Background: This study investigates socio-economic differentials in herpes simplex virus type 2 (HSV-2) seroprevalence in Australian men and women using individual and geographic measures of socio-economic status.
Methods: HSV-2 seropositivity among men and women aged over 25 years was investigated by levels of individual and area-based measures of socio-economic status (SES) in a series of Poisson regression models, variously adjusting for age, country of birth, marital status, indigenous status, and urban/rural residence as potential confounders. Serum and socio-demographics were collected during 1999 and 2000 in a population-based Australia-wide prevalence survey.
Results: HSV-2 seroprevalence was significantly lower in areas of low SES than in high SES areas among both men (P for trend <0.001) and women (P for trend = 0.004) for all ages. A similar pattern was evident for individual education level for men with lower rates of HSV-2 in respondents with lower educational achievement (relative risk = 0.77, 95% CI 0.61–0.97, P = 0.024). In contrast, HSV-2 prevalence was higher for women with lower individual levels of education for all ages (relative risk = 1.22, 95% CI 1.04–1.44, P = 0.017). Analyses stratifying HSV-2 prevalence for individual education level by area-based SES showed the highest prevalence of HSV-2 in women with the lowest education level residing in the highest SES areas. This pattern was not evident in men, with a greater concordance between individual and area-based SES.
Conclusion: HSV-2 seroprevalence is not consistently distributed across individual and area measures of SES, suggesting that upward and downward mixing between social strata in men and women is an important mode of HSV-2 transmission.
This Australian seroprevalence study showed that upward and downward mixing between social strata in men and women is an important mode of herpes simplex virus type 2 transmission.
From the *Discipline of Epidemiology and Biostatistics, School of Population Health and †Australian Centre for International and Tropical Health, School of Population Health, University of Queensland, Herston Queensland, Australia; ‡School of Public Health and Community Medicine, University of New South Wales, Kensington New South Wales, Australia; §International Diabetes Institute, Caulfield, Victoria, Australia; |Westmead Millenium Institute, Westmead New South Wales, Australia; and ¶Sexually Transmitted Infections Research Centre, University of Sydney, Westmead New South Wales, Australia
The authors thank the following for their support of the study: The Commonwealth Department of Health and Aged Care, Abbott Australasia Pty Ltd., Alphapharm Pty Ltd., AstraZeneca, Aventis Pharmaceutical, Bristol-Myers Squibb Pharmaceuticals, Eli Lilly (Australia) Pty Ltd., GlaxoSmithKline, Janssen-Cilag (Australia) Pty Ltd., Merck Lipha s.a., Merck Sharp and Dohme (Australia), Novartis Pharmaceutical (Australia) Pty Ltd., Novo Nordisk Pharmaceutical Pty Ltd., Pharmacia and Upjohn Pty Ltd., Pfizer Pty Ltd., Roche Diagnostics, Sanofi Synthelabo (Australia) Pty Ltd., Servier Laboratories (Australia) Pty Ltd., BioRad Laboratories Pty Ltd., HITECH Pathology Pty Ltd., Australian Kidney Foundation, Diabetes Australia, Diabetes Australia (Northern Territory), Queensland Health, South Australian Department of Human Services, Tasmanian Department of Health and Human Services, Territory Health Services and Victorian Department of Human Services and Health Department of Western Australia and also, for their invaluable contribution to the set-up and field activities of AusDiab, the authors also thank A. Allman, B. Atkins, S. Bennett, S. Chadban, S. Colagiuri, M. de Courten, M. Dalton, M. D’Embden, D. Dunstan, T. Dwyer, D. Jolley, I. Kemp, P. Magnus, J. Mathews, D. McCarty, A. Meehan, K. O’Dea, P. Phillips, P. Popplewell, C. Reid, A. Stewart, R. Tapp, H. Taylor, T. Welborn, F. Wilson, and P. Zimmet.
Correspondence: Andrew Page, BA (Psych) (Hons), PhD, Discipline of Epidemiology and Biostatistics, School of Population Health, Public Health Building, Herston Rd, University of Queensland, Herston Queensland 4006, Australia. E-mail: email@example.com.
