Ms S.Revathi,Published a Paper on the topic "Inequalities on Pwer Series With Real Coefficients and Univalent Function Theory"

 

 

 

ANALYZING THE IMPACT OF INFLAMMATORY BOWEL DISEASE (IBD) BY USING R-PROGRAMMING

 

J. Vennila, P. Basker^*, K. Thenmozhi, K. Rajeswari and P. Nithyakala

Department of Statistics

Kristu Jayanti College (Autonomous)

K. Narayanapura, Bangaluru-560077 Karnataka, India

Department of Mathematics Chandigarh University Punjab-140413, India

Department of Computer Science (UG) Kristu Jayanti College

K. Narayanapura, Bangaluru-560077 Karnataka, India

Received: October 11, 2021; Accepted: November 25, 2021

Keywords and phrases: IBD, control group, educational group, socio-demographic, R-programming.

^*Corresponding author

How to cite this article: J. Vennila, P. Basker, K. Thenmozhi, K. Rajeswari and P. Nithyakala, Analyzing the impact   of inflammatory bowel disease (IBD) by using R-programming, JP Journal of Biostatistics 19 (2022), 123-144. DOI: 10.17654/0973514322009

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Published Online: January 31, 2022

Department of Statistics

Marudhar Kesari Jain College for Woman Vaniyambadi, Tamilnadu, India

Department of Pharmacy Practice

Swamy Vivekananda College of Pharmacy Tiruchengode, Namakkal

Tamilnadu-637205, India

 

Abstract

 

Inflammatory bowel disease (IBD) is a wide range of conditions involving persistent inflammation of the digestive system. This analysis looks at the severity of IBD diseases such as Crohn’s disease and ulcerative colitis. It is an observational prospective research. The findings of this investigation are analyzed using R-programming. To compare the patient’s knowledge between the control and educational groups, as well as their effectiveness, R was used. The IBD has been revealed in recent papers. Food insecurity and a lack of social support affect one out of every eight individuals with inflammatory bowel disease. With socio-demographic data and R, there is a link between eating habits and kinds of food habits. The patients’ knowledge was analyzed and it is similar in the control group and educational group.

 

Introduction of R in Pharma

 

Use of R in operations related to Medical Research Services and Pharma Regulatory Reports is now less than 10%, according to market trends. R is, on the other hand, widely used in public health, healthcare economics, exploratory/scientific research, pattern identification, plots/graphs production, basic statistical analysis, and machine learning applications. In past few years, data science has aided industry executives in making important business choices. Scientists who work with data are narrators. In regulatory writing services, they frequently need to dive through data, clean,

transform, develop and verify models, analyze trends, produce insights, and, most importantly, effectively convey outcomes. Today, data science and big data have shown to be beneficial and necessary in a wide range of industries and various other fields. R has also proven its worth in research by allowing researchers to prepare large volumes of data in less time. R is a statistical analysis programme. It is utilized in machine learning and deep learning research.

 

Introduction of Inflammatory Bowel Disease (IBD)

 

According to the Indian Medical Association, there was little knowledge of the disease severity and symptoms in 2017. According to data from India, around 50 lakhs individuals worldwide suffer with IBD, with the number of cases reaching 12 lakhs per annum. Furthermore, this information may be used to help direct research into the role of socio-demographic variables, clinical features, and IBD expertise in tertiary care hospitals. Crohn’s disease (CD) affects the entire gastrointestinal (GI) tract, while ulcerative colitis (UC) primarily affects the colon and rectum. Inflammatory bowel disease (IBD) is a chronic relapsing and remitting disease comprised of Crohn’s disease (CD) affects the entire GI tract and ulcerative colitis (UC) primarily affects the colon and rectum. In Asia and Africa, the incidence and prevalence rates of IBD have been on the rise. Despite several epidemiologic studies on IBD, the cause of the disease remains unknown, despite of the fact that the disease’s impact is quickly increasing in India and other Asian nations.

