UNIVERSITY OF THE WITWATERSRAND FACULTY OF MEDICINE SCHOOL OF PUBLIC HEALTH RESEARCH REPORT DETERMINANTS OF VACCINATION COVERAGE AMONG CHILDREN AGED 12-23 MONTHS IN RURAL KWAZULU-NATAL JAMES N NDIRANGU A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Masters in Science (Medicine) in Population - based Field Epidemiology JULY 2008 2 DECLARATION I, James Nganga Ndirangu declare that this research report is my own work. It is being submitted for the degree of Master of Science in Medicine in the field of Population ? Based Field Epidemiology in the University of Witwatersrand, Johannesburg. It has not been submitted before any degree or examination at this or any other University. Signature: ________________________ Full Name: James Nganga Ndirangu 15th day of April, 2008 3 DEDICATION This work is dedicated to my lovely mother, Jane Thairu, for her prayers throughout my stay away from home. I also dedicate it to Grace Wambui, my wife, who has supported me throughout my study period. 4 ABSTRACT To evaluate the impact of maternal HIV-infection on routine childhood immunization coverage, comparison was made on the immunization status of children born to HIV-infected and HIV-uninfected women in rural KwaZulu Natal. The study population was all children enrolled in the routine demographic surveillance system as at 31st December 2005 (n=18,171) in Africa Centre Demographic Surveillance Area. Sampling of subjects was done based on the dates of birth that were between the period 1st Jan 2004 and 31st December 2005 (n=2,020). This was further divided based on maternal HIV status namely; 236 HIV (+), 777 HIV (-) and 1,007 HIV (unknown). The main outcome measure was the percent of complete routine childhood immunizations recommended by the WHO as assessed from the Road-to- Health cards or maternal recall during household interviews. For all vaccines, children born to HIV-infected mothers had lower immunization coverage than children born to HIV-negative mothers (78.21% vs. 86.52%). The children of mothers who were HIV-infected were 31-55% (P-value <0.020) less likely to be immunized compared to children of mothers who were HIV- uninfected. We conclude that maternal HIV-infection is associated with childhood under immunisation. VCT health workers should encourage HIV-infected mothers to complete childhood immunization. Improving access to immunization services could benefit vulnerable populations such as children born to HIV-infected mothers. 5 ACKNOWLEDGEMENTS Firstly, I am greatly indebted to INDEPTH NETWORK and other donors for their financial support throughout the course of this programme. Secondly, my sincere thanks to Dr Till Barnighausen of Africa Centre for Health and Population studies, University of KwaZulu Natal for his mentorship and guidance during my attachment, and to Dr Khin Tint, both academic coordinator and supervisor, for her guidance and support throughout this research. Special thanks to all the lecturers at the School of Public Health, University of The Witwatersrand. You have shaped me to be a much better researcher. Thanks to Lawrence Mpinga and Lindy Mataboge for assisting us in all administrative aspects. My sincere thanks to the Director, Prof Marie-Louise Newell and the entire staff of Africa Centre for Health and Population studies, University of KwaZulu Natal for hosting me in the course of my attachment and allowing me to use their dataset for purposes of this research. Thanks to Rhana Naicker for doing everything possible to make us feel at home in the course of our stay there. Thanks to Mr Colin Newell for assisting in the extraction of my variables. Lastly, I would like to express my thanks to my Director, Prof Kayla Laserson, Mr Frank Odhiambo and Dr Adazu Kubaje who facilitated my application to study here in South Africa. 6 TABLE OF CONTENTS DECLARATION ..................................................................................................................................... 2 DEDICATION ......................................................................................................................................... 3 ABSTRACT............................................................................................................................................. 4 ACKNOWLEDGEMENTS ..................................................................................................................... 5 LIST OF FIGURES.................................................................................................................................. 8 LIST OF TABLES ................................................................................................................................... 9 DEFINITION OF TERMS..................................................................................................................... 10 ACRONYMS AND ABBREVIATIONS .............................................................................................. 11 CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW....................................................... 12 1.1 Background information????????????????????????????????12 1.2 Problem statement??????????????????????????????????. 14 1.3 Justification????????????????????????????????????... 15 1.4 Hypothesis?????????????????????????????????????. 15 1.5 Literature review??????????????????????????????????... 15 CHAPTER 2: METHODOLOGY ......................................................................................................... 21 2.1 Study aims and objectives???????????????????????????????. 21 2.2 Research design???????????????????????????????????. 21 2.3 Study area and study population????????????????????????????... 21 2.4 Sample size????????????????????????????????????? 23 2.5 Data sources????????????????????????????????????.. 24 2.5.1 Data...................................................................................................................................................... 24 2.5.2 Immunization data ............................................................................................................................... 25 2.5.3 HIV surveillance data .......................................................................................................................... 26 2.5.4 Household asset data............................................................................................................................ 27 7 2.6 Measurements???????????????????????????????????? 27 2.7 Quality control???????????????????????????????????... 30 2.8 Data cleaning????????????????????????????????????. 31 2.9 Data processing methods and analysis??????????????????????????.. 31 2.10 Ethics??????????????????????????????????????... 32 CHAPTER 3: RESULTS ....................................................................................................................... 34 3.1 Immunization coverage????????????????????????????????. 34 3.2 Characteristics of the sample??????????????????????????????. 37 3.3. Maternal HIV status?????????????????????????????????. 38 3.4 Determinants of vaccination coverage??????????????????????????.. 39 CHAPTER 4: DISCUSSION................................................................................................................. 44 4.1 Immunization coverage????????????????????????????????. 45 4.2 Maternal HIV status?????????????????????????????????.. 46 4.3 Distance to health care facility?????????????????????????????.. 47 4.4 Limitations?????????????????????????????????????.50 CHAPTER 5: CONCLUSION............................................................................................................... 52 CHAPTER 6: RECOMMENDATIONS................................................................................................ 53 REFERENCES....................................................................................................................................... 55 APPENDICES?...?????????????????????????????..???64 8 LIST OF FIGURES Figure 1 Summary of sampling and sample size selection Figure 2 Percent of immunization coverage by vaccine type among children aged 12-23 months at the time of interview, Africa Centre DSA South Africa, 2000-2006. Figure 3 Distribution of all vaccines, showing a decrease in timeliness of vaccines, Africa Centre DSA South Africa 9 LIST OF TABLES Table 1: National vaccination schedule for children under 1 year of age in South Africa Table 2: Data collected at each routine household visit, 2000 and ongoing Table 3: Comparing the mean vaccination coverage in Africa Centre DSA and the National Estimates for the period 2000-2006 Table 4: Characteristics of the sample population Table 5: Characteristics of the sample by maternal HIV status Tables 6: Adjusted odds for BCG vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) Table 7: Adjusted odds for Polio vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) Table 8: Adjusted odds for Diphtheria vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) Table 9: Adjusted odds for Hepatitis b vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) Table 10: Adjusted odds for Measles vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) 10 DEFINITION OF TERMS APGAR score- a simple and repeatable method to quickly and summarily assess the health of newborn children immediately after childbirth. It is determined by evaluating the newborn baby on five simple criteria on a scale from zero to two and summing up the five values thus obtained.1 Resident members ? Are members of the households within the Africa Centre DSA who have been residing within those households for a period exceeding four months. 