Article Sidebar

Download
PDF
Statistic
Read Counter : 1207 Download : 613

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Main Article Content

Abstract

Demographic and socio-economic (SE) factors are associated with modifiable, clinical factors and cardiovascular disease (CVDs). However, the inclusion of mediators in these relationships creates complex pathways which extend the roles of factors within the model. The traditional hierarchal logistic regression (HLR) is unable to estimate the transmittal effects of factors through different layers of factors of CVDs. The study aims to simultaneously estimate and validate the probable linear and non-linear relationships among factors of CVDs considering potential mediators. Four hundred sixty participants (312 males and 148 females) were selected through systematic sampling in this sex-matched case control (1:1 ratio) study conducted in the largest Cardiac Center of Pakistan. The information on demographic, SE, modifiable and clinical factors of CVDs was recorded. Warp partial least squares (PLS) based on warp 3 algorithm was used to estimate the simultaneous linear and non-linear path coefficients of the proposed model of study. The study found that demographic and SE factors played a significant role in shaping the modifiable factors which further transmit their impact to CVDs. However, this transmitted impact of modifiable factors on CVDs was mediated through metabolic syndrome abnormalities (MSA) except self-reported subjective stress (SSS). Sleep satisfaction and negative dietary habits were the mediators between the relationship of SSS and MSA. Physical activity is the strongest factor associated with CVDs status. The proposed path analyses, verifying the mediation role of MSA in the pathways of relationships which would help in identifying the risky group of population and guide in formulating the health promotion policies for the reduction of CVDs burden. Further, Warp 3 algorithm is the better option to estimate complex models containing linear and non-linear relationship in the same model.

Keywords

Cardiovascular Diseases Risk Factors Path Analyses Partial Least Squares Warp 3 Algorithm Mediation

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following License

CC BY: This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

 