PraveenKumar [1] has worked on the processing of medical data and prediction of future health condition using data mining techniques and analyzing with R-programming. Dolgikh et al. [2] have analyzed experience in developing graphical user interface to R-programming language for clinical data. Rimal et al. [3] have investigated COVID-19 cases using R-programming. Morales et al. [4] have examined infancy and childhood obesity using R-programming. Alexander and Powell [5] have studied SARS-CoV-2 vaccination for patients with inflammatory bowel disease.

Lee et al. [6] have examined multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease. Singh et al. [7] have surveyed the risk and outcomes of coronavirus disease in patients with inflammatory bowel disease. Kumric et al. [8] have analyzed the impact of the COVID-19 on inflammatory bowel disease patients. Attauabi et al. [9] have studied association between 5-aminosalicylates in patients with IBD and risk of severe COVID-19.

The goal of this research study is to report the use of R to analyze the severity level of inflammatory bowel disease (IBD). According to the findings of this study, knowing the severity of a disease can lead to substantial improvements in the treatment of IBD. With the help of the healthcare team, education plays a critical role in providing improved patient care. Pharmacists are among the healthcare personnel who work with educational materials in respect to disease-specific knowledge, therapy compliance, and general care in order to enhance the patient’s condition. In terms of patient well-being and complication reduction, emphasizing the significance of frequent follow-up may help to prevent cancer in the future. Insufficient understanding of patients at the baseline study may be enhanced by providing visual pamphlets as teaching material during the patients’ post-visit.

Aim

·  To study about the knowledge in inflammatory bowel disease between control group and educational group using R-programming.

Objective

·   To assess the knowledge with disease severity in control group and educational group using R.

·    To associate the knowledge with the disease severity of both the groups using R.

·    To compare the knowledge between control group and educational group in pre- and post-tests using R.

Flow chart of research process

 

Research Methodology

 

ü Study site. The study was conducted at the Department of Internal Medicine and obesity clinic in Kovai Medical Center and Hospital (KMCH) at Coimbatore.

ü Study design. This study is conducted based on a prospective - observational study.

ü Study duration. The study was conducted for a period of six months.

 

ü Study population and sample. Population identified is 180, from the population of the sample size using statistical calculation. It is observed that there are 23 each from Control Group and Education Group.

ü Phase of study

 

◦   Phase I: Baseline period: To identify the sample size and samples taken (pre-test conducted).

 

◦  Phase II: To educate patients with patient information leaflet (post-test conducted).

◦   Phase III: Comparing the efficacy of knowledge between the  two groups.

◦  Phase IV: Statistical analysis using R.

 

◦  Phase V: Result and discussion.

 

Sources of data

 

◦  A data collection form was designed to collect the patient information

including demographic data (age, gender, food habits, education, type of food) and medical data (past medical history, types of IBD, duration of IBD and severity of IBD, knowledge based question (pre and post) and their current medication).

◦   A leaflet containing information for patient education was designed and distributed.

◦  Questionnaires were used to determine clinical education.

 

Study protocol

The institutional research and ethics committee approved the study and issued the letter of permission to conduct the study (Proposal No: EC/AP/591/02/2018). A suitably designed data collection form was used to collect the patient data. The study patient was split into two groups (control group and educational group). After 8 weeks, the educational group receives a patient information booklet, whereas the control group does not get anything. After 16 weeks, the control group receives a patient information booklet, whereas the experimental group does not. The patient’s demographic details group and past medical history, education history, patient’s height, weight, BMI, complaints and lab investigations were recorded from the first visit to the sixth visit. The BMI from the first visit is

compared to the final visit BMI in both the groups 1 and 2. A well designed patient information leaflet was provided to the patient during the visit and counselled appropriately.

