Imperfect Diagnostic Tests Research Articles

Efficient modelling of infectious diseases in wildlife: A case study of bovine tuberculosis in wild badgers.

Bovine tuberculosis (bTB) has significant socio-economic and welfare impacts on the cattle industry in parts of the world. In the United Kingdom and Ireland, disease control is complicated by the presence of infection in wildlife, principally the European badger. Control strategies tend to be applied to whole populations, but better identification of key sources of transmission, whether individuals or groups, could help inform more efficient approaches. Mechanistic transmission models can be used to better understand key epidemiological drivers of disease spread and identify high-risk individuals and groups if they can be adequately fitted to observed data. However, this is a significant challenge, especially within wildlife populations, because monitoring relies on imperfect diagnostic test information, and even under systematic surveillance efforts (such as capture-mark-recapture sampling) epidemiological events are only partially observed. To this end we develop a stochastic compartmental model of bTB transmission, and fit this to individual-level data from a unique > 40-year longitudinal study of 2,391 badgers using a recently developed individual forward filtering backward sampling algorithm. Modelling challenges are further compounded by spatio-temporal meta-population structures and age-dependent mortality. We develop a novel estimator for the individual effective reproduction number that provides quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. We also infer measures of the hidden burden of infection in the host population through time; the relative likelihoods of competing routes of transmission; effective and realised infectious periods; and longitudinal measures of diagnostic test performance. This modelling framework provides an efficient and generalisable way to fit state-space models to individual-level data in wildlife populations, which allows identification of high-risk individuals and exploration of important epidemiological questions about bTB and other wildlife diseases.

A hierarchical model-based framework for evaluating probabilities of area-wide freedom from lymphatic filariasis infection based on sentinel site surveillance data

The design of population surveys to substantiate the elimination of disease transmission across large implementation units (IUs) has become important as many parasite control efforts approach their final stages. This is especially true for the global program to eliminate lymphatic filariasis (LF), which has successfully reduced infection prevalence in many endemic countries, such that the focus has shifted to how best to determine that the area-wide elimination of this macroparasitic disease has been achieved. The WHO has recommended a two-stage lot quality assurance sampling (LQAS) framework based on sampling children from selected clusters within an IU, called the Transmission Assessment Survey (TAS), for supporting such decision-making, but questions have emerged regarding the reliability of this strategy for assessing if LF transmission is broken effectively everywhere within an area. In this study, we develop and describe an alternative probabilistic framework that combines infection status information from longitudinal parasitological surveys of whole communities carried out in sentinel sites, imperfect diagnostic tests, and locally-applicable extinction thresholds predicted by transmission models, to overcome the problems associated with TAS. We applied the framework to LF infection and intervention data from the country of Malawi, and demonstrated how our hierarchical coupled model-sentinel site survey tool can be used to estimate the probability that LF transmission has occurred at the individual survey, village, and countrywide scales. We also further demonstrated how the framework can be used in conjunction with zonal or areal design prevalences to estimate the number of sentinel sites and durations of interventions required to acquire sufficiently high confidence that an area is free from infection. Our results indicate that the application of the spatially driven model-data freedom-from-infection tool developed here to follow up data from high-risk sentinel sites in a region may offer a highly cost-effective framework for guiding the making of high-fiducial and defensible area-wide LF intervention stopping decisions.

Bayesian evaluation of the accuracy of a thoracic auscultation scoring system in dairy calves with bronchopneumonia using a standard lung sound nomenclature.

Although thoracic auscultation (AUSC) in calves is quick and easy to perform, the definition of lung sounds is highly variable and leads to poor to moderate accuracy in diagnosing bronchopneumonia (BP). Evaluate the diagnostic accuracy of an AUSC scoring system based on a standard lung sound nomenclature at different cut-off values, accounting for the absence of a gold standard test for BP diagnosis. Three hundred thirty-one calves. We considered the following pathological lung sounds: increased breath sounds (score 1), wheezes and crackles (score 2), increased bronchial sounds (score 3), and pleural friction rubs (score 4). Thoracic auscultation was categorized as AUSC1 (positive calves for scores ≥1), AUSC2 (positive calves for scores ≥2), and AUSC3 (positive calves for scores ≥3). The accuracy of AUSC categorizations was determined using 3 imperfect diagnostic tests with a Bayesian latent class model and sensitivity analysis (informative vs weakly informative vs noninformative priors and with vs without covariance between ultrasound and clinical scoring). Based on the priors used, the sensitivity (95% Bayesian confidence interval [BCI]) of AUSC1 ranged from 0.89 (0.80-0.97) to 0.95 (0.86-0.99), with a specificity (95% BCI) of 0.54 (0.45-0.71) to 0.60 (0.47-0.94). Removing increased breath sounds from the categorizations resulted in increased specificity (ranging between 0.97 [0.93-0.99] and 0.98 [0.94-0.99] for AUSC3) at the cost of decreased sensitivity (0.66 [0.54-0.78] to 0.81 [0.65-0.97]). A standardized definition of lung sounds improved AUSC accuracy for BP diagnosis in calves.

