ABSTRACT

Objective

To determine the association between risk factors and hospital admission.

Methods

The 1998 Scottish Health Survey was linked to the Scottish hospital admission database.

Findings

Smoking was the most important behavioural risk factor (hazard ratio: 1.90, 95% CI: 1.59–2.27). Other behavioural risk factors yielded small but largely anticipated results. Hazard ratios for biological risks increased predictably but with some exceptions (blood pressure and total cholesterol). The top quintile for C-reactive protein showed almost double the risk of admission compared with the bottom quintile (hazard ratio: 1.93, 95% CI: 1.52–2.46). Elevated body mass index (BMI) increased the risk of serious admission (hazard ratio: 1.23, 95% CI: 1.03–1.47) and raised gamma-GT increased this risk by 20% (hazard ratio: 1.20, 95% CI: 1.04–1.38). Forced expiratory volume was the ‘biological’ factor with the largest risk (hazard ratio for lowest category: 1.82, 95% CI: 1.49–2.22). All the measures of social position showed variable effects on the risk of hospital admission. Large effects on risk were associated with self assessed health, longstanding illness and previous admission.

Conclusion

The linkage of national surveys with a prospective hospitalization database will develop into an increasingly powerful tool.

Introduction

Epidemiology is the ‘study of the distribution and determinants of health-related states and events in populations’.1 However, within epidemiology, more is known about the aetiology of disease than is understood about some of the determinants of health service utilization.2 Yet, both are important.

We know that the factors that determine demand for health services are complex and interacting. They include the levels of disease in a population, the volume and nature of health service supply, the behaviour of key ‘gate keepers’, the expectations and help-seeking behaviours of the population, demographic factors, social capital and much else.3–6

Although these general insights are valuable, relatively little work has been done, using large national samples, on the interaction between established disease risk factors like obesity, raised blood pressure, elevated cholesterol and smoking on the pattern of hospital utilization. Still less is understood about newer risk factors like C-reactive protein and fibrinogen.7,8 Also, although the relationship between deprivation and high levels of health service demand is well established,9 the degree to which deprivation acts through known biological and behavioural risk factors is less well understood.

The purpose of this study is to address these areas of relative ignorance by taking advantage of a new resource created by the linkage of lifestyle and hospital utilization data across Scotland. It is only in the past 3 years that it has been possible, using probability matching techniques,10 to link Scottish Morbidity Records (SMRs) with the Scottish Health Survey (SHS). SHS respondents are asked at the end of their interview whether they would be willing to be re-contacted and to allow their records to be checked against NHS registers. The survey achieves a response rate of over 80% and 90% of respondents consent to record linkage.

The SMR system records details of all in-patient and day case admissions (but not accident and emergency attendances) to Scottish NHS hospitals. The record includes information on demographic factors (e.g. age, sex and address), diagnoses, clinical procedures and means of discharge. Using patient identifying information, acute hospitalization records (SMR1) are routinely linked to mental health hospital records (SMR4), cancer registrations (SOCRATES—formerly SMR6) and Registrar General death registrations, resulting in a linked database of all such patient records covering the period 1981 to the present day.

The SHS is a national survey that collects in-depth information covering a wide range of health and behavioural topics; socio-demographic information (social class, housing tenure, car ownership, state benefits, etc.) and physiological measurements (taken by nurses) for a large representative sample of the Scottish population.11 At the time this project was initiated, there had been two waves, the first in 1995 in which 7932 adults (aged 16–64) were interviewed and the second in 1998 in which 9047 adults (aged 16–74) were interviewed. The results from the third SHS, conducted in 2003, had not at that stage been released.

Methods

Ethical approval was sought and obtained from the Multi-centre Research Ethics Committees and the Privacy Advisory Committee's approval for the linkage was also sought and granted.

The aim was to estimate the risks of hospital admission associated with a wide range of risk factors. All analysis was undertaken on the 1998 SHS respondents. This survey population was selected (in favour of the 1995 or combined SHS populations) because there was a wider age group (16–74 years), more variables of interest were included (e.g. C-reactive protein), better measures of risk factors like exercise were employed and it became possible to restrict the outcomes to the most costly admissions.

Table 1 shows the reduced list of variables from the SHS employed by this study. A total of 8305 respondents from the 1998 SHS were linked to the April 2006 version of the Scottish hospital admission database which contains details of all SMR01 general hospital admissions, SMR04 psychiatric admissions, death records from 1981 to the end of September 2005 and cancer registrations from 1981 to December 2003. In each case, the date of event is recorded.

