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Research Article | Volume 15 Issue 1 (Jan - Feb, 2025) | Pages 106 - 111
A Study to determine Socio-demographic Risk factors of Myocardial Infarction in a Tertiary Care Hospital of Indore.
 ,
 ,
 ,
1
Assistant Professor, Department of Community Medicine, NMCRC, Kanpur, Uttar Pradesh, India
2
Professor, Department of Community Medicine, SAIMS, Indore, Madhya Pradesh, India
3
Assistant Professor, Department of Psychiatry, NMCRC, Kanpur, Uttar Pradesh, India
4
Assistant Professor, Department of Community Medicine, SMC, Unnao, Uttar Pradesh, India
Under a Creative Commons license
Open Access
Received
Nov. 9, 2024
Revised
Nov. 25, 2024
Accepted
Dec. 3, 2024
Published
Jan. 11, 2025
Abstract

Objective: To determine socio-demographic risk factors of Myocardial Infarction (MI). Study Design: Hospital based, case - control study. Result: 125 cases of MI were matched with 125 controls for age, sex and socioeconomic status. The mean age of MI in the present study was 55.63±9.473 years, with maximum number (n=29) in age group 60-64 years & minimum number of patients (n=02) were below 35 years. Distribution of cases according to place of residence revealed equal number of patients from urban (n=62) and rural areas. Maximum number of cases were from three generation family (n=68), while most of the controls were from nuclear (n=59). Unskilled workers contributed to more than 50 % of cases in current study in comparison to no cases amongst professionals. Similar trends were observed in as per literacy status where illiteracy contributed to more than 40 % of MI cases. Almost equal number of patients belonged to Kuccha & Pucca house. No statistically significant correlation was found between source of water and MI in this study. Conclusion: Associations of socio-demographic factors with MI i.e. area of residence, type of family, type of house, occupation, education status and source of water were found to be non-significant.

Keywords
INTRODUCTION

As per WHO, myocardial Infarction and stroke together contributes to 85% of deaths due to cardio-vascular diseases (CVD). Many studies have been done in past to correlate socio demographic factors with incidence of MI as interventions can be planned at mass level. There is compelling evidence to demonstrate that socio-demographic factors carry a significant weightage as compared to traditional major risk factors [1]. It has been shown that belonging to lower socio-economic status has positive correlation with mortality due to CVD [1]. In a case control study, that prevalence of coronary artery disease (CAD) was inversely related to the level of education and income. The study reported that being poor and illiterate is an independent risk factor for MI [2].

 

Similarly, although cause and effect relationship between area of residence, type of family and water source remains yet to be proven, a few studies do mention them as one of the predisposing factors for pathophysiology of MI. The epidemiological transition is manifested differently in various regions, as there are varied economic development in India [3]. These are compounded by varied prevalence of risk factors in different geo-political regions [3]. However, the use of tobacco and prevalence of hypertension in urban area are frequently associated with lower income and education. In a study conducted by Somya S et. al, in 2013, on 78 patients of MI at Kasturba Hospital, Manipal revealed that 46 (59.0%) were from joint family, 41 (52.6%) were from rural area and perceived family support was rated as moderate in 73 (93.6%) patients [3]. In a study conducted in south Asian country, low level of education was found to be associated with significant rise in adverse cardiovascular outcome [4]. However inconsistent association were reported from studies done in western population [4]. Two major sources of water are ground water and surface water. On one hand the surface is easily accessible but prone to contamination whereas ground water has considerable hardness in form of calcium and magnesium salts. Very few studies have penned down the correlation of hardness of water and MI [5]. Since 1980s, researchers were only able to obtain weak inverse relationship of MI with source/ hardness of water, after considering socioeconomic factors, hypertension, smoking and elevated serum lipids [6]. It was proposed that presence of high level of magnesium in hard water may possess some anti-stress actions, which may protect against coronary heart disease [5,6]. Studies done in South Africa and Finland reported inverse correlation of concentration of magnesium in drinking water to ischemic heart disease [5,6].

