A survey of non-communicable disease and the associated risk factors in three different study sites in Papua New Guinea

© 2020 Patricia RarauIntroduction Non-Communicable Diseases (NCDs) are the leading cause of death and morbidity throughout the world, with the greatest burden in low- and middle-income countries (LMICs). Of the estimated 17.9 million CVD deaths in 2016, more than 10 million occurred in LMIC countries. Coronary heart disease and stroke were the two major causes contributing to these deaths. Papua New Guinea (PNG) is categorized as a lower-middle-income country according to World Bank criteria and it is experiencing rapid economic growth as a result of large-scale mineral and gas resource developments. These economic changes are contributing to epidemiological transitions associated with rapid lifestyle changes that, in turn, are leading to increases in cardiovascular diseases and diabetes. NCDs and associated risk factors have not been well investigated in PNG, however, several small studies conducted over the past 40 years have been suggestive of increasing prevalence of NCDs and their associated risk factors (Chapter 3). The aim of this research is to establish an up-to-date NCD risk factor prevalence data enabling a better understanding of the differences in these risk factors in relation to socio-economic status in three locations in PNG. The present study was undertaken during the construction phase of a large-scale gas development which was projected to more than double the gross domestic product (GDP) of PNG. The study was designed to provide baseline prevalence data on NCD risk factors in the initial years of a gas project impact site (West Hiri) and in two non-project impact sites (Asaro and Karkar Island). It was also anticipated that the study findings would provide up-to-date NCD risk factor prevalence data to help the national government plan services and develop cost-effective interventions. This thesis describes the methods used and presents the initial findings for NCD risk factors in a survey of three different socio-demographic populations of PNG. Methods The analysis of the data presented in the results sections of this thesis was based on the NCD Risk factor survey. The survey was a cross-sectional study of the prevalence of NCD risk factors across three different sites namely West Hiri in Central province, Asaro in Eastern Highlands province and Karkar Island in Madang province. A modified questionnaire based on the WHO STEPwise instrument was used for data collection. In addition, physical measurement and biochemical samples were collected from participants (Chapter 4). Results A total of 785 participants participated in the survey. The prevalence of NCD risk factors varied markedly different across the three sites. The metabolic risk factors such as obesity, elevated blood pressure, increased total cholesterol and HbA1c levels were higher in West Hiri compared to the other two sites (Chapter 5). Further analysis of the data was done to investigate the association between socio-economic status (SES), and the CVD risk factors. Findings show that elevated CVD risk factors were common among all SES groups, but metabolic risk factors were more prevalent among homemakers, peri-urban West Hiri and Asaro and among the highest quintile groups. Adults in the peri-urban West Hiri had higher risk of obesity, hypertension, abnormal lipids and elevated HbA1c. Similarly, Asaro adults had increased risk of central obesity, hypertension, elevated triglycerides and MetS compared to the residents of rural Karkar Island (Chapter 6). Conclusion The research reported in this thesis adds to our understanding of the associations between SES and CVD risk factors in a LMIC like PNG. Data from high income countries show a negative correlation with socio-economic status, however findings from our study showed the association between CVD risk factors and SES varied greatly depending on the type of SES measure used. Understanding these associations is important to inform the government to develop appropriate and effective prevention and control strategies. With this information at hand, the government would be able to make informed decisions and prioritize its prevention and control strategies targeting high risk populations or settings in the country