R Notebook

library(tidyverse)
library(gtsummary)
library(broom)
library(gt)

ncd <- read.csv("ncd.csv")

# Previewdata
head(ncd)

NA

Interpretation of the Dataset (n = 6)

The dataset includes 6 participants with 11 variables describing their demographic and health characteristics.

Participants’ ages range from 31 to 56 years, and most are male (5 out of 6).

Residency is predominantly urban, with only one participant from a rural area.

Education levels vary from no formal education to higher education, indicating a diverse educational background.

Occupations include service, business, farming, unemployment, and retirement, reflecting mixed socioeconomic profiles.

BMI values range from 18.9 to 30.1, suggesting participants span from underweight to obese categories.

Systolic blood pressure ranges from 92 to 188 mmHg, and diastolic pressure from 78 to 116 mmHg, showing both normal and hypertensive readings.

ncd %>% 
   tbl_summary()

Characteristic	N = 2201
participant_id	111 (56, 166)
age	50 (38, 67)
sex
Female	118 (54%)
Male	102 (46%)
residence
Rural	133 (60%)
Urban	87 (40%)
education
Higher	50 (23%)
No formal	25 (11%)
Primary	66 (30%)
Secondary	79 (36%)
occupation
Business	46 (21%)
Farmer	61 (28%)
Retired	24 (11%)
Service	66 (30%)
Unemployed	23 (10%)
bmi	25.4 (21.3, 28.3)
systolic_bp	134 (114, 165)
diastolic_bp	91 (76, 107)
diabetes	51 (23%)
hypertension	84 (38%)
1 Median (Q1, Q3); n (%)

Interpretation:

Interpretation of Overall Participant Characteristics

The dataset includes 2,201 participants with a median ID of 111 (56, 166).

The median age of participants was 50 years (38, 67), indicating a predominantly middle-aged group.

Females accounted for 54% of the sample, while males made up 46%, reflecting a fairly balanced gender distribution.

In terms of residence, a majority of participants were from rural areas (60%), with urban residents comprising 40%.

Educational attainment varied: 36% had secondary education, 30% had primary education, 23% had higher education, and 11% had no formal education.

Regarding occupation, the most common categories were service (30%) and farming (28%), followed by business (21%), retirement (11%), and unemployment (10%).

The median BMI was 25.4 (21.3, 28.3), suggesting that participants generally fell within the normal to overweight range.

The median systolic blood pressure was 134 mmHg (114, 165) and diastolic was 91 mmHg (76, 107), indicating variability across individuals with a tendency toward elevated levels.

The prevalence of diabetes was 23%, while hypertension was reported in 38% of participants, suggesting a moderate burden of cardiometabolic risk factors in the sample.

ncd %>%
  tbl_summary(
    by = residence)

Characteristic	Rural N = 1331	Urban N = 871
participant_id	108 (64, 156)	115 (44, 176)
age	51 (38, 69)	49 (37, 65)
sex
Female	74 (56%)	44 (51%)
Male	59 (44%)	43 (49%)
education
Higher	33 (25%)	17 (20%)
No formal	13 (9.8%)	12 (14%)
Primary	38 (29%)	28 (32%)
Secondary	49 (37%)	30 (34%)
occupation
Business	20 (15%)	26 (30%)
Farmer	39 (29%)	22 (25%)
Retired	14 (11%)	10 (11%)
Service	42 (32%)	24 (28%)
Unemployed	18 (14%)	5 (5.7%)
bmi	25.5 (21.1, 28.3)	24.9 (21.9, 28.4)
systolic_bp	136 (114, 166)	131 (111, 164)
diastolic_bp	91 (74, 104)	93 (77, 108)
diabetes	34 (26%)	17 (20%)
hypertension	55 (41%)	29 (33%)
1 Median (Q1, Q3); n (%)

Interpretation:

The dataset comprises 1331 rural and 871 urban participants.

The median participant ID (as an index) was slightly higher in the urban group (115 [44, 176]) than in the rural group (108 [64, 156]).

The median age was comparable between groups — 51 years (38, 69) in rural areas and 49 years (37, 65) in urban areas.

Sex distribution was fairly balanced: females represented 56% of the rural group and 51% of the urban group.

In terms of education, rural participants had a slightly higher proportion with no formal education (9.8% vs 14%), while both groups had the largest proportion with secondary education (37% rural, 34% urban).

Occupational patterns differed slightly: farmers (29%) and service workers (32%) were more common in rural areas, while business (30%) was more prevalent among urban participants.

BMI values were similar between groups, with medians of 25.5 (21.1, 28.3) for rural and 24.9 (21.9, 28.4) for urban participants.

Blood pressure levels were marginally higher in rural areas — systolic BP: 136 (114, 166) vs 131 (111, 164); diastolic BP: 91 (74, 104) vs 93 (77, 108).

The prevalence of diabetes was slightly higher among rural participants (26% vs 20%), as was hypertension (41% vs 33%).

model <- glm( factor(hypertension) ~ age + sex + bmi + residence + education + occupation + diabetes, data = ncd, family = binomial)
tbl_regression(model)

Characteristic	log(OR)	95% CI	p-value
age	0.00	-0.02, 0.02	0.8
sex
Female	—	—
Male	0.30	-0.27, 0.87	0.3
bmi	-0.01	-0.07, 0.04	0.6
residence
Rural	—	—
Urban	-0.41	-1.0, 0.18	0.2
education
Higher	—	—
No formal	0.52	-0.48, 1.5	0.3
Primary	0.05	-0.72, 0.84	0.9
Secondary	0.09	-0.66, 0.85	0.8
occupation
Business	—	—
Farmer	-0.41	-1.2, 0.41	0.3
Retired	-0.56	-1.7, 0.49	0.3
Service	-0.12	-0.91, 0.67	0.8
Unemployed	-0.26	-1.4, 0.80	0.6
diabetes
No	—	—
Yes	0.06	-0.61, 0.71	0.9
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Interpretation of Logistic Regression Results

The analysis examined associations between selected demographic and health variables and the outcome of interest (log odds reported as log(OR)).

Age showed no significant association with the outcome (log(OR) = 0.00; 95% CI: -0.02, 0.02; p = 0.8).

Sex was not a significant predictor, with males having slightly higher odds compared to females, though not statistically significant (log(OR) = 0.30; 95% CI: -0.27, 0.87; p = 0.3).

BMI was also not significantly associated with the outcome (log(OR) = -0.01; 95% CI: -0.07, 0.04; p = 0.6).

Residence showed no significant difference between urban and rural participants (log(OR) = -0.41; 95% CI: -1.0, 0.18; p = 0.2).

Across education levels, none of the categories (No formal, Primary, or Secondary) significantly differed from the reference group (Higher education), with all p-values > 0.05.

Similarly, occupation was not a significant predictor; compared to business, other categories (farmer, retired, service, and unemployed) showed non-significant associations.

Diabetes status did not demonstrate a significant effect on the outcome (log(OR) = 0.06; 95% CI: -0.61, 0.71; p = 0.9).

Overall, none of the included predictors were statistically significant, indicating that within this sample, the variables assessed did not show strong or independent associations with the outcome variable.