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Submitted: 11 May 2020 | Approved: 20 May 2020 | Published: 21 May 2020

How to cite this article: Munir S, Riaz S, Arshad T, Riaz A. Risk Factors Associated to Patients with Type 2 Diabetes in Lahore District. Ann Clin Endocrinol Metabol. 2020; 4: 011-019.

DOI: 10.29328/journal.acem.1001014

ORCiD: orcid.org/0000-0001-8836-0710

Copyright: © 2020 Munir S, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: Major risk factors; Meta-analysis; Convenient sampling; Associations; Industrial area

Abbreviations: Pops: Persistent Organic Pollutants; Pcdds: Polychlorinated Dibenzo-P-Dioxins; Pcdfs: Polychlorinated Dibenzofurans; HRGC: High Resolution Gas Chromatography; HRMS: High Resolution Mass Spectrometry; PREDIMED: The Prevención Con Dietamediterránea; ADA: American Diabetes Association; T2DM: Type 2 Diabetes Mellitus

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Risk Factors Associated to Patients with Type 2 Diabetes in Lahore District

Snobia Munir*, Samreen Riaz, Tooba Arshad and Aasma Riaz

Department of Microbiology and Molecular Genetics, Quaid-e-Azam Campus, University of the Punjab, Lahore, Pakistan

*Address for Correspondence: Snobia Munir, Department of Microbiology and Molecular Genetics, Quaid-e-Azam Campus, University of the Punjab, Lahore, Pakistan, Tel: 03158269694; Email: snobiaamunir@yahoo.com

Our research aimed to check the impact of some significant risk variables on diabetes growth and the specific goal of this study was to evaluate the connection of industrial fields with diabetes risk variables. The current research also informs us about the most important risk factor for male and female people with diabetes. A cross-section and convenient sample of 100 people, male and female, without discernment of risk factors and diabetes mellitus (Meta-Analysis on the effect of major risk factors on the diabetic patients form Jinnah Hospital Lahore). The risk factors in the general assessment i.e. lack of exercise, kidney problems, high ranges of tests and residence in industrial areas are found to be significant. Assessment of these factors is helpful in early diagnosis and in prognosis of diabetes.

Diabetes mellitus is a wide occurring ailment. Globally more than 400 million individuals have diabetes, and if present studies prevail, the incidence is expected to increase. Diabetes is a significant cause of early death, heart attack, and stroke. In 2016, it is the seventh major cause of death [1]. Pakistan with a diabetic population of 5.2 million, 90% of which are Type 2 was ranked 6th in 2000 in a World Health Organization (WHO) list of nations with the largest amount of diabetics [2]. Diabetes is due to either the pancreas not producing enough insulin or the cells of the body not responding properly to the insulin produced [3,11,12]. Worldwide, individuals with diabetes were roughly “171 million (2.8 percent of the world’s population)” in the year 2000, and anticipated 366 million individuals will be from developing regions of the world from this figure [4]. Recent studies demonstrate that “environmental chemicals” are a significant cause and contributor to increased diabetes proportion [4]. One of the main causes of diabetes and long-lasting health conditions is fatness and heavy weight. Diabetes occurs mostly in developing nations between 40-60 years of age and mostly in developed areas over 60 years of age [4].

Sometimes, studies of individuals exposed to elevated concentrations of environmental variables (dioxin, POPs, PCDDs) (TCDD) discovered enhanced rates of type 2 diabetes. Other studies also discovered that diabetes is associated with exposure to environmental variables (dioxin, POPs, PCDDs) in Vietnam (e.g., [5,6] and in veterans exposed to Agent Orange in Korea [7].

Environmental factors (dioxin, POPs, PCDDs)s are mainly by-products of industrial processes but can also result from natural processes, such as volcanic eruptions and forest fires. Environmental factors (dioxin, POPs, PCDDs) are unwanted by-products of a wide range of manufacturing processes including smelting, chlorine bleaching of paper pulp and the manufacturing of some herbicides and pesticides. Uncontrolled waste incinerators (solid waste and hospital waste) are often the worst culprits for releasing environmental variables (dioxin, POPs, PCDDs) owing to incomplete burning. Technology is accessible that enables the controlled incineration of waste with low environmental emissions (dioxin, POPs, PCDDs).

