Relation of adult lifestyle and socioeconomic factors to the prevalence of Helicobacter pylori infection

Paul Moayyedi, Anthony TR Axona, Richard Feltbowerb, Sara Duffetta, Will Crocombec, David Braunholtzd, ID Gerald Richardse, Anthony C Dowellf and David Formang for the Leeds HELP Study Group

a Centre for Digestive Diseases, The General Infirmary at Leeds, Leeds, UK.
b Paediatric Epidemiology Group, University of Leeds, Leeds, UK.
c Northern and Yorkshire Clinical Trials and Research Unit, University of Leeds, Leeds, UK.
d The Department of Public Health and Epidemiology, University of Birmingham, Birmingham, UK.
e Institute of Epidemiology and Health Services Research, University of Leeds, Leeds, UK.
f Department of General Practice, Wellington School of Medicine, New Zealand.
g Cancer Epidemiology Group, Epidemiology and Health Services Research Unit, University of Leeds, Leeds, UK.

Paul Moayyedi, The General Infirmary at Leeds, Great George Street, Leeds LS1 3EX, UK. E-mail: paulmo{at}ulth.northy.nhs.uk


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
Introduction The influence of adult socioeconomic status, co-habitation, gender, smoking, coffee and alcohol intake on risk of Helicobacter pylori infection is uncertain.

Methods Subjects between aged 40–49 years were randomly invited to attend their local primary care centre. Participants were interviewed by a researcher on smoking, coffee and alcohol intake, history of living with a partner, present and childhood socioeconomic conditions. Helicobacter pylori status was determined by 13C-urea breath test.

Results In all, 32 929 subjects were invited, 8429 (26%) were eligible and 2327 (27.6%) were H. pylori positive. Helicobacter pylori infection was more common in men and this association remained after controlling for childhood and adult risk factors in a logistic regression model (odds ratio [OR] = 1.15; 95% CI: 1.03–1.29). Living with a partner was also an independent risk factor for infection (OR = 1.30; 95% CI: 1.01–1.67), particularly in partners of lower social class (social class IV and V—OR = 1.47; 95% CI: 1.19–1.81, compared with social class I and II). Helicobacter pylori infection was more common in lower social class groups (I and II—22% infected, III—29% infected, IV and V—38% infected) and there was a significant increase in risk of infection in manual workers compared with non-manual workers after controlling for other risk factors (OR = 1.18; 95% CI: 1.03–1.34). Alcohol and coffee intake were not independent risk factors for infection and smoking was only a risk factor in those smoking >35 cigarettes a day.

Conclusions Male gender, living with a partner and poor adult socioeconomic conditions are associated with increased risk of H. pylori infection.

Keywords Helicobacter pylori, gender, socioeconomic status

Accepted 8 October 2001


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
Helicobacter pylori infection is a major cause of peptic ulcers and is an important risk factor for gastric cancer.1 Helicobacter pylori infection is implicated in over a third of a million deaths each year worldwide.2 In order to develop public health measures that could limit transmission of this potentially fatal pathogen, it is important to understand the epidemiology of H. pylori. Childhood appears to be the critical period during which H. pylori is acquired,3 especially in areas of overcrowding and socioeconomic deprivation.4,5 However, adults can also become infected with H. pylori and, in developed countries, this has been reported to occur at a rate of around 0.3–0.5% per year.6 In many developing countries, the majority of people appear to acquire an infection during childhood and acquisition in adult life is, therefore, relatively unimportant.7 In the developed world, as the rate of childhood infection declines, more adults become susceptible to infection and the relative importance of acquisition in adult life increases. Mathematical modelling indicates that up to a quarter of those infected in developed countries may acquire H. pylori after childhood.8 Adult factors associated with prevalence of H. pylori are poorly characterized and in particular it is unclear whether socioeconomic conditions and household overcrowding in later life are independent predictors of infection.

