The Conclusion Generator.

PURPOSE: Reliance on null hypothesis significance testing often leads to misinterpretation of research results. Common misinterpretations include that a statistically nonsignificant difference (p ≥ 0.05) implies no difference between groups, and that a statistically significant finding (p < 0.05) is unbiased and clinically important. We aimed to develop a tool - the Conclusion Generator - to mitigate these misconceptions. METHODS: We reviewed the content of the Conclusion Generator and validated its output using published and simulated data. RESULTS: The Conclusion Generator is a free online application designed to generate conclusions for scientific papers based on the values and clinical interpretation of the point estimate and confidence interval. Both relative and absolute measures of effect are supported. It offers two modes for interpretation: (1) Statistical mode provides an accurate statistical interpretation of results, with an optional specification of superiority and noninferiority bounds; (2) Clinical mode evaluates the clinical importance of the point estimate and confidence limits as specified by the user. Both modes assume no uncontrolled biases. Users must specify the number of decimals, the direction of a beneficial effect (e.g., relative risk <1 vs. >1), and the level of detail (concise vs. elaborated) for the output. The validation confirmed the Conclusion Generator's capability to interpret research results, considering random error and clinical relevance, while avoiding common misinterpretations associated with null hypothesis significance testing. CONCLUSIONS: The Conclusion Generator facilitates an appropriate interpretation of research results by emphasizing estimation and clinical relevance over hypothesis testing.

A comprehensive assessment of quality management methods in the SMESH study.

BACKGROUND: This paper aims to instigate discussion and publication of methodologies applied to enhance quality management through comprehensive scientific reports. It provides a detailed description of the design, implementation, and results of the quality control program employed in the SMESH study. METHODS: Cross-sectional, multicenter, national study designed to assess the prevalence of human papillomavirus in sex workers and in men who have sex with men (MSM). Respondent-driven sampling recruitment was used. An online system was developed for the study and checkpoints were defined for data entry. The system checked the quality of biological samples and performed a retest with part of the sample. RESULTS: A total of 1.598 participants (442 sex workers and 1.156 MSM) were included. Fifty-four health professionals were trained for face-to-face data collection. The retest showed Kappa values ranging between 0.3030 and 0.7663. CONCLUSION: The retest data were mostly classified as indicating a strong association. The data generated by the checkpoints showed the successful implementation of the quality control program.

Using a novel 'difference-in-differences' method and syndromic surveillance to estimate the change in local healthcare utilisation during periods of media reporting in the early stages of the COVID-19 pandemic in England.

OBJECTIVES: Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using 'difference-in-differences' (DiD). STUDY DESIGN: A retrospective observational study was conducted using five media events in January-February 2020 in England on four routinely monitored syndromic surveillance indicators. METHODS: Dates 'exposed' to a media event were estimated using Google Trends internet search intensity data (terms = 'coronavirus' and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect. RESULTS: We estimated a four-fold increase in telehealth 'cough' calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called 'superspreading event' in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively. CONCLUSIONS: We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.

Study of Health in Pomerode (SHIP-Brazil): aims, methodological issues and descriptive results / Estudo Vida e Saúde em Pomerode (SHIP-Brazil): objetivos, aspectos metodológicos e resultados descritivos

Background Few studies compared populations with similar genetic and culture background on different continents with standardized methods. Objective To describe methodological issues of the Study of Health in Pomerode - SHIP-Brazil and some characteristics of the participants of the baseline examination. Design and Setting Prospective, population-based cohort study of a representative sample of residents (aged 20 to 79 years) of Pomerode, Santa Catarina, Brazil. Methods Data for the baseline survey (from 2014 to 2018) were collected through interviews and medical examinations, including socio-demographic and lifestyle information, clinical and subclinical conditions, oral and mental health, among others. Biosamples (blood, urine, stool, and saliva) were collected and stored. Methods of data collection and quality control are described. Preliminary descriptive statistics were performed. Results The response rate was 67.6% (n=2,488 individuals). The Kappa test-retest of some variables varied from 0.54 to 1.0. German culture participants are older (46.5 vs 38.7 years), self-declared white (97.3% vs 82.1%), more frequently never smokers (71.4% vs 66.9%) but had higher risk of consuming alcohol (16.9% vs 13.4%) compared to participants with non-German background. Germans were taller (169 cm vs 166 cm), had greater abdominal circumference among men (101.9 cm vs 97.3 cm). Furthermore, they reported more multimorbidity (56.7% vs 43.6%) , had more arterial hypertension (30.7% vs 18.5%), but less depression (15.4% vs 19,1%) than non-Germans. Conclusions The interaction of genetic and social/environmental issues should be examined to understand the role of risk factors on clinical conditions observed.

