A review of the use of health examination data from the Health Survey for England in government policy development and implementation

Background Information is needed at all stages of the policy making process. The Health Survey for England (HSE) is an annual cross-sectional health examination survey of the non-institutionalised general population in England. It was originally set up to inform national policy making and monitoring by the Department of Health. This paper examines how the nurse collected physical and biological measurement data from the HSE have been essential or useful for identification of a health issue amenable to policy intervention; initiation, development or implementation of a strategy; choice and monitoring of targets; or assessment and evaluation of policies. Methods Specific examples of use of HSE data were identified through interviews with senior members of staff at the Department of Health and the Health and Social Care Information Centre. Policy documents mentioned by interviewees were retrieved for review, and reference lists of associated policy documents checked. Systematic searches of Chief Medical Officer Reports, Government ‘Command Papers’, and clinical guidance documents were also undertaken. Results HSE examination data have been used at all stages of the policy making process. Data have been used to identify an issue amenable to policy-intervention (e.g. quantifying prevalence of undiagnosed chronic kidney disease), in strategy development (in models to inform chronic respiratory disease policy), for target setting and monitoring (the 1992 blood pressure target) and in evaluation of health policy (the effect of the smoking ban on second hand smoke exposure). Conclusions A health examination survey is a useful part of a national health information system

The effect of mode and context on survey results: analysis of data from the Health Survey for England 2006 and the Boost Survey for London.

BACKGROUND: Health-related data at local level could be provided by supplementing national health surveys with local boosts. Self-completion surveys are less costly than interviews, enabling larger samples to be achieved for a given cost. However, even when the same questions are asked with the same wording, responses to survey questions may vary by mode of data collection. These measurement differences need to be investigated further. METHODS: The Health Survey for England in London ('Core') and a London Boost survey ('Boost') used identical sampling strategies but different modes of data collection. Some data were collected by face-to-face interview in the Core and by self-completion in the Boost; other data were collected by self-completion questionnaire in both, but the context differed. Results were compared by mode of data collection using two approaches. The first examined differences in results that remained after adjusting the samples for differences in response. The second compared results after using propensity score matching to reduce any differences in sample composition. RESULTS: There were no significant differences between the two samples for prevalence of some variables including long-term illness, limiting long-term illness, current rates of smoking, whether participants drank alcohol, and how often they usually drank. However, there were a number of differences, some quite large, between some key measures including: general health, GHQ12 score, portions of fruit and vegetables consumed, levels of physical activity, and, to a lesser extent, smoking consumption, the number of alcohol units reported consumed on the heaviest day of drinking in the last week and perceived social support (among women only). CONCLUSION: Survey mode and context can both affect the responses given. The effect is largest for complex question modules but was also seen for identical self-completion questions. Some data collected by interview and self-completion can be safely combined

Does the use of prediction equations to correct self-reported height and weight improve obesity prevalence estimates? A pooled cross-sectional analysis of Health Survey for England data.

OBJECTIVE: Adults typically overestimate height and underestimate weight compared with directly measured values, and such misreporting varies by sociodemographic and health-related factors. Using self-reported and interviewer-measured height and weight, collected from the same participants, we aimed to develop a set of prediction equations to correct bias in self-reported height and weight and assess whether this adjustment improved the accuracy of obesity prevalence estimates relative to those based only on self-report. DESIGN: Population-based cross-sectional study. PARTICIPANTS: 38 940 participants aged 16+ (Health Survey for England 2011-2016) with non-missing self-reported and interviewer-measured height and weight. MAIN OUTCOME MEASURES: Comparisons between self-reported, interviewer-measured (gold standard) and corrected (based on prediction equations) body mass index (BMI: kg/m2) including (1) difference between means and obesity prevalence and (2) measures of agreement for BMI classification. RESULTS: On average, men overestimated height more than women (1.6 cm and 1.0 cm, respectively; p<0.001), while women underestimated weight more than men (2.1 kg and 1.5 kg, respectively; p<0.001). Underestimation of BMI was slightly larger for women than for men (1.1 kg/m2 and 1.0 kg/m2, respectively; p<0.001). Obesity prevalence based on BMI from self-report was 6.8 and 6.0 percentage points (pp) lower than that estimated using measured BMI for men and women, respectively. Corrected BMI (based on models containing all significant predictors of misreporting of height and weight) lowered underestimation of obesity to 0.8pp in both sexes and improved the sensitivity of obesity over self-reported BMI by 15.0pp for men and 12.2pp for women. Results based on simpler models using age alone as a predictor of misreporting were similar. CONCLUSIONS: Compared with self-reported data, applying prediction equations improved the accuracy of obesity prevalence estimates and increased sensitivity of being classified as obese. Including additional sociodemographic variables did not improve obesity classification enough to justify the added complexity of including them in prediction equations

