Estimates Of Disease Prevalence Research Articles

Aggregating multiple test results to improve medical decision-making.

Gathering observational data for medical decision-making often involves uncertainties arising from both type I (false positive) and type II (false negative) errors. In this work, we develop a statistical model to study how medical decision-making can be improved by aggregating results from repeated diagnostic and screening tests. Our approach is relevant to not only clinical settings such as medical imaging, but also to public health, as highlighted by the need for rapid, cost-effective testing methods during the SARS-CoV-2 pandemic. Our model enables the development of testing protocols with an arbitrary number of tests, which can be customized to meet requirements for type I and type II errors. This allows us to adjust sensitivity and specificity according to application-specific needs. Additionally, we derive generalized Rogan-Gladen estimates of disease prevalence that account for an arbitrary number of tests with potentially different type I and type II errors. We also provide the corresponding uncertainty quantification.

Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model.

Wastewater-based estimation of infectious disease prevalence in real-time assists public health authorities in developing effective responses to current outbreaks. However, wastewater-based estimation for IAV remains poorly demonstrated, partially because of a lack of knowledge about temporal variation in shedding amount of an IAV-infected person. In this study, we applied two mathematical models to previously collected wastewater and clinical data from four U.S. states during the 2022/2023 influenza season, dominated by the H3N2 subtype. First, we modeled the relationship between the detection probability of IAV in wastewater and FluA case counts, using a logistic function. The model revealed that a 50% probability of IAV detection in wastewater corresponds to 0.53 (95% CrI: 0.35-0.78) cases per 100,000 people, as observed in clinical surveillance over two weeks. Next, we applied the previously developed PRESENS model to IAV wastewater concentration data from California, revealing rapid and prolonged virus shedding patterns. The estimated shedding model was incorporated into an extended version of the PRESENS model to assess the variability in the relationship between IAV concentrations and case numbers across other states, including Massachusetts, New Jersey, and Utah. As a result, our analysis demonstrated the effectiveness of normalizing IAV concentrations with PMMoV (Pepper mild mottle virus) to accurately understand spatial distribution patterns of IAV prevalence. We successfully estimated FluA case counts from wastewater concentrations within a factor of two for 80% of data from a state where 34% of the state population was monitored by wastewater surveillance. Importantly, wastewater-based estimates provided real-time or leading insights (0-2days) compared to clinical case detection in the three states, enabling early understanding of the incidence trends by limiting delays in data publication. These findings highlight the potential of wastewater surveillance to detect IAV outbreaks in near real-time and enhance efficiency of the infectious disease management.

Estimating the rates of dementia using administrative data linked to cohort studies.

Current estimates of dementia and Alzheimer's disease incidence and prevalence are required to understand the health needs of the elderly. We used two Australia cohort studies, administrative datasets, and data linkage techniques to estimate dementia rates in Australia. The study used Australian Longitudinal Study on Women's Health and the Health in Men Cohort Study. Records of dementia were obtained from linked sources and incidence and prevalence estimates were produced. Capture-recapture methods were used to estimate numbers of dementia cases not identified through data linkage. There were 3399 (28.5%) men with dementia identified from any source and 3767 (34.8%) women. Rates of dementia incidence and prevalence were similar between sexes but were raised in men once estimates of unidentified cases were included. Cohort studies and linked administrative data can be used together to produce current estimates of dementia prevalence and incidence comparable to other population estimates.

Current risks of byssinosis and chronic obstructive pulmonary disease in textile workers.

The textile manufacturing sector is a major contributor to the economies of several emergent economies in the global South. Respiratory disease, including byssinosis and chronic obstructive pulmonary disease (COPD), among textile workers appears to be common in this setting. Notably, little or no contemporary research has been published from countries, which are major global producers of textiles. Most recent epidemiological studies are of a cross-sectional design, with deficiencies that repeat those of research in this area over the past 20 years and do little to advance the cause of prevention. Their estimates of disease prevalence vary wildly for reasons that are unclear. An important exception was the publication of a successful, randomized trial of a low-cost intervention, the first in this field. Research into the respiratory problems of textile work needs to embrace modern approaches to the understanding of (occupational) COPD and move beyond the estimation of disease prevalence. There is an important need for cheaper and simpler techniques for workplace dust measurement. Occupational health researchers should pool their skills, resources and protocols towards the prevention of a problem that is common to many low- and middle-income economies.

