Abstract
Introduction
Myocardial infarction (MI) is one of the most life-threatening pathologies characterized by sudden cardiac death and is among the leading causes of mortality in the developed world.
Aims
This study investigates the demographic, socio-economic, and healthcare access disparities in the US among patients with myocardial infarction (MI).
Methodology
This was a retrospective original research study conducted using the BRFSS (Behavioural risk factor surveillance system) database of CDC (Centers for disease control and prevention).Data was extracted from the BRFSS on 3rd January 2024 to identify patients with MI in the year 2021 and multivariate models were used to assess the relationship between factors such as age, gender, income levels, and education in patients with myocardial infarction.
Results
Individuals in the age group of 65 years or older constituted the highest percentage of MI cases at 66.33 % (OR, 16.66; 95 % CI, 10.27-27.02; p-value <0.0001).Males showed a higher prevalence of MI, accounting for 61.19 % of cases, and females demonstrated lower susceptibility (OR, 0.46; 95 % CI, 0.43-0.50; p-value <0.0001).High school graduates (Grade 12 or GED) exhibited the highest incidence at 32.08 % (OR, 1.44; 95 % CI, 0.81-2.56; p-value 0.2084). Retirees accounted for the highest incidence at 56.06 %, with significantly increased odds compared to those employed for wages (OR, 1.93; 95 % CI, 1.71-2.19; p-value <0.0001) . The analysis of income levels indicated the highest MI incidence in the $25,000 <= Income < $35,000 group (17.31 % of cases).
Conclusions
Additional research is necessary to further disentangle the interaction between MI and factors such as age, gender, education level, race, employment status, and income level, and as the findings of this study suggest, retired individuals and individuals from lower-income groups indicate a disparity in access to timely treatment regarding MI.
Thus, the determination of such discrepancies needs to be addressed regarding how such factors affect access to timely healthcare, especially in matters of widely prevalent diseases such as MI.
Introduction
Acute myocardial infarction (AMI) stands as a formidable challenge to global public health, ranking among the leading causes of mortality in the developed world. The prevalence of myocardial infarction is 3 million worldwide, contributing to over 1 million deaths annually in the United States alone. The global prevalence of MI under the age of 60 is 3.8 % and over the age of 60 is 9.5 %. These figures underscore the pervasive nature of myocardial infarction and the critical need for comprehensive research and interventions. Patients suffering from myocardial infarction exhibit multiple risk factors, such as obesity, unhealthy dietary habits, sedentary lifestyles, and hypertension. While significant advances have been made in the treatment of MI, disparities in awareness, diagnosis, and treatment continue to exist among different populations. Gender disparities introduce another layer of complexity, Women are less likely than men to receive timely diagnosis and treatment for MI, even when presenting with similar symptoms. ,
In writing this article Behavioural risk factor surveillance system (BRFSS) data was used to examine and analyze trends, prevalence, and disparities related to myocardial infarction. BRFSS is the largest and most comprehensive telephone survey system in the United States, collecting data on health behaviours, chronic conditions, and preventive care among adults.
There are many studies done on BRFSS in the topic of healthcare access disparities in the United States among patients with myocardial infarction (MI) such as “the role of socioeconomic status, race/ethnicity, and insurance coverage in access to cardiac rehabilitation among patients with acute myocardial infarction” used BRFSS and collected data from 2014 to 2015 and found that lower socioeconomic status, minority race/ethnicity, and lack of health insurance were all associated with reduced odds of receiving cardiac rehabilitation (CR).
Aims and objectives
The primary aim of this study is to evaluate demographic, socio-economic, health status and healthcare access characteristics in patients of myocardial infarction.
Methodology
This retrospective original research study was conducted using BRFSS (Behavioural risk factor surveillance system) and database of CDC (Centers for disease control and prevention). Data was extracted from the BRFSS on 3rd January 2024 to identify patients with myocardial infarction in the year 2021 and multivariate models were used to assess the relationship between factors such as age, gender, income levels, and education in patients with myocardial infarction. Data extracted from the BRFSS (Behavioural risk factor surveillance system) in the year 2021 with independent variable, chronic health care condition-Ever told you had a heart attack, also called as myocardial infarction? (CVDINFR4) and other dependant variables-
Demographic characteristics:- age: calculated variable for 6-level age category, gender: (SEX1), race: calculated variable for 8-level race; socio-economic characteristics: -education level (EDUCA), employment status (EMPLOY1),annual household income (INCOME3); health condition:-mental health status: calculated variable for 3 level not good mental health status (_MENT14D), physical health status: calculated variable for 3 level not good physical health status (_PHYS14D), healthcare access: do you have one person or a group of doctors that you think as your personal healthcare provider(PERSDOC3),the current primary source of your health insurance (PRIMINSR),in the past 12 months, needed to see a doctor but could not afford(MEDCOST1).
