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The effect of deprivation on coronary heart disease mortality rate

The effect of deprivation on coronary heart disease mortality rate Abstract Deprivation and socioeconomic status are associated with cardiovascular diseases. The aim of this study is to describe a relationship between socioeconomic deprivation and coronary heart disease (CHD) mortality. This study used publicly available data for English Indices of Multiple Deprivation (IMD) and CHD mortality rate. IMD data is a ranked score based on seven factors related to socioeconomic status. The average IMD rank and the average CHD mortality rate were collected from the Office of National Statistics website for each Clinical Commissioning Group (CCG) in UK. To investigate the relationship between IMD score and CHD mortality a linear regression was fitted using logged CHD data, because the spread of the CHD data was largest for higher values of IMD. This achieved a more symmetrical distribution of the data. The results from the regression model were retransformed to values representing the original CHD data. Analysis was performed using data from 209 CCGs. CHD mortality rate for the lowest IMD ranked CCG is 27.6 and for the highest 97.4. Using the log-CHD values, the linear regression model revealed that for a one-point increase in IMD rank score, CHD mortality rate increases by 1.03 on average (95% CI 1.02–1.03; P < 0.001). These data show that areas with higher deprivation had higher rates of mortality from CHD. These findings are supported by previous research, which emphasize the important relationship between socioeconomic status and public health crises. coronary heart disease, mortality rate, clinical commissioning group, deprivation, socioeconomic status and epidemiology Introduction Cardiovascular diseases (CVDs) are a group of disorders of the heart and blood vessels, including stroke, coronary heart disease (CHD), hypertensive diseases and diseases of veins (Townsend et al., 2015). CVD is the number one cause of death globally accounting for 17.7 million deaths in 2015 (Townsend et al. 2015). In the UK, CVDs account for approximately 160 000 deaths each year (Townsend et al. 2015; British Heart Foundation, 2017). In 2014, CHD was the major contributor of NHS expenditure out of all the CVDs. It was also the leading cause of death for both males (15%) and females (10%) (Townsend et al. 2015). Disability and deaths from CVD can often be prevented with healthy lifestyles (Newman et al., 2017). Non-modifiable risk factors such as age, sex and family history as well as modifiable risk factors such as smoking, inactivity, type 2 diabetes, high blood pressure and obesity have been identified and linked to CVDs (Heart UK, 2017). A positive relationship between increasing socioeconomic deprivation and increasing CHD mortality rate was reported in many European countries as well as in the USA (Mackenbach et al., 2000; Hawkins et al., 2012). Deprivation is a comparative measure of an individual’s economic and social position (Clark et al., 2009). It is based on factors such as income, education, marital status, country of birth and comorbidities (Rawshani et al., 2015; Pan et al., 2016). A recent study by Cundiff et al. analyzed international population datasets from 168 countries of risk factors for CVD (Cundiff and Agutter, 2016). The authors identified a number of modifiable risk factors for CVD, including poor diet, tobacco smoke and air pollution all primarily associated with lower socioeconomic status in the countries studied. However, the relationship between CVD and socioeconomic deprivation in the UK remains unclear. This research report aims to establish the relationship between CHD mortality rate and socioeconomic deprivation in the UK. It is hypothesized that increased CHD mortality rate is associated with increased socioeconomic deprivation in the UK. Methods Study design and sampling The study design was a retrospective analysis of publically available population-level datasets in the UK. Using publicly available data from the Department for Communities and Local Government (2015), data on English indices of multiple deprivation (IMD) for 2015 was collected. A low IMD score is for an area with low deprivation and as the IMD rank increases, the deprivation increases as well. Data of IMD takes into account income, employment, education, health, crime, barriers and living at a Clinical Commissioning Group (CCG) level. The average IMD score is calculated by firstly taking into account a population-weighted measure of smaller areas within the CCG, the Lower-Layer Super Output Areas (LSOA). CCG LSOA deprivation is calculated taking into account income, employment, education, health, crime, barriers and living. The average scores of the LSOA in each CCG are then used to find the average IMD score for that CCG. The average score is taken for each CCG. Within NHS UK, there are 209 CCGs responsible for planning and commissioning of healthcare services for their local areas. The data on CHD mortality rate was obtained from National Cardiovascular Intelligence network (Public Health England, 2016), which was last updated on April 2016. As for IMD, CHD data was gathered for the 209 CCGs within NHS England. CHD mortality rate is crude CHD mortality rate per 100 000 deaths. CHD mortality data are reported as a specific sub-category for all CVDs deaths. There is also time restriction as the IMD data was collected in 2015 and CHD data collected in 2016. The data on deprivation is relatively stable over a 12-month period and therefore unlikely to have changed during the period of CHD data collection. Analysis Both datasets are numeric continues data types. The data were summarized using mean values, standard deviation and range. Data for CHD mortality rate is presented per 100 000 deaths. Data for IMD score is presented as mean and standard deviation. The spread of the CHD data around the line of best fit was largest for higher values of IMD (see Fig. 1) Therefore, CHD data was logged (using the natural log) which achieved a more symmetrical distribution of the data (see Fig. 2). Then, linear regression analysis was used to quantify the relationship between IMD rank and the log-CHD mortality rank for each CCG in the UK. Results from the linear regression model are presented as log-coefficient (log-95% confidence interval (CI)). In addition, to aid interpretation of the results, the regression model outputs were retransformed to values representing the original CHD units, using the exponential. The retransformed model outpars are presented as coefficient (95%CI). The statistical significance threshold was set at 0.05. In addition, the adjusted R-square was calculated in order to find out the percentage IMD average score that accounts for the CHD mortality rate. Figure 1. Open in new tabDownload slide Scatter plot demonstrating a significant relationship between CHD mortality rates against IMD average scores. Figure 2. Open in new tabDownload slide Scatter plot demonstrating a significant relationship between log-CHD mortality rates against IMD average scores. Regression diagnosis was undertaken by checking residuals-versus-fit plot for the normal CHD data and log-CHD data. Ethics Data was publicly available and therefore consent for use was not required. In addition, the data was anonymised and it only contained figures of CHD mortality rate for each CCG. Results Summary statistics calculated and presented in Table 1 shows the mean (standard deviation, SD) scores for IMD and crude CHD mortality rate per 100 000 deaths. The mean (SD) IMD rank was 21.91 (8.35). The lowest deprivation score (i.e. the least deprived CCG) of 5.652 was observed in Wokingham CCG. The highest IMD score (i.e. the most deprived) of 51.549 was observed in Bradford CCG. This represents almost a 10-fold increase in deprivation rank between the least and most deprived communities. The mean (SD) crude CHD mortality rate was 43.38 (12.01). The spread of CHD mortality was plotted against IMD rank (Fig. 1) this indicates a greater spread of the CHD data for higher vales of IMD. The CHD data was therefore logged. A scatter plot of log-CHD mortality rate over IMD score (Fig. 2) demonstrates that as CCG deprivation increases so does the crude CHD mortality rate. Table 1. Summary statistics for both datasets. (CHD mortality rate is presented per 100 00 deaths) . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 Open in new tab Table 1. Summary statistics for both datasets. (CHD mortality rate is presented per 100 00 deaths) . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 Open in new tab The linear regression model, using log-CHD outcome, revealed that for a one-point increase in IMD rank score, the log of CHD mortality rate increased by 0.03 (95%CI 0.02–0.03, P < 0.0001). The retransformed model parameters revealed that for a one-point increase in IMD rank score, the CHD mortality rate increased by 1.03 (95% CI 1.02–1.03; P < 0.0001). These findings demonstrate a 3% average increase in CHD mortality rate for each unit increase in IMD rank. The P-value of less than 0.001 indicates that there is a statistically significant association between increased deprivation and the risk of CHD mortality rate. It is therefore accepted that the evidence against the null hypothesis are strong and the null hypothesis can be rejected. The adjusted R-square value for the linear regression model was 0.73 indicating that 73% of the variability of CHD mortality rate is accounted for by the variability in IMD score. Discussion Significance of data These results demonstrate a significant positive relationship between socioeconomic deprivation and crude CHD mortality rate. However, it is important to note that liner regression analysis cannot demonstrate a causative relationship. These results are supported by other studies in this area. A recent cohort study following 1.2 million UK women without heart disease demonstrated a strong positive relationship between deprivation and CHD (P < 0.0001; Floud et al., 2016). Another cohort study aimed to explore the link between population-level income, income equality and CHD mortality (Massing et al., 2004). The authors of this study showed that the income inequality was directly related to CHD mortality. CHD mortality rate has been positively linked with socioeconomic deprivation in other studies carried out in other countries (Kim et al., 2014). However, studies in Korea (Kim et al. 2014) and Finland (Salomaa et al., 2000) that took into account different factors that influence socioeconomic status and particularly education demonstrated an inverse association of education and CHD mortality rate. These studies suggest that the relationship between CHD mortality and deprivation may be more complex than at first hypothesized. Potential cofounding factors are likely to be differences in education attainment, which is associated with individual lifestyle factors such as health and risk taking behaviors, smoking, access to healthy foods and regular exercise (Lawlor et al., 2005; Tu et al., 2016). UK, International policies and suggested interventions The data presented here indicate a need to reduce inequalities across regions in the UK. Currently, there are a number of national and international policies used in developed countries like UK and in developing countries (Pettinger, 2016). Sustained economic growth, reduce unemployment through both supply and demand, progressive taxes, increasing benefits to the poor, national minimum wage and benefits are some of the policies in developed countries. Education, aid and development of manufacturing are policies in developing countries. However, additional policies may be required that aim a general left shift of the whole population to decrease CHD. Strategies of preventative medicine as proposed by Rose (1985) aim the decrease of disease by targeting the whole population rather than the high-risk groups. Such policies will improve the health of the general population and therefore the CHD mortality rate will decrease. This can be achieved by educating the younger generations about lifestyle choices and the consequences of unhealthy habits like smoking, sedentary lifestyle and a diet high in saturated fats. This is more likely to be effective for the people with lower deprivation levels. Unfortunately, the education strategy will fail if healthy choices remain economically inaccessible for those of lower socioeconomic strata and therefore other policies must be established. Furthermore, data presented by Bowdon et al. (2018) indicates that areas with lower socioeconomic status (i.e. scoring higher IMD average scores) may be associated with populations that are required to work more hours than less deprived regions and consequently have less free time for regular exercise. Exercise is a very important environmental, modifiable factor that is linked to CVDs. Exercise has been proven to reduce well-established risk factors causing atherosclerosis leading to CVD, like hypertension, insulin resistance and glucose intolerance. These are all factors that contribute to poor health of the heart and the vessels. Introducing exercise is one of the ways to prevent CVDs and also help improve health in patients already suffering from CVDs. Some well-known risk factors for CHD are behavioral and include smoking, poor diet, physical inactivity and alcohol consumption (Townsend et al. 2015). An underlying cause of poor diet and physical inactivity can be suggested to be the socioeconomic deprivation as people of lower socioeconomic status is suggested that they have received little or no education around the importance of these two major contributors of CVDs an more specifically CHD and also prices of healthy food items are more expensive choices over the cheaper ‘fast food’. Therefore, deprivation is the root of the problem and hence it is the one that healthcare interventions should target. Another important parameter is the regional North–South divide in the UK that describes the general increasing deprivation in the northern regions of the UK compared with southern regions (with the exception of the South–West regions). However, this can be a potential pitfall when trying to interpret the relationship of IMD scores and CHD mortality rate and careful analysis must be done. NHS Bradford CCG, a city of the North, has the highest IMD score and also the highest CHD mortality rate. However, looking at the population composition, the district of Bradford has the largest proportion of people of Pakistani ethnic origin (20.3%) in the UK (City of Bradford Metropolitan Council, 2018). This is of a high importance as previous research papers have identified that CHD rate is higher among the Asian population as people of this ethnic group have been identified to have smaller diameter vessels raising the risk for atheromatous occlusion formation. This has also an impact on operations that become more challenging and consequently having more complications later (Makarvus et al., 2017). Therefore, after deriving a positive relationship between IMD scores and CHD mortality rate further analysis is needed to identify any potential high-risk groups that need special interventions. South Asian System of Intervention (SASI) has identified that the South Asian population has a six-time higher risk to suffer from Diabetes II and therefore has designed an intervention specifically targeting this population by offering simple education by a consultant and also clinics are held in Hindi, Punjabi, Urdu, Gunjarati, Bengali as well as in English in order to ensure that the patients get the most out of their consultation (NHS, 2018). This is a focused intervention in contrast to Rose proposition for the whole shift population intervention but when these two are used combined, an important decrease in CHD mortality rate may be achieved. Introducing this program to areas like Bradford could result in lower the CHD mortality rate. Furthermore, in the future, data analysis could be repeated and population divided into ethnicity groups in order to have clear view of the differences if any. A factor to be considered when taking into account the North–South divide and its relationship with deprivation and consequently its effect on CHD coronary arteries is weather and climate. According to a study (Yang et al., 2015), temperature has a significant effect on systolic blood pressure and CVD mortality as supported by other studies as well (Brennan et al., 1982; Lewington et al., 2012). It was presented that during winter, systolic blood pressure was significantly higher than in summer and systolic blood pressure was then used to predict mortality that was shown to be variable according to the season. In this research paper, data from all the CCG areas was used from North to South UK. The North parts have generally lower temperatures than South and it may be suggested that CHD mortality rates for the North CCGs is high due to the weather rather than due to the North–South divide relating to IMD average score. They may also act synergistically. Data limitations Unfortunately the two datasets, for the exposure and the outcome, do not much chronologically as the data of IMD was obtained in 2015 and for CHD mortality rate in 2016. There was not CHD data available for 2015 and this limits extend they are related. However, it can be assumed that the data of deprivation does not change over 1 year and hence the results of the linear regression analysis can be assumed to be reliable. In the future, when the study will be repeated, data could be collected on the same year to exclude this limitation and increase reliability of the results. Another very important