The same strain of H. pylori has been isolated from husband and wife, raising the possibility of intrafamilial transmission between spouses.9 Epidemiological studies have not definitively confirmed living with a partner as a factor associated with prevalence for infection but most studies have been under-powered or have not adequately controlled for confounding factors.10,11 The relationship between lifestyle factors and H. pylori infection is also uncertain. Smoking has been reported as a factor associated with H. pylori prevalence12 but this has not been a universal finding.13,14 A recent study suggested alcohol protected against infection while coffee had the opposite effect,14 but results have been inconsistent.6,13 Again the reasons for these discrepancies may relate to the sample size and failure to control adequately for confounding variables.

We investigated the relationship between H. pylori infection, adult socioeconomic status, adult household crowding, marital status and lifestyle in a large cross-sectional study that controlled for childhood factors associated with prevalence.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
Study population
This paper presents the baseline data from an intervention trial evaluating the medical benefits and health economics of H. pylori eradication in the community. The study was carried out in the Leeds and Bradford area (a predominately urban community in the north of England) between January 1994 and July 1995. Subjects aged 40–49 years were randomly selected from the lists of 36 general practices and invited by letter to attend their local surgery (evening clinics were held for recruitment). The age range was chosen to target the population with the highest prevalence of H. pylori infection that would remain largely free from serious morbidity and mortality during follow-up. Subjects were excluded if they had taken antibiotics, proton pump inhibitors or bismuth salts within the previous 2 weeks. Those unwilling to give up alcohol for 1 week, those with an allergy to macrolides, proton pump inhibitors or 5-nitroimidazoles and those taking warfarin, digoxin, cisapride, antihistamines or theophyllines were also excluded.

A trained research nurse interviewed eligible participants about past and present living conditions using a standard questionnaire. Household crowding at the age of 8 years and at present, was expressed as occupants/room (excluding bathroom and kitchen if less than 6 ft x 6 ft). Childhood socioeconomic conditions were recorded at the age of 8 years as this has been described by other workers.3 Social class was based on present occupation according to published guidelines.15 Tables 1 and 2GoGo outline other questions asked in this survey. We also enquired about pet ownership but the details of this are not presented here. Informed written consent was obtained from all participants and the relevant local research ethics committees approved the study.


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Table 1 Analysis of childhood factors associated with Helicobacter pylori (Hp) infection
 

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Table 2 Analysis of adult factors associated with Helicobacter pylori (Hp) infection
 
Determination of H. pylori status
Active H. pylori infection was assessed by a non-fasting 13C-urea C-urea breath test. Participants were given 4 g citric acid and a baseline breath sample was obtained in a 10 ml exetainer. 100 mg of 13C-labelled urea (99% pure, Boston Isotopes, Boston, Massachusetts, US) was ingested and a further breath sample was collected at 30 minutes. Samples were analysed using a mass spectrometer (ABCA-NT, Europa Scientific, UK). Subjects with an excess {delta}13CO2 value of >5 per ml were defined as H. pylori positive. This protocol has been previously validated in the Leeds population and has 98% sensitivity (95% CI: 93–100%) and 96% specificity (95% CI: 90–99%).16

Statistical analysis
Odds ratios (OR) for the unadjusted data were calculated using EpiInfo version 5.01 (Center for Disease Control, Atlanta, GA, US). All data (apart from pet ownership) were then incorporated into an unconditional logistic regression model to determine independent predictors of H. pylori infection. The categories were predefined before data collection apart from the groupings for number of siblings and household crowding. These were categorized retrospectively in a manner that avoided small numbers in any stratum. Calculations were performed using the SPSS statistical package version 7.0 (US).


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
In all, 32 929 subjects were invited to participate, 9262 attended their local general practice and 8429 (25.6%) were eligible for the study. The mean age of the 8429 participants was 45.3 ± 2.9 years and 2327 (27.6%) were H. pylori positive with breath samples missing or inadequate in 25 (0.3%) cases. Most of the subjects were Caucasian (97.3%) and born in the UK (95.3%).

Representativeness of the sample
The social class distribution of subjects participating in this survey was compared with the 1991 Census data for West Yorkshire. This suggested that there were differences between participants and the general population, particularly with an over-representation of social class III non-manual and an under-representation of social class III manual in the study (Figure 1Go).



