Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.
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ABSTRACT:
SUBMITTER: Hund L
PROVIDER: S-EPMC3649931 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Pooled testing can enhance the efficiency of diagnosing individuals with diseases of low prevalence. Often, pooling is implemented using standard groupings (2, 5, 10, etc.). On the other hand, optimization theory can provide specific guidelines in finding the ideal pool size and pooling strategy. This article focuses on optimizing the precision of disease prevalence estimators calculated from multiplex pooled testing data. In the context of a surveillance application of animal diseases, we study the estimation efficiency (i.e., precision) and cost efficiency of the estimators with adjustments for the number of expended tests. This enables us to determine the pooling strategies that offer the highest benefits when jointly estimating the prevalence of multiple diseases, such as theileriosis and anaplasmosis. The outcomes of our work can be used in designing pooled testing protocols, not only in simple pooling scenarios but also in more complex scenarios where individual retesting is performed in order to identify positive cases. A software application using the shiny package in R is provided with this article to facilitate implementation of our methods. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00511-4.
| S-EPMC9373899 | biostudies-literature
Project description:BackgroundBoth common and rare mitochondrial DNA (mtDNA) variants may contribute to genetic susceptibility to some complex human diseases. Understanding of the role of mtDNA variants will provide valuable insights into the etiology of these diseases. However, to date, there have not been any large-scale, genome-wide association studies of complete mtDNA variants and disease risk. One reason for this might be the substantial cost of sequencing the large number of samples required for genetic epidemiology studies. Next-generation sequencing of pooled mtDNA samples will dramatically reduce the cost of such studies and may represent an appealing approach for large-scale genetic epidemiology studies. However, the performance of the different designs of sequencing pooled mtDNA has not been evaluated.MethodsWe examined the approach of sequencing pooled mtDNA of multiple individuals for estimating allele frequency using the Illumina genome analyzer (GA) II sequencing system. In this study the pool included mtDNA samples of 20 subjects that had been sequenced previously using Sanger sequencing. Each pool was replicated once to assess variation of the sequencing error between pools. To reduce such variation, barcoding was used for sequencing different pools in the same lane of the flow cell. To evaluate the effect of different pooling strategies pooling was done at both the pre- and post-PCR amplification step.ResultsThe sequencing error rate was close to that expected based on the Phred score. When only reads with Phred ≥ 20 were considered, the average error rate was about 0.3%. However, there was significant variation of the base-calling errors for different types of bases or at different loci. Using the results of the Sanger sequencing as the standard, the sensitivity of single nucleotide polymorphism detection with post-PCR pooling (about 99%) was higher than that of the pre-PCR pooling (about 82%), while the two approaches had similar specificity (about 99%). Among a total of 298 variants in the sample, the allele frequencies of 293 variants (98%) were correctly estimated with post-PCR pooling, the correlation between the estimated and the true allele frequencies being >0.99, while only 206 allele frequencies (69%) were correctly estimated in the pre-PCR pooling, the correlation being 0.89.ConclusionSequencing of mtDNA pooled after PCR amplification is a viable tool for screening mitochondrial variants potentially related to human diseases.
| S-EPMC3268604 | biostudies-literature
Project description:BackgroundContraception is one of the most important health interventions currently available and yet, many women and couples still do not have reliable access to modern contraceptives. The best indicator for monitoring family planning is the proportion of women using contraception among those who need it. This indicator is frequently called demand for family planning satisfied and we argue that it should be called family planning coverage (FPC). This indicator is complex to calculate and requires a considerable number of questions to be included in a household survey.ObjectivesWe propose a model that can predict FPC from a much simpler indicator - contraceptive use prevalence - for situations where it cannot be derived directly.DesignUsing 197 Multiple Indicator Cluster Surveys and Demographic and Health Surveys from 82 countries, we explored least-squares regression models that could be used to predict FPC. Non-linearity was expected in this situation and we used a fractional polynomial approach to find the best fitting model. We also explored the effect of calendar time and of wealth on the models explored.ResultsGiven the high correlation between the variables involved in FPC, we managed to derive a relatively simple model that depends only on contraceptive use prevalence but explains 95% of the variability of the outcome, with high precision for the estimated regression line. We also show that the relationship between the two variables has not changed with time. A concordance analysis showed agreement between observed and fitted results within a range of ±9 percentage points.ConclusionsWe show that it is possible to obtain fairly good estimates of FPC using only contraceptive prevalence as a predictor, a strategy that is useful in situations where it is not possible to estimate FPC directly.
| S-EPMC4642361 | biostudies-literature
Project description:BackgroundEstimates of dengue transmission intensity remain ambiguous. Since the majority of infections are asymptomatic, surveillance systems substantially underestimate true rates of infection. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing both the burden of disease from dengue and the likely impact of interventions.Methodology/principal findingsThe force of infection (λ) and corresponding basic reproduction numbers (R0) for dengue were estimated from non-serotype (IgG) and serotype-specific (PRNT) age-stratified seroprevalence surveys identified from the literature. The majority of R0 estimates ranged from 1-4. Assuming that two heterologous infections result in complete immunity produced up to two-fold higher estimates of R0 than when tertiary and quaternary infections were included. λ estimated from IgG data were comparable to the sum of serotype-specific forces of infection derived from PRNT data, particularly when inter-serotype interactions were allowed for.Conclusions/significanceOur analysis highlights the highly heterogeneous nature of dengue transmission. How underlying assumptions about serotype interactions and immunity affect the relationship between the force of infection and R0 will have implications for control planning. While PRNT data provides the maximum information, our study shows that even the much cheaper ELISA-based assays would provide comparable baseline estimates of overall transmission intensity which will be an important consideration in resource-constrained settings.
