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The need for an integrated approach for chronic disease research and care in Africa

Published online by Cambridge University Press:  29 November 2016

A. L. Barr
Affiliation:
Department of Medicine, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
E. H. Young
Affiliation:
Department of Medicine, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
L. Smeeth
Affiliation:
Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
R. Newton
Affiliation:
Medical Research Council/Uganda Virus Research Institute (MRC/UVRI), Uganda Research Unit on AIDS, Entebbe, Uganda
J. Seeley
Affiliation:
Medical Research Council/Uganda Virus Research Institute (MRC/UVRI), Uganda Research Unit on AIDS, Entebbe, Uganda Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK
K. Ripullone
Affiliation:
Department of Medicine, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
T. R. Hird
Affiliation:
Department of Medicine, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
J. R. M. Thornton
Affiliation:
Department of Medicine, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
M. J. Nyirenda
Affiliation:
Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
S. Kapiga
Affiliation:
Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK Mwanza Intervention Trials Unit, National Institute for Medical Research, Mwanza, Tanzania
C. A. Adebamowo
Affiliation:
Department of Epidemiology and Public Health, Greenebaum Comprehensive Cancer Center and Institute of Human Virology, University of Maryland School of Medicine, Baltimore MD 21201 USA Institute of Human Virology, Nigeria
A. G. Amoah
Affiliation:
Department of Medicine, University of Ghana Medical School, Korlebu, Ghana
N. Wareham
Affiliation:
MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
C. N. Rotimi
Affiliation:
Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
N. S. Levitt
Affiliation:
Division of Diabetic Medicine and Endocrinology, Department of Medicine, University of Cape Town, Cape Town, South Africa
K. Ramaiya
Affiliation:
Shree Hindu Mandal Hospital, Dar es Salaam, Tanzania
B. J. Hennig
Affiliation:
MRC Unit, The Gambia, Fajara, The Gambia MRC International Nutrition Group, London School of Hygiene & Tropical Medicine, London, UK
J. C. Mbanya
Affiliation:
Department of Internal Medicine and Specialties, Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, Yaoundé, Cameroon
S. Tollman
Affiliation:
MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa INDEPTH Network, Accra, Ghana
A. A. Motala
Affiliation:
Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
P. Kaleebu
Affiliation:
Medical Research Council/Uganda Virus Research Institute (MRC/UVRI), Uganda Research Unit on AIDS, Entebbe, Uganda
M. S. Sandhu*
Affiliation:
Department of Medicine, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
*
*Address for correspondence: M. S Sandhu, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, CB10 1SA, UK. (Email: [email protected])
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Abstract

With the changing distribution of infectious diseases, and an increase in the burden of non-communicable diseases, low- and middle-income countries, including those in Africa, will need to expand their health care capacities to effectively respond to these epidemiological transitions. The interrelated risk factors for chronic infectious and non-communicable diseases and the need for long-term disease management, argue for combined strategies to understand their underlying causes and to design strategies for effective prevention and long-term care. Through multidisciplinary research and implementation partnerships, we advocate an integrated approach for research and healthcare for chronic diseases in Africa.

Type
Perspective
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited
Copyright
Copyright © The Author(s) 2016

Burden of non-communicable diseases (NCDs) in Africa

Chronic NCDs such as diabetes, cardiovascular diseases (CVD), and cancers are emerging as leading causes of mortality and morbidity in Africa. In this region, with a population of around 1.1 billion, there were an estimated 28 million adults living with diabetes in 2015 [1, 2]. It is anticipated that in sub-Saharan Africa (SSA) alone, the number of people living with the disease will rise to 34.2 million by 2040 [2]. Similarly, in 2015, a projected 1.6 million deaths were attributable to CVD in Africa [3]. This figure is expected to rise by another 1 million by 2030 [3]. Cancer-related deaths are also anticipated to double to 1.2 million by 2030 [3]. Close to one-third of NCD-related deaths in low- and middle-income countries (LMICs) are premature and occur before the age of 60 [4]. Thus, NCDs already present a major health burden for the African continent and are expected to be the most common cause of death, exceeding the number of deaths from communicable, maternal, perinatal, and nutritional diseases combined, by 2030 [5].

