Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These ...models allow us to account for 'conditional dependence' between two or more diagnostic tests, meaning that the results from tests are correlated even after conditioning on the person's true disease status. The challenge is that it is not always clear to researchers whether conditional dependence exists between tests and whether it exists in all or just some latent classes. Despite the increasingly widespread use of latent class models to estimate diagnostic test accuracy, the impact of the conditional dependence structure chosen on the estimates of sensitivity and specificity remains poorly investigated.
A simulation study and a reanalysis of a published case study are used to highlight the impact of the conditional dependence structure chosen on estimates of sensitivity and specificity. We describe and implement three latent class random-effect models with differing conditional dependence structures, as well as a conditional independence model and a model that assumes perfect test accuracy. We assess the bias and coverage of each model in estimating sensitivity and specificity across different data generating mechanisms.
The findings highlight that assuming conditional independence between tests within a latent class, where conditional dependence exists, results in biased estimates of sensitivity and specificity and poor coverage. The simulations also reiterate the substantial bias in estimates of sensitivity and specificity when incorrectly assuming a reference test is perfect. The motivating example of tests for Melioidosis highlights these biases in practice with important differences found in estimated test accuracy under different model choices.
We have illustrated that misspecification of the conditional dependence structure leads to biased estimates of sensitivity and specificity when there is a correlation between tests. Due to the minimal loss in precision seen by using a more general model, we recommend accounting for conditional dependence even if researchers are unsure of its presence or it is only expected at minimal levels.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Plasmodium vivax exacts a significant toll on health worldwide, yet few efforts to date have quantified the extent and temporal trends of its global distribution. Given the challenges associated with ...the proper diagnosis and treatment of P vivax, national malaria programmes—particularly those pursuing malaria elimination strategies—require up to date assessments of P vivax endemicity and disease impact. This study presents the first global maps of P vivax clinical burden from 2000 to 2017.
In this spatial and temporal modelling study, we adjusted routine malariometric surveillance data for known biases and used socioeconomic indicators to generate time series of the clinical burden of P vivax. These data informed Bayesian geospatial models, which produced fine-scale predictions of P vivax clinical incidence and infection prevalence over time. Within sub-Saharan Africa, where routine surveillance for P vivax is not standard practice, we combined predicted surfaces of Plasmodium falciparum with country-specific ratios of P vivax to P falciparum. These results were combined with surveillance-based outputs outside of Africa to generate global maps.
We present the first high-resolution maps of P vivax burden. These results are combined with those for P falciparum (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The burden of P vivax malaria decreased by 41·6%, from 24·5 million cases (95% uncertainty interval 22·5–27·0) in 2000 to 14·3 million cases (13·7–15·0) in 2017. The Americas had a reduction of 56·8% (47·6–67·0) in total cases since 2000, while South-East Asia recorded declines of 50·5% (50·3–50·6) and the Western Pacific regions recorded declines of 51·3% (48·0–55·4). Europe achieved zero P vivax cases during the study period. Nonetheless, rates of decline have stalled in the past five years for many countries, with particular increases noted in regions affected by political and economic instability.
Our study highlights important spatial and temporal patterns in the clinical burden and prevalence of P vivax. Amid substantial progress worldwide, plateauing gains and areas of increased burden signal the potential for challenges that are greater than expected on the road to malaria elimination. These results support global monitoring systems and can inform the optimisation of diagnosis and treatment where P vivax has most impact.
Bill & Melinda Gates Foundation and the Wellcome Trust.
The incidence of cryptococcosis amongst HIV-negative persons is increasing. Whilst the excellent performance of the CrAg testing in people living with HIV is well described, the diagnostic ...performance of the CrAg LFA has not been systematically evaluated in HIV-negative cohorts on serum or cerebrospinal fluid.
We performed a systematic review to characterise the diagnostic performance of IMMY CrAg® LFA in HIV-negative populations on serum and cerebrospinal fluid. A systematic electronic search was performed using Medline, Embase, Global Health, CENTRAL, WoS Science Citation Index, SCOPUS, Africa-Wide Information, LILACS and WHO Global Health Library. Studies were screened and data extracted from eligible studies by two independent reviewers. A fixed effect meta-analysis was used to estimate the diagnostic sensitivity and specificity.
Of 447 records assessed for eligibility, nine studies met our inclusion criteria, including 528 participants overall. Amongst eight studies that evaluated the diagnostic performance of the IMMY CrAg
LFA on serum, the pooled median sensitivity was 96% (95% Credible Interval (CrI) 68-100%) with a pooled specificity estimate of 96% (95%CrI 84-100%). Amongst six studies which evaluated the diagnostic performance of IMMY CrAg
LFA on CSF, the pooled median sensitivity was 99% (95%CrI 95-100%) with a pooled specificity median of 99% (95%CrI 95-100%).
