IJE Advance Access originally published online on January 18, 2008
International Journal of Epidemiology 2008 37(1):88-105; doi:10.1093/ije/dym255
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HIV incidence in 3 years of follow-up of a Zimbabwe cohort—1998–2000 to 2001–03: contributions of proximate and underlying determinants to transmission
1Department of Infectious Disease Epidemiology, Imperial College London, UK.
2 Biomedical Research and Training Institute, Harare, Zimbabwe.
* Corresponding author. Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK. E-mail: sajgregson{at}aol.com
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Abstract |
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Background: In recent years, HIV prevalence has begun to decline in Zimbabwe, which has been associated with reductions in sexual risk behaviour. Here, we analyse the determinants of HIV incidence in this period of decline and estimate the population-level impact of identified risk factors.
Methods: A population-based cohort of 1672 HIV-negative adult males and 2465 HIV-negative adult females was recruited between 1998 and 2000. Each individual was then followed-up 3 years later. The influence and inter-relationship of social, behavioural and demographic variables were examined using a proximate determinants framework. To explore the population-level influence of a variable, methods were developed for estimating a risk factor's contribution to the reproductive number (CRN).
Results: HIV incidence was 19.9 [95% confidence interval (CI) 16.3–24.2] per 1000 person years in men and 15.7 (95% CI 13.0–18.9) in women. Multiple sexual partners, having an unwell partner, and reporting another sexually transmitted disease were risk factors that captured the main aspects of the proximate determinants framework: individual behaviour, partnership characteristics and the probability of transmission, respectively. If the proximate determinants fully captured risk of HIV infection, underlying factors would not influence a fully parameterized model. However, a number of underlying social and demographic determinants remained important in regression models after including the proximate determinants. For both sexes, having multiple sexual partners made a substantial CRN, but, for women, no behaviour explained more than 10% of new infections.
Conclusions: The proximate determinants did not explain the majority of new infections at the population level. This may be because we have been unable to measure some risks, but identifying risk factors assumes that those acquiring infections are somehow different from others who do not acquire infections. That they are not suggests that in this generalized epidemic there is little difference in readily identifiable characteristics of the individual between those who acquire infection and those who do not.
Keywords HIV/AIDS, epidemiological methods, Zimbabwe, incidence, population attributable fraction, Africa
Accepted 29 November 2007
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Introduction |
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Despite the wide spread of HIV in sub-Saharan Africa relatively few studies have followed cohorts of individuals exploring risk factors for acquiring infection. Published examples include cohorts from Uganda, Tanzania and Zimbabawe.1–4 The sequence of risks and outcomes only becomes transparent if we observe changes amongst individuals and populations over time in cohort studies. Such studies are critically important to the design and targeting of prevention activities. They also allow us to explore changing patterns of acquisition as the HIV epidemic spreads and how the community responds to it, but the wide range of risks, the exploratory nature of many statistical analyses and the use of epidemiological methods developed to explore the aetiology of chronic rather than infectious disease limits our ability to interpret results. Here we analyse cohort data from rural Zimbabwe exploring both patterns of risk following declines in prevalence and also the methods available to understand these risks.
Zimbabwe is experiencing a widespread HIV epidemic with prevalence having peaked at around 25% in the adult population in the late 1990s.5 However, recent evidence suggests that HIV prevalence is now declining in Zimbabwe6,7 as it may be in a few other countries with generalized epidemics, such as Haiti and Kenya.8 The most recent nationally representative estimate of prevalence in Zimbabwe was 18%.9 Prevalence and incidence may rise and then fall as a ‘natural’ pattern for an epidemic but in Zimbabwe the prevalence decline has been associated with observed reductions in casual partnerships, a delay in sexual debut and increases in condom use.7
Epidemics of sexually transmitted diseases may be conceptualized as progressing through phases.10 Due to the long incubation of AIDS, which ultimately results in death, the effect of interventions or behaviour change on prevalence will be slow to accrue and difficult to observe empirically.8 As the HIV epidemic passes through different phases we can expect that the risks and the context of those acquiring infection will change. Therefore, this recent period in Zimbabwe, when prevalence began to decline, is a particularly important phase in which to analyse and understand risk factors for incidence.