Risk factors for NCDs in Africa

Because of the diverse social, environmental, and biological settings within Africa, the distributions of known and other potentially novel risk factors, and their determinants, are likely to differ from those of European populations or those of African descent living outside Africa [Reference Gurdasani6]. The higher incidence of certain cancers, such as liver, cervical and oesophageal, in Africa compared with high-income countries (HICs) may reflect underlying differences in their risk factors in these regions [Reference Ferlay7]. Population growth and the concomitant rise in life expectancy are likely to only partly explain the increase in NCDs. Many of the known risk factors for NCDs in HICs are the same for LMICs, including smoking, alcohol, diet, obesity, raised cholesterol and blood pressure [Reference Yusuf8, Reference Murphy9]. However, the distribution and relative contribution of these risk factors to the burden of NCDs in Africa are unclear. We also have only a limited understanding of the social, environmental and biological drivers of these risk factors within African populations. Rapid urbanisation and globalisation, and the associated trends towards unhealthy lifestyles, contribute to the burden of NCDs [Reference BeLue10]. Small increases in urbanisation are associated with lower levels of physical activity and higher body mass index [Reference Riha11Reference Sobngwi13]. Globalisation has increased the availability of cheap, nutrient-poor, and energy-dense foods, which are likely to increase the risk of obesity and associated cardiometabolic risk factors [Reference Hawkes14]. Notably, the high burden of NCDs in rural areas, as well as urban centres, suggest there may be additional contributing or distinct factors [Reference Kavishe15].

Impact of maternal and childhood health on NCDs in Africa

Maternal and neonatal health and risk of NCDs are interrelated. The prevalence of overweight and obesity in adult women in Africa has been rising; between 1980 and 2013, the burden of overweight and obesity in females increased by an average of 10% [Reference Ng16]. Maternal obesity increases the risk of developing gestational diabetes [Reference Torloni17], and is associated with poor maternal and neonatal outcomes [Reference Yu, Teoh and Robinson18Reference Wendland20]. Women who develop diabetes and hypertension during pregnancy have an increased risk of type 2 diabetes (T2D), CVD, and metabolic syndromes [Reference Yogev and Visser19, Reference Ben-Haroush, Yogev and Hod21Reference Kaul23]. Children of obese or diabetic mothers also have a higher risk of metabolic disease in later life [Reference Boney24]. By contrast, under-nutrition in utero or in early life may also result in an increased risk of T2D and CVD in adulthood [Reference Uauy, Kain and Corvalan25Reference Gluckman, Hanson and Beedle28]. Adaptive responses to exposures in utero are thought to prepare the foetus for the postnatal environment [Reference Uauy, Kain and Corvalan25]. Rapid changes in the nutritional environments, as seen in many LMICs, could lead to an increase in NCDs [Reference Uauy, Kain and Corvalan25Reference Barker27]. These maternal and developmental risk factors may have a social, environmental, or biological basis, and are intergenerational – highlighting the complexity of the epidemiological transitions across the African continent, and in other LMICs [Reference Gluckman, Hanson and Beedle28, Reference Barker29, Reference Barker, Bagby and Hanson30].

Changing burden of infectious disease in Africa

Substantial progress has been made in reducing the burden of many types of infectious diseases, including those in early childhood. However, tuberculosis (TB), malaria, and HIV, as well as hepatitis B and C, remain endemic across the region. Africa has the highest burden of HIV in the world, with approximately 26 million prevalent cases and 1.3 million new infections recorded in 2014 [31]. TB and HIV co-infection is a growing issue in many LMICs [Reference Pawlowski32]; as such, it is the most common cause of death for people with AIDS [33]. Anti-retroviral therapy (ART) coverage in Africa has rapidly increased over the past decade; 51% of known cases in SSA received treatment in 2012 [34]. Expanding use of ART has led to a notable decline in HIV-associated morbidity and mortality in Africa; HIV is rapidly becoming a chronic disease, requiring long-term treatment and management. This and the emergence of drug resistant strains of HIV, malaria, TB, and other pathogens pose a major challenge for the continent's infectious disease control and management programmes, which may also have implications for the burden of NCDs [35, Reference Okeke36].

