Low-prevalence Conditions Research Articles

A Comparison of Green, Delta, and Monte Carlo Methods to Select an Optimal Approach for Calculating the 95% Confidence Interval of the Population-attributable Fraction: Guidance for Epidemiological Research.

This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the "GDM-PAF CI Explorer," was developed to facilitate the analysis and visualization of these computations. No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland's method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

DxGoals: A Software Tool for Determining and Analyzing Clinically Meaningful Classification Accuracy Goals for Diagnostic Tests.

To evaluate diagnostic tests for low prevalence conditions, classification accuracy metrics such as sensitivity, specificity, and positive likelihood ratio (PLR) and negative likelihood ratio (NLR) are advantageous because they are prevalence-independent and thus estimable in studies enriched for the condition. However, classification accuracy goals are often chosen without a clear understanding of whether they are clinically meaningful. Pennello (2021) proposed a risk stratification framework for determining classification accuracy goals. A software application is needed to determine the goals and provide data analysis. We introduce DxGoals, a freely available, R-Shiny software application for determining, visualizing, and analyzing classification accuracy goals for diagnostic tests. Given prevalence p for the target condition and specification that a test's positive and negative predictive values PPVand NPV=1-cNPV should satisfy PPV>PPV* and cNPV<cNPV*, DxGoals uses Bayes Theorem to determine equivalent goals for PLR and NLR and implied goals for sensitivity and specificity. When study data are provided, DxGoals analyzes whether the determined goals are met with statistical significance. When comparing 2 tests, DxGoals translates a superiority or noninferiority goals for the differences PPV-p and p-cNPV to equivalent goals for PLR and NLR and analyzes the goals when data are provided. We illustrate DxGoals on tests for penicillin allergy, ovarian cancer, and cervical cancer. The inputs cNPV*,p, and PPV* were informed by clinical management guidelines. DxGoals facilitates determination, visualization, and analysis of clinically meaningful standalone and comparative classification accuracy goals. It is a potentially useful tool for diagnostic test evaluation.

Choices of measures of association affect the visualisation and composition of the multimorbidity networks

BackgroundNetwork analysis, commonly used to describe the patterns of multimorbidity, uses the strength of association between conditions as weight to classify conditions into communities and calculate centrality statistics. Our aim was to examine the robustness of the results to the choice of weight.MethodsData used on 27 chronic conditions listed on Australian death certificates for women aged 85+. Five statistics were calculated to measure the association between 351 possible pairs: odds ratio (OR), lift, phi correlation, Salton cosine index (SCI), and normalised-joint frequency of pairs (NF). Network analysis was performed on the 10% of pairs with the highest weight according to each definition, the ‘top pairs’.ResultsOut of 56 ‘top pairs’ identified, 13 ones were consistent across all statistics. In networks of OR and lift, three of the conditions which did not join communities were among the top five most prevalent conditions. Networks based on phi and NF had one or two conditions not part of any community. For the SCI statistics, all three conditions which did not join communities had prevalence below 3%. Low prevalence conditions were more likely to have high degree in networks of OR and lift but not SCI.ConclusionUse of different statistics to estimate weights leads to different networks. For exploratory purposes, one may apply alternative weights to identify a large list of pairs for further assessment in independent studies. However, when the aim is to visualise the data in a robust and parsimonious network, only pairs which are selected by multiple statistics should be visualised.

First year results and insights from the Mexican Rare Disease Patient Registry

Rare diseases are defined based on their low-prevalence occurrence in the general population, individually affecting a small number of people, but having a major health and social impact collectively. It is estimated that at least 300 million people live with a rare disorder globally, however the exact numbers are not available for most countries. Patient registries are valuable tools that allow to better understand the prevalence of diseases in the population, the needs of patients and their health outcomes, and provide information for improved healthcare planning. The Mexican Rare Disease Patient Registry is an open, online, self-reporting registry study launched in 2022 to gather information about the prevalence of rare diseases in the Mexican population, the need for access to timely diagnosis, and the challenges faced by patients living with these conditions in the country. We report the analysis and results of the data collected for 144 patients that completed the questionnaire during the first year of the Registry. Results show that the mean time to clinical diagnosis for patients registered during the first year was 8 years, with an average of 6 medical specialists consulted. Additionally, while 71.53 % of registered patients reported having been informed that their condition could have a genetic origin, only 39.58 % were referred to a genetics specialist. Only 27.77 % of patients have had genetic or molecular testing performed, despite more than 80 % of patients reporting positive attitudes towards genetic testing shall it be offered or available to them. Through these initial analyses and data gathered, we are starting to elucidate relevant trends in access to diagnosis, care and treatment for patients living with rare and low-prevalence diseases in Mexico. These data will be useful for better health services planning and addressing the needs of patients and families living with these low-prevalence conditions in the country to ensure better outcomes and quality of life.