While environmental factors (dioxin, POPs, PCDDs) are formed locally, the distribution of the environment is global. Environmental factors (dioxin, POPs, PCDDs) are discovered in the setting around the globe. Some soils, sediments and foods, particularly dairy products, meat, fish and shellfish, have the largest concentrations of these compounds. In crops, water and air, very small concentrations are discovered.

Motivating background

The first motivation was from the findings of Chin-Chi, et al. [8] conducted a study to evaluate the epidemiologic and experimental evidence on the relationship of environmental chemicals with obesity, diabetes and metabolic syndrome. They identified a total of 29 articles (7 on arsenic, 3 on cadmium, 2 on mercury, 11 on persistent organic pollutants, 3 on phthalates and 4 on bisphenol A) including 7 prospective studies. Considering consistency, temporality, strength, dose-response, and biological plausibility (confounding), they concluded that the evidence is suggestive but not sufficient for a relationship between arsenic and persistent organic pollutants, and insufficient for mercury, phthalates and bisphenol A. For cadmium the epidemiologic evidence does not seem to suggest an association with diabetes.

A second motivation stems from the findings of Fernández [9]. Performed a study to evaluate the iron excess and risk of type 2 diabetes mellitus in a prospective cohort of Mediterranean population. He conducted three studies on the PREDIMED cohort with data collected in men and women aged 55 to 80 from centers located in Reus-Tarragona, Pamplona and/or Barcelona. A conditional logistic regression model was fitted and adjusted for socio-demographic, anthropometric, lifestyle, dietary and inflammation variables. The methodology included the diagnosis of T2DM based on ADA criteria. These results obtained were that in an adult Mediterranean population at high cardiovascular risk, high dietary iron and body iron stores increase the risk of T2DM after adjustment for potential confounding variables.

Thirdly work of Kanan and Samara, [10] gave us motivation who conducted a study that encompasses the historical presence of PCDDs and PCDFs in the world-wide environment. Information on exposure indicated that the main route of exposure of dioxins/furans to humans is through ingestion, which is discussed in this paper. The extraction methods including USEPA 8290 are the most used with HRGC/HRMS preferred as a detection tool. Moreover, a detailed compilation of studies of the PCDD concentrations and environmental sources from major industrial regions in several countries are presented. In summary, the major sources of dioxins in the worldwide environment include combustion and industrial sources with major challenges related to the lack of data availability in the Middle East especially with the current Warfare conflicts in the region.

Selection of patients

Over 100 Type 2 diabetic patients were included in the study. All other patients having disease other than diabetes were excluded.

Ethical approval of the study

Ethical approval for the study was taken from the Ethical/Protocol/Synopsis Committee of Jinnah Hospital, Lahore. Pakistan.

Target population

The target population of the present study comprised of all the diabetic patients of the Jinnah Hospital, Lahore.

Sampling

A convenient sampling was used to collect data. 100 patients were included in the study.

Survey method

The success of the survey depends upon the accuracy of data collection. The collection of accurate data depends upon the correct choice of survey method. For current study a questionnaire and face to face interview was used to collect data.

Data collection

For this particular study the face to face interviews were conducted along with the questionnaire. Data was collected within a month. By keeping in mind the difficulty of understanding and language among senior (old) patients and those who came from rural areas. So it was the best way to ask questions from the patients and fill the questionnaire on our own.

Field experience

There came some difficulties in field experience. The respondents’ behavior was very good but some of them refused to answer the questions. After explaining the objectives of the study they agreed to co-operate with the interview. Some respondents praised our research topic and some even gave us their contact numbers to inform them the result of our study but it was a good experience on the whole.

Questionnaire

The questionnaire contained a total of 54 questions. First 7 questions were used to collect personal information of the patients, the remaining questions access the factors affecting diabetic patients.

Analysis

Data was analyzed by using multiple tests including tests for associations Pearson’s chi-squared test (χ2), the goodness-of-fit test and Mann-Whitney U test.

