1 Office of Surveillance, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA.
2 Division of STD Prevention, National Center for HIV, STD, and Tuberculosis Prevention, Centers for Disease Control and Prevention, Atlanta, GA.
3 Department of Pediatrics, Emory University School of Medicine, Atlanta, GA.
4 Division of Health Examination Statistics, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD.
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ABSTRACT |
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herpes simplex; herpesvirus 2, human; incidence; mathematical computing; models, theoretical; prevalence; serologic tests
Abbreviations: CI, confidence interval; HSV-1 (2), herpes simplex virus type 1 (2); NHANES II (III), Second (Third) National Health and Nutrition Examination Survey.
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INTRODUCTION |
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Since the early 1970s, public awareness and concern about genital herpes have increased considerably, in part because of the impression that the burden of HSV-2-related disease has been rising (12). From the late 1960s to the mid-1990s, the annual number of outpatient visits for genital herpes in the United States increased steadily from 20,000 to over 150,000 (13
). A recent national survey revealed that the prevalence of antibody to HSV-2 in US residents aged 12 years or older had increased from 16.4 percent in the late 1970s to 21.9 percent in the early 1990s (14
, 15
). The increase in prevalence was greatest in White teenagers and young adults.
There are no nationally representative data on the incidence of HSV-2 infection in the United States. Direct measurement of incidence is difficult because infection is most often asymptomatic and unrecognized. Furthermore, genital herpes infection is not reportable in most states. Determining seroconversion rates in a large, representative cohort would be possible but costly. A more practical approach is to estimate the past incidence of infection from existing seroprevalence data by means of "catalytic modeling" (1620
). We applied a variation of this methodology to data from the Second National Health and Nutrition Examination Survey (NHANES II) and the Third National Health and Nutrition Examination Survey (NHANES III) to estimate the incidence of HSV-2 infection in the general population as well as the risk of infection during pregnancy.
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MATERIALS AND METHODS |
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General description of the incidence models
Mathematical details of the models are given in the Appendix. What follows is a general description of how the models function.
The models estimated force of infection (25), which, for the purposes of this paper, is considered synonymous with annual incidence in the susceptible (i.e., seronegative) population. Force of infection was always modeled independently for males and females and for three race/ethnic groups: non-Hispanic Whites, non-Hispanic Blacks, and others (including Hispanics, Native Americans, Asians, and Pacific Islanders), referred to in this paper as Whites, Blacks, and others, respectively.
The past force of infection was modeled iteratively. At the beginning of each iteration, the modeled force of infection from the previous iteration was used to calculate what the prevalence of antibody to HSV-2 would be at the time of NHANES II and NHANES III (the "modeled prevalence"). The modeled prevalence was then compared with the actual prevalence according to the two surveys, and the force of infection was adjusted by using mathematical algorithms. This process was repeated until further adjustments resulted in no further improvement of the goodness of fit of the model as measured by the weighted deviance (a lower weighted deviance indicates a better fit of the model).
Because the seroprevalence of HSV-2 increased from NHANES II to NHANES III, we assumed that incidence had also increased. To model this increase, we assumed that the force of infection had been constant up to a certain date, year 0 (yr0), and increased linearly thereafter. The values for yr0 and for the rate of increase after yr0 were determined empirically by determining which values resulted in the best fit of the modeled prevalence to the actual prevalence.
The age-specific force-of-infection curve was determined by regression analysis and was assumed to have a constant shape but to increase in height after yr0. The shape of this curve was modeled by using three different methods, all of which gave similar results. Results presented in this paper are from the third model, which functioned much like a locally weighted smoothing algorithm (26) in that the force of infection estimated at a certain age depended most on the prevalence of antibody to HSV-2 in survey participants who were close to that age and little or not at all on the prevalence in survey participants who were much older or much younger. Because acquisition of HSV-2 by pregnant women around the time of birth increases the risk of neonatal HSV-2 infection (7
), we estimated the force of infection in women during pregnancy by assuming that it would not change as a result of the pregnancy.
Standardization
In most instances, force of infection was averaged across ages, races, and genders after weighting with the 1990 civilian, noninstitutionalized population and adjusting for the estimated prevalence of HSV-2 infection in the year of interest. The number of infections in 1985 was estimated by using the intercensal estimate of the civilian, noninstitutionalized population in that year. To estimate the risk of seroconversion during pregnancy, force of infection in women was weighted by using the 1996 US natality data set (27).
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RESULTS |
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The model fit best when the annual rates of increase in force of infection were set to 7, 2, and 9 percent of the pre-1970 baselines for Whites, Blacks, and others, respectively. Within each race/ethnic group, the estimated rates of increase, as a percentage of the baseline force of infection, were equal for males and females.