Received for publication June 23, 2008, and accepted December 5, 2008.
Numerous studies have considered demographic and behavioral risk factors associated with herpes simplex virus type 2 (HSV-2) seropositivity. Previous studies of demographic factors have shown a higher prevalence of HSV-2 seropositivity in women than in men,1–7 a higher prevalence of HSV-2 with increasing age,1–4,6,8–12 and significant differentials in HSV-2 prevalence by ethnicity and country of birth groups.6,9,11–14 Investigations of more proximate behavioral factors have also shown higher prevalence of HSV-2 in those reporting a greater number of sexual partners,3–5,8,9,15 those engaging in sexual intercourse at a younger age,5,9,14,15 and those with a history of other sexually transmitted infections.5,9,10,14
A number of studies have also considered HSV-2 prevalence by markers of socio-economic status (SES) such as education level2–4,8–11,14–16 and income level.9–11 Few studies have explicitly considered socio-economic factors associated with HSV-2 seroprevalence in representative samples, with most focusing on clinical samples,14,15 commercial sex workers or other occupational groups,3,8,10,13 or population-based samples of subgroups of a population such as low-income women,9 unmarried heterosexuals with multiple partners,11 or particular geographic regions.16,17 Previous studies investigating SES differentials in HSV-2 prevalence have found higher prevalence in lower socio-economic groups, although this has not always been the case in western developed contexts where higher prevalence of HSV-2 is evident in higher socio-economic groups.2,16
These studies of socio-economic factors and HSV-2 indicate that both contextual and compositional SES factors are important in understanding HSV-2 seroprevalence in populations. Individual markers of SES, such as income, education level, and area-based or geographic markers of SES are likely to show differing patterns of HSV-2 seroprevalence. There are no representative studies of HSV-2 prevalence in which both individual and area-based measures of SES have been considered simultaneously. Geographic areas of residence are not necessarily homogeneous in terms of the individual SES of residents. Also, ecological socio-economic associations do not necessarily address the compositional effects of individual SES. An investigation of socio-economic and other demographic factors within particular geographic areas is useful in understanding “at risk” subpopulations and potential modes of transmission within populations. Accordingly, the current study investigates socio-economic differentials in HSV-2 seroprevalence in a nationally representative sample of Australian men and women using both individual and geographic measures of SES.
Materials and Methods
The study used serum and socio-demographic data already collected for AusDiab, a nationwide, representative study designed to investigate diabetes and related risk factors in the Australian population (age ≥25 years). Detailed methodology of this survey can be found elsewhere.18 The survey involved a short household interview, followed by a biomedical examination at a local survey centre. The final survey sample included 11,247 adults. From the AusDiab survey, a stratified random sample was obtained (n = 4000) that involved oversampling some demographic groups to ensure sufficient numbers to address descriptive objectives concerning HSV-2 seroprevalence.1 Sample weights were generated using poststratified ratio estimates of sex, age, indigenous status, and urban/rural residence strata, using the 2001 Australian Census population as the standard.1
SES as measured by individual educational achievement was recorded for each respondent. Education level was based on the categories: “University or Technical and Further Education,” “Completed high school,” “Some high school,” and “Primary school.” Preliminary analyses found small frequencies in those completing “Some high school” or “Primary school.” Accordingly, for the analysis, educational achievement was aggregated and specified as a binary variable: “Completed high school” (and above) or “Did not complete high school.” Two area-based SES indices, the Index of education and occupation and the Index of economic resources, were also investigated. These indices were derived from Australian Bureau of Statistics Census information in 2001 and use socio-economic characteristics of a given Local Government Area (average income level, educational attainment, occupational status, and unemployment rates, among other factors).19 Local Government Areas are spatial units, which represent the geographic area of responsibility of an incorporated Local Government Council. The median LGA size during the period of this seroprevalence survey was approximately 6000; however, population (and geographic area) range from small units of a few hundred people to large municipalities of approximately 200,000 people. Preliminary analyses showed similar trends for both these area-based measures, with greater SES differentials evident for the Index of education and occupation. The education and occupation measure was used in analyses as the primary area-based SES measure, given conceptual similarities with the individual SES measure of education level. Weighted prevalences of the SES index scores were used to create approximate population quintiles of area-based SES. After preliminary analyses of SES trends that showed similar HSV-2 seroprevalence in intermediate groups between the highest and lowest SES quintiles, these quintiles were grouped into “low” (20%), “middle” (60%), and “high” (20%) SES groups.