The proximate determinants framework of HIV transmission elaborated by Boerma and Weir provides a conceptual basis for examining both underlying social and demographic determinants as well as individual level proximate and biological determinants predicting risk of HIV infection.11 Within this framework, the proximate determinants can be affected by contextual factors and intervention programs. The proximate determinants (such as condom use)—which can be measured in empirical studies—are markers for biological determinants (such as transmission probability per contact) which are unobserved but ultimately lead to transmission. In the framework, underlying determinants act through the proximate determinants, and ultimately through biological determinants. For example, poverty may predispose females to sex work, which exposes them to high number of sexual partners and, thereby, to greater exposure to infection. Previously we have explored risk factors associated with prevalent HIV infections from our baseline survey. Analyses of incidence are needed to understand the causal process leading to new infections as well as to disentangle historical from contemporary trends.
There is no single correct or straightforward statistical method for analysing data using this framework. We take two approaches: first, we explore the ability of measured proximate determinants to explain the influence of underlying determinants; then, second, we attempt to evaluate the importance of each determinant of infection in terms of how it influences the mean number of secondary cases arising from a new case (the effective reproductive ratio or R).8,12
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Methods |
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Population
The Manicaland HIV/STD Prevention Project is an ongoing population-based open cohort study, full details of which can be found elsewhere.6 The study population were resident in four subsistence farming areas, two roadside trading centres, four forestry, tea and coffee estates and two small towns in the rural province of Manicaland in eastern Zimbabwe. All local residents were enumerated in an initial household census (conducted between July 1998 and February 2000) which was repeated 3 years later in each site. Males aged 17–54 years and females aged 15–44 years were recruited into a cohort study of HIV transmission. Every household in the study area was visited up to three times or until interviews were completed. To be eligible for the cohort, individuals had to be regular members of the household. Only one member of a marital pair was recruited.
At baseline and follow-up, demographic, socioeconomic and sexual behaviour data were collected through an interviewer-led questionnaire. To reduce social desirability bias, responses to sensitive questions about sexual behaviour were collected using an informal confidential system, in which responses were written on ballot slips and placed in a locked box.6 Dried blood spots were collected for HIV serological testing for the purposes of the research only which was performed using a highly sensitive and specific antibody dipstick assay.13 Different tests were used in baseline and follow-up (ICL-HIV 1 & 2 Dipstick, Thailand (baseline) and Abbott 3rd Generation HIV 1 & 2 EIA, USA or Genelavia MIXT HIV1&2, Sanofi Diagnostics Pasteur S.A., France (follow-up), which have been shown to work equivalently in Zimbabwe.6 As was standard practice at the time the data collection was undertaken, participants were not given test results but were given a voucher for counselling and testing and were provided with the services of a mobile clinic (details are published elsewhere6). Lifetime uptake increased from 6% to 11% from baseline to follow-up; 21.5% of those who accepted testing were HIV-positive. The majority who went for testing reported either a pre- or post-test counselling session that included information on preventing infection. Anti-retrovirals were not available at the time of study, though efforts have been made such that the study population is now being included in the first wave of anti-retroviral programmes in Zimbabwe. Local clinics provided treatment of opportunistic infections, though availability was reported to be variable during the study period. In collaboration with local NGOs, the study nurses monitored and supplemented cotrimoxazole stocks at local clinics. Written informed consent was obtained as a condition of enrolment and continuation in the study. Participants unable to sign their name provided a finger print after being explained about the study by research assistants. Prior ethical approval for the study was obtained from the Research Council of Zimbabwe—Number 02187—and the Applied and Qualitative Research Ethics Committee in Oxford, United Kingdom—N97.039.
Follow-up
As reported previously 54% (2242/4142) of the males and 66% (3265/4922) of the females interviewed at baseline—and not known to have died subsequently—were re-interviewed at follow-up.6 Follow-up rates of individuals were lower within households of higher wealth status (chi-squared P < 0.0001), better education (primary/none: 70%; secondary/higher: 55%, chi-squared P < 0.001) and being more mobile at baseline (64%, 53%, chi-squared P < 0.0001). Amongst the follow-ups, we analysed individuals at risk of seroconversion in HIV negative males (1777/2242) and females (2566/3265). Individuals with missing key data or ambiguous HIV test results were dropped leaving 1672 (94%) males and 2465 (96%) females for analysis. Date of seroconversion was randomly assigned (uniform distribution) between the last negative and first positive test. Follow-up time was censored at this date.
