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Health and the 2008 Economic Recession: Evidence From UK
鉴于失业率和健康状况不佳之间的关系,2008年以来,在英国和美国,裁员人数的急剧增加已经为公共健康领域的评论家敲响了警钟。而上一次在美国欧洲,经济衰退对健康的影响的证据是模棱两可。从最近的这次危机初步发现似乎证实了对自杀人数上升的担忧。
2008年的经济衰退对每个人的影响程度都相同是不可能的。失业者健康状况倾向于比未失业者健康状况要差,但常规/体力劳动职业人也遭受了比同样从事管理岗位或专业性岗位更多的健康问题。这是在2008年和2009年之间,英国的制造业就业人数出现了最大的下降。源于失业和增加的工作不安全感带来的健康问题因此很可能对常规/体力劳动职业中失业者和未失业影响不同。从行业风险空间指数来看,英国的一些地区的人很可能比其他地区的人更容易受到经济衰退的影响,进而有可能扩大地理不平等健康系数。
Given the association between unemployment and poor health [1], [2] the rapid increase in layoffs from 2008 onwards in the United Kingdom (UK) and United States (US) has raised alarm bells among many public health commentators [3], [4], [5], [6], [7]. While evidence of an effect of previous recessions on health in the US [8], [9], [10], [11], Europe [12], [13], [14], [15], [16], [17], [18] and Asia [19], [20] is equivocal, preliminary findings from the most recent crisis appear to confirm fears of a rise in the number of suicides [17], [18].
It is unlikely that the 2008 economic recession has influenced everyone to the same extent. The unemployed tend to have poorer health than the employed [1], [2], but people in routine/manual labour occupations are also known to suffer more health problems than their peers in managerial/professional occupations [21]. It was the UK's manufacturing sector which experienced the most substantial fall in employment between 2008 and 2009 [22]. Health problems stemming from job loss and increased job insecurity [9], [10], [23], [24] are therefore likely to have been borne disproportionately among the unemployed and those employed in routine/manual labour occupations. As industries at risk are spatially patterned, some areas of the UK were likely to be more vulnerable to the effects of the recession than others, potentially widening geographical inequalities in health [18].
Using a large survey repeated every three months from January 2006 to December 2010, we investigated to what extent poor health status, and inequalities thereof according to geographical and socioeconomic circumstance, rose during the 2008 economic recession.
Research in this field, according to Catalano and colleagues [10], can be classified into two groups characterised by design: i) risk factor studies; and ii) net effect studies. Risk factor studies are those which attempt to identify the effects of unfavourable economic circumstances among individuals, such as financial insecurity or involuntary job loss, on their health and behaviours. In contrast, net effect studies provide an insight into the sum of the economic effects on population health by using groups or geographical areas as the analytical units, and estimating temporal change in association between economic indicators (such as the rate of unemployment) and prevalence or incidence rates.
In this paper, we used individual-level data on health status, demographic and socioeconomic circumstances extracted from the UK Quarterly Labour Force Survey (QLFS) [25] to conduct research most akin to the ‘net effects’ study design. The QLFS is among the largest regular social surveys in the UK, collecting cross-sectional information every three months (‘quarters’) from over 100,000 people living in private households across all areas of England, Scotland, Wales and Northern Ireland. To capture circumstances before, during and since the economic recession which began in 2008, we pooled 60 months (20 quarters) of the QLFS from January 2006 to December 2010 to create a dataset of 2.38 million survey responses (1.15 million men, 1.23 million women). Our focus was on the health of the working-age population, so we kept all women aged 16 y–59 y and men aged 16 y–64 y in our data (reflecting gender differences in the national retirement age). This left a sample of 1.36 million survey responses, approximately 57% of the original dataset surveyed across 20 quarters from 2006 to 2010. This very large sample size and high frequency of update were key reasons for using the QLFS in our study, in comparison to alternative sources of data which are less frequent (e.g. the decennial UK Census) or have far smaller sample sizes (e.g. British Household Panel Survey). A narrow risk factor study (e.g. to test the effect of job loss on health) was clearly possible with the QLFS, however, our main objective was to use this rich source of individual-level data to estimate the net effects of the economic recession on population health over time, across regions, and between selected groups.
