Bayesian evidence synthesis for a transmission dynamic model for HIV among men who have sex with men

Understanding infectious disease dynamics and the effect on prevalence and incidence is crucial for public health policies. Disease incidence and prevalence are typically not observed directly and increasingly are estimated through the synthesis of indirect information from multiple data sources. We demonstrate how an evidence synthesis approach to the estimation of human immunodeficiency virus (HIV) prevalence in England and Wales can be extended to infer the underlying HIV incidence. Diverse time series of data can be used to obtain yearly "snapshots" (with associated uncertainty) of the proportion of the population in 4 compartments: not at risk, susceptible, HIV positive but undiagnosed, and diagnosed HIV positive. A multistate model for the infection and diagnosis processes is then formulated by expressing the changes in these proportions by a system of differential equations. By parameterizing incidence in terms of prevalence and contact rates, HIV transmission is further modeled. Use of additional data or prior information on demographics, risk behavior change and contact parameters allows simultaneous estimation of the transition rates, compartment prevalences, contact rates, and transmission probabilities.     

A General HIV Incidence Inference Scheme Based on Likelihood of Individual Level Data and a Population Renewal Equation

We derive a new method to estimate the age specific incidence of an infection with a differential mortality, using individual level infection status data from successive surveys. The method consists of a) an SI-type model to express the incidence rate in terms of the prevalence and its derivatives as well as the difference in mortality rate, and b) a maximum likelihood approach to estimate the prevalence and its derivatives. Estimates can in principle be obtained for any chosen age and time, and no particular assumptions are made about the epidemiological or demographic context. This is in contrast with earlier methods for estimating incidence from prevalence data, which work with aggregated data, and the aggregated effect of demographic and epidemiological rates over the time interval between prevalence surveys. Numerical simulation of HIV epidemics, under the presumption of known excess mortality due to infection, shows improved control of bias and variance, compared to previous methods. Our analysis motivates for a) effort to be applied to obtain accurate estimates of excess mortality rates as a function of age and time among HIV infected individuals and b) use of individual level rather than aggregated data in order to estimate HIV incidence rates at times between two prevalence surveys.     

Periodic Active Case Finding for TB: When to Look?

Objective

To investigate the factors influencing the performance and cost-efficacy of periodic rounds of active case finding (ACF) for TB.

Methods

A mathematical model of TB dynamics and periodic ACF (PACF) in the HIV era, simplified by assuming constant prevalence of latent TB infection, is analyzed for features that control intervention outcome, measured as cases averted and cases found. Explanatory variables include baseline TB incidence, interval between PACF rounds, and different routine and PACF case-detection rates among HIV-infected and uninfected TB cases.

Findings

PACF can be cost-saving over a 10 year time frame if the cost-per-round is lower than a threshold proportional to initial incidence and cost-per-case-treated. More cases are averted at higher baseline incidence rates, when more potent PACF strategies are used, intervals between PACF rounds are shorter, and when the ratio of HIV-negative to positive TB cases detected is higher. More costly approaches, e.g. radiographic screening, can be as cost-effective as less costly alternatives if PACF case-detection is higher and/or implementation less frequent.

Conclusion

Periodic ACF can both improve control and save medium-term health care costs in high TB burden settings. Greater costs of highly effective PACF at frequent (e.g. yearly) intervals may be offset by higher numbers of cases averted in populations with high baseline TB incidence, higher prevalence of HIV-uninfected cases, higher costs per-case-treated, and more effective routine case-detection. Less intensive approaches may still be cost-neutral or cost-saving in populations lacking one or more of these key determinants.     

Risk-adjusted female breast cancer incidence rates in the United States

A method has been previously proposed for estimating risk-adjusted incidence rates (RAIRs) from cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. Unlike conventionally reported SEER-based cancer incidence rates in the United States, but similar to the approach taken by the International Association of Cancer Registries and the International Agency for Research on Cancer, the method uses only the first primary cancer of the given site. In addition, it also adjusts for population-based cancer prevalence in order to obtain a better population-based measure of cancer risk. For most cancers multiple cancer primaries are rare and the prevalence of the disease is low. However, female breast cancer has a comparatively high risk of subsequent breast cancers and is the most prevalent cancer in women. Hence, in white women RAIRs are 3.0% lower in ages 30-39, 4.2% lower in ages 40-49, 4.0% lower in ages 50-59, 4.1% lower in ages 60-69, 3.8% lower in ages 70-79, and 4.3% lower in ages 80 years and older compared with conventional rates. Corresponding lower percentages for black women are 3.9%, 6.9%, 5.1%, 7.8%, 6.0%, and 2.2%, respectively. Age-group specific trends in breast cancer incidence rates differed between RAIRs and conventional incidence rates, increasingly so with older age. The number of cancer cases in the United States is estimated from conventional incidence rates and population estimates. In 2007, the estimated number of malignant breast cancer cases was 181,665 for white women and 20,203 for black women. The estimated number of breast cancer cases decreased by 4.8% for whites and 6.5% for blacks when based on RAIRs. RAIRs are a better measure of breast cancer risk and trends in RAIRs are better for monitoring the effect of risk factors.     