The study comprises 100 diabetic patients including male and female. Approximately 22 risk factors like age, gender. marital status, profession, performance of exercise, taking proper meals, usage of fast food, eating away from home, usage of tobacco, usage of alcohol and heavy drinkers, usage of cigarettes. cigar, pipe and chew, availability of blood sugar meter, recording sugar levels, usage of medications, living in industrial areas, satisfaction with the sanitation system, usage of processed meat, usage of additives in food like sugar tablets were recorded (Figure 1).


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Figure 1: Graphical representation of different variables against different factors.

Some of these variables, including age, are regarded as quantitative. All other factors are qualitative, most of which are in dichotomous form (yes/no). This portion of the research is split into two sections in order to present the full and thorough analysis; descriptive and analytical section.

Descriptive analysis

In this section the frequency and percentages of the different environmental factors of diabetes for patients will be discussed. There are 100 subjects (diabetic patients). The debate of the results will base on the frequency (counts), percentages (Table 1). 

Table 1: Descriptive Statistics (Demographic Variables).
Frequencies (percentages) of demographic questions:
Variable Classification Patients’ Gender Total Count %
Male Female
Count % Count %
1. Age 20-35 5 35.7 9 64.3 14 100
36-50 16 41.0 23 59.0 39 100
51-65 10 28.6 25 71.4 35 100
66-80 5 41.7 7 58.3 12 100
2. Marital status Single 2 50.0 2 50.0 4 100
Married 34 35.4 62 64.6 96 100
3. Family strength 1-6 19 38.8 30 61.2 49 100
7-12 16 33.3 32 66.7 48 100
13-18 1 33.3 2 66.7 3 100
4. Patients in family 0-3 34 41.0 49 59.0 83 100
4-8 1 7.7 12 92.3 13 100
9-12 1 25.0 3 75.0 4 100
5. Profession House wife 0 0.0 55 100.0 55 100
Office Job 16 72.7 6 27.3 22 100
Others 20 87.0 3 13.0 23 100
Inferential analysis

In Table we are testing the association of all the major risk factors with people living in industrial areas. Here from this table we can see that the type of exercise, kidney problems, range of tests and kind of industry are significantly associated with whether patients live in an industrial area or not. It can be seen that age, profession and satisfaction with sanitary system is significantly associated with marital status while all the other risk factors showed no association with marital status (Table 2). 