Statistical methods
Table 1 documents the variables that were investigated and whether they were modelled as proximate or underlying determinants. The current analysis is based upon the risk factors reported at follow up where individuals described both recent behaviours and those occurring in the period following their first interview. The association between the determinants and HIV incidence was investigated by fitting Poisson regression models. Variables (or stratum of variables) determined to improve crude models or age adjusted models (Wald test P-value 
0.05) are presented and were retained for multivariable analysis. When a set of variables was highly correlated (correlation coefficient >0.5) and measured a similar behaviour, the variable most highly associated with HIV incidence was retained for the multivariable model. The role of a variable was assessed by comparing a model without the variable to a full model using the likelihood ratio test.
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Separate models were constructed for proximate determinants and underlying determinants.14 Then, significant proximate determinants were added to the underlying determinants model. If the underlying determinants all acted through the proximate determinant, the underlying determinants should no longer substantially influence a model containing terms for the proximate determinants.
Population-level impact of risk factors
Because of the dynamics of how an infectious disease moves through a population, there is no standard statistical method to decompose the importance of particular determinant on the totality of transmission, such as the population attributable fraction (PAF) for non-infectious diseases.10,11,15,16 We propose that determinants of infection can be conceptualized as having a bearing on the reproductive ratio, R, by influencing its constituent components of: (i) the rate of exposure to infectious persons; (ii) the transmission probability; and (iii) the duration of infectiousness. Ideally, we would be able to track the spread of infection from individual to individual and examine the impact of risk in both the infective and susceptible partners. However, whilst we have data on the infected and those who infect them, we do not have information on the links between them. We approximate R at time t (Rt) for the unexposed and exposed population of men and women using an incidence: prevalence ratio as described previously6:
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We then estimate the reproductive number at time t for a hypothetical cohort where the risk factor was absent (Rtu), as described in the Appendix. The contribution to the reproductive number (CRN) is estimated as one minus the ratio Rtu in the unexposed sub-cohort to Rt in the entire cohort, using the formula
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| (2) |
This method provides an imperfect measure since it assumes that all transmission stays within the group exposed to a particular risk. Therefore, infections occurring across groups (i.e. from high risk to low risk groups) do not contribute to the estimate of R. The true value of R is affected by the behaviour of both the infected and susceptible population, although only the susceptible population is considered in this method. Therefore the CRN is a conservative underestimate of the real influence of a risk factor (see the Appendix). The CRN does—unlike the PAF—take some account of transmission as well as acquisition and can account for the changing size of the exposed populations.
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Results |
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Incidence of HIV
Ninety-eight men seroconverted in a total of 4916 years of follow-up, giving an incidence rate (IR) of 19.9 [95% confidence interval (CI) 16.3–24.2] per 1000 person years. One hundred and thirteen incident cases amongst females in 7184 years of follow-up resulted in a marginally lower IR of 15.7 (95% CI 13.0–18.9). Age-standardizing the rates made little difference (male 20.4, female 15.4) compared with the crude rates.
Proximate determinants
Tables 2 and 3 show the proximate determinants associated with HIV incidence in men and women, respectively. For men, in multivariable models controlling for other proximate determinants, multiple partners (RR = 2.4), experience of genital sores (RR = 2.7), having an unwell partner (RR = 1.8), and local prevalence amongst women (RR = 1.3, per 10% increase in prevalence) were predictors of incident infection. Male circumcision was not associated with HIV incidence (P = 0.9) though only 3% of men in this population reported as having been circumcised.
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For women, predictors of incident infection were similar to those for men: with multiple partners (RR = 3.2), experiencing genital sores (RR = 2.0), and having an unwell partner (RR = 2.0). In addition, multiple partners in the last month was an independent predictor (RR = 8.8), even after controlling for total number of partners in the follow-up period. Dry sex, reportedly practiced by 24% of women, was not associated with HIV incidence (P = 0.4).