limitation of the data is that both the CHD mortality rates and the IMD average scores are area- level statistics. This is an ecological study, an observational study, and data is analyzed at the level of population. Data represents the general picture of the whole population of a CCG but they cannot be used to make conclusions about individual level. In addition, the data boundaries for age, 0–74, are very wide and they therefore cannot provide any useful information for specific, smaller age groups to allow for effective intervention design and implementation. Conclusion The results suggest that CHD mortality rate is highly influenced by environmental factors that stem from low socioeconomic status and poverty. It is emphasized that there is a need for policies that will aim to reduce poverty in order to prevent CHD and therefore CHD mortality rate. Despite a global reduction in mortality rates, a further decrease must be achieved. The data used indicates that there is a greater burden of health related inequalities associated with individuals living in poverty. Current literature is focused on documenting health inequalities rather than explaining why these inequalities persist in order to try to tackle this problem. It is suggested that additional research should focus on understanding the factors that drive the relationship between socioeconomic status and mortality rate and proceed to the development of appropriate policies. Author Biography Lambrini Theocharidou, a third year undergraduate medical student at the University of Leeds Medical School and she is very interested in research with target to achieve to publish a paper. The author, designed the study, collected the data required and analyzed it and has the primary responsibility for final content. Dr Matt R. Mulvey. The author was the supervisor of the study and assisted in writing up the paper. Acknowledgments Acknowledgments to my tutor Dr Matt R. Mulvey, whose valuable guidance made this possible. Furthermore, I am very grateful to University of Leeds. References Bowdon , M. , Marcovitz , P., Lain , S. K. et al. . ( 2018 ) Exercise training in ‘at Risk’ Black and White woman: a comparative cohort analyses , Medicine and Science in Sports and Exercise , 50 ( 7 ), 1350 – 1356 . Google Scholar Crossref Search ADS PubMed WorldCat Brennan , P. J. , Greenberg , G., Miall , W. E. et al. . ( 1982 ) Seasonal variation in arterial blood pressure , British Medical Journal , 258 , 919 – 923 . Google Scholar Crossref Search ADS WorldCat British Heart Foundation . ( 2017 ) BHF CVD statistics compendium 2017 , BHF , London . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC City of Bradford Metropolitan Council . Population, available at https://www.bradford.gov.uk/open-data/our-datasets/population/, accessed 21 January 2018. Clark , A. M. , DesMeules , M., Luo , W. et al. . ( 2009 ) Socioeconomic status and cardiovascular disease: risks and implications for care , Nature Reviews Cardiology , 6 ( 11 ), 712 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat Cundiff , D. K. and Agutter , P. S. ( 2016 ) Cardiovascular disease death before age 65 in 168 countries correlated statistically with biometrics, socioeconomic status, tobacco, gender, exercise, macronutrients and vitamin K , Cureus , 8 ( 8 ), 748 . Google Scholar OpenURL Placeholder Text WorldCat Department of Communities and Local Government . ( 2015 ) English Indices of Deprivation, available at https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015, accessed 11 June 2017. Floud , S. , Balkwill , A., Moser , K. et al. . Million Women Study Collaborators . ( 2016 ) The role of health- related behavioural factors in accounting for inequalities in coronary heart disease risk by education and area deprivation: prospective study of 1.2 million UK women , BMC Medicine , 14 ( 1 ), 145 . Google Scholar Crossref Search ADS PubMed WorldCat Hawkins , N. M. , Jhund , P. S., McMurray , J. J. et al. . ( 2012 ) Heart failure and socioeconomic status: accumulating evidence of inequality , European Journal of Heart Failure , 14 ( 2 ), 138 – 46 . Google Scholar Crossref Search ADS PubMed WorldCat Heart UK . ( 2017 ), available at http://www.heartuk.org.uk, accessed 14 August 2017. Kim , J. H. , Jeong , M. H., Park , I. H. et al. . ( 2014 ) The association of socioeconomic status with three-year clinical outcomes in patients with acute myocardial infarction who underwent percutaneous coronary intervention , Journal of Korean Medical Science , 29 , 536 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat Lawlor , D. A. , Davey Smith , G., Patel , R. et al. . ( 2005 ) Life-course socioeconomic position, area deprivation, and coronary heart disease: findings from the British Women’s Heart and Health Study , American Journal of Public Health , 95 , 91 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat Lewington , S. , Li , L., Sherliker , P. et al. . ( 2012 ) Seasonal variation in blood pressure and its relationship with outdoor temperature in 10 diverse regions of China: the China Kadoorie Biobank , Journal of Hypertension , 30 , 1383 – 1391 . Google Scholar Crossref Search ADS PubMed WorldCat Mackenbach , J. P. , Cavelaars , A. E. J. M., Kunst , A. E. et al. . ( 2000 ) Socioeconomic inequalities in cardiovascular disease mortality , European Heart Journal , 21 , 1141 – 51 . Google Scholar Crossref Search ADS PubMed WorldCat Makarvus , A. N. , Jauhar , R., Tortez , L. M. et al. . ( 2017 ) Comparison of the diameters of major epicardial coronary arteries by angiogram in Asian-Indians versus European Americans <40 years of age undergoing percutaneous coronary artery intervention , The American Journal of Cardiology , 120 ( 6 ), 924 – 926 . Google Scholar Crossref Search ADS PubMed WorldCat Massing , M. W. , Rosamond , W. D., Wing , S. B. et al. . ( 2004 ) Income, income inequality, and cardiovascular disease mortality: relations among countypopulations of the United States, 1985 to 1994 , Southern Medical Journal , 97 ( 5 ), 475 – 84 . Google Scholar Crossref Search ADS PubMed WorldCat Newman , J. D. , Schwartzbard , A. Z., Weintraub , H. S. et al. . ( 2017 ) Primary prevention of cardiovascular disease in diabetes mellitus , Journal of the American College of Cardiology , 70 ( 7 ), 883 – 93 . Google Scholar Crossref Search ADS PubMed WorldCat NHS . South Asian Specialist Intervention (SASI) system. video, available at https://www.england.nhs.uk/challenge-prizes/case-studies/, accessed on 21 January 2018. Pan , Y. , Song , T., Chen , R. et al. . ( 2016 ) Socioeconomic deprivation and mortality in people after ischemic stroke: the China National Stroke Registry , International Journal of Stroke , 11 ( 5 ), 557 – 64 . Google Scholar Crossref Search ADS PubMed WorldCat Pettinger , T. ( 2016 ). Policies to reduce poverty, available at http://www.economicshelp.org/macroeconomics/inequality/policies_reduce_poverty/, accessed 5 July 2017 Public Health England . ( 2016 ) Cardiovascular disease profiles, available at http://www.yhpho.org.uk/ncvincvd/, accessed 11 June 2017. Rawshani , A. , Svensson , A. M., Rosengren , A. et al. . ( 2015 ) Impact on socioeconomic status on cardiovascular disease and mortality in 24,947 individuals with type 1 diabetes , Diabetes Care , 38 ( 8 ), 1518 – 27 . Google Scholar Crossref Search ADS PubMed WorldCat Rose , G. ( 1985 ) Sick individuals and sick populations , International Journal of Epidimiology , 14 ( 1 ), 32 – 38 . Google Scholar Crossref Search ADS WorldCat Salomaa , V. , Niemela , M., Miettinem , H. et al. . ( 2000 ) Relationship of socioeconomic status to the incidence and prehospital, 28- day, and 1- yr mortality rates of acute coronary events in the FINMONICA myocardial infarction register study , Circulation , 101 , 1913 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Townsend , N. , Bhatnagar , P., Wilkins , E. et al. . ( 2015 ) Cardiovascular disease statistics 2014 , British Heart Foundation , London . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Tu , D. , Newcombe , R., Edwards , R. et al. . ( 2016 ) Socio-demographic characteristics of New Zealand adult smokers, ex-smokers and non-smokers: results from the 2013 Census , The New Zealand Medical Journal , 129 ( 1447 ), 43 – 56 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Yang , L. , Li , L., Lewington , S. et al. . ( 2015 ) Outdoor temperature, blood pressure, and cardiovascular disease mortality among 23 000 individuals with diagnosed cardiovascular disease in China , European Heart Journal , 36 ( 19 ), 1178 – 1185 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes Supervisor: Dr Matt R. Mulvey, Leeds Institute of Health Sciences, Room 10.39, Level 10, Worsley Building, Clarendon Way, Leeds, LS2 9NL, UK © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2018. Published by Oxford University Press. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BioScience Horizons Oxford University Press