How to Cite
Sajid, M. R., Muhammad, N., Zakaria, R., Shahbaz, A., & Nauman, A. (2020). Associated Factors of Cardiovascular Diseases in Pakistan: Assessment of Path Analyses Using Warp Partial Least Squares Estimation. Pakistan Journal of Statistics and Operation Research, 16(2), 265-277. https://doi.org/10.18187/pjsor.v16i2.3075
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Akhuemonkhan, E., & Lazo, M. (2017). Association between family history of diabetes and cardiovascular disease and lifestyle risk factors in the United States population: the 2009-2012 National Health and Nutrition Examination Survey. Preventive Medicine, 96, 129-134.
  2. Alberti, K., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., . . . Smith Jr, S. C. (2009). Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation, 120(16), 1640-1645.
  3. Arber, S., Fenn, K., & Meadows, R. (2014). Subjective financial well-being, income and health inequalities in mid and later life in Britain. Social Science and Medicine, 100, 12-20.
  4. Association, A. L. (2012). Cutting tobacco's rural roots: Tobacco use in rural communities Disparities in Lung Health Series.
  5. Association, A. P. (2012). Stress by generations (pp. 19-21).
  6. Barrington, W. E., Beresford, S. A., McGregor, B. A., & White, E. (2014). Perceived stress and eating behaviors by sex, obesity status, and stress vulnerability: findings from the vitamins and lifestyle (VITAL) study. Journal of the Academy of Nutrition and Dietetics, 114(11), 1791-1799.
  7. Bauman, A., Bull, F., Chey, T., Craig, C. L., Ainsworth, B. E., Sallis, J. F., . . . Pratt, M. (2009). The international prevalence study on physical activity: results from 20 countries. International journal of behavioral nutrition and physical activity, 6(1), 21.
  8. Becerra-Tomás, N., Babio, N., Martínez-González, M. Ã., Corella, D., Estruch, R., Ros, E., . . . Lamuela-Raventós, R. M. (2016). Replacing red meat and processed red meat for white meat, fish, legumes or eggs is associated with lower risk of incidence of metabolic syndrome. Clinical Nutrition, 35(6), 1442-1449.
  9. Berglund, E., Lytsy, P., & Westerling, R. (2013). Adherence to and beliefs in lipid-lowering medical treatments: a structural equation modeling approach including the necessity-concern framework. Patient Education and Counseling, 91(1), 105-112.
  10. Braveman, P., & Gottlieb, L. (2014). The social determinants of health: it's time to consider the causes of the causes. Public Health Reports, 129(1_suppl2), 19-31.
  11. Cappelletti, E. R., Kreuter, M. W., Boyum, S., & Thompson, T. (2015). Basic needs, stress and the effects of tailored health communication in vulnerable populations. Health Education Research, 30(4), 591-598.
  12. Cappuccio, F. P., Cooper, D., D'elia, L., Strazzullo, P., & Miller, M. A. (2011). Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. European Heart Journal, 32(12), 1484-1492.
  13. Chen, C.-C., Li, T.-C., Chang, P.-C., Liu, C.-S., Lin, W.-Y., Wu, M.-T., . . . Lin, C.-C. (2008). Association among cigarette smoking, metabolic syndrome, and its individual components: the metabolic syndrome study in Taiwan. Metabolism: Clinical and Experimental, 57(4), 544-548.
  14. Fan, L., Hao, Z., Gao, L., Qi, M., Feng, S., & Zhou, G. (2020). Non-linear relationship between sleep duration and metabolic syndrome: A population-based study. Medicine, 99(2), e18753.
  15. Federation, I. D. (2006). The IDF consensus worldwide definition of the metabolic syndrome IDF Communications (pp. 1-23).
  16. Genin, P., Dessenne, P., Finaud, J., Pereira, B., Dutheil, F., Thivel, D., & Duclos, M. (2018). Effect of Work-Related Sedentary Time on Overall Health Profile in Active vs. Inactive Office Workers. Frontiers in public health, 6, 279. doi: 10.3389/fpubh.2018.00279
  17. Godwin, M., Streight, S., Dyachuk, E., van Den Hooven, E. C., Ploemacher, J., Seguin, R., & Cuthbertson, S. (2008). Testing the simple lifestyle indicator questionnaire: initial psychometric study. Canadian Family Physician, 54(1), 76-77.
  18. Grandner, M. A., Patel, N. P., Gehrman, P. R., Xie, D., Sha, D., Weaver, T., & Gooneratne, N. (2010). Who gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints. Sleep medicine, 11(5), 470-478.
  19. Greer, A. E., Sui, X., Maslow, A. L., Greer, B. K., & Blair, S. N. (2015). The effects of sedentary behavior on metabolic syndrome independent of physical activity and cardiorespiratory fitness. Journal of Physical Activity and Health, 12(1), 68-73.
  20. Grundy, S. M. (2016). Metabolic syndrome update. Trends in Cardiovascular Medicine, 26(4), 364-373.
  21. Havranek, E. P., Mujahid, M. S., Barr, D. A., Blair, I. V., Cohen, M. S., Cruz-Flores, S., . . . Lockwood, D. W. (2015). Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation, 132(9), 873-898.
  22. Hernández, A. V., Pasupuleti, V., Deshpande, A., Bernabé-Ortiz, A., & Miranda, J. J. (2012). Effect of rural-to-urban within-country migration on cardiovascular risk factors in low-and middle-income countries: a systematic review. Heart, 98(3), 185-194.
  23. Hung, H.-C., Yang, Y.-C., Ou, H.-Y., Wu, J.-S., Lu, F.-H., & Chang, C.-J. (2013). The association between self-reported sleep quality and metabolic syndrome. PloS one, 8(1), e54304.
  24. Janjua, N. Z., Mahmood, B., Bhatti, J. A., & Khan, M. I. (2015). Association of household and community socioeconomic position and urbanicity with underweight and overweight among women in Pakistan. PloS one, 10(4), e0122314.
  25. Kastorini, C.-M., Milionis, H. J., Georgousopoulou, E., Kalantzi, K., Nikolaou, V., Vemmos, K. N., . . . Panagiotakos, D. B. (2015). Defining the path between social and economic factors, clinical and lifestyle determinants, and cardiovascular disease. Global heart, 10(4), 255-263.
