Evaluation of a risk factor scoring model in screening for undiagnosed diabetes in China population showed that in the Finnish model, the variables were age (45-54 and 55-64 years old), BMI (25-30 and greater than or equals to 30 kg/m2), waist circumference (men ranged from 94 to less than 102, women from 80 to less than 88; men greater than or equal to 102, women greater than or equals to 88), history of antihypertensive drug treatment, high blood glucose, physical activity, daily consumption of fruits, berries or vegetables and their performances were that the cut-off point greater than or
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equal to 9 with sensitivity of 77%, specificity of 66% and AUC of 0.80 in original population. The sensitivity was 45%, specificity 86% and AUC 0.76 when the model was applied to the total participants.60
In the Danish model, the variables were age (45-50, and 55-60 years old), sex (male and female), BMI (25-29 and 30kg/m2), known hypertension, physical activity at leisure time, family history of diabetes and their performance was highest when the cut-off point was greater than or equal to 31 with sensitivity of 76%, specificity of 72% and AUC of 0.80 in original population. The sensitivity was 51%, specificity 76% and AUC 0.71 when the model was applied to total participants.60
In the Chinese model, the variables with the age (45-54, 55-64, and greater than or equals to 65 years old), BMI (24-28 and greater than or equal to 28kg/m2), WHR (male greater than or equals to 0.9 and female greater than 0.85), SBP (greater than or equals to 140 mmHg), DBP (greater than or equals to 90 mmHg), heart rate (greater than or equals to 90 beats / min), family history of diabetes mellitus, history of hyperglycemia and their performance had a cut-off point greater than or equals to 51 with sensitivity of 83%, specificity of 66% and AUC of 0.82 in original population.60 The use of a larger sample size might have resulted in some modification to this model which could have led to a reduction its false negative predictive value. It also lacked external validation scoring model.
In the TRAQ-D study a total of 688 participants, aged 18-92 years, were recruited for the study; of these, 321 (46.7%) had type 2 diabetes. In addition, 59.9% were females (female male ratio 1.5:1). Of the 16 risk factors investigated age was the most significant independent risk factors for diabetes (<0.05), 66% of patients with diabetes being > 45 years of age.59
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Moreover the independent OR of family history of diabetes was the most significant non-modifiable risk factor (OR 7.93, 95% CI 5.00-12.57, p<0.005), while independent odds ratios for BMI (p<0.005, OR 3.84, 95% CI 2.05-7.17), smoking (<0.005, OR 6.60, 95% CI 4.08-10.69) were also established as significant modifiable risk actors.
Its total scorecard is 30. The average score of patients with diabetes was significantly higher than those without (17.79, SD + 5.01 vs. 9.20, SD + 5.08).
When tested on the 232 phase two participants the ROC plot yielded an AUC of 0.94. However, testing on the entire sample of 688 participants yielded an AUC of 0.884 which was significantly better than BMI (AUC of 0.817).
Hence, TRAQ-D provides greater predicative ability than BMI or age alone.
A comparative analysis of performance of risk score using AUC shows that it has similar value of AUC in Africa and India, which is low compare to other parts of the world.53
In a study in Lagos the intercept of the analysis was - 129.04. The risk score with maximal efficiency was 0.45. It had a sensitivity of 89.40%, specificity of 18.20%, positive predictive value of 10.40, and negative predictive value of 95%. The efficiency was 107.60%. The area under the receiver operating characteristic curve was 0.54. A comparative analysis of the performance of risk score using AUC showed that it was similar to the value of AUC in Africa and India, which is low compared to other parts of the world.53
The performance of this study is relatively comparable with others in terms of sensitivity and specificity.
In a study to validate the American Diabetes Association risk score, the performance of the score was cross-sectionally analysed in subjects who had
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either fasting or 2-h glucose levels exceeding the threshold of diabetes. A total of 2,525 subjects in the 1987 cohort and 1,976 subjects in the 1992 cohort could be classified according to results of oral glucose tolerance test and had complete Diabetes Risk Score data. The crude prevalence of undiagnosed diabetes was 3.5% (n = 87) in the 1987 survey and 5.7% (n = 112) in the 1992 survey (known diabetic patients treated with anti-diabetic drugs and subjects with incomplete baseline data excluded from the analyses).
The ROC plot indicated good performance of the Diabetes Risk Score also in the cross-sectional setting (AUC = 0.80 for both surveys). For cut-off point Diabetes Risk Score of ≥9, sensitivity was 0.77 (95% CI 0.66–0.85) and 0.76 (0.67–0.83), specificity was 0.66 (0.64–0.68) and 0.68 (0.66–0.70), PPV was 0.07 (0.06–0.09) and 0.12 (0.10–0.15), and negative predictive value (the probability of not having diabetic glucose levels if Diabetes Risk Score was
<9) was 0.99 (0.98–0.99) and 0.98 (0.97–0.99) in the 1987 and 1992 oral glucose tolerance tests, respectively.13
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CHAPTER THREE
MATERIALS AND METHOD
3.1 STUDY AREA
Rivers State is one of the thirty-six states of the Federal Republic of Nigeria.
It is located in the Niger Delta, the oil-rich region of Southern Nigeria. It is bounded on the North by Imo and Abia States, the East by Akwa Ibom State, on the West by Delta and Bayelsa States, and on the South by the Atlantic ocean. The state is rapidly being urbanized with attendant effects of westernization, over-crowding, pollution and environmental degradation.
There are 23 Local Government Areas which consist of 5 urban and 18 rural areas, 319 political wards, with 1,583 Communities and settlements and Port Harcourt is the administrative capital. It has an area of 12,190 square kilometers with an estimated population of 6,128,931 (at 3.1% growth rate
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from 2006 population figure) and sex ratio above 1 from NPHCDA/WHO 2011 population data.
The study was conducted in communities in Ikwerre Local Government Area (LGA) in Rivers State. Ikwerre LGA is one the 23 LGAs in Rivers State and is made of thirteen political wards. The headquarters is located in Isiokpo and is bounded in the East by Emohua, West by Etche, North by Imo State and South by Obio-Akpor LGA. Its people are predominantly farmers and traders and speak Ikwerre as the native language of the people. There are nine model health centres and two general hospitals to cater for the health needs of its people. The total population of the LGA is 235,470 (estimated from 2006 National Population Census result of Nigeria) and generally the communities have a rural setting because populations are less than 20,000 people.7-10