Table 1

Risk factors chosen for analysis

Behavioural 
 Smoking status 
 Alcohol consumption 
 Physical activity 
 Diet 
Biological 
 Body mass index 
 Waist hip ratio 
 Blood pressure 
 Total cholesterol 
 HDL cholesterol 
 Gamma-GT 
 Fibrinogen 
 C-reactive protein 
 FEV 
Social 
 In receipt of income related benefita 
 Social class 
 Car ownership 
 Highest educational qualification 
 Economic activity 
 Unemployment benefit 
 Housing tenure 
 Overcrowding 
 Central heating 
 Area deprivation 
 Rurality 
 Access to the nearest GP practice 
 Access to the nearest main hospital 
 Drive time to nearest hospital 
Estimates of health at survey 
 Self-assessed general health 
 Psychosocial health (GHQ-12) 
 Longstanding illness 
 Number of longstanding illnesses 
 In receipt of incapacity benefit 
Prior hospital admissions 
 Number of admissions 5 years prior to survey 
Behavioural 
 Smoking status 
 Alcohol consumption 
 Physical activity 
 Diet 
Biological 
 Body mass index 
 Waist hip ratio 
 Blood pressure 
 Total cholesterol 
 HDL cholesterol 
 Gamma-GT 
 Fibrinogen 
 C-reactive protein 
 FEV 
Social 
 In receipt of income related benefita 
 Social class 
 Car ownership 
 Highest educational qualification 
 Economic activity 
 Unemployment benefit 
 Housing tenure 
 Overcrowding 
 Central heating 
 Area deprivation 
 Rurality 
 Access to the nearest GP practice 
 Access to the nearest main hospital 
 Drive time to nearest hospital 
Estimates of health at survey 
 Self-assessed general health 
 Psychosocial health (GHQ-12) 
 Longstanding illness 
 Number of longstanding illnesses 
 In receipt of incapacity benefit 
Prior hospital admissions 
 Number of admissions 5 years prior to survey 

aOne composite variable was created to represent the respondent being in receipt of any income-related benefit. The income-related benefits that make-up this variable are: income support, family credit, unemployment benefit, housing benefit and council tax benefit.

We analysed the risk of hospital admission in a ‘time to first event’ framework implemented via a Cox proportional hazards model. We accounted for different exposure times across individuals using the full date of interview and the date of death for those that died without any admission.

We began by analysing the time to first hospital admission. However, some of these admissions are brief, low cost and increasingly being dealt with in community settings or in hospital outpatient clinics. We refined the analysis to focus on admissions above average cost, which we label ‘serious’. This was achieved using published costs for each Healthcare Resource Group (HRG) to create a ‘severity index’: calculated as the reference cost for each HRG divided by the average cost for all SMR1 admissions. A severity index of more than one indicated an above average cost and was defined as a potentially serious admission. It turned out that the value of 1 was assigned to acute myocardial infarction without complications because that was equal to the average cost of all SMR1 admissions. So, a serious/costly admission was at least as costly as an acute myocardial infarction.

The results using all hospital admissions were similar to, but less powerful than, those restricted to serious hospital admissions and we present these latter results in this paper.

To estimate the impact of emigration or other reasons for loss to follow up, the SHS datasets were linked to the then most current Scottish Community Health Index (CHI) (CHI is a general practice based population register in Scotland).12 Respondents who were known to have died or to be currently on the CHI comprised 96.2% of the 1998 survey respondents which reduced the subjects to 7876. Two Cox's proportional hazard models were run. First, analysis was carried out on ‘all respondents’ and then excluding emigrants. Both models had identical statistically significant risk factor categories and there was almost no difference between the two models in terms of hazard ratios. It was, therefore, decided that all analysis would be run excluding emigrants as this would result in a cleaner dataset.

Each of the 33 risk factors was modelled individually (in age and sex adjusted Cox's proportional hazards regression models) and we represent the differences in risk using hazard ratios.

Results

Consent was granted for 8305 SHS responses including person-identifiable information to be made available. The linkage of the SHS data to the April 2006 version of linked SMR01 ‘catalogue’ [The SMR01 catalogue is a linked file that, as well as SMR01 records, includes SMR04 records, cancer registrations and death records)] successfully linked 75% of the survey records to an admission record between March 1981, when the linked catalogue began, and September 2005. The remainder were assumed not to have experienced a hospital admission. Only admissions that occurred after the date of the SHS (1998) were used in the analysis. During the 7.5 years of follow-up, 1715 individuals had a serious hospital admission. Tables 2–5 show the results of the age and sex adjusted models for four different categories of risk factors.