Introdução Poucos estudos compararam populações com histórico genético e cultural semelhante em diferentes continentes com métodos padronizados. Objetivos Descrever questões metodológicas do estudo de "Vida e Saúde em Pomerode - SHIP-Brazil" e algumas características dos participantes do exame inicial do estudo. Desenho de estudo e local Estudo de coorte prospectivo de base populacional em amostra representativa de moradores (20 a 79 anos) de Pomerode, Santa Catarina. Métodos As informações para a linha de base (de 2014 a 2018) foram coletadas por meio de entrevistas e exames médicos, incluindo dados sociodemográficos, de estilo de vida, condições clínicas e subclínicas, saúde bucal e mental, entre outros. Amostras biológicas (sangue, urina, fezes e saliva) foram coletadas e armazenadas. A coleta de dados e o controle de qualidade foram descritos. Foram realizadas análises descritivas preliminares. Resultados A taxa de resposta foi de 67,6% (n=2.488 indivíduos). O Kappa teste-reteste de algumas variáveis variou  de 0,54 a 1,0. Os participantes de cultura alemã são mais velhos (46,5 vs 38,7 anos ), autodeclarados brancos (97,3% vs 82,1%), com menor número de fumantes (71,4% vs 66,9%), mas tiveram maior risco de consumir álcool (16,9% vs 13,4%), eram mais altos (169 cm vs 166 cm), tinham maior circunferência abdominal entre os homens (101,9 cm vs 97,3 cm) em comparação com participantes "não-alemães". Pessoas de cultura alemã relataram mais multimorbidade (56,7% vs 43,6%), apresentavam mais hipertensão arterial (30,7% vs 18,5%), mas menos depressão (15,4% vs 19,1%). Conclusões A interação genética e social/ambiental devem ser examinadas para melhor entender o papel desses fatores de risco nas condições clínicas observadas.

[Germina: tool for automatic detection of clusters of COVID-19 cases]. / Germina: herramienta para la detección automática de agrupaciones de casos de COVID-19.

The COVID-19 pandemic showed that epidemiological surveillance was under-resourced to respond to increases in cases and outbreaks. The high community transmissibility among the school population in the city of Barcelona at the beginning of the sixth wave strained the local COVID-19 surveillance unit. Using SCRUM methodology, Germina was developed and implemented as a software tool capable of capturing, harmonizing, integrating, storing, analysing and visualizing data from multiple information sources on a daily basis. Germina identifies clusters of three or more school cases and calculates epidemiological indicators. The implementation of Germina facilitated the epidemiological response to the sixth wave of COVID-19 in the school setting in the city of Barcelona. This tool is transferable to other exposure settings and communicable diseases. The use of automated informatics tools such, as Germina, improves epidemiological surveillance systems and supports evidence-based decision making.

Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022.

Analysis of wastewater is used in many settings for surveillance of SARS-CoV-2, but it remains unclear how well wastewater testing results reflect incidence. Denmark has had an extensive wastewater analysis system that conducts 3 weekly tests in ≈200 sites and has 85% population coverage; the country also offers free SARS-CoV-2 PCR tests to all residents. Using time series analysis for modeling, we found that wastewater data, combined with information on circulating variants and the number of human tests performed, closely fitted the incidence curve of persons testing positive. The results were consistent at a regional level and among a subpopulation of frequently tested healthcare personnel. We used wastewater analysis data to estimate incidence after testing was reduced to a minimum after March 2022. These results imply that data from a large-scale wastewater surveillance system can serve as a good proxy for COVID-19 incidence and for epidemic control.

Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared.

During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.

COVID-19 severity scale for claims data research.

OBJECTIVE: To create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data. DATA SOURCE: Secondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020. STUDY DESIGN: The World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality. DATA COLLECTION/EXTRACTION METHODS: The strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC). PRINCIPAL FINDINGS: A total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p < 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19. CONCLUSION: A standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19.

