Influence of Rurality on the Awareness of Myocardial Infarction Symptoms in the US

Overview

Journal Ther Adv Cardiovasc Dis

Publisher Sage Publications

Specialties Cardiology & Vascular Diseases
Pharmacology

Date 2019 Dec 5

PMID 31797738

Citations 2

Authors

Phoebe Tran

Lam Tran

Affiliations

Soon will be listed here.

Abstract

Background: Myocardial infarctions (MIs) are the leading cause of death in the United States (US). Differences in MI mortality rates exist between rural and urban areas in the US. Early recognition of MI symptoms can lead to receiving prompt lifesaving treatment. In this study, we identified the influence of living in a rural area, rurality, on disparities in MI symptom awareness across the US.

Methods: We examined 2007 and 2009 Behavioral Risk Factor Surveillances System survey data using logistic regressions to model the impact of rurality on MI symptom awareness while controlling for sociodemographic and MI clinical factors. From the results of these models, we created a type of marginal probability, known as average adjusted predictions (AAPs) and the difference in AAPs, called average marginal effects (AMEs), to determine patterns of awareness for each MI symptom between rural, suburban, and urban areas.

Results: We found that there were similar odds and probabilities of being aware of all five MI symptoms between rural, suburban, and urban areas, although rural residents consistently had a slightly higher odds and probability of being aware of all five MI symptoms compared with suburban and urban residents. Rural, suburban, and urban residents had the highest probability of being aware of chest pain/discomfort (95.5-96.1%) and the lowest probability of being aware of jaw/back/neck pain (68.6-72.0%). After adjustment, more than 25% of rural, suburban, and urban residents were found to be unaware that jaw/back/neck pain and feeling weak/light-headed/faint were symptoms of MI. AMEs were greatest for all areas for jaw/back/neck pain (-3.5% to -3.2%) and smallest for chest pain/discomfort (-0.6% to -0.2%).

Conclusions: The study's results highlight the need to increase awareness of the MI symptoms of jaw/back/neck pain and feeling weak/light-headed/faint to shorten hospital delay and time to treatment, especially for rural areas where cardiovascular disease mortality is high.

Citing Articles

Engaging social activities prevent stroke and myocardial infraction by raising awareness of warning symptoms: A cross-sectional survey study.

Kim G, Jang H, Kwon S, Lee B, Jang S, Chae W Front Public Health. 2023; 10:1043875.

PMID: 36726633 PMC: 9885037. DOI: 10.3389/fpubh.2022.1043875.

Spatial and temporal analysis of myocardial infarction incidence in Zanjan province, Iran.

Soleimani M, Bagheri N BMC Public Health. 2021; 21(1):1667.

PMID: 34521362 PMC: 8438974. DOI: 10.1186/s12889-021-11695-8.

References

Cheng Y, Shu J, Hsu H, Liang Y, Chou R, Hsu P . High health literacy is associated with less obesity and lower Framingham risk score: Sub-study of the VGH-HEALTHCARE trial. PLoS One. 2018; 13(3):e0194813. PMC: 5874050. DOI: 10.1371/journal.pone.0194813. View

Cha E, Kim K, Lerner H, Dawkins C, Bello M, Umpierrez G . Health literacy, self-efficacy, food label use, and diet in young adults. Am J Health Behav. 2014; 38(3):331-9. PMC: 4039409. DOI: 10.5993/AJHB.38.3.2. View

Turin T, Okamura T, Afzal A, Rumana N, Watanabe M, Higashiyama A . Impact of hypertension on the lifetime risk of coronary heart disease. Hypertens Res. 2016; 39(7):548-51. DOI: 10.1038/hr.2016.23. View

Dhingra R, Vasan R . Age as a risk factor. Med Clin North Am. 2012; 96(1):87-91. PMC: 3297980. DOI: 10.1016/j.mcna.2011.11.003. View

Bieler G, Brown G, Williams R, Brogan D . Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data. Am J Epidemiol. 2010; 171(5):618-23. DOI: 10.1093/aje/kwp440. View

Mosca L, Ferris A, Fabunmi R, Robertson R . Tracking women's awareness of heart disease: an American Heart Association national study. Circulation. 2004; 109(5):573-9. DOI: 10.1161/01.CIR.0000115222.69428.C9. View

