live births) District municipalities 37,9 45,5 44,2 31,1 42,1 39,3 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0 Namakwa Pixley ka Seme Siyanda Frances Baard John Taolo Gaetsewe Northern Cape IMR (per 1000 live births) District municipalities
SLIDE 19
Figure 5: Estimated infant mortality rate by district municipalities in Free State, Census 2011 Figure 6: Estimated infant mortality rate by district municipalities in KwaZulu-Natal, Census 2011
41,3 51,9 47,6 43,0 35,1 44,2 0,0 10,0 20,0 30,0 40,0 50,0 60,0 Xhariep Lejweleputswa Thabo Mofutsanyane Fezile Dabi Mangaung Free State IMR (per 1000 live births) District municipalities 25,6 30,6 40,0 22,5 28,7 32,3 32,5 38,3 42,5 35,2 25,7 30,6 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 IMR (per 1000 live births) District municipalities
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Figure 7: Estimated infant mortality rate by district municipalities in North West, Census 2011 Figure 8: Estimated infant mortality rate by district municipalities in Gauteng, Census 2011
30,3 45,8 62,2 48,3 41,9 0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 Bojanala Ngaka Modiri Molema Dr Ruth Segomotsi Mompati Dr Kenneth Kaunda North West IMR (per 1000 live births) District municipalities 31,3 32,0 27,3 19,2 19,0 23,1 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 Sedibeng West Rand Ekurhuleni City of Johannesburg City of Tshwane Gauteng IMR (per 1000 live births) District municipalities
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Figure 9: Estimated infant mortality rate by district municipalities in Mpumalanga, Census 2011 Figure 10: Estimated infant mortality rate by district municipalities in Limpopo, Census 2011
46,1 27,5 24,8 30,8 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0 Gert Sibande Nkangala Ehlanzeni Mpumalanga IMR (per 1000 live births) District municipalities 20,7 16,5 23,1 26,6 20,8 20,9 0,0 5,0 10,0 15,0 20,0 25,0 30,0 Mopani Vhembe Capricorn Waterberg Greater Sekhukhune Limpopo IMR (per 1000 live births) District municipalities