Table 2

‘Age and sex standardized association’ between behavioural risk factors and hospital admission

Risk factor n % Hazard ratio 95% CI Significance 
Smoking      
 Never regularly smokeda 3301 44.0 1.00   
 Ex smoker 1477 17.6 1.26 1.08–1.46 ** 
 Light smoker, (<10) or cigar, pipe or high cotinine 856 11.3 1.22 1.00–1.49 
 Moderate smoker, 10–20 per day 1128 13.8 1.54 1.30–1.83 *** 
 Heavy smoker, 20 plus per day 1099 13.1 1.90 1.59–2.27 *** 
 Missing 15 0.3 n/a n/a n/a 
Drinking      
 Never drank and trivial 475 5.4 0.89 0.72–1.10 n/s 
 Ex-drinker 381 4.1 1.38 1.11–1.70 ** 
 Light drinkera 3713 45.8 1.00   
 Moderate drinker 1591 21.1 0.87 0.75–1.00 n/s 
 Heavy drinker 670 9.1 1.03 0.82–1.28 n/s 
 Excessive drinker 978 13.5 0.98 0.82–1.18 n/s 
 Missing 68 1.1 1.95 0.88–4.34 n/s 
Physical activity      
 Below recommended levela 5530 67.8 1.00   
 Achieving recommended level 2336 32.1 0.79 0.69–0.91 ** 
 Missing 10 0.1 1.26 0.44–3.62 n/s 
Diet      
 Not reaching fruit and vegetable daily target 6544 83.5 1.15 1.00–1.33 
 Reaching fruit and vegetable daily targeta 1332 16.5 1.00   
Risk factor n % Hazard ratio 95% CI Significance 
Smoking      
 Never regularly smokeda 3301 44.0 1.00   
 Ex smoker 1477 17.6 1.26 1.08–1.46 ** 
 Light smoker, (<10) or cigar, pipe or high cotinine 856 11.3 1.22 1.00–1.49 
 Moderate smoker, 10–20 per day 1128 13.8 1.54 1.30–1.83 *** 
 Heavy smoker, 20 plus per day 1099 13.1 1.90 1.59–2.27 *** 
 Missing 15 0.3 n/a n/a n/a 
Drinking      
 Never drank and trivial 475 5.4 0.89 0.72–1.10 n/s 
 Ex-drinker 381 4.1 1.38 1.11–1.70 ** 
 Light drinkera 3713 45.8 1.00   
 Moderate drinker 1591 21.1 0.87 0.75–1.00 n/s 
 Heavy drinker 670 9.1 1.03 0.82–1.28 n/s 
 Excessive drinker 978 13.5 0.98 0.82–1.18 n/s 
 Missing 68 1.1 1.95 0.88–4.34 n/s 
Physical activity      
 Below recommended levela 5530 67.8 1.00   
 Achieving recommended level 2336 32.1 0.79 0.69–0.91 ** 
 Missing 10 0.1 1.26 0.44–3.62 n/s 
Diet      
 Not reaching fruit and vegetable daily target 6544 83.5 1.15 1.00–1.33 
 Reaching fruit and vegetable daily targeta 1332 16.5 1.00   

Sample percentages and regression results are weighted using the survey weights.

n/s, not significant; n/a, not applicable (category cases excluded from model, due to zero admissions).

aReference category of variable.

Significance level: *P < 0.05, **P < 0.01, ***P < 0.001.

Table 3

‘Age and sex standardized association’ between biological risk factors and hospital admission