The effect of deprivation on coronary heart disease mortality rate

The effect of deprivation on coronary heart disease mortality rate

BioScience Horizons , Volume 11 – Jan 1, 2018

Abstract

Abstract Deprivation and socioeconomic status are associated with cardiovascular diseases. The aim of this study is to describe a relationship between socioeconomic deprivation and coronary heart disease (CHD) mortality. This study used publicly available data for English Indices of Multiple Deprivation (IMD) and CHD mortality rate. IMD data is a ranked score based on seven factors related to socioeconomic status. The average IMD rank and the average CHD mortality rate were collected from the Office of National Statistics website for each Clinical Commissioning Group (CCG) in UK. To investigate the relationship between IMD score and CHD mortality a linear regression was fitted using logged CHD data, because the spread of the CHD data was largest for higher values of IMD. This achieved a more symmetrical distribution of the data. The results from the regression model were retransformed to values representing the original CHD data. Analysis was performed using data from 209 CCGs. CHD mortality rate for the lowest IMD ranked CCG is 27.6 and for the highest 97.4. Using the log-CHD values, the linear regression model revealed that for a one-point increase in IMD rank score, CHD mortality rate increases by 1.03 on average (95% CI 1.02–1.03; P < 0.001). These data show that areas with higher deprivation had higher rates of mortality from CHD. These findings are supported by previous research, which emphasize the important relationship between socioeconomic status and public health crises. coronary heart disease, mortality rate, clinical commissioning group, deprivation, socioeconomic status and epidemiology Introduction Cardiovascular diseases (CVDs) are a group of disorders of the heart and blood vessels, including stroke, coronary heart disease (CHD), hypertensive diseases and diseases of veins (Townsend et al., 2015). CVD is the number one cause of death globally accounting for 17.7 million deaths in 2015 (Townsend et al. 2015). In the UK, CVDs account for approximately 160 000 deaths each year (Townsend et al. 2015; British Heart Foundation, 2017). In 2014, CHD was the major contributor of NHS expenditure out of all the CVDs. It was also the leading cause of death for both males (15%) and females (10%) (Townsend et al. 2015). Disability and deaths from CVD can often be prevented with healthy lifestyles (Newman et al., 2017). Non-modifiable risk factors such as age, sex and family history as well as modifiable risk factors such as smoking, inactivity, type 2 diabetes, high blood pressure and obesity have been identified and linked to CVDs (Heart UK, 2017). A positive relationship between increasing socioeconomic deprivation and increasing CHD mortality rate was reported in many European countries as well as in the USA (Mackenbach et al., 2000; Hawkins et al., 2012). Deprivation is a comparative measure of an individual’s economic and social position (Clark et al., 2009). It is based on factors such as income, education, marital status, country of birth and comorbidities (Rawshani et al., 2015; Pan et al., 2016). A recent study by Cundiff et al. analyzed international population datasets from 168 countries of risk factors for CVD (Cundiff and Agutter, 2016). The authors identified a number of modifiable risk factors for CVD, including poor diet, tobacco smoke and air pollution all primarily associated with lower socioeconomic status in the countries studied. However, the relationship between CVD and socioeconomic deprivation in the UK remains unclear. This research report aims to establish the relationship between CHD mortality rate and socioeconomic deprivation in the UK. It is hypothesized that increased CHD mortality rate is associated with increased socioeconomic deprivation in the UK. Methods Study design and sampling The study design was a retrospective analysis of publically available population-level datasets in the UK. Using publicly available data from the Department for Communities and Local Government (2015), data on English indices of multiple deprivation (IMD) for 2015 was collected. A low IMD score is for an area with low deprivation and as the IMD rank increases, the deprivation increases as well. Data of IMD takes into account income, employment, education, health, crime, barriers and living at a Clinical Commissioning Group (CCG) level. The average IMD score is calculated by firstly taking into account a population-weighted measure of smaller areas within the CCG, the Lower-Layer Super Output Areas (LSOA). CCG LSOA deprivation is calculated taking into account income, employment, education, health, crime, barriers and living. The average scores of the LSOA in each CCG are then used to find the average IMD score for that CCG. The average score is taken for each CCG. Within NHS UK, there are 209 CCGs responsible for planning and commissioning of healthcare services for their local areas. The data on CHD mortality rate was obtained from National Cardiovascular Intelligence network (Public Health England, 2016), which was last updated on April 2016. As for IMD, CHD data was gathered for the 209 CCGs within NHS England. CHD mortality rate is crude CHD mortality rate per 100 000 deaths. CHD mortality data are reported as a specific sub-category for all CVDs deaths. There is also time restriction as the IMD data was collected in 2015 and CHD data collected in 2016. The data on deprivation is relatively stable over a 12-month period and therefore unlikely to have changed during the period of CHD data collection. Analysis Both datasets are numeric continues data types. The data were summarized using mean values, standard deviation and range. Data for CHD mortality rate is presented per 100 000 deaths. Data for IMD score is presented as mean and standard deviation. The spread of the CHD data around the line of best fit was largest for higher values of IMD (see Fig. 1) Therefore, CHD data was logged (using the natural log) which achieved a more symmetrical distribution of the data (see Fig. 2). Then, linear regression analysis was used to quantify the relationship between IMD rank and the log-CHD mortality rank for each CCG in the UK. Results from the linear regression model are presented as log-coefficient (log-95% confidence interval (CI)). In addition, to aid interpretation of the results, the regression model outputs were retransformed to values representing the original CHD units, using the exponential. The retransformed model outpars are presented as coefficient (95%CI). The statistical significance threshold was set at 0.05. In addition, the adjusted R-square was calculated in order to find out the percentage IMD average score that accounts for the CHD mortality rate. Figure 1. Open in new tabDownload slide Scatter plot demonstrating a significant relationship between CHD mortality rates against IMD average scores. Figure 2. Open in new tabDownload slide Scatter plot demonstrating a significant relationship between log-CHD mortality rates against IMD average scores. Regression diagnosis was undertaken by checking residuals-versus-fit plot for the normal CHD data and log-CHD data. Ethics Data was publicly available and therefore consent for use was not required. In addition, the data was anonymised and it only contained figures of CHD mortality rate for each CCG. Results Summary statistics calculated and presented in Table 1 shows the mean (standard deviation, SD) scores for IMD and crude CHD mortality rate per 100 000 deaths. The mean (SD) IMD rank was 21.91 (8.35). The lowest deprivation score (i.e. the least deprived CCG) of 5.652 was observed in Wokingham CCG. The highest IMD score (i.e. the most deprived) of 51.549 was observed in Bradford CCG. This represents almost a 10-fold increase in deprivation rank between the least and most deprived communities. The mean (SD) crude CHD mortality rate was 43.38 (12.01). The spread of CHD mortality was plotted against IMD rank (Fig. 1) this indicates a greater spread of the CHD data for higher vales of IMD. The CHD data was therefore logged. A scatter plot of log-CHD mortality rate over IMD score (Fig. 2) demonstrates that as CCG deprivation increases so does the crude CHD mortality rate. Table 1. Summary statistics for both datasets. (CHD mortality rate is presented per 100 00 deaths) . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 Open in new tab Table 1. Summary statistics for both datasets. (CHD mortality rate is presented per 100 00 deaths) . