The Impact Of Socio-Economic Deprivation On The Health Of The Population

Hypothesis 1: As overall deprivation (outlined by the IMD) increases, the health of the population decreases. The main aim for this investigation was to determine to what extent varying levels of socio-economic deprivation, outlined by the Index for Multiple Deprivation (IMD), influence health inequalities and perception of health and attitudes towards the NHS in Harlow. The seven domains influencing IMD rank are summarised in the methodology, however the weighting of these factors in determining the rank of the LSOA is unclear and hence the influence of each individual factor on health inequalities cannot be determined. Despite this, my primary data collection has allowed me to spot trends between deprivation and a range of both quantitative and qualitative measures of health and attitudes towards the National Healthcare Service (NHS). The prevalence of chronic health conditions (CHCs) is one example of a health inequality created by varying levels of deprivation. Figure 11 shows the cumulative frequency of CHCs in 5 households in each LSOA I sampled.

The spatial variation in relation to IMD rank and percentile is further defined by figure 12, a choropleth GIS map. Whilst the data displayed in figure 10, suggests that there is the highest number of CHCs (9) present in LSOA 003C that ranks 3rd highest out of the LSOAs I sampled, the small scale at which the data was collected and the overall low number of diseases, means this data is not really representative of the LSOAs as a whole. The data does clearly show that, with the exceptions of LSOAs 007E and 005C, asthma is the most common CHC affecting people in all LSOAs. Whilst the link between the socio-economic factor of living environment and asthma has been hypothesised, it has been scientifically proven, that asthma is not independently associated with deprivation, but the symptoms may be aggravated by active or passive smoking, smoking being a characteristic associated with low socio-economic status. Research has also shown that the other CHCs - anxiety and depression, Type 2 diabetes and heart conditions are all to a large extent related to socio-economic aspects of a person’s lifestyle. Anxiety and depression can be induced by low socio-economic status, which often causes social isolation (Luthar, 2007). Unhealthy eating habits, lack of exercise smoking, drug abuse and other poor lifestyle choices, often induced by socio-economic factors have been clinically proven to cause CHCs such as diabetes and heart conditions.

Furthermore, poor nutrition during pregnancy, as well as smoking and exposure to stress, all of which are linked with low socio-economic status, can contribute to low birth weight, which in turn has been linked to adult coronary heart disease, blood pressure and diabetes. This is just a few of many mechanisms that demonstrate how deprivation can in the long-term affect chronic health.

From the data compiled in figures 13 and 14, and spatially illustrated in figure 15, it is evident that the percentage of households where someone has been hospitalised in the past 5 years in the LSOAs I sampled in my primary data collection also increases with deprivation. Recent findings have shown that the nation’s poorest young people are almost 70% more likely to end up in A&E than their less deprived counterparts. This is because education, diet, environment and the pressures on families living in deprived areas mean poor children often end up in hospital when their health issues could have been diagnosed earlier. Furthermore, people with diabetes from deprived backgrounds in England are twice as likely to end up in hospital with a heart attack or stroke than those who are better off, according to a study by Imperial College, London. In this study, socio-economic position was classified using all seven domains of the IMD. My primary data shown in figure 14 simplistically demonstrates how the percentage of households where someone has been hospitalised in the past 5 years is highest in the LSOAs in the 20% most deprived bracket (53. 3%) and lowest in the 20% least deprived bracket (26. 7%). My data, along with the finding from the aforementioned study highlight the importance of improved risk stratification strategies and outreach population-based policy interventions to reduce inequalities health outcomes that result in hospitalisation. In my primary data collection, I collected data on how people personally rate their health. Whilst this is open to subjectivity and bias, self-rated health (SRH) shows strong associations with measures of health and well-being. Many studies have used self-rated mental health (SRMH) as a predictor of various outcomes, independently or together with SRH. (Magwene et al. , 2017).