49 Primary road - a road that forms the major routes between the major urban centres.48 In the South African context, it is known by as N-series. Mobile clinic points? Designated points throughout the district (e.g. primary schools, shopping centers, churches etc) that are visited twice monthly by a mobile clinic. The services offered during this visits are; family planning services, child immunization, antenatal care and treatment of chronic illnesses. 48 11 ACRONYMS AND ABBREVIATIONS BCG Bacillus Calmette Guerin OPV Oral Polio Vaccine DTP Diphtheria Tetanus Pertusis DSA Demographic Surveillance Area ACDIS Africa Centre Demographic Information System DHS Demographic Health Survey GIS Geographic Information System EPI Expanded Programme on Immunization WHO World Health Organization UNICEF United Nations International Children?s Emergency Fund CHF Child Health Form ARV Anti-retroviral VCT Voluntary Counseling and Testing 12 CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW In this chapter, background information including the importance and general coverage of vaccination is reviewed. The problem statement and justification for the study is explained. The published literature on the possible determinants of vaccination coverage is reviewed and the chapter ends by describing the aims and objectives of the study. 1.1 Background information The Expanded Programme on Immunization was started in 1974 by WHO/UNICEF for less developed countries. It was called ?expanded? for two reasons. First, polio and measles vaccines were added to BCG and DTP vaccines. Second, the proportion of children in developing countries who were immunized was substantially increased.2 Immunization has led to spectacular reductions in mortality in both developed and developing countries.3 Vaccines, which protect against disease by inducing immunity around the world, are widely and routinely administered based on the common-sense principle that it is better to prevent people from falling ill than to treat them once they do. Suffering, disability and preventable death are avoided. It is estimated that immunization averted about two million deaths of infants in 2003 as well as an additional 600,000 hepatitis-B related deaths that would have occurred in adulthood (from cirrhosis and cancer).4 Routine immunization is now provided in developing countries and many parts of sub-Saharan Africa against Measles, Polio, Diphtheria, Tetanus, Pertussis, Hepatitis-B and Tuberculosis. The global coverage of infants in 2004 with 3 doses of DTP 13 vaccine was 78%, 3 doses of polio vaccine 80% and one dose of measles 76%. Further, by 2004, a total of 153 countries had integrated Hepatitis-B vaccination.5 The use of conjugate vaccines for the prevention of Haemophilus influenza type b (Hib) disease in children has substantially decreased the burden of disease in developing countries.6,7,8 However, they are not used in some developing countries due to their high cost and because Hib remains under-recognized as a cause of severe disease and death.9 South Africa was the first country in Africa to self-finance and incorporate the Hib vaccine into its routine child immunization schedule from July 1999.10 Since its incorporation, the number of Hib cases, among children below one year of age decreased by 65%, from 55 cases in 1999- 2000 to 19 cases in 2003-04. This is based on the number of cases reported to the national surveillance system 11 The national vaccination status schedule in South Africa includes BCG, OPV, DTP, Hib, Hepatitis B and Measles vaccine. These vaccines are provided free of charge at local clinics and community health centers in South Africa. All children have a right to basic health care, of which immunization is one of the components. The Expanded Programme on Immunization in South Africa aims for 90% full immunization coverage amongst children at 1 year of age. According to the 2003 statistics, the routine immunization coverage was 82%, which was above the WHO recommendation for routine immunization coverage of 80%.12 The routine vaccination schedule in South Africa for children?s first year of life is shown in the table below: 14 Table 1: National vaccination schedule for children under 1 year of age in South Africa Age Vaccine Birth BCG, Polio0 6 weeks Polio1, DTP1, HepB1, Hib1 10 weeks Polio2, DTP2, HepB2, Hib2 14 weeks Polio3, DTP3, HepB3, Hib3 9 months Measles Immunization is a proven tool for controlling and even eradicating disease. However, the determinants of vaccination coverage are thought to vary with different settings, for instance, across social groups, distance traveled to health care facilities, distance traveled to transport infrastructure, maternal HIV status or with varying maternal education level. Thus, it is imperative to examine these factors that determine vaccination coverage so as to advocate for interventions that will increase childhood vaccination coverage. This study assessed the vaccination coverage and the determinants of this coverage in rural KwaZulu-Natal (Hlabisa district). However, for data reasons, we could not include vaccination against Haemophilus influenza type b (Hib), which may also prevent a relatively large number of childhood deaths. 13 1.2 Problem statement Routine child vaccination has been known to reduce infant mortality by preventing diseases. However, a previous study conducted in Hlabisa health district indicated that 76% of children had received all the vaccines due to a 12-month-old child, and 78% of these had received all 15 doses by 12 months of age.14 The study aims at investigating why some children were vaccinated while others were not in an area served by Africa Centre. 1.3 Justification Vaccination coverage rates are accepted as an indicator of the performance and adequacy of primary paediatric health care services15 and are also a useful tool in program management and decision making.16 Vaccination coverage has been shown to be primarily hampered by difficulty in assessing primary care, complex transport requirements and by user characteristics, such as parental education, late birth order, household structure and socio- economic status in other rural areas.17,18,19 A clear understanding of these factors will assist in strengthening the primary paediatric health care services in the Africa Centre Demographic Surveillance Area (DSA) which also has a high HIV prevalence (40%) among mothers attending prenatal clinics.20 1.4 Hypothesis ? There is no difference in vaccination coverage by maternal HIV status. ? There is no difference in vaccination coverage by distance from the household to the nearest fixed clinic, mobile clinic point and primary road. 1.5 Literature review Infant mortality has traditionally been viewed as an indicator of the social and economic well- being of a society. It reflects not only the magnitude of those health problems which are directly responsible for the death of infants but the effect of a multitude of other factors, 16 including prenatal and post-natal care of mother and infant, and the environmental conditions to which the infant is exposed.21 Due to the association between child immunization and child survival4, a large component of the factors affecting child survival also affect child immunisation for instance maternal HIV status, maternal education level, access to health care facility, socio-economic status of the household where the child is brought up among others. Maternal HIV Status A previous study conducted in Rakai District, Uganda showed that the percent coverage for all childhood vaccines was lower among children born to HIV-infected mothers than children born to HIV-uninfected mothers. The difference was greatest for measles and DTP3, with coverage being 7% lower for children of HIV-infected mothers.22 Coverage was also affected by whether a mother had knowledge of her HIV status. The odds of being under immunized, for children of both HIV-infected and HIV-uninfected mothers who did not know their status, increased to 1.56 (95% CI 0.86-2.83) & 1.68 (95% CI 1.07- 2.63) respectively, while the odds of children born to HIV-infected women who knew their status was doubled to 2.21 (95% CI 1.14-4.29). This implies that the risk of under immunization increases not only with HIV- infection but also with the knowledge of maternal HIV status. This could be due to the fact that women who are HIV-infected tend to change their health seeking behaviour when they know their HIV-status and this may negatively affect their child?s heath. The impact of learning about their HIV-infection may cause psychological effects like depression which have a direct effect on their child-care. 17 Maternal Education Widespread awareness of the importance of mother?s education on their children?s health is commonplace in research. For instance, Caldwell argued that maternal education is the single most significant determinant of child health and child mortality and no other factor has such impact. Maternal education cannot thus be employed as a proxy for general social and economic change but must be examined as an important factor in its own right.23 Some possible links and pathways that result in greater utilization of modern health services are; education is associated with greater knowledge of the protective functions of child immunization and greater awareness of proper immunization schedules.24 Schooling equips women with special skills and dispositions that significantly predict the use of medical services and changes in household health behaviour.25 Potential health benefits from delayed and lower fertility among an educated woman enable greater investments in the child.26 Finally, education