Unveiling Adolescent Suicidality: Holistic Analysis of Protective and Risk Factors Using Multiple Machine Learning Algorithms

Adolescent suicide attempts are on the rise, presenting a significant public health concern. Recent research aimed at improving risk assessment for adolescent suicide attempts has turned to machine learning. But no studies to date have examined the performance of stacked ensemble algorithms, which are more suitable for low-prevalence conditions. The existing machine learning-based research also lacks population-representative samples, overlooks protective factors and their interplay with risk factors, and neglects established theories on suicidal behavior in favor of purely algorithmic risk estimation. The present study overcomes these shortcomings by comparing the performance of a stacked ensemble algorithm with a diverse set of algorithms, performing a holistic item analysis to identify both risk and protective factors on a comprehensive data, and addressing the compatibility of these factors with two competing theories of suicide, namely, The Interpersonal Theory of Suicide and The Strain Theory of Suicide. A population-representative dataset of 173,664 Norwegian adolescents aged 13 to 18 years (mean = 15.14, SD = 1.58, 50.5% female) with a 4.65% rate of reported suicide attempt during the past 12 months was analyzed. Five machine learning algorithms were trained for suicide attempt risk assessment. The stacked ensemble model significantly outperformed other algorithms, achieving equal sensitivity and a specificity of 90.1%, AUC of 96.4%, and AUCPR of 67.5%. All algorithms found recent self-harm to be the most important indicator of adolescent suicide attempt. Exploratory factor analysis suggested five additional risk domains, which we labeled internalizing problems, sleep disturbance, disordered eating, lack of optimism regarding future education and career, and victimization. The identified factors provided stronger support for The Interpersonal Theory of Suicide than for The Strain Theory of Suicide. An enhancement to The Interpersonal Theory based on the risk and protective factors identified by holistic item analysis is presented.

The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study.

BackgroundNetwork analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures.MethodsA retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges.ResultsRelative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes.ConclusionsMultimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12874-022-01607-8.

Should COVID-19 symptoms be used to cohort patients in the emergency department? A retrospective analysis

ObjectiveTo determine how cohorting patients based on presenting complaints affects risk of nosocomial infection in crowded Emergency Departments (EDs) under conditions of high and low prevalence of COVID-19. MethodsThis was a retrospective analysis of presenting complaints and PCR tests collected during the COVID-19 epidemic from 4 EDs from a large hospital system in Bronx County, NY, from May 1, 2020 to April 30, 2021. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were calculated for a symptom screen based on the CDC list of COVID-19 symptoms: fever/chills, shortness of breath/dyspnea, cough, muscle or body ache, fatigue, headache, loss of taste or smell, sore throat, nasal congestion/runny nose, nausea, vomiting, and diarrhea. PPV was calculated for varying values of prevalence. ResultsThere were 80,078 visits with PCR tests. The sensitivity of the symptom screen was 64.7% (95% CI: 63.6, 65.8), specificity 65.4% (65.1, 65.8). PPV was 16.8% (16.5, 17.0) and NPV was 94.5% (94.4, 94.7) when the observed prevalence of COVID-19 in the ED over the year was 9.7%. The PPV of fever/chills, cough, body and muscle aches and nasal congestion/runny nose were each approximately 25% across the year, while diarrhea, nausea, vomiting and headache were less predictive, (PPV 4.7%–9.6%) The combinations of fever/chills, cough, muscle/body aches, and shortness of breath had PPVs of 40–50%. The PPV of the screen varied from 3.7% (3.6, 3.8) at 2% prevalence of COVID-19 to 44.3% (44.0, 44.7) at 30% prevalence. ConclusionThe proportion of patients with a chief complaint of COVID-19 symptoms and confirmed COVID-19 infection was exceeded by the proportion without actual infection. This was true when prevalence in the ED was as high as 30%. Cohorting of patients based on the CDC's list of COVID-19 symptoms will expose many patients who do not have COVID-19 to risk of nosocomially acquired COVID-19. EDs should not use the CDC list of COVID-19 symptoms as the only strategy to minimize exposure.