Risk factor n % Hazard ratio 95% CI Significance 
BMI group      
 Underweight (Under 20) 378 4.9 1.45 1.08–1.96 
 Desirable (20–25)a 2495 32.5 1.00   
 Overweight (25–30) 2699 34.3 1.11 0.95–1.29 n/s 
 Obese (Over 30) 1596 19.5 1.23 1.03–1.47 ** 
 Missing 708 8.8 1.45 1.15–1.82 n/s 
Waist–hip ratio      
 Normala 4856 63.6 1.00   
 Raised 1691 19.2 1.26 1.12–1.42 *** 
 Missing 1329 17.2 1.29 1.09–1.53 ** 
Blood pressure      
 Hypertensive untreated 1366 15.7 1.14 0.99–1.31 n/s 
 Hypertensive treated 551 5.5 1.58 1.33–1.89 *** 
 Normotensive treated 422 4.6 1.65 1.33–2.06 *** 
 Normotensive Untreateda 4253 57.7 1.00   
 Missing 1284 16.6 1.27 1.06–1.51 ** 
Total cholesterol      
 Desirable rangea 2148 29.6 1.00   
 Mildly raised 2137 26.4 1.02 0.88–1.19 n/s 
 Moderately raised 871 10.0 0.74 0.60–0.91 ** 
 Severely raised 189 2.0 0.83 0.60–1.14 n/s 
 Missing 2531 32.0 1.17 1.00–1.37 n/s 
HDL cholesterol      
 Low 1126 13.5 1.27 1.11–1.47 ** 
 Desirablea 4190 54.0 1.00   
 Missing 2560 32.4 1.31 1.15–1.49 *** 
Gamma-GT      
 Normala 3945 51.6 1.00   
 High 1522 17.7 1.20 1.04–1.38 
 Missing 2409 30.8 1.29 1.13–1.48 *** 
Fibrinogen      
 Quintile1a 930 13.2 1.00   
 Quintile2 865 11.9 0.96 0.72–1.29 n/s 
 Quintile3 1176 15.4 1.06 0.80–1.39 n/s 
 Quintile4 886 10.5 1.22 0.93–1.59 n/s 
 Quintile5 1061 12.1 1.73 1.33–2.25 *** 
 Missing 2958 36.9 1.57 1.24–1.99 *** 
C-reactive protein      
 Quintile1a 1072 14.9 1.00   
 Quintile2 1033 13.6 1.04 0.79–1.38 n/s 
 Quintile3 1003 12.5 1.42 1.09–1.84 ** 
 Quintile4 1106 13.6 1.39 1.08–1.77 ** 
 Quintile5 1184 13.8 1.93 1.52–2.46 *** 
 Missing 2478 31.6 1.68 1.34–2.12 *** 
FEV1      
 Equal to or in excess of predicted valuesa 3196 41.2 1.00   
 Within 1 SD below the predicted values 1673 21.1 1.31 1.12–1.52 *** 
 1–1.64 SD below the predicted values 595 7.6 1.54 1.26–1.89 *** 
 More than 1.64 SD below the pred values (‘low’)” 547 6.6 1.82 1.49–2.22 *** 
 Missing 1865 23.5 1.61 1.39–1.88 *** 
Risk factor n % Hazard ratio 95% CI Significance 
BMI group      
 Underweight (Under 20) 378 4.9 1.45 1.08–1.96 
 Desirable (20–25)a 2495 32.5 1.00   
 Overweight (25–30) 2699 34.3 1.11 0.95–1.29 n/s 
 Obese (Over 30) 1596 19.5 1.23 1.03–1.47 ** 
 Missing 708 8.8 1.45 1.15–1.82 n/s 
Waist–hip ratio      
 Normala 4856 63.6 1.00   
 Raised 1691 19.2 1.26 1.12–1.42 *** 
 Missing 1329 17.2 1.29 1.09–1.53 ** 
Blood pressure      
 Hypertensive untreated 1366 15.7 1.14 0.99–1.31 n/s 
 Hypertensive treated 551 5.5 1.58 1.33–1.89 *** 
 Normotensive treated 422 4.6 1.65 1.33–2.06 *** 
 Normotensive Untreateda 4253 57.7 1.00   
 Missing 1284 16.6 1.27 1.06–1.51 ** 
Total cholesterol      
 Desirable rangea 2148 29.6 1.00   
 Mildly raised 2137 26.4 1.02 0.88–1.19 n/s 
 Moderately raised 871 10.0 0.74 0.60–0.91 ** 
 Severely raised 189 2.0 0.83 0.60–1.14 n/s 
 Missing 2531 32.0 1.17 1.00–1.37 n/s 
HDL cholesterol      
 Low 1126 13.5 1.27 1.11–1.47 ** 
 Desirablea 4190 54.0 1.00   
 Missing 2560 32.4 1.31 1.15–1.49 *** 
Gamma-GT      
 Normala 3945 51.6 1.00   
 High 1522 17.7 1.20 1.04–1.38 
 Missing 2409 30.8 1.29 1.13–1.48 *** 
Fibrinogen      
 Quintile1a 930 13.2 1.00   
 Quintile2 865 11.9 0.96 0.72–1.29 n/s 
 Quintile3 1176 15.4 1.06 0.80–1.39 n/s 
 Quintile4 886 10.5 1.22 0.93–1.59 n/s 
 Quintile5 1061 12.1 1.73 1.33–2.25 *** 
 Missing 2958 36.9 1.57 1.24–1.99 *** 
C-reactive protein      
 Quintile1a 1072 14.9 1.00   
 Quintile2 1033 13.6 1.04 0.79–1.38 n/s 
 Quintile3 1003 12.5 1.42 1.09–1.84 ** 
 Quintile4 1106 13.6 1.39 1.08–1.77 ** 
 Quintile5 1184 13.8 1.93 1.52–2.46 *** 
 Missing 2478 31.6 1.68 1.34–2.12 *** 
FEV1      
 Equal to or in excess of predicted valuesa 3196 41.2 1.00   
 Within 1 SD below the predicted values 1673 21.1 1.31 1.12–1.52 *** 
 1–1.64 SD below the predicted values 595 7.6 1.54 1.26–1.89 *** 
 More than 1.64 SD below the pred values (‘low’)” 547 6.6 1.82 1.49–2.22 *** 
 Missing 1865 23.5 1.61 1.39–1.88 *** 

Sample percentages and regression results are weighted using the survey weights.

n/s, not significant; n/a, not applicable (category cases excluded from model, due to zero admissions).

aReference category of variable.