Statistical analysis

 

R is used for statistical analysis of the descriptive data for analyzing frequency, percentage, chi-square and t-test. The student t-test was performed to compare the changes in pre- and post-visits in the two groups

from the first visit to the final visit. A value for

statistically significant.

p < 0.05

was considered

 

CODE OF R (sample coding of IBD knowledge with Control_group and Educational_group)

#Importing csv into R

 

>   ibd_numerical<-read.csv(“1.Edu Group demo.csv”,stringsAsFactors = FALSE,na.strings =

“.”)

 

 

 

 

 

 

 

#Frequency Distribution for Socio demographic of Control_Group and Educational_Group ibd1= c(ibd_datas$Age)

>  table = transform(table(count))

>  table count Freq 1 (21,26] 4

2 (26,31] 2

3 (31,36] 4

4 (36,41] 5

5 (41,46] 4

6 (46,51] 2

7 (51,56] 0

8 (56,61] 0

#Bar Plot for Age

>  barplot(table$Freq,main=“Frequency Distribution for Age”,xlab=“AGE”,ylab=“No. of Patients”,col=c(“Red”,“Blue”,“Yellow”,“Green”,“Orange”,“pink”,“gray”,“black”)) #Histogram for Age

>  hist(ibd1,main=“Frequency Distribution for Age”,xlab=“AGE”,ylab=“No. of Patients”,col=c(“Red”,“Blue”,“Yellow”,“Green”,“Orange”,“gray”,“pink”,“black”)) #Paired t-Test

>pairedt.test(table$pre_test,table$post_test) pairedt = -0.371, df = 22, p-value = 0.714

alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval:

-0.3172167 0.4751114

 

sample estimates:

 

mean of x mean of y

 

3.171053 3.092105

 

#Chi-Square Test

 

Results and Discussions

 

Age group. Table 1 shows that the study sample of the highest range of the patients is identified under the age group of 21-30 and 31-40 years (26.10%) in control group and 31-40 years (47.8) in educational group. The average range of the patients is identified as 41-50 and 51-60 years (17.40%) in control group and 21-30 years (21.7%) in educational group. Low

responses were identified in more than 60 years (0%) in both the groups. From this data, it is observed that the higher rate control group responses were received from under the age group 31-40 years, and educational group responses were received from under the age group of 31-40 years. From the above assessment, the chi-square test was used to find the association between age group and disease severity (12.075) in control group and (5.845) in educational group. It is observed that there is no significant value

in both the groups ( p > 0.05).

It is concluded that there is no association

between the age group and disease severity in both the groups.

 

Table 1. Descriptive analysis of control group and educational group

 

Characteristics	Group	Control group frequency (percentage)	Educational group frequency (percentage)
Age	21-30	6 (26.1)	5(21.7)
	31-40	6 (26.1)	11(47.8)
	41-50	4 (17.4)	7(30.4)
	51-60	4 (17.4)	0(0)
	More than 60	3(13.0)	0(0)
Gender	Male	12(52.2)	7 (30.4)
	Female	11(47.8)	16(69.6)
Marital status	Married	11(47.8)	16(69.6)
	Unmarried	8(34.8)	4(17.4)
	Divorce	2(8.7)	1(4.3)
	Widow	2(8.7)	2(8.7)
Area	Urban	14 (60.9)	14(60.9)
	Rural	9 (39.1)	9(39.1)
Occupation	Professional	2 (8.7)	3(13.0)
	Student	1 (4.3)	1(4.3)
	Home maker	4(17.4)	4(17.4)
	Business	7(30.4)	8(34.8)
	Agriculture	2(8.7)	2(8.7)
	Others	7(30.4)	5(21.7)
	Labours	0(0)	0(0)

Gender

Table 1 indicates that the study sample of the highest range of the patients is identified under the gender group of male (52.2%) in control group and female (69.6%) in educational group. In the control group, males (47.8%) and females (30.4%) had minimal replies, whereas in the educational group, females (30.4%) had low responses. From this data, it is detected that the higher rate control group responses were received from males under the gender group, while that of educational group responses were received from females. From the above assessment, the chi-square test was used to find the association between gender and disease severity (4.476) in control group and (5.996) in educational group. It is observed that there is

no significant value in both the groups ( p > 0.05). It is concluded that there

is no association between the gender and disease severity in both the groups.