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Figure 1 Social class of subjects participating in the study compared with the 1991 Census of the Yorkshire region in the UK

 
A letter was written to 847 randomly selected non-participants for permission to review their general practice notes and 74% consented. This information was compared with the note review of 4711 subjects participating in the study. The ages of the two groups was similar (mean ± standard deviation age for participants = 45.3 ± 2.9 years, non-participants = 45.8 ± 2.9 years). H2 receptor antagonist prescriptions were also similar between the two groups (4% versus 3.8%). Participants were almost twice as likely to visit their primary care physician for dyspepsia in the previous 2 years compared with non-participants (15.1% versus 8.2%).

General factors associated with H. pylori infection
Infected subjects were marginally older than uninfected subjects (mean age of H. pylori positive cases = 45.4 ± standard deviation of 2.9 years, H. pylori negative cases = 45.2 ± 2.9 years P = 0.004) and infection was more common in non-Caucasians (Table 1Go). Helicobacter pylori infection was also more prevalent in men than women (29% versus 26%). This remained statistically significant after controlling for confounding factors (Table 1Go).

Childhood factors associated with prevalence of H. pylori infection
Current H. pylori infection was associated with the following childhood variables in the unadjusted analysis: type of house, presence of bathroom, number of siblings, sharing a bedroom with a sibling, sharing a bed with a sibling, sharing a bed with a parent, household crowding and head of household social class (Table 1Go). All variables in Tables 1 and 2GoGo were included in a multivariate model with prevalence of H. pylori as the dependent variable. Type of accommodation, sharing a bed with a sibling and number of siblings (OR = 1.16 per extra sibling, 95% CI: 1.13–1.20) remained strongly associated with prevalence of H. pylori. Childhood social class and household crowding (OR = 1.14 per extra occupant per room, 95% CI: 0.99–1.31) also remained associated with H. pylori infection (Table 1Go).

Adult socioeconomic status and prevalence of H. pylori infection
The prevalence of H. pylori infection had a statistically significant association with present social class (Table 2Go) in an unadjusted analysis. Present social class is correlated with childhood social class (Pearson's correlation coefficient = 0.22, P < 0.0001) and the unadjusted results of present social class could be confounded by childhood socioeconomic conditions. However, the prevalence of H. pylori infection increased with decreasing social class even within levels of childhood social class (Table 3Go).


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Table 3 Adult social class and prevalence of Helicobacter pylori infection stratified for childhood social class
 
Helicobacter pylori infection was also associated with other measures of current socioeconomic status. Housing tenure, educational level, present household crowding, number of cars in household, telephone ownership and presence of central heating all showed a statistically significant association with infection (Table 2Go). When these factors were entered into a multivariate model together with variables from Table 1Go, marital status, smoking, alcohol and coffee intake, telephone ownership, educational level, housing tenure and central heating remained associated with H. pylori infection. Present social class became statistically non-significant in this model but was associated with H. pylori infection when dichotomously categorized as non-manual/ manual (manual versus non-manual OR = 1.18, 95% CI: 1.03– 1.34). No OR were greater than 1.60 in magnitude.

Marital status and prevalence of H. pylori infection
Subjects that had lived with a partner were more likely to be infected with H. pylori (lived with partner 2179/7786—28.0% infected, never lived with partner 120/502—23.9% infected, P = 0.048 {chi}2). Prevalence of infection did not increase with increasing time spent with a partner (<10 years—30% infected, 10–20 years—27% infected, >20 years—28% infected, P = 0.79 {chi}2). Marital status remained independently associated with H. pylori infection in a logistic regression model controlling for factors outlined in Tables 1 and 2GoGo with partners' social class omitted (OR = 1.30, 95% CI: 1.01–1.67, for subjects ever had a partner compared with never had a partner).

Lifestyle factors and prevalence of H. pylori infection
Smoking was associated with increased prevalence of H. pylori infection in both univariate and multivariate analysis (Table 2Go). The proportion of subjects infected increased with increasing numbers of cigarettes smoked (Table 4Go) but when adjusted for childhood and adult socioeconomic status the 95% CI for the OR did not include unity only in those that smoked >35 cigarettes/day.