Between baseline and follow-up the frequency of these determinants declined: multiple partners (men 40% to 21%; women 6% to 3%) and having an STI in the last year (men 13% to 6%, women 35% to 16%) (P < 0.0001 for all in chi-squared tests). We did not have a measure of partner's heath at baseline for comparison.
Underlying determinants
In men, HIV incidence increased with onset of sexual activity in young age groups, falling sharply after age group 35–39, then slowly rising again (perhaps because of widows and divorcees acquiring new partners and married men starting second families) although numbers are small (Figure 1). A number of socio-demographic factors were associated with HIV incidence in crude models: being unmarried (protective), being widowed or divorced, living in a subsistence farming area (protective, relative to towns), being a student (protective) and lower socioeconomic status.6 Increased mobility was associated with incidence in a number of measures: living outside the study site for more then one month, previously living in the countryside and visiting a bar. A number of personal beliefs about condom use acceptability and ways to avoid contracting HIV were associated with incidence. Men who acknowledged that a relative died of AIDS had reduced incidence of infection. Including the proximate determinants (as in Table 2 and above) had little effect aside from slightly reducing the statistical power and parsimony of the models, suggesting that the underlying determinants are measuring different aspects of proximate risk from those measured directly (Table 4).
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For women, incidence was observed to rise up the 25–29-year age group, then fall sharply and stay at a low level which parallels the mean number of sex partners (Figure 1). Being widowed or divorced, having secondary school or higher education, being a member of a traditional church and attending a bar were associated with higher incidence, as were beliefs about marriage, beer drinking and getting paid for sex. Only the effect estimate for attending a bar moved substantially towards the null when controlling for the proximate determinants. For women, attending a bar is highly associated with multiple sex partners, which explains the loss of significance of the underlying determinant in the full model (Table 5).
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Estimating PAF by approximating Rt
We estimate the reproductive number for men (Rtm) was 0.78 and for women (Rtw) 0.51 (Figure 2). This suggests a declining epidemic, as has been observed in this cohort.6 For men, we estimate that having multiple sexual partners accounts for approximately one third of the total reproductive number [(1–Rti)/Rt = 0.35], while the other identified proximate determinants (genital sores and having an unwell partner) accounted for only a small proportion of transmission, due to the relative rarity of these factors. For women, no factor had a substantial impact (>10%), again owing to the low reported prevalence of the proximate determinants. Underlying determinants for men (going to bars, not believing that HIV can be avoided by sticking to one partner and using condoms, and not acknowledging that a family member died of AIDS) may contribute to transmission [(1 – Rti)/Rt = 0.29, 0.28, and 0.14, respectively]. However, since we presume that these factors work through proximate determinants, it is incorrect to think that by hypothetically removing the underlying determinant, some proportional amount of transmission would necessarily be averted. The estimated effect on the reproduction number was consistently greater than the conventional PAF but neither measure accounted for a large proportion of the HIV transmission.
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Discussion |
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We identified three variables (number of sexual partners, having an STI and having an unwell partner) that capture the main aspects of the proximate determinants framework: individual behaviour, partnership characteristics and the probability of transmission. Total number of reported sexual partnerships was the individual behavioural measure predictive of incident infection. Suffering an ulcerative STI was the best marker for transmission probability. And, having an unwell partner (and the local prevalence in the opposite sex for men) can be seen as a limited proxy measure for contact with an infected individual, which, of course, is a causal necessity in the sexual transmission of HIV.
Some underlying determinants—if related to sexual behaviour or networks—would be expected to be associated with HIV incidence in univariate analysis. But if the proximate determinants fully captured the mechanism through which the underlying determinants worked, they should no longer carry an effect in fully parameterized models. An example of this is bar attendance, which itself is not a cause of infection. Attending a bar was a marker for infection in women, but after controlling for proximate determinants—i.e. number of partners—bar attendance was no longer a predictor. However, there was surprising residual importance of variables like traditional religion and secondary education (for women) acknowledging that a family member died of AIDS, beliefs about condom use (for men) and beliefs about beer drinking (both sexes). Men who had a prior residence in rural areas or who lived outside their site of residence during the survey period had higher rates of infection. Indeed, these variables may be markers for position in a sexual network, rather than specific risk behaviour. Earlier in the epidemic, mobility was thought to be an important driver of the spread of HIV into rural areas.17 In Manicaland, migrants themselves did not have higher prevalence at baseline,17,18 but mobility may continue to play an important role by connecting the sexual network of different geographical sites.