Development and evaluation of a kidney health questionnaire and estimates of chronic kidney disease prevalence in the Cooperative Health Research in South Tyrol (CHRIS) study.

Kidney diseases are a public health burden but are poorly investigated in the general population. In light of inadequate survey tools, we developed a novel questionnaire for use in population-based studies, to retrospectively assess kidney diseases. The questionnaire covered general kidney diseases, reduced kidney function, and renal surgeries. It was administered between 2011 and 2018 to 11,684 participants (median age = 45 years) of the Cooperative Health Research in South Tyrol (CHRIS) study. Fasting estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) were measured. By factor analysis we contextualized the questionnaire content with respect to thebiochemical measurements. We estimated overall and sex-stratified prevalence of kidney diseases, including possible CKD, calibrating them to the general target population via relative sampling weights. Population-representative prevalence of glomerulonephritis, pyelonephritis, and congenital kidney diseases was 1.0%, 3.0%, and 0.2%, respectively, with corresponding odds ratios for females versus males of 1.4 (95% confidence interval: 1.0, 2.0), 8.7 (6.2, 12.3), and 0.7 (0.3, 1.6), respectively. Prevalence of kidney dysfunction (eGFR < 60mL/min/1.73m2 or UACR > 30mg/g) was 8.59%, while prevalence of self-reported CKD was 0.69%, indicating 95.3% oflack of disease awareness, with a similar figure in people with diabetes or hypertension. Overall, 15.76% of the population was affected by a kidney disease of any kind. In the Val Venosta/Vinschgau alpine district, CKD prevalence aligned with Western European estimates. Kidney health questionnaire implementation in population studies is feasible and valuable to assess CKD awareness, which we found to be dramatically low.

Prevalence of epilepsy: a population-based cohort study in Denmark with comparison to Global Burden of Disease (GBD) prevalence estimates

BackgroundThe Global Burden of Disease Study (GBD) produces prevalence estimates for ‘idiopathic epilepsy’ (ie, of unknown aetiology) and ‘secondary epilepsy’ (ie, with known aetiology) but does not report prevalence by...

Abstract 4124675: Deep Learning Screening of Cardiac MRIs Uncovers Undiagnosed Hypertrophic Cardiomyopathy in the UK BioBank

Introduction: The prevalence of hypertrophic cardiomyopathy (HCM) in the UK Biobank based on ICD-10 codes (.07%) is lower than global estimates of disease prevalence (0.2 - 0.5%). Prior studies using this data have remarked on the limitations of findings given likely underdiagnosis. The availability of cardiac MRI scans on a fraction of the participants offers an opportunity to identify missed diagnoses. Aims: This study seeks to utilize a generalizable deep learning model to detect likely cases of undiagnosed hypertrophic cardiomyopathy from cardiac MRIs in the UK Biobank. Methods: The foundational model was trained on a multi-institutional dataset of 14,073 cardiac MRIs via a self-supervised contrastive learning approach that sought to minimize the divergence between scans and their associated radiology reports. The pre-trained model was fine-tuned to diagnose hypertrophic cardiomyopathy on a distinct cohort of 4,870 MRIs with 368 cases of HCM, achieving an AUC of 0.94. The fine-tuned model was applied to the UK Biobank cardiac MRI dataset to ascertain predicted probabilities of HCM. Cases exceeding a threshold of 95% – correlating to the top 0.5% of cases (expected specificity of 97% and sensitivity of 60%) – were screened in for manual reading. In a blinded fashion, a board-certified radiologist was tasked with diagnosing HCM on a sample of cases composed of high and low predicted probabilities. Results: Of the 43,017 patients with cardiac MRIs, only 9 (.02%) had an ICD diagnosis of HCM. 266 cardiac MRIs were manually reviewed: 216 had greater than 95% predicted probability of HCM; 50 negative controls were randomly selected amongst cases with predicted probability less than 10%. The radiologist concurred with an HCM diagnosis for 115 cases (sensitivity 53%, specificity 98%), 112 of which were previously undiagnosed. The prevalence of hypertension and aortic stenosis did not significantly differ between the cohort of true positives (69.2%) and false positives (76.6%). The corrected prevalence of HCM in the UK BioBank MRI cohort is estimated at 0.28%. Conclusions: The findings of this study illustrate the remarkable ability of a generalizable deep learning model to detect undiagnosed cases of a rare disease process from cardiac MRIs. This is an important milestone that may allow for widespread screening of hypertrophic cardiomyopathy while minimizing demand for radiologist labor, and thereby allow patients to reap the substantial benefits of earlier treatment.