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 Open in new tab The linear regression model, using log-CHD outcome, revealed that for a one-point increase in IMD rank score, the log of CHD mortality rate increased by 0.03 (95%CI 0.02–0.03, P < 0.0001). The retransformed model parameters revealed that for a one-point increase in IMD rank score, the CHD mortality rate increased by 1.03 (95% CI 1.02–1.03; P < 0.0001). These findings demonstrate a 3% average increase in CHD mortality rate for each unit increase in IMD rank. The P-value of less than 0.001 indicates that there is a statistically significant association between increased deprivation and the risk of CHD mortality rate. It is therefore accepted that the evidence against the null hypothesis are strong and the null hypothesis can be rejected. The adjusted R-square value for the linear regression model was 0.73 indicating that 73% of the variability of CHD mortality rate is accounted for by the variability in IMD score. Discussion Significance of data These results demonstrate a significant positive relationship between socioeconomic deprivation and crude CHD mortality rate. However, it is important to note that liner regression analysis cannot demonstrate a causative relationship. These results are supported by other studies in this area. A recent cohort study following 1.2 million UK women without heart disease demonstrated a strong positive relationship between deprivation and CHD (P < 0.0001; Floud et al., 2016). Another cohort study aimed to explore the link between population-level income, income equality and CHD mortality (Massing et al., 2004). The authors of this study showed that the income inequality was directly related to CHD mortality. CHD mortality rate has been positively linked with socioeconomic deprivation in other studies carried out in other countries (Kim et al., 2014). However, studies in Korea (Kim et al. 2014) and Finland (Salomaa et al., 2000) that took into account different factors that influence socioeconomic status and particularly education demonstrated an inverse association of education and CHD mortality rate. These studies suggest that the relationship between CHD mortality and deprivation may be more complex than at first hypothesized. Potential cofounding factors are likely to be differences in education attainment, which is associated with individual lifestyle factors such as health and risk taking behaviors, smoking, access to healthy foods and regular exercise (Lawlor et al., 2005; Tu et al., 2016). UK, International policies and suggested interventions The data presented here indicate a need to reduce inequalities across regions in the UK. Currently, there are a number of national and international policies used in developed countries like UK and in developing countries (Pettinger, 2016). Sustained economic growth, reduce unemployment through both supply and demand, progressive taxes, increasing benefits to the poor, national minimum wage and benefits are some of the policies in developed countries. Education, aid and development of manufacturing are policies in developing countries. However, additional policies may be required that aim a general left shift of the whole population to decrease CHD. Strategies of preventative medicine as proposed by Rose (1985) aim the decrease of disease by targeting the whole population rather than the high-risk groups. Such policies will improve the health of the general population and therefore the CHD mortality rate will decrease. This can be achieved by educating the younger generations about lifestyle choices and the consequences of unhealthy habits like smoking, sedentary lifestyle and a diet high in saturated fats. This is more likely to be effective for the people with lower deprivation levels. Unfortunately, the education strategy will fail if healthy choices remain economically inaccessible for those of lower socioeconomic strata and therefore other policies must be established. Furthermore, data presented by Bowdon et al. (2018) indicates that areas with lower socioeconomic status (i.e. scoring higher IMD average scores) may be associated with populations that are required to work more hours than less deprived regions and consequently have less free time for regular exercise. Exercise is a very important environmental, modifiable factor that is linked to CVDs. Exercise has been proven to reduce well-established risk factors causing atherosclerosis leading to CVD, like hypertension, insulin resistance and glucose intolerance. These are all factors that contribute to poor health of the heart and the vessels. Introducing exercise is one of the ways to prevent CVDs and also help improve health in patients already suffering from CVDs. Some well-known risk factors for CHD are behavioral and include smoking, poor diet, physical inactivity and alcohol consumption (Townsend et al. 2015). An underlying cause of poor diet and physical inactivity can be suggested to be the socioeconomic deprivation as people of lower socioeconomic status is suggested that they have received little or no education around the importance of these two major contributors of CVDs an more specifically CHD and also prices of healthy food items are more expensive choices over the cheaper ‘fast food’. Therefore, deprivation is the root of the problem and hence it is the one that healthcare interventions should target. Another important parameter is the regional North–South divide in the UK that describes the general increasing deprivation in the northern regions of the UK compared with southern regions (with the exception of the South–West regions). However, this can be a potential pitfall when trying to interpret the relationship of IMD scores and CHD mortality rate and careful analysis must be done. NHS Bradford CCG, a city of the North, has the highest IMD score and also the highest CHD mortality rate. However, looking at the population composition, the district of Bradford has the largest proportion of people of Pakistani ethnic origin (20.3%) in the UK (City of Bradford Metropolitan Council, 2018). This is of a high importance as previous research papers have identified that CHD rate is higher among the Asian population as people of this ethnic group have been identified to have smaller diameter vessels raising the risk for atheromatous occlusion formation. This has also an impact on operations that become more challenging and consequently having more complications later (Makarvus et al., 2017). Therefore, after deriving a positive relationship between IMD scores and CHD mortality rate further analysis is needed to identify any potential high-risk groups that need special interventions. South Asian System of Intervention (SASI) has identified that the South Asian population has a six-time higher risk to suffer from Diabetes II and therefore has designed an intervention specifically targeting this population by offering simple education by a consultant and also clinics are held in Hindi, Punjabi, Urdu, Gunjarati, Bengali as well as in English in order to ensure that the patients get the most out of their consultation (NHS, 2018). This is a focused intervention in contrast to Rose proposition for the whole shift population intervention but when these two are used combined, an important decrease in CHD mortality rate may be achieved. Introducing this program to areas like Bradford could result in lower the CHD mortality rate. Furthermore, in the future, data analysis could be repeated and population divided into ethnicity groups in order to have clear view of the differences if any. A factor to be considered when taking into account the North–South divide and its relationship with deprivation and consequently its effect on CHD coronary arteries is weather and climate. According to a study (Yang et al., 2015), temperature has a significant effect on systolic blood pressure and CVD mortality as supported by other studies as well (Brennan et al., 1982; Lewington et al., 2012). It was presented that during winter, systolic blood pressure was significantly higher than in summer and systolic blood pressure was then used to predict mortality that was shown to be variable according to the season. In this research paper, data from all the CCG areas was used from North to South UK. The North parts have generally lower temperatures than South and it may be suggested that CHD mortality rates for the North CCGs is high due to the weather rather than due to the North–South divide relating to IMD average score. They may also act synergistically. Data limitations Unfortunately the two datasets, for the exposure and the outcome, do not much chronologically as the data of IMD was obtained in 2015 and for CHD mortality rate in 2016. There was not CHD data available for 2015 and this limits extend they are related. However, it can be assumed that the data of deprivation does not change over 1 year and hence the results of the linear regression analysis can be assumed to be reliable. In the future, when the study will be repeated, data could be collected on the same year to exclude this limitation and increase