| S-EPMC4400108 | biostudies-literature
Project description:Understanding HIV incidence, the rate at which new infections occur in populations, is critical for tracking and surveillance of the epidemic. In this article, we derive methods for determining sample sizes for cross-sectional surveys to estimate incidence with sufficient precision. We further show how to specify sample sizes for two successive cross-sectional surveys to detect changes in incidence with adequate power. In these surveys biomarkers such as CD4 cell count, viral load, and recently developed serological assays are used to determine which individuals are in an early disease stage of infection. The total number of individuals in this stage, divided by the number of people who are uninfected, is used to approximate the incidence rate. Our methods account for uncertainty in the durations of time spent in the biomarker defined early disease stage. We find that failure to account for this uncertainty when designing surveys can lead to imprecise estimates of incidence and underpowered studies. We evaluated our sample size methods in simulations and found that they performed well in a variety of underlying epidemics. Code for implementing our methods in R is available with this article at the Biometrics website on Wiley Online Library.
| S-EPMC4715554 | biostudies-literature
Project description:Recombination can occur either as a result of crossover or gene conversion events. Population genetic methods for inferring the rate of recombination from patterns of linkage disequilibrium generally assume a simple model of recombination that only involves crossover events and ignore gene conversion. However, distinguishing the 2 processes is not only necessary for a complete description of recombination, but also essential for understanding the evolutionary consequences of inversions and other genomic partitions in which crossover (but not gene conversion) is reduced. We present heRho, a simple composite likelihood scheme for coestimating the rate of crossover and gene conversion from individual diploid genomes. The method is based on analytic results for the distance-dependent probability of heterozygous and homozygous states at 2 loci. We apply heRho to simulations and data from the house mouse Mus musculus castaneus, a well-studied model. Our analyses show (1) that the rates of crossover and gene conversion can be accurately coestimated at the level of individual chromosomes and (2) that previous estimates of the population scaled rate of recombination ρ=4Ner under a pure crossover model are likely biased.
| S-EPMC9434185 | biostudies-literature
Project description:SummaryMassively parallel sequencing technologies hold incredible promise for the study of DNA sequence variation, particularly the identification of variants affecting human disease. The unprecedented throughput and relatively short read lengths of Roche/454, Illumina/Solexa, and other platforms have spurred development of a new generation of sequence alignment algorithms. Yet detection of sequence variants based on short read alignments remains challenging, and most currently available tools are limited to a single platform or aligner type. We present VarScan, an open source tool for variant detection that is compatible with several short read aligners. We demonstrate VarScan's ability to detect SNPs and indels with high sensitivity and specificity, in both Roche/454 sequencing of individuals and deep Illumina/Solexa sequencing of pooled samples.
| S-EPMC2734323 | biostudies-literature
Project description:The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.
| S-EPMC8525136 | biostudies-literature
Project description:OBJECTIVE:To estimate HIV incidence in the general population in countries where there have been two recent household-based HIV prevalence surveys (the Dominican Republic, Mali, Niger, Tanzania, and Zambia). METHODS:We applied a validated method to estimate HIV incidence using HIV prevalence measurement in two surveys. RESULTS:We estimate incidence among men and women aged 15-44 years to be: 0.5/1000 person-years at risk in the Dominican Republic 2002-2007, 1.1/1000 in Mali 2001-2006, 0.6/1000 in Niger 2002-2006, 3.4/1000 in Tanzania 2004-2008, and 11.2/1000 in Zambia 2002-2007. The groups most at risk in these epidemics are typically 15-24-year-old women and 25-39-year-old men. Incidence appears to have declined in recent years in all countries, but only significantly among men in the Dominican Republic and Tanzania and women in Zambia. CONCLUSION:Using prevalence measurements to estimate incidence reveals the current level and age distribution of new infections and the trajectory of the HIV epidemic. This information is more useful than prevalence data alone and should be used to help determine priorities for interventions.
| S-EPMC3535828 | biostudies-literature
Project description:Knowledge of incidence rates of HIV and other infectious diseases is important in evaluating the state of an epidemic as well as for designing interventional studies. Estimation of disease incidence from longitudinal studies can be expensive and time consuming. Alternatively, Janssen et al. (1998, Journal of the American Medical Association 280, 42-48) proposed the estimation of HIV incidence at a single point in time based on the combined use of a standard and "detuned" antibody assay. This article frames the problem from a longitudinal perspective, from which the maximum likelihood estimator of incidence is determined and compared with the Janssen estimator. The formulation also allows estimation for general situations, including different batteries of tests among subjects, inclusion of covariates, and a comparative evaluation of different test batteries to help guide study design. The methods are illustrated with data from an HIV interventional trial and a seroprevalence survey recently conducted in Botswana.
| S-EPMC3449098 | biostudies-literature