Table 2: Other variables.
Variable Classification Patients’ Gender Total
Male Female   Count   %
Count % Count %
follow regular ` No 11 36.7 19 63.3 30 30.0
Yes 25 35.7 45 64.3 70 70.0
type of exercise None 10 37.0 17 63.0 27 27.0
Walk 26 36.6 45 63.4 71 71.0
other 0 0.0 2 100 2 2.0
days per week you exercise none 10 37.0 17 63.0 27 27.0
daily 24 38.1 39 61.9 63 63.0
after 1 day 0 0.0 4 100.0 4 4.0
after 2 days 2 33.3 4 66.7 6 6.0
each session none 10 37.0 17 63.0 27 27.0
15 min 6 21.4 22 78.6 28 28.0
30 min 8 30.8 18 69.2 26 26.0
1 hour 12 63.2 7 63.0 19 19.0
time of day you exercise none 10 37.0 17 63.0 27 27.0
morning 22 40.7 32 59.3 54 54.0
afternoon 1 10.0 9 90.0 10 10.0
evening 3 33.3 6 66.7 9 9.0
avoid any specific exercise Yes 0 0.0 3 100.0 3 3.0
No 36 37.1 61 62.9 97 97.0
health interfered with hobbies No 15 51.7 14 48.3 29 29.0
Yes 21 29.6 50 70.4 71 71.0
diabetes making more emotional No 10 38.5 16 61.5 26 26.0
Yes 26 35.1 48 64.9 74 74.0
diabetes affecting routine life No 16 51.6 15 48.4 31 31.0
Yes 20 29.0 49 71.0 69 69.0
health interfered with household chores No 21 51.2 20 48.8 41 41.0
Yes 15 25.4 44 74.6 59 59.0
diabetes affecting  social life No 21 42.0 29 58.0 50 50.0
Yes 15 30.0 35 70.0 50 50.0
feel any change after taking insulin No 28 46.7 32 53.3 60  60.0
Yes 8 20.0 32 80.0 40 40.0
diabetes is curable No 9 20.5 35 79.5 44 44.0
Yes 27 48.2 29 51.8 56 56.0
smoking Yes 5 100 0 0.0 5 5.0
No 31 32.6 64 67.4 95 95.0
drinking alcohol weekly 1 100 0 0.0 1 1.0
monthly 1 50.0 1 50.0 2 2.0
never 34 35.1 63 64.9 97 97.0
using tobacco No 33 34.7 62 65.3 95 95.0
cigarette 2 100 0 0.0 2 2.0
any other 1 33.3 2 66.7 3 3.0
Since when. 0-5 years 31 33.0 63 67.0 94 94.0
6-40 years 5 83.3 1 16.7 6 6.0
meals usually eaten per day 2 times 12 35.3 22 64.7 34 34.0
3 times 24 38.7 38 61.3 62 62.0
4 times 0 0.0 4 100.0 4 4.0
Snacks taken per day? One time 11 26.8 30 73.2 41 41.0
two time 21 46.7 24 53.3 45 45.0
never 4 28.6 10 71.4 14 14.0
no. of times a week eaten away from home Once per week 13 52.0 12 48.0 25 25.0
Twice in week 3 50.0 3 50.0 6 6.0
never 20 29.0 49 71.0 69 69.0
meals usually eaten away from home junk food 1 6.3 15 93.8 16 16.0
Chinese 3 100 0 0.0 3 3.0
Desi 32 39.5 49 60.5 81 81.0
skipping meals Yes 12 34.3 23 65.7 35 35.0
No 24 36.9 41 63.1 65 65.0
weight loss after diabetes Yes 12 30.0 28 70.0 40 40.0
No 24 40.0 36 60.0 60 60.0
Weight gain after diabetes? Yes 29 40.3 43 59.7 72 72.0
No 7 25.0 21 75.0 28 28.0
weak eyesight Yes 10 47.6 11 52.4 21 21.0
No 26 32.9 53 67.1 79 79.0
kidney problem Yes 32 42.1 44 57.9 76 76.0
No 4 16.7 20 83.3 24 24.0
numbness/tingling/loss of feeling in your feet Yes 5 38.5 8 61.5 13 13.0
No 31 35.6 56 64.4 87 87.0
dental problem Yes 18 54.5 15 45.5 33 33.0
No 18 26.9 49 73.1 67 67.1
wound healing problem Yes 17 37.0 29 63.0 46 46.0
No 19 35.2 35 64.8 54 54.0
testing of blood sugar Yes 3 33.3 6 66.7 9 9.0
No 33 36.3 58 63.7 91 91.0
owning blood sugar meter Yes 14 46.7 16 53.3 30 30.0
No 22 31.4 48 68.6 70 70.0
Difficulties in  monitoring  blood sugar Yes 15 34.1 29 65.9 44 44.0
No 21 37.5 35 62.5 56 56.0
usual range of tests 0-200 11 34.4 21 65.6 32 32.0
201-400 18 40.0 27 60.0 45 45.0
410-600 6 35.3 11 64.7 17 17.0
recording results Yes 26 42.6 35 57.4 61 61.0
No 10 26.3 28 73.7 38 38.0
getting signs or symptoms when blood sugar is low Yes 8 40.0 12 60.0 20 20.0
No 28 35.4 51 64.6 79 79.0
taking diabetes medications Yes 5 45.5 6 54.5 11 11.0
No 31 35.2 57 64.8 88 88.0
  kind of medicine none 5 45.5 6 54.5 11 11.0
Diabetes Pills 16 35.6 29 64.4 45 45.0
Insulin Injections 8 34.8 15 65.2 23 23.0
Combination 7 35.0 13 65.0 20 20.0
regular in taking medicine Yes 10 52.6 9 47.4 19 19.0
No 26 32.5 54 67.5 80 80.0
Taking any other medications. Yes 24 42.1 33 57.9 57 57.0
No 12 29.3 29 70.7 41 41.0
  meeting doctor Weekly .9 47.4 10 52.6 19 19.0
Monthly 20 35.7 36 64.3 56 56.0
Once a year 7 30.4 16 69.6 23 23.0
living in an industrial area Yes 30 40.5 44 59.5 74 74.0
No 6 25.0 18 75.0 24 24.0
  kind of industry near Eatables 2 25.0 6 75.0 8 8.0
garments 3 50.0 3 50.0 6 6.0
others 0 0.0 9 100.0 9 9.0
none 31 40.3 46 59.7 77 77.0
living place Rural Area 16 45.7 19 54.3 35 35.0
Urban Area 20 31.7 43 68.3 63 63.0
sanitary system is satisfactory Yes 5 41.7 7 58.3 12 12.0
No 31 36.0 55 64.0 86 86.0
kind of water drunk tap water 20 45.5 23 53.5 43 43.0
filter water 16 29.1 39 70.9 55 55.0
usage of processed meat Yes 29 37.7 48 62.3 77 77.0
no 7 33.3 14 66.7 21 21.0
usage of food additives yes 28 34.6 53 65.4 81 81.0
no 8 47.1 9 52.9 17 17.0
Testing of patient’s gender with the factors