Other factors considered were age, country of birth, marital status, indigenous status, and urban/rural residence. Previous examination of HSV-2 seroprevalence in Australia has shown significant differences by age, indigenous status (higher rates of HSV-2 in indigenous than in nonindigenous groups), and urban/rural residence (higher rates of HSV-2 in metropolitan than in rural areas).1 Age was grouped into 3 broad age-groups, a younger (25–40 years), a middle age-group (41–54 years), and an older (≥55 years) group, based on age-specific patterns of HSV-2 prevalence, which indicate a peak (followed by a plateau) in prevalence in those aged in their 40s.1 Mean age of participants was 52 years (SD = 15 years, median = 50 years, interquartile range = 40–62). The indigenous status of respondents was grouped as “indigenous” or “not indigenous,” and urban/rural residence was grouped as “capital city,” “other metropolitan centres,” and “rural and remote areas.” Differences in HSV-2 by marital status categories have also been demonstrated20 (higher prevalence of HSV-2 among those divorced or separated than those married or in de facto relationships). Respondents’ marital status was grouped according to: “married or de facto,” “divorced or separated,” “widowed,” and “never married.”
Differences in HSV-2 seropositivity between country of birth groups in Australia are relatively small;21 however, country of birth is an important confounding factor in analyses of SES, given that migrant groups are differentially distributed by SES. Respondents’ countries of birth were grouped according to “Australian-born” or “not Australian-born.”
HSV-2 seropositivity was considered by stratifying individual respondents into levels of individual and area-based strata of SES in a series of Poisson regression models, variously adjusting for age, country of birth, marital status, indigenous status, and urban/rural residence as potential confounders. Seroprevalence of HSV-2 generally increases with age, representing cumulative rates of infection, and is also significantly higher in females than males.1 Consequently, base models of associations between SES and HSV-2 were stratified by sex and adjusted for age, followed by multivariate models adjusting for potential confounding of other socio-demographic factors.
Additional models also examined interactions in HSV-2 seropositivity between individual and area-based measures of SES. After preliminary investigation of interaction terms (between individual and area-based SES measures), models were stratified by approximate quintile of area-based SES to determine whether SES differentials in individual educational achievement differed across the area-based measure of SES. A composite SES variable combining the individual and area-based measures was specified such that estimates could be calculated for each combination of individual and area-based SES category, to investigate the extent to which individual-level SES prevalence varied within area-level SES measures, and assess the extent of cross-level bias in HSV-2 prevalence. Combinations of individual/area SES categories were compared to a “high” SES reference group, defined as those individuals who completed high school, and who resided in the highest SES area.
Poisson regression, rather than logistic regression, was used because prevalences were considerable and the odds ratio from logistic regression only approximates the relative risk (RR) when proportions are small. In this analysis, the antilog of β estimates correspond to adjusted ratios of prevalences. Statistical analyses were conducted in SAS Version 9.122 using PROC GENMOD. Multilevel Poisson regression models (using PROC MIXED and the GLIMMIX SAS macro)23 were also specified to investigate the proportion of variance accounted for by individual and area SES measures, using area-SES intercepts specified as random effects, and individual SES (and other covariates) specified as fixed effects. The proportion of area-level SES variance attributable to individual-level SES was investigated by evaluating changes in the ρ statistic from the variance components of models adjusted and unadjusted for individual SES.23
HSV Antibody Testing
All sera were tested for antibody to HSV-2 using an indirect enzyme-linked immunosorbent assay (ELISA), specifically the HerpeSelect 2 ELISA IgG (Focus Technologies). The manufacturer recommends that an index value of 1.10 is presumptive for the presence of antibody to HSV-2. However, we have found that using this cut-off value yields a high rate of false positive results.24 To overcome this problem, a cut-off value of 3.5 was used to determine HSV-2 seropositive sera. Hence sera that had a cut off of ≤0.9 were classified as negative and those with a cut-off of ≥3.5 were classified as positive. Sera that gave an equivocal result in the HerpeSelect 2 ELISA (index values 0.9–3.5) were resolved using a type specific “in house” Western blot.25
HSV-2 seroprevalence was significantly lower in low SES areas (compared to high SES areas) among both men (P for trend <0.001) and women (P for trend = 0.004) for all ages (Table 1). A similar pattern was evident for individual education level for men of all ages, with significantly lower rates of HSV-2 in respondents with lower educational achievement (RR = 0.77, 95% CI 0.61–0.97, P = 0.024). Age-specific analyses showed that the magnitude of this difference between high and low educational achievement was slightly higher in younger men (though not statistically significant) who had not completed high school (compared to those who completed high school) (Table 1).