Inferring diagnoses from prescription data: a machine-learning approach

Abstract Estimating disease prevalence is a fundamental while challenging public-health goal: in the absence of solid diagnostic data, it typically requires expensive and time intensive studies. Analyzing existing medical records is an alternative, complementary approach. In particular, prescription records offer a glimpse into the health status of populations, prescribing patterns, and operational dynamics. We asked whether these could be used to infer diagnostics and, therefore, estimate disease prevalence. We used the Portuguese electronic medical prescriptions dataset (ADD NUMBERS) and trained a machine learning model that captures the probability that specific drugs are prescribed for the same or related medical conditions. We 1) constructed a manually-curated dataset of diseases and associated medications; 2) used this dataset to train embeddings models; 3) tested these models against human-classified data; 4) used the trained/tested model to uncover comorbidities associated with a) antibiotic prescription (to validate the model), and b) the impact of COVID-19 in prescription patterns of chronic-diseases. The model was able to accurately identify drugs used for targeting ten specific diseases, with few false positives and false negatives. Our analysis showed that the COVID-19 pandemic had almost no effect on the number of prescriptions for some chronic diseases (such as diabetes and depression), but, importantly, had a strong impact on the number of new diagnoses, with new users of diabetes medication in 2019 dropping by more than 16.8% in 2020. Medical prescriptions offer a promising tool to quickly assess disease prevalence, underdiagnosis, and allow a broad overview of the medical status of the population, especially in the face of health emergencies that strongly pressure the medical system. Key messages • We propose a new approach to infer diagnostics from prescription data with several promising applications. • We propose a new approach to infer diagnostics from prescription data with several promising applications.

Persistent biological invasions alter ecological network topology, impacting disease transmission during community assembly

Ecological networks experiencing persistent biological invasions may exhibit distinct topological properties, complicating the understanding of how network topology affects disease transmission during invasion-driven community assembly. We developed a trait-based network model to assess the impact of network topology on disease transmission, measured as community- and species-level disease prevalence. We found that trait-based feeding interactions between host species determine the frequency distribution of the niche of co-occurring species in steady-state communities, being either bimodal or multimodal. The width of the growth kernel influences the degree-biomass relationship of species, being either weakly positive or strongly negative. When this relationship is weakly positive, species-level disease prevalence is primarily correlated with biomass. However, when the degree-biomass relationship is strongly negative, species-level disease prevalence is determined by the difference between a host species’ in-degree and out-degree closeness centrality. At the community level, disease prevalence is generally amplified by increasing host richness, community biomass, and the standard deviation of interaction generality, while it is diluted by higher network connectance. Our framework verifies the amplification effects of host richness during invasion-driven community assembly and offers valuable insights for estimating disease prevalence based on host network topology.

Estimating disease prevalence from partially-sampled clusters using the conditional linear family for multivariate Bernoulli data

In periodontal disease surveillance in human populations, full-mouth clinical examinations to classify the disease status of individuals are the gold standard for estimating periodontitis prevalence. However, conducting full-mouth exams is resource intensive, time consuming, and costly, especially in studies involving thousands of participants. Partial-recording protocols have been utilized in oral health surveys worldwide to gather correlated binary outcomes of periodontal disease on selected teeth in lieu of full-mouth exams. Since the use of partial-recording protocols tends to underestimate disease prevalence, a statistical distributional approach considering the pattern of tooth-level disease in the mouth was proposed to substantially reduce bias for the estimation of periodontitis prevalence. This approach employed multivariate Bernoulli distributions for observation (tooth)-level disease indicators to define formulae for the prevalence of disease (periodontitis) at the cluster (person)-level for various full-mouth case definitions. In turn, prevalence estimators were based on plug- in estimates of parameters from a conditional linear family for binary data gathered under partial recording protocols. Work in this article extended existing prevalence estimators for simple case definitions based on single clinical measures of tooth-level periodontal disease to a definition of severe periodontitis using two measures as defined by the Centers for Disease Control and Prevention and the American Academy of Periodontology, and later adopted by the 2017 World Workshop in Periodontology. Simulations evaluated the finite-sample performance of the proposed estimators and their confidence intervals for three established partial-recording protocols. In general, the prevalence estimators performed well relative to bias and coverage when tooth-level probabilities of disease and within-mouth correlation structures were correctly specified and even when the pattern of tooth-pair correlations was misspecified. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 3-31.