We have applied Mann-Whitney U test to find the effect of gender on major risk factors that are physical activities, daily activities, meals, other complications, blood sugar monitoring, medications and environmental factors. There was effect of patients’ gender regarding other complications involved in diabetes while there is no effect of gender on physical activities of patients, daily activities of patients, meals taken by patients, blood sugar monitoring, proper medications and environmental factors. It is observed that patients who came from rural areas have a poor environmental exposure and medications are not properly available there whereas patients coming from urban areas have good environmental exposure and can take proper medications.

The primary goal of the research was to identify effect of environmental factors on diabetes patients. To attain this task a time based study had been carried out in the city of Lahore and the data has been collected from The Diabetic Centre, Jinnah hospital Lahore. A sample of 100 patients has been obtained. These patients were considered to be the respondents’ once diagnosed by the doctor to be having diabetes. Duration of study was fixed and convenient sampling method was used. The data was gathered through questionnaire and interview.

In Hypothesis Testing following results have been found

Here we have seen that the type of exercise, kidney problems, range of tests and kind of industry are significantly associated with whether patients live in an industrial area or not. It was observed that people coming from areas having industries nearby were having certain complications like kidney problems. We conclude that industrial areas have waste discharges; this waste gets in drains and canals and thus becomes a reason for water pollution and other pollutions too. Drinking water of these areas can cause many problems i.e. kidney problems, diarrhea, nausea etc. This results in the increased range of blood sugar tests of the patients since patients already ill will have less energy and hence their power will be less too. This depends upon the kind of industry that is in their locality.

The variables that are insignificantly associated with industrial area such as weight loss, weight gain, eyesight, numbness in feet, dental problem etc. indicate that proper medications were used by patients which gave considerably better results even when they were exposed i.e. living in industrial areas.

It can be seen that age, profession and satisfaction with sanitary system is significantly associated with marital status. Most of the females were housewives and married whereas only a few were working and single. Only 12 patients said they were satisfied with sanitary system in their area while others were not satisfied.

There was effect of patients’ gender regarding other complications involved in diabetes whereas medications and environmental factors were affected by the habitat of patients.

There were seven variables. Results from our study suggested that patients’ gender had effect on this factor (other complications). It can be said that the complications such as weight loss, weight gain, eyesight, kidney problems, dental problems, numbness/tingling/loss of feeling in feet, wound healing problems were effected by gender of patients (Figure 1). Male patients suffered from fewer complications as compared to female patients because males were active, most of them were working i.e. laborers, farmers, job holders while most of females were housewives, who were less active as compared to men and hence faced more complications.

The habitat of patients meant whether they lived in rural or urban areas. The risk factor medication was effected by habitat of patients. Patients who lived in urban areas could conveniently buy authorized medicines, which gave better results. There was availability of medical stores and pharmacies in city area whereas patients coming from a rural area faced many problems. The availability of medical stores and pharmacies was not easy for patients living in rural areas. These medical stores and pharmacies were very distant and secondly the medicines available there at those medical stores and pharmacies, situated in rural areas, were not authorized i.e. Insulin was not authorized and hence proper health results could not be seen. This created the effect of habitat on the medications.