Interrelationship between non-communicable and infectious disease in Africa

The interrelated risk factors for infectious and non-communicable diseases are likely to have an important impact on the spectrum and distribution of chronic diseases in Africa, and other LMICs undergoing similar epidemiological transitions. The immune and metabolic systems are closely integrated, with each system's response dependent on the other for normal function [Reference Hotamisligil37]. Evolving from a common antecedent organ, they have shared and overlapping signalling pathways [Reference Hotamisligil37]. Chronic inflammation has been unequivocally linked to obesity, insulin resistance, T2D, and an increased risk of malignancy [Reference Hotamisligil37, Reference Crusz and Balkwill38]. Likewise, several infectious diseases and their treatments are associated with an increased risk of NCDs [Reference Young39]. HIV and ART may increase the burden of cardiometabolic risk factors, including lipid and glucose abnormalities [Reference Dillon40Reference Triant43]. Hepatitis B and C infection may also increase the risk of developing T2D [Reference Shintani44Reference Wang46], as well as chronic liver disease and hepatocellular cancers, in addition to other oncogenic pathogens [Reference Chen and Morgan47]. Similarly, insulin resistance and T2D may influence clinical outcomes in patients with hepatitis-associated liver disease and cancer [Reference Wang48]. Endemic infectious diseases may have also had an impact on selective adaptation and risk of NCDs – for example, renal function and African trypanosomiasis [Reference Genovese49]. Thus, the body's own immune response, endemic and chronic infections, and their treatments, may play an important role in the development and progression of NCDs in Africa, although the underlying mechanisms are not well understood.

The need for surveillance and prospective studies in Africa

Understanding the aetiology and determinants of NCDs in Africa is a fundamental step in developing strategies for disease prevention, management and control. Crucially, the impact of population and individual risk factors on disease susceptibility is largely unknown. Usually, chronic disease risk prediction models applied to African populations are based on regional comparisons of national indices, cross-sectional or case-control assessments, and the extrapolation of risk prediction algorithms developed in populations in Europe and elsewhere [Reference Gaziano50Reference Crowther and Norris52]. Furthermore, those studies conducted in African populations often use varying methods and definitions, or only assess a small subset of potential risk factors, limiting comparative analysis [Reference Mensah53Reference Murphy55].

Whilst research institutions in Africa have clearly developed research frameworks for assessing the epidemiological and clinical burden of chronic disease across the region, there is a need to integrate and scale up such efforts [Reference Gurdasani6, Reference Dillon40, Reference Motala51, Reference Kahn56]. The INDEPTH Network of health and demographic surveillance systems is an example of a pragmatic model for examining disease burden across different settings [57Reference Ng59]. Utilizing such established surveillance systems and analogous research initiatives to implement high quality and comparable large-scale population-based studies across the spectrum of chronic diseases and their risk factors will be crucial to understanding the aetiology and burden of chronic diseases in Africa. Likewise, implementation of standardised tools for the measurement of NCD risk factors in LMICs, such as those developed by the WHO, will enable comparability [60]. Importantly, establishing prospective studies in these settings will provide an invaluable framework to evaluate the utility of existing generic cut-off points or develop specific risk prediction algorithms for African populations, and provide the foundations for future aetiological and healthcare interventions.

The need for research into the implementation of integrated health services and the management of chronic diseases in Africa

Aligning epidemiological and implementation research will provide the most effective strategy to identify pragmatic solutions for delivering chronic disease health care. The emerging double burden of chronic infectious and non-communicable disease imposes a substantial strain on limited healthcare resources and has implications for health policy and planning. In many African countries, where health systems are fragile, under-resourced or targeted primarily to infectious diseases, the capacity to effectively deal with the burden of chronic NCDs and accompanying co-morbidities is severely limited [Reference Beran and Yudkin61, Reference Maseko, Chirwa and Muula62]. These health system challenges will only become more apparent as the prevalence of NCDs increases.