Seroprevalence and associated risk factors of brucellosis, Rift Valley fever and Q fever among settled and mobile agro-pastoralist communities and their livestock in Chad.

Brucellosis, Rift Valley fever (RVF) and Q fever are zoonoses prevalent in many developing countries, causing a high burden on human and animal health. Only a few studies are available on these among agro-pastoralist communities and their livestock in Chad. The objective of our study was to estimate brucellosis, RVF and Q fever seroprevalence among Chadian agro-pastoralist communities and their livestock, and to investigate risk factors for seropositivity. We conducted a multi-stage cross-sectional serological survey in two rural health districts, Yao and Danamadji (966 human and 1041 livestock (cattle, sheep, goat and equine) samples)). The true seroprevalence were calculated applying a Bayesian framework to adjust for imperfect diagnostic test characteristics and accounting for clustering in the study design. Risk factors for each of the zoonotic diseases were estimated using mixed effects logistic regression models. The overall prevalence for brucellosis, Q fever and RVF combined for both regions was estimated at 0.2% [95% credibility Interval: 0-1.1], 49.1% [%CI: 38.9-58.8] and 28.1% [%CI: 23.4-33.3] in humans, and 0.3% [%CI: 0-1.5], 12.8% [%CI: 9.7-16.4] and 10.2% [%CI: 7.6-13.4] in animals. Risk factors correlating significantly with the respective disease seropositivity were sex for human brucellosis, sex and Q fever co-infection for animal brucellosis, age for human Q fever, species and brucellosis co-infection for animal Q fever, age and herd-level animal RVF seroprevalence within the same cluster for human RVF, and cluster-level human RVF seroprevalence within the same cluster for animal RVF. In Danamadji and Yao, Q fever and RVF are notably seroprevalent among agro-pastoralist human and animal communities, while brucellosis appears to have a low prevalence. Correlation between the seroprevalence between humans and animals living in the same communities was detected for RVF, highlighting the interlinkage of human and animal transmissible diseases and of their health, highlighting the importance of a One Health approach.

Profile likelihood confidence interval for the prevalence assessed by an imperfect diagnostic test.

We present a new confidence interval for the prevalence of a disease for a situation when sensitivity and specificity of the diagnostic test are estimated from validation samples independent of the study sample. The new interval is based on profile likelihood and incorporates an adjustment improving the coverage probability. Its coverage probability and expected length were assessed by simulation and compared to two other methods for this problem, namely those by Lang and Reiczigel (2014) and Flor et al. (2020). Expected length of the new interval is less than that of the Lang and Reiczigel interval while its coverage is about the same. Comparison to the Flor interval resulted in similar expected length but higher coverage probabilities for the new interval. All in all, the new interval proved to be better than both its competitors.

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic tests: application to COVID-19 serosurveillance in Haiti

BackgroundLot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in “high” or “low” classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study design in Haiti.MethodsWe first derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. We then apply the novel LQAS-IMP to design an LQAS system to classify prevalence of SARS-CoV-2 antibodies among healthcare workers at eleven Zanmia Lasante health facilities in Haiti. Finally, we show the performance of the LQAS-IMP procedure in a simulation study.ResultsWe found that when an imperfect diagnostic test is used, the classification errors in the standard LQAS procedure are larger than specified. In the modified LQAS-IMP procedure, classification errors are consistent with the specified maximum classification error. We then utilized the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti.ConclusionThe LQAS-IMP procedure accounts for imperfect sensitivity and specificity in system design; if the accuracy of a test is known, the use of LQAS-IMP extends LQAS to applications for indicators that are based on laboratory tests, such as SARS-CoV-2 antibodies.

A systematic review and meta-analysis of human and zoonotic dog soil-transmitted helminth infections in Australian Indigenous communities.