1409. Pulmonary Non-tuberculous Mycobacterium Infection (PNTMI) and COVID-19: Characterization of the National COVID Collaborative Cohort (N3C)

BackgroundEstablishing whether a low-prevalence clinical condition is a risk factor for COVID-19 infection, or serious adverse outcomes, is difficult due to a limited number of patients, and lack of access to patient’s data by researchers. The National COVID Collaborative Cohort (N3C), a centralized national data resource to study COVID-19, provides access to structured clinical data derived from electronic health records. As of June 2021, N3C contains data on 6,193,738 patients (2,090,138 with COVID-19, 33.7%) from 55 participating sites (Figure 1). We describe the characteristics of patients with PNTMI based on COVID-19 infection status.Figure 1 N3C Basic Demographic DataMethodsCOVID-19 is defined by positive lab result (PCR, antigen, or antibody) or COVID-19 coding diagnosis, as defined by N3C. PNTMI phenotype was built with N3C Data Enclave concept set tool, and ATLAS (https://atlas.ohdsi.org/). We limited analysis to adults (18 years-old or older). We used de-identified data sets stripped of protected health information (PHI). We used N3C Data Enclave analytical tools for exploratory data analysis, and descriptive statistics.ResultsWe identified five hundred and eighty six individuals from 19 sites fulfilling the PNTMI phenotype (9.46 cases per 100,000 people). After our age limit, 555 individuals were included for analysis (Figure 2). 340 were females (61.3%), 447 of white race (80.5%), and 30 were Hispanic (5.4%). Additional descriptive statistics and statistical significance testing are provided (Table 1). The most common concept were "Non-tuberculous mycobacterial pneumonia", and "Pulmonary Mycobacterium avium complex infection". Four sites accounted for more than 50% of identified patients (Figure 2). We identified 24 individuals with COVID-19 (4.32%), and 44 deaths in this cohort (7.9%). Deaths were unrelated to COVID-19 event. Figure 2. Basic demographic data of pulmonary non-tuberculous Mycobacterium infection phenotype in N3C Figure 3. Concepts and data sources of pulmonary non-tuberculous Mycobacterium infection phenotype in N3C ConclusionIn N3C, the PNTMI cohort has a lower proportion of COVID-19 infection than the general population, and it was not a cause of mortality. Further analysis to study impact of comorbidities, and differences in race and geographical location are warranted. N3C is a powerful research platform to study the impact of COVID-19 in special populations with low prevalence, and it can be used to study other populations of interest.Disclosures All Authors: No reported disclosures

Psychiatric comorbidity in the Baltimore ECA follow-up study: the matrix approach.

Psychiatric comorbidity is defined as the joint occurrence of two or more mental or substance use disorders. Widespread psychiatric comorbidity has been reported in treatment and population-based studies. The aim of this study was to measure the extent and impact of psychiatric comorbidity in a cohort of the Baltimore Epidemiologic Catchment Area study. We examined the comorbidity burden of 16 mental disorders in a cohort of 847 participants using both established and novel analytical approaches The Comorbidity to Diagnosis Inflation Ratio (CDIR), is a statistical instrument that quantifies impact of pairwise comorbid associations, both on the whole sample, as well as on each specific disorder. Most anxiety disorders had substantial co-occurrence with each other, as well as with Major Depressive Disorder (MDD). In addition, mood disorders had a high degree of comorbidity with Alcohol Dependence (AD). The CDIR for the whole sample was 1.32, indicating a ratio of 132 comorbidities per 100 diagnoses. The conditions with high sample prevalence were relatively less comorbid than the low prevalence conditions. Obsessive Compulsive Disorder had a comorbidity burden that was 89% greater than the overall sample. Anxiety disorders are highly interrelated, as well as highly comorbid with depression. The comorbidity phenomenon is linked to the differential prevalence of the analyzed conditions. Comorbidity frequency (most prevalent comorbid condition) appears mutually exclusive to comorbidity burden (most widely interrelated condition). While AD and MDD were the most frequently diagnosed disorders; low prevalence conditions as OCD and GAD were the most widely interrelated.

Low prevalence match and mismatch detection in simultaneous face matching: Influence of face recognition ability and feature focus guidance.

