Association Between Psychological Distress and Mortality
Association Between Psychological Distress and Mortality
Participants were taken from the Health Survey for England, a representative health examination study sampling people from the general population living in private households in that country. From 1994 to 2004, 11 independent, cross sectional studies with identical methodologies took place on an annual basis. Consenting study members (75 936 (89.1%)) were linked to National Health Service mortality data up to February 2008. For this analysis, we used raw data from people aged 35 years and over from all these study years, with the exception of 1996 when psychological distress was not measured. Ethical approval was obtained from the London Research Ethics Council.
During a household visit, interviewers collected information using computer-assisted personal interviewing modules. We measured psychological distress using the 12 item version of the General Health Questionnaire (GHQ-12), a widely used measure of distress in population studies. The GHQ-12 is generally considered to be a unidimensional scale of psychological distress, consisting of items capturing symptoms of anxiety, depression, social dysfunction, and loss of confidence. Study members respond to whether a symptom is present by using a four point Likert scale ("not at all"=0, "same as usual"=0, "more than usual"=1, "much more than usual"=1). A total GHQ-12 score of four or greater leads to people being defined as psychological distress "cases" and scores 0–3 as "non-cases"; this definition has been validated against standardised psychiatric interviews and has been strongly associated with various psychological disorders such as depression and anxiety. Most previous studies used such a dichotomy and few have examined associations across the full range of psychological distress. No standard cut-off values exist for dividing up "cases" identified by a GHQ-12 score threshold. We therefore chose to divide people into four groups based on their GHQ-12 score: asymptomatic (score 0), subclinically symptomatic (score 1–3), symptomatic (score 4–6), and highly symptomatic (score 7–12).
Causes of death recorded on death certificates were coded using the international classification of diseases, 9th and 10th revisions (ICD-9 and ICD-10, respectively). We identified cardiovascular disease deaths (including ischaemic heart disease, cerebrovascular disease, peripheral vascular disease and heart failure) using codes 410–414, 430–438, 440, 443–5, and 428 (ICD-9); and I20-I25, I50, I60–70, I73 and I74 (ICD-10). Cancer deaths were identified using codes 140–239 (ICD-9) and C00-D48 (ICD-10). We identified deaths from external causes using codes 800–999 and E800-E999 (ICD-9) and S00-Y98 (ICD-10). For the main analyses, any mention of a condition on the death certificate was counted but a subgroup analysis restricted cases to those where the condition was the underlying cause of death.
We ascertained that the proportional hazards assumption had not been violated by inspecting the log(−log(survival)) plot. We then used Cox proportional hazards models to compute study-specific hazard ratios with accompanying 95% confidence intervals for the association of GHQ-12 score with mortality outcomes. Heterogeneity in the effect estimates between studies was examined using the I statistic, which indicates the proportion of the total variation in the estimates due to between-studies variation. The I varied between 0% and 81.1%, depending on the mortality outcome and psychological distress variable used in the analysis. Owing to this heterogeneity, we pooled the study-specific effect estimates and their standard errors in random effects meta-analyses. Study members scoring 0 on the GHQ-12 were regarded as being free of psychological distress and used as the reference group. We compared this group with the three GHQ-12 score groups (scores 1–3, 4–6, and 7–12), and also reported the hazard ratio per one standard deviation increment in GHQ-12 score (calculated with sex specific standard deviations: men 2.41, women 2.75).
Days were the time scale and, for participants with no record of an event, the data were censored at 15 February 2008. Models were adjusted for age (years), sex, current occupational social class (professional, managerial or technical, skilled non-manual, skilled manual, partly skilled, and unskilled), body mass index, systolic blood pressure (mm Hg), physical activity (any moderate to vigorous physical activity in a week), smoking status (not a current smoker; or <5, 5–10, 10–15, 15–20, and >20 cigarettes per day), alcohol consumption (units per week), and diabetes at baseline (yes or no). Details on the measurement protocols and data handling of these covariates can be found elsewhere. We calculated the population proportional attributable risk for each mortality outcome and the four categories of GHQ-12 score using a standard equation.
To further examine the association between crude GHQ-12 score and mortality (all cause, cardiovascular disease, cancer, and external causes), we meta-analysed study specific Cox proportional hazard models to calculate age and sex adjusted hazard ratios and 95% confidence intervals for each GHQ-12 score, with score 0 as the reference. In addition, we did a subgroup analysis to investigate potential reverse causality; analyses were repeated dropping deaths within the first five years of follow-up. This analysis did not include deaths from external causes.
We compared people with data missing for one or more variable with those with complete data. Covariates were compared with Student's t test for continuous variables and χ tests for categorical variables. In the sensitivity analysis, we imputed missing values for covariates with Predictive Analytics Software version 18.0, using five imputations. All other analyses were conducted using R version 2.15.0 and the survival and metafor packages. Figures were constructed using the Rmeta and gplots packages. The reporting of this study conforms to the STROBE statement.