 

Objective: To determine socio-demographic risk factors of MI in cases and controls.

 

The present study has been carried out on the patients of MI admitted in tertiary care hospital of a private medical college i.e. SAMC& PGI, Indore, MP. The study was conducted under guidance and directions of Department of community medicine with necessary liaison with Department of cardiology.

Study Design:

Study was hospital based (tertiary care centre), case - control study.

 

Period of Study:

The period of study was from December 2018 to June 2020 i.e.18 months. Collection of data was done over a period of one year from December 2018 to December 2019.

 

Sample size:

Patients admitted with MI in year 2019 in cardiology ward, with age matched controls admitted in same hospital. A sample of 125 cases and 125 controls i.e. 250 (cases and controls) were taken after implementing exclusion criteria in the study. The controls were matched to cases for age group, sex and socioeconomic status.

 

Sampling Frame:

All patients diagnosed as MI (including both newly diagnosed cases and cases who have suffered MI in past), admitted under department of cardiology during study period, and fulfilling inclusion criteria were included in the study.

 

Inclusion Criteria:

Admitted patient with MI following acute management /stabilization (cases) .


Admitted patient of other diseases (control) .

Controls should not have any ECG changes corresponding to MI.

 

He/ she must give consent to participate in the study.

He/she must be above the age of 18 years.

He/she should not have been hospitalized for cardiac event earlier (for controls).

 

Exclusion Criteria:

Patient not giving consent.

Patients with poor general condition – needing him/ her to stay in Intensive care unit (ICU) or required referral to higher centre during ICU stay.

 

Data Collection:

The permission from institutional research and ethical committee of SAMC and PGI was taken as part of mandatory requirement. Subsequently a written permission was sought from Head of Department of cardiology for the present study to be conducted in cardiology ward, following stabilization of MI patients. However complete study was conducted under guidance and direct supervision of Department of Community Medicine. According to European Society of Cardiology guidelines 2017, It is considered safe to shift patient or discharge patients with MI after 02-03 days after admission and therapy to a lower center [7]. Hence in this study patients were included only after 72 hours of hospitalization or 24 hours after shifting in ward from intensive care unit, whichever was later. Consenting patients were informed in detail about the study procedure and informed that they were free to refuse/ provide information at any point.

 

The predesigned proforma was used to collect information about epidemiological factors like place of residence, age, sex, occupation, education, socio-economic status, source of water & type of family.

 

Area of Residence:

Urban Area: In Indian context urban areas are the “towns (places with municipal corporation, municipal area committees, notified area committees a or cantonment board); also all places having 5000 or more inhabitants, a density of not less than 1000 persons per square mile (390 per square kilometre), pronounced urban characteristics and adult male population (at least 3/4) employed in pursuits/ jobs other than agriculture” [8].

 

Types of Family:

Families were classified as Nuclear Family, Joint Family, Three Generation Family [8,9].

 

Education:

As per year 2011 Census of India “a person, aged seven years or above, who can both read and write with understanding in any language, is treated as literate”. Hence an individual, who can read but cannot write, is not considered literate [10].

 

Literates Include - Educated subjects were subdivided in groups: Primary Education: Secondary Education, Higher Secondary Level and Graduate.

 

Occupation Classification:

It was defined as participation in any economically productive activity either physical or intellectual in nature. It was classified in following groups - Professional - Managerial (Executive)/ Intermediate - Skilled non manual/ Clerical /Skilled Manual/ Semi-skilled -Unskilled - Unemployed [11, 12].

 

Socioeconomic Status:

Assessment of Socioeconomic status was done by modified BG Prasad's Classification for 2018.**

 

The modified BG Prasad Scale is based on consumer price index (CPI) and generates income categories at any given point of time. However, as our hospital drains people from other neighbouring states as well, CPI Index for India was taken rather than MP state in particular. The CPI (Jan 2018) for current study was taken as 288 and income groups were calculated based on All India Average Consumer Price Index Numbers for Industrial Workers. Calculation for socioeconomic class was done as, new income value

= 2.88 × (old value × 4.63 × 4.93).