Marital status

Table 1 displays that the study sample of the highest range of the patients is identified under the martial status of married (47.8%) in control group and (69.6%) in educational group. The average range of the patients is identified as unmarried (34.8%) in control group and (17.4%) in educational group. Low responses were identified as divorced and widows (8.7%) in control group and widows (4.3%) in educational group. From this data, it is observed that the higher rate control group and educational responses were received from under the marital status married from the above assessment. The chi-square test was used to find the association between marital status and disease severity (3.61) in control group and (5.777) in educational group. It is observed that there is no significant value in both the groups

( p > 0.05)

and it  is concluded that  there is no association between the

marital status and disease severity in both the groups.

Area

Table 1 illustrates that the study sample of the highest range of the patients identified under the area is urban (60.9%) in control group as well as educational group, and low responses were identified in rural area

(39.1%) in both the groups. From this data, it is detected that the higher rate control and educational group responses were received from urban area. From the above assessment, the chi-square test was used to find the association between area and disease severity (0.958) in control group and (0.668) in educational group. It is observed that there is no significant value

in both the groups ( p > 0.05).

It is concluded that there is no association

between the area and disease severity in both the groups.

Occupation

Table 1 displays that the study sample of the highest range of the patients is identified under the business (30.4%) in control group (34.8%) in educational group, the average range of the patients is identified as other occupations (30.4%) in control group (21.7%) in educational group and low responses were identified as student (4.3%) in both the groups. From this data, it is observed that the higher rate of control and educational group responses were received from under the occupation of business. From the above assessment, the chi-square test was used to find the association between occupation and disease severity (18.386) in control group and (14.709) in educational group. It is observed that there is an association

between occupation and disease severity in control group ( p < 0.05)

and

that there is no association between the occupation and disease severity in educational group.

Table 2. Descriptive analysis of control group and educational group

 

Characteristics	Group	Control group frequency (percentage)	Educational group frequency (percentage)
Education	Illiterate	0(0)	1(4.3)
	Elementary	6(26.1)	3(13.0)
	High School	8(34.8)	9(39.1)
	Graduate	9(39.1)	10(43.5)
Family history	Yes	1(4.3)	3(13.0)
	No	22(95.7)	20(87.0)
Alcoholism	Yes	2(8.7)	3(13.0)
	No	16(69.6)	19(82.6)
	Past	5(21.7)	1(4.3)

 

Smoking	Yes	1(4.3)	3(13.0)
	No	20(87.0)	17(73.9)
	Past	2(8.7)	3(13.0)
Past medical history	Endocrine	0(0)	3(13.0)
	Heart disease	4(17.4)	4(17.4)
	GI problems	5(21.7)	1(4.3)
	Nervous problems	1(4.3)	0(0)
	Renal problems	3(13.0)	0(0)
	Others	1(4.3)	4(17.4)
	No	9(39.1)	11(47.8)

 

Table 2 displays the descriptive analysis of socio-demographic and Table 4 shows the association between socio-demographic and disease severity. From the tables, the following results are observed:

(1)    Education. From the study sample, the more patients identified in control group and educational group are under the category of graduates. There is no association between disease severity and educational qualification in both the groups ( p > 0.05).

(2)  Family history. There is no family history found in both the groups

( p > 0.05).

 

(3)  Alcoholism. There is no association between alcoholism and disease severity in both the groups ( p > 0.05).

 

(4)    Smoking. There is no association between smoking and disease severity in both the groups ( p > 0.05).

 

(5)     Past medical history. There is mild association between past medical history and disease severity in both the groups ( p < 0.05).