reliability of the results. Another very important limitation of the data is that both the CHD mortality rates and the IMD average scores are area- level statistics. This is an ecological study, an observational study, and data is analyzed at the level of population. Data represents the general picture of the whole population of a CCG but they cannot be used to make conclusions about individual level. In addition, the data boundaries for age, 0–74, are very wide and they therefore cannot provide any useful information for specific, smaller age groups to allow for effective intervention design and implementation. Conclusion The results suggest that CHD mortality rate is highly influenced by environmental factors that stem from low socioeconomic status and poverty. It is emphasized that there is a need for policies that will aim to reduce poverty in order to prevent CHD and therefore CHD mortality rate. Despite a global reduction in mortality rates, a further decrease must be achieved. The data used indicates that there is a greater burden of health related inequalities associated with individuals living in poverty. Current literature is focused on documenting health inequalities rather than explaining why these inequalities persist in order to try to tackle this problem. It is suggested that additional research should focus on understanding the factors that drive the relationship between socioeconomic status and mortality rate and proceed to the development of appropriate policies. Author Biography Lambrini Theocharidou, a third year undergraduate medical student at the University of Leeds Medical School and she is very interested in research with target to achieve to publish a paper. The author, designed the study, collected the data required and analyzed it and has the primary responsibility for final content. Dr Matt R. Mulvey. The author was the supervisor of the study and assisted in writing up the paper. Acknowledgments Acknowledgments to my tutor Dr Matt R. Mulvey, whose valuable guidance made this possible. Furthermore, I am very grateful to University of Leeds. References Bowdon , M. , Marcovitz , P., Lain , S. K. et al. . ( 2018 ) Exercise training in ‘at Risk’ Black and White woman: a comparative cohort analyses , Medicine and Science in Sports and Exercise , 50 ( 7 ), 1350 – 1356 . Google Scholar Crossref Search ADS PubMed WorldCat Brennan , P. J. , Greenberg , G., Miall , W. E. et al. . ( 1982 ) Seasonal variation in arterial blood pressure , British Medical Journal , 258 , 919 – 923 . Google Scholar Crossref Search ADS WorldCat British Heart Foundation . ( 2017 ) BHF CVD statistics compendium 2017 , BHF , London . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC City of Bradford Metropolitan Council . Population, available at https://www.bradford.gov.uk/open-data/our-datasets/population/, accessed 21 January 2018. Clark , A. M. , DesMeules , M., Luo , W. et al. . ( 2009 ) Socioeconomic status and cardiovascular disease: risks and implications for care , Nature Reviews Cardiology , 6 ( 11 ), 712 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat Cundiff , D. K. and Agutter , P. S. ( 2016 ) Cardiovascular disease death before age 65 in 168 countries correlated statistically with biometrics, socioeconomic status, tobacco, gender, exercise, macronutrients and vitamin K , Cureus , 8 ( 8 ), 748 . Google Scholar OpenURL Placeholder Text WorldCat Department of Communities and Local Government . ( 2015 ) English Indices of Deprivation, available at https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015, accessed 11 June 2017. Floud , S. , Balkwill , A., Moser , K. et al. . Million Women Study Collaborators . ( 2016 ) The role of health- related behavioural factors in accounting for inequalities in coronary heart disease risk by education and area deprivation: prospective study of 1.2 million UK women , BMC Medicine , 14 ( 1 ), 145 . Google Scholar Crossref Search ADS PubMed WorldCat Hawkins , N. M. , Jhund , P. S., McMurray , J. J. et al. . ( 2012 ) Heart failure and socioeconomic status: accumulating evidence of inequality , European Journal of Heart Failure , 14 ( 2 ), 138 – 46 . Google Scholar Crossref Search ADS PubMed WorldCat Heart UK . ( 2017 ), available at http://www.heartuk.org.uk, accessed 14 August 2017. Kim , J. H. , Jeong , M. H., Park , I. H. et al. . ( 2014 ) The association of socioeconomic status with three-year clinical outcomes in patients with acute myocardial infarction who underwent percutaneous coronary intervention , Journal of Korean Medical Science , 29 , 536 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat Lawlor , D. A. , Davey Smith , G., Patel , R. et al. . ( 2005 ) Life-course socioeconomic position, area deprivation, and coronary heart disease: findings from the British Women’s Heart and Health Study , American Journal of Public Health , 95 , 91 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat Lewington , S. , Li , L., Sherliker , P. et al. . ( 2012 ) Seasonal variation in blood pressure and its relationship with outdoor temperature in 10 diverse regions of China: the China Kadoorie Biobank , Journal of Hypertension , 30 , 1383 – 1391 . Google Scholar Crossref Search ADS PubMed WorldCat Mackenbach , J. P. , Cavelaars , A. E. J. M., Kunst , A. E. et al. . ( 2000 ) Socioeconomic inequalities in cardiovascular disease mortality , European Heart Journal , 21 , 1141 – 51 . Google Scholar Crossref Search ADS PubMed WorldCat Makarvus , A. N. , Jauhar , R., Tortez , L. M. et al. . ( 2017 ) Comparison of the diameters of major epicardial coronary arteries by angiogram in Asian-Indians versus European Americans <40 years of age undergoing percutaneous coronary artery intervention , The American Journal of Cardiology , 120 ( 6 ), 924 – 926 . Google Scholar Crossref Search ADS PubMed WorldCat Massing , M. W. , Rosamond , W. D., Wing , S. B. et al. . ( 2004 ) Income, income inequality, and cardiovascular disease mortality: relations among countypopulations of the United States, 1985 to 1994 , Southern Medical Journal , 97 ( 5 ), 475 – 84 . Google Scholar Crossref Search ADS PubMed WorldCat Newman , J. D. , Schwartzbard , A. Z., Weintraub , H. S. et al. . ( 2017 ) Primary prevention of cardiovascular disease in diabetes mellitus , Journal of the American College of Cardiology , 70 ( 7 ), 883 – 93 . Google Scholar Crossref Search ADS PubMed WorldCat NHS . South Asian Specialist Intervention (SASI) system. video, available at https://www.england.nhs.uk/challenge-prizes/case-studies/, accessed on 21 January 2018. Pan , Y. , Song , T., Chen , R. et al. . ( 2016 ) Socioeconomic deprivation and mortality in people after ischemic stroke: the China National Stroke Registry , International Journal of Stroke , 11 ( 5 ), 557 – 64 . Google Scholar Crossref Search ADS PubMed WorldCat Pettinger , T. ( 2016 ). Policies to reduce poverty, available at http://www.economicshelp.org/macroeconomics/inequality/policies_reduce_poverty/, accessed 5 July 2017 Public Health England . ( 2016 ) Cardiovascular disease profiles, available at http://www.yhpho.org.uk/ncvincvd/, accessed 11 June 2017. Rawshani , A. , Svensson , A. M., Rosengren , A. et al. . ( 2015 ) Impact on socioeconomic status on cardiovascular disease and mortality in 24,947 individuals with type 1 diabetes , Diabetes Care , 38 ( 8 ), 1518 – 27 . Google Scholar Crossref Search ADS PubMed WorldCat Rose , G. ( 1985 ) Sick individuals and sick populations , International Journal of Epidimiology , 14 ( 1 ), 32 – 38 . Google Scholar Crossref Search ADS WorldCat Salomaa , V. , Niemela , M., Miettinem , H. et al. . ( 2000 ) Relationship of socioeconomic status to the incidence and prehospital, 28- day, and 1- yr mortality rates of acute coronary events in the FINMONICA myocardial infarction register study , Circulation , 101 , 1913 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Townsend , N. , Bhatnagar , P., Wilkins , E. et al. . ( 2015 ) Cardiovascular disease statistics 2014 , British Heart Foundation , London . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Tu , D. , Newcombe , R., Edwards , R. et al. . ( 2016 ) Socio-demographic characteristics of New Zealand adult smokers, ex-smokers and non-smokers: results from the 2013 Census , The New Zealand Medical Journal , 129 ( 1447 ), 43 – 56 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Yang , L. , Li , L., Lewington , S. et al. . ( 2015 ) Outdoor temperature, blood pressure, and cardiovascular disease mortality among 23 000 individuals with diagnosed cardiovascular disease in China , European Heart Journal , 36 ( 19 ), 1178 – 1185 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes Supervisor: Dr Matt R. Mulvey, Leeds Institute of Health Sciences, Room 10.39, Level 10, Worsley Building, Clarendon Way, Leeds, LS2 9NL, UK © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2018. Published by Oxford University Press.