Loss to follow-up was 
40% over the 3-year intersurvey period. Although this figure is similar to other population-based cohorts in Africa and followed-up migrants do not have higher risk of HIV,17 there is potential for unmeasured differences between those lost to follow-up and those re-interviewed. STD status was based on self-reported symptoms, rather than biological testing, which may have resulted in underreporting, recall bias or inclusion of non-infectious causes of genital complaints. Participation rates were high, but it remains possible that difficult-to-reach groups, for example female sex workers, were poorly identified in the survey. Therefore the study population may not be completely representative of the population as a whole. Perhaps the most important limitation here is that the serostatus of partners were not known. In the late stage of this epidemic, serostatus of long-term partners may be the most important single individual determinant of risk.
Because of the long incubation of AIDS, prevalent HIV infections are a product of many years of risk, so longitudinal studies on incidence are especially important for understanding the contemporary epidemiology. However, even in this analysis of a prospective cohort, the proximate determinants failed to account for a large proportion of transmission. The proximate determinants framework has proven a very useful concept for studying fertility and child survival.19,20 But, reconciling the framework to HIV epidemiology may prove difficult because the risk of HIV—as an infectious disease—depends on proximate factors beyond the individual's own behaviour. Removing a risk factor from a population may eliminate proportionally more or less of the disease than would be expected from the risk ratio and the prevalence of the factor depending on how the risk factor affects transmissibility and how individuals with the risk factor mix with the population.16 Thus, the relative risk of a given factor is dependent on the magnitude and stage of the epidemic. Any empirical study is limited in its follow-up time and, therefore, fails to fully describe sexual transmission as a result of the lifetime sexual network of subjects and their partners.
That our analysis failed to identify the influence of underlying determinants on proximate determinants suggests that the latter are poorly measured. However, this may be because many infections are occurring in those with no discernable risks, as well as infection having spread through the sexual partner networks to those at the ends of chains of transmission who are otherwise typical.
Comparing risk factors for incidence with risk factors for prevalence may elucidate which factors are necessary for an epidemic and which change as the epidemic goes through different phases (Table 6). The main proximate determinants of incidence are similar to those previously found to be risk factors for prevalent infection in this population: number of sexual partners, presence of STI cofactors and (for men) local HIV prevalence in women.8 However, certain underlying determinants are different. For example, being widowed was a predictor of prevalence, whereas being divorced or separated was a predictor of incidence, suggesting that widow(er)s were infected in marriage, while divorcees were at heightened risk as they acquire new partners. Beliefs, which were not predictors of prevalent infection, were associated with incidence. ‘Protective beliefs’ included acknowledging that a relative died of AIDS, believing one can avoid AIDS with condoms and fidelity and that condoms did not reduce the pleasure of sex (for male respondents) and beer drinking was not an essential activity for men (for female respondents). All of these protective beliefs increased in frequency between the baseline and follow-up survey. For men, the proximate determinants of multiple partners in last month and genital sores also decreased in frequency between baseline and follow-up. These substantial reductions in risk behaviour were not a result of the intervention trial that was ongoing during the study period,6 but rather a combination of higher mortality in risky populations and, importantly, individual reductions in risk behaviour including increased condom use, reductions in casual partnerships and delayed first sex.6
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One counter-intuitive finding for prevalence at baseline (that consistent condom use was a predictor of infection) was not found with incidence. This may suggest that condom use is associated with past risk or that infected people are motivated to use condoms to protect their partner(s). A paradoxical finding was that, amongst women, secondary/higher education was associated with lower risk for prevalence but higher risk for incidence. This contradicts most other studies that show a trend towards lower rates of infection in educated groups as the epidemic progresses.21,22 This finding requires further investigation.