Spatial modelling of soil-transmitted helminth infections in Kenya: a disease control planning tool

Background

Implementation of control of parasitic diseases requires accurate, contemporary maps that provide intervention recommendations at policy-relevant spatial scales. To guide control of soil transmitted helminths (STHs), maps are required of the combined prevalence of infection, indicating where this prevalence exceeds an intervention threshold of 20%. Here we present a new approach for mapping the observed prevalence of STHs, using the example of Kenya in 2009.

Methods and Findings

Observed prevalence data for hookworm, Ascaris lumbricoides and Trichuris trichiura were assembled for 106,370 individuals from 945 cross-sectional surveys undertaken between 1974 and 2009. Ecological and climatic covariates were extracted from high-resolution satellite data and matched to survey locations. Bayesian space-time geostatistical models were developed for each species, and were used to interpolate the probability that infection prevalence exceeded the 20% threshold across the country for both 1989 and 2009. Maps for each species were integrated to estimate combined STH prevalence using the law of total probability and incorporating a correction factor to adjust for associations between species. Population census data were combined with risk models and projected to estimate the population at risk and requiring treatment in 2009. In most areas for 2009, there was high certainty that endemicity was below the 20% threshold, with areas of endemicity ≥20% located around the shores of Lake Victoria and on the coast. Comparison of the predicted distributions for 1989 and 2009 show how observed STH prevalence has gradually decreased over time. The model estimated that a total of 2.8 million school-age children live in districts which warrant mass treatment.

Conclusions

Bayesian space-time geostatistical models can be used to reliably estimate the combined observed prevalence of STH and suggest that a quarter of Kenya''s school-aged children live in areas of high prevalence and warrant mass treatment. As control is successful in reducing infection levels, updated models can be used to refine decision making in helminth control.     

A non-parametric method for the reconstruction of age- and time-dependent incidence from the prevalence data of irreversible diseases with differential mortality.

A method is proposed for reconstructing the time and age dependence of incidence rates from successive age-prevalence cross sections taken from the sentinel surveys of irreversible diseases when there is an important difference in mortality between the infected and susceptible subpopulations. The prevalence information at different time-age points is used to generate a surface; the time-age variations along the life line profiles of this surface and the difference in mortality rates are used to reconstruct the time and age dependence of the incidence rate. Past attempts were based on specified parametric forms for the incidence or on the hypothesis of time-invariant forms for the age-prevalence cross sections. The proposed method makes no such assumptions and is thus capable of coping with rapidly evolving prevalence situations. In the simulations carried out, it is found to be resilient to important random noise components added to a prescribed incidence rate input. The method is also tested on a real data set of successive HIV age-prevalence cross sections from Burundi coupled to differential mortality data on HIV(+) and HIV(-) individuals. The often-made assumption that the incidence rate can be written as the product of a calendar time component and an age component is also examined. In this case, a pooling procedure is proposed to estimate the time and the age profiles of the incidence rate using the reconstructed incidence rates at all time-age points.     

A cost effective,community based heart health promotion project in England: prospective comparative study.

OBJECTIVE: To determine whether a community based coronary heart disease health promotion project, undertaken over four years, was associated with changes in the prevalence in adults of lifestyle risk factors known to affect the development of coronary heart disease, and to estimate whether such an approach was cost effective. DESIGN: Prospective, comparative study of the effects of a health promotion intervention on coronary heart disease lifestyle risk factors, assessed by postal questionnaire sent to a randomly chosen sample, both at baseline and after four years. SUBJECTS: Intervention and control populations of adults aged 18-64 in Rotherham, both from areas with a high incidence of coronary heart disease and similar socioeconomic composition. MAIN OUTCOME MEASURES: Changes in prevalence of lifestyle risk factors between the control and intervention communities from 1991 to 1995. The effect of the intervention on certain lifestyle behaviours was evaluated using multiple logistic regression to model the proportion with a particular behaviour in the study communities as a function of age (18-40 or 41-64 years), sex, the year of observation (1991 or 1995), and area (intervention of control). RESULTS: 6.9% fewer people smoked and 8.7% more drank low fat milk in the intervention area, but no other statistically significant changes between the areas were detected. The estimated cost per life year gained was pounds 31. CONCLUSIONS: It is possible to have a cost effective impact on coronary heart disease lifestyle risk factors in a population of adults over four years using only modest resources.     