Simultaneous face matching to verify identity is key to security and policing. However, matching is error-prone, particularly when target-item prevalence is low. Two experiments examined whether superior face recognition ability and the use of internal or external facial feature guidance scales would reduce low prevalence effects. In Experiment 1, super-recognisers (n = 317) significantly outperformed typical-ability controls (n = 452), while internal feature guidance enhanced accuracy across all prevalence conditions. However, an unexpected effect in controls revealed higher accuracy in low prevalence conditions, probably because no low-match or low-mismatch prevalence information was provided. In Experiment 2, top-end-of-typical range ability participants (n = 841) were informed of their low prevalence condition and demonstrated the expected low-prevalence effects. Findings and implications are discussed.

Introduction to Endo-ERN\u2013scope and mission

The official installation of the European Reference Networks in 2017 formed the foundation to improve quality and safety and access to highly specialized health care across the EU for patients affected by rare or low prevalence and complex conditions. The European Reference Network on Rare Endocrine Conditions (Endo-ERN) covers specific expertise from birth to senescence with a specific governance structure characterized by both a pediatric and an adult chair, and equal responsibilities for patient representatives and health care providers. The introduction on the scope and mission of Endo describes the complexity of the Endo-ERN mission and will thrive toward the ultimate aim and mission of the network of reducing health care inequalities across Europe. Specific knowledge and medical expertise of the existing rare endocrine condition is urgently needed, and therefore, raising awareness for Rare Disease Day from the Endo-ERN perspective is imperative.

Exaggerated prevalence effect with the explicit prevalence information: The description-experience gap in visual search.

Despite the increasing focus on target prevalence in visual search research, few papers have thoroughly examined the effect of how target prevalence is communicated. Findings in the judgment and decision-making literature have demonstrated that people behave differently depending on whether probabilistic information is made explicit or learned through experience, hence there is potential for a similar difference when communicating prevalence in visual search. Our current research examined how visual search changes depending on whether the target prevalence information was explicitly given to observers or they learned the prevalence through experience with additional manipulations of target reward and salience. We found that when the target prevalence was low, learning prevalence from experience resulted in more target-present responses and longer search times before quitting compared to when observers were explicitly informed of the target probability. The discrepancy narrowed with increased prevalence and reversed in the high target prevalence condition. Eye-tracking results indicated that search with experience consistently resulted in longer fixation durations, with the largest difference in low-prevalence conditions. Longer search time was primarily due to observers re-visited more items. Our work addressed the importance of exploring influences brought by probability communication in future prevalence visual search studies.

Vaping in the News: The Influence of News Exposure on Perceived e-Cigarette Use Norms

ABSTRACTBackground: Research has documented the impact of descriptive norms on tobacco use, but few studies have investigated how media exposure shapes e-cigarette use norms.Purpose: The purpose of this study was to examine how exposure to e-cigarette-related news articles shapes individuals’ descriptive norm perceptions about real-world e-cigarette use.Method: The study implemented an experiment with a 2 normative direction (high vs low prevalence) × 2 exposure dosage (single vs double dose) between-subjects factorial design (N = 298). Analysis of variance and thematic analysis were conducted.Results: Normative direction and exposure dosage of prevalence information contained in the news articles interacted to influence perceived descriptive norms. Increasing the dosage of prevalence information enhanced descriptive norm perceptions in low-prevalence conditions only. Participants relied on institutional signals and behavioral cues to infer descriptive norms when prevalence information was absent in the news.Discussion: The study investigates the underlying mechanism of how news articles may influence normative perceptions.Translation to Health Educational Practice: Given that news media may inadvertently form social norms that are conducive to e-cigarette use behavior, Health Educators should pay attention to descriptive norms emanated from the news media environment. They should also consider norm debiasing strategies and the integration of dosage of low-prevalence information into social norm messages.

When visual search target is rare: overweighting or under weighting the probability?

The prevalence of items in visual search may have substantial performance consequences. In laboratory visual search tasks in which the target is rare, viewers are likely to miss the target. A dual-threshold model proposed by Wolfe and Van Wert (2010) assumes that in the low prevalence condition, viewers shift their criteria resulting in more miss errors. However, from the prospective of prospect theory (Kahneman &amp; Tversky, 1979), decision makers tend to overweight small probability. To explore how viewers subjectively weight the probability in the low prevalence visual search task, we compared viewers’ criteria with the optimal criteria by presenting different probability descriptions for a fixed prevalence rate. The data from this experiment indicated that target presence had an effect on viewers’ accuracy and response times but not probability descriptions. Viewers’ criteria under different probability descriptions were higher than optimal. These results are in accordance with the dual-threshold model assumption that viewers respond “target absent” more frequently than optimal, leading to more miss errors in the low prevalence condition.