endows a woman with the ability to access relevant health services, interact effectively and assimilate information relating to family planning, prenatal care, childhood immunizations and nutritional needs.27 Distance to health care facility Geographic accessibility can be measured by the following factors: the physical separation that limits contact, represented as Euclidean distance; distance along a road network; travel time or travel costs.28 Stronger associations have been found with road distance and estimated travel time than with Euclidean distance.29 Further, there is a cogent argument that Euclidean distance is a sub-optimal measure of isolation since it ignores physical barriers such as rivers, swamps, hills, road and traffic system and socio-cultural factors. These can greatly influence the actual distance traveled.30 18 Travel time models have some limitations when compared to actual distances travelled. Travel time assumes ubiquitous coverage of public transport which is unlikely in a rural setting. It is usual to wait hours for another bus or taxi. Similarly, it assumes public transport is used on average behaviour while this differs from area to area. Further, travel time differs with age and sex. These individual factors are not accounted for in travel time.28 Distance to the clinic, a proxy measure of accessibility, has been found to affect immunization coverage in 2006 in Kenya31. Close proximity to the clinic was also associated with an increased likelihood of vaccination, with immunization coverage declining with increasing distance from vaccination clinics in Egypt32 and in Pakistan.33 A possible explanation of this could be that visibility of a clinic may attract a parent?s attention and/or act as a reminder to the parent of the immunization status of the child. A study that investigated the accessibility and utilization of clinics in Hlabisa district, rural South Africa showed that the mean travel time to the nearest clinic is 77 min (median 81 min) and that 64.8% of the homestead travel at least one hour to attend a clinic. Compared with other districts in South Africa and Africa, this suggests that patients in the district travel 80% longer than the reported provincial coverage and that the proportion of the population who live in the vicinity of more than one hour travel time to health care facility is double the national rural average.28 Travel time to clinic is only a proportion of the total time expenditure in getting health care. Studies in South Africa measure the time taken to receive a treatment as 42 minutes34 and estimate that 35% of patients wait for an hour or more to receive primary health care.35 Other studies in Africa show that people spend more time waiting to receive treatment than 19 travelling to clinic with an average waiting time of 150-160 minutes.36 Travel time becomes an indicator of vaccination coverage because where the majority of mothers/caregivers have to make a journey to a health facility, they may prefer more accessible alternate sources of child care. Urban-rural differentials Urban-rural differences in vaccination coverage have been previously observed in Niger and Nigeria. In Niger, only 1 in 10 rural children have received all the eight immunizations compared to half of urban children while the overall vaccination coverage levels in rural areas are less than half of those for urban children for BCG and measles. Further, due to higher drop out rates from the health system in rural areas in Niger, coverage levels for the third dose of DPT and polio are less than one-quarter of the rates seen in urban areas.37 In Nigeria, rural- urban differences in vaccination coverage, though marked, are not as large as those seen in Niger. In the case of BCG and measles vaccines, rural coverage levels are about two-thirds of the levels in urban areas.33 In Eastern Turkey, measles vaccination was found to be higher in urban regions than suburban and rural regions.38 Socio-economic status Previous studies have shown that socio-economic status of the family play an important role in determining vaccination compliance with higher socio-economic status being associated with higher rate of vaccination.39,40 In Bangladesh, children of relatively better-off households (owning 4-6 household articles) had an 80% higher chance of being fully immunized compared to the economically disadvantaged group (owning at most one household article).41 This may be due to the fact that children who are poor are hardest to reach by the health 20 services and parents may encounter more barriers to reach them compared to those of better socio-economic status. However, other studies have found no difference in vaccination rates with respect to socio-economic status.18, 34 Other factors Sex discrimination in child immunization has been found to exist in rural Bangladesh with female children being 30% less likely to be fully vaccinated compared to male children.41 Similar findings have been reported in other studies. 42,43 Marital status of the mother has been found to play an important role in predicting child health in developing countries. 44,45 In Central Asia, the risk of under vaccination for children of unmarried mothers was 70% higher than for married mothers.40 This may be due to the fact that in many societies, marriage provides a better social life which may be associated with better child health. 21 CHAPTER 2: METHODOLOGY 2.1 Study aims and objectives Primary objective The main aim of this study was to investigate why some children are vaccinated while others are not. Secondary objective 1. To determine the vaccination coverage among children aged 12-23 months in the Africa Centre Demographic Surveillance Area (DSA). 2. To investigate whether maternal HIV status influences vaccination coverage in children aged 12-23 months when controlling for maternal age, maternal education, household wealth, the child?s sex, birth order, and the distance from the household to the nearest fixed clinic, mobile clinic point and primary road. 2.2 Research design This was an analytical cross-sectional study of children recruited and followed up from 1st January 2000 to 31st December 2005. The study utilized primary data collected twice per year at Africa Centre DSA. 2.3 Study area and study population Hlabisa health sub-district is located within the rural district of Umkhanyakude in northern KwaZulu Natal (Appendix 1), covering an area of 1,430 km2. The sub-district is about 250 km 22 north of the city of Durban (the third largest city in South Africa). Since 1998, the Africa Centre for Health and Population studies has maintained a Demographic Information System (ACDIS) in KwaZulu-Natal, the largest province South Africa, stretching from the southern borders of Swaziland and Mozambique to the Eastern Cape border in the south. Its headquarters are located near Mtubatuba in Hlabisa sub-district. Births, deaths, migrations, marriages, and socioeconomic status are recorded on some 11,000 households in a 438 square kilometer area of the Hlabisa sub-district. The total population surveyed was approximately 90,000 Zulu speaking people, of whom 3.3% were located in a formal urban township (KwaMsane), 19.9% in peri-urban areas (informal settlements with a population density of more than 400 people per km2), and the remaining 76.8% were classified as living in a rural area.46 The rural population lives in scattered homesteads that are not concentrated in villages or households, as is the case in many parts of Africa. The community, like many others in the province of KwaZulu Natal is in the throes of an unprecedented health crisis, with HIV prevalence close to 40 percent in women attending prenatal clinics.20 In the sub-district, there is a central community hospital and 13 fixed clinics which provide the bulk of the primary health care in Hlabisa. In addition, 30 mobile clinic points are visited twice monthly, and 130 community health workers are each expected to visit regularly a group of assigned homesteads.47 To access primary health care, two-thirds of the population walks to clinics, while the remaining one-third uses public transport. A negligible number (0.4 %) use their own transport to access care.48 The whole population of children, born between 1st Jan 2000 and 31st Dec 2005, in the 11,000 households in the Demographic Surveillance System, was included. The age group was chosen because the WHO recommends that if vaccination coverage is to be investigated, the children 23 should be older than 11 months of age. This is because by this age, they have received all the vaccines that are due to them in their first year of life. Secondly, this was consistent with other surveys on vaccination and more so with the DHS. Thus comparisons between the Demographic Surveillance Area with provincial results and also other studies done in different countries could be done. 2.4 Sample size The sample included all children born between 1st Jan 2004 and 31st Dec 2005, in Africa Centre which totalled to 3,058 children. This was from a total of 18,171 children born between 1st Jan 2000 and 31st Dec 2005, whose vaccination information was available in the ACDIS. We could not include all the 18,171 children because data on socio-economic status and maternal HIV status information were available from the year 2004 and these were important independent variables that were explored in this study. Out of the 3,058, 2,020 (66%) were included in the logistic regression analysis because they had complete information on the independent variables. The rest (n=1,038) were dropped because they had missing information on independent variables. For the purposes of this analysis, we categorized maternal HIV status as; HIV Positive 236 (12%), HIV Negative 777 (38%) and HIV unknown 1,007 (50%). 