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Abstract

Abstract Deprivation and socioeconomic status are associated with cardiovascular diseases. The aim of this study is to describe a relationship between socioeconomic deprivation and coronary heart disease (CHD) mortality. This study used publicly available data for English Indices of Multiple Deprivation (IMD) and CHD mortality rate. IMD data is a ranked score based on seven factors related to socioeconomic status. The average IMD rank and the average CHD mortality rate were collected from the Office of National Statistics website for each Clinical Commissioning Group (CCG) in UK. To investigate the relationship between IMD score and CHD mortality a linear regression was fitted using logged CHD data, because the spread of the CHD data was largest for higher values of IMD. This achieved a more symmetrical distribution of the data. The results from the regression model were retransformed to values representing the original CHD data. Analysis was performed using data from 209 CCGs. CHD mortality rate for the lowest IMD ranked CCG is 27.6 and for the highest 97.4. Using the log-CHD values, the linear regression model revealed that for a one-point increase in IMD rank score, CHD mortality rate increases by 1.03 on average (95% CI 1.02–1.03; P < 0.001). These data show that areas with higher deprivation had higher rates of mortality from CHD. These findings are supported by previous research, which emphasize the important relationship between socioeconomic status and public health crises. coronary heart disease, mortality rate, clinical commissioning group, deprivation, socioeconomic status and epidemiology Introduction Cardiovascular diseases (CVDs) are a group of disorders of the heart and blood vessels, including stroke, coronary heart disease (CHD), hypertensive diseases and diseases of veins (Townsend et al., 2015). CVD is the number one cause of death globally accounting for 17.7 million deaths in 2015 (Townsend et al. 2015). In the UK, CVDs account for approximately 160 000 deaths each year (Townsend et al. 2015; British Heart Foundation, 2017). In 2014, CHD was the major contributor of NHS expenditure out of all the CVDs. It was also the leading cause of death for both males (15%) and females (10%) (Townsend et al. 2015). Disability and deaths from CVD can often be prevented with healthy lifestyles (Newman et al., 2017). Non-modifiable risk factors such as age, sex and family history as well as modifiable risk factors such as smoking, inactivity, type 2 diabetes, high blood pressure and obesity have been identified and linked to CVDs (Heart UK, 2017). A positive relationship between increasing socioeconomic deprivation and increasing CHD mortality rate was reported in many European countries as well as in the USA (Mackenbach et al., 2000; Hawkins et al., 2012). Deprivation is a comparative measure of an individual’s economic and social position (Clark et al., 2009). It is based on factors such as income, education, marital status, country of birth and comorbidities (Rawshani et al., 2015; Pan et al., 2016). A recent study by Cundiff et al. analyzed international population datasets from 168 countries of risk factors for CVD (Cundiff and Agutter, 2016). The authors identified a number of modifiable risk factors for CVD, including poor diet, tobacco smoke and air pollution all primarily associated with lower socioeconomic status in the countries studied. However, the relationship between CVD and socioeconomic deprivation in the UK remains unclear. This research report aims to establish the relationship between CHD mortality rate and socioeconomic deprivation in the UK. It is hypothesized that increased CHD mortality rate is associated with increased socioeconomic deprivation in the UK. Methods Study design and sampling The study design was a retrospective analysis of publically available population-level datasets in the UK. Using publicly available data from the Department for Communities and Local Government (2015), data on English indices of multiple deprivation (IMD) for 2015 was collected. A low IMD score is for an area with low deprivation and as the IMD rank increases, the deprivation increases as well. Data of IMD takes into account income, employment, education, health, crime, barriers and living at a Clinical Commissioning Group (CCG) level. The average IMD score is calculated by firstly taking into account a population-weighted measure of smaller areas within the CCG, the Lower-Layer Super Output Areas (LSOA). CCG LSOA deprivation is calculated taking into account income, employment, education, health, crime, barriers and living. The average scores of the LSOA in each CCG are then used to find the average IMD score for that CCG. The average score is taken for each CCG. Within NHS UK, there are 209 CCGs responsible for planning and commissioning of healthcare services for their local areas. The data on CHD mortality rate was obtained from National Cardiovascular Intelligence network (Public Health England, 2016), which was last updated on April 2016. As for IMD, CHD data was gathered for the 209 CCGs within NHS England. CHD mortality rate is crude CHD mortality rate per 100 000 deaths. CHD mortality data are reported as a specific sub-category for all CVDs deaths. There is also time restriction as the IMD data was collected in 2015 and CHD data collected in 2016. The data on deprivation is relatively stable over a 12-month period and therefore unlikely to have changed during the period of CHD data collection. Analysis Both datasets are numeric continues data types. The data were summarized using mean values, standard deviation and range. Data for CHD mortality rate is presented per 100 000 deaths. Data for IMD score is presented as mean and standard deviation. The spread of the CHD data around the line of best fit was largest for higher values of IMD (see Fig. 1) Therefore, CHD data was logged (using the natural log) which achieved a more symmetrical distribution of the data (see Fig. 2). Then, linear regression analysis was used to quantify the relationship between IMD rank and the log-CHD mortality rank for each CCG in the UK. Results from the linear regression model are presented as log-coefficient (log-95% confidence interval (CI)). In addition, to aid interpretation of the results, the regression model outputs were retransformed to values representing the original CHD units, using the exponential. The retransformed model outpars are presented as coefficient (95%CI). The statistical significance threshold was set at 0.05. In addition, the adjusted R-square was calculated in order to find out the percentage IMD average score that accounts for the CHD mortality rate. Figure 1. Open in new tabDownload slide Scatter plot demonstrating a significant relationship between CHD mortality rates against IMD average scores. Figure 2. Open in new tabDownload slide Scatter plot demonstrating a significant relationship between log-CHD mortality rates against IMD average scores. Regression diagnosis was undertaken by checking residuals-versus-fit plot for the normal CHD data and log-CHD data. Ethics Data was publicly available and therefore consent for use was not required. In addition, the data was anonymised and it only contained figures of CHD mortality rate for each CCG. Results Summary statistics calculated and presented in Table 1 shows the mean (standard deviation, SD) scores for IMD and crude CHD mortality rate per 100 000 deaths. The mean (SD) IMD rank was 21.91 (8.35). The lowest deprivation score (i.e. the least deprived CCG) of 5.652 was observed in Wokingham CCG. The highest IMD score (i.e. the most deprived) of 51.549 was observed in Bradford CCG. This represents almost a 10-fold increase in deprivation rank between the least and most deprived communities. The mean (SD) crude CHD mortality rate was 43.38 (12.01). The spread of CHD mortality was plotted against IMD rank (Fig. 1) this indicates a greater spread of the CHD data for higher vales of IMD. The CHD data was therefore logged. A scatter plot of log-CHD mortality rate over IMD score (Fig. 2) demonstrates that as CCG deprivation increases so does the crude CHD mortality rate. Table 1. Summary statistics for both datasets. (CHD mortality rate is presented per 100 00 deaths) . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 Open in new tab Table 1. Summary statistics for both datasets. (CHD mortality rate is presented per 100 00 deaths) . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 . Mean . Standard deviation . Minimum . Maximum . Range . IMD average scores 21.91 8.35 5.65 51.55 45.90 CHD mortality rate 43.38 12.01 20.70 97.40 76.70 Open in new tab The linear regression model, using log-CHD outcome, revealed that for a one-point increase in IMD rank score, the log of CHD mortality rate increased by 0.03 (95%CI 0.02–0.03, P < 0.0001). The retransformed model parameters revealed that for a one-point increase in IMD rank score, the CHD mortality rate increased by 1.03 (95% CI 1.02–1.03; P < 0.0001). These findings demonstrate a 3% average increase in CHD mortality rate for each unit increase in IMD rank. The P-value of less than 0.001 indicates that there is a statistically significant association between increased deprivation and the risk of CHD mortality rate. It is therefore accepted that the evidence against the null hypothesis are strong and the null hypothesis can be rejected. The adjusted R-square value for the linear regression model was 0.73 indicating that 73% of the variability of CHD mortality rate is accounted for by the variability in IMD score. Discussion Significance of data These results demonstrate a significant positive relationship between socioeconomic deprivation and crude CHD mortality rate. However, it is important to note that liner regression analysis cannot demonstrate a causative relationship. These results are supported by other studies in this area. A recent cohort study following 1.2 million UK women without heart disease demonstrated a strong positive relationship between deprivation and CHD (P < 0.0001; Floud et al., 2016). Another cohort study aimed to explore the link