Risk Of Coronary Heart Disease In India

Risk Of Coronary Heart Disease In India

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Risk Of Coronary Heart Disease In India

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Risk Of Coronary Heart Disease In India

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Question:
Discuss about the Physical Activity And Risk Of Coronary Heart Disease In India.
 
 
Answer:

Physical activity and risk of coronary heart disease in India
The evidence presented in the paper 
The selected case-control study was carried out with an objective to know the relationship between physical exercise and coronary heart disease (CHD) in the urban areas of India i.e. Delhi and Banglore (Rastogi et al 2004). The urban hospitals of New Delhi and Bangalore were carefully chosen to draw a sample of 350 cases and 700 controls. The cases and controls were matched for age and gender. They were asked certain questions regarding socioeconomic status, smoking history, history of hypertension, diabetes, hypercholesterolaemia, family history of Cardio Vascular Diseases, dietary intake, types of fat or oils used in cooking, nutritional supplement use, and physical activity. The anthropometric measurments were also taken regarding height, weight, hip & waist circumferences, body mass index (BMI) and waist to hip ratio. The data analysis was done using conditional logistic regression where confounders were controlled in the analysis. The results found that 48% of controls and 38% of cases participated in some kind of leisure-time physical activity. The participants who engaged in highest level of leisure-time physical activity had lowest risk of developing CHD. On the other hand; participants with comparatively increased levels of sedentary lifestyles, had higher risk of developing CHD. The paper concluded that leisure-time exercise had a protective effect on heart as compared to sedentary lifestyle. The recommendation of paper was that physical activities in daily lives should be promoted in urban India.
Exposure or Intervention
The exposure or intervention was leisure time exercise which was assessed with the help of physical activity questionnaire.  The metabolic equivalents (MET) or intensity of the reported activities were assessed with the help of Compendium of Physical Activities (Rastogi et al 2004, pg 2, para 5).
Outcome
The outcome was acute Myocardial Infarction. Cases were the respondents having outcome. Controls were the respondents in which outcome was absent. Cases aged 21–74 with a diagnosis of AMI were selected from eight urban hospitals in New Delhi and Bangalore between January 1999 and January 2000 (Rastogi et al 2004, pg 2, para 1).
Study Design
The study design was case-control study having 350 cases of acute myocardial infarction and 700 controls matched for age and gender. The study used conditional logistic regression for data analysis (Rastogi et al 2004, pg 1, Abstract).
Study Population
The study population was patients in urban hospitals of New Delhi and Banglore.
Main findings of study
People with exposure to highest level of leisure time exercise i.e. 145 MET-minutes per day, equivalent to 36 minutes of brisk walking per day, had relative risk of 0.45 (95% CI 0.31-0.66) as compared to non-exercise group. It means leisure-time exercise had a protective effect for the risk of AMI. On the other hand people with greater than 3.6 hours per day of sedentary activity had about 1.88 times (95% CI 1.09-3.20) higher risk of developing AMI, as compared to people with less than 70 minutes per day of sedentary activity per day.
 
Extent to which the observed association between the exposure and outcome be attributed to non-causal explanations –
Non-causal explanations of the observed association could be attributed to selection and/or measurement bias, confounding, or chance variation. Following section explains all three in detail.
Selection and/ or measurement bias 
The patients in both the groups were matched by age and gender; but there always remains risk of selection bias in the study as the controls were relatively healthy and their ailments were minor, as compared to cases having diagnosis of Acute Myocardial Infarction (AMI). The studies have shown selection bias to be a common cause of bias in case-control studies (van Rein et al 2014). The selection of controls could also be inappropriate. Also in the present study, the disease is very severe and the chances of pre-mature death are high. Thus the study selected only those patients who survived the condition of AMI and thus had comparatively less severe illness, which is again a major cause of bias (van Rein et al 2014). This is also called “survivor bias in case-control studies” (ibid).
In the discussion section, the author has discussed that controls were chosen from seven different out-patient clinics and in-patient wards; and so there were chances of association being present in one particular group and not in other groups; and such a situation might induced bias in the results. Also the study interviewed only those cases who survived. Thus, although there were 25 cases that did not survive, but they could not be included in the study. Also the author himself has argued the possibility of only health conscious individuals participating in the study, which may have induced bias in the study. Moreover controls in the study were more educated and had lower incomes than cases; which might be the source of bias in the study.
Confounding 
The study adjusted the following confounders: age, gender, cigarette and bidi smoking, BMI, WHR, alcohol intake, education, or income; but there might be other unknown confounding factors which might have introduced bias in the results. One such example is the use of chewing tobacco. Cigarette and Bidi smoking have been adjusted, but not chewing tobacco. There is also a possibility of introducing the bias by doing matching. However matching is supposed to remove bias but studies have shown that it may also introduce bias (Pearce 2016). The reason being; while attempting to match for the confounders; matching may also be done for exposure itself (ibid). Further matched case-control design must include matched analysis (ibid). In this particular research various potential confounders were controlled in the analysis. Thus every possible effort was done to remove confounding.
 
