Population characteristics of stigma and chronic health conditions.
Dr Dermot O’Reilly, Dr Michael Rosato, Dr David Wright, Dr Aideen Maguire & Ms Foteini Tseliou
Queen’s University Belfast and University of Ulster
The stigmatisation of individuals is believed to have various negative consequences. In terms of health care, for example, it is frequently argued that stigma functions so as to impede help seeking behaviour - such as would be evident in the seeking of a diagnosis or form of treatment. The impact of stigma in impeding help seeking has also been observed in relation to many other conditions such as, STDs, HIV/AIDS, epilepsy, Huntington’s disease and even (though to a lesser degree) relatively common disorders such as diabetes.As well as functioning as an impediment to diagnosis and treatment, it has also been argued that stigma can have negative effects on an individual’s sense of wellbeing and emotional stability. Attempts to measure or assess the extent to which stigmatisation is actually practiced or perceived have normally been undertaken by means of community surveys on attitudes and perceptions or reports concerning stigma. However, to our knowledge no-one has sought to assess the prevalence of stigma using unobtrusive methods and large data sets such as are proposed here.
The overall aim of the proposed study is to assess the perceived sigma, by comparing self-reported and objectively measured prevalence of a disease, specifically
1. To measure the extent of perceived stigma across a range of conditions. The proposed range of diseases will allow a measure of the variation in perceived stigma; for example it is anticipated that disclosure will be significantly lower for patient’s epilepsy and mental health problems than for those with asthma or diabetes.
2. To measure the socio-demographic, socio-economic and spatial factors associated with stigma:
a. By comparing those with a disease/condition and the non-use of appropriate medication it will be possible to assess the levels and social characteristics of those with unmet need.
b. Linking BSO data to Census records shows that there are a proportionof people who cannot be matched to the NILS database. Previous work by this group has demonstrated that this is most often due to non-enumeration. By comparing the characteristics of this group derived from BSO data (area characteristics, EPD consumption, institutional residence etc) it will provide a better understanding of the representativeness of those in NILS. This will be very important for those undertaking studies relating to the health of NILS members.
Publications to Date:
- Wright, D.M., Rosato, M. & O’Reilly, D. (2016) 'Which long-term illnesses do patients find most limiting? A census-based cross-sectional study of 340,000 people' Int J Public Health (2016). doi:10.1007/s00038-016-0929-2
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