R Notebook

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

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

# Previewdata
head(ncd)

Interpretation : The dataset contains information from six participants, each described by 11 variables related to demographic and health characteristics. Participants’ ages range from 31 to 56 years, including both males and females, with most residing in urban areas. The education levels vary from no formal education to higher education, and occupations include service, unemployed, farmer, business, and retired categories. Body Mass Index (BMI) values range from 18.9 to 30.1, indicating participants with normal weight to obese levels. Systolic blood pressure ranges from 92 to 188 mmHg, and diastolic pressure from 78 to 116 mmHg, suggesting variability in blood pressure status among individuals. Some participants, such as those with systolic values above 140 mmHg and diastolic above 90 mmHg, may be considered hypertensive. Overall, this dataset reflects diverse sociodemographic and health profiles, which can be useful for analyzing associations between lifestyle factors, residence, and blood pressure levels.

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: The descriptive summary of 2,201 participants indicates a middle-aged population with nearly equal gender distribution. Most participants (60%) live in rural areas, suggesting that rural health characteristics dominate the dataset. Educational levels are varied, but secondary and primary education are the most common, reflecting moderate literacy among participants. Occupation data show that a large share work in service or farming sectors, which is typical for mixed rural-urban populations.

The median BMI of 25.4 suggests that many participants are overweight, increasing the risk for chronic diseases. Blood pressure levels (median systolic 134 mmHg, diastolic 91 mmHg) are slightly above normal, indicating a tendency toward hypertension. Indeed, 38% of participants are classified as hypertensive, and 23% have diabetes—both substantial figures. These findings highlight a significant burden of noncommunicable diseases, particularly hypertension and diabetes, and suggest the need for preventive strategies focusing on healthy lifestyle promotion and early screening in both rural and urban areas.

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 (%)

The dataset compares health and demographic characteristics between rural (N = 1,331) and urban (N = 871) participants. The median age is slightly higher in rural areas (51 years) than urban areas (49 years), with females slightly more represented in rural (56%) than urban areas (51%). Educational attainment differs, with a higher proportion of rural participants having no formal education (9.8% vs. 14% in urban) and higher education being more common in rural areas (25% vs. 20%).

Occupational patterns show rural participants are more often farmers (29% vs. 25%), while urban participants are more engaged in business (30% vs. 15%). Service employment is similar between groups, but unemployment is higher in rural areas (14% vs. 5.7%). Median BMI is slightly higher in rural participants (25.5 vs. 24.9), and blood pressure levels indicate elevated values in both groups, with rural median systolic BP higher (136 vs. 131 mmHg). Diabetes and hypertension are more prevalent in rural areas (26% vs. 20% for diabetes, 41% vs. 33% for hypertension), suggesting a higher burden of noncommunicable diseases in rural populations. Overall, rural participants show slightly higher risk factors for NCDs compared to urban counterparts.

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

Logistic Regression Analysis: The logistic regression results show the association between various participant characteristics and the outcome of interest.

Age has a log odds ratio (log(OR)) of 0.00 (95% CI: -0.02, 0.02; p = 0.8), indicating no significant association with the outcome.

Sex: Males have a log(OR) of 0.30 (95% CI: -0.27, 0.87; p = 0.3) compared to females, suggesting no statistically significant difference between genders.

BMI shows a log(OR) of -0.01 (95% CI: -0.07, 0.04; p = 0.6), indicating that BMI is not significantly associated with the outcome.

Residence: Urban participants have a log(OR) of -0.41 (95% CI: -1.0, 0.18; p = 0.2) relative to rural participants, showing no significant effect.

Education levels (no formal, primary, secondary) compared to higher education all show non-significant associations with the outcome (p-values 0.3–0.9).

Occupation categories (farmer, retired, service, unemployed) compared to business also show no significant associations (p-values 0.3–0.8).

Diabetes status (yes vs. no) has a log(OR) of 0.06 (95% CI: -0.61, 0.71; p = 0.9), indicating no significant effect.

Overall, none of the variables analyzed show statistically significant associations with the outcome, as all p-values are greater than 0.05, and confidence intervals include the null value. This suggests that in this dataset, these demographic, socioeconomic, and health factors are not significantly linked to the outcome under study.