The persistent low-prevalence effect in unfamiliar face-matching: The roles of feedback and criterion shifting.

In visual search, relatively infrequent targets are more likely to be "missed," a phenomenon known as the low-prevalence effect (LPE). Across five experiments, we examined the LPE in unfamiliar face matching, focusing on the roles of feedback and criterion shifting. Across experiments, observers made identity match/mismatch decisions to photograph pairs, and we manipulated target (i.e., identity mismatch) prevalence. Experiment 1 established the necessity of feedback for the LPE; observers were only sensitive to prevalence disparities when provided trial-level feedback. In Experiment 2, target prevalence affected decision criteria, without concomitant effects on perceptual sensitivity. In Experiments 3 through 5, we adopted a "retraining" paradigm, in which observers encountered blocks of high-prevalence targets midway through four 50-trial face-matching quartiles. High-prevalence targets were visually obvious (Experiment 3) or less obvious (Experiments 4 and 5). Whereas observers in equal-prevalence conditions remained unbiased throughout the experiments, those in low-prevalence conditions adopted conservative criteria by the second quartile. This criterion shift was largely resistant to "unbiasing" efforts. Only Experiment 5, which used an 18-item retraining block, revealed a successful (albeit slight) third-quartile liberal criterion shift, but observers were strongly conservative again by the fourth quartile. We discuss the applied and theoretical consequences of these results. (PsycINFO Database Record

The digital epidemiology of dysencephalia splanchnocystica, AKA meckel–gruber syndrome: Retrospective analysis and geographic mapping via google trends

Background: Genetic diseases are diverse and many of which have debilitating consequences affecting the individual, the society, and the economy. Trends databases, including Google Trends database, can be used to estimate the digital epidemiology of these diseases. Digital Epidemiology is valuable when it comes to conditions of low prevalence as in the case of ciliopathies including that of Meckel–Gruber Syndrome.Aims and Objectives: To assess the digital epidemiology and the geographic mapping of Meckel- Gruber syndrome via a trends database of the surface web.Materials and Methods: Google Trends database will be usedfor geographic mapping and retrospective analysis of interest of users of the Surface Web. The aim is to infer and predict the digital epidemiology of Meckel–Gruber Syndrome. A retrospective analysis is conducted as far as the trends database permits (2004-2017). The trends database was explored using the thematic expression of keywords specific to Meckel–Gruber Syndrome including its synonyms. Subsequently, descriptive and inferential statistics were carried out to estimate the digital epidemiology as well as the geographic mapping. The aim was to conclude the existence of any significant change in web users’ interest and the variation of that interest versus geography (country) and chronology (time).Results: Concerning geographic mapping, signals of web users were found to be originating from the United States (68.49%) and Finland (31.51%). Globally, the average value of the relative interest of surface web users in Meckel–Gruber Syndrome was 34.10 (+/- 14.59). There was an overall decline in web users’ attention towards the condition for the period 2004−2010 versus 2011−2017 (20.06 vs 4.88, p-value&lt;0.001) and for period 2004-2006 versus 2007-2009 (29.14 vs 14.19, p=0.001).Conclusion: Digital epidemiological analysis has been proven feasible with good accuracy via Googles Trends. In the case of Dysencephalia Splanchnocystica, the geographic mapping of the surface web has been limited to the developed world. Prospectively, Google Trends can be integrated into a predictive early warning system to anticipate any change in the interest of the users of the Indexed Web in a particular disease including genetic ones.Asian Journal of Medical Sciences Vol.9(5) 2018 81-86

SimPEL: Simulation-based power estimation for sequencing studies of low-prevalence conditions.

Power estimations are important for optimizing genotype-phenotype association study designs. However, existing frameworks are designed for common disorders, and thus ill-suited for the inherent challenges of studies for low-prevalence conditions such as rare diseases and infrequent adverse drug reactions. These challenges include small sample sizes and the need to leverage genetic annotation resources in association analyses for the purpose of ranking potential causal genes. We present SimPEL, a simulation-based program providing power estimations for the design of low-prevalence condition studies. SimPEL integrates the usage of gene annotation resources for association analyses. Customizable parameters, including the penetrance of the putative causal allele and the employed pathogenic scoring system, allow SimPEL to realistically model a large range of study designs. To demonstrate the effects of various parameters on power, we estimated the power of several simulated designs using SimPEL and captured power trends in agreement with observations from current literature on low-frequency condition studies. SimPEL, as a tool, provides researchers studying low-frequency conditions with an intuitive and highly flexible avenue for statistical power estimation. The platform-independent "batteries included" executable and default input files are available at https://github.com/precisionomics/SimPEL.