Force of infection, incidence, and prevalence of HSV-2 infection
The estimated annual force of infection increased from 4.6 per 1,000 (95 percent confidence interval (CI): 4.2, 5.0) before 1970 to 8.4 per 1,000 (95 percent CI: 7.7, 9.1) in 1985, a relative increase of 82 percent (figure 4). During this time, increases in prevalence of HSV-2 infection would have lagged behind increases in force of infection. In the overall population (aged 0 years or older), prevalence would have risen from 13.6 percent to 15.7 percent between 1970 and 1985, a relative increase of only 16 percent. Taking into account the increasing seroprevalence, the incidence averaged over the entire US population (i.e., the combined HSV-2-seronegative and -seropositive population) would have increased from 4.0 per 1,000 (95 percent CI: 3.7, 4.3) to 7.1 per 1,000 (95 percent CI: 6.5, 7.6) between 1970 and 1985, a relative increase of 78 percent.
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DISCUSSION |
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The results of this model are consistent with other published estimates of incidence in European and North American populations. In a prospectively studied cohort of Swedish girls born in 1958 and 1959, HSV-2 seroconversion occurred at annual rates of 5, 24, and 23 per 1,000 from ages 13 to 18, 17 to 22, and 21 to 29 years, respectively (29). In a cohort of US university students in the mid-1980s, the annual seroconversion rate was 20 per 1,000 (30, 31
). In pregnant women in Washington State and Birmingham, Alabama, 16 and 20 per 1,000 seronegative women, respectively, seroconverted over the course of their pregnancy (7
, 31
), corresponding to respective annual rates of 21 and 27 per 100,000. During two recent HSV-2 vaccine trials, groups of women and men at increased risk for infection seroconverted at rates of 68 and 44 per 1,000, respectively (32). Among women attending an antenatal clinic in London, United Kingdom, in 1980 and 1981, Ades et al. (19
) estimated seroconversion rates of 2.4, 5, and 20 per 1,000 pregnancies in Asians, Whites, and Blacks, respectively, on the basis of a model that assumed a constant rate of acquisition.
The peak incidence in persons aged 2029 years is consistent with studies in the United States and the United Kingdom showing that outpatient visits for genital herpes reach their maximum in this age group (12, 27
, 33
). This is older than the peak age for notifications in the United States of acute bacterial, sexually transmitted diseases such as chlamydia and gonorrhea, which are reported most frequently in women in their late teens and men in their early twenties (13
). This finding may reflect the chronic nature of the infection, in that a person infected at a young age who continues to shed the virus intermittently may infect others for many years, even long after entering into a stable, monogamous relationship. Thus, as birth cohorts age, the pool of potentially infectious persons increases.
The high estimated incidence in the second and third decades of life underscores the need to focus prevention efforts on both teenagers and young adults. Prevention messages should include practicing healthful sexual behavior such as reducing the number of sexual partners. Promoting regular condom use is prudent, although few data support their effectiveness in preventing HSV-2 transmission (31, 34
). An effective vaccine against HSV-2 would provide an invaluable prevention tool; developing an HSV-2 vaccine remains a field of active investigation despite disappointing results thus far from published large-scale clinical trials (35
, 36
).
We estimate that the rate of HSV-2 seroconversion per 1,000 pregnancies in 1985 was 16.7, which, given the 3.76 million US livebirths that year (37) and the estimated prevalence of HSV-2-susceptible mothers (81 percent, data not shown) implies that 51,000 women would have become newly infected with HSV-2 during their pregnancy in that year. The prevalence of antibody to HSV-1 in HSV-2-negative women would have been 39 percent (data, not shown, from NHANES III); thus, 20,000 would have represented primary infections (i.e., HSV-2 infections in women without prior HSV infection), and 31,000 would have represented nonprimary first infections (i.e., HSV-2 infections in women with prior HSV-1 infections). Children of these women would be at the highest risk for neonatal infection if they were born during the time window between infection and HSV-2 seroconversion. If viral shedding during this window lasts 11.4 days in primary infection and 6.8 days in nonprimary first infection (38
), and a constant risk of acquisition of HSV-2 throughout pregnancy is assumed (7
), then 1,600 neonates would be at risk each year as a result of new maternal HSV-2 infections. Approximately 37 percent of these (6
, 7
), or 600, would become infected. Neonatal HSV-1 infection would account for approximately half as many cases (6
8
), bringing the total to 900 neonatal herpes infections annually as a result of primary and nonprimary first maternal infections. This figure is consistent with another estimate of 7002,300 cases of neonatal herpes annually in the United States (39
) and is higher than the rate of neonatal herpes observed in King County, Washington, in the early 1980s (11.9 per 100,000 births, corresponding to 450 cases in a birth cohort of 3.76 million) (40
).
These rates of seroconversion per pregnancy are predicated on similar forces of infection in pregnant and nonpregnant women. However, pregnant women may engage in sexual intercourse less often, may be less likely to change partners, and may have other characteristics that put them at lower risk for HSV-2 infection relative to nonpregnant women. Conversely, other factors, such as decreased condom use or increased partner changing, could increase this risk.
Persons citing our results should understand their limitations. As with any model, the estimates and confidence intervals are conditional on the assumptions of the model. The confidence intervals, for example, do not account for uncertainties in yr0 and the rate of increase over time or for uncertainties about the dynamics of this increase. Also, estimates of incidence in the oldest age groups should be considered less reliable than those in younger age groups, since the prevalence in the former can be greatly affected by the incidence in younger age groups. Thus, the prevalence in older age groups may be subject to cohort effects not taken into account by the model. Furthermore, recent studies have documented that HSV-2 antibody levels in infected persons occasionally fall below the level of detection (41). Such seroreversion would cause our model to underestimate incidence.