RESULTS

Table No. 1: Distribution of patients (cases and controls) by matching factors (Age & sex)

 

Factors

Cases (n=125) N %

Controls (n=125) N%

Age group (years)

18-34

2 (1.6 %)

2 (1.6 %)

35-39

6 (4.8 %)

6 (4.8 %)

40-44

4 (3.2 %)

4 (3.2 %)

45-49

19 (15.2 %)

19 (15.2 %)

50-54

21 (16.8 %)

21 (16.8 %)

55-59

22 (17.6 %)

22 (17.6 %)

60-64

29 (23.2 %)

29 (23.2 %)

≥65

22 (17.6 %)

22 (17.6 %)

Sex

Male

117 (93.6 %)

117 (93.6 %)

Female

8 (6.4 %)

8 (6.4 %)

 

Sex

The distribution of total 125 patients (cases of MI), according to age and sex showed that 117 (93.6 %) were males and 8 (6.4 %) were females. The patients of MI were matched with controls for age & sex. The mean age of MI (cases) in the present study was 55.63±9.473 years, with maximum number (n=29, 23.2 %) in age group 60-64 years and minimum number of patients (n=02, 1.6 %) were below 35 years. All females had onset of MI after 50 years of age. In current study it was found that over 75% (n=94) patients were hospitalized for MI were of more than 50 years and only 1.6 % (n=2) were less than 34 years.

 

Table No. 2: Distribution of cases according to Socio-economic status

Socio-economic Status

Cases

I

3(2.4%)

II

9(7.2%)

III

25(20%)

IV

58 (46.4%)

V

30 (24%)

Total

125

 

In this study, maximum numbers of cases belonged to socioeconomic class IV according to modified BG Prasad Scale i.e. 58 (46.4 %). Only 2.4 % of cases belonged to class I socioeconomic status. More than 70 % (n=88) of patient’s belonged to Socio economic class (SEC) IV & V combined and less than 10% were from SEC I & II.

 

Table No. 3: Distribution of patients according to area of residence

Area of residence

Cases (%)

Controls (%)

Total (%)

Urban area

62 (49.6%)

75(60 %)

137 (54.8%)

Rural area

63 (50.4%)

50 (40%)

113 (45.2 %)

Total

125 (50 %)

125 (50 %)

250 (100 %)

 

(Pearson Chi-Square =2.729, p=0.099, statistically insignificant)

Out of 125 cases 63 (50.4 %) were from rural area and rest were from rural area. Also in controls this study revealed 75 patients, i.e. 60 %patients were from urban area. Proportion of controls residing in urban area were more than rural area, however with the above sample size it represented no statistically significant value. Distribution of patients according to place of residence is revealed in Table No 08 in which almost equal number of patients were from urban (n=62) and rural areas (n=63), with p value of 0.099. The Odds ratio for residence in urban area was 0.66 with confidence interval 0.4.-1.08.

 

Table No. 4: Distribution of patients according to type of family

Type of family

Cases (%)

Controls (%)

Total (%)

Nuclear

44(35.2 %)

59 (47.2 %)

103(41.2%)

Joint

13 (10.4%)

11 (8.8%)

24(9.6%)

Three generation

68 (54.4%)

55(44%)

123(49.2%)

Total

125(50%)

125(50%)

250(100%)

 

(Pearson Chi Square = 3.725, df=2, p=0.155; statistically insignificant)

In this study, maximum number of cases i.e. 68 (54.4 %) were residing in three generation family and 59 controls (47.2 %) resided in nuclear family. However no statistically significant correlation could be found between MI and type of family Distribution of patients according to type of family is shown in Table no 4 in which maximum number of cases were from three generation family (n=68, 54.4 %), followed by nuclear (n=44, 35.2 %) and joint family (n=13, 10.4 %). However, controls revealed higher number from nuclear (n=59, 47.2 %), followed by three generation family (n=55, 44 %) and joint family (n=11, 8.8 %).