The researcher has also reasoned that physical activity may also be protective for some of the ailments in control group, other than AMI, which may have induced confounding bias in the research.
Chance Variation 
The researcher had set the level of significance at 95 percent, which left only 5 percent chances of chance variation.
Chance Variation is inherent in any research based on statistical predictions. Chance variation is also called chance error or random error. It is the difference between the predicted value and actual value/ population value. In other words it is the probability by which the estimates differ from the true value/ population value. In a normal distribution curve, if we take a range up to 1.96  SD (standard deviations) above and below the estimated mean; then there are 95% chances that true value will fall in that range; which will leave only 5 percent likelihoods of any variation from this range, which is called chance variation. There will be 2.5% chances that true value/ population value will fall above this range and 2.5% chances will be that true value will fall below this range. (Sowey, & Petocz, 2017)
Internal Validity of the study –
The quantitative research is considered to be internally valid if it could minimise Systematic errors or bias. The researcher has to ensure that the cause-effect relationship is not a spurious one. There are several benchmarks defined by various epidemiologists from time to time; which authenticates the evidence of causal relationship between the exposure and the outcome; within the study. Some of these principles are as follows. The first principle is that the cause must precede the effect i.e. there should be temporal relationship between the cause and effect. The second principle is that covariation between cause and effect should be high. It means, by changing the one, there should be clear and visible change in the other. The third principle is that there should be a dose-response relationship between the cause and effect. It means higher the change in one, higher should be the change in the other or vice-versa (Neuman 2016). These conditions for internal validity of study are further deliberated in detail as follows.
Temporal relationship
Yes, the research have shown that there is a temporal relationship between exposure and outcome. It was a case-control study and the exposure or non-exposure to leisure-time exercise preceded the development or non-development of AMI.
Strong relationship
Yes, the relationship between leisure time exercise and AMI was very strong as the P value was less than 0.0001 for the relationship. Participants with 35-40 minutes of brisk walking had 55% lower risk of developing AMI as compared to controls who did not exercise.
Dose-response relationship
Yes, there was a dose-response relationship between exposure and the outcome. Participants in the highest level of physical exercise group had lowest risk of developing AMI and this observation was significant at p<0.0001. Consistency within the study   Yes, the results were consistent within the study. Age and Sex adjusted analysis showed that leisure-time physical exercise lowered the risk of AMI. After adjusting for confounders like cigarette/ bidi smoking, the leisure time exercise had protective effect on AMI risk. The results were also consistent in multi-variate analysis. Accordance with other evidence Yes, the findings are consistent with other evidence. The findings are consistent with evidence from some recent research. In 2014, Andersen and colleagues found that leisure time physical activity had a protective effect towards the risk of developing AMI and the benchmark of a dose-response relationship also got fulfilled (Andersen et al 2014). Similarly the INTERHEART study of China found a protective role of leisure-time physical exercise towards AMI as compared to sedentary lifestyle (Cheng et al 2014). Moreover in Copenhagen City Heart study, it was found that leisure-time physical exercise had a protective effect in post-MI patients (Saevereid et al 2013). A recent meta-analysis by Claes et al also revealed that home-based physical activity is protective for cardiovascular rehabilitation (Claes et al 2017). Some other studies around the world have also found that exercise-based rehabilitation helps in the improvement of quality of life and functional capacity of heart (Peixoto et al 2015). Also some studies have been mentioned in the article itself. The article talk about one prospective study from US on women that established that more than 3 hours per week of leisure time physical exercise had protective effect on heart. Another cohort study on US men determined that individuals who were doing more than 30 minutes per day of moderate-intensity physical exercise had 20 percent lower chances of developing CHD. The selected paper discusses another US-based prospective study on post-menopausal women, which determined that walking daily have a protective effect on heart. Biological Plausibility – Yes, the results are plausible in terms of a biological mechanism. The leisure-time physical exercise results in lipid lowering in Atherosclerotic plaques. It also reduces thrombotic potential and increases fibrinolytic potential (Libby 2013). The study has also discussed underlying biological mechanisms due to which physical activity has protective effects on CVD risk. These include reduced blood pressure, increased HDL (High-Density Lipoproteins), increase in insulin sensitivity, improvement in endothelial function, and reduction in atherogenic cytokine production.   