Chronic infectious and non-communicable disease programmes in Africa have traditionally been distinct at all levels of healthcare provision. However, emerging evidence suggests that an integrated approach to the broad spectrum of chronic diseases may provide the most cost-effective mechanism for disease treatment and control due to the related underlying pathogenesis and strategies for management [Reference Janssens63, Reference Atun64]. Integrating NCD management with existing HIV/AIDS/TB, malaria, and maternal and child health programmes would utilise existing infrastructure, and allow for more rapid implementation of NCD health care [Reference van Olmen65]. Ideally these services would be placed within a strengthened and well-resourced primary health care system that can provide pro-active, patient-centric and long-term community-based care [66]. However, a more complete understanding of the broad range of risk factors, and their interrelation, will be critical in designing such integrated health care systems – and provide mechanisms for broader preventative strategies. In these contexts, it is vital to conduct public health implementation research to fully explore the most effective and economic strategies to deliver accessible and integrated health care for chronic diseases [Reference Beaglehole67Reference de-Graft Aikins, Boynton and Atanga70]. Universal implementation of existing low cost interventions for the diagnosis and management of NCDs and infectious diseases would be a pivotal first phase [66].

Likewise, it will be important to identify the most effective strategies for chronic disease management for the African context; reliable information on the efficacy of drug treatments for NCDs and infectious diseases, and their adverse reactions in African populations is limited [Reference Aminkeng71, Reference Ramos72]. Harnessing and integrating existing health and pharmacovigilance systems could also facilitate drug efficacy trials and monitoring. Evaluation of current and novel chronic disease diagnostics, treatment and management strategies, including point of care testing and low-cost technology-based interventions within resource-limited settings, will be vital to implementing efficient mechanisms for integrated chronic disease research and care across the region, and in translating research findings into health care policy and services [Reference Drain73, Reference Pai74].

An integrated approach to chronic disease research and care in Africa

NCDs and infectious diseases should not be viewed as distinctive fields within global health research [Reference Horton75]. There is a critical need to combine research efforts across both acute and chronic infectious diseases and NCDs to better understand their interrelation and to develop more effective health systems to provide long-term management and care. Research and implementation partnerships will need to adopt innovative multidisciplinary research agendas that both strengthen and integrate existing infrastructure and advance implementation science. Combined, such structures could allow the formation of large-scale population health resources that would enable comprehensive studies into the diagnosis, prevention and management of chronic diseases, and their complex interactions, over diverse settings [Reference Collins76].

Acknowledgements

This work was funded by the African Partnership for Chronic Disease Research strategic award from the UK Medical Research Council under the MRC/DFID Concordat agreement (grant number MR/K013491/1). We also acknowledge the National Institute for Health Research Cambridge Biomedical Research Centre.

Declaration of Interest

All authors have no conflicts of interest to declare.