Perspectives from cystinosis: access to healthcare may be a confounding factor for variant classification.

Genetic variability persists across diverse populations, and it may impact the characterization of heritable diseases in different ancestral groups. Cystinosis is a metabolic disease caused by pathogenic variants in the CTNS gene causing the cellular accumulation of cystine. We attempted to assess the currently poorly characterized prevalence of cystinosis by employing a population genetics methodology. However, we encountered a significant challenge due to genetic variations across different populations, and the consideration of potential disparities in access to healthcare made our results inconclusive. Pathogenic CTNS variants were identified in a representative global population cohort using The Human Gene Mutation Database (HGMD) and the 1000 Genomes (1KG) database. The c.124G>A (p.Val42Ile) variant was reported to be pathogenic based on an observation in the white population presenting with atypical phenotypes, but it would be reclassified as benign in the African ancestral group if applying the ACMG allele frequency guideline due to its high allele frequency specifically in this population. Inclusion or exclusion of this c.124G>A (p.Val42Ile) variant results in a significant change in estimated disease prevalence, which can impact the diagnosis and treatment of affected patients with a broad range of phenotypic presentations. This observation led us to postulate that pathogenic manifestations of the disease may be underdiagnosed due to variable expressivity and systemic inequities in access to care, specifically in the African subpopulation. We call for a more cautious and inclusive approach to achieve more equitable care across diverse populations.

Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods

Objective or PurposeTo compare the performance of three phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. DesignThree phenotyping methods were used to identify clinical conditions including unspecified DR, non-proliferative diabetic retinopathy (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative diabetic retinopathy (PDR), diabetic macular edema (DME), vitreous hemorrhage (VH), retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next two methods used a Bidirectional Encoder Representations from Transformers (BERT) with a dense Multilayer Perceptron (MLP) output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-ICD NLP System). Subjects, Participants, and/or ControlsAdults ≥18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods, Intervention or TestingWe compared the performance of the three phenotyping methods in identifying the diabetic retinopathy related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome MeasuresPerformance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. ResultsA total of 91,097 patients and 692,486 office visits were included in the study. Compared to the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39 to 0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21 to 0.81). The prevalence of diabetic retinopathy and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System). ConclusionsThe prevalence of diabetic retinopathy and related conditions varied significantly depending on the methodology of identifying cases. The best performing phenotyping method was the Text-and-ICD NLP System that used information in both diagnosis codes as well as free-text notes.

A Loop-Mediated Isothermal Amplification Assay for the Rapid Detection of Didymella segeticola Causing Tea Leaf Spot.

Tea leaf spot caused by Didymella segeticola is an important disease that threatens the healthy growth of tea plants (Camellia sinensis) and results in reductions in the productivity and quality of tea leaves. Early diagnosis of the disease is particularly important for managing the infection. Loop-mediated isothermal amplification (LAMP) assay is an efficient diagnostic technique with the advantages of simplicity, specificity, and sensitivity. In this study, we developed a rapid, visual, and high-sensitivity LAMP assay for D. segeticola detection based on sequence-characterized amplified regions. Two pairs of amplification primers (external primers F3 and B3 and internal primers FIP and BIP) were designed based on a specific sequence in D. segeticola (NCBI accession number: OR987684). Compared to common pathogens of other genera in tea plants and other species in the Didymella genus (Didymella coffeae-arabicae, Didymella pomorum, and Didymella sinensis), the LAMP method is specific for detecting the species D. segeticola. The assay was able to detect D. segeticola at a minimal concentration of 1 fg/μL genomic DNA at an optimal reaction temperature of 65 °C for 60 min. When healthy leaves were inoculated with D. segeticola in the laboratory, the LAMP method successfully detected D. segeticola in diseased tea leaves at 72 h post inoculation. The LAMP assays were negative when the DNA samples were extracted from healthy leaves. Leaf tissues with necrotic lesions from 18 germplasms of tea plants tested positive for the pathogen by the LAMP assay. In summary, this study established a specific, sensitive, and simple LAMP method to detect D. segeticola, which provides reliable technical support for estimating disease prevalence and facilitates sustainable management of tea leaf spot.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Disease Prevalence Estimation