24 Figure 1 Summary of sampling and sample size selection 2.5 Data sources 2.5.1 Data Data were obtained from the Africa Centre Demographic Information System (ACDIS) in South Africa. ACDIS has two separate cycles of data collection ? household and individual. During the household data collection cycle, a set of questionnaires are routinely administered every six months (Table 2) to a key informant in each household.49 Information recorded are Unknown HIV status 1,007 Socio-economic status and maternal HIV collected from year 2004 HIV Positive 236 HIV Negative 777 Sample Date of Birth: 1st Jan 2004 to 31st Dec 2005 (3,058) Study Population Date of Birth: 1st Jan 2000 to 31st Dec 2005 (18,171) 1,038 children dropped because had missing information on independent variables in regression analysis Sample in logistic regression analysis 2,020 25 key attributes and events regarding physical structures, household and individuals and their relationship to each other. Additional modules administered include; household socio- economic data, individual socio-economic data and child grants. The HIV surveillance comprises part of the individual data collection cycle, undertaken annually, and requires an interview with the eligible persons due to the sensitivity of the questions. Table 2: Data collected at each routine household visit, 2000 and ongoing Subject Types of information Homestead Latitude, longitude Owner Number of households Household Formation and dissolution Household head Individual Individual details: inc. date of birth, sex, parents Household membership(s) Household members Update household list: members who join, leave or die Residency status, marital and partnership status. Relationship to household head Births Pregnancy outcomes: abortions, still and live births. Delivery environment: inc. assistance, place birth weight, APGAR Deaths Location and care provision at time of death. Open description of circumstances Migrations Details of place of origin or destination Type of migration, e.g. household or individual migration Child health On first birthday: vaccination history 2.5.2 Immunization data A Child Health Form (CHF) is used to collect immunization data at Africa Centre (Appendix 4). This form is only filled in for children who are between 1 year and 4 years of age and are resident in the households within the DSA. 26 Data on immunization has been collected annually since 2000, and once retrospectively for all children ever born between 1995 and 2000 to mothers who were residents in the ACDIS DSA in 2000. Immunization data, including the dates of immunization, are available for the following vaccines: BCG, Diphtheria-Tetanus-Pertussis (DTP), Oral Polio Vaccine (OPV), Hepatitis B (HepB) and measles vaccinations. These vaccines are the complete set of vaccines included in the South African National Childhood Immunization Schedule (Table 1), with the exception of the Haemophilus influenzae type B (Hib) vaccine, which has recently been added to the ACDIS data collection schedule as from January 2008. 2.5.3 HIV surveillance data Data were used from the first round (January to December 2004) and the second round (January to December 2005) of a prospective population-based HIV survey. During data collection process (Appendix 5), teams of two trained fieldworkers visited each eligible individual in his or her household. Fieldworkers revisited households up to four times to contact absentees. If a subject no longer lived at the household, the field worker handed the case to a specially trained tracking team that made up to 10 attempts to find the individual in his or her new residence. After written informed consent, the field workers collected blood by finger stick and prepared dried blood spots for HIV testing according to the Joint United Nations Programme on HIV/AIDS (UNAIDS) and World Health Organization (WHO) Guidelines for Using HIV Testing Technologies in Surveillance.50 HIV status was determined by antibody testing with a broad-based HIV-1/HIV-2 enzyme-linked immunosorbent assay (ELISA; Vironostika, Organon Teknika, Boxtel, the Netherlands) followed by a confirmatory ELISA (GAC-ELISA; Abbott, Abbott Park, Illinois, USA). 27 2.5.4 Household asset data A household assets index is used as a measure of wealth. Household assets indices are valid proxies of wealth in rural Africa as shown by Morris and colleagues.51 The household assets included in this analysis are: Household furnishings (Bed, sofa, table/chair), bicycle, block- maker, car, electric stove with oven, electric hot plate, electric kettle, fridge/freezer, gas cooker, lorry/tractor, motorcycle/scooter, radio, sewing machine, telephone, cellphone, television set, video cassette recorder and wheelbarrow. Wealth index was calculated from these assets using the principal component analysis method.52 Households are categorised households as either belonging to the poorest 40%, the middle 40% or the wealthiest 20% on the assets index scale. 2.6 Measurements Vaccination coverage The main outcome measure was complete vaccination for children who are aged 12-23 months. The vaccinations given by the first year of life are; measles vaccine, Diptheria- Tetanus-Pertusis (DTP3), Haemophilus influenzae type B (Hib), Poliomyelitis (Polio 3), Hepatitis B (HepB3) and BCG vaccine. Each of these was treated as separate dependent variables. BCG in South Africa is given at birth and if not it is given at any other time the child is presented to the clinic. Though Polio is given at the same time with DTP, Hib & HepB (6, 10 & 14 weeks), unlike the latter, it can be administered though routine immunization or through immunization campaigns. Measles is administered much later on, at 9 months of age and it can be administered through routine immunizations or through immunization 28 campaigns. The age cut-off corresponding to the first birthday was chosen because WHO recommends that vaccination should be assessed for children after this age.4 This allows a three months period for children to receive measles immunization. To allow an appraisal of the trends in timeliness of vaccines given as children get older, four categories were used. For each vaccine, the proportion of children receiving their vaccines on/before their due dates, within 28 days after their due date, within 42 days of the due date and after 42 days of the due date were calculated. This method has been used in other rural areas of South Africa.53 Determinants of vaccination coverage The determinants of vaccination included as independent variable were; maternal education, maternal HIV status, maternal age, shortest distance from the household to the health care facility (mobile/fixed), shortest distance from the household to the nearest primary road, economic status of the household (wealth index) and sex of child. Maternal education was included because the association between a mother?s education and the likelihood of her child being vaccinated is well documented.30,33,54,55 We also included distance to health care facility because various studies have found an association between distance to health facility and vaccination coverage.16,26,32,56 Socio economic status, measured in terms of household wealth, was included as an independent variable because children from the poorest households, who also live furthest from the health facilities, incur indirect costs (in terms of travel time to health facilities) making it difficult to avail themselves for services 29 that exist in the community.16,40 Maternal HIV status was included as an independent variable because given the high HIV prevalence of women visiting prenatal clinics43 in the study area, it was important to investigate the effect of maternal HIV status on vaccination coverage. Further, maternal HIV status has been documented to be associated with vaccination coverage.20 The difference between our study and the Rakai study in Uganda that investigated maternal HIV status and vaccination coverage22 was that; the Rakai study was based on participants who visited a Voluntary Counseling and Testing (VCT) centre while our study was population-based and participants were visited in their homes by community interviewers and tested for HIV after consenting. Further, the Rakai sample included children aged 6 to 35 months of age while our sample was children aged 12-23 months of age. The Rakai study adjusted the interaction between HIV-infection and maternal knowledge of HIV status, for number of antenatal visits, knowledge that a child should complete immunization by nine months of age, belief that it is permissible to immunize a symptomatic child born to an HIV infected mother and usual immunization venue (government or other). Our study adjusted for distance to health care facility among other factors because of the general weakness associated with the HIV-related disease which makes it more difficult for the HIV-positive mother to bridge the distance to the health facility. A mother was defined as HIV-infected if she had a positive HIV test before/at the birth of the child for which immunization was to be assessed for the purposes of this study. Vaccination coverage was calculated based on the standard Demographic and health survey definition. The numerator was the number of children aged 12-23 months at the time of the 30 survey who had received specified vaccine(s), at any time before the survey, according to information from a mother Road-to-Health card or report by respondent. The mothers report was used only when no card was shown to the interviewer or there was no record of the vaccination. In the absence of both the Road-to-Health card and the mothers report, the child was treated as not having been vaccinated for the purposes of this study. The denominator was the total number of children aged between 12-23 months at the time of the survey. 