References

1. United Nations. Population and Vitals Statistics Report. 2015.Google Scholar
2. IDF. IDF Diabetes Atlas, 7th edn. Brussels: International Diabetes Federation, 2015.Google Scholar
3. WHO. Global health estimates summary tables: projection of deaths by cause, age and sex, by World Bank income groups: World Health Organization; 2013 [8th of June 2015]. (http://www.who.int/healthinfo/global_burden_disease/projections/en/).Google Scholar
4. WHO. Global Status Report on Non-communicable Diseases 2010. Geneva: World Health Organization, 2011.Google Scholar
5. WHO. The Global Burden of Disease: 2004 Update. Geneva: World Health Organization, 2008.Google Scholar
6. Gurdasani, D, et al. The African Genome Variation Project shapes medical genetics in Africa. Nature 2014; 517: 327332.Google Scholar
7. Ferlay, J, et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International Journal of Cancer 2010; 127: 28932917.Google Scholar
8. Yusuf, S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364: 937952.Google Scholar
9. Murphy, GA, et al. Sociodemographic distribution of non-communicable disease risk factors in rural Uganda: a cross-sectional study. International Journal of Epidemiology 2013; 42: 17401753.Google Scholar
10. BeLue, R, et al. An overview of cardiovascular risk factor burden in sub-Saharan African countries: a socio-cultural perspective. Globalization and Health 2009; 5: 10.Google Scholar
11. Riha, J, et al. Urbanicity and lifestyle risk factors for cardiometabolic diseases in rural Uganda: a cross-sectional study. PLoS Medicine 2014; 11: e1001683.Google Scholar
12. Assah, FK, et al. Urbanization, physical activity, and metabolic health in sub-Saharan Africa. Diabetes Care 2011; 34: 491496.Google Scholar
13. Sobngwi, E, et al. Exposure over the life course to an urban environment and its relation with obesity, diabetes, and hypertension in rural and urban Cameroon. International Journal of Epidemiology 2004; 33: 769776.Google Scholar
14. Hawkes, C. Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Globalization and Health 2006; 2: 4.Google Scholar
15. Kavishe, B, et al. High prevalence of hypertension and of risk factors for non-communicable diseases (NCDs): a population based cross-sectional survey of NCDS and HIV infection in Northwestern Tanzania and Southern Uganda. BMC Medicine 2015; 13: 126.Google Scholar
16. Ng, M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384: 766781.Google Scholar
17. Torloni, MR, et al. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obesity Reviews 2009; 10: 194203.Google Scholar
18. Yu, CK, Teoh, TG, Robinson, S. Obesity in pregnancy. British Journal of Obstetrics and Gynaecology 2006; 113: 11171125.Google Scholar
19. Yogev, Y, Visser, GHA. Obesity, gestational diabetes and pregnancy outcome. Seminars in Fetal and Neonatal Medicine 2009; 14: 7784.Google Scholar
20. Wendland, EM, et al. Gestational diabetes and pregnancy outcomes – a systematic review of the World Health Organization (WHO) and the International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria. BMC Pregnancy and Childbirth 2012; 12: 23.Google Scholar
21. Ben-Haroush, A, Yogev, Y, Hod, M. Epidemiology of gestational diabetes mellitus and its association with Type 2 diabetes. Diabetic Medicine 2004; 21: 103113.CrossRefGoogle ScholarPubMed
22. Kim, C, Newton, KM, Knopp, RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care 2002; 25: 18621868.Google Scholar
23. Kaul, P, et al. Impact of gestational diabetes mellitus and high maternal weight on the development of diabetes, hypertension and cardiovascular disease: a population-level analysis. Diabetic Medicine 2014; 32: 164173.Google Scholar
24. Boney, CM, et al. Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics 2005; 115: e290e296.Google Scholar
25. Uauy, R, Kain, J, Corvalan, C. How can the Developmental Origins of Health and Disease (DOHaD) hypothesis contribute to improving health in developing countries? American Journal of Clinical Nutrition 2011; 94(6 Suppl.): 1759s1764s.Google Scholar
26. Barker, DJ. Fetal origins of coronary heart disease. BMJ (Clinical research ed) 1995; 311: 171174.Google Scholar