Significance level: *P < 0.05, **P < 0.01, ***P < 0.001.

Table 4

‘Age and sex standardized association’ between social risk factors and hospital admission

Risk factor n % Hazard ratio 95% (CI) Significance 
Income-related benefits      
 Yes 2271 24.0 1.49 1.32–1.68 *** 
 Noa 5605 76.0 1.00   
Social class      
 I—Professional and II—Managerial technicala 2490 33.3 1.00   
 IIIN—Skilled non-manual 1256 14.4 1.13 0.95–1.35 n/s 
 IIIM—Skilled manual 2135 28.5 1.27 1.12–1.45 *** 
 IV—Semi-skilled manual 1242 15.0 1.31 1.10–1.57 ** 
 V—Unskilled manual 498 5.4 1.40 1.14–1.71 ** 
 Other 14 0.2 3.04 0.79–11.60 n/s 
 Missing 241 3.2 1.26 0.90–1.75 n/s 
Car ownership      
 Nonea 2348 23.6 1.00   
 One 3759 46.8 0.80 0.71–0.91 ** 
 Two 1506 24.0 0.64 0.53–0.77 *** 
 Three or more 263 5.6 0.82 0.56–1.21 n/s 
Employment status      
 In Employmenta 4312 59.8 1.00   
 Unemployment 306 3.9 1.24 0.83–1.83 n/s 
 Retired 3239 36.1 1.85 1.58–2.17 *** 
 Missing 19 0.2 2.49 1.07–5.79 
Unemployment benefit      
 Yes 162 2.0 1.46 0.91–2.34 n/s 
 Noa 7714 98.0 1.00   
Highest qualification      
 A-level(s) or a degreea 3780 52.5 1.00   
 GCSE at A-C or equivalent 1138 14.5 1.32 1.10–1.59 ** 
 Other formal qualifications 598 6.7 1.46 1.19–1.78 *** 
 No formal qualifications 2347 26.1 1.41 1.22–1.62 *** 
 Missing 13 0.2 1.16 0.23–5.78 n/s 
Housing tenure      
 House owned outright or with mortgagea 4977 67.3 1.00   
 Publicly rented 2094 23.1 1.55 1.37–1.76 *** 
 Privately rented 801 9.6 1.24 1.00–1.53 
 Missing  4 0.1 n/a  n/a n/a 
Carstairs area deprivation      
 Bottoma 1373 20.1 1.00   
 Second 1589 18.7 1.28 1.06–1.55 
 Third 1915 22.3 1.36 1.13–1.62 ** 
 Forth 1452 19.1 1.49 1.22–1.83 *** 
 Top 1540 19.9 1.63 1.35–1.96 *** 
 Missing 0.1 n/a n/a n/a 
Risk factor n % Hazard ratio 95% (CI) Significance 
Income-related benefits      
 Yes 2271 24.0 1.49 1.32–1.68 *** 
 Noa 5605 76.0 1.00   
Social class      
 I—Professional and II—Managerial technicala 2490 33.3 1.00   
 IIIN—Skilled non-manual 1256 14.4 1.13 0.95–1.35 n/s 
 IIIM—Skilled manual 2135 28.5 1.27 1.12–1.45 *** 
 IV—Semi-skilled manual 1242 15.0 1.31 1.10–1.57 ** 
 V—Unskilled manual 498 5.4 1.40 1.14–1.71 ** 
 Other 14 0.2 3.04 0.79–11.60 n/s 
 Missing 241 3.2 1.26 0.90–1.75 n/s 
Car ownership      
 Nonea 2348 23.6 1.00   
 One 3759 46.8 0.80 0.71–0.91 ** 
 Two 1506 24.0 0.64 0.53–0.77 *** 
 Three or more 263 5.6 0.82 0.56–1.21 n/s 
Employment status      
 In Employmenta 4312 59.8 1.00   
 Unemployment 306 3.9 1.24 0.83–1.83 n/s 
 Retired 3239 36.1 1.85 1.58–2.17 *** 
 Missing 19 0.2 2.49 1.07–5.79 
Unemployment benefit      
 Yes 162 2.0 1.46 0.91–2.34 n/s 
 Noa 7714 98.0 1.00   
Highest qualification      
 A-level(s) or a degreea 3780 52.5 1.00   
 GCSE at A-C or equivalent 1138 14.5 1.32 1.10–1.59 ** 
 Other formal qualifications 598 6.7 1.46 1.19–1.78 *** 
 No formal qualifications 2347 26.1 1.41 1.22–1.62 *** 
 Missing 13 0.2 1.16 0.23–5.78 n/s 
Housing tenure      
 House owned outright or with mortgagea 4977 67.3 1.00   
 Publicly rented 2094 23.1 1.55 1.37–1.76 *** 
 Privately rented 801 9.6 1.24 1.00–1.53 
 Missing  4 0.1 n/a  n/a n/a 
Carstairs area deprivation      
 Bottoma 1373 20.1 1.00   
 Second 1589 18.7 1.28 1.06–1.55 
 Third 1915 22.3 1.36 1.13–1.62 ** 
 Forth 1452 19.1 1.49 1.22–1.83 *** 
 Top 1540 19.9 1.63 1.35–1.96 *** 
 Missing 0.1 n/a n/a n/a 

