Empirical Bayes approach to estimating the number of HIV-infected individuals in hidden and elusive populations

Hepatitis C time trends in reported cases and estimates of the hidden population born before 1965, Denmark and Sweden, 1990 to 2020

BackgroundAccording to the World Health Organization, hepatitis C virus (HCV) infection should be under control by 2030.AimOur aim was to describe the size and temporal changes in reported cases of chronic HCV infection in Denmark and Sweden and to estimate the size of the hidden (undiagnosed) population born before 1965.MethodsWe extracted all HCV infections reported to national surveillance systems in Denmark and Sweden from 1990 to 2020. Prediction of the size of the hidden HCV-infected population was restricted to the cohort born before 1965 and cases reported up to 2017. We applied a model based on removal sampling from binomial distributions, estimated the yearly probability of diagnosis, and deducted the original HCV-infected population size.ResultsDenmark (clinician-based) reported 10 times fewer hepatitis C cases annually than Sweden (laboratory and clinician-based), peaking in 2007 (n = 425) and 1992 (n = 4,537), respectively. In Denmark, the birth year distribution was monophasic with little change over time. In recent years, Sweden has had a bimodal birth year distribution, suggesting ongoing infection in the young population. In 2017, the total HCV-infected population born before 1965 was estimated at 10,737 living persons (95% confidence interval (CI): 9,744-11,806), including 5,054 undiagnosed, in Denmark and 16,124 (95% CI: 13,639-18,978), including 10,580 undiagnosed, in Sweden.ConclusionsThe reporting of HCV cases in Denmark and Sweden was different. For Denmark, the estimated hidden population was larger than the current national estimate, whereas in Sweden the estimate was in line with the latest published numbers.

Estimating hidden populations by transferring knowledge from geographically misaligned levels

The estimation of hidden sub-populations is a hard task that appears in many fields. For example, public health planning in Brazil depends crucially of the number of people who holds a private health insurance plan and hence rarely uses the public services. Different sources of information about these sub-populations may be available at different geographical levels. The available information can be transferred between these different geographic levels to improve the estimation of the hidden population size. In this study, we propose a model that use individual level information to learn about the dependence between the response variable and explanatory variables by proposing a family of link functions with asymptotes that are flexible enough to represent the real aspects of the data and robust to departures from the model. We use the fitted model to estimate the size of the sub-population at any desired level. We illustrate our methodology estimating the sub-population that uses the public health system in each neighborhood of large cities in Brazil.

Removal modelling in ecology: A systematic review

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique.

Power estimation in biomarker studies where events are already observed

Background The clinical utility of a new biomarker should ideally be established in a prospective randomized clinical trial. However, such trials are not always practical. As such, it is common for investigators to identify promising biomarkers using archived specimens and clinical data collected from previously completed therapeutic trials. Simon et al. defined such biomarker studies as prospective-retrospective studies and proposed specific conditions to satisfy for such evaluations to be more than hypothesis generating. One condition they proposed is that archived tissues must be available on a sufficiently large number of patients from the pivotal trials to ensure adequately powered analyses. Purpose The purpose of this article is to provide a new perspective on how to estimate power for assessing the prognostic and predictive values of a single binary biomarker in prospective-retrospective biomarker studies. Computer programs are provided to facilitate the use of these methods in practice. Methods The proposed methods utilize additional information that becomes available during the course of the treatment trial including sample size, accrual time, additional follow-up time, and the observed number of events at time of biomarker analysis. These methods involve solving for the exponential hazard rates that give rise to the event numbers that are consistent with those observed while satisfying other design parameter constraints. Conclusion Simon et al. proposed a new paradigm for biomarker design, conduct, analysis, and evaluation in prospective-retrospective studies that offers a more efficient alternative than fully prospective biomarker studies. As a general rule, they suggest that samples from at least two-thirds of the patients be available for analysis. In this article, I expand on this issue and provide a methodological tool useful for estimating study power in prospective-retrospective biomarker studies. It is my hope that these incremental efforts to improve the quality and statistical rigor in biomarker studies will hasten the introduction of useful tumor biomarkers into clinical practice.

Ascertaining the 2004-2006 HIV type 1 CRF07_BC outbreak among injecting drug users in Taiwan

Objective

To ascertain the explosive 2004–2006 outbreak of HIV-1 CRF07_BC among intravenous drug users (IDU) in Taiwan, which more than doubled the total number of reported HIV cases in less than 3 years, resulting in a 45-fold increase in cumulative IDU/HIV cases and a 40-fold increase in previously seldom-reported female IDU/HIV cases.

Methods

A mathematical model was utilized to fit the monthly case data, in order to estimate the turning points (peak incidence) and the reproduction number R of the outbreak. Furthermore, correlation analysis was carried out to assess the correlation between infections among the male and female IDUs.

Results

Model fit revealed a two-wave epidemic during April 2004–March 2007. The larger second wave started shortly after May 2005 and peaked in October 2005 before gradually subsiding. R was estimated to be 3.15 (3.14–3.16) and 27.21 (26.73–28.05) for the two respective waves. The time series of monthly differences in male and female case data were found to be most significantly correlated at lag 0 (i.e., r > 0.7) with r = 0.906 and 0.804, respectively in each direction. The Granger causality test indicated that the male time series caused the corresponding female time series with a lag of 2 months or less.

Conclusions

The modeling results revealed the presence of a small first wave in 2004, before an explosion of cases after May 2005. Furthermore, a harm reduction program implemented in August 2005 contributed to the downturn in the epidemic after October. Correlation results also suggest that the upsurge in male HIV cases led to the subsequent drastic surge in female cases.