Control strategies for endemic childhood scabies

Human scabies is a major global public health issue, with an estimated 300 million cases per year worldwide. Prevalence rates are particularly high in many third-world regions and within various indigenous communities in developed countries. Infestation with Sarcoptes Scabiei is associated with group-A streptococcal pyoderma which in turn predisposes to rheumatic fever, acute glomerulonephritis and their respective long-term sequelae: rheumatic heart disease and chronic renal insufficiency. The documented difficulties inherent in achieving scabies control within affected communities have motivated us to develop a network-dependent Monte-Carlo model of the scabies contagion, with the dual aims of gaining insight into its dynamics, and in determining the effects of various treatment strategies. Here we show that scabies burden is adversely affected by increases in average network degree, prominent network clustering, and by a person-to-person transmissibility of greater magnitude. We demonstrate that creating a community-specific model allows for the determination of an effective treatment protocol that can satisfy any pre-defined target prevalence. We find frequent low-density treatment protocols are inherently advantageous in comparison with infrequent mass screening and treatment regimes: prevalence rates are lower when compared with protocols that administer the same number of treatments over a given time interval less frequently, and frequent low-density treatment protocols have economic, practical and public acceptance advantages that may facilitate their long-term implementation. This work demonstrates the importance of stochasticity, community structure and the heterogeneity of individuals in influencing the dynamics of the human scabies contagion, and provides a practical method for investigating the outcomes of various intervention strategies.     

Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence

Summary In the last decade, interest has been focused on human immunodeficiency virus (HIV) antibody assays and testing strategies that could distinguish recent infections from established infection in a single serum sample. Incidence estimates are obtained by using the relationship between prevalence, incidence, and duration of recent infection (window period). However, recent works demonstrated limitations of this approach due to the use of an estimated mean “window period.” We propose an alternative approach that consists in estimating the distribution of infection times based on serological marker values at the moment when the infection is first discovered. We propose a model based on the repeated measurements of virological markers of seroconversion for the marker trajectory. The parameters of the model are estimated using data from a cohort of HIV‐infected patients enrolled during primary infection. This model can be used for estimating the distribution of infection times for newly HIV diagnosed subjects reported in a HIV surveillance system. An approach is proposed for estimating HIV incidence from these results.     

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds

ABSTRACT: BACKGROUND: A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. RESULTS: A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. CONCLUSIONS: The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.     

Epidemiology of Plasmodium malariae infection in Gambela, Ethiopia

Malaria incidence and prevalence surveys were performed from December, 1967 to February, 1969 among the indigenous Nilotic inhabitants of Gambela, a small administrative centre in the western lowlands of Ethiopia. Entomological data suggested that malaria transmission was seasonal and this was consistent with monthly P. falciparum parasite rates. Monthly P. malariae parasite rates, however, were consistent with an hypothesis of homogeneity. The age-specific incidence of quartan malaria among 26 children zero to 11 years old at the start of study was examined at 28 day intervals over a 15-month period. The resulting data suggested that parasite acquisition was a slow process and an annual P. malariae incidence of 0.17 was derived. This statistic was supported by studies performed five years later: The incidence of P. malariae among 102 infants followed from birth up to 48 months of age was 0.16-0.20. An attempt was then made to account for the prevalence of P. malariae in terms of the entomological conditions observed in Gambela. Macdonald's formula for the sporozoite rate was used to derive hypothetical relative proportions of P. falciparum and P. malariae among the observed sporozoite-positive mosquito populations. About 4% of the sporozoite challenges were estimated to be of P. malariae. An hypothetical annual entomological P. malariae inoculation rate was then made by multiplying the number of observed sporozoite inoculations per person (approximately 10/year) by the proportion of them estimated to be of P. malariae. The annual P. malariae sporozoite challenge was thus estimated at 0.4 per person, in good agreement with the annual incidence estimates from parasite rates in children.     