Soil-transmitted helminths (STH) infect 1.5 billion people and countless animals worldwide. In Australian Indigenous communities, STH infections have largely remained endemic despite control efforts, suggesting reservoirs of infection may exist. Dogs fulfil various important cultural, social and occupational roles in Australian Indigenous communities and are populous in these settings. Dogs may also harbour zoonotic STHs capable of producing morbidity and mortality in dogs and humans. This review provides an overview of human and zoonotic STH infections, identifies the Australian Indigenous locations affected and the parasite species and hosts involved. The meta-analysis provides estimates of individual study and pooled true prevalence of STH infections in Australian Indigenous communities and identifies knowledge gaps for further research on zoonotic or anthroponotic potential. A systematic literature search identified 45 eligible studies documenting the presence of Strongyloides stercoralis, Trichuris trichiura, Ancylostoma caninum, Ancylostoma duodenale, Ancylostoma ceylanicum, undifferentiated hookworm, and Ascaris lumbricoides. Of these studies, 26 were also eligible for inclusion in meta-analysis to establish true prevalence in the light of imperfect diagnostic test sensitivity and specificity by Rogan-Gladen and Bayesian methods. These studies revealed pooled true prevalence estimates of 18.9% (95% CI 15.8-22.1) for human and canine S. stercoralis infections and 77.3% (95% CI 63.7-91.0) for canine A. caninum infections indicating continued endemicity, but considerably more heterogenous pooled estimates for canine A. ceylanicum infections, and A. duodenale, undifferentiated hookworm and T. trichiura in humans. This review suggests that the prevalence of STHs in Australian Indigenous communities has likely been underestimated, principally based on imperfect diagnostic tests. Potential misclassification of hookworm species in humans and dogs due to outdated methodology, also obscures this picture. High-quality contemporary studies are required to establish current true prevalence of parasite species in all relevant hosts to guide future policy development and control decisions under a culturally sound One Health framework.

Implications of covariate induced test dependence on the diagnostic accuracy of latent class analysis in pulmonary tuberculosis

BackgroundIn application studies of latent class analysis (LCA) evaluating imperfect diagnostic tests, residual dependence among the diagnostic tests still remain even after conditioning on the true disease status due to measured variables known to affect prevalence and/or alter diagnostic test accuracy. Presence of severe comorbidities such as HIV in pulmonary tuberculosis (PTB) diagnosis alter the prevalence of PTB and affect the diagnostic performance of the available imperfect tests in use. This violates two key assumptions of LCA: (1) that the diagnostic tests are independent conditional on the true disease status (2) that the sensitivity and specificity remain constant across subpopulations. This leads to incorrect inferences. MethodsThrough simulation we examined implications of likely model violations on estimation of prevalence, sensitivity and specificity among passive case-finding presumptive PTB patients with or without HIV. Jointly conditioning on PTB and HIV, we generated independent results for five diagnostic tests and analyzed using Bayesian LCA with Probit regression, separately for sets of five and three diagnostic tests using four working models allowing: (1) constant PTB prevalence and diagnostic accuracy (2) varying PTB prevalence but constant diagnostic accuracy (3) constant PTB prevalence but varying diagnostic accuracy (4) varying PTB prevalence and diagnostic accuracy across HIV subpopulations. Vague Gaussian priors with mean 1 and unknown variance were assigned to the model parameters with unknown variance assigned Inverse Gamma prior. ResultsModels accounting for heterogeneity in diagnostic accuracy produced consistent estimates while the model ignoring it produces biased estimates. The model ignoring heterogeneity in PTB prevalence only is less problematic. With five diagnostic tests, the model assuming homogenous population is robust to violation of the assumptions. ConclusionWell-chosen covariate-specific adaptations of the model can avoid bias implied by recognized heterogeneity in PTB patient populations generating otherwise dependent test results in LCA.

Analytical framework to evaluate and optimize the use of imperfect diagnostics to inform outbreak response: Application to the 2017 plague epidemic in Madagascar.

