4.1. Introduction

This chapter presents the results and findings of this study. Descriptive statistics are presented using tables and charts. Further analysis with inferential statistics is also presented where results are considered statistically significant if p value is less than .05 level of significance. The analysis was done using the R Statistical Computing Software version 4.1.3.

4.2. Demographic characteristics

In this section we present frequency tables for all the demographics characteristics of the population.

4.2.1. Region

A majority of respondents in this study came from South 2,853 (28%) followed by North West 2,773 (27%) regions.

4.2.2. Age group

A majority of respondents in this study came from South 2,852 (28%). Respondents in the age brackets 70+, 50-59 and 40-49 years were the least with 985 (10%), 1,289 (12%), 1,270 (12%) respectively.

4.3. Health characteristics

In this section we present frequency tables for all the demographics characteristics of the population.

4.2.1. High blood pressure

Out of all the sampled respondents, the study found that 4,372 (42%) suffer from high blood pressure and 5,965 (58%) do not suffer from it. The prevalence of high blood pressure for this population is thus 42%.

4.3.2. Diabetes

Table 4.x presents the results of the distribution of diabetes. The prevalence among the surveyed respondents was 5% (n = 499).

4.3.3 Prevalence of high blood pressure by sex

The study disaggregated the results of high blood pressure above by sex to compare the prevalence across gender. The results of Table 4.x show that high blood pressure is more prevalent among males 2,302 (53%) than females 2,070 (47%).

4.3.4 Prevalence of high blood pressure by sex

Prevalence for diabetes was lower among men 217 (43%) compared to women 282 (57%). These results are presented in Table 4.x.

Descriptive statistics, M(SD)

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Descriptive statistics, Median(IQR)

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Descriptive statistics by categorical variables

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Chi-square and ANOVA tests

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Table name.
0 (N=5965) 1 (N=4372) Total (N=10337) p value
region 0.0261
   1 1165 (19.5%) 921 (21.1%) 2086 (20.2%)
   2 1663 (27.9%) 1110 (25.4%) 2773 (26.8%)
   3 1636 (27.4%) 1217 (27.8%) 2853 (27.6%)
   4 1501 (25.2%) 1124 (25.7%) 2625 (25.4%)
sex < 0.0011
   1 2607 (43.7%) 2302 (52.7%) 4909 (47.5%)
   2 3358 (56.3%) 2070 (47.3%) 5428 (52.5%)
race < 0.0011
   1 5307 (89.0%) 3744 (85.6%) 9051 (87.6%)
   2 545 (9.1%) 541 (12.4%) 1086 (10.5%)
   3 113 (1.9%) 87 (2.0%) 200 (1.9%)
agegroup < 0.0011
   1 1928 (32.3%) 392 (9.0%) 2320 (22.4%)
   2 1166 (19.5%) 455 (10.4%) 1621 (15.7%)
   3 768 (12.9%) 502 (11.5%) 1270 (12.3%)
   4 588 (9.9%) 701 (16.0%) 1289 (12.5%)
   5 1189 (19.9%) 1663 (38.0%) 2852 (27.6%)
   6 326 (5.5%) 659 (15.1%) 985 (9.5%)
Body mass index < 0.0012
   Count 5965 4372 10337
   Median 23.69 26.53 24.82
   Q1, Q3 21.37, 26.37 23.72, 30.09 22.14, 28.03
Serum cholesterol (mg/dL) < 0.0012
   Count 5965 4372 10337
   Median 203.00 226.00 213.00
   Q1, Q3 175.00, 238.00 195.00, 259.00 183.00, 247.00
Age in years < 0.0012
   Count 5965 4372 10337
   Median 38.00 60.00 49.00
   Q1, Q3 27.00, 60.00 45.00, 67.00 31.00, 63.00
  1. Pearson’s Chi-squared test
  2. Linear Model ANOVA

Linear regression

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Logistic regression

We will do the following:

Binary logistic regression

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Exponents

Multinomial logistic regression

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weights: 20 (12 variable)

initial value 14330.124811 iter 10 value 14195.338995 final value 14150.572286 converged

Ordinal logistic regression

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