 

Table No. 5 Distribution of patients according to type of house

Type of House

Cases (%)

Controls (%)

Total (%)

Pucca House

68 (54.4 %)

69 (55.2 %)

137 (54.8 %)

Kaccha House

57 (45.6 %)

56 (44.8 %)

113 (45.2 %)

Total

125 (50 %)

125 (50 %)

250 (100 %)

 

(Pearson Chi Square = 0.016, df=1, p=0.899; statistically insignificant)

In this study more than 50 % of cases and controls resided in pucca house, but it was statistically insignificant in relation to MI. Distribution of patients according to type of house is shown in Table no 5 in which 57 cases (45.6 %) and 56 controls (44.8%) lived in kaccha house. Similarly, this study did not reveal place of residence to be statistically significant risk factor for MI.

 

Table No. 6 Distribution of patients according to source of water

Source of water

Cases (%)

Controls (%)

Total (%)

Overground (Piped water supply)

47 (37.6 %)

51 (40.8 %)

98 (39.2 %)

Underground water

78 (62.4 %)

74 (59.2 %)

152 (60.8 %)

Total

125 (50 %)

125 (50 %)

250 (100 %)

 

(Pearson Chi Square = 0.269, df=3, p=, 0.604; statistically nonsignificant)

The study revealed that 47 (37.6 %) cases and 51 (40.8 %) of controls had piped water supply in their residence. However more than 60 % of cases and control used water from underground sources like well, use of boring and hand pump. No statistically significant correlation was found between source of water and MI in this study.

 

Table No. 7 Distribution of patients according to occupation

Occupation

Cases (%)

Controls (%)

Total (%)

Professional

0 (0 %)

2 (1.6 %)

2 (0.8 %)

Managerial(Executive)

11 (8.8 %)

14 (11.2 %)

25 (10 %)

Clerical and skilled

36 (28.8 %)

41 (32.8 %)

77 (30.8 %)

Semi Skilled

12 (9.6 %)

10 (8 %)

22 (8.8%)

Unskilled

64 (51.2 %)

57 (45.6 %)

121 (48.4 %)

Unemployed

2 (1.6 %)

1 (0.8 %)

3 (1.2%)

Total

125 (50 %)

125 (50 %)

250 (100 %)

 

(Pearson Chi Square = 3.605, df=5, p=0.608; statistically insignificant)

Distribution of patients according to occupation reveals that more than 50 % who had MI were unskilled and no case was  reported amongst professionals. Around one fourth of cases were from clerical and skilled group. Semi-skilled personals had one third cases as compared to skilled workers.

 

Table No. 8 Distribution of patients according to literacy status

Literacy status

Cases (%)

Controls (%)

Total (%)

Illiterate

51 (40.8 %)

36 (28.8 %)

87 (34.8 %)

Primary

17 (13.6 %)

25 (20 %)

42 (16.8 %)

Secondary

25 (20 %)

21 (16.8 %)

46 (18.4 %)

Higher Secondary

18 (14.4 %)

28 (22.4 %)

46 (18.4 %)

Graduate

11 (8.8 %)

10 (8 %)

21 (8.4 %)

Postgraduate

3 (2.4 %)

5 (4 %)

8 (3.2 %)

Total

125 (50 %)

125 (50 %)

250 (100 %)

 

(Pearson Chi Square value = 7.179, d.f. = 5, p=0.208 statistically insignificant)

Literacy status revealed that 51 cases (40.8 %) were illiterate. The school dropouts following primary were 42 (16.8 %) of both cases and controls (combined). Significantly lower number (only 11 %) of patients had completed graduate or postgraduate degree. In cases, incidence of MI shows a decreasing trend with increasing education level except at primary level i.e. as the education level increases, incidence of MI decreases. However, the literacy rate failed to demonstrate statistical significance with MI. 