Sample percentages and regression results are weighted using the survey weights.

n/s, not significant; n/a, not applicable (category cases excluded from model, due to zero admissions).

aReference category of variable.

Significance level: *P < 0.05, **P < 0.01, ***P < 0.001.

Table 5

‘Age and sex standardized association’ between ‘estimates of health at survey/prior hospital admissions’ risk factors and hospital admission

Risk factor n % Hazard ratio 95% CI Significance 
Self-assessed health      
 Very gooda 2751 36.6 1.00   
 Good 3081 40.0 1.37 1.20–1.58 *** 
 Fair 1521 17.7 2.27 1.97–2.62 *** 
 Bad 438 4.8 3.84 3.20–4.61 *** 
 Very bad 85 0.9 4.91 3.35–7.18 *** 
Psychosocial health (GHQ 12 score)      
 zero scorea 4490 58.3 1.00   
 1–3 score 2023 25.8 1.40 1.23–1.60 *** 
 4 plus score 1303 15.3 1.92 1.68–2.20 *** 
 Missing 60 0.7 1.25 0.75–2.06 n/s 
Longstanding illness      
 Limiting longstanding illness 2114 23.7 2.55 2.24–2.91 *** 
 Non-limiting longstanding illness 1341 17.1 1.33 1.14–1.55 *** 
 No longstanding illnessa 4421 59.2 1.00   
Number of longstanding illnesses      
 No longstanding illnessa 4433 59.4 1.00   
 One longstanding illness 2248 27.8 1.72 1.51–1.97 *** 
 Two longstanding illnesses 858 9.4 2.49 2.10–2.94 *** 
 Three or more longstanding illnesses 337 3.5 3.06 2.49–3.76 *** 
Incapacity benefit      
 Yes 546 6.6 2.24 1.81–2.77 *** 
 Noa 7330 93.4 1.00   
Prior hospital admissions      
 Nonea 4749 62.8 1.00   
 One 1594 19.7 1.57 1.35–1.82 *** 
 Two 662 7.9 1.79 1.48–2.17 *** 
 Three 333 3.7 2.18 1.73–2.75 *** 
 Four or more 538 5.9 4.27 3.63–5.02 *** 
Risk factor n % Hazard ratio 95% CI Significance 
Self-assessed health      
 Very gooda 2751 36.6 1.00   
 Good 3081 40.0 1.37 1.20–1.58 *** 
 Fair 1521 17.7 2.27 1.97–2.62 *** 
 Bad 438 4.8 3.84 3.20–4.61 *** 
 Very bad 85 0.9 4.91 3.35–7.18 *** 
Psychosocial health (GHQ 12 score)      
 zero scorea 4490 58.3 1.00   
 1–3 score 2023 25.8 1.40 1.23–1.60 *** 
 4 plus score 1303 15.3 1.92 1.68–2.20 *** 
 Missing 60 0.7 1.25 0.75–2.06 n/s 
Longstanding illness      
 Limiting longstanding illness 2114 23.7 2.55 2.24–2.91 *** 
 Non-limiting longstanding illness 1341 17.1 1.33 1.14–1.55 *** 
 No longstanding illnessa 4421 59.2 1.00   
Number of longstanding illnesses      
 No longstanding illnessa 4433 59.4 1.00   
 One longstanding illness 2248 27.8 1.72 1.51–1.97 *** 
 Two longstanding illnesses 858 9.4 2.49 2.10–2.94 *** 
 Three or more longstanding illnesses 337 3.5 3.06 2.49–3.76 *** 
Incapacity benefit      
 Yes 546 6.6 2.24 1.81–2.77 *** 
 Noa 7330 93.4 1.00   
Prior hospital admissions      
 Nonea 4749 62.8 1.00   
 One 1594 19.7 1.57 1.35–1.82 *** 
 Two 662 7.9 1.79 1.48–2.17 *** 
 Three 333 3.7 2.18 1.73–2.75 *** 
 Four or more 538 5.9 4.27 3.63–5.02 *** 

Sample percentages and regression results are weighted using the survey weights.

n/s, not significant; n/a, not applicable (category cases excluded from model, due to zero admissions).

aReference category of variable.