Risk factor studies for incident infection have not been plentiful, but the published studies have largely identified the same proximate determinants as were found here. Numbers of sexual partners and presence of other STIs (as measured by genital ulcer or urethral discharge) are nearly universal risk factors found in Ugandan,1,23 Tanzanian2,24 and Zimbabwean3 cohorts. Being the victim of forced sex for women,25,26 and not being circumcised27,28 for men have been identified as risk factors elsewhere. Perhaps unsurprisingly, distal socio-demographic factors, such as religion are less consistent between studies, apart from marriage which is generally protective.1,4,23,24
Our estimates of population impact [(1–Rtu)/Rt] tended to be greater than those for the PAF, as it estimates the influence on the biological parameter R—the basic reproductive number. This measure approximated the impact of relative susceptibility and, to some extent, accounts for changing dynamics, such as the changing size of the risk groups in follow-up. Methods that conceptualize the population-level impact of a risk factor in terms of the reproductive number R may be more satisfactory than the PAF since they are well grounded in the theory of infectious disease epidemiology.8 These methods do not assume independence of events, an important violation of directly transmitted diseases of assumptions underlying PAF calculations. However, these R methods still do not account for how the risk factor affects infectiousness through concurrency, variable levels of virus shedding or tendencies to mix with susceptible groups. In particular, the population impact of STIs, which increase both susceptibility and infectiousness,29 will be underestimated using either PAF or R methods.
Despite these limitations in the framework, the empirical data and statistical approaches to test the framework, this study enhances the evidence base for understanding the contemporary HIV epidemic in Zimbabwe and for implementing prevention activities. Reported ‘high risk’ behaviours were quite uncommon, especially in women, highlighting the degree to which the epidemic has become generalised. Indeed, 70% of infections occurred amongst married men and women.
Having multiple sexual partners remains the key proximate risk factor, although an unwell partner has also emerged as important. The change in underlying risk factors illustrates the barriers to further reductions in HIV incidence, particularly the role of beer drinking in increased risk and the protective effect of variables suggesting an acknowledgement of HIV risk. Most striking is our limited ability to identify factors contributing to the epidemic. This may be because we have been unable to measure some risks, but identifying risk factors assumes that those acquiring infections are somehow different from others who do not acquire infections. That they are not suggests that in this generalized epidemic there is little difference in individual characteristics and behaviours between those who acquire infection and those who do not.
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Appendix |
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Estimation of the basic reproductive number in unexposed individuals
Following on from the incidence prevalence ratio described in formula (1), we can calculate the expected number of cases E[G(t,t + n)u] that would have occurred if the whole population were unexposed to the risk factor. We estimate this by applying the rate of infection in unexposed individuals to the whole population:
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In this case, F(t)u is the number of unexposed cases at time t. Similarly G(t,t + n)u refers to incident cases, but their exposure status is based on the follow-up period, so a person exposed at baseline can become unexposed and, if infected, contribute to G(t, t + n)u. The ratio of R in the unexposed to R in the whole population is then expressed as in formula (2).
Why the CRN is a conservative estimate
In the proposed estimation of a risk factor's CRN, Rtu is calculated as the ratio of incident to prevalent cases that are unexposed to the risk factor. This will always underestimate the CRN because unexposed cases will have been infected by both exposed and unexposed prevalent cases, though we assume they all come from unexposed in the formula for Rtu. Diagrammatically:
So, in reality, exposed and unexposed incident cases are generated as follows:
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uu and eu. We can only observe incidence in the exposed e and in the unexposed e. This requires us to make assumptions about the source of infection. The conservative assumption is that mixing is completely assortative: exposed only infect exposed and unexposed only infect unexposed. We therefore assume that: |
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Therefore Ru is smaller than we have approximated. And in turn, the CRN of a risk factor is larger than we have approximated since:
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Just as the contribution of risk factors to the PAF can sum to greater than one, the contribution of risk factors to the reproductive number in this formulation can add to more than one, because individuals can have more than one risk and risks can combine to exacerbate the spread of infection.
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Acknowledgements |
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We thank the people of Manicaland, Zimbabwe for their time and commitment to the study, all of the field work staff of the Manicaland HIV/STD Prevention Project and the ALPHA network for helpful contributions. We also thank Christophe Fraser for sharing ideas leading to the development of the CRN methods.
Conflict of interest: None declared.
KEY MESSAGES
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References |
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