Table 3. Descriptive analysis of control group and educational group

 

Characteristics	Group	Control group frequency (percentage)	Educational group frequency (percentage)
Appendectomy	Yes	4(17.4)	3(13.0)
	No	19(82.6)	20(87.0)
Previous surgery	Yes	4(17.4)	3(13.0)
	No	19(82.6)	20(87.0)
Food habits	Vegetarian	8(34.8)	7(30.4)
	Both vegetarian and non-vegetarian	15(65.2)	16(69.6)
Types of food habits	Traditional foods	9(39.1)	7(30.4)
	Fibrous foods	7(30.4)	3(13.0)
	Non-fibrous foods	3(13.0)	6(26.1)
	Junk foods	4(17.4)	7(30.4)
Types of inflammatory bowel disease	Crohn’s disease	13(56.5)	13(56.5)
	Ulcerative colitis	10(43.5)	10(43.5)
Duration of inflammatory bowel disease	One to five months	7(30.4)	7(30.4)
	Six months to eleven months	12(52.2)	15(65.2)
	One year and above	4(17.4)	1(4.3)

Table 3 demonstrates the descriptive analysis of socio-demographic and Table 4 displays the relationship between socio-demographic data with disease severity. From the tables, we note the following outcomes:

(1)  Appendectomy. From the observation of samples, no appendectomy patient is found in control group and educational group. There is no association between disease severity and appendectomy in control groups

( p > 0.05)

and when associated with disease severity, educational group is

statistically significant ( p < 0.05).

 

(2)   Previous surgery. From the observation of samples, it is seen that there is no previous surgery patient found in control group and educational group. There is no association between disease severity and previous surgery

in control group ( p > 0.05)

and when associated with disease severity,

educational group is statistically significant ( p < 0.05).

 

(3)   Food habits. From the observation of samples, non-vegetarian food habits patients were found in control group and educational group. An association between disease severity and food habits in control groups and educational group is statistically significant ( p < 0.05).

(4)   Types of food habits. From the observation of samples, traditional food habit patients were found in control group and junk food and traditional food habit patients were found in educational group. There is no association between disease severity and types of food habits in control group and

educational group ( p > 0.05).

(5)     Types of   inflammatory bowel disease. From the observation of samples, a large number of Crohn’s disease patients were found in educational group and control group. There is an association among disease severity and types of inflammatory bowel disease in control group and

educational group ( p < 0.05).

(6)  Duration of inflammatory bowel disease. From the observation of samples, a large number of six months to eleven months patients were found in educational group and control group. There is an association among disease severity and duration of inflammatory bowel disease in control group

and educational group ( p < 0.05).

 

Table 4. Descriptive analysis and chi-square test association between disease severity and demographic data of control group and educational group