27. Barker, DJ, et al. Fetal nutrition and cardiovascular disease in adult life. Lancet 1993; 341: 938941.Google Scholar
28. Gluckman, PD, Hanson, MA, Beedle, AS. Non-genomic transgenerational inheritance of disease risk. BioEssays 2007; 29: 145154.Google Scholar
29. Barker, DJ. The origins of the developmental origins theory. Journal of Internal Medicine 2007; 261: 412417.Google Scholar
30. Barker, DJ, Bagby, SP, Hanson, MA. Mechanisms of disease: in utero programming in the pathogenesis of hypertension. Nature Clinical Practice Nephrology 2006; 2: 700707.Google Scholar
31. UNAIDS. How AIDS Changed Everything Report – 2015. Geneva: UNAIDS, 2015.Google Scholar
32. Pawlowski, A, et al. Tuberculosis and HIV Co-Infection. Plos Pathogens 2012; 8: e1002464.Google Scholar
33. WHO. Tuberculosis; fact sheet No. 104 2015. (http://www.who.int/mediacentre/factsheets/fs104/en/).Google Scholar
34. UNAIDS. Access to Antiretroviral Therapy in Africa; Status Report on Progress Towards the 2015 Targets. Geneva: UNAIDS, 2013.Google Scholar
35. WHO. Antimicrobial Resistance; Global Report on Surveillance. Geneva: World Health Organisation, 2014.Google Scholar
36. Okeke, IN, et al. Antimicrobial resistance in developing countries. Part I: recent trends and current status. Lancet Infectious Diseases 2005; 5: 481493.Google Scholar
37. Hotamisligil, GS. Inflammation and metabolic disorders. Nature 2006; 444: 860867.Google Scholar
38. Crusz, SM, Balkwill, FR. Inflammation and cancer: advances and new agents. Nature Reviews Clinical Oncology 2015; 12: 584596.Google Scholar
39. Young, F, et al. A review of co-morbidity between infectious and chronic disease in Sub Saharan Africa: TB and Diabetes Mellitus, HIV and Metabolic Syndrome, and the impact of globalization. Globalization and Health 2009; 5: 9.Google Scholar
40. Dillon, DG, et al. Association of HIV and ART with cardiometabolic traits in sub-Saharan Africa: a systematic review and meta-analysis. International Journal of Epidemiology 2013; 42: 17541771.Google Scholar
41. Brown, TT, et al. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Archives of Internal Medicine 2005; 165: 11791184.Google Scholar
42. De Wit, S, et al. Incidence and risk factors for new-onset diabetes in HIV-infected patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. Diabetes Care 2008; 31: 12241229.Google Scholar
43. Triant, VA, et al. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. Journal of Clinical Endocrinology and Metabolism 2007; 92: 25062512.CrossRefGoogle ScholarPubMed
44. Shintani, Y, et al. Hepatitis C virus infection and diabetes: direct involvement of the virus in the development of insulin resistance. Gastroenterology 2004; 126: 840848.Google Scholar
45. Marzouk, D, et al. Metabolic and cardiovascular risk profiles and hepatitis C virus infection in rural Egypt. Gut 2007; 56: 11051110.Google Scholar
46. Wang, CS, et al. Hepatitis C virus infection and the development of type 2 diabetes in a community-based longitudinal study. American Journal of Epidemiology 2007; 166: 196203.Google Scholar
47. Chen, SL, Morgan, TR. The natural history of hepatitis C virus (HCV) infection. International Journal of Medical Sciences 2006; 3: 4752.Google Scholar
48. Wang, C, et al. Increased risk of hepatocellular carcinoma in patients with diabetes mellitus: a systematic review and meta-analysis of cohort studies. International Journal of Cancer Journal international du cancer 2012; 130: 16391648.Google Scholar
49. Genovese, G, et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 2010; 329: 841845.Google Scholar
50. Gaziano, TA, et al. Comparative assessment of absolute cardiovascular disease risk characterization from non-laboratory-based risk assessment in South African populations. BMC Medicine 2013; 11: 170.Google Scholar
51. Motala, AA, et al. The prevalence of metabolic syndrome and determination of the optimal waist circumference cutoff points in a rural South African community. Diabetes Care 2011; 34: 10321037.Google Scholar
52. Crowther, NJ, Norris, SA. The current waist circumference cut point used for the diagnosis of metabolic syndrome in Sub-Saharan African women is not appropriate. PLoS ONE 2012; 7: e48883.Google Scholar