The environmental factors include variables that are industrial areas, kind of industries, sanitary system, water, use of processed meat and food additives. There was effect of habitat (urban/rural) on these variables.

The aim of this research was to study the effects of certain major risk factors on the development of diabetes and the specific objectives were to assess the associations of gender of patients with the risk factors of diabetes. After selecting the topic, introduction and literature review of problem was collected from different sources including journals, articles books and electronic media. The questionnaire consisted of the bio-data of persons and following risk factors of (1)physical activities, (2)daily activities, (3)meals, (4)other complications, (4)blood sugar monitoring, (5)medications and (6)environmental factors as demonstrated in figure 1 and table 2. A cross-section and convenient sampling of 100 persons was conducted consisting of both males and females without any discrimination.

After the information was collected, it was selected for statistical analysis according to pre-coded criteria. The specified information was then entered into a private computer compatible with IBM. The study was conducted using software S.P.S.S. version 23.0 (Social Science Statistical Package) based on descriptive and analytical bases. In descriptive assessment, frequency distribution, percentages and cross tabs were calculated to verify the connection between distinct information characteristics and, in inferential assessment, chi-square tests were used to verify the importance of distinct factors by relating this statistics with P-value, normality tests were applied to check whether data is normal or not and Mann-Whitney U test was applied to check the effect of different risk factors on the variables.

The results of this cross sectional study provided information regarding the risk factors of Diabetes in Lahore, Pakistan. It is observed that the females (64) were more than males (36) as shown in table 1. The reason for large number of females then males may be the population (Hospital) from which the data was collected therefore ratio of female person was greater than males. In the overall analysis the risk factors i.e. type of exercise, kidney problems, range of tests and kind of industry are significantly associated with marital status. Age, profession and satisfaction with sanitary system are significantly associated with marital status.

There was effect of patients’ gender regarding other complications involved in diabetes whereas medications and environmental factors were affected by the habitat of patients (Table 3a-e).