  26. Kock, N. (2015). PLS-based SEM algorithms: The good neighbor assumption, collinearity, and nonlinearity. Information Management and Business Review, 7(2), 113-130.
  27. Kock, N. (2017). WarpPLS user manual: Version 6.0. ScriptWarp Systems: Laredo, TX, USA.
  28. Lavoie, C. J., Zeidler, M. R., & Martin, J. L. (2018). Sleep and aging. Sleep Science and Practice, 2(1), 3. doi: 10.1186/s41606-018-0021-3
  29. Leblanc, V., Hudon, A.-M., Royer, M.-M., Corneau, L., Dodin, S., Bégin, C., & Lemieux, S. (2015). Differences between men and women in dietary intakes and metabolic profile in response to a 12-week nutritional intervention promoting the Mediterranean diet. Journal of nutritional science, 4, e13.
  30. Li, L., & Zhang, M. (2011). Population versus hospital controls for case-control studies on cancers in Chinese hospitals. BMC medical research methodology, 11(1), 167.
  31. Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., . . . Andrews, K. G. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2224-2260.
  32. Menotti, A., Alberti-Fidanza, A., Fidanza, F., Lanti, M., & Fruttini, D. (2012). Factor analysis in the identification of dietary patterns and their predictive role in morbid and fatal events. Public health nutrition, 15(7), 1232-1239.
  33. Misra, A., & Khurana, L. (2009). The metabolic syndrome in South Asians: epidemiology, determinants, and prevention. Metabolic syndrome and related disorders, 7(6), 497-514.
  34. Muhammad, N., Sajid, M. R., Zakaria, R., & Shahbaz, A. (2019). The mediating role of metabolic syndrome in cardiovascular diseases. AIP Conference Proceedings, 2138(1), 050020. doi: 10.1063/1.5121125
  35. Olsen, J. R., Nicholls, N., & Mitchell, R. (2019). Are urban landscapes associated with reported life satisfaction and inequalities in life satisfaction at the city level? A cross-sectional study of 66 European cities. Social Science and Medicine, 226, 263-274.
  36. Organization, W. H. (2018). Noncommunicable Diseases (NCD) Country Profiles, .
  37. Osmanovic-Thunström, A., Mossello, E., Åkerstedt, T., Fratiglioni, L., & Wang, H.-X. (2015). Do levels of perceived stress increase with increasing age after age 65? A population-based study. Age and Ageing, 44(5), 828-834.
  38. Panel, E. (2001). Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) JAMA (Vol. 285, pp. 2486-2497).
  39. Papathanasiou, G., Georgoudis, G., Papandreou, M., Spyropoulos, P., Georgakopoulos, D., Kalfakakou, V., & Evangelou, A. (2009). Reliability measures of the short International Physical Activity Questionnaire (IPAQ) in Greek young adults. Hellenic J Cardiol, 50(4), 283-294.
  40. Patel, N. P., Grandner, M. A., Xie, D., Branas, C. C., & Gooneratne, N. (2010). " Sleep disparity" in the population: poor sleep quality is strongly associated with poverty and ethnicity. BMC public health, 10(1), 475.
  41. Quan, S.-A., Li, Y.-C., Li, W.-J., Li, Y., Jeong, J.-Y., & Kim, D.-H. (2016). Gender differences in sleep disturbance among elderly Koreans: Hallym Aging Study. Journal of Korean Medical Science, 31(11), 1689-1695.
  42. Rehkopf, D. H., Krieger, N., Coull, B., & Berkman, L. F. (2010). Biologic risk markers for coronary heart disease: nonlinear associations with income. Epidemiology, 38-46.
  43. Révész, D., Milaneschi, Y., Verhoeven, J. E., Lin, J., & Penninx, B. W. (2015). Longitudinal associations between metabolic syndrome components and telomere shortening. The Journal of Clinical Endocrinology & Metabolism, 100(8), 3050-3059.
  44. Sajid, M., Ansar, A., Hanif, A., Waheed, K., & Tufail, S. (2017). Non Clinical Risk Factors of Myocardial Infarction: A Meta-Analysis Approach. J Biom Biostat, 8(4), 363.
  45. Sajid, M. R., Muhammad, N., Zakaria, R., & Shahbaz, A. (2019). Validation of Modified Dietary Habits Instrument in Cardiovascular Diseases Study. Paper presented at the Simposium Kebangsaan Sains Matematik ke-27 (SKSM 27), 26-27 November.
  46. Scollo, M., & Winstanley, M. (2016). Tobacco in Australia: facts and issues. Melbourne: Cancer Council Victoria; 2012.
  47. Silva, A. A. M. d., Vasconcelos, A. G. G., Bettiol, H., & Barbieri, M. A. (2010). Socioeconomic status, birth weight, maternal smoking during pregnancy and adiposity in early adult life: an analysis using structural equation modeling. Cadernos de Saude Publica, 26, 15-29.
  48. Singh-Manoux, A., Marmot, M. G., & Adler, N. E. (2005). Does subjective social status predict health and change in health status better than objective status? Psychosomatic Medicine, 67(6), 855-861.
  49. Song, H., Fang, F., Arnberg, F. K., Mataix-Cols, D., de la Cruz, L. F., Almqvist, C., . . . Valdimarsdóttir, U. A. (2019). Stress related disorders and risk of cardiovascular disease: population based, sibling controlled cohort study. BMJ, 365, l1255.
  50. Statistics, P. B. o. (2017). Provisional Summary Results of 6th Population and Housing Census-2017.
  51. Thivel, D., Tremblay, A., Genin, P. M., Panahi, S., Rivière, D., & Duclos, M. (2018). Physical Activity, Inactivity, and Sedentary Behaviors: Definitions and Implications in Occupational Health. Frontiers in public health, 6, 288.
  52. Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information technology theory and application, 11(2), 5-40.
  53. Wareham, N. J., Jakes, R. W., Rennie, K. L., Schuit, J., Mitchell, J., Hennings, S., & Day, N. E. (2003). Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Public health nutrition, 6(4), 407-413.
  54. Wu, M.-C., Yang, Y.-C., Wu, J.-S., Wang, R.-H., Lu, F.-H., & Chang, C.-J. (2012). Short sleep duration associated with a higher prevalence of metabolic syndrome in an apparently healthy population. Preventive Medicine, 55(4), 305-309.
  55. Xi, B., Veeranki, S. P., Zhao, M., Ma, C., Yan, Y., & Mi, J. (2017). Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in US adults. Journal of the American College of Cardiology, 70(8), 913-922.