This review demonstrates a high pooled sensitivity and specificity for the IMMY CrAg
LFA in HIV-negative populations, in keeping with findings in HIV-positive individuals. The review was limited by the small number of studies. Further studies using IMMY CrAg
LFA in HIV-negative populations would help to better determine the diagnostic value of this test.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Leptospirosis is an underdiagnosed infectious disease with non-specific clinical presentation that requires laboratory confirmation for diagnosis. The serologic reference standard remains the ...microscopic agglutination test (MAT) on paired serum samples. However, reported estimates of MAT's sensitivity vary. We evaluated the accuracy of four index tests, MAT on paired samples as well as alternative standards for leptospirosis diagnosis: MAT on single acute-phase samples, polymerase chain reaction (PCR) with the target gene Lfb1, and ELISA IgM with Leptospira fainei serovar Hurstbridge as an antigen.
We performed a systematic review of studies reporting results of leptospirosis diagnostic tests. We searched eight electronic databases and selected studies that tested human blood samples and compared index tests with blood culture and/or PCR and/or MAT (comparator tests). For MAT selection criteria we defined a threshold for single acute-phase samples according to a national classification of leptospirosis endemicity. We used a Bayesian random-effect meta-analysis to estimate the sensitivity and specificity of MAT in single acute-phase and paired samples separately, and assessed risk of bias using the Quality Assessment of Studies of Diagnostic Accuracy Approach- 2 (QUADAS-2) tool.
For the MAT accuracy evaluation, 15 studies were included, 11 with single acute-phase serum, and 12 with paired sera. Two included studies used PCR targeting the Lfb1 gene, and one included study used IgM ELISA with Leptospira fainei serovar Hurstbridge as antigen. For MAT in single acute-phase samples, the pooled sensitivity and specificity were 14% (95% credible interval CrI 3-38%) and 86% (95% CrI 59-96%), respectively, and the predicted sensitivity and specificity were 14% (95% CrI 0-90%) and 86% (95% CrI 9-100%). Among paired MAT samples, the pooled sensitivity and specificity were 68% (95% CrI 32-92%) and 75% (95% CrI 45-93%) respectively, and the predicted sensitivity and specificity were 69% (95% CrI 2-100%) and 75% (2-100%).
Based on our analysis, the accuracy of MAT in paired samples was not high, but it remains the reference standard until a more accurate diagnostic test is developed. Future studies that include larger numbers of participants with paired samples will improve the certainty of accuracy estimates.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Parasitological investigation of bone marrow, splenic or lymph node aspirations is the gold standard for the diagnosis of visceral leishmaniasis (VL). However, this invasive test requires skilled ...clinical and laboratory staff and adequate facilities, and sensitivity varies depending on the tissue used. The direct agglutination test (DAT) is a serological test that does not need specialised staff, with just minimal training required. While previous meta-analysis has shown DAT to have high sensitivity and specificity when using parasitology as the reference test for diagnosis, meta-analysis of DAT compared to other diagnostic techniques, such as PCR and ELISA, that are increasingly used in clinical and research settings, has not been done.
We conducted a systematic review to determine the diagnostic performance of DAT compared to all available tests for the laboratory diagnosis of human VL. We searched electronic databases including Medline, Embase, Global Health, Scopus, WoS Science Citation Index, Wiley Cochrane Central Register of Controlled Trials, Africa-Wide Information, LILACS and WHO Global Index. Three independent reviewers screened reports and extracted data from eligible studies. A meta-analysis estimated the diagnostic sensitivity and specificity of DAT.
Of 987 titles screened, 358 were selected for full data extraction and 78 were included in the analysis, reporting on 32,822 participants from 19 countries. Studies included were conducted between 1987-2020. Meta-analysis of studies using serum and DAT compared to any other test showed pooled sensitivity of 95% (95%CrI 90-98%) and pooled specificity of 95% (95%CrI 88-98%). Results were similar for freeze-dried DAT and liquid DAT when analysed separately. Sensitivity was lower for HIV-positive patients (90%, CrI 59-98%) and specificity was lower for symptomatic patients (70%, CrI 43-89%). When comparing different geographical regions, the lowest median sensitivity (89%, CrI 67-97%) was in Western Asia (five studies).
This systematic review and meta-analysis demonstrates high estimated pooled sensitivity and specificity of DAT for diagnosis of VL, although sensitivity and specificity were lower for different patient groups and geographical locations. This review highlights the lack of standardisation of DAT methods and preparations, and the lack of data from some important geographical locations. Future well-reported studies could provide better evidence to inform test implementation for different patient populations and use cases.
CRD42021240830.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Aim
The distribution of Yersinia pestis, the pathogen that causes plague in humans, is reliant upon transmission between host species; however, the degree to which host species distributions dictate ...the distribution of Y. pestis, compared with limitations imposed by the environmental niche of Y. pestis per se, is debated. We test whether the present‐day environmental niche of Y. pestis differs between its native range and an invaded range and whether biotic factors (host distributions) can explain observed discrepancies.