ASCERTAINING HIV UNDERREPORTING IN LOW PREVALENCE COUNTRIES USING THE APPROXIMATE RATIO OF UNDERREPORTING

Underreporting of HIV/AIDS cases is a common problem in HIV epidemiology which often skews epidemiologic projections on which public health policy decisions are often based, especially in the cases of low HIV prevalence countries or in early phases of an emerging epidemic when the HIV incidence is still low, but might be growing rapidly. In this work, we propose a simple mathematical model with groups of known and undetected HIV-positives. Using this model with the annual HIV incidence data of new HIV cases and new AIDS cases detected at onset of symptoms, we are able to obtain an estimate for the number of undetected HIV-positives. Moreover, using Taiwan data of 1993–2000, we are able to predict the number of new cases in the next two years within 5% accuracy. We also give an approximate ratio of underreporting which approximates the magnitude of underreporting of HIV cases in low HIV prevalence settings. The procedure is illustrated with the HIV data of Taiwan and Cuba. The result shows that underreporting in Cuba is low, probably due to its intense contact tracing program. For Taiwan, the level of underreporting is higher, but has improved slightly since 1999. The method is useful as a simple tool to gauge the immediate impact of an emerging epidemic, as well as for the purpose of public health policy planning and short-term future projections.

Bayesian statistics in medicine: a 25 year review

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Factors associated with condom use among brothel-based female sex workers in Thailand

This study sought to determine the actual levels of condom use by female sex workers (FSW) in Thailand brothels. Specifically, it examined the influence that the country of origin of the FSW patrons exerted on usage of a condom, and it assessed the relevancy of constructs from the Health Belief Model in predicting FSW requesting condom usage by their clients. A survey was administered to a cross-sectional non-randomized convenience sample of 150 FSW in four geographic locations in Thailand, which included: Bangkok, Chang Mai, Mae Hong Son and Other (comprised mainly of brothels in small villages). Statistical interpretation of the data indicated that Thai FSW requested condom use in 63% of the cases, while the overall mean of condom use was only 51%. Condom use by patrons' country of origin was as follows: westerners 76%, foreign Asians 52% and native Thai men 27%. These results fall considerably short of the Ministry of Health's goal of 100% condom use in Thai brothels. Furthermore, public health education initiatives need to effectively target the native Thai FSW patrons since they represent the clients least likely to use a condom (27%).

Participatory mapping of sex trade and enumeration of sex workers using capture-recapture methodology in Diego-Suarez, Madagascar

BACKGROUND

Capture-recapture methodology has been employed to estimate the size of hidden or difficult-to-reach human populations such as sex workers, homeless persons, and intravenous drug users. This study took place in the context of efforts to improve the quality of curative and preventive services for sexually transmitted infections offered to sex workers in Diego-Suarez, Madagascar in partnership with sex workers.

GOAL

The goal of this study was to better understand sex-trading practices, map out sex work sites, and estimate the size of a mobile sex-worker population in Diego-Suarez, Madagascar using methods that can be reproduced in other resource-poor settings.

STUDY DESIGN

Forty sex workers were trained to function as field researchers. Key informants in designated neighborhoods were identified and interviewed. Basic maps of sex-trade sites were designed by hand. The number of sex workers in Diego-Suarez was estimated using capture-recapture methodology. An educational brochure was used to "capture" sex workers during a first phase, and "recapture" 2 weeks later.

RESULTS

Most sex trade was street based and mobile. Capture-recapture methods estimated a total of 2684 sex workers (95% CI: 2588-2780) in Diego-Suarez, or approximately 12% of the 15- to 49-year-old female population in the city.

CONCLUSION

Geographic mapping and capture-recapture proved to be low-cost and relatively easy to use techniques for counting a hard-to-reach population, such as female street-based sex workers. Sex workers can be capable fieldworkers, and their active participation in research concerning sex work should be considered whenever possible. A high proportion of women of reproductive age were found to engage in sex work, indicating that there is an urgent need to scale up sexually transmitted infection and HIV prevention activities targeting sex workers, their sexual partners, and the general population.

Estimating the number of Cubans infected sexually by human immunodeficiency virus using contact tracing data

BACKGROUND

To estimate the yearly number of people in Cuba who are living with human immunodeficiency virus (HIV) and were infected through sexual contact but who have not developed acquired immunodeficiency syndrome (AIDS). Estimation was made directly from the yearly HIV seroprevalence data of the Cuban Partner Notification Programme from 1991 to 2000.

METHODS

The generalized removal model for open populations is utilized for the estimation. The total number of known HIV-infected Cubans at each sampling time is used in the prior to provide more reasonable approximations.

RESULTS

We estimated a yearly survival rate of 93%. The median estimates for the number of all living asymptomatic HIV-positive Cubans, infected by sexual contact, tripled from 714 in 1991 to 2170 in 2000. The number of unknown HIV-positive Cubans infected sexually is estimated to range from 174 in 1991 to 401 in 2000.

CONCLUSIONS

A consistent increase in the number of sexually infected HIV-positive individuals in Cuba from 1991 to 2000 is evident from the estimates. From 1996 onwards more sexually active homosexual/bisexual contacts were traced and consequently more sexually-infected HIV-positives were detected. A consequence of increased detection is the levelling off and subsequent decrease in the number of unknown HIV-positives during this time period. The estimation procedure is useful in estimating prevalent population sizes of epidemiological and public health interest.