Methods
Study Samples
Participants were taken from the Health Survey for England, a representative health examination study sampling people from the general population living in private households in that country. From 1994 to 2004, 11 independent, cross sectional studies with identical methodologies took place on an annual basis. Consenting study members (75 936 (89.1%)) were linked to National Health Service mortality data up to February 2008. For this analysis, we used raw data from people aged 35 years and over from all these study years, with the exception of 1996 when psychological distress was not measured. Ethical approval was obtained from the London Research Ethics Council.
Measurement of Psychological Distress
During a household visit, interviewers collected information using computer-assisted personal interviewing modules. We measured psychological distress using the 12 item version of the General Health Questionnaire (GHQ-12), a widely used measure of distress in population studies. The GHQ-12 is generally considered to be a unidimensional scale of psychological distress, consisting of items capturing symptoms of anxiety, depression, social dysfunction, and loss of confidence. Study members respond to whether a symptom is present by using a four point Likert scale ("not at all"=0, "same as usual"=0, "more than usual"=1, "much more than usual"=1). A total GHQ-12 score of four or greater leads to people being defined as psychological distress "cases" and scores 0–3 as "non-cases"; this definition has been validated against standardised psychiatric interviews and has been strongly associated with various psychological disorders such as depression and anxiety. Most previous studies used such a dichotomy and few have examined associations across the full range of psychological distress. No standard cut-off values exist for dividing up "cases" identified by a GHQ-12 score threshold. We therefore chose to divide people into four groups based on their GHQ-12 score: asymptomatic (score 0), subclinically symptomatic (score 1–3), symptomatic (score 4–6), and highly symptomatic (score 7–12).
Mortality Data
Causes of death recorded on death certificates were coded using the international classification of diseases, 9th and 10th revisions (ICD-9 and ICD-10, respectively). We identified cardiovascular disease deaths (including ischaemic heart disease, cerebrovascular disease, peripheral vascular disease and heart failure) using codes 410–414, 430–438, 440, 443–5, and 428 (ICD-9); and I20-I25, I50, I60–70, I73 and I74 (ICD-10). Cancer deaths were identified using codes 140–239 (ICD-9) and C00-D48 (ICD-10). We identified deaths from external causes using codes 800–999 and E800-E999 (ICD-9) and S00-Y98 (ICD-10). For the main analyses, any mention of a condition on the death certificate was counted but a subgroup analysis restricted cases to those where the condition was the underlying cause of death.
Statistical analyses
We ascertained that the proportional hazards assumption had not been violated by inspecting the log(−log(survival)) plot. We then used Cox proportional hazards models to compute study-specific hazard ratios with accompanying 95% confidence intervals for the association of GHQ-12 score with mortality outcomes. Heterogeneity in the effect estimates between studies was examined using the I statistic, which indicates the proportion of the total variation in the estimates due to between-studies variation. The I varied between 0% and 81.1%, depending on the mortality outcome and psychological distress variable used in the analysis. Owing to this heterogeneity, we pooled the study-specific effect estimates and their standard errors in random effects meta-analyses. Study members scoring 0 on the GHQ-12 were regarded as being free of psychological distress and used as the reference group. We compared this group with the three GHQ-12 score groups (scores 1–3, 4–6, and 7–12), and also reported the hazard ratio per one standard deviation increment in GHQ-12 score (calculated with sex specific standard deviations: men 2.41, women 2.75).
Days were the time scale and, for participants with no record of an event, the data were censored at 15 February 2008. Models were adjusted for age (years), sex, current occupational social class (professional, managerial or technical, skilled non-manual, skilled manual, partly skilled, and unskilled), body mass index, systolic blood pressure (mm Hg), physical activity (any moderate to vigorous physical activity in a week), smoking status (not a current smoker; or <5, 5–10, 10–15, 15–20, and >20 cigarettes per day), alcohol consumption (units per week), and diabetes at baseline (yes or no). Details on the measurement protocols and data handling of these covariates can be found elsewhere. We calculated the population proportional attributable risk for each mortality outcome and the four categories of GHQ-12 score using a standard equation.
To further examine the association between crude GHQ-12 score and mortality (all cause, cardiovascular disease, cancer, and external causes), we meta-analysed study specific Cox proportional hazard models to calculate age and sex adjusted hazard ratios and 95% confidence intervals for each GHQ-12 score, with score 0 as the reference. In addition, we did a subgroup analysis to investigate potential reverse causality; analyses were repeated dropping deaths within the first five years of follow-up. This analysis did not include deaths from external causes.
We compared people with data missing for one or more variable with those with complete data. Covariates were compared with Student's t test for continuous variables and χ tests for categorical variables. In the sensitivity analysis, we imputed missing values for covariates with Predictive Analytics Software version 18.0, using five imputations. All other analyses were conducted using R version 2.15.0 and the survival and metafor packages. Figures were constructed using the Rmeta and gplots packages. The reporting of this study conforms to the STROBE statement.