Significance level: *P < 0.05, **P < 0.01, ***P < 0.001.

Behavioural risk factors (data are presented for males and females combined but analysis has been done by sex)

Heavy smokers had almost twice the risk of a serious admission (hazard ratio: 1.90, 95% CI: 1.59–2.27) (Table 2). Smoking is the behavioural risk factor associated with the largest single risk. Reaching the recommended level of physical activity was associated with decreased risk. For example, the risk of a serious admission for those reporting the recommended levels of activity was 21% lower (hazard ratio: 0.79, 95% CI: 0.69–0.91) than those reporting not reaching the recommended levels. Not reaching daily fruit and vegetable targets was associated with an elevated risk of 15% (hazard ratio: 1.15, 95% CI: 1.00–1.33). These results are as might have been expected but the results for alcohol consumption require more interpretation. High risk of admission for ex-drinkers may reflect their previous rather than current consumption and the fact that moderate drinkers were less at risk of admission than light drinkers may or may not reflect a protective effect of moderate alcohol consumption.

Biological risk factors

In general, for this group of risk factors, hazard ratios increased as risk increased. There were, however, some interesting exceptions (Table 3). Blood pressure, when elevated, is now routinely detected and treated. Therefore, being on treatment reflects a risk status; in broad terms, being treated carried approximately a 30% additional risk of admission but being hypertensive and untreated carried no additional risk. This probably reflects the fact that those measured as hypertensive during the survey who were not on treatment were either giving falsely elevated readings or were suffering from very mild hypertension. For total cholesterol, the moderately raised category showed less risk of serious admission (hazard ratio: 0.74, 95% CI: 0.60–0.91). However, those with a low (less desirable) level of HDL cholesterol showed 27% greater risk of hospital admission (hazard ratio: 1.27, 95% CI: 1.11–1.47). C-reactive protein and fibrinogen have both been established as risk factors for heart disease.7,8 The top quintile for C-reactive protein showed almost double the risk of a serious admission compared with those in the bottom quintile (hazard ratio: 1.93, 95% CI: 1.52–2.46), although the equivalent increase in risk for the top quintile in the distribution of fibrinogen was 73% (hazard ratio: 1.73, 95% CI: 1.33–2.25).

Obesity, reflected in an elevated BMI (obesity is defined here as a BMI  >  30), was associated with a higher risk of serious admission (hazard ratio: 1.23, 95% CI: 1.03–1.47). The waist–hip ratio also predicted admission, e.g. the chance of a serious admission was elevated by 26% (hazard ratio: 1.26, 95% CI: 1.12–1.42) for those with raised, compared with ‘normal’, waist–hip ratio. The risks measured as a result of obesity were slightly higher for females than for males (not shown). Gamma-GT, which can be elevated through alcoholic liver damage, was associated with increased risk: for example, serious admissions were higher by 20% in those with an elevated level (hazard ratio: 1.20, 95% CI: 1.04–1.38) compared with those with a ‘normal’ level. Forced expiratory volume (FEV) is a strong predictor of a serious admission (hazard ratio: 1.82, 95% CI: 1.49–2.22) among those with the lowest recorded levels.

Social risk factors

Those with markers of poverty or lower social position were at greater risk of experiencing a hospital admission than those without (Table 4). For example, poorer educational achievement, being in lower social class, not owning a car, renting rather than owning your home or being unemployed were associated with a higher risk of serious admission. Typically, these markers were associated with increased risks of between 20% and 40%.

The level of neighbourhood deprivation had a measurable effect. The more deprived your neighbourhood, the greater the risk of experiencing a hospital admission. Being resident in the most deprived quintile, compared with the least deprived quintile, showed 63% increased risk for a serious admission (hazard ratio: 1.63, 95% CI: 1.35–1.96). However, measures of central heating, overcrowding, rurality, drive time to GP, straight-line distance to A&E and drive time to the nearest hospital had no significant association with the risk of admission.