My primary data collection found that the relationship between these two health-related characteristics differed across Harlow LSOAs with varying levels of deprivation, however despite trends in the data, the correlation between data sets was not significant at a significance level of 0. 05. My rs value for the Spearman’s rank correlation coefficient between general health and IMD rank was -0. 554, which is smaller than the critical value for 9 observations (0. 683). My rs value for the Spearman’s rank correlation coefficient between mental health and IMD rank was 0. 400, which is also smaller than the critical value for 9 observations (0. 683). Both however suggested a weak correlation. Therefore, as I was unable to gain statistically significant results to back up my hypothesis that high IMD rank results in poor perceived mental and general health, I used census data from the Office of National Statistics (ONS) to back up my hypothesis that those in lower deprivation areas would rate their overall health more highly.

However, this data is equally subjective, as someone who has ill health may view themselves as very healthy as many people are in denial or oblivious to their health issues. Figure 22, spatially presents the data as pie charts on a GIS map showing the IMD rank of the LSOAs. Here there is a correlation between percentage of people who rate their health as very good and IMD rank. The 2nd most deprived LSOA 002C has the lowest percentage of people rating their health as very good (40. 41%) and the least deprive LSOA 005C has the highest percentage rating their health as very good (53. 62%). 005C does have slightly anomalous results for fair and bad health as very few people ticked these boxes in the LSOA and the very bad health percentage (2. 25%) is anomalous for LSOA 003A, as it is significantly higher than the others (figure 21). This data is very representative of the LSOAs, as everyone is legally required to complete the census, meaning all household in the LSOAs are accounted for. The relationship between deprivation and number of sick days taken by workers is a complex one. In deprived areas where workers generally do not have paid sick leave, more people go into work when they are ill, especially in circumstances where people are living ‘hand-to-mouth’.

However, it could equally be argued that due to ill health in deprived areas as a result of a combination of poor social policies, unfair economic disadvantages and bad politics (Marmot, 2010) people in deprived areas require (and take) more sick days. My primary data concerning sick days taken by respondents of my survey in each LSOA has proven to be inconclusive. 80% of the people I spoke to from LSOAs 007E and 001C claim they take zero ‘sick days’ in a year. 007E is the most deprived LSOA and 001C is amongst the 20% least deprived, thus illustrating there is no clear-cut trend in my findings. LSOA 004E also appears to be an anomaly with 40% of respondents saying they call in sick 10 or more days of the year. The GIS map (figure 20) layers pie charts showing the proportion of respondents in each ‘sick days taken’ bracket onto the IMD rank choropleth map. One potential reason why I am unable to identify any correlations between the two variable is because as a large proportion respondents to my survey were retired, it was therefore difficult to place them in any of the following brackets, (zero, 1-4, 5-9 or 10+). I therefore consistently put all retired people in the zero ‘sick days taken’ bracket, but this would have significantly skewed my results.

The word cloud, figure 25, illustrates how much attitudes towards the NHS can vary based on levels of deprivation. With the exception of a few respondents, I found that most people from the 20% least deprived LSOAs used positive adjectives such as ‘kind, helpful, crucial, excellent, friendly and caring’ when asked to describe the NHS. Those in the 40% least deprived LSOA used adjectives such as ‘necessary, struggling, thoughtful, short-staffed, pressured and professional’ to describe the NHS. And people from the 20% most deprived LSOAs used more negative descriptors such as ‘okay, slow, bad, terrible, rubbish and sh*t’ to describe the NHS. I also found that 15% of people used the adjective ‘good’, suggesting many people are unsure where they stand with respect to their opinion of the NHS. Figures 23 and 24 are word clouds I used to display the positive and negative aspects of the local area according to LSOA respondents. The outcome suggested that other IMD factors that could be influencing how people rate their health are aspects of their living environment including quality of housing, roads, noise levels and green spaces. In lower deprivation LSOAs. Many people from more deprived LSOAs said that noise was a negative aspect of their local area and while others from less deprived LSOAs said the quiet was what they liked most about their local area.