Ethnic differences in multimorbidity after accounting for social-economic factors, findings from The Health Survey for England

BACKGROUND: Social-economic factors and health behaviours may be driving variation in ethnic health inequalities in multimorbidity including among distinct ethnic groups. METHODS: Using the cross-sectional nationally representative Health Surveys for England 2011-18 (N = 54 438, aged 16+), we performed multivariable logistic regression on the odds of having general multimorbidity (≥2 longstanding conditions) by ethnicity [British White (reference group), White Irish, Other White, Indian, Pakistani, Bangladeshi, Chinese, African, Caribbean, White mixed, Other Mixed], adjusting for age, sex, education, area deprivation, obesity, smoking status and survey year. This was repeated for cardiovascular multimorbidity (N = 37 148, aged 40+: having ≥2 of the following: self-reported diabetes, hypertension, heart attack or stroke) and multiple cardiometabolic risk biomarkers (HbA1c ≥6.5%, raised blood pressure, total cholesterol ≥5mmol/L). RESULTS: Twenty percent of adults had general multimorbidity. In fully adjusted models, compared with the White British majority, Other White [odds ratio (OR) = 0.63; 95% confidence interval (CI) 0.53-0.74], Chinese (OR = 0.58, 95% CI 0.36-0.93) and African adults (OR = 0.54, 95% CI 0.42-0.69), had lower odds of general multimorbidity. Among adults aged 40+, Pakistani (OR = 1.27, 95% CI 0.97-1.66; P = 0.080) and Bangladeshi (OR = 1.75, 95% CI 1.16-2.65) had increased odds, and African adults had decreased odds (OR = 0.63, 95% CI 0.47-0.83) of general multimorbidity. Risk of cardiovascular multimorbidity was higher among Indian (OR = 3.31, 95% CI 2.56-4.28), Pakistani (OR = 3.48, 95% CI 2.52-4.80), Bangladeshi (OR = 3.67, 95% CI 1.98-6.78), African (OR = 1.61, 95% CI 1.05-2.47), Caribbean (OR = 2.18, 95% CI 1.59-2.99) and White mixed (OR = 1.98, 95% CI 1.14-3.44) adults. Indian adults were also at risk of having multiple cardiometabolic risk biomarkers. CONCLUSION: Ethnic inequalities in multimorbidity are independent of social-economic factors. Ethnic minority groups are particularly at risk of cardiovascular multimorbidity, which may be exacerbated by poorer management of cardiometabolic risk requiring further investigation

Prevalence of and factors associated with herpes zoster in England: a cross-sectional analysis of the Health Survey for England