Table 3a: Association of Patients living in industrial area with major risk factors.
Statements Chi square d.f p-value Conclusion
Ho: There is no association of industrial area and Family Patients. 1.468 2 0.480 Insignificant
Ho: There is no association of industrial area and regular exercise 0.322 1 0.571 Insignificant
Ho: There is no association of industrial area and type of exercise 6.296 2 0.043* Significant
Ho: There is no association of industrial area and days per week you exercise 1.812 3 0.612 Insignificant
Ho: There is no association of industrial area and length of each session 0.690 3 0.876 Insignificant
Ho: There is no association of industrial area and when you usually exercise 5.958 3 0.114 Insignificant
Ho: There is no association of industrial area and avoid any specific exercise 0.131 1 0.718 Insignificant
Ho: There is no association of industrial area and health interfered hobbies or activities 0.353 1 0.552 Insignificant
Ho: There is no association of industrial area and making more emotional 0.755 1 0.385 Insignificant
Ho: There is no association of industrial area and affected routine life 0.031 1 0.860 Insignificant
Ho: There is no association of industrial area and health affected household chores 0.870 1 0.351 Insignificant
Ho: There is no association of industrial area and affected social life 0.883 1 0.347 Insignificant
Ho: There is no association of industrial area and change after taking insulin injections 2.345 1 0.126 Insignificant
Ho: There is no association of industrial area and Its curable disease 1.660 1 0.198 Insignificant
Ho: There is no association of industrial area and smoking 1.709 1 0.191 Insignificant
Ho: There is no association of industrial area and drink alcohol 1.004 2 0.605 Insignificant
Ho: There is no association of industrial area and usage of tobacco 3.570 2 0.168 Insignificant
Ho: There is no association of industrial area and Since when smoking. 0.270 1 0.603 Insignificant
Ho: There is no association of industrial area and meals usually eaten per day 5.808 2 0.055 Insignificant
Ho: There is no association of industrial area and snacks taken per day 0.209 2 0.901 Insignificant
Ho: There is no association of industrial area and times a week do you eat out 1.433 2 0.489 Insignificant
Ho: There is no association of industrial area and meals eaten away from home 1.004 2 0.605 Insignificant
Ho: There is no association of industrial area and skip meals 1.318 1 0.251 Insignificant
Ho: There is no association of industrial area and weight loss 0.145 1 0.704 Insignificant
Ho: There is no association of industrial area and weight gain 0.104 1 0.748 Insignificant
Ho: There is no association of industrial area and eyesight weakness 0.007 1 0.935 Insignificant
Ho: There is no association of industrial area and kidney problem 5.860 1 0.015* Significant
Ho: There is no association of industrial area and numbness/tingling/loss of feeling in your feet 0.672 1 0.412 Insignificant
Ho: There is no association of industrial area and dental problem 0.208 1 0.648 Insignificant
Ho: There is no association of industrial area and wound healing problem 0.000 1 0.992 Insignificant
Ho: There is no association of industrial area and testing blood sugar 0.419 1 0.517 Insignificant
Ho: There is no association of industrial area and own a blood sugar meter 2.910 1 0.088 Insignificant
Ho: There is no association of industrial area and difficulties monitoring your blood sugar 0.063 1 0.802 Insignificant
Ho: There is no association of industrial area and Usual range of tests. 6.641 2 0.036* Significant
Ho: There is no association of industrial area and record blood sugars 0.022 1 0.883 Insignificant
Ho: There is no association of industrial area and getting signs or symptoms when your blood sugar is low 0.274 1 0.601 Insignificant
Ho: There is no association of industrial area and taking diabetes medications 0.267 1 0.606 Insignificant
Ho: There is no association of industrial area and kind of medicine taken 6.490 3 0.090 Insignificant
Ho: There is no association of industrial area and regular in taking medicine 0.151 1 0.698 Insignificant
Ho: There is no association of industrial area and any other medications 0.209 1 0.648 Insignificant
Ho: There is no association of industrial area and meeting doctor 0.832 2 0.660 Insignificant
Ho: There is no association of industrial area and kind of industry 72.887 3 0.000* Significant
Ho: There is no association of industrial area and habitat 0.078 1 0.779 Insignificant
Ho: There is no association of industrial area and sanitary system 0.002 1 0.965 Insignificant
Ho: There is no association of industrial area and kind of water drunk 0.063 1 0.802 Insignificant
Ho: There is no association of industrial area and Usage of processed meat 0.007 1 0.935 Insignificant
Ho: There is no association of industrial area and Usage of food additives 3.097 1 0.078 Insignificant
Table 3b: Association of Marital Status with all other variables.
Statements Chi square d.f. p-value
Ho: There is no association of marital status and age. 82.639 37 .000*
Ho: There is no association of marital status and gender .354 1 .552
Ho: There is no association of marital status and family strength 7.676 13 .864
Ho: There is no association of marital status and family Patients 5.042 8 .753
Ho: There is no association of marital status and profession 60.937 35 .004*
Ho: There is no association of marital status and if you follow a regular exercise program or routine. .050 1 .824
Ho: There is no association of marital status and type of exercise done. 1.702 2 .427
Ho: There is no association of marital status and no. of days per week exercise was done 2.447 3 .485
Ho: There is no association of marital status and how long at each session of exercise was 2.140 3 .544