In contrast, HSV-2 prevalence in women was significantly higher for lower individual levels of education for all ages (RR = 1.22, 95% CI 1.04–1.44, P = 0.017) and women aged 25 to 40 years (RR = 1.39, 95% CI 1.02–1.90, P = 0.038) (Table 1). HSV-2 seroprevalence in women was lower in lower SES areas for all ages (P for trend <0.05) and those aged 25 to 40 (P for trend = 0.131), and differences were of a similar magnitude and direction to area-based SES differentials in men (Table 1).
Analyses stratifying HSV-2 prevalence for individual education level by area-based SES showed that the highest prevalence of HSV-2 was in women with the lowest education level residing in the highest SES areas (RR = 1.50, 95% CI 1.04–2.15) (Table 2, Fig. 1). This pattern was not evident in men, for whom a greater concordance between individual and area-based SES measures was evident. For men, higher rates of HSV-2 were evident in respondents with a higher (compared to lower) education level and those residing in higher (compared to lower) SES areas (Table 2, Fig. 1). Approximately 12% of the variance in area-SES estimates of HSV-2 prevalence for males (ρ = 0.168 vs. P = 0.149 adjusted for individual education level) and 40% for females (ρ = 0.041 vs. ρ = 0.058) could be attributed to individual-level SES. Neither area nor individual-level SES differentials changed substantially in subgroup analyses conducted separately for the Australian-born, the nonindigenous, and metropolitan residents.
This study examined HSV-2 seroprevalence in Australia by individual and area-based measures of SES, adjusting for sex, age, marital status, indigenous status, and urban/rural residence. HSV-2 seroprevalence was significantly higher in high SES areas in both men and women. Similar trends were evident for individual education level in men, with similar SES differentials evident in younger and older aged men. In contrast, HSV-2 prevalence in women was significantly higher for lower individual levels of education, with largest differentials evident for younger women (25–40 years). Analyses stratifying HSV-2 prevalence for individual education level by area-based SES showed that the highest prevalence of HSV-2 was in women with the lowest education level residing in the highest SES areas, and that approximately 40% of within area variation was attributable to individual-level education.