Characteristics	Group	Control group frequency (percentage)			Chi- square test	p-value	Educational group frequency (percentage)			Chi- square test	p-value
		Mild	Moderate	Severe			Mild	Moderate	Severe
Age	21-30	3(50)	1(16.7)	2(33.3)	12.075	0.148	0(0)	4(800	1(20)	5.845	0.211
	31-40	2(33.3)	4(66.7)	0(0)			4(36.4)	7(63.6)	0(0)
	41-50	2(50)	2(50)	0(0)			3(42.9)	4(57.1)	0(0)
	51-60	1(25)	3(75)	0(0)			0(0)	0(0)	0(0)
	More than 60	1(33.3)	0(0)	2(66.7)			0(0)	0(0)	0(0)
Gender	Male	4(33.3)	5(33.3)	6(33.3)	4.476	0.107	0(0)	6(85.7)	1(14.3)	5.996	0.045*
	Female	5(45.5)	6(54.5)	0(0)			7(43.8)	9(56.3)	0(0)
Marital status	Married	5(45.5)	4(36.4)	2(18.2)	3.61	0.729	5(31.3)	11(68.8)	0(0)	5.777	0.449
	Unmarried	3(37.5)	3(37.5)	2(25.0)			1(25.0)	2(50.0)	1(25.0)
	Divorce	1(50)	1(50)	0(0)			0(0)	1(100)	0(0)
	Widow	0(0)	2(100)	0(0)			7(30.4)	15(65.2)	1(4.3)
Area	Urban	6(42.5)	5(35.7)	3(21.4)	0.958	0.619	4(28.6)	9(64.3)	1(7.1)	0.688	0.709
	Rural	3(33.3)	5(55.6)	1(11.1)			3(33.3)	6(66.7)	0(0)
Occupation	Professional	0(0)	2(100)	0(0)	18.386	0.049*	0(0)	3(100)	0(0)	14.709	0.143
	Student	1(100)	0(0)	0(0)			1(100)	0(0)	0(0)
	Home maker	0(0)	1(25)	3(75)			0(0)	3(75.0)	1(25.0)
	Business	3(42.9)	4(57.1)	0(0)			5(62.5)	3(37.5)	0(0)
	Agriculture	2(100)	0(0)	0(0)			0(0)	2(100)	0(0)
	Others	3(42.9)	3(42.9)	1(14.3)			1(20)	4(80)	0(0)
	Labours	0(0)	0(0)	0(0)			0(0)	0(0)	0(0)

 

Education	Illiterate	0(0)	0(0)	0(0)	2.827	0.587	0(0)	1(100)	0(0)	4.727	0.579
	Elementary	3(50)	1(16.7)	2(33.3)			0(0)	3(100)	0(0)
	High School	3(37.5)	4(50)	1(12.5)			4(44.4)	4(44.4)	1(11.1)
	Graduate	3(33.3)	5(55.6)	1(11.1)			3(30.0)	7(70.0)	0(0)
Family history	Yes	0(0)	1(100)	0(0)	1.359	0.507	0(0)	3(100)	0(0)	1.84	0.399
	No	9(40.9)	9(40.9)	4(18.2)			7(35)	12(60.0)	1(5.0)
Alcoholism	Yes	0(0)	2(100)	0(0)	3.838	0.428	0(0)	3(100)	0(0)	2.582	0.63
	No	6(37.5)	7(43.8)	3(18.8)			7(36.8)	11(57.9)	1(5.3)
	Past	3(60)	1(20)	1(20)			0(0)	1(100)	0(0)
Smoking	Yes	0(0)	1(100)	0(0)	3.533	0.473	0(0)	3(100)	0(0)	9.801	0.444
	No	8(40.0)	9(45)	3(15.0)			7(41.2)	10(58.8)	0(0)
	Past	1(50.0)	0(0)	1(50.0)			0(0)	2(66.7)	1(33.3)
Past medical history	Endocrine	0(0)	0(0)	0(0)	16.714	0.081	0(0)	3(100)	0(0)	13.471	0.097
	Heart disease	1(25.0)	2(50.0)	1(25.0)			0(0)	0(0)	0(0)
	GI problems	2(40.0)	0(0)	3 (60.0)			0(0)	3(75.0)	1(25)
	Nervous problems	1(100)	0(0)	0(0)			1(100)	0(0)	0(0)
	Renal problems	2(66.7)	1(33.3)	0(0)			0(0)	0(0)	0(0)
	Others	1(100)	0(0)	0(0)			3(75.0)	1(25.0)	0(0)
	No	2(22.2)	7(77.8)	0(0)			3(27.3)	8(72.7)	0(0)
Appendectomy	Yes	2(50.0)	1(25.0)	1(25.0)	0.688	0.709	1(33.3)	1(33.3)	1(33.3)	7.214	0.027*
	No	7(36.8)	9 (47.4)	3(15.8)			6(30.0)	14(70.0)	0(0)
Previous surgery	Yes	2(50.0)	1(25.0)	1(25.0)	0.688	0.709	0(0)	2(66.7)	1(33.3)	7.718	0.021*
	No	7(36.8)	9(47.4)	3(15.8)			7(35.0)	13(65.0)	0(0)
Food habits	Vegetarian	1(12.5)	3(37.5)	4(50.0)	9.824	0.007*	0(0)	6(85.7)	1(14.3)	5.996	0.045*
	Both vegetarian and non- vegetarian	8(53.3)	7(46.7)	0(0)			7(43.8)	9(56.3)	0(0)
Types of food habits	Traditional foods	4(44.4)	3(33.3)	2(22.2)	8.787	0.186	4(57.1)	2(66.7)	1(33.3)	10.827	0.094
	Fibrous foods	1(14.3)	4(57.1)	2(28.6)			0(0)	3(42.9)	0(0)
	Non-fibrous foods	3(100)	0(0)	0(0)			2(33.3)	4(66.7)	0(0)
	Junk foods	1(25.0)	3(75.0)	0(0)			1(14.3)	6(85.7)	0(0)
Types of inflammatory bowel disease	Crohn’s disease	8(61.5)	5(38.5)	0(0)	9.21	0.01*	7(53.8)	6(46.2)	0(0)	8.351	0.015*
	Ulcerative colitis	1(10.0)	5(50.0)	4(40.0)			0(0)	9(90.0)	1(10.0)
Duration o inflammatory bowel disease	One to five months	7(100.0)	0(0)	0(0)	19.507	0.001*	7(30.4)	0(0)	0(0)	23.102	0.000*
	Six months to eleven months	1(8.3)	9(75.0)	2(16.7)			0(0)	14(93.1)	1(6.7)
	One year and above	1(25.0)	1(25.0)	2(50.0)			7(30.4)	15(65.2)	1(4.3)

