Session 3: Nick Wareham
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The document discusses training and capacity building efforts for international public health research on NCD prevention. It describes the Cambridge Seminar program which provides epidemiology training to researchers from low and middle income countries. It also highlights several examples of collaborative research projects between researchers from different countries investigating NCD risk factors, interventions for diabetes prevention, and physical activity patterns in rural and urban settings in Africa. The overall aim is to build global capacity to undertake translational public health research for preventing non-communicable diseases.
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Session 3: Nick Wareham
- 1. Supporting and Undertaking International Public Health Research in NCD prevention Nick Wareham
- 2. Source: Global Diabetes Plan, IDF 2011
- 6. The 1 st Cambridge Seminar 1981 Harry Keen John Jarrett
- 7. The 11 th Cambridge Seminar, 2011
- 8. Regional linked seminars Cambridge Seminar in Hangzhou, China 2010
- 11. Source: The International Expert Committee. Diabetes Care 2009
- 12. The IDF Diabetes Atlas 5 th Edition A summary of the figures and key findings
- 13. Prevalence of diabetes, 2011
- 15. WHO multinational study International collaboration for study on disease outcomes Source: Fuller et al, Diabetologia 2001
- 17. Source: Gillies et al, BMJ 2007 Meta-analysis of RCTs
- 22. The Indian Diabetes Prevention Programmes Prof A Ramachandran Source: Ramachandran et al, Diabetologia 2006
- 23. MRC-ICMR initiative Chennai-Imperial-Cambridge Evaluation of a scaleable and feasible mass intervention to promote lifestyle changes
- 24. Professor Jean-Claude Mbanya President IDF
- 25. Diabetes prevalence in Africa Source: Mbanya et al, Lancet 2010
- 27. Felix Assah Attendee Cambridge Seminar MPhil in Epidemiology PhD in Epidemiology Wellcome Trust Clinical Fellow “ The Cambridge sequence … has been invaluable in providing an Introduction to NCD research and then going further to provide expert level training over a five year period. I am ready to give back to my society the knowledge and skills acquired through hands-on training of students and junior researchers.”
- 29. Rural Area in Cameroon Mbankomo
- 30. Rural lifestyle
- 31. Urban Area in Cameroon Biyem-Assi, Yaounde
- 32. Urban lifestyle
- 34. Population distribution of physical activity energy expenditure (PAEE) Assah FK et al, Diabetes Care 2011
- 35. Activity and Clustered Metabolic Risk in Cameroon Rural and urban differences in 552 adults p<0.001 for trend Source: Assah et al, Diabetes Care 2011
- 36. Domains of Physical Activity in Rural and Urban dwellers Assah FK et al, Unpublished, 2010
- 37. Seasonal trends of physical activity Dark bars = Rural Light bars = Urban
- 38. Seasonal trends of physical activity Dark bars = Rural Light bars = Urban
- 39. Some correlates of physical activity PAEE (kJ/kg/day) Rural (N=271) Urban ( N= 317) Correlates β SE p value β SE p value Demographic and anthropometric BMI (kg/m 2 ) -1.53 0.33 <0.001 -1.0 0.21 <0.001 Normal -- -- Overweight -11.11 3.41 0.001 -4.50 2.60 0.09 Obese -17.15 4.78 <0.001 -11.52 2.69 <0.001 Related lifestyle behaviours Smoking 0.57 5.70 0.9 12.77 3.81 0.001 Alcohol drinking 5.05 3.56 0.2 4.84 2.56 0.06 Fruits and vegetables <3 times/week -- -- 3 – 6 times/week 9.19 3.35 0.007 4.34 2.23 0.05 >=7 times/week 11.53 3.98 0.004 8.28 3.23 0.01 School duration (years) -0.55 0.35 0.1 -0.98 0.19 <0.001
Notas del editor
- Big changes from the last edition are mostly due to new data from China, MENA, and Africa Most people with diabetes are 40 – 59 years of age 80% of people with diabetes live in LMICs 2/3 people with diabetes are under 60 More people live in urban than rural areas (63% in urban, almost 2:1 ratio)
- This slide shows the MAIN FINDINGS OF MY ANALYSIS: Explain graph Highly significant, inverse trend People in Norfolk who met all 5 goals, none went on to develop diabetes, highest rate in those who met no goals Overall incidence similar to that in other UK populations Sensitivity analyses show robustness of the trend –missing data, sex
- Biggest changes will be in Africa, followed very closely by MENA. NAC and Europe will change the least.
- Note sparse, mainly linear habitation pattern. Abundance of surrounding forest/farmland.
- Note dense cluster habitation with almost inexistent green space!
- Urban dwellers had a significantly lower PAEE than rural dwellers (44 · 6 ± 20 · 2 kJ/kg/day vs. 58 · 6 ± 24 · 5 kJ/kg/day, p<0 · 001). However, note that this difference corresponds to a rural-to-urban left shift in the population distribution of PAEE. So, rural vs. urban difference in physical activity may not only be attributable to a few high risk individuals, but involves a change in the whole population.
- Trend analysis adjusted for age, sex, residential area, smoking, alcohol consumption, fruit and vegetable consumption, and number of years of education.
- Note that physical activity in rural Cameroonians is predominantly carried out for work or travel, and very little for leisure. In urban dwellers, the is a much lower time spent for work or travel related activity, with no significant difference in leisure time activity. Urbanization may be causing a large reduction in physical activity for work/travel, but there is still no culture of replacing this with leisure time activity.
- Seasonal trends and differences in physical activity levels between rural and urban dwellers in Cameroon (N=588). Dark bars = Rural, Light bars = Urban. Bars are means and error bars are SEM. Seasons are: Long Dry, Dec-May; Light Rain, June; Short Dry, July-Sept; Heavy Rain, Oct-Nov
- Seasonal trends and differences in physical activity levels between rural and urban dwellers in Cameroon (N=588). Dark bars = Rural, Light bars = Urban. Bars are means and error bars are SEM. Seasons are: Long Dry, Dec-May; Light Rain, June; Short Dry, July-Sept; Heavy Rain, Oct-Nov
- BMI, level of education and light intensity occupations all showed significant inverse associations with PAEE independent of age, sex or residential area. Frequent consumption of fruits and vegetables was associated with higher PAEE. Data are regression coefficients, which represent the expected change in PAEE (kJ/kg/day) for a unit change in exposure. Estimates are adjusted for age, sex and residential area.