2.7 Quality control A CHF form is only filled in for Children who are between 1 year and 4 years and are resident in the households within the DSA. When a second visit is being made to the household, two pieces of information exist, on the case report forms of that particular household, that help avoid double filling the CHF forms. 1. Last Visit date to the household 2. Age of the children in the household. The effect of this is that: if the age of the child at the last visit was 12 months and above, then a field worker does not bother with a CHF form. However, if the age of a child at the last visit was less that 12 months, then the field worker can find out the current age of the child, and if the child is 12 months and above, as of the second visit date, then he/she fills in a CHL form. 31 2.8 Data cleaning During data cleaning, we used the premise that vaccinations were given in sequence. For instance; DTP1 - DTP2 - DTP3. We compared the dates when DTP2 was administered to ensure it was after DTP1 was given. Where the dates indicated that DTP1 was given after DTP2, we compared the dates of administration of DTP1 with those of OPV1 and HepB1, since they are administered at the same time, and made the corrections to DTP1 as appropriate. This was done across all the vaccines that are administered at the same time, namely; OPV, DTP and HepB. Secondly, we checked for the dates when OPV0 & BCG vaccines were given to ensure this was within a few days after birth or at birth. 2.9 Data processing methods and analysis Data processing The data were captured raw from the household interviews and hand delivered to the Data Centre. This was in the form of case report forms. Data in the center is normally captured using SQL 2000 and was then transferred to STATA 9.2 for cleaning and analysis. Data cleaning was done to check for missing values, incorrect dates, skip patterns as required by the questionnaire. This was ensured for accuracy and completeness of the data before analysis. Data Analysis The primary response variable was vaccination status (1=fully vaccinated; 0=partially vaccinated). Differences in proportions were compared using the chi-square or Fisher?s exact tests as appropriate. Education level was dichotomized as 1-7 (primary school) years and >7 years (post primary school), as per the school life in South Africa. Maternal age was dichotomized as <25 and >25 years. Wealth index was dichotomized as poorest 40%, the 32 middle 40% or the wealthiest 20% on the assets index scale. These categories of relative wealth have been used in other studies in poor provinces in South Africa. 57,58 The association between Maternal HIV status, distance to health facility, distance to primary road, maternal education level, maternal age and socio-economic status with vaccination status were examined in the bivariate analysis. For multivariate analysis, we controlled for factors that were significant in the univariate analysis in addition to factors that were not significant in the univariate but were found significant in other rural areas in the developing countries. These included maternal age, maternal education, birth order and sex of the child which appeared in all models. Logistic regression was preferred because the independent variables were continuous and categorical in nature. Further, the dependant variable (vaccination status) was dichotomous. Sequential logistic regression was used to explore the effect of the independent variables on vaccination status. Maternal HIV status was considered to be a more important predictor variable, followed by distance to health care facility and to the primary road (model 1). The other independent variables were then subsequently adjusted for in model l and a comparison of the two models done to find out the best fit. 2.10 Ethics The primary project was cleared by an ethics committee in KwaZulu Natal University at baseline (2000). Application for recertification is logged annually with the same Ethics committee, the last recertification being in November 2006, ethics clearance number E009/00. 33 Approval from the University of Witwatersrand Committee for Research on Human Subjects was also obtained; M061028. Permission was obtained from Africa Centre for the use of their dataset after filling in a data user agreement formed which was signed by my supervisor and I. Study participants were identified using their study IDs instead of their real names during data extraction. This was maintained during analysis and reporting. For the participants in the HIV surveillance, HIV test results were obtained confidentially in a number of counselling centres which have been set up for that purpose in the survey area. A linked, anonymous voluntary HIV testing system with pre- and post-result counselling that used confidential personal pin numbers and handheld computers for result communication had been established.49 34 CHAPTER 3: RESULTS 3.1 Immunization coverage Routine immunization coverage of five vaccinations from 2000 to 2006 is shown in Figure 3.1.1. BCG vaccine has the highest mean immunization coverage, 92.3% (95% CI 88.4-96.2), while measles vaccine is recorded the lowest mean immunization coverage, 79.1% (95% CI 76.0-82.2). Immunization coverage for the two vaccines is statistically different (P<0.001). This can be attributed to the time difference between administrations of the two vaccines. BCG is given at birth while measles is given much later ? 9 months. Besides, babies who were not given BCG vaccine at birth are immunized whenever they are taken to the clinic during the first year or for the next immunization visit, at 6 weeks of age. This could account for BCG having the highest vaccination coverage. All vaccinations have the highest coverage in the years 2001-2002, and then have been on the decline reaching the lowest levels in 2006. This trend is significant for OPV3 at P=0.03 and HepB3 at P=0.04 while BCG and DTP3 were at boarder line significance, at P=0.05 and P=0.06 respectively. Similar trends were observed in the national vaccination estimates as reported by UNICEF/WHO.59 It is noteworthy that 2068 (67.6%) of all the children sampled (n=3058) had been fully vaccinated with BCG, OPV3, DTP3, HepB3 and measles, by the end of their first year of age. 35 Routine Immunization coverage by vaccine, children aged 12-23 months (n=18,171) in Africa Centre 0 20 40 60 80 100 BCG OPV3 DTP3 HepB3 Measles Vaccine % C ov er ag e 2000 2001 2002 2003 2004 2005 2006 Figure 2 Percent of immunization coverage by vaccine type among children aged 12-23 months at the time of interview, Africa Centre DSA South Africa A comparison of the mean vaccination coverage between an area served by Africa Centre and the mean national vaccination estimates, as reported by UNICEF/WHO59, was done (Table 3). There were no significant differences observed between vaccination coverage in the Africa Centre DSA and the national estimates across all vaccines (P-values>0.05). Except for BCG vaccine, whose coverage was 3.9% higher in the Africa Centre DSA compared to the national estimates; all the other vaccines had lower coverage in the Africa Centre DSA than nationally (Table 3). Table 3: Comparing the mean vaccination coverage in Africa Centre DSA and the mean national estimates for the period 2000-2006 Vaccine DSA (%) 95% CI National (%) 95% CI P-values BCG 92.33 88.44 ? 96.22 88.43 80.86 ? 95.99 0.254 OPV3 89.01 85.22 ? 92.79 91.29 84.67 ? 97.91 0.432 DTP3 87.13 83.34 ? 90.93 91.14 84.40 ? 97.89 0.196 HepB3 83.46 79.70 ? 87.22 90.00 83.46 ? 96.54 0.051 Measles 79.07 75.98 ? 82.16 82.14 75.44 ? 88.84 0.304 36 Timeliness of vaccination The results indicate that across all vaccines, 13% to 34% of all children received their vaccines according to the immunization schedule, depending on the vaccine, with the majority of them receiving their vaccines within 28 days of their due dates. Timeliness of all vaccines in Africa Centre DSS 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 BCG OPV3 DTP3 HepB3 Measles Vaccine C hi ld re n (% ) At due date 28 days within due date 42 days withing due date > 42 days after due date Figure 3 Distribution of all vaccines, showing a decrease in timeliness of vaccines, Africa Centre DSA South Africa 37 3.2 Characteristics of the sample The majority of the mothers (64%) had post-primary education (Table 4). The median age of the mothers was 24 years (ranging from 13-54). There were an equal number of male and female children in the sample. No statistical differences were observed between the total number of children vaccinated (3058) and the number of children included in the regression analysis (2020) (Table 4). The average distance from a household to a fixed clinic was 3.2 kms, while the average distance from a household to a mobile vaccination point was 5.7 kms. Table 4: Characteristics of the sample population Variable Total children vaccinated, % Included in regression, % (n=3058) (n=2020) P-value Sex of child Female 50.1 50.1 Male 49.9 49.9 0.973 Maternal Education Primary Education 35.9 36.2 Post primary education 64.1 63.8 0.837 Maternal Age (years) Under 20 22.9 22.8 20-29 50.7 49.6 30+ 26.4 27.6 0.609 Wealth index of household Poorest 40.0 39.8 Middle 40.0 40.2 Wealthiest 20.0 20.0 0.985 Mean, 95% CI Mean, 95% CI P-value Distance to fixed clinic (km) 3.13, (3.06-3.21) 3.19, (3.11-3.28) 0.251 