DISCUSSION

According to the INTERHEART study, a global case–control study including 27,098 participants from 52 countries, the median age for first presentation of acute MI in South Asian (Bangladesh, India, Nepal, Pakistan, Sri Lanka) population is 53 years, whereas that in Western Europe, China, and Hong Kong is 63 years, with more men than women affected (6,787 women out of total participants 27,098) [13]. In another case control study carried out by Zodpey et. al, in 2013 on risk factors for acute MI in central India, the maximum cases (n=82, 30.94 %) were from age group 61-70 years and minimum cases were of age group less than 30 years (n=05, 1.89 %) [14]. In a study conducted on 25,748 patients of MI from Kerala state Mohanan P et. al, in year 2007 to 2009, reported prevalence of in age groups <50 years, 51-70 years and >70 years to be of 22.2%, 57.2% and 20.6% respectively [15]. In a retrospective study of ischemic heart disease patients (n=130) admitted in Intensive care unit, conducted by Abhraham et. al, in 2010, revealed that mean age of patients was 58.4 ± 12.5 years with maximum number of patients between 40 to 60 years i.e. 66 % and showed that middle age person were more affected [16]. The age distribution of this study was comparable to above studies for onset of MI.

 

In this study female patients (Table No. 01) were only 08 i.e. 6.4 % out of 125 MI cases and all were reported after 50 years of age. In a multi-centric hospital-based registry – CREATE registry, data reveals that 4826 (23.6 %) of participants were females and had later age of onset (60.90 years vs 56.4 years; p< 0.0001) [17].

 

In this study, maximum numbers of cases belonged to socioeconomic class IV according to modified BG Prasad Scale i.e. 58 (46.4 %) and only 2.4 % of cases belonging to class I socioeconomic status. Gupta et. al, in 2009, concluded in a case control study, that prevalence of CAD was inversely related to the level of education and income [2]. Other risk factors like uncontrolled hypertension, low physical activity, smoking, dietary fat, hypercholesterolemia and diabetes were found to be commonly present in low-socioeconomic status [2]. In a case control study (200 cases with age matched controls) conducted by Ferreira Aet. al, in 2018, at Goa, revealed maximum patients (n=111, 55.5 %) from lower middle class and minimum number of patients from upper class (n=08, 4%) [18]. A community-based cross-sectional study, carried out amongst 980 adults, residing in rural area of Belagavi district, India from January 2013 to June 2015 by Kavi A et. al, revealed no increase in risk factors of MI was linked to socioeconomic status [19]. However, in this study there were maximum number of cases from lower middle class/lower class and less number of cases from upper class, which is comparable to study conducted by Gupta et. al and Ferreira R et. al.

 

Prabhakaran D, based on several community-based studies concluded that the prevalence of IHD has increased 07 folds in urban area from 02% to around 14 % and quadrupled in rural areas, from 1.7% to 7.4% between year 1970 and year 2013 (20). However, in a community-based study conducted by Anjana RM et. al, in 2010, showed that prevalence of coronary artery disease in rural area (3.8%) was less than urban area (8.8 %) with p value=0.01 [21].

 

The finding of significant cases of MI amongst residents of urban area was not demonstrated in this study. 

 

In a multicentric case control study conducted by Gupta R et. al, in 2009 revealed more cases of MI amongst joint family in males (n=1691, 50.9 %) and females (n=1298, 47.9 %) [02]. In current study around two third of patients either belonged to joint or three generation family and only around one third were from nuclear family. However, type of family was not statistically significant as risk factor for MI with p value of 0.155.

 

In this study more than 50 % of cases and controls resided in pucca house, but it was statistically insignificant in relation to MI. Distribution of patients according to type of house is shown in Table no 5 in which 57 cases (45.6 %) and 56 controls (44.8%) lived in kaccha house. In a cross-sectional study conducted (n=289) by Shekhar et. al, at West Godawari district in 2013, 138 individuals lived in semi pucca houses, 77 people lived in kaccha houses and 74 people lived in pucca houses [22]. Cardiovascular diseases were reported in Pucca house, semi pucca house and kaccha house as 29 (39.1 %), 54 (39.1 %) and 30 (38.9 %) respectively, revealing no significant difference according to place of residence [22]. Similarly, this study did not reveal place of residence to be statistically significant risk factor for MI.