Low prevalence search for cancers in mammograms: Evidence using laboratory experiments and computer aided detection.

People miss a large proportion of targets when they only appear rarely. This Low Prevalence (LP) Effect could lead to serious consequences if it occurred in the real-world task of searching for cancers in mammograms. Using a novel mammogram search task, we asked participants to search for a prespecified cancer (Experiments 1-2) or a range of masses (Experiments 3-5) under high or low prevalence conditions. Experiment 1 showed that an LP Effect occurred using these stimuli. Experiment 2 tested an overreliance hypothesis and showed that the use of Computer Aided Detection (CAD) led to fewer missed cancers with a valid CAD prompt yet, a large proportion of cancers were missed when CAD was incorrect. Experiment 3-5 showed that false alarms also increased when searching for a range of masses and that CAD reduced miss errors when it correctly cued the target but increased miss errors and false alarms when it did not. Furthermore, when a mass fell outside the CAD prompt it was more likely to be misidentified. No LP Effect was observed with the addition of CAD when people were asked to search for a range of targets. Theories and implications for mammogram search are discussed. (PsycINFO Database Record

Use of medical administrative data for the surveillance of psychotic disorders in France

BackgroundPsychotic disorders are among the most severe psychiatric disorders that have great effects on the individuals and the society. For surveillance of chronic low prevalence conditions such as psychotic disorders, medical administrative databases can be useful due to their large coverage of the population, their continuous availability and low costs with possibility of linkage between different databases. The aims of this study are to identify the population with psychotic disorders by different algorithms based on the French medical administrative data and examine the prevalence and characteristics of this population in 2014.MethodsThe health insurance system covers the entire population living in France and all reimbursements of ambulatory care in private practice are included in a national health insurance claim database, which can be linked with the national hospital discharge databases. Three algorithms were used to select most appropriately persons with psychotic disorders through data from hospital discharge databases, reimbursements for psychotropic medication and full insurance coverage for chronic and costly conditions.ResultsIn France in 2014, estimates of the number of individuals with psychotic disorders were 469,587 (54.6% males) including 237,808 with schizophrenia (63.6% males). Of those, 77.0% with psychotic disorders and 70.8% with schizophrenia received exclusively ambulatory care. Prevalence rates of psychotic disorders were 7.4 per 1000 inhabitants (8.3 in males and 6.4 in females) and 3.8 per 1000 inhabitants (4.9 in males and 2.6 in females) for schizophrenia. Prevalence of psychotic disorders reached a maximum of 14 per 1000 in males between 35 and 49 years old then decreased with age while in females, the highest rate of 10 per 1000 was reached at age 50 without decrease with advancing age. No such plateau was observed in schizophrenia.DiscussionThis study is the first in France using an exhaustive sample of medical administrative data to derive prevalence rates for psychotic disorders. Although only individuals in contact with healthcare services were included, the rates were congruent with reported estimates from systematic reviews. The feasibility of this study will allow the implementation of a national surveillance of psychotic disorders essential for healthcare management and policy planning.

Tuberculosis detection by pouched rats: Opportunities for reinforcement under low-prevalence conditions

Giant African pouched rats (Cricetomys ansorgei) have been employed successfully in two operational tuberculosis-detection projects in which they sniff sputum samples from symptomatic individuals who have visited tuberculosis clinics. The prevalence of pulmonary tuberculosis in this population is high, approximately 20% in the regions where the rats have been used. If the rats are to be used to screen individuals from lower-prevalence populations, their performance under such conditions must first be evaluated. In this study, the prevalence of tuberculosis-positive samples presented to eight pouched rats was reduced to approximately 5%, and the percentage of known-positive samples included as opportunities for reinforcement was varied in sequence from 10 to 8, 6, 4, 2, 4, and 2. Liquid food reinforcers were delivered for identification responses to known-positive samples and at no other time. The rats’ accuracy was clinically and statistically significantly lower at 2% than at the other values. These results indicate that the rats can perform well in low-prevalence scenarios but, if they are used under the conditions of the present study, at least 4% of the samples presented to them must be opportunities for reinforcement.