Descriptive and logistic regression analyses were performed using the web enabled analysis tool in the BRFFS descriptive data generated including frequencies / numbers and percentages of responses to demographic, socio-economic, health access questions. Logistic regression analysis was used to assess the independent variable of myocardial infarction was « YES », while the reference categories for the independent variables were unique to each question. It yielded statistics; P values odds ratios (ORs), and 95 % Cis. P value 0.05 was considered significant data were stored in Microsoft excel and graphical analysis was performed in Graph pad prism; version 9.4.1
Results
In the year 2021and location all, 436038 people participated in the study. Out of this, 22831 self-identified OR answered “Yes” to the question- Ever told you had a heart attack, also called myocardial infarction (CVDINFR4).
The demographic characteristics were studied in a sample size of 22831 population as described in Table 1 .
| Table 1 : Demographic and socio-economic characteristics of study population | |
|---|---|
| Variable | Value n (%) |
| Demographic | |
| Age | Total = 22831 (100 %) |
| -Age 18 to 24 | 87 (0.38 %) |
| -Age 25 to 34 | 279 (1.22 %) |
| -Age 35 to 44 | 717 (3.14 %) |
| -Age 45 to 54 | 1967 (8.62 %) |
| -Age 55 to 64 | 4638 (20.31 %) |
| -Age 65 Or older | 15143 (66.33 %) |
| Gender | Total = 22831 (100 %) |
| -Male | 13970 (61.19 %) |
| -Female | 8861 (38.81 %) |
| -Don’t know/Not Sure | 0 (0 %) |
| -Refused | 0 (0 %) |
| Race | Total = 22831 (100 %) |
| -White, non-Hispanic | 18341 (80.33 %) |
| -Black, non-Hispanic | 1530 (6.7 %) |
| -Hispanic | 1298 (5.69 %) |
| -American Indian/Alaskan Native, non-Hispanic | 537 (2.35 %) |
| -Asian, non-Hispanic | 215 (0.94 %) |
| -Native-Hawaiian/other Pacific Islander, non-Hispanic | 83 (0.36 %) |
| -Other race, non-Hispanic | 304 (1.33 %) |
| -Multiracial, non-Hispanic | 523 (2.29 %) |
| Socioeconomic | |
| Education | Total = 22716 (100 %) |
| -Never attended school or only kindergarten | 38 (0.17 %) |
| -Grades 1 – 8 (Elementary) | 740 (3.26 %) |
| -Grades 9 – 11 (Some high school) | 1624 (7.15 %) |
| -Grade 12 or GED (High school graduate) | 7287 (32.08 %) |
| -College 1 year to 3 years (Some college or technical) | 6723 (29.6 %) |
| -College 4 years or more (College graduate) | 6304 (27.75 %) |
| Employment | Total = 22510 (100 %) |
| -Employed for wages | 3669 (16.3 %) |
| -Self-employed | 1405 (6.24 %) |
| -Out of work for 1 year or more | 613 (2.72 %) |
| -Out of work for less than 1 year | 321 (1.43 %) |
| -A homemaker | 579 (2.57 %) |
| -A student | 50 (0.22 %) |
| -Retired | 12619 (56.06 %) |
| -Unable to work | 3254 (14.46 %) |
| Income | Total = 17885 (100 %) |
| -Income < $10,000 | 943 (5.27 % %) |
| -$10,000 <= Income < $15,000 | 1188 (6.64 %) |
| -$15,000 <= Income < $20,000 | 1464 (8.19 %) |
| -$20,000 <= Income < $25,000 | 1778 (9.94 %) |
| -$25,000 <= Income < $35,000 | 3096 (17.31 %) |
| -$35,000 <= Income < $50,000 | 2774 (15.51 %) |
| -$50,000 <= Income < $75,000 | 2753 (15.39 %) |
| -$75,000 <= Income < $100,000 | 1723 (9.63 %) |
| -$100,000 <= Income < $150,000 | 1334 (7.46 %) |
| -$150,000 <= Income < $200,000 | 440 (2.46 %) |
| -Income >= $200,000 | 392 (2.19 %) |
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