 

Only few studies have penned down the correlation of hardness of water and MI [23]. In a small area study using Bayesian modelling and the geo-referenced registry based data conducted in Finland, by Kausa A, et al(n=18 946 men) between 1983 to 1993, revealed inverse relation between water hardness and CHD [23]. In another case control study, by Nasiri B et. al, (n=547) upon patients undergoing coronary angiography revealed no relationship between the levels of calcium and magnesium in the drinking water to severity of atherosclerosis [24]. In this study also source of drinking water did not achieve statistical significance as risk factor to MI.

 

Distribution of patients according to occupation is shown in Table no 07 in which that maximum cases were unskilled i.e. n=64 (51.2%) and least in professionals. In a multicentric case control study conducted by Gupta R et. al, in 2002 revealed more cases of MI amongst males in business (n=1117, 33.6%) and professionals (n=799, 24.1 %), as compared to females that were unemployed (724, 27.2 %) or who were doing business (676, 25.4 %) [25]. Only few studies have included the occupation as a risk factor for MI of which the results of above study were in contrast to this study as they reported more MI cases businessman and professionals. This difference could be due to affordable health schemes, which are funded by government, for poor people and increase in healthcare facilities. However, this study revealed no statistical significance of occupation to occurrence of MI with p value of 0.608.

 

Distribution of patients according to literacy is shown in Table no 8 in which 51 cases (40.8 %) were illiterate and around 10 % were graduate or postgraduates. In a multicentric case control study conducted by Gupta R et. al, in 2002 revealed more cases of MI amongst males in education of 11-15 years (n=1463, 46.7 %) in males and 0-10 years (n=1160, 47.6 %) in females [25]. However, another study conducted by Gupta R et. al, in 2009 revealed illiteracy and lower socioeconomic status to be an independent risk factor for MI [2]. The relation between literacy and MI is complex but in current study high illiteracy was reported amongst cases. In this study incidence of MI shows a decreasing trend of increasing education level with exception at primary level.

 

Multiple studies to correlate socio-demographic data with onset of cardiovascular disease have been done in past in many geographical regions with varied results. Countries like India are currently undergoing demographic transition and are rapidly creating health awareness in underprivileged population. Government funded health schemes like Ayushmaan Bharat have significantly reduced out of pocket expenditure of poor population of the country. 

CONCLUSION

This hospital-based case-control study aimed to identify socio-demographic risk factors for myocardial infarction (MI) among patients admitted to a tertiary care hospital . Our study included 125 cases and 125 controls, matched for age, sex, and socioeconomic status . Majority of MI cases were males (93.6%) which Mostly belonged to socioeconomic class IV (46.4%) and V (24%) and the 50-64 years age group .  An equal number of cases came from urban and rural areas and Maximum participants resided in three-generation families (54.4%). Majority of cases (51.2%) were Unskilled workers and Illiteracy rate was 40.8% . Though our study did not find statistically significant correlations between MI and most socio-demographic factors, that’s why it highlights the importance of considering these factors in the prevention and management of MI even in unskilled workers and those with lower socioeconomic status suggests the need for targeted interventions to address modifiable risk factors in these populations.

 

Recommendations

The main four recommendations of this study are: 1.) Public Health Initiatives: Implement public health programs to promote cardiovascular health, particularly among vulnerable populations. 2.) Occupational Health: Develop workplace health programs to reduce cardiovascular risk factors among unskilled workers. 3.) Education and Awareness: Educate patients and their families about MI risk factors, prevention, and management. 4.) Further Research: Conduct longitudinal studies to explore the temporal relationship between socio-demographic factors and MI.

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