Brooks E, Preis S, Hwang S, Murabito J, Benjamin E, Kelly-Hayes M . Health insurance and cardiovascular disease risk factors. Am J Med. 2010; 123(8):741-7. PMC: 2913281. DOI: 10.1016/j.amjmed.2010.02.013. View

Loucks E, Buka S, Rogers M, Liu T, Kawachi I, Kubzansky L . Education and coronary heart disease risk associations may be affected by early-life common prior causes: a propensity matching analysis. Ann Epidemiol. 2012; 22(4):221-32. PMC: 3327475. DOI: 10.1016/j.annepidem.2012.02.005. View

Critchley J, Capewell S . Mortality risk reduction associated with smoking cessation in patients with coronary heart disease: a systematic review. JAMA. 2003; 290(1):86-97. DOI: 10.1001/jama.290.1.86. View

10.

Liu Y, Wang Y, Liang F, Chen Y, Liu L, Li Y . The Health Literacy Status and Influencing Factors of Older Population in Xinjiang. Iran J Public Health. 2015; 44(7):913-9. PMC: 4645762. View

11.

McNamee R . Regression modelling and other methods to control confounding. Occup Environ Med. 2005; 62(7):500-6, 472. PMC: 1741049. DOI: 10.1136/oem.2002.001115. View

12.

Prasad A, Gersh B, Mehran R, Brodie B, Brener S, Dizon J . Effect of Ischemia Duration and Door-to-Balloon Time on Myocardial Perfusion in ST-Segment Elevation Myocardial Infarction: An Analysis From HORIZONS-AMI Trial (Harmonizing Outcomes with Revascularization and Stents in Acute Myocardial Infarction). JACC Cardiovasc Interv. 2016; 8(15):1966-1974. DOI: 10.1016/j.jcin.2015.08.031. View

13.

Emberson J, Bennett D . Effect of alcohol on risk of coronary heart disease and stroke: causality, bias, or a bit of both?. Vasc Health Risk Manag. 2007; 2(3):239-49. PMC: 1993990. DOI: 10.2147/vhrm.2006.2.3.239. View

14.

Herlitz J, Wireklintsundstrom B, Bang A, Berglund A, Svensson L, Blomstrand C . Early identification and delay to treatment in myocardial infarction and stroke: differences and similarities. Scand J Trauma Resusc Emerg Med. 2010; 18:48. PMC: 2944143. DOI: 10.1186/1757-7241-18-48. View

15.

Thompson A, Anisimowicz Y, Miedema B, Hogg W, Wodchis W, Aubrey-Bassler K . The influence of gender and other patient characteristics on health care-seeking behaviour: a QUALICOPC study. BMC Fam Pract. 2016; 17:38. PMC: 4815064. DOI: 10.1186/s12875-016-0440-0. View

16.

Clouston S, Manganello J, Richards M . A life course approach to health literacy: the role of gender, educational attainment and lifetime cognitive capability. Age Ageing. 2016; 46(3):493-499. PMC: 5405758. DOI: 10.1093/ageing/afw229. View

17.

Wacholder S, Hartge P, Lubin J, Dosemeci M . Non-differential misclassification and bias towards the null: a clarification. Occup Environ Med. 1995; 52(8):557-8. PMC: 1128296. DOI: 10.1136/oem.52.8.557. View

18.

Brookhart M, Sturmer T, Glynn R, Rassen J, Schneeweiss S . Confounding control in healthcare database research: challenges and potential approaches. Med Care. 2010; 48(6 Suppl):S114-20. PMC: 4024462. DOI: 10.1097/MLR.0b013e3181dbebe3. View

19.

Estacio E, Whittle R, Protheroe J . The digital divide: Examining socio-demographic factors associated with health literacy, access and use of internet to seek health information. J Health Psychol. 2017; 24(12):1668-1675. DOI: 10.1177/1359105317695429. View

20.

Muller C, MacLehose R . Estimating predicted probabilities from logistic regression: different methods correspond to different target populations. Int J Epidemiol. 2014; 43(3):962-70. PMC: 4052139. DOI: 10.1093/ije/dyu029. View