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Table 4 Association between smoking, alcohol and coffee intake and prevalence of Helicobacter pylori infection
 
Subjects drinking alcohol had a decreased prevalence of H. pylori infection with an OR of 0.82 compared with non-drinkers in the multivariate model (Table 2Go). When analysed according to the amount consumed, the 95% CI for OR for prevalence of H. pylori infection included unity for all categories except those that drank <1 unit of alcohol/week (Table 4Go).

The proportion of subjects infected with H. pylori was lower in those that drank coffee but this relationship was of borderline statistical significance in the multivariate model (Table 2Go). There was no clear dose response between the amount of coffee drunk and prevalence of H. pylori infection when adjusted for the childhood and adult factors associated with prevalence (Table 4Go).


    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
To our knowledge, this is the largest study assessing factors associated with the prevalence of H. pylori infection using the 13C-UBT. Epidemiological surveys normally use serology but the sensitivity and specificity of these tests limit the accuracy of H. pylori prevalence estimates.17,18 We have used the 13C-urea breath test, which is more accurate than serology16 and, together with the large sample size, allows more precise evaluation of factors associated with prevalences for H. pylori. It is important to recognize that the risk factors that influence acquisition of infection and those that influence persistence of infection in adult life may not be the same and this study is not designed to distinguish between these events.

We have confirmed that childhood socioeconomic deprivation and overcrowding are important determinants of the prevalence of H. pylori infection as others have shown.4,5,19 Number of siblings was a strong predictor of infection suggesting that transmission between siblings is an important mode of acquisition. This is consistent with the observation that individuals of higher birth order have an increased prevalence of H. pylori, particularly if the age gap between sibs is small.20,21

The influence of adult socioeconomic factors on prevalence of infection has been less well characterized.4,22 Our survey suggests that some markers of adult socioeconomic status are independent factors associated with prevalences for infection supporting the hypothesis that either H. pylori can be acquired in adult life or that adult socioeconomic factors contribute to the persistence of infection. Odds ratios for adult socioeconomic variables were generally not as high as for childhood factors suggesting that the latter remain more important determinants of infection status.

Data from previous studies suggested an increased prevalence of H. pylori infection in men compared with women but it frequently did not reach statistical significance.23 A meta-analysis indicated that male gender was a factor associated with prevalence for infection but the authors acknowledged that the study was limited by the unavailability of primary data from some studies, making it difficult to control for confounding variables.24 This large study suggests that the odds of being H. pylori positive increase by 15% in middle-aged males. The reason for the possible gender difference in H. pylori prevalence is unclear but may relate to young boys having poorer hygiene than young girls.25

Studies investigating transmission of H. pylori within households have often assessed prevalence of H. pylori in spouses of infected and uninfected index cases.10,11 These case-control studies have given conflicting results due to possible bias in the patient groups selected and the lack of adjustment for confounding factors. This cross-sectional survey avoids some of these problems and has identified living with a partner as a factor associated with prevalence for H. pylori infection. The odds of infection for cohabiting are relatively small but the plausibility of this finding is strengthened by the observation that prevalence of infection increases in subjects with lower social class partners.

The influence of lifestyle on prevalence of H. pylori infection remains controversial. In our study smokers were more likely to have H. pylori infection than non-smokers but when adjusted for confounding factors this association was only observed in those smoking >35 cigarettes/day. Smaller cross-sectional surveys have reported an association between smoking and H. pylori prevalence12,26 but these may not have adequately controlled for confounding factors.

Several studies have investigated the association between H. pylori and alcohol or coffee intake with conflicting results.6,13,14,26 The mechanisms that would promote an association between coffee or alcohol intake are unclear and this hypothesis is not supported by our data. We cannot, however, formally exclude the possibility that participants may have altered their lifestyle in middle age and this has masked an earlier association between H. pylori and alcohol.

