Holtgrave, David R PhD*; Hall, H Irene PhD†; Rhodes, Philip H PhD†; Wolitski, Richard J PhD†
*Department of Health, Behavior, and Society Johns Hopkins Bloomberg School of Public Health Baltimore, MD
†Centers for Disease Control and Prevention Atlanta, GA
Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.
To the Editor:
Comprehensive HIV surveillance in the United States involves continually measuring and reporting on at least the following factors: (1) HIV-related risk behaviors; (2) HIV incidence; (3) HIV diagnoses; (4) AIDS diagnoses; (5) HIV prevalence; and (6) deaths among persons with HIV.1-3 An additional helpful measure would be the annual HIV transmission rate (T). Although various definitions of the HIV transmission rate are possible, we define it here as follows: for every 100 persons living with HIV, the number of HIV infections transmitted to HIV-seronegative partners in 1 year. The transmission rate for a given year, x, is calculated as follows: T(x) = [I(x)/P(x)] × 100 [where I(x) is the number of new HIV infections in year x, and P(x) is the prevalence of persons living with HIV in year x]. Although a simple derivative of I(x) and P(x), the transmission rate provides an important gauge of the speed of spread of HIV infection. Further, by examining trends in T(x) over the course of the epidemic, it provides some crude indication of whether prevention efforts are serving to control the rapidity of the spread of the epidemic.
While a potentially highly useful component of a surveillance system, T(x) has received little attention in the literature.4,5 Previously, we estimated the HIV transmission rate for the years 1978 through 2000.4 The prior estimates should now be updated because Centers for Disease Control and Prevention (CDC) has recently released new estimates of I(x) through 2006 using BED testing technology, improved statistical methods, and extended backcalculation. Using these methods, CDC has now estimated that the HIV incidence curve in the mid-1980s is slightly lower than previously thought, and HIV incidence throughout the 1990s and 2000s is higher than previously thought. The new estimates of I(x) are a substantial improvement over old estimates, especially for the 1990s and 2000s in which the previous estimate of I(x) was 40,000 per year based on uncertain, “informal” methods.1 Therefore, in this research letter, we provide an updated estimate of T(x) from 1977 through 2006.
Key parameter values and results are contained in Table 1. We use annual estimates of I(x) as recently published by CDC.3 CDC's back-calculation estimates of I(x) were calculated in multiyear time blocks, not unique estimates for each year.3 Our estimate of deaths among persons with AIDS for a given year [DA(x)] was obtained from CDC's HIV surveillance database. The value of DA(x) for a given year is limited to persons 13 years and older so as to match the age restrictions in CDC's recent updates of I(x).3 Hence, all calculations in this research letter relate only to persons 13 years and older. Besides deaths among persons with AIDS, there may be deaths among persons living with HIV (not AIDS) in a given year, DH(x). The total number of deaths among persons living with HIV or AIDS, D(x), equals DA(x) + DH(x). We calculated DH(x) by taking the number of persons living with HIV (not AIDS) in a given year and multiplying by a general population mortality rate. For this analysis, we selected 427.1 per 100,000 as the general population mortality rate because this is the preliminary 2006 death rate for persons 45-54 years old in the United States (an age group just older than the age at which most new infections occur in the nation).6 The number of AIDS cases in a given year, A(x), is needed for this calculation and is provided in Table 1.
To calculate P(x), we use the following formula: P(x) = P(x−1) + I(x) − D(x). Then, T(x) is calculated as [I(x)/P(x)] × 100 (in sensitivity analysis, we examined the influence of using P(x−1) in the denominator of T(x); we found that it did not appreciably alter the results and certainly did not alter the qualitative conclusions of our analysis).
Table 1 also contains the calculation of year-to-year percentage changes from T(x) to T(x+1). Further, we examine percentage changes from T(x = 1984) to T(x > 1984) (as incidence peaked in 1984 in the United States) and percentage changes from T(x = 1997) to T(x > 1997) (as the transmission rate increased in 1997 after multiple years of stability).
Several aspects of the results deserve comment. First, the calculated values of P(x) over time are highly similar to previously published ranges.7-10 This may seem surprising because I(x) in the 1990s and 2000s is higher than previously estimated. However, I(x) in the mid-1980s is somewhat lower than had been thought, and these countervailing changes roughly offset each other in our simplified estimation of P(x).
Second, T(x) was very high during the explosive growth phases of the epidemic in the early and mid-1980s, but dropped substantially during the late 1980s. T(x) reached a period of stability in the early and mid-1990s until 1997 when, by our estimation, there was an apparent upturn. After 1997, T(x) dropped progressively from year to year. T(x = 2006) is estimated at 5.0, which means that for every 100 persons living with HIV in the United States, there are 5 HIV transmissions per year. Stated another way, this indicates that in 2006, over 95% of persons living with HIV in the United States did not transmit the virus to a seronegative partner. In fact, approximately 95% of persons living with HIV would not have transmitted to a seronegative partner in 2006 if and only if each transmission was from an HIV-seropositive person who had not already transmitted that year; clearly, the more that HIV transmission occurs in clusters, the fewer the number of HIV-seropositive persons who transmit the virus in 1 year. (Of course, the percentage of persons living with HIV who engage in any risk behavior that could possibly cause transmission is more than 5.0% because HIV has a relatively low probability of transmission per risk act.11,12)
The general decline in HIV transmission rates over time could be considered a rough measure of prevention success, in that even as prevalence grew over time incidence did not grow proportionately. HIV diagnosis is known to significantly reduce HIV risk behavior,11,12 and in the past decade, there has been an increasing emphasis on prevention programs for persons living with HIV that further reduce HIV risk behavior.13,14
Since the early 1990s, HIV prevention resources (adjusted for inflation) have been relatively flat and in fact shrinking in recent years.15,16 Although part of the recent decline in T(x) could theoretically be attributed to new therapies that lower viral load, it is useful to note that T(x) was declining (and sometimes stable) in periods before effective antiretroviral therapies were even developed. Further, when antiretroviral therapies were introduced in the United States, HIV transmission may have temporarily increased; the study of this relationship is an area of needed further research.
Finally, we note that CDC has just published new HIV prevalence estimates for the United States for the years 2003 and 2006.17 These estimates differ by less than 0.8% from our prevalence estimates calculated here. The impact of the new prevalence estimates17 on transmission rate estimates for those 2 years is minimal. For 2003, our transmission rate estimate of 5.53 (rounded to 5.5 in Table 1) would become 5.57. For 2006, our transmission rate estimate of 4.98 (rounded to 5.0 in Table 1) would become 5.00.
David R. Holtgrave, PhD*
H. Irene Hall, PhD†
Philip H. Rhodes, PhD†
Richard J. Wolitski, PhD†
*Department of Health, Behavior, and Society
Johns Hopkins Bloomberg School of Public Health
†Centers for Disease Control and Prevention
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