BACKGROUND: Herpes zoster (commonly called shingles) is caused by the reactivation of varicella zoster virus, and results in substantial morbidity. While the risk of zoster increases significantly with age and immunosuppression, relatively little is known about other risk factors for zoster. Moreover, much evidence to date stems from electronic healthcare or administrative data. Hence, the aim of this study was to explore potential risk factors for herpes zoster using survey data from a nationally-representative sample of the general community-dwelling population in England. METHODS: Data were extracted from the 2015 Health Survey for England, an annual cross-sectional representative survey of households in England. The lifetime prevalence of self-reported herpes zoster was described by age, gender and other socio-demographic factors, health behaviours (physical activity levels, body mass index, smoking status and alcohol consumption) and clinical conditions, including; diabetes, respiratory, digestive and genito-urinary system and mental health disorders. Logistic regression models were then used to identify possible factors associated with shingles, and results were presented as odds ratios with 95% confidence intervals. RESULTS: The lifetime prevalence of shingles among the sample was 11.5% (12.6% among women, 10.3% among men), which increased with age. After adjusting for a range of covariates, increased age, female gender (odds ratio: 1.21; 95%CI: 1.03, 1.43), White ethnic backgrounds (odds ratio: 2.00; 95%CI: 1.40, 2.88), moderate physical activity 7 days per week (odds ratio: 1.29; 95%CI: 1.01, 1.66) and digestive disorders (odds ratio: 1.51; 95%CI: 1.13, 1.51) were each associated with increased odds of having had herpes zoster. CONCLUSIONS: Age, gender, ethnicity and digestive disorders may be risk factors for herpes zoster among a nationally representative sample of adults in England. These potential risk factors and possible mechanisms should be further explored using longitudinal studies

The effect of survey method on survey participation: Analysis of data from the Health Survey for England 2006 and the Boost Survey for London

BACKGROUND: There is a need for local level health data for local government and health bodies, for health surveillance and planning and monitoring of policies and interventions. The Health Survey for England (HSE) is a nationally-representative survey of the English population living in private households, but sub-national analyses can be performed only at a regional level because of sample size. A boost of the HSE was commissioned to address the need for local level data in London but a different mode of data collection was used to maximise participant numbers for a given cost. This study examines the effects on survey and item response of the different survey modes. METHODS: Household and individual level data are collected in HSE primarily through interviews plus individual measures through a nurse visit. For the London Boost, brief household level data were collected through interviews and individual level data through a longer self-completion questionnaire left by the interviewer and collected later. Sampling and recruitment methods were identical, and both surveys were conducted by the same organisation. There was no nurse visit in the London Boost. Data were analysed to assess the effects of differential response rates, item non-response, and characteristics of respondents. RESULTS: Household response rates were higher in the 'Boost' (61%) than 'Core' (HSE participants in London) sample (58%), but the individual response rate was considerably higher in the Core (85%) than Boost (65%). There were few differences in participant characteristics between the Core and Boost samples, with the exception of ethnicity and educational qualifications. Item non-response was similar for both samples, except for educational level. Differences in ethnicity were corrected with non-response weights, but differences in educational qualifications persisted after non-response weights were applied. When item non-response was added to those reporting no qualification, participants' educational levels were similar in the two samples. CONCLUSION: Although household response rates were similar, individual response rates were lower using the London Boost method. This may be due to features of London that are particularly associated with lower response rates for the self-completion element of the Boost method, such as the multi-lingual population. Nevertheless, statistical adjustments can overcome most of the demographic differences for analysis. Care must be taken when designing self-completion questionnaires to minimise item non-response

Sociodemographic and Built Environment Associates of Travel to School by Car among New Zealand Adolescents: Meta-Analysis.

Travelling to school by car diminishes opportunities for physical activity and contributes to traffic congestion and associated noise and air pollution. This meta-analysis examined sociodemographic characteristics and built environment associates of travelling to school by car compared to using active transport among New Zealand (NZ) adolescents. Four NZ studies (2163 adolescents) provided data on participants' mode of travel to school, individual and school sociodemographic characteristics, distance to school and home-neighbourhood built-environment features. A one-step meta-analysis using individual participant data was performed in SAS. A final multivariable model was developed using stepwise logistic regression. Overall, 60.6% of participants travelled to school by car. When compared with active transport, travelling to school by car was positively associated with distance to school. Participants residing in neighbourhoods with high intersection density and attending medium deprivation schools were less likely to travel to school by car compared with their counterparts. Distance to school, school level deprivation and low home neighbourhood intersection density are associated with higher likelihood of car travel to school compared with active transport among NZ adolescents. Comprehensive interventions focusing on both social and built environment factors are needed to reduce car travel to school