53. Mensah, GA. Descriptive epidemiology of cardiovascular risk factors and diabetes in sub-Saharan Africa. Progress in Cardiovascular Diseases 2013; 56: 240250.Google Scholar
54. Bonita, R. Guest editorial: strengthening NCD prevention through risk factor surveillance. Global Health Action 2009; 2: 46.Google Scholar
55. Murphy, GA, et al. Cardiometabolic risk in a rural Ugandan population. Diabetes Care 2013; 36: e143.Google Scholar
56. Kahn, K, et al. Validation and application of verbal autopsies in a rural area of South Africa. Tropical Medicine & International Health: TM & IH 2000; 5: 824831.Google Scholar
57. INDEPTH Network. 2015. (http://www.indepth-network.org).Google Scholar
58. Asiki, G, et al. The general population cohort in rural south-western Uganda: a platform for communicable and non-communicable disease studies. International Journal of Epidemiology 2013; 42: 129141.Google Scholar
59. Ng, N, et al. Combining risk factors and demographic surveillance: potentials of WHO STEPS and INDEPTH methodologies for assessing epidemiological transition. Scandinavian Journal of Public Health 2006; 34: 199208.Google Scholar
60. WHO. WHO STEPS Instrument (Core and Expanded): The WHO STEPwise Approach to Noncommunicable Disease Risk Factor Surveillance (STEPS). Geneva: World Health Organization, 2016.Google Scholar
61. Beran, D, Yudkin, JS. Diabetes care in sub-Saharan Africa. Lancet 2006; 368: 16891695.Google Scholar
62. Maseko, FC, Chirwa, ML, Muula, AS. Health systems challenges in cervical cancer prevention program in Malawi. Global Health Action 2015; 8: 26282.Google Scholar
63. Janssens, B, et al. Offering integrated care for HIV/AIDS, diabetes and hypertension within chronic disease clinics in Cambodia. Bulletin of the World Health Organization 2007; 85: 880885.Google Scholar
64. Atun, R, et al. Improving responsiveness of health systems to non-communicable diseases. Lancet 2013; 381: 690697.Google Scholar
65. van Olmen, J, et al. Management of chronic diseases in Sub-Saharan Africa: cross-fertilisation between HIV/AIDS and diabetes care. Journal of Tropical Medicine 2012; 2012: 349312.Google Scholar
66. WHO. Package of Essenrtial Noncommunicable (PEN) Disease Interventions for Primary Health Care in Low-Resource Settings. Geneva: World Health Organization, 2010.Google Scholar
67. Beaglehole, R, et al. Improving the prevention and management of chronic disease in low-income and middle-income countries: a priority for primary health care. Lancet 2008; 372: 940949.Google Scholar
68. Maher, D, et al. Research needs for an improved primary care response to chronic non-communicable diseases in Africa. Tropical Medicine & International Health: TM & IH 2010; 15: 176181.Google Scholar
69. Mahomed, OH, Asmall, S. Development and implementation of an integrated chronic disease model in South Africa: lessons in the management of change through improving the quality of clinical practice. International Journal of Integrated Care 2015; 15: e038.Google Scholar
70. de-Graft Aikins, A, Boynton, P, Atanga, LL. Developing effective chronic disease interventions in Africa: insights from Ghana and Cameroon. Globalization and Health 2010; 6: 6.Google Scholar
71. Aminkeng, F, et al. Higher frequency of genetic variants conferring increased risk for ADRs for commonly used drugs treating cancer, AIDS and tuberculosis in persons of African descent. Pharmacogenomics Journal 2014; 14: 160170.Google Scholar
72. Ramos, E, et al. Pharmacogenomics, ancestry and clinical decision making for global populations. Pharmacogenomics Journal 2014; 14: 217222.CrossRefGoogle ScholarPubMed
73. Drain, PK, et al. Diagnostic point-of-care tests in resource-limited settings. Lancet Infectious Diseases 2014; 14: 239249.Google Scholar
74. Pai, NP, et al. Barriers to implementation of rapid and point-of-care tests for human immunodeficiency virus infection: findings from a systematic review (1996–2014). Point of Care 2015; 14: 8187.Google Scholar
75. Horton, R. Offline: chronic diseases – the social justice issue of our time. Lancet 2016: 386: 2378.Google Scholar
76. Collins, R. What makes UK Biobank special? Lancet 2012: 379: 11731174.Google Scholar