A weakness of the study is that only 26% of the invited subjects were evaluated for H. pylori status. It is, however, extremely rare for individuals to know their H. pylori status and so there is unlikely to be any systematic bias that would threaten the internal validity of our results. The study participants may, however, not be representative and indeed there were differences between this group and the general population in terms of social class distribution and frequency of consultation for dyspepsia. Our results concerning the estimated levels of infection prevalence may not, therefore, apply to the general population but this should not be the case for our estimates of relative risk for the identified variables.


KEY MESSAGES

  • Childhood socioeconomic conditions have the strongest association with Helicobacter pylori infection.
  • Risk of H. pylori infection increases with increasing number of siblings.
  • There is a small but statistically significant increase in prevalence of H. pylori infection in men.
  • Some markers of present socioeconomic status remained a risk factor for prevalence of H. pylori infection in a logistic regression model controlling for childhood socioeconomic conditions.
  • There is little evidence from this cross-sectional survey that smoking, alcohol or coffee intake have a strong influence on H. pylori prevalence.

 


    Appendix
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
The Leeds HELP Study Group
Study supervisors
: Prof. ATR Axon, Consultant Gastroenterologist, Centre for Digestive Diseases, The General Infirmary at Leeds, Great George Street, Leeds LS1 3EX, UK. Prof. IDG Richards, Professor of Public Health Medicine, Institute of Epidemiology and Health Services Research, 30 Hyde Terrace, University of Leeds, Leeds LS2 9LN, UK. Prof. AC Dowell, Professor of Primary Care, Department of General Practice, Wellington School of Medicine, PO Box 7343, Wellington, New Zealand. Prof. P Heywood, Professor of Primary Care Development, Centre for Research in Primary Care, The Hallas Wing, Nuffield Institute for Health, 71–75 Clarendon Road, Leeds LS2 9PL, UK. Prof. A Walan, ASTRA Hassle AB, S-431 83, Molndal, Sweden.

Clinical co-ordinator
: Dr P Moayyedi, Senior Lecturer in Gastroenterology, Centre for Digestive Diseases, The General Infirmary at Leeds, Great George Street, Leeds LS1 3EX, UK.

Data management and trial co-ordinators
: Dr S Mason, Joint Operations Director, Northern and Yorkshire Clinical Trials and Research Unit, University of Leeds, Hospital Lane, Leeds LS16 6QB, UK. Mr W Crocombe, Senior Trial Co-ordinator, Mrs R Muthukumar, Assistant Trial Co-ordinator, Miss J Norton, Assistant Trial Co-ordinator—Northern and Yorkshire Clinical Trials and Research Unit, University of Leeds, Hospital Lane, Leeds LS16 6QB, UK.

Data collection
: Mrs S Duffett, Research Sister. Mrs P Atha, Research Nurse, Mrs M Liptrott, Research Nurse, Mrs J Nathan, Research Nurse, Mrs C Youings, Research Nurse, Mrs R Hall, Research Nurse, Mrs J Greatrex, Research Nurse, Mrs J Hammacott, Research Nurse, Miss A Zilles, Research Nurse, Mrs J Welsby, Research Nurse, Miss C Walton, Research Nurse—Institute of Epidemiology and Health Services Research, 30 Hyde Terrace, University of Leeds, Leeds LS2 9LN, UK.

Statistical support
: Dr D Braunholtz, Senior Statistician, The Department of Public Health and Epidemiology, University of Birmingham, Birmingham B15 2TT, UK. Mrs J Brown, Chief Medical Statistician, Northern and Yorkshire Clinical Trials and Research Unit, University of Leeds, Hospital Lane, Leeds LS16 6QB, UK. Dr P McKinney, Senior Research Fellow, Paediatric Epidemiology Group, 30 Hyde Terrace, University of Leeds, Leeds LS2 9LN, UK. Mr R Feltbower, Statistician, Paediatric Epidemiology Group, 30 Hyde Terrace, University of Leeds, Leeds LS2 9LN, UK. Dr S Eriksson, ASTRA Hassle AB, S-431 83, Molndal, Sweden.