We chose to model the increase in force of infection over time as a simple linear function; our model included data from only two points in time and thus did not allow a more complex analysis. In reality, the increase was almost certainly nonlinear and may have varied by age group. For this reason, and because the estimates of incidence in the late 1980s depended greatly on the value chosen for yr0, it would be a mistake to extrapolate our results beyond the mid-1980s. Furthermore, since the incidence of gonorrhea, syphilis, chlamydia, and primary human immunodeficiency virus infection has probably decreased since the early 1990s (4244
), the upward trend in the incidence of HSV-2 infection may also have reversed, although condom use, which may account for much of the decline in the incidence of sexually transmitted disease, is probably less effective in preventing HSV-2 transmission.
The estimated 82 percent increase in incidence from 1970 to 1985 is considerably greater than the 16 percent relative increase in prevalence we estimated to have occurred during this time or the 34 percent relative increase in HSV-2 seroprevalence between the time that NHANES II and NHANES III were conducted (15). Trends in the overall seroprevalence of infectious diseases generally lag behind trends in incidence and may not reflect them accurately. For example, if the incidence of HSV-2 infection increased substantially in the 1970s and 1980s, as we believe it did, then overall seroprevalence may currently be increasing even if incidence has stabilized or is decreasing slightly. Factors other than increasing incidence could have contributed to the observed increase in HSV-2 seroprevalence, although their effects would likely have been very small: a decreasing age at acquisition or a decreasing mortality rate amongpopulations at high risk for HSV-2 infection would both have caused prevalence to increase.
The next NHANES survey is now in progress and will soon provide new estimates of the seroprevalence of HSV-2 in the United States. These data can be incorporated into our model as they become available, making the results more robust. Data from teenagers and young adults will be especially important, since prevalence among these persons is most sensitive to recent changes in incidence and is thus more useful for delineating trends. If the current trend of increasing prevalence is to be reversed, strong prevention efforts will be needed as well as new technologies, such as vaccines.
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APPENDIX |
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in which f(a) was the baseline age-specific force of infection at age a and g(yr) was the time-dependent multiplier in year yr. Estimated prevalence at any given age A in the year of a survey, yrs, P(A,yrs), was estimated as
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in which yr = yrs - A + a.
We modeled f(a) separately with three different functions, which were chosen because they allowed force of infection to increase with age, then decrease, and finally to level off at older ages. In all models, several parameters were modeled by regression analysis, including the age at which force of infection peaked, the force of infection at this peak, and the force of infection in the oldest age groups.
The first model assumed that infection did not occur before age 10 years and that the baseline force of infection was constant with respect to age in persons aged 50 years or older. Baseline force of infection between ages 10 and 50 years was described by a polynomial,
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in which the ßs were coefficients that could be estimated in a generalized linear model.
The second model assumed that
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in which max was the maximum baseline force of infection, apeak was the age at which this maximum occurred,
was a parameter inversely related to the width of the age range at highest risk for infection,
late was the baseline force of infection at older ages, and STEP(apeak) was a Heavyside function equal to 0 before apeak and 1 thereafter. The four parameters were estimated by using nonlinear regression analysis after specifying a large grid of starting values.
In the third model, the mean baseline force of infection was taken from K different submodels. In each submodel, f(a) was modeled as a step function with a finite number of steps of width K years, followed by a final step of variable width. The first step began at age 0 years in the first submodel, age 1 year in the second model, age 2 years in the third model, and so on. In the nth step of the kth submodel, f(k,a) = ßk,n. After each of the K submodels was fit independently to the data, f(a) was taken as the mean of the ßs that included that particular age. K was varied from 6 to 20 to find the minimum value that produced a smooth, age-specific, force-of-infection curve.
In all models, g(yr) was assumed to be 1.0 prior to a certain year, yr0, and to increase linearly thereafter by m percent of the baseline. yr0 and m were determined empirically by varying yr0 from 1940 to 1988, determining the value of m that produced the best fit of the model for each yr0, and then charting goodness of fit by yr0.
The modeling was performed with SAS software (SAS Institute, Cary, North Carolina). The models were fit with either PROC GENMOD, a generalized linear modeling procedure (first and third models), or PROC NLIN, a nonlinear modeling procedure (second model), such that the estimated prevalence, P(A,yrs), matched the actual prevalence observed in the two surveys. Goodness of fit was assessed by the weighted deviance (minus two times the logarithm of the unconditional binomial likelihood ratio, weighted with the normalized survey weight). Variance was estimated by using Faye's variation of the balance repeat replicate method (45). Replicate weights were calculated with WESVARPC software (WESTAT, Rockville, Maryland).
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NOTES |
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Parts of this paper were presented at the Conference on Advances in Mathematical Modeling of Sexually Transmitted Diseases, Santa Fe, New Mexico, April 1999, and at the 13th Meeting of the International Society for Sexually Transmitted Diseases Research, Denver, Colorado, July 1999.
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REFERENCES |
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