Distance to mobile clinic point (km) 5.81, (5.69-5.93) 5.74, (5.62-5.88) 0.488 Distance to primary road (km) 7.74, (7.48-8.02) 7.76, (7.47-8.05) 0.952 38 3.3. Maternal HIV status Of the 3,058 mothers included in the survey, 1217 (40%) knew their HIV status while 1841 (60%) did not know their HIV status. Among the mothers aged less than 20 years, 33% were HIV Positive compared to 13% who were HIV Negative, the rest being of unknown status (Table 4). There was no differential effect of wealth index of the households between HIV Negative and positive groups. The HIV Positive women lived closer to a fixed clinic (2.98 kms vs. 3.31 kms) and a mobile vaccination point (5.75 kms vs. 6.10 kms) compared to those who were HIV Negative. Table 5: Characteristics of the sample by maternal HIV status Variable HIV Positive, % HIV Negative, % (n=236) (n=777) P-value Sex of child Female 48.9 49.9 Male 51.1 50.1 0.770 Maternal Education Primary Education 38.7 37.4 Post primary education 61.3 62.6 0.709 Maternal Age (years) Under 20 32.8 13.2 20-29 43.8 58.6 30+ 23.4 28.1 <0.001 Wealth index of household Poorest 44.2 40.0 Middle 40.9 40.4 Wealthiest 14.9 19.6 0.188 Mean, 95% CI Mean, 95% CI P-value Distance to fixed clinic (km) 2.98, (2.76-3.21) 3.31, (3.18-3.44) <0.001 Distance to mobile clinic 5.75, (5.56-5.95) 6.10, (5.74-6.48) 0.001 39 point (km) Distance to primary road (km) 8.28, (7.83-8.73) 7.37, (6.57-8.17) <0.001 3.4 Determinants of vaccination coverage Tables 6: Adjusted odds for BCG vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) BCG vaccine Univariate Multivariate Characteristic OR 95% CI P-value aOR 95% CI P-value Maternal HIV Status Negative 1.00 1.00 Unknown 0.82 (0.57-1.15) 0.256 0.80 (0.55-1.15) 0.239 Positive 0.49 (0.31-0.78) 0.003 0.45 (0.28-0.72) 0.001 Sex Females 1.00 1.00 Males 1.24 (0.90-1.71) 0.194 1.27 (0.92-1.77) 0.139 Maternal Age (years) Linear age 1.01 (0.98-1.03) 0.492 1.02 (0.99-1.05) 0.171 Maternal Education (years) Primary education 1.00 1.00 Post primary education 0.94 (0.36-2.44) 0.894 0.73 (0.51-1.05) 0.089 Wealth index Poorest 1.00 1.00 Medium 1.28 (0.77-2.10) 0.341 1.37 (0.93-2.01) 0.102 Wealthiest 1.50 (0.89-2.53) 0.130 1.83 (1.06-3.16) 0.030 Birth order First borne 1.00 1.00 Second and older borne 1.26 (1.01-1.55) 0.032 0.79 (0.53-1.17) 0.244 Fixed clinic distance (kms) 1.02 (0.93-1.11) 0.722 1.01 (0.91-1.12) 0.792 Distance to mobile clinic point (km) 0.99 (0.93-1.04) 0.622 0.90 (0.82-0.99) 0.035 Distance from a primary road (kms) 0.99 (0.96-1.01) 0.210 0.96 (0.93-0.99) 0.039 OR=odds ratio, aOR=adjusted odds ratio, 95% CI=95% confidence interval 40 In multivariate logistic regression analysis, living further from a primary road and a mobile clinic point were associated with being significantly less likely to receive BCG vaccine (Table 6), adjusted for maternal HIV status, maternal education, maternal age, wealth index, child?s sex, birth order, distance to health facility and to a primary road. Children born to mothers who are HIV Positive were significantly less likely to be vaccinated with BCG compared to those born to HIV Negative mothers. Further, children born to wealthier households were also significantly more likely to be vaccinated with BCG compared to those born in the poorest households. Being second born or older was also associated with higher likelihood of vaccination with BCG vaccine in the univariate but not in the multivariate. Table 7: Adjusted odds for Polio vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) OPV3 vaccine Univariate Multivariate Characteristic OR 95% CI P-value aOR 95% CI P-value Maternal HIV Status Negative 1.00 1.00 Unknown 0.79 (0.58-1.09) 0.156 0.81 (0.60-1.09) 0.169 Positive 0.53 (0.34-0.80) 0.003 0.56 (0.37-0.85) 0.006 Sex Females 1.00 1.00 Males 1.24 (0.90-1.71) 0.194 1.07 (0.80-1.14) 0.634 Maternal Age (years) Linear age 0.99 (0.98-1.01) 0.913 1.01 (0.99-1.04) 0.228 Maternal Education (years) Primary education 1.00 1.00 Post primary education 0.94 (0.36-2.44) 0.894 0.97 (0.72-1.32) 0.877 Wealth index Poorest 1.00 1.00 Medium 1.28 (0.77-2.10) 0.341 1.51 (1.08-2.11) 0.014 Wealthiest 1.50 (0.89-2.53) 0.130 1.72 (1.08-2.75) 0.022 Birth order First borne 1.00 1.00 Second and older borne 1.12 (0.93-1.36) 0.235 0.76 (0.54-1.08) 0.137 Fixed clinic distance (kms) 1.02 (0.93-1.11) 0.722 1.02 (0.93-1.12) 0.624 41 Distance to mobile clinic point (km) 0.99 (0.93-1.04) 0.622 0.89 (0.83-0.96) 0.006 Distance from a primary road (kms) 0.99 (0.96-1.01) 0.210 0.95 (0.92-0.98) 0.001 OR=odds ratio, aOR=adjusted odds ratio, 95% CI=95% confidence interval In the multivariate logistic regression analysis, living further from a primary road and a mobile clinic point were associated with being significantly less likely to be vaccinated with Polio vaccine (Table 7), adjusted for maternal HIV status, maternal education, maternal age, wealth index, child?s sex, birth order, distance to health facility and to a primary road. Other factors associated with polio vaccination included; maternal HIV status and wealth index. Children born in wealthier/medium families were more likely to be vaccinated with polio compared to those born in poorer families while those born to HIV Positive mothers were less likely to be vaccinated compared to those born to HIV Negative mothers. Table 8: Adjusted odds for Diphtheria vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) DTP3 vaccine Univariate Multivariate Characteristic OR 95% CI P-value aOR 95% CI P-value Maternal HIV Status Negative 1.00 1.00 Unknown 0.83 (0.62-1.09) 0.187 0.79 (0.59-1.07) 0.133 Positive 0.58 (0.39-0.86) 0.007 0.56 (0.37-0.85) 0.006 Sex Females 1.00 1.00 Males 1.24 (0.90-1.71) 0.194 1.05 (0.81-1.37) 0.695 Maternal Age (years) Linear age 1.00 (0.98-1.02) 0.839 1.00 (0.98-1.02) 0.839 Maternal Education (years) Primary education 1.00 1.00 Post primary education 0.94 (0.36-2.44) 0.894 0.94 (0.71-1.25) 0.688 42 Wealth index Poorest 1.00 1.00 Medium 1.28 (0.77-2.10) 0.341 1.46 (1.07-2.00) 0.016 Wealthiest 1.50 (0.89-2.53) 0.130 1.68 (1.08-2.59) 0.019 Birth order First borne 1.00 1.00 Second and older borne 1.11 (0.92-1.32) 0.275 0.83 (0.60-1.14) 0.257 Fixed clinic distance (kms) 1.02 (0.93-1.11) 0.722 1.01 (0.92-1.09) 0.805 Distance to mobile clinic point (km) 0.99 (0.93-1.04) 0.622 0.88 (0.82-0.95) 0.002 Distance from a primary road (kms) 0.99 (0.96-1.01) 0.210 0.95 (0.92-0.97) 0.001 OR=odds ratio, aOR=adjusted odds ratio, 95% CI=95% confidence interval In the multivariate logistic regression analysis, living further from a primary road and a mobile clinic point were associated with being significantly less likely to be vaccinated with Diphtheria vaccine (Table 8), adjusted for maternal HIV status, maternal education, maternal age, wealth index, child?s sex, birth order, distance to health facility and to a primary road. Other factors associated with Diphtheria vaccination included; maternal HIV status and wealth index. Children born in wealthier/medium families were more likely to be vaccinated with Diphtheria compared to those born in poorer families while those born to HIV Positive mothers were less likely to be vaccinated with Diphtheria compared to those born to HIV Negative mothers. Table 9: Adjusted odds for Hepatitis vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) HepB3 vaccine Univariate Multivariate Characteristic OR 95% CI P-value aOR 95% CI P-value Maternal HIV Status 43 Negative 1.00 1.00 Unknown 0.78 (0.61-1.01) 0.058 0.75 (0.57-0.98) 0.035 Positive 0.67 (0.46-0.97) 0.036 0.63 (0.43-0.93) 0.020 Sex Females 1.00 1.00 Males 1.24 (0.90-1.71) 0.194 1.01 (0.79-1.28) 0.925 Maternal Age (years) Linear age 1.00 (0.98-1.02) 0.640 1.01 (0.99-1.03) 0.204 Maternal Education (years) Primary education 1.00 1.00 Post primary education 0.94 (0.36-2.44) 0.894 0.86 (0.67-1.12) 0.292 Wealth index Poorest 1.00 1.00 Medium 1.28 (0.77-2.10) 0.341 1.36 (1.02-1.80) 0.033 Wealthiest 1.50 (0.89-2.53) 0.130 1.70 (1.15-2.52) 0.008 Birth order First borne 1.00 1.00 Second and older borne 1.13 (0.95-1.34) 0.152 0.85 (0.63-1.14) 0.283 Fixed clinic distance (kms) 1.02 (0.93-1.11) 0.722 1.02 (0.95-1.11) 0.530 Distance to mobile clinic point (km) 0.99 (0.93-1.04) 0.622 0.88 (0.82-0.94) <0.001 Distance from a primary road (kms) 0.99 (0.96-1.01) 0.210 0.95 (0.94-0.98) 0.001 OR=odds ratio, aOR=adjusted odds ratio, 95% CI=95% confidence interval In the multivariate logistic regression analysis, living further from a primary road and a mobile clinic point were associated with being significantly less likely to be vaccinated with Hepatitis vaccine (Table 9), adjusted for maternal HIV status, maternal education, maternal age, wealth index, child?s sex, birth order, distance to health facility and to a primary road. Other factors associated with Hepatitis vaccination included; maternal HIV status and wealth index. Children born in wealthier/medium families were more likely to be vaccinated with Hepatitis compared to those born in poorer families while those born to HIV Positive mothers were less likely to be vaccinated with Hepatitis compared to those born to HIV Negative mothers. 