Nowadays, disease prevalence estimation is a significant concern, with heart disease being one of the most common ailments. Unfortunately, the treatment of such diseases can be costly, often beyond the means of the average individual. However, we can mitigate this issue to some extent by accurately estimating disease prevalence before it reaches dangerous levels, using techniques such as Machine Learning and Data Mining. In the healthcare biomedical field, there's a vast amount of health data available, ranging from text to images. However, much of this data remains unexplored and unmined. Introducing a Disease Prevalence Estimation System could address this gap. Such a system would not only help in reducing costs but also enhance the quality of treatment for patients. Machine Learning and Data Mining techniques can be employed to construct this Disease Prevalence Estimation System. By analyzing patient profiles including factors like blood pressure, age, sex, cholesterol, and blood sugar levels, the system can predict the likelihood of individuals developing various health issues. Furthermore, the system can identify complex problems and make intelligent medical decisions, thereby improving overall healthcare outcomes. Performance evaluation can be done using metrics such as the confusion matrix, allowing for the calculation of accuracy, precision, and recall. In conclusion, a Disease Prevalence Estimation System has the potential to offer high performance and better accuracy, thus significantly contributing to the early detection and management of various diseases

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error.

Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.

Best Disease: Global Mutations Review, Genotype-Phenotype Correlation, and Prevalence Analysis in the Israeli Population.

To review all reported disease-causing mutations in BEST1, perform genotype-phenotype correlation, and estimate disease prevalence in the Israeli population. Medical records of patients diagnosed with Best disease and allied diseases from nine Israeli medical centers over the past 20 years were collected, as were clinical data including ocular findings, electrophysiology results, and retina imaging. Mutation detection involved mainly whole exome sequencing and candidate gene analysis. Demographic data were obtained from the Israeli Bureau of Statistics (January 2023). A bibliometric study was also conducted to gather mutation data from online sources. A total of 134 patients were clinically diagnosed with Best disease and related conditions. The estimated prevalence of Best disease was calculated to be 1 in 127,000, with higher rates among Arab Muslims (1 in 76,000) than Jews (1 in 145,000). Genetic causes were identified in 76 individuals (57%), primarily showing autosomal-dominant inheritance due to BEST1 mutations (58 patients). Critical conserved domains were identified consisting of a high percentage of dominant missense mutations, primarily in transmembrane domains and the intracellular region (Ca2+ binding domain) of the BEST1 protein. This study represents the largest cohort of patients with Best disease reported in Israel and globally. The prevalence in Israel is akin to that in Denmark but is lower than that in the United States. Critical conserved domains within the BEST1 protein are pivotal for normal functioning, and even minor missense alterations in these areas lead to a dominant disease manifestation. Genetic testing is indispensable as the gold standard for Best disease diagnosis due to the variable clinical presentation of the disease.

Burden of Disease in Psoriatic Arthritis in Latin America: a SystematicLiterature Review.

Psoriatic arthritis is a chronic inflammatory pathology that generates a substantial and progressive deterioration of functionality and quality of life. It is associated with comorbidities (cardiovascular and metabolic) and involvement of mental health. In Latin America, information regarding the disease is limited. This study reviews the burden of disease (disease activity, functional involvement, clinical manifestations, comorbidities, patient-reported outcomes, quality of life, and use of health resources) in PsA patients in Latin America. Systematic literature review of publications in PUBMED, EMBASE, Cochrane Database of Systematic Reviews-CDSR/Database of Abstracts of Reviews of Effects, LILACS, Scielo, Redalyc, conference abstracts, and grey literature. Two independent assessors selected studies and extracted information. Quality was assessed according to the type of study. We identified 692 references, selecting 50 studies: 41 cross-sectional, four economic-studies, four cohort studies and one systematic review. The information comes mainly from Brazil, Argentina, and Mexico. The estimated disease prevalence for Latin America ranges from 0.004 to 0.08% (95% CI 0.02-0.20). Measurements with validated instruments suggest suboptimal assessment of disease domains, significant functional compromise, loss of productivity, and high frequency of comorbidities, including mental health. Methodological and population considerations limit the generalizability of the findings. The available information reports a considerable burden of disease in patients with PsA in Latin America, with involvement of quality of life associated with disability in relation to disease activity and its various manifestations. Future research and funding efforts should be aimed at generating more standardized information about the impact of PsA in the region. Key Points •The functional involvement related to disease activity, the impact on the quality of life, and the frequency of cardiometabolic and psychological comorbidities are remarkable in Latin American patients with PsA. •The current synthesis offers an overview of the burden of disease (disease activity, functional involvement, clinical manifestations, comorbidities, patient-reported outcomes, quality of life, and use of health resources) in PsA patients in Latin America. •Future research efforts and clinical strategies are required in order to generate standardized data on the patients and better estimate the burden of disease in the region.