Is poliomyelitis a serious problem in developing countries?--lameness in Ghanaian schools.

A postal survey of lameness in schools throughout Ghana showed an estimated prevalence of lameness attributable to poliomyetitis of 5-8 per 1000 school-aged children and an estimated mean annual incidence of paralytic poliomyelitis of 23 per 100 000 population. Official reported incidence rates range from 0-1 to 2-1 per 100 000 population, indicating that at least 90% of cases are not reported. No evidence of epidemics was found to account for these high rates. These suggest that mean annual incidence rates in tropical endemic countries have always been as great, if not greater, than those experienced by temperate countries during epidemic periods in the twentieth century and that the total number of cases of paralytic poliomyelitis occurring in the world each year has been reduced by only 25% since the advent of polio vaccine. Immunisation against poliomyelitis must have a high priority in Ghana and other tropical countries where the disease is endemic.     

Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data

Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with different transmission routes, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the previously observed non-MSM HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health.     

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model

Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time‐consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties is described. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost.     

Ring-Screening to Control Endemic Transmission of Taenia solium

Background

Taenia solium is a major cause of preventable epilepsy in developing nations. Screening and treatment of human intestinal stage infection (taeniasis) within high-risk foci may reduce transmission and prevent epilepsy by limiting human exposure to infective eggs. We piloted a ring-strategy that involves screening and treatment for taeniasis among households located nearby pigs heavily-infected with the larval stage (cysticercosis). These pigs mark areas of increased transmission and can be identified by tongue examination.

Methodology

We selected two villages in northern Peru for a controlled prospective interventional cohort pilot study. In the intervention village (1,058 residents) we examined the tongues of all pigs every 4 months for nodules characteristic of cysticercosis. We then screened all residents living within 100-meters of any tongue-positive pig using enzyme-linked immunosorbent assay to detect Taenia antigens in stool. Residents with taeniasis were treated with niclosamide. In both the intervention and control (753 residents) we measured incidence of exposure by sampling the pig population every 4 months for serum antibodies against cysticercosis using enzyme-linked immunoelectrotransfer blot.

Principal Findings

Baseline seroincidence among pigs born during the study was 22.6 cases per 100 pigs per-month (95% confidence interval [CI] 17.0–30.0) in the intervention and 18.1 (95% CI 12.7–25.9) in the control. After one year we observed a 41% reduction in seroincidence in the intervention village compared to baseline (incidence rate ratio 0.59, 95% CI 0.41–0.87) while the seroincidence in the control village remained unchanged. At study end, the prevalence of taeniasis was nearly 4 times lower in the intervention than in the control (prevalence ratio 0.28, 95% CI 0.08–0.91).

Conclusions/Significance

Ring-screening reduced transmission of T. solium in this pilot study and may provide an effective and practical approach for regions where resources are limited. However, this strategy requires validation in larger populations over a greater period of time.     

Stochastic simulations of a multi-group compartmental model for Johne's disease on US dairy herds with test-based culling intervention

Infection elimination may be an important goal of control programs. Only in stochastic infection models can true infection elimination be observed as a fadeout. The phenomena of fadeout and variable prevalence are important in understanding the transmission dynamics of infectious diseases and these phenomena are essential to evaluate the effectiveness of control measures. To investigate the stochastic dynamics of Mycobacterium avium subsp. paratuberculosis (MAP) infection on US dairy herds with test-based culling intervention, we developed a multi-group stochastic compartmental model (a continuous time Markov chain model) with both horizontal and vertical transmission. The stochastic model predicted fadeout and within-herd prevalence to have a large variance. Although test-based culling intervention generally decreased prevalence over time, it took longer than desired by producers to eliminate the endemic MAP infection from a herd. Uncertainty analysis showed that, using annual culture test and culling of only high shedders or culling of both low and high shedders with a 12-month delay in culling of low shedders, MAP infection persisted in many herds beyond 20 years. While using semi-annual culture test and culling of low and high shedders with a 6-month delay in culling of low shedders, MAP infection in many herds would be extinct within 20 years. Sensitivity analysis of the cumulative density function of fadeout suggested that combining test-based culling intervention and reduction of transmission rates through improved management between susceptible calves and shedding animals may be more effective than either alone in eliminating endemic MAP infection. We also discussed the effects of other factors such as herd size, heifer replacement, and adult cow infection on the probability of fadeout.     