44 Table 10: Adjusted odds for Measles vaccination among children aged 12- 23 months in Africa Centre DSA, 2006 (n=2020) Measles vaccine Univariate Multivariate Characteristic OR 95% CI P-value aOR 95% CI P-value Maternal HIV Status Negative 1.00 1.00 Unknown 0.86 (0.68-1.09) 0.215 0.79 (0.62-1.02) 0.071 Positive 0.73 (0.51-1.02) 0.066 0.69 (0.48-1.00) 0.052 Sex Females 1.00 1.00 Males 1.24 (0.90-1.71) 0.194 0.91 (0.73-1.14) 0.408 Maternal Age (years) Linear age 1.00 (0.98-1.02) 0.577 1.01 (0.99-1.02) 0.330 Maternal Education (years) Primary education 1.00 1.00 Post primary education 0.94 (0.36-2.44) 0.894 0.93 (0.73-1.19) 0.590 Wealth index Poorest 1.00 1.00 Medium 1.28 (0.77-2.10) 0.341 1.38 (1.06-1.80) 0.015 Wealthiest 1.50 (0.89-2.53) 0.130 1.63 (1.13-2.36) 0.008 Birth order First borne 1.00 1.00 Second and older borne 1.06 (0.91-1.25) 0.418 0.94 (0.72-1.24) 0.699 Fixed clinic distance (kms) 1.02 (0.93-1.11) 0.722 1.02 (0.95-1.09) 0.518 Mobile clinic point distance (kms) 0.99 (0.93-1.04) 0.622 0.95 (0.89-1.01) 0.112 Distance from a primary road (kms) 0.99 (0.96-1.01) 0.210 0.98 (0.96-1.00) 0.081 OR=odds ratio, aOR=adjusted odds ratio, 95% CI=95% confidence interval In the multivariate logistic regression analysis, children born to mothers who were HIV Positive were less likely to be vaccinated with measles vaccine compared to those born to HIV Negative mothers, (Table 10), adjusted for maternal HIV status, maternal education, maternal age, wealth index, child?s sex, birth order, distance to health facility and to a primary road. Children born in wealthier/medium families were also more likely to be vaccinated with measles compared to those born in poorer families adjusting for the same factors. 44 CHAPTER 4: DISCUSSION Vaccination coverage has been used as a proxy measure of health service utilization in Africa10. We investigated the effect of maternal HIV status on vaccination coverage in children aged 12-23 months when controlling for accessibility to the fixed clinic, mobile clinic points and primary road and other maternal and child characteristics. We focused on this because there is limited research on immunization coverage among children born to HIV-positive mothers. The findings from our analysis can be summarized as follows: children born to HIV- positive mothers are between 31-55% less likely to be immunized compared to those born to HIV-negative mothers. Children living near mobile clinic points are between 10- 12% more likely to be immunized; those living near the primary road are 4-5% more likely to be immunized whereas children from wealthier households are between 1.63- 1.83 times more likely to be immunized. This study confirms the hypothesis that immunization coverage is lower among children born to HIV-positive mothers compared to HIV-negative mothers. Also that distance to health care facility and to primary road affects the likelihood of vaccination. This is especially so in the Africa Centre DSA where people have to walk for 3 to 8 kms to access any kind of health care.48 45 4.1 Immunization coverage The results indicate that the WHO recommended coverage for routine immunization of 80% routine coverage60 was attained in the Africa Centre DSA across all vaccines except for measles. The routine vaccination coverage in the study area also attained at least 80% DTP3 coverage, a strategic goal defined in the EPI Regional Strategic Plan, 2001-2005, as set by the WHO. Only 22 countries (48%) in Africa attained the WHO recommended level by mid-2005.61 Measles coverage in the study area would be categorized as high routine coverage (>75%) by the standards of the regional EPI. Overall coverage in the Africa Centre DSA was not significantly different from South Africa?s national coverage as reported by the WHO.62 Vaccination coverage differentials among different socio-economic groups, namely the poorest, medium and wealthiest, were observed in the multivariate analysis. A study of health inequalities in Northern Ghana observed that children in the poorest quintiles were less likely to have received measles vaccination.16 However the plausible reason for the differential effect could be that the wealthiest households appear to be more likely to live near the fixed clinics and also close to the primary roads compared to those living in the poorest households (appendix 6). The poor thus depend on mobile clinic points, which are closer to them, or have to travel to fixed clinics for immunization. Increasing mobile vaccination points will thus reduce these indirect costs and hence reduce these inequalities of vaccination among the different socio-economic groups. 46 4.2 Maternal HIV status Maternal HIV status was found to positively and significantly affect vaccination coverage across all the vaccines, in our study area. Children born to HIV-positive mothers were less likely to be vaccinated compared to those born to HIV-negative mothers. A previous study in Rakai district Uganda showed that children born to HIV-infected mothers had lower immunization coverage compared to children born to HIV-negative mothers.25 Further, maternal HIV status has been found to be associated with child survival in South west Uganda. Children born to HIV-positive mothers and those born during the period when their mothers seroconverted were four times more likely to die compared to those born to HIV-negative mothers.63 This could be because maternal HIV status is associated with higher maternal mortality. A mother whose health is adversely affected by virtue of her HIV status could find it more difficult to walk to a health care facility and have her child vaccinated, compared to a more healthy mother. This may cause the child to be deprived of maternal care, which involves immunization. The role of the extended family is important in such a case, to ensure the continued support of the child, though the child support which is given in this case might not be as adequate as the mother would give. This is especially common in settings with high HIV prevalence which may cause increased burden of childcare among relatives living in already resource constrained settings.64 In this study, we cannot comment on the effect of the child?s HIV status on their likelihood of vaccination. However, previous studies have shown that irrespective of their 47 own infection status, all children born to mothers who were at an advanced level of HIV or who died during follow-up were at considerably increased risk of death compared to those whose mothers survived or were at a less advanced stage of the disease. This association was especially strong for uninfected children.65 Another plausible explanation could be that mothers who are HIV-positive have lower scores of perception to general health, physical functioning and overall quality of life.66 This may have a negative effect on the health seeking behaviour of the mother where she may prefer to use her resources for other household purposes other than improving her health and that of her child. Further, mothers who are HIV-positive are faced with a challenge of distribution of scarce economic resources. They either use the scarce resources to seek health care for themselves or for their children or to feed their households. This burden is further increased if their children are HIV-positive and in need of extra care, as well as the fear of leaving orphaned children. HIV-positive women are also more vulnerable than others because they will have diminishing strength with which to continue functioning in their roles as mothers and caretakers of the family. 64 4.3 Distance to health care facility The results shown here are based on the actual distance from the household to a fixed clinic or a mobile clinic point or a primary road. Distance to fixed clinic was not found to be significant. However, distance to primary road was positively associated with 48 vaccination coverage except for measles vaccine. Children living further from a primary road were less likely to be vaccinated. A probable explanation would be that a mother/caregiver who is taking a child for vaccination would have to overcome the distance to primary road. From there public transport can be used to get the clinic. The time spent while using public transport is of no consequence, so long as a mother/caregiver gets to the primary road and hence is able to access the transport. One study done at the Africa Centre DSA found that though the majority of the people walk to their nearest facility, one-third of the population use public transport.67 We found that distance to the primary road was significant for BCG, DTP3, HepB3 and OPV3 vaccines, but it was not significant for measles vaccine. This could possibly be due to the fact that BCG, DTP3, HepB3 and OPV3 are administered much closer to birth (within the first four months after birth) while measles is administered much later than 9 months. More attention is given to a child at this early age compared to when a child is older. This is evident from the fact that the mean immunization coverage for these vaccines is higher compared to that of measles vaccine. The mother/caregiver would therefore make every effort to overcome the distance to the primary road for the sake of her child?s? health at this tender age. Distance to the mobile clinic point is positively associated with vaccination coverage. BCG is administered only once whereas OPV3, DTP3 and HepB3 are administered three times at specified intervals. These subsequent visits to the clinic need advance planning and an appreciation of the importance of receiving vaccines at the right intervals. BCG is 49 given at birth or at any subsequent visit to the clinic before the child is 12 months and hence the diminishing effect of distance to mobile clinic points. Mobile clinic points are located near poor people who live further from the fixed clinics (appendix 6). Thus mobile clinic points could be bridging the gap of mothers living in poor households walking for very long distances to get to fixed clinics. Such a mother would have to weigh the opportunity cost of waiting for the scheduled time(s) of a mobile clinic versus travelling a long distance to attend a fixed clinic. This however assumes that mobile clinics in the study area have consistent planning and there are no disparities in the quality of service between the fixed and the mobile clinic. Such disparities can occur when health workers in the mobile clinics have long days when they have to deal with the numerous clients. This may cause them to rush their services or lose their patience when handling the clients. Quality of the health care services has been found to affect their utilization.68 It is noteworthy that mothers? education was not significantly associated with immunization coverage across all the vaccines. This has been found in other studies.36 In our study area, 64% of the mothers had post-primary education, while the rest had primary education and below. This indicates that the literacy level in our study area is higher than most rural areas in developing countries. In the Africa Centre DSA, there were no gender differences in immunization coverage, as found in other studies.36 This is a representation of actual practice where there is no 50 gender discrimination in relation to child immunization. The gender ratio in our study was 1: 0.99 which is equal to the average gender ratio in South Africa69, an expression of equal preference of children. 