Ho: There is no association of marital status and the time of day do you usually exercise 3.223 3 .358
Ho: There is no association of marital status and has your physician told you to avoid any specific exercise .575 2 .750
Ho: There is no association of marital status and is your health interfered with your hobbies or recreational activities? .032 1 .857
Ho: There is no association of marital status and do you think diabetes makes you more emotional? .002 1 .963
Ho: There is no association of marital status and has diabetes affected your routine life. .070 1 .791
Ho: There is no association of marital status and if health was interfered with household chores. .140 1 .709
Ho: There is no association of marital status and if diabetes affected your social life. 1.042 1 .307
Ho: There is no association of marital status and if you feel any change in your body after taking insulin injections. .203 2 .903
Ho: There is no association of marital status and if you think diabetes is curable disease. .610 1 .435
Ho: There is no association of marital status and if you smoke. 3.509 1 .061
Ho: There is no association of marital status and if you drink alcohol. .129 2 .938
Ho: There is no association of marital status and if you use tobacco. .219 2 .896
Ho: There is no association of marital status and since how long ago you are using tobacco. .266 5 .998
Ho: There is no association of marital status and no. of meals do you usually eat per day. .530 3 .912
Ho: There is no association of marital status and no.of times you take snacks per day. 1.266 3 .737
Ho: There is no association of marital status and no. of times a week do you eat away from home. 3.701 3 .296
Ho: There is no association of marital status and meals that are usually eaten away from home. .380 3 .944
Ho: There is no association of marital status and if you ever skip meals. .530 3 .912
 Ho: There is no association of marital status and if you feel any weight loss after diabetes. .159 1 .690
Ho: There is no association of marital status and if you feel any weight gain after diabetes. .011 1 .917
Ho: There is no association of marital status and if your eyesight become weak after diabetes. .036 1 .850
Ho: There is no association of marital status and if you have any kidney problem after diabetes. .007 1 .932
Ho: There is no association of marital status and  if you have any numbness/tingling/loss of feeling in your feet. .547 2 .761
Ho: There is no association of marital status and if you have any dental problem after diabetes. 3.263 2 .196
Ho: There is no association of marital status and if you have any wound healing problem after diabetes. 1.374 2 .503
Ho: There is no association of marital status and if you test your blood sugar. .417 1 .519
Ho: There is no association of marital status and if you have your own blood sugar meter. .766 1 .382
Ho: There is no association of marital status and if you had any difficulties monitoring your blood sugar. .087 2 .958
Ho: There is no association of marital status and Usual range of tests. 100.000 84 .112
Ho: There is no association of marital status and record blood sugars. .238 1 .626
Ho: There is no association of marital status and getting signs or symptoms when your blood sugar is low. 1.055 1 .304
Ho: There is no association of marital status and taking diabetes medications. .814 1 .367
Ho: There is no association of marital status and kind of medicine taken. 1.986 3 .575
Ho: There is no association of marital status and regular in taking medicine. 2.484 2 .289
Ho: There is no association of marital status and any other medications. .114 1 .735
Ho: There is no association of marital status and meeting doctor. 1.279 3 .734
Ho: There is no association of marital status and kind of industry. 1.353 2 .509
Ho: There is no association of marital status and habitat. 25.655 21 .220
Ho: There is no association of marital status and sanitary system. .230 2 .891
Ho: There is no association of marital status and kind of water drunk. 6.306 2 .043*
Ho: There is no association of marital status and usage of processed meat. .120 2 .942
Ho: There is no association of marital status and usage of food additives. .071 2 .965
Table 3c: Normality tests.
Factors N Kolmogorov-Smirnov Test Statistic Asymp. Sig. (2-tailed) Conclusion
Ho: Physical activities are normal 98 0.262 0.000 Non normal
Ho: Daily activities  are normal 100 0.373 0.000 Non normal
Ho: meals are normal 98 0.164 0.000 Non normal
Ho: Other complications are normal 99 0.191 0.000 Non normal
Ho: Blood sugar monitoring is normal 99 0.255 0.000 Non normal
Ho: medications are normal 96 0.169 0.000 Non normal
Ho: Environmental factors  are normal 98 0.185 0.000 Non normal
Table 3d: Testing of patient’s gender with the factors.
No. Alternative Hypothesis U p- value
1 Gender effects physical activities 1100.000 0.985
2 Gender effects daily activities 953.000 0.146
3 Gender effects meals 862.000 0.071
4 Gender effects other complications 862.500  0.042*
5 Gender effects blood sugar monitoring 999.500 0.308
6 Gender effects medications 890.500 0.168
7 Gender effects environmental factors 905.500 0.109
Table 3e: Habitat with other factors.
No. Alternative Hypothesis U p - value
1 Habitat effects physical activities 896.500 0.185
2 Habitat effects daily activities 1060.500 0.751
3 Habitat effects meals 987.000 0.456
4 Habitat effects other complications 885.000 0.096
5 Habitat effects blood sugar monitoring 975.000 0.325
6 Habitat effects medications 711.000  0.010*
7 Habitat effects environmental factors 379.000  0.000*

We would like to oblige University of the Punjab, Lahore for funds and Ms. Aasma Riaz (Assistant Professor Statistics Department, University of the Punjab, Lahore) for analysis of statistics of data.

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