Location
North America and Central Asia.
Major taxa studied
Yersinia pestis.
Methods
We use environmental niche models to determine whether the current climatic niche of Y. pestis differs between its native range in Asia and its invaded range in North America. We then test whether the inclusion of information on the distribution of host species improves the ability of models to capture the North American niche. We use geographical null models to guard against spurious correlations arising from spatially autocorrelated occurrence points.
Results
The current climatic niche of Y. pestis differs between its native and invaded regions. The Asian niche overpredicted the distribution of Y. pestis across North America. Including biotic factors along with the native climatic niche increased niche overlap between the native and invaded models, and models containing only biotic factors performed better than the native climatic niche alone. Geographical null models confirmed that the increased niche overlap through inclusion of biotic factors did not, with a couple of exceptions, arise solely from spatially autocorrelated occurrences.
Main conclusions
The current climatic niche in Central Asia differs from the current climatic niche in North America. Inclusion of biotic factors improved the fit of models to the Y. pestis distribution data in its invaded region better than climate variables alone. This highlights the importance of host species when investigating zoonotic disease introductions and suggests that climatic variables alone are insufficient to predict disease distribution in novel environments.
The disease burden of Plasmodium falciparum malaria illness is generally estimated using one of two distinct approaches: either by transforming P. falciparum infection prevalence estimates into ...incidence estimates using conversion formulae; or through adjustment of counts of recorded P. falciparum-positive fever cases from clinics. Whilst both ostensibly seek to evaluate P. falciparum disease burden, there is an implicit and problematic difference in the metric being estimated. The first enumerates only symptomatic malaria cases, while the second enumerates all febrile episodes coincident with a P. falciparum infection, regardless of the fever's underlying cause.
Here, a novel approach was used to triangulate community-based data sources capturing P. falciparum infection, fever, and care-seeking to estimate the fraction of P. falciparum-positive fevers amongst children under 5 years of age presenting at health facilities that are attributable to P. falciparum infection versus other non-malarial causes. A Bayesian hierarchical model was used to assign probabilities of malaria-attributable fever (MAF) and non-malarial febrile illness (NMFI) to children under five from a dataset of 41 surveys from 21 countries in sub-Saharan Africa conducted between 2006 and 2016. Using subsequent treatment-seeking outcomes, the proportion of MAF and NMFI amongst P. falciparum-positive febrile children presenting at public clinics was estimated.
Across all surveyed malaria-positive febrile children who sought care at public clinics across 41 country-years in sub-Saharan Africa, P. falciparum infection was estimated to be the underlying cause of only 37.7% (31.1-45.4, 95% CrI) of P. falciparum-positive fevers, with significant geographical and temporal heterogeneity between surveys.
These findings highlight the complex nature of the P. falciparum burden amongst children under 5 years of age and indicate that for many children presenting at health clinics, a positive P. falciparum diagnosis and a fever does not necessarily mean P. falciparum is the underlying cause of the child's symptoms, and thus other causes of illness should always be investigated, in addition to prescribing an effective anti-malarial medication. In addition to providing new large-scale estimates of malaria-attributable fever prevalence, the results presented here improve comparability between different methods for calculating P. falciparum disease burden, with significant implications for national and global estimation of malaria burden.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria ...Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.
As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low‐prevalence areas are increasingly needed. For low‐burden areas, disaggregation regression ...models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However, in areas with both routine surveillance data and prevalence surveys, models that make use of the spatial information from prevalence point‐surveys might make more accurate predictions. Using case studies in Indonesia, Senegal and Madagascar, we compare the out‐of‐sample mean absolute error for two methods for incorporating point‐level, spatial information into disaggregation regression models. The first simply fits a binomial‐likelihood, logit‐link, Gaussian random field to prevalence point‐surveys to create a new covariate. The second is a multi‐likelihood model that is fitted jointly to prevalence point‐surveys and polygon incidence data. We find that in most cases there is no difference in mean absolute error between models. In only one case, did the new models perform the best. More generally, our results demonstrate that combining these types of data has the potential to reduce absolute error in estimates of malaria incidence but that simpler baseline models should always be fitted as a benchmark.
Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is ...becoming more widely available. Disaggregation regression is an important model framework for estimating high resolution risk maps from aggregated data. However, the aggregation of incidence over large, heterogeneous areas means that these data are underpowered for estimating complex, non-linear models. In contrast, prevalence point-surveys are directly linked to local environmental conditions but are not common in many areas of the world. Here, we train multiple non-linear, machine learning models on Plasmodium falciparum prevalence point-surveys. We then ensemble the predictions from these machine learning models with a disaggregation regression model that uses aggregated malaria incidences as response data. We find that using a disaggregation regression model to combine predictions from machine learning models improves model accuracy relative to a baseline model.
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