between population-level income, income equality and CHD mortality (Massing et al., 2004). The authors of this study showed that the income inequality was directly related to CHD mortality. CHD mortality rate has been positively linked with socioeconomic deprivation in other studies carried out in other countries (Kim et al., 2014). However, studies in Korea (Kim et al. 2014) and Finland (Salomaa et al., 2000) that took into account different factors that influence socioeconomic status and particularly education demonstrated an inverse association of education and CHD mortality rate. These studies suggest that the relationship between CHD mortality and deprivation may be more complex than at first hypothesized. Potential cofounding factors are likely to be differences in education attainment, which is associated with individual lifestyle factors such as health and risk taking behaviors, smoking, access to healthy foods and regular exercise (Lawlor et al., 2005; Tu et al., 2016). UK, International policies and suggested interventions The data presented here indicate a need to reduce inequalities across regions in the UK. Currently, there are a number of national and international policies used in developed countries like UK and in developing countries (Pettinger, 2016). Sustained economic growth, reduce unemployment through both supply and demand, progressive taxes, increasing benefits to the poor, national minimum wage and benefits are some of the policies in developed countries. Education, aid and development of manufacturing are policies in developing countries. However, additional policies may be required that aim a general left shift of the whole population to decrease CHD. Strategies of preventative medicine as proposed by Rose (1985) aim the decrease of disease by targeting the whole population rather than the high-risk groups. Such policies will improve the health of the general population and therefore the CHD mortality rate will decrease. This can be achieved by educating the younger generations about lifestyle choices and the consequences of unhealthy habits like smoking, sedentary lifestyle and a diet high in saturated fats. This is more likely to be effective for the people with lower deprivation levels. Unfortunately, the education strategy will fail if healthy choices remain economically inaccessible for those of lower socioeconomic strata and therefore other policies must be established. Furthermore, data presented by Bowdon et al. (2018) indicates that areas with lower socioeconomic status (i.e. scoring higher IMD average scores) may be associated with populations that are required to work more hours than less deprived regions and consequently have less free time for regular exercise. Exercise is a very important environmental, modifiable factor that is linked to CVDs. Exercise has been proven to reduce well-established risk factors causing atherosclerosis leading to CVD, like hypertension, insulin resistance and glucose intolerance. These are all factors that contribute to poor health of the heart and the vessels. Introducing exercise is one of the ways to prevent CVDs and also help improve health in patients already suffering from CVDs. Some well-known risk factors for CHD are behavioral and include smoking, poor diet, physical inactivity and alcohol consumption (Townsend et al. 2015). An underlying cause of poor diet and physical inactivity can be suggested to be the socioeconomic deprivation as people of lower socioeconomic status is suggested that they have received little or no education around the importance of these two major contributors of CVDs an more specifically CHD and also prices of healthy food items are more expensive choices over the cheaper ‘fast food’. Therefore, deprivation is the root of the problem and hence it is the one that healthcare interventions should target. Another important parameter is the regional North–South divide in the UK that describes the general increasing deprivation in the northern regions of the UK compared with southern regions (with the exception of the South–West regions). However, this can be a potential pitfall when trying to interpret the relationship of IMD scores and CHD mortality rate and careful analysis must be done. NHS Bradford CCG, a city of the North, has the highest IMD score and also the highest CHD mortality rate. However, looking at the population composition, the district of Bradford has the largest proportion of people of Pakistani ethnic origin (20.3%) in the UK (City of Bradford Metropolitan Council, 2018). This is of a high importance as previous research papers have identified that CHD rate is higher among the Asian population as people of this ethnic group have been identified to have smaller diameter vessels raising the risk for atheromatous occlusion formation. This has also an impact on operations that become more challenging and consequently having more complications later (Makarvus et al., 2017). Therefore, after deriving a positive relationship between IMD scores and CHD mortality rate further analysis is needed to identify any potential high-risk groups that need special interventions. South Asian System of Intervention (SASI) has identified that the South Asian population has a six-time higher risk to suffer from Diabetes II and therefore has designed an intervention specifically targeting this population by offering simple education by a consultant and also clinics are held in Hindi, Punjabi, Urdu, Gunjarati, Bengali as well as in English in order to ensure that the patients get the most out of their consultation (NHS, 2018). This is a focused intervention in contrast to Rose proposition for the whole shift population intervention but when these two are used combined, an important decrease in CHD mortality rate may be achieved. Introducing this program to areas like Bradford could result in lower the CHD mortality rate. Furthermore, in the future, data analysis could be repeated and population divided into ethnicity groups in order to have clear view of the differences if any. A factor to be considered when taking into account the North–South divide and its relationship with deprivation and consequently its effect on CHD coronary arteries is weather and climate. According to a study (Yang et al., 2015), temperature has a significant effect on systolic blood pressure and CVD mortality as supported by other studies as well (Brennan et al., 1982; Lewington et al., 2012). It was presented that during winter, systolic blood pressure was significantly higher than in summer and systolic blood pressure was then used to predict mortality that was shown to be variable according to the season. In this research paper, data from all the CCG areas was used from North to South UK. The North parts have generally lower temperatures than South and it may be suggested that CHD mortality rates for the North CCGs is high due to the weather rather than due to the North–South divide relating to IMD average score. They may also act synergistically. Data limitations Unfortunately the two datasets, for the exposure and the outcome, do not much chronologically as the data of IMD was obtained in 2015 and for CHD mortality rate in 2016. There was not CHD data available for 2015 and this limits extend they are related. However, it can be assumed that the data of deprivation does not change over 1 year and hence the results of the linear regression analysis can be assumed to be reliable. In the future, when the study will be repeated, data could be collected on the same year to exclude this limitation and increase reliability of the results. Another very important limitation of the data is that both the CHD mortality rates and the IMD average scores are area- level statistics. This is an ecological study, an observational study, and data is analyzed at the level of population. Data represents the general picture of the whole population of a CCG but they cannot be used to make conclusions about individual level. In addition, the data boundaries for age, 0–74, are very wide and they therefore cannot provide any useful information for specific, smaller age groups to allow for effective intervention design and implementation. Conclusion The results suggest that CHD mortality rate is highly influenced by environmental factors that stem from low socioeconomic status and poverty. It is emphasized that there is a need for policies that will aim to reduce poverty in order to prevent CHD and therefore CHD mortality rate. Despite a global reduction in mortality rates, a further decrease must be achieved. The data used indicates that there is a greater burden of health related inequalities associated with individuals living in poverty. Current literature is focused on documenting health inequalities rather than explaining why these inequalities persist in order to try to tackle this problem. It is suggested that additional research should focus on understanding the factors that drive the relationship between socioeconomic status and mortality rate and proceed to the development of appropriate policies. Author Biography Lambrini Theocharidou, a third year undergraduate medical student at the University of Leeds Medical School and she is very interested in research with target to achieve to publish a paper. The author, designed the study, collected the data required and analyzed it and has the primary responsibility for final content. Dr Matt R. Mulvey. The author was the supervisor of the study and assisted in writing up the paper. Acknowledgments Acknowledgments to my tutor Dr Matt R. Mulvey, whose valuable guidance made this possible. Furthermore, I am very grateful to University of Leeds. References Bowdon , M. , Marcovitz , P., Lain , S. K. et al. . 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Mulvey, Leeds Institute of Health Sciences, Room 10.39, Level 10, Worsley Building, Clarendon Way, Leeds, LS2 9NL, UK © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2018. Published by Oxford University Press.

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BioScience HorizonsOxford University Press

Published: Jan 1, 2018

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