Note: * indicates statistically significant ( p < 0.05).

Table 5. Paired t-test between pre- and post-tests control group and educational group

Questions	Knowledge control group pre and post		Knowledge educational group pre and post		Knowledge of post EG and post CG
	t	p-value	t	p-value	t	p-value
Q1. The terminal ileum is the last part of the small bowel located in right lower abdomen	-.371	.714	-1.447	.162	2.011	.057
Q2. The rectum is part of the colon starts approximately 15cm from the anus and finishes at the anus	0.000	1.000	-1.447	.162	.901	.377
Q3. The function of the colon is to absorb nutrients	-1.164	.257	-1.164	.257	.463	.648
Q4. People can survive without the colon, but not without the small bowel	-1.164	.257	-1.000	.328	.272	.788
Q5. Specific foods to be avoided in IBD are well known	.624	.035*	-2.152	.043*	.526	.604
Q6. Smoking cessation is important to prevent worsening of Crohn’s disease	-.526	.604	-2.152	.043*	1.311	.203
Q7. Risk of IBD increases with family history of the condition	-1.164	.257	-.720	.479	.492	.628
Q8. IBD can develop in all age groups, but is more frequent at younger ages	0.000	1.000	-1.699	.103	.295	.770
Q9. Anemia may develop if severe inflammation persists	.624	.539	-2.598	.016*	-.295	.770
Q10. Crohn’s disease can occur anywhere in the digestive tract, from the mouth to the anus	-1.226	.233	-.327	.747	-.901	.377
Q11. Ulcerative colitis rarely involves the rectum	-1.164	.257	-.768	.451	.569	.575
Q12. IBD can involve organs other than the bowels	-.371	.714	-1.000	.328	0.000	1.000
Q13. IBD is considered cure if symptoms do not recur after a few years	-2.598	.016*	-1.000	.328	1.283	.213
Q14. Inflammation in the bowels may persist even if the symptoms improve after treatment initiation	-.371	.714	-1.447	.162	0.000	1.000
Q15. Long term steroid administration is advised to reduce inflammation recurrence	-1.073	.295	-2.598	.016*	-.253	.803
Q16. Constant blood monitoring is indicated for patients who are on immunocompromised agents such as Azathioprine, because their WBC may decrease	-1.164	.257	.624	.035*	0.000	1.000
Q17. Biological agents are mainly used in patients with mild symptoms	-1.141	.266	.569	.575	.439	.665
Q18. Suppository or enema is used to treat cecal inflammation in patients with Ulcerative Colitis	-.371	.714	.439	.665	.439	.665
Q19. Patients with IBD for 8- 10 yrs. should have colorectal screening	-1.417	.171	-1.699	.103	.720	.479
Q20. Permanent colostomy is performed if surgery is indicated for patients with Ulcerative colitis	-1.164	.257	-.439	.665	0.000	1.000
Q21. Patients with Crohn’s disease of the small bowel may be cured after surgery	-1.417	.171	-.810	.426	.463	.648
Q22. Patients with IBD should stop all the medications when considering pregnancy	-1.417	.171	-1.447	.162	0.000	1.000
Q23. Most patients with IBD are advised cesarean section delivery	-.439	.665	1.141	.266	.810	.426
Q24. Immunocompromised patients with IBD should avoid any kind of vaccination	-2.598	.016*	-1.817	.083	.272	.788