Uneven demographic consequences of the 2022 disease outbreak for the sea urchin Diadema antillarum in Puerto Rico.

Pervasive epizootic events have had a significant impact on marine invertebrates throughout the Caribbean, leading to severe population declines and consequential ecological implications. One such event was the regional collapse of herbivory, partly caused by the Diadema antillarum mortality event in 1983-84, resulting in a trophic cascade and altering the structure of reef communities. Consequently, there was a notable decrease in coral recruitment and an increase in the coverage of macroalgae. Nearly four decades later, in early 2022, the Caribbean basin experienced another widespread mass mortality event, further reducing the populations of D. antillarum. To assess the effects of this recent mortality event on the current demographics of D. antillarum, we surveyed eight populations along the eastern, northeastern, northern, and northwestern coast of Puerto Rico from May to July 2022, estimating their population density, size distribution, and disease prevalence. Additionally, the study compared these population parameters with data from four sites previously surveyed in 2012 and 2017 to understand the impact of the recent mortality event. The survey conducted in 2022 showed varying population densities at the surveyed reefs. Some populations exhibited mean densities of nearly one individual per square meter, while others had extremely low or no living individuals per square meter. The four populations with the highest density showed no evidence of disease, whereas the four populations with the lowest D. antillarum densities exhibited moderate to high disease prevalence. However, when considering all sites, the estimated disease prevalence remained below 5%. Nevertheless, the comparison with data from 2012 and 2017 indicated that the recent mortality event had a negative impact on D. antillarum demographics at multiple sites, as the densities in 2022 were reduced by 60.19% compared to those from the previous years. However, it is still too early to determine the severity of this new mortality event compared to the 1983-84 mortality event. Therefore, it is imperative to continue monitoring these populations.

Variations in Incidence and Prognosis of Stage 4 CKD Among Adults Identified Using Different Algorithms: A Population-Based Cohort Study

Rationale & ObjectiveClinical guidelines define chronic kidney disease (CKD) as abnormalities of kidney structure or function for >3 months. Assessment of the duration criterion may be implemented in different ways, potentially impacting estimates of disease incidence or prevalence in the population, individual diagnosis, and treatment decisions, especially for more severe cases. We investigated differences in incidence and prognosis of CKD G4 identified by one of four algorithms. Study DesignPopulation-based cohort study in Alberta, Canada. Setting & ParticipantsResidents >18 years old with incident CKD G4 (eGFR 15-29 mL/min/1.73 m2) diagnosed between April 1, 2015 and March 31, 2018, based on administrative and laboratory data. ExposuresFour outpatient eGFR-based algorithms, increasing in stringency, for defining cohorts with CKD G4 were evaluated: (1) a single test, (2) first eGFR <30 mL/min/1.73 m2 and a second eGFR 15-29 mL/min/1.73 m2 >90 days apart (two tests), (3) ≥2 eGFR measurements <30 mL/min/1.73 m2 sustained for >90 days (qualifying period) and the last eGFR in the qualifying period of 15-29 mL/min/1.73 m2 (relaxed sustained), (4) ≥2 consecutive measurements 15-29 mL/min/1.73 m2 for >90 days (rigorous sustained). OutcomesTime to the earliest of death, eGFR improvement (a sustained increase in eGFR to ≥30 mL/min/1.73 m2 for >90 days and >25% increase from the index eGFR), or kidney failure. Analytical ApproachFor each of the 4 cohorts, incidence rates and event-specific cumulative incidence functions at 1 year from cohort entry were estimated. ResultsThe incidence rates of CKD G4 decreased as algorithms became more stringent, from 190.7 (single test) to 79.9 (rigorous sustained) per 100,000 person-years. The two cohorts based on sustained reductions in eGFR were of comparable size and 1-year event-specific probabilities. The two cohorts based on a single test and a two-test sequence were larger and experienced higher probabilities of eGFR improvement. LimitationsA short follow-up period of 1 year and a predominantly White population. ConclusionsThe use of more stringent algorithms for defining CKD G4 results in substantially lower estimates of disease incidence, the identification of a group with a lower probability of eGFR improvement, and a higher risk of kidney failure. These findings can inform implementation decisions of disease definitions in clinical reporting systems and research studies.