External Validity The external validity of a research denotes the extent to which the results of the study could be generalised across heterogenous populations. The sampling bias may be a threat to external validity of research if the sample is not true representative of study population (Pearl 2017). In other words the results of the study could be generalised to whole population which is possible when the chosen sample is true representative of the study population. It is always important to reinforce the reporting of results on external validity so that the context of application of results could be understood i.e. whether the results are applicable to local settings or group settings or wider country settings. It becomes important to transform research in to practice as the interventions may also be applicable to similar context or settings (Steckler, & McLeroy, 2008). Generalisability This particular research was conducted on hospital patients from the urban settings of Delhi and Banglore. Thus results of the study could be generalised to urban cities of India only, that too particularly Delhi and Banglore. If some other study is conducted by taking controls from general populations, the results might be different. Thus to conclude, the results of this research could be generalised to urban hospital patients of New Delhi and Banglore cities of India. Can the findings be applied to the source population from which the study population was derived? The study population was derived from urban hospitals of New Delhi and Banglore cities of India. The chosen sample was sufficient to generalise the findings to the source population; but as the research was conducted on patients selected from hospitals only, the generalisation of results to general population or whole population of a country or city is uncertain. Can the study results be applied to other relevant populations? The study results are specific to New Delhi and Banglore cities of India as the selected sample was representative of hospital patients of these two cities only; and thus the results could not be generalised to other relevant populations.   References Andersen, K., Mariosa, D., Adami, H. O., Held, C., Ingelsson, E., Lagerros, Y. T., ... & Sundström, J. (2014). Dose-response relations of total and leisure-time physical activity to risk of heart failure: a prospective cohort study. Circulation: Heart Failure, CIRCHEARTFAILURE-113. Cheng, X., Li, W., Guo, J., Wang, Y., Gu, H., Teo, K., ... & Yusuf, S. (2014). Physical activity levels, sport activities, and risk of acute myocardial infarction: results of the INTERHEART study in China. Angiology, 65(2), 113-121. Claes, J., Buys, R., Budts, W., Smart, N., & Cornelissen, V. A. (2017). Longer-term effects of home-based exercise interventions on exercise capacity and physical activity in coronary artery disease patients: A systematic review and meta-analysis. European journal of preventive cardiology, 24(3), 244-256. Libby, P. (2013). Mechanisms of acute coronary syndromes and their implications for therapy. New England Journal of Medicine, 368(21), 2004-2013. Neuman, W. L. (2016). Understanding research. Pearson. Pearce, N. (2016). Analysis of matched case-control studies. bmj, 352, i969. Pearl, J. (2017). The Eight Pillars of Causal Wisdom (Lecture notes for the UCLA WCE conference, April 24, 2017). Peixoto, T. C., Begot, I., Bolzan, D. W., Machado, L., Reis, M. S., Papa, V., ... & Guizilini, S. (2015). Early exercise-based rehabilitation improves health-related quality of life and functional capacity after acute myocardial infarction: a randomized controlled trial. Canadian Journal of Cardiology, 31(3), 308-313. Rastogi, T., Vaz, M., Spiegelman, D., Reddy, K. S., Bharathi, A. V., Stampfer, M. J., ... & Ascherio, A. (2004). Physical activity and risk of coronary heart disease in India. International journal of epidemiology, 33(4), 759-767. Saevereid, H. A. S., Schnohr, P. S., & Prescott, E. P. (2013). Speed and duration of walking and other leisure time physical activity and the risk of heart failure: the Copenhagen City Heart study. European Heart Journal, 34(suppl 1), P3646. Sowey, E., & Petocz, P. (2017). A Panorama of Statistics: Perspectives, Puzzles and Paradoxes in Statistics. John Wiley & Sons. Steckler, A., & McLeroy, K. R. (2008). The importance of external validity. American Journal of Public Health, 98(1), pp. 9–10. van Rein, N., Cannegieter, S. C., Rosendaal, F. R., Reitsma, P. H., & Lijfering, W. M. (2014). Suspected survivor bias in case–control studies: stratify on survival time and use a negative control. Journal of clinical epidemiology, 67(2), 232-235.   Free Membership to World's Largest Sample Bank To View this & another 50000+ free samples. Please put your valid email id. E-mail Yes, alert me for offers and important updates Submit  Download Sample Now Earn back the money you have spent on the downloaded sample by uploading a unique assignment/study material/research material you have. After we assess the authenticity of the uploaded content, you will get 100% money back in your wallet within 7 days. UploadUnique Document DocumentUnder Evaluation Get Moneyinto Your Wallet Total 10 pages PAY 6 USD TO DOWNLOAD *The content must not be available online or in our existing Database to qualify as unique. 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