4.4 Limitations In the light of the above findings, the methodology has some limitations. All vaccinations are recorded on an individual?s vaccination card (Road-to-Health card) kept by the mother. In the absence of a Road-to-Health card, maternal spontaneous vaccination recall was used. This may cause recall bias since the mother may not adequately recall the precise vaccines and the vaccination dates. However, this has been used in other studies estimating immunization coverage in other settings.25,70 Moreover; a study in Egypt showed that using ?card plus history? is a commonly used source of vaccination information because mother?s reports of their children?s vaccination status are of remarkable quality - even among uneducated mothers. 70 In our study, only 42.5% of the respondents had vaccination cards. This is much lower compared to the SADHS which shows that 75% of children aged 12-23 months had road- to-health cards.69 Had our survey relied on immunization cards only, we were unlikely to cover a large population of children in our study area. HIV status of the mother was available for only 1217 out of 3058 (40%) of the sample. To solve this problem of a small sample size, we also included respondents whose HIV status was unknown. 51 Data is collected once every six months from a key informant in the household. This may introduce recall bias since the informant may not recall accurately the information concerning all the members of the household over a period of the last six months. HIV surveillance is not conducted on all the resident members within the demographic surveillance area. It is collected voluntarily on members of the household who consent to be included in the survey. This reduced the sample size of the mothers who knew their HIV status. HIV status of the mother was available for only 1217 out of 3058 (40%) of the sample. For purposes of our analysis, we included both groups of mothers who had known and unknown HIV status. Therefore the strength of the association for unknown group is an estimate between the range for HIV Negative and Positive status and could not be quantified to reflect the true picture. Further, our study defined maternal HIV Positive status as having had a positive HIV test before/at the birth of the child for which immunization was to be assessed. However, a mother might have become infected after birth, with consequent negative effects on child?s health and immunization status, but was considered HIV Negative for the purposes of this study. This may underestimate the real numbers of HIV positive mothers compared with HIV negative mothers. The sampling strategy used in this study could have introduced selection bias. Out of 18, 171 children whose vaccination information was available, only 2,020 were analysed because they had complete information on the independent variables. 52 Vaccination data did not include Haemophilus influenzae type B (Hib) vaccine which was routine in EPI since 1999. This is because data on Hib vaccine was only collected in the ACDIS as from January 2008. CHAPTER 5: CONCLUSION This study leads to the conclusion that children living near a mobile clinic point and near a primary road network were more likely to be vaccinated compared to those whose households were located further. Children born to HIV-positive mothers were less likely to be vaccinated compared to those born to HIV-negative mothers. Immunization coverage improves with wealth: children from the less poor households being more likely to be vaccinated compared to those in the poorest households. There is no gender difference among children in the society in terms of vaccination status. Immunization has without doubt been shown to improve child survival, and given the fact that vaccines are given free of charge in sub-Saharan Africa, it is imperative to address the determinants that are barriers to vaccination coverage. Vaccination coverage especially in developing countries is further hampered by the HIV/AIDS epidemic. This is not only because the children born to HIV-positive mothers are less likely to be immunized, but also because their survival rate is reduced by virtue of their mothers? increased risk of death as a result of their HIV Status. Further, these children are at high risk of mother-to-child transmission of HIV infection during birth. 53 CHAPTER 6: RECOMMENDATIONS Interventions aimed at HIV-positive mothers are well needed especially in the rural areas. ARV rollouts need to be intensified especially in these areas where there are scarce resources and hence the burden of HIV is higher. Alongside the ARV rollouts, socio mobilisation aimed at increasing the demand for immunization could be done to reduce the differentials in immunization of children due to maternal HIV status. These include conducting trainings to health educators, such as VCT workers, on vaccination schedules and on vaccine policies. These VCT workers would then encourage HIV-infected mothers to complete childhood immunisation. Given the primacy of distance to the health care facility in vaccination coverage coupled with coverage differentials by socio-economic status, steps to improve access especially to the poor need to be taken. Mobile clinics are one of the interventions that have without doubt helped reduce health utilization differentials among the different socio economic groups. It has targeted the poor who have limited access to fixed clinics. There is need however to increase the number of vaccination points in the study area to cater for the differentials in vaccination across the social economic groups. These mobile vaccination points should be located further from the cities and should target the poorest areas within the study site. Further, intensive outreach services including placement of community health workers within communities with limited access to heath care facilities can be adopted to reduce prevailing socio economic differentials in vaccination. 54 There is also need for continued community awareness on immunization. The health workers should continue disseminating information to the members for instance on the importance of the timely completion of immunizations. Heath workers can target mothers coming for ante-natal clinic and educate them on the benefits of vaccination of their unborn children. There should be periodic limited and targeted mass campaigns (mop ups) in the high-risk populations. This includes areas within the sub-district that have the poorest EPI vaccination coverage, poor surveillance information, densely populated areas and people living in areas with heavy migration. To improve the quality of immunization services, the local authorities should establish a mechanism of tracking children who have defaulted their vaccinations. Further research into this subject is needed. For instance qualitative research exploring reasons why mothers do not take their children to be vaccinated. Drop out rates were observed, in this study, to be between (OPV3, DTP3, HepB3) and measles ranging between 9% and 15%. A further investigation of factors associated with these drop-out rates is necessary. 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Social Science and Medicine 1998; 46: 1205-1212. 64 APPENDICES APPENDIX 1 Location of Hlabisa District and the DSA within South Africa 65 APPENDIX 2 Child Health Form Questionnaire 66 67 APPENDIX 3 HIV surveillance Questionnaire 68 69 70 APPENDIX 4 Ethics approval from KwaZulu Natal University 71 72 APPENDIX 5 Ethics approval from University of The Witwatersrand Human Research Ethics Committee 73 APPENDIX 6 Relationship between wealth index categories and distance (to fixed clinic, mobile clinic and primary road) Comparing distance to the fixed clinic by wealth index of the household Wealth index of household Mean 95% CI Wealth index of household Mean 95% CI P-value Poorest 4.09 (3.95-4.21) Medium 2.95 (2.83-3.06) <0.001 Poorest 4.09 (3.95-4.21) Wealthiest 1.94 (1.80-2.07) <0.001 Comparing distance to mobile clinic by wealth index Wealth index of household Mean 95% CI Wealth index of household Mean 95% CI P-value Poorest 4.37 (4.21-4.54) Medium 5.99 (5.79-6.18) <0.001 Poorest 4.37 (4.21-4.54) Wealthiest 7.98 (7.76-8.22) <0.001 Comparing distance to primary road by wealth index Wealth index of household Mean 95% CI Wealth index of household Mean 95% CI P-value Poorest 10.35 (9.92-10.78) Medium 7.23 (6.77-7.69) <0.001 Poorest 10.35 (9.92-10.78) Wealthiest 3.69 (3.22-4.17) <0.001