During outbreaks, the lack of diagnostic "gold standard" can mask the true burden of infection in the population and hamper the allocation of resources required for control. Here, we present an analytical framework to evaluate and optimize the use of diagnostics when multiple yet imperfect diagnostic tests are available. We apply it to laboratory results of 2,136 samples, analyzed with 3 diagnostic tests (based on up to 7 diagnostic outcomes), collected during the 2017 pneumonic (PP) and bubonic plague (BP) outbreak in Madagascar, which was unprecedented both in the number of notified cases, clinical presentation, and spatial distribution. The extent of these outbreaks has however remained unclear due to nonoptimal assays. Using latent class methods, we estimate that 7% to 15% of notified cases were Yersinia pestis-infected. Overreporting was highest during the peak of the outbreak and lowest in the rural settings endemic to Y. pestis. Molecular biology methods offered the best compromise between sensitivity and specificity. The specificity of the rapid diagnostic test was relatively low (PP: 82%, BP: 85%), particularly for use in contexts with large quantities of misclassified cases. Comparison with data from a subsequent seasonal Y. pestis outbreak in 2018 reveal better test performance (BP: specificity 99%, sensitivity: 91%), indicating that factors related to the response to a large, explosive outbreak may well have affected test performance. We used our framework to optimize the case classification and derive consolidated epidemic trends. Our approach may help reduce uncertainties in other outbreaks where diagnostics are imperfect.

Bayesian latent class models for identifying canine visceral leishmaniosis using diagnostic tests in the absence of a gold standard.

Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information. In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age. Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.

Bayesian credible intervals for population attributable risk from case–control, cohort and cross‐sectional studies

SummaryPopulation attributable risk (PAR) and population attributable fraction (PAF) are used in epidemiology to predict the impact of removing a risk factor from the population. Until recently, no standard approach for calculating confidence intervals or the variance for PAR in particular was available in the literature. Previously we outlined a fully Bayesian approach to provide credible intervals for the PAR and PAF from a cross‐sectional study, where the data was presented in the form of a 2×2 table. However, extensions to cater for other frequently used study designs were not provided. In this paper we provide methodology to calculate credible intervals for the PAR and PAF for case–control and cohort studies. Additionally, we extend the cross‐sectional example to allow for the incorporation of uncertainty that arises when an imperfect diagnostic test is used. In all these situations the model becomes over‐parameterised, or non‐identifiable, which can result in standard ‘off‐the‐shelf’ Markov Chain Monte Carlo (MCMC) updaters taking a long time to converge or even failing altogether. We adapt an importance sampling methodology to overcome this problem, and propose some novel MCMC samplers that take into consideration the shape of the posterior ridge to aid in the convergence of the Markov chain.

Assessing the accuracy of an imperfect dichotomous test in a multiple testing context

In this paper we address the situation where a well-established, but invasive, expensive or time-consuming diagnostic test may be replaced by multiple repetitions of a different diagnostic test which is known to be imperfect but less invasive or expensive. With an imperfect diagnostic test repeated several times on the same patient, we first introduce the sensitivity and specificity of that test for any given number of test repetitions. We also derive the corresponding asymptotic limits for sensitivity and specificity as the number n of test repetitions increases. Given those limits, we then derive sharp upper bounds for the differences between the sensitivity and specificity, obtained for a given number n of test repetitions, and the corresponding asymptotic limit. Specifically, we provide exact rates for the convergence of the empirical sensitivities and specificities. Our analysis is motivated, among other things, by the current discussion on rapid SARS-CoV-2 testing where the real-time polymerase chain reaction (RT-PCR) test may be replaced with a sequence of rapid lateral-flow antigen tests.

An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests.

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false‐positives and false‐negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio‐temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.

A simulation-extrapolation approach for regression analysisof misclassified current status data with the additive hazards model.

Current status data arise when each subject is observed only once and the failure time of interest is only known to be either smaller or larger than the observation time rather than observed exactly. For the situation, due to the use of imperfect diagnostic tests, the failure status could often suffer misclassification or one observes misclassified data, which may result in severely biased estimation if not taken into account. In this article, we discuss regression analysis of such misclassified current status data arising from the additive hazards model, and a simulation-extrapolation (SIMEX) approach is developed for the estimation. Furthermore, the asymptotic properties of the proposed estimators are established, and a simulation study is conducted to assess the empirical performance of the method, which indicates that the proposed procedure performs well. In particular, it can correct the estimation bias given by the naive method that ignores the existence of misclassification. An application to a medical study on gonorrhea is also provided.