Health status

The SHS asks about self-assessed health, longstanding illnesses and receipt of incapacity benefit (Table 5). The General Health Questionnaire (GHQ 12) is also used to assess aspects of psychosocial illhealth. Those who perceived their own health as ‘very bad’ had an increased risk of a serious admission nearly five times that of those who answered ‘very good’ (hazard ratio: 4.91, 95% CI: 3.35–7.18). A score of 4 or more on the GHQ12 was associated with a near doubling of the risk of admission compared with those with a score of zero (hazard ratio: 1.92, 95% CI: 1.68–2.20). Reporting a longstanding illness was associated with an increased risk of admission. That risk increased if the longstanding illness was described as limiting daily activities but also with increased reporting of the number of longstanding illnesses. Being in receipt of incapacity benefit also showed increased risk (hazard ratio 2.24, 95% CI: 1.81–2.77).

Finally, the relationship between previous hospital admissions and the risk of subsequent hospital admissions was investigated. Not surprisingly, there was a strong relationship. For example, those with four or more recorded admissions in the previous 5 years had a 4-fold increase in risk of any hospital admission (hazard ratio: 4.22, 95% CI: 3.68–4.84) compared with those with none.

Discussion

Main findings of the study

One of the main reasons for carrying out this study was to create a resource for the future. With each successive wave of the SHS and rapidly escalating numbers of person years of hospitalization to analyse, it will be possible to create a much more sophisticated understanding of the way in which risk factors affect hospital admission. This study represents the start of this process and establishes that this approach is practical and potentially useful.

The second important finding is that a large selection of established risk factors do associate with the risk of hospital admission. This is not surprising but the relative size of each association was less predictable. Before embarking on this study, a range of local experts were informally asked by the authors for their estimation of the size of effect and relative importance of established risk factors on hospital admission. Most answered that the data were simply not available and that they would find it hard to estimate the scale of the impact. The value of this paper, therefore, is that it demonstrates how record linkage can establish the absolute scale and relative importance of these relationships.

Of the behavioural risk factors, smoking had the largest effect. This almost certainly reflects the large number of diseases known to have a causative relationship with smoking. However, it is striking how modest was the effect of, for example, diet and physical activity.

Of the biological markers, FEV1 had the largest association with the risk of hospital admission, confirming a finding from a previous study on a much older cohort.4 C-reactive protein has attracted increasing attention in recent years because of its association with inflammation, chronic stress and the metabolic syndrome13, so it was interesting to note that higher levels were associated with increased risk of admission. However, it should also be noted that the results for high blood pressure and total cholesterol were less straightforward. Most individuals with high levels of blood pressure are now treated as are many with elevated cholesterol. Consequently, their measurement of risk at the time of SHS may have been reduced through treatment but their chances of requiring a hospital admission may be higher because of the underlying condition. Consequently, it is difficult to interpret these findings.

All the measures of social position had variable but unsurprising associations with the risk of hospital admission: none of these associations were large. It is likely that much of this association is mediated through higher levels of behavioural and biological risk but further analysis, as data are accumulated over time, will allow us to establish the degree of interaction between social, biological and behavioural risk factors.

The relatively small size of the increased risk of hospital admission associated with behavioural, social and biological factors may surprise some. In contrast, large effects on risk were associated with self-assessed health, longstanding illness and history of previous admission. It may seem obvious that indicators that reflect the existence of established disease are associated with the risk of subsequent admission but the size of these effects is worthy of note.

What is already known on this topic

It is well established that record linkage can be a powerful tool for health service research.14 A large literature has established each item in Table 1 as a risk factor for disease. A very few studies have partially established the nature and size of the link between risk factors and hospital utilization4–6 but not for general population cohorts. This is the first time that an analysis has been carried out for a national population cohort.

What this study adds

A large and expanding linked database has been used to establish the presence and scale of association between risk factors and hospital admission. With the passage of time, person years of follow-up will increase. The result will be an increasingly powerful tool for analysing factors that influence hospital utilization.

Limitations of the study

The fundamentals of this study are very sound. The SHS is large, well validated and rigorously conducted.11 The SMR covers all admissions to NHS facilities and is maintained with high standards of quality control10 and the record linkage methodology employed is tried and tested.10 The main limitation of this study is the concentration on univariate associations. Plans are in place to extend to multivariate analysis but, nonetheless, the size and pattern of univariate association are of sufficient interest to merit detailed reporting in the first instance. Further research will also investigate the association between risk factors and admissions resulting from specific diagnostic categories, once sufficient records and time have accumulated to perform this work.

Future applications

In time, many applications may be found for this tool. These include scenario modelling of changes in risk factor, providing further evidence to motivate behavioural change and to support anticipatory care intervention programmes.

Funding

NHS Health Scotland provided financial support and ISD Scotland provided staff members and substantial logistic support.

Acknowledgment

The project team wish to thank: NHS Health Scotland for contribution of staff members; ISD Scotland for staff members and substantial logistic support; The Chief Scientist Office, and in particular Dr Peter Craig, for enabling access to the source dataset and for continuing financial support which allows ongoing linkage of this important resource.

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