 

In this research study, commonly 24 questions were prepared and answers were collected from patients. Patients were educated using leaflet. The data were collected based on the knowledge of pre- and post-visits of both the groups using paired t-test statistical tool in R-programming.

From Table 5, the following outcomes are observed:

(1)   Knowledge control group pre and post. It is concluded that there is a statistically significant difference between the questions Q5, Q13, Q24

pre- and post-tests knowledge of control group ( p < 0.05)

and the rest

of the questions of pre- and post-tests knowledge of control group are not statistically significant ( p > 0.05).

 

(2)    Knowledge educational group pre and post. From this, it has been concluded that there is a statistically significant difference between the questions Q5, Q6, Q9, Q15, Q16 pre- and post-tests knowledge of

educational group ( p < 0.05)

and the rest of the questions of pre- and

post-tests knowledge of educational group are not statistically significant

( p > 0.05).

 

(3)    Knowledge of post EG and post CG. From this, it has been concluded that there is statistically no significant difference between post-

test knowledge of control group and educational group ( p > 0.05),

which

means that there is no IBD knowledge in the control group and educational group patients.

Figure 1. Descriptive of age group.                 Figure 2. Descriptive of gender.

Figure 3. Descriptive of marital status.             Figure 4. Descriptive of gender.

 

          

Figure 5. Descriptive of past medical history.

 

Figure 7. Duration of inflammatory bowel disease.

Figure 6. Descriptive of appendectomy.

 

Figure 8. Types of inflammatory bowel disease.

 

Figure 9. Occupation.                                    Figure 10. Education.

 

Figure 11. Family history.

Figure 12. Alcoholism.

 

         

Figure 13. Smoking.                                Figure 14. Previous surgery.

 

          

Figure 15. Types of food habits.                         Figure 16. Food habits.

 

 

Conclusion

 

A large and diverse group of international gastroenterologists have reported the most challenging issues they face in the care of patients with IBD. As a result of the education about the significance of the disease, patients have little awareness of IBD. This is the most challenging issue facing gastroenterologists worldwide. R is an object-oriented language. The use of R-programming in clinical trials has not been the most popular and obvious, despite its recent growth over the past few years. Furthermore, pre- and post-visits of control group and educational group are compared based upon the knowledge of the disease. From this, there is no major knowledge about IBD in groups. Thus it is concluded that there is no statistically significant difference between knowledge level in control group and educational group. Finally, one out of every eight inflammatory bowel disease patients is food insecure and socially isolated. There is a statistical association between food habits and types of food habits with socio- demographic data using R. The patients’ knowledge was similar in the control group and educational group.

 

References

 

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