Health status in the QLFS was self-rated. The primary outcome variable was health status measured using responses to the question “Do you have any health problems or disabilities that you expect will last for more than a year?” 95% of all survey responses of working age in the QLFS indicated a response (yes or no). Non-responders were not asked this question on health status as they were too ill or distressed to continue with the survey; we omitted these survey responses from the analysis.
In secondary analyses, we investigated specific types of health problems reported in the QLFS that could have been influenced by the 2008 economic recession. In the interests of brevity, we assigned each group a name. The exact wording from the QLFS is given in parentheses.
Depression (“Depression, bad nerves or anxiety”);
Mental illness (“Mental illness, or suffer from phobia, panics or other nervous disorders”);
Cardiovascular (“Heart, blood pressure or blood circulation problems”);
Respiratory (“Chest or breathing problems, asthma, bronchitis”).
Individual economic status (employed, unemployed, and economically inactive – including early retirees, students, the long-term sick, and homemakers) was measured according to the International Labour Organization (ILO) definition [26]. Among those who were employed, their occupational class was identified using the UK National Statistics Socio-Economic Classification (NS-SEC) [27]. The NS-SEC groups occupations into classes to reflect working relations between employers and employees, the salaried and the temporary labour force, and the manual and non-manual workers. All occupations in the QLFS were classified into three recognized NS-SEC classes: ‘routine/manual’; ‘intermediate’; or ‘professional/managerial’.
We also utilised a number of conventional demographic measures which were surveyed in each quarter, including age, gender, country of birth, ethnicity (White, Mixed, Indian, Pakistani, Bangladeshi, Chinese, Other Asian, Black Caribbean or Black African, Other), educational qualifications (none, GCSE, A-Level, Degree, Other), couple status (married or living with partner, single never married, separated/divorced/widowed), household tenure (owned outright/with mortgage, rented, rent free, part rent/part mortgage), number of dependents (0 to 4+), and geographical region (n = 20).
To test whether overall levels of health worsened during the 2008 economic recession, we first assessed the prevalence of each health status across all other characteristics of the study sample and each of the 20 quarters using descriptive statistics. Association between health and each explanatory variable was investigated using logit regression. Coefficients were exponentiated to odds ratios (OR), indicating the likelihood of a person reporting a health problem compared to the likelihood of not reporting a health problem.
For assessing the extent to which health varied across time, we used an explanatory variable containing 20 categories. Each category represented a single quarter (i.e. three months of data). We fitted logit regression models of each outcome variable, adjusted for this categorical variable denoting time, with the reference category set as quarter 1 (January to March 2006). An advantage of adopting this categorical approach was that we did not impose any presupposition of the distribution of health status across time. This approach was more favourable in comparison to using a combination of linear and polynomial functions of time (e.g. square and cubic functions), which were found to have a smoothing effect, resulting in underestimation of rapid changes in health occurring from one quarter to the next.
We constructed multivariate models in several steps. First, our models were adjusted by age group and gender. An interaction term between age and gender was fitted to account for differences in health between males and females at different ages. Second, we added dummy variables denoting economic status, occupational class, and geographic region into the models separately, and then simultaneously. Third, all other explanatory variables were added sequentially to the models. For each model, 95% confidence intervals (95% CI) were used to assess whether change in the unemployment rate and prevalence of health problems between each quarter was statistically significant (p<0.05).
With a view towards addressing whether changes in each of the outcome variables coincided with the 2008 economic recession, we fitted an age, gender and time adjusted logistic regression model of unemployment (vs. employment) separately. Only economically active people were included in these models, as these people were either in work or actively looking for jobs, in line with the ILO definition [26]. Indirect comparisons between trajectories in unemployment and health status were made in this way, rather than adjusting for a regional measure of unemployment, as our use of a fixed effect for region precluded the analysis of any explanatory variables at this level. However, individual economic status was included in the health models. Indirect comparisons also helped us to avoid imposing any assumptions regarding the length of time required for the economic recession to have observable influences on population health.
To investigate influence on health inequalities, we extended our multivariate logit regression of health status with interactions between time, economic status, occupational class, and geographic region. The model interacting time with occupational class omitted all people who were not employed. All of these models identified the extent of change in health status across time between people in different geographical and socioeconomic circumstances during the economic recession. All analyses were conducted in Stata v.12 (StataCorp, TX, USA).
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