Exploration of chronic kidney disease prevalence estimates using new measures of kidney function in the health survey for England

Background: chronic kidney disease (CKD) diagnosis relies on glomerular filtration rate (eGFR) estimation, traditionally using the creatinine-based Modification of Diet in Renal Disease (MDRD) equation. The Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) equation performs better in estimating eGFR and predicting mortality and CKD progression risk. Cystatin C is an alternative glomerular filtration marker less influenced by muscle mass. CKD risk stratification is improved by combining creatinine eGFR with cystatin C and urinary albumin to creatinine ratio (uACR). We aimed to identify the impact of introducing CKDEPI and cystatin C on the estimated prevalence and risk stratification of CKD in England and to describe prevalence and associations of cystatin C.Methods and findings: cross sectional study of 5799 people in the nationally representative 2009 and 2010 Health Surveys for England. Primary outcome measures: prevalence of MDRD, CKDEPI and cystatin C-defined eGFR&lt;60ml/min/1.73m2; prevalence of CKD biomarker combinations (creatinine, cystatin C, uACR). Using CKDEPI instead of MDRD reduced the prevalence of eGFR&lt;60ml/min/1.73m2 from 6.0% (95% CI 5.4–6.6%) to 5.2% (4.7–5.8%) equivalent to around 340,000 fewer individuals in England. Those reclassified as not having CKD evidenced a lower risk profile. Prevalence of cystatin C eGFR&lt;60ml/min/1.73m2 was 7.7% and independently associated with age, lack of qualifications, being an ex-smoker, BMI, hypertension, and albuminuria. Measuring cystatin C in the 3.9% people with CKDEPI-defined eGFR&lt;60ml/min/1.73m2 without albuminuria (CKD Category G3a A1) reclassified about a third into a lower risk group with one of three biomarkers and two thirds into a group with two of three. Measuring cystatin C in the 6.7% people with CKDEPI eGFR &gt;60ml/min/1.73m2 with albuminuria (CKD Category G1-2) reclassified almost a tenth into a higher risk group.Limitations: cross sectional study, single eGFR measure, no measured (‘true’) GFR.Conclusions: introducing the CKDEPI equation and targeted cystatin C measurement reduces estimated CKD prevalence and improves risk stratification<br/

Trends in socioeconomic inequalities in behavioural non-communicable disease risk factors : analysis of repeated cross-sectional health surveys in England between 2003 and 2019

Background: Previous studies have shown that those in lower socioeconomic positions (SEPs) generally have higher levels of behavioural non-communicable disease (NCD) risk factors. However, there are limited studies examining recent trends in inequalities. This study examined trends in socioeconomic inequalities in NCD behavioural risk factors and their co-occurrence in England from 2003–19. Methods: This time-trend analysis of repeated cross-sectional data from the Health Survey for England examined the relative index of inequalities (RII) and slope index of inequalities (SII) in four NCD behavioural risk factors: smoking; drinking above recommended limits; insufficient fruit and vegetables consumption; and physical inactivity. Findings: Prevalence of risk factors has reduced over time, however, this has not been consistent across SEPs. Absolute and relative inequalities increased for physical inactivity; relative inequalities also increased for smoking; for insufficient fruit and vegetable consumption, the trends in inequalities depended on SEPs measure. Those in lower SEPs experienced persistent socioeconomic inequalities and clustering of behavioural risk factors. In contrast, those in higher SEPs had higher prevalence of excessive alcohol consumption; this inequality widened over the study period. Interpretation: Inequalities in smoking and physical inactivity are persisting or widening. The pattern of higher drinking in higher SEPs obscure the fact that the greatest burden of alcohol-related harm falls on lower SEPs. Policy attention is required to tackle increasing inequalities in smoking prevalence, low fruit and vegetable consumption and physical inactivity, and to reduce alcohol harm