IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i21p11284-d666017.html
   My bibliography  Save this article

Examining Predictors of Myocardial Infarction

Author

Listed:
  • Diane Dolezel

    (Health Information Management Department, Texas State University, San Marcos, TX 78666, USA)

  • Alexander McLeod

    (Computer Information Systems & Quantitative Methods Department, Texas State University, San Marcos, TX 78666, USA)

  • Larry Fulton

    (School of Health Administration, Texas State University, San Marcos, TX 78666, USA)

Abstract

Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Center for Disease (CDC) Control Behavioral Risk Factor Surveillance System (BRFSS) survey for the year 2019. Multiple quasibinomial models were generated on an 80% training set hierarchically and then used to forecast the 20% test set. The final training model proved somewhat capable of prediction with a weighted F1-Score = 0.898. A complete model based on statistically significant variables using the entirety of the dataset was compared to the same model built on the training set. Models demonstrated coefficient stability. Similar to previous studies, age, gender, marital status, veteran status, income, home ownership, employment status, and education level were important demographic and socioeconomic predictors. The only geographic variable that remained in the model was associated with the West North Central Census Division (in-creased risk). Statistically important behavioral and risk factors as well as comorbidities included health status, smoking, alcohol consumption frequency, cholesterol, blood pressure, diabetes, stroke, chronic obstructive pulmonary disorder (COPD), kidney disease, and arthritis. Three access to healthcare variables proved statistically significant: lack of a primary care provider (Odds Ratio, OR = 0.853, p < 0.001), cost considerations prevented some care (OR = 1.232, p < 0.001), and lack of an annual checkup (OR = 0.807, p < 0.001). The directionality of these odds ratios is congruent with a marginal effects model and implies that those without MI are more likely not to have a primary provider or annual checkup, but those with MI are more likely to have missed care due to the cost of that care. Cost of healthcare for MI patients is associated with not receiving care after accounting for all other variables.

Suggested Citation

  • Diane Dolezel & Alexander McLeod & Larry Fulton, 2021. "Examining Predictors of Myocardial Infarction," IJERPH, MDPI, vol. 18(21), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11284-:d:666017
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/21/11284/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/21/11284/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Domenico Di Raimondo & Gaia Musiari & Giuliana Rizzo & Edoardo Pirera & Salvatore Santo Signorelli, 2022. "New Insights in Prevention and Treatment of Cardiovascular Disease," IJERPH, MDPI, vol. 19(4), pages 1-7, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maciej Berk{e}sewicz & Herman Cherniaiev & Robert Pater, 2021. "Estimating the number of entities with vacancies using administrative and online data," Papers 2106.03263, arXiv.org.
    2. J. Michael Brick & Michael E. Jones, 2008. "Propensity to respond and nonresponse bias," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 51-73.
    3. Jacques Muthusi & Samuel Mwalili & Peter Young, 2019. "%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
    4. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    5. Fenton, Alex, 2013. "Small-area measures of income poverty," LSE Research Online Documents on Economics 58053, London School of Economics and Political Science, LSE Library.
    6. repec:cep:sticas:/173 is not listed on IDEAS
    7. Mei‐Chih Meg Tseng & Yi‐Ping Lin & Fu‐Chang Hu & Tsun‐Jen Cheng, 2013. "Risks Perception of Electromagnetic Fields in Taiwan: The Influence of Psychopathology and the Degree of Sensitivity to Electromagnetic Fields," Risk Analysis, John Wiley & Sons, vol. 33(11), pages 2002-2012, November.
    8. Raphael Nishimura & James Wagner & Michael Elliott, 2016. "Alternative Indicators for the Risk of Non-response Bias: A Simulation Study," International Statistical Review, International Statistical Institute, vol. 84(1), pages 43-62, April.
    9. Camelia Herman & Colleen M. Leonard & Perpetua Uhomoibhi & Mark Maire & Delynn Moss & Uwem Inyang & Ado Abubakar & Abiodun Ogunniyi & Nwando Mba & Stacie M. Greby & McPaul I. Okoye & Nnaemeka C. Iriem, 2023. "Non-falciparum malaria infection and IgG seroprevalence among children under 15 years in Nigeria, 2018," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    10. Elijah O. Onsomu & DaKysha Moore & Benta A. Abuya & Peggy Valentine & Vanessa Duren-Winfield, 2013. "Importance of the Media in Scaling-Up HIV Testing in Kenya," SAGE Open, , vol. 3(3), pages 21582440134, July.
    11. Vinas-Forcade, Jennifer & Seijas, María Noé, 2021. "To teach or not to teach: Negative selection into the teaching profession in Uruguay," International Journal of Educational Development, Elsevier, vol. 84(C).
    12. Zhongqi Fan & Amy M. Yang & Marcus Lehr & Ana B. Ronan & Ryan B. Simpson & Kimberly H. Nguyen & Elena N. Naumova & Naglaa H. El-Abbadi, 2024. "Food Insecurity across Age Groups in the United States during the COVID-19 Pandemic," IJERPH, MDPI, vol. 21(8), pages 1-19, August.
    13. Lenis, David & Ackerman, Benjamin & Stuart, Elizabeth A., 2018. "Measuring model misspecification: Application to propensity score methods with complex survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 48-57.
    14. Sofia Gil-Clavel & Emilio Zagheni & Valeria Bordone, 2022. "Close Social Networks Among Older Adults: The Online and Offline Perspectives," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 1111-1135, June.
    15. Matthew R. Williams & Terrance D. Savitsky, 2021. "Uncertainty Estimation for Pseudo‐Bayesian Inference Under Complex Sampling," International Statistical Review, International Statistical Institute, vol. 89(1), pages 72-107, April.
    16. Jeanna Parsons Leigh & Kirsten Fiest & Rebecca Brundin-Mather & Kara Plotnikoff & Andrea Soo & Emma E Sypes & Liam Whalen-Browne & Sofia B Ahmed & Karen E A Burns & Alison Fox-Robichaud & Shelly Kupsc, 2020. "A national cross-sectional survey of public perceptions of the COVID-19 pandemic: Self-reported beliefs, knowledge, and behaviors," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    17. Wang, Jianqiang C., 2012. "Sample distribution function based goodness-of-fit test for complex surveys," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 664-679.
    18. Liu, Chia & Olamijuwon, Emmanuel, 2024. "The link between intimate partner violence and spousal resource inequality in lower- and middle-income countries," Social Science & Medicine, Elsevier, vol. 345(C).
    19. Alejandro Aybar-Flores & Alvaro Talavera & Elizabeth Espinoza-Portilla, 2023. "Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms," IJERPH, MDPI, vol. 20(7), pages 1-29, March.
    20. Joseph R Starnes & Chiara Di Gravio & Rebecca Irlmeier & Ryan Moore & Vincent Okoth & Ash Rogers & Daniele J Ressler & Troy D Moon, 2021. "Characterizing multidimensional poverty in Migori County, Kenya and its association with depression," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-10, November.
    21. Christian A. Maino Vieytes & Ruoqing Zhu & Francesca Gany & Amirah Burton-Obanla & Anna E. Arthur, 2022. "Empirical Dietary Patterns Associated with Food Insecurity in U.S. Cancer Survivors: NHANES 1999–2018," IJERPH, MDPI, vol. 19(21), pages 1-21, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11284-:d:666017. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.