Health economic support
: Prof. M Drummond, Director, Dr J Mason, Senior Research Fellow—Centre for Health Economics, University of York, Heslington, York YO1 5DD, UK. Dr N-O Stalhammar, ASTRA Hassle AB, S-431 83, Molndal, Sweden.

Data monitoring committee
: Dr R Spiller, Reader in Gastroenterology, Queens Medical Centre, Nottingham, UK. Dr M Jones, Senior Statistician, ICRF, St James' and Seacroft University Hospitals, Beckett Street, Leeds LS9 7TF, UK. Prof. D Forman, Professor of Cancer Epidemiology, Centre for Cancer Research, University of Leeds, Arthington House, Cookridge Hospital, Leeds LS16 6QB, UK.

13C-UBT analysis
: Mr M Clough, MLSO, Centre for Digestive Diseases, The General Infirmary at Leeds, Great George Street, Leeds LS1 3EX, UK.

Trial pharmacist
: Ms C Bedford, Out-patient pharmacy manager, Out-patient pharmacy, The General Infirmary at Leeds, Great George Street, Leeds LS1 3EX, UK.

Financial administration
: Mrs A Starkey, Research School of Medicine, 24 Hyde Terrace, University of Leeds, Leeds LS2 9LN, UK.

Participating general practices
: Meanwood Health Centre, 548 Meanwood Road, Leeds; Kippax Health Centre, Gibson Lane, Kippax, Leeds; The Croft Surgery, Town Street, Horsforth, Leeds; Windsor House Surgery, Corporation Street, Morley, Leeds; Woodsley Health Centre, Woodsley Road, Leeds; Bridge Street Surgery, 3 Bridge Street, Otley; Marsh Street Surgery, 25a Marsh Street, Rothwell, Leeds; High Field Surgery, Holtdale Approach, Leeds; Lingwell Croft Surgery, Ring Road, Middleton, Leeds; Beeston Hill Health Centre, 134 Beeston Road, Leeds; Woodhouse Medical Centre, Woodhouse Street, Leeds; Leigh View Medical Practice, Bradford Road, Tingley, Wakefield; Fountain Medical Centre, Corporation Street, Morley, Leeds; Dib Lane Practice, 112A Dib Lane, Leeds; Hunslet Health Centre, 24 Church Street, Leeds; Grange Medical Centre, Seacroft Crescent, Leeds; Crossland Surgery, 218A Dewsbury Road, Leeds; St. Martin's Practice, 319 Chapeltown Road, Leeds; Robin Lane Medical Centre, Robin Lane, Pudsey, Leeds; Manor Park Surgery, Bellmount Close, Bramley, Leeds; Carlton Surgery, 27 Carlton Gardens, Leeds; Burton Croft Surgery, 5 Burton Crescent, Headingley, Leeds; Ridge Medical Practice, 3 Paternoster Lane, Great Horton, Bradford; West Lodge Surgery, New Street, Farsley, Leeds; Windmill Health Centre, Mill Green View, Leeds; Yeadon Health Centre, 17 South View Road, Yeadon, Leeds; The Medical Centre, 30 Buttershaw Lane, Bradford; Garforth Medical Centre, Church Lane, Leeds; The Chapeloak Practice, 347 Oakwood Lane, Leeds; The Health Centre, King;'s Road, Wrose, Bradford; Burley Park Medical Centre, 273 Burley Road, Leeds; The Street Lane Practice, 12 Devonshire Avenue, Leeds; Silver Lane Surgery, 1 Suffolk Court, Yeadon, Leeds; New Wortley Health Centre, 15 Green Lane, Leeds; Cullingworth Medical Centre, 12 Mill Street, Cullingworth, Bradford; Westcliffe Medical Centre, Westcliffe Road, Shipley, Bradford.


    Acknowledgments
 
This study was funded by a Northern and Yorkshire Research and Development Grant and by AstraZeneca. Paul Moayyedi is currently funded by a UK Medical Research Council Training Fellowship in Health Services Research.


    References
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix
 References
 
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