A General Framework For Prioritizing Research To Reduce Injuries And Diseases in Mining

A strategy for prioritizing mining health and safety research by evaluating the potential for risk reduction through interventions is proposed. Mining has one of the highest incidence rates of injury and disease found in major industries. The main premise of this paper is that often the best opportunities to reduce these rates are not revealed by retrospective analysis of injury and illness data. Instead, a proactive approach is needed that accounts for risks to specific hazards that can be abated by engineering or behavioral interventions. The process proposed here begins with development of prospective interventions. The degree of reduction in risk to be expected from an intervention then is determined from statistics on the mining worker population, the expected degree of success of the intervention, and the expected change in the severity of injuries resulting from the intervention. Three disparate mining health and safety concerns are presented to demonstrate common problems in assessing risks of injury and illness and describe additional data needs. Information on events preceding injuries and illnesses and more detailed demographic data on the mining work force are needed to analyze injury and illness data more precisely. Detailed information on exposure to specific hazards is necessary to evaluate the potential for an intervention to reduce risk of injury or illness.     

Multivariate Markov process models for the transmission of methicillin-resistant Staphylococcus aureus in a hospital ward

Summary .   Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.     

Impact of High-Risk Sex and Focused Interventions in Heterosexual HIV Epidemics: A Systematic Review of Mathematical Models

Background

The core-group theory of sexually transmitted infections suggests that targeting prevention to high-risk groups (HRG) could be very effective. We aimed to quantify the contribution of heterosexual HRGs and the potential impact of focused interventions to HIV transmission in the wider community.

Methods

We systematically identified studies published between 1980 and 2011. Studies were included if they used dynamical models of heterosexual HIV transmission, incorporated behavioural heterogeneity in risk, and provided at least one of the following primary estimates in the wider community (a) the population attributable fraction (PAF) of HIV infections due to HRGs, or (b) the number per capita or fraction of HIV infections averted, or change in HIV prevalence/incidence due to focused interventions.

Findings

Of 267 selected articles, 22 were included. Four studies measured the PAF, and 20 studies measured intervention impact across 265 scenarios. In low-prevalence epidemics (≤5% HIV prevalence), the estimated impact of sex-worker interventions in the absence of risk compensation included: 6–100% infections averted; 0.9–6.2 HIV infections averted per 100,000 adults; 11–94% and 4–47% relative reduction in prevalence and incidence respectively. In high-prevalence epidemics (>5% HIV prevalence), sex-worker interventions were estimated to avert 6.8–40% of HIV infections and up to 564 HIV infections per 100,000 adults, and reduce HIV prevalence and incidence by 13–27% and 2–14% respectively. In both types of epidemics, greater heterogeneity in HIV risk was associated with a larger impact on the fraction of HIV infections averted and relative reduction in HIV incidence.

Conclusion

Focused interventions, as estimated by mathematical models, have the potential to reduce HIV transmission in the wider community across low- and high-prevalence regions. However, considerable variability exists in estimated impact, suggesting that a targeted approach to HIV prevention should be tailored to local epidemiological context.     

Projected progression of the prevalence of obesity in Australia

Several country-specific and global projections of the future obesity prevalence have been conducted. However, these projections are obtained by extrapolating past prevalence of obesity or distributions of body weight. More accurate would be to base estimates on the most recent measures of weight change. Using measures of overweight and obesity incidence from a national, longitudinal study, we estimated the future obesity prevalence in Australian adults. Participants were adults aged ≥25 years in 2000 participating in the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study (baseline 2000, follow-up 2005). In this population, approximately one-fifth of those with normal weight or overweight progressed to a higher weight category within 5 years. Between 2000 and 2025, the adult prevalence of normal weight was estimated to decrease from 40.6 to 28.1% and the prevalence of obesity to increase from 20.5 to 33.9%. By the time, those people aged 25-29 in 2000 reach 60-64 years, 22.1% will be normal weight, and 42.4% will be obese. On average, normal-weight females aged 25-29 years in 2000 will live another 56.2 years: 26.6 years with normal weight, 15.6 years with overweight, and 14.0 years with obesity. Normal-weight males aged 25-29 years in 2000 will live another 51.5 years: 21.6 years with normal weight, 21.1 years with overweight, and 8.8 years with obesity. If the rates of weight gain observed in the first 5 years of this decade are maintained, our findings suggest that normal-weight adults will constitute less than a third of the population by 2025, and the obesity prevalence will have increased by 65%.