Importance of sample size for estimating prevalence: a case example of infectious hematopoietic necrosis viral RNA detection in mixed-stock Fraser River sockeye salmon (Oncorhynchus nerka), British Columbia, Canada

Proper sample size must be considered when designing infectious-agent prevalence studies for mixed-stock fisheries, because bias and uncertainty complicate interpretation of apparent (test)-prevalence estimates. Sample size varies between stocks and are often smaller than expected during wild-salmonid surveys. Our case example of 2010–2016 survey of sockeye salmon (Oncorhynchus nerka) from different stocks of origin in British Columbia, Canada, illustrated the effect of sample size on apparent-prevalence interpretation. Molecular testing (viral RNA RT-qPCR) for infectious hematopoietic necrosis virus (IHNv) revealed large differences in apparent prevalence across wild salmon stocks (much higher from Chilko Lake) and sampling location (fresh water or marine), indicating differences in both stock and host life-stage effects. Ten of the 13 marine non-Chilko stock–years with IHNv-positive results had small sample sizes (<30 samples per stock–year), which, with imperfect diagnostic tests (particularly lower diagnostic sensitivity), could lead to inaccurate apparent-prevalence estimation. When calculating sample size for expected apparent prevalence using different approaches, smaller sample sizes often led to decreased confidence in apparent-prevalence results and decreased power to detect a true difference from a reference value.

Bayesian inference for multistrain epidemics with application to ESCHERICHIA COLI O157:H7 in feedlot cattle

For most pathogens, testing procedures can be used to distinguish between different strains with which individuals are infected. Due to the growing availability of such data, multistrain models have increased in popularity over the past few years. Quantifying the interactions between different strains of a pathogen is crucial in order to obtain a more complete understanding of the transmission process, but statistical methods for this type of problem are still in the early stages of development. Motivated by this demand, we construct a stochastic epidemic model that incorporates additional strain information and propose a statistical algorithm for efficient inference. The model improves upon existing methods in the sense that it allows for both imperfect diagnostic test sensitivities and strain misclassification. Extensive simulation studies were conducted in order to assess the performance of our method, while the utility of the developed methodology is demonstrated on data obtained from a longitudinal study of Escherichia coli O157:H7 strains in feedlot cattle.

Bayesian variable selection for high dimensional predictors and self-reported outcomes

BackgroundThe onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.MethodsWe adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women’s Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.ResultsSimulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women’s Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.ConclusionsVariable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification

BackgroundVarious methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals’ coverage.MethodsIn this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method.ResultsAcross all data sets, the Bayesian point estimate and the RGE produced similar error distributions with slight advantages of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE’s truncation problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length.ConclusionThe Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval.

The Influence of Screening, Misclassification, and Reporting Biases on Reported Chlamydia Case Rates Among Young Women in the United States, 2000 Through 2017.

National chlamydia case rate trends are difficult to interpret because of biases from partial screening coverage, imperfect diagnostic tests, and underreporting. We examined the extent to which these time-varying biases could influence reported annual chlamydia case rates. Annual reported case rates among women aged 15 through 24 years from 2000 through 2017 were obtained from the Centers for Disease Control and Prevention's National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention AtlasPlus tool. Estimates of reporting completeness, diagnostic test sensitivity and specificity, and screening coverage were derived from literature review and expert opinion. We adjusted annual reported case rates for incomplete reporting, imperfect diagnostic tests, and partial screening coverage through a series of corrections, and calculated annual adjusted case rates of correctly diagnosed chlamydia. Adjusted chlamydia case rates among young women were higher than reported case rates throughout the study period. Reported case rates increased over the study period, but adjusted rates declined from 12,900 to 7900 cases per 100,000 person-years between 2000 and 2007. After 2007, adjusted case rates declined to 7500 cases per 100,000 person-years in 2017. Bias from partial screening coverage had a larger impact on case rate magnitude and trend shape than bias from imperfect diagnostic tests or underreporting. Reported chlamydia case rates may be substantially lower than true chlamydia case rates because of incomplete reporting, imperfect diagnostic tests, and partial screening coverage. Because the magnitude of these biases has declined over time, the differences between reported and adjusted case rates have narrowed, revealing a sharp decline in adjusted case rates even as reported case rates have risen. The decline in adjusted case rates suggests that the rise in reported case rates should not be interpreted strictly as increasing chlamydia incidence, as the observed rise can be explained by improvements in screening coverage, diagnostic tests, and reporting.

On the importance of population-based serological surveys of SARS-CoV-2 without overlooking their inherent uncertainties

The SARS-CoV-2 epidemic has caused an unprecedented public health situation and more than ever it is important to be well informed on methods to monitor and analyse the progression of the epidemic. This brief note aims to explain the scope in conducting large-scale serological surveys of SARS-CoV-2 to define the landscape of population immunity, without overlooking the inherent uncertainty steaming from sampling design and diagnostic validity. The note completes with a succinct appendix of simple statistical methods for estimating prevalence from random population samples using imperfect diagnostic tests.