3.2 Relationship between Income and Diabete Prevalence
4 REGRESSION MODELS
According to Figure 2, household income shows similar pattern with diabetes prevalence. To further explore possible relationship between these two factors, the correlation of income and diabetes are calculated.
Based on previous analysis, I choose fractions of energy intake from protein, carbohydrate and fibre and diversity/entropy of energy from nutrients as indicators to predict household income. To make predicitons more accurate, fractions of consumption of dairy, fruit and vegetable and grains are also used as indicators.
4.1 Nutrition/Food Intakes and Income Higher annual income areas are usually with lower prevalence of diabetes. The coefficient is around -0.7. Regions with higher income population probably have lower rates of diabetes patients. However, there are various and complicated factors which may influence diabetes prevalence, so we can not simply conclude direct relationships between household income and diabetes prevalence. The observed are positively correlated with the predicted with a coefficient of 0.83. Dietary habit can be an indicator to household income.
Figure 4: Correlation between income and diabetes prevalence
3.3 Correlation between Nutrition Intakes, Income and Diabetes Prevalence To explore the relationships between nutrition structure, income and diabetes prevalence, I calculated the correlation between different nutrient intakes which are shown as unhealthy above (fat, saturated fat, sugar, carbohydrate), diversity/entropy of energy from nutrients, income and diabetes prevalence. Incomes are positively correlated with nutrition diversity and negatively correlated with diabetes prevalence and fraction of energy intake from carbohydrate.
Figure 7: Correlation between the observed and the predicted of diet - income model
4.1 Nutrition/Food Intakes and Income
Diabetes prevalence is positively correlated with fraction of energy intake from carbohydrate and negatively correlated with nutrition diversity and income. Unhealthy dietary habit with over fat, saturated fat and sugar doesn’t have a strong correlation with household income and diabetes prevalence, while carbohydrate intake and nutrition diversity have a relatively strong correlation. Purely correlating nutrition structure (with unhealthy nutrient intakes) with income and diabetes may be insufficient to explore relationships diet, income and diabetes.
The observed are positively correlated with the predicted with a coefficient of 0.86. Dietary habit can be an indicator to household income.
Figure 8: Correlation between the observed and the predicted of diet - diabetes model
Figure 5: Correlation between nutrition structure, income and diabetes prevalence
3.4 Correlation between Nutrition/Food Intakes, Income and Diabetes Prevalence To further explore relationships between dietary behaviour, income and diabetes prevalence, I calculated the correlation between other nutrient intakes (other than the ones in figure 4), proportion of food and liquids consumption, income and diabetes prevalence. Incomes are positively correlated with fractions of fibre and protein intake, consumption of wine, dairy, fruits and vegetables, and negatively correlated with consumption of soft drinks and grains. Diabetes prevalence is positively correlated with consumption of grains and soft drinks, and negatively correlated with fractions of protein and fire intake, consumption of wine, dairy, fish, fruits and vegetables. Higher incomes are probably more related to healthier diet with more fibre and protein intakes through more consumtion of dairy, fruits and vegetables. Also, healthier diets are probably more related to lower diabetes prevalence.
Figure 6: Correlation between nutrition & food structure, income and diabetes prevalence
5 CONCLUSION Overall, general diet habits in London are unhealthy with high intakes of fats, saturated fats and sugar. Dietary pattern has some correlations with incomes and diabetes prevalence. Areas with higher household income are likely to have low diabetes prevalence, which is probably due to people’s different dietary behaviours in certain areas. Higher incomes are more likely related to healthier dietary habits with more energy intake from protein and fibre, more consumption of dairy, fruits and vegetables and high nutrition diversity. However, the overly intake of fats, saturated fats and sugar doesn’t represent a high correlation with household incomes. Also, healthier diet with more energy intake from protein and fibre, more consumption of dairy, fish, fruits and vegetables and high nutrition diversity are related to lower prevalence of diabetes.
6 LIMITATIONS 1. The data was collected from Clubcard customers of Tesco, which may be not representative for the overall population. 2. There were food consumption other than food purchasing from Tesco, so the dietary habits may be different when we take all kinds of food consumption into consider. 3. The distribution of Tesco may influence people’s purchasing pattern. 4. Food consumption cab be influenced by many social economic factors other than income, for instance, employment, education status, etc. 5. Diabetes can be influenced by many factors like accessibility to health care facilities, daily physical activities, etc.