A limitation of this study is that there is no explicit information on the sexual behavior of respondents. This study is based on a subsample of a population prevalence survey recruited for purposes other than investigating HSV-2 seroprevalence, and as such, key sexual behavior risk factors (such as number of sexual partners) were not collected. It is not possible to derive from this study the extent to which observed SES differentials in HSV-2 seroprevalence reflect current (and previous) sexual behavior in individuals comprised within the combinations of individual and area-based SES strata investigated. An additional limitation, given the geographical investigation of HSV-2 prevalence, is that there is no information on respondents “length of residence.” Sexual behavior and partner history may be differentially distributed between those respondents with more transient backgrounds than those respondents with less transient backgrounds and a less mobile residential history. It is not clear from previous Australian studies of HSV-2 prevalence what effect residential mobility might have on the level of HSV-2 prevalence reported in the current study. However, findings are based on seroprevalence data, which represent the biologic consequences of sexual behavior, and the implication of these findings is that sexual behavior associated with HSV-2 infection may also be differentially distributed by social class. The results of the current study accord with a previous investigation of SES differentials in sexual behavior in Australian women, which found a higher number of lifetime partners, greater number of partners in the last year, and earlier age at coitarche in lower, compared to higher, SES groups.26
The advantage of this study is that it is derived from a national population survey and not subject to potential selection bias, such as that found in clinical samples with overrepresentation of high-risk subpopulations such as sex workers, bisexuals, or homosexual men.27–30 Also, unlike previous studies of HSV-2 prevalence in Australia,31 and in other contexts,32,33 HSV-2 status was defined according to antibody testing and not based on self-reported symptoms of genital herpes, thereby reducing potential measurement bias in the outcome associated with self-reported data. This bias is likely to be considerable in the case of herpes, given the low proportion of people with HSV antibodies who are aware of being infected.20 Higher prevalence of reported sexually transmitted infections in higher SES groups in Australia has previously been attributed to reporting bias and the more frank and honest responses to questions relating to sexual activity in those from higher SES groups,34 and the possibility that better educated people are more likely to be aware of and/or remember the name of the condition with which they were diagnosed.31 However, the current study using antibody testing indicates that previously reported socio-economic differences in sexually transmitted infections on individual measures of SES in Australia are apparently real.
The pattern of SES differences in the current study may illustrate inherent gender inequalities in work, income, and education. It may be that women of lower education are married or partnered with men of a higher education level, reflecting lesser opportunities for women for further education, and traditional notions of female roles (in this cohort who would have been educated in the 1970s and 1980s). HSV-2 infection is likely to have occurred at earlier ages, and the socio-economic pattern in the current study may reflect partnerships where men may have gone on to further education whereas women did not, as they entered age-groups associated with child birth and child rearing.
A number of studies have also considered HSV-2 prevalence by markers of SES such as education level2–4,8–11,14–16,20 and income level.9–11 However, few studies have explicitly considered socio-economic factors associated with HSV-2 seroprevalence in representative samples. Most previous studies consider SES as a confounder in multivariate models usually in clinical samples,14,15 sex workers or other occupational groups,3,8,10,13 or population-based samples of subgroups of a population such as low income women,9 unmarried heterosexuals with multiple partners,11 or particular geographic regions.16,17 Most of these studies have found higher prevalence of HSV-2 in lower socio-economic groups, although in Western developed contexts, such as France and Switzerland, higher prevalence of HSV-2 was evident in higher socio-economic groups.2,16 Differential SES trends are also evident in different socio-economic contexts and subpopulations,3,8–10 particularly in developing countries.3,10,13 For example, in studies of HSV-2 prevalence in factory workers in Ethiopia and Zimbabwe, those with lower education had a higher HSV-2 seroprevalence than those with higher education levels.10,11 Similarly, higher HSV-2 seroprevalence was found in women with lower income and education (compared to higher SES groups) in a low income Californian neighborhood.9 A study from the United Kingdom of the geospatial distribution of cases of genital herpes enumerated through a central clinic found few differences between areas of high and low SES.17
These studies of socio-economic factors and HSV-2, and the findings of the current study, indicate that both contextual and compositional SES factors are important in understanding HSV-2 seroprevalence in populations. Individual markers of SES and area-based or geographic markers of SES have shown different patterns of HSV-2 seroprevalence. Geographic areas of residence are not necessarily homogeneous in terms of the individual SES of residents. Also, ecological socio-economic associations do not necessarily address the compositional effects of individual SES.
This study suggests higher HSV-2 prevalence in upper SES group men than among lower status men, whether measured by individual or area-based measures. However, when the interaction of individual and area SES was examined, it seems that the women residing in the same upper SES areas as these upper SES men are more likely to be women with lesser education. The inconsistent distribution of HSV-2 seroprevalence across individual and area measures of SES may suggest that upward and downward mixing between social strata in men and women is an important mode of HSV-2 transmission. These findings are limited by a lack of behavioral data; however, the significant socio-economic differentials within and between measures of individual and area-SES represent the biologic consequences of particular sexual behaviors and suggest that health promotion should not solely focus on lower social strata or high risk subgroups comprised within these lower social strata.
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