Self-reported health, socio-economic status and area of residence: understanding the relationships

Dermot O'Reilly and Michael Rosato, Queen's University Belfast

[Project number 30027]

Traditionally studies to examine the variations in health status between populations such as in health inequalities have used mortality data [1,2] as they are readily available and easily understood. Measures of morbidity on the other hand, are difficult to obtain and the causes of variation more open to interpretation [3]. The latter are usually based upon self-reported measures of health, which depend not only on ‘objective health’ but also on individual response to this and on the propensity to report health problems [4]. Though self-assessed health has been shown to predict health care use [5] and subsequent mortality [6], questions remain about the subjectivity of the responses.

It has been shown that for a given level of ill health, economically better off areas are more likely to report good health and that regions in the North of England report higher levels of LLTI than would be expected from their mortality rates [7,8]. This may not indicate additional ‘needs’ over and above those suggested by mortality rates, but could reflect the additional influence of socio-economic and cultural factors [9] making interpretation for resource allocation purposes difficult. Variations in levels of self-reported health status may also be influenced by age, sex and ethnicity [10-13].

It is important therefore to better understand the relationship between Self Reported Health and more ‘objective’ measures of health.

The proposed study aims to look at how the relationship between LLTI and future mortality is modified by factors such as demography, socio-economic status and area of residence. The theory is that mortality (for non-injury related causes of death) is more closely related to underlying ‘objective’ health and that differences in the relative hazards between, say, older and younger people or men and women, reflects the underlying effect modification on the relationship between objective and self-reported health status.

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In this study we aim to explore the predictive power (and variations in the predictive power) of LLTI, as measured at the 1991 census, and subsequent mortality over the following 10-12 years. We are particularly interested in any effect modification associated with demographic factors (such as age, sex, ethnicity), socioeconomic status (based on a combination of tenure, overcrowding, car availability, unemployment, and social class) and area of residence (Standard Region).

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