Assessing Mbiotisho: A smartphone application used to collect high-frequency health and nutrition data from difficult-to-reach populations.

There is an urgent need for improved and timely health and nutrition data. We developed and tested a smartphone application that caregivers from a pastoral population used to measure, record and submit high-frequency and longitudinal health and nutrition information on themselves and their children. The data were assessed by comparing caregiver-submitted measurements of mid-upper arm circumference (MUAC) to several benchmark data sets, including data collected by community health volunteers from the participating caregivers during the project period and data generated by interpreting photographs of MUAC measurements submitted by all participants. We found that the caregivers participated frequently and consistently over the 12-month period of the project; most of them made several measurements and submissions in at least 48 of the 52 weeks of the project. The evaluation of data quality was sensitive to which data set was used as the benchmark, but the results indicate that the errors in the caregivers' submissions were similar to that of enumerators in other studies. We then compare the costs of this alternative approach to data collection through more conventional methods, concluding that conventional methods can be more cost-effective for large socioeconomic surveys that value the breadth of the survey over the frequency of data, while the alternative we tested is favoured for those with objectives that are better met by high-frequency observations of a smaller number of well-defined outcomes.

Response to Tsuda et al. "demonstrating the undermining of science and health policy after the Fukushima nuclear accident by applying the toolkit for detecting misused epidemiological methods".

BACKGROUND: The SHAMISEN (Nuclear Emergency Situations - Improvement of Medical And Health Surveillance) European project was conducted in 2015-2017 to review the lessons learned from the experience of past nuclear accidents and develop recommendations for preparedness and health surveillance of populations affected by a nuclear accident. Using a toolkit approach, Tsuda et al. recently published a critical review of the article by Cléro et al. derived from the SHAMISEN project on thyroid cancer screening after nuclear accident. MAIN BODY: We address the main points of criticism of our publication on the SHAMISEN European project. CONCLUSION: We disagree with some of the arguments and criticisms mentioned by Tsuda et al. We continue to support the conclusions and recommendations of the SHAMISEN consortium, including the recommendation not to launch a mass thyroid cancer screening after a nuclear accident, but rather to make it available (with appropriate information counselling) to those who request it.

Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.

BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type. METHODS: Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors. RESULTS: For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review. CONCLUSIONS: The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

Why and how we can use data linkage in oral health research: a narrative review.

OBJECTIVES: Poor oral health, impacting health and wellbeing across the life-course, is a costly and wicked problem. Data (or record) linkage is the linking of different sets of data (often administrative data gathered for non-research purposes) that are matched to an individual and may include records such as medical data, housing information and sociodemographic information. It often uses population-level data or 'big data'. Data linkage provides the opportunity to analyse complex associations from different sources for total populations. The aim of the paper is to explore data linkage, how it is important for oral health research and what promise it holds for the future. METHODS: This is a narrative review of an approach (data linkage) in oral health research. RESULTS: Data linkage may be a powerful method for bringing together various population datasets. It has been used to explore a wide variety of topics with many varied datasets. It has substantial current and potential application in oral health research. CONCLUSIONS: Use of population data linkage is increasing in oral health research where the approach has been very useful in exploring the complexity of oral health. It offers promise for exploring many new areas in the field.

The Epidemiology of Moderate and Severe Traumatic Brain Injury in Central Norway.

INTRODUCTION: Few studies account for prehospital deaths when estimating incidence and mortality rates of moderate and severe traumatic brain injury (msTBI). In a population-based study, covering both urban and rural areas, including also prehospital deaths, the aim was to estimate incidence and mortality rates of msTBI. Further, we studied the 30-day and 6-month case-fatality proportion of severe TBI in relation to age. METHODS: All patients aged ≥17 years who sustained an msTBI in Central Norway were identified by three sources: (1) the regional trauma center, (2) the general hospitals, and (3) the Norwegian Cause of Death Registry. Incidence and mortality rates were standardized according to the World Health Organization's world standard population. Case-fatality proportions were calculated by the number of deaths from severe TBI at 30 days and 6 months, divided by all patients with severe TBI. RESULTS: The overall incidence rates of moderate and severe TBI were 4.9 and 6.7 per 100,000 person-years, respectively, increasing from age 70 years. The overall mortality rate was 3.4 per 100,000 person-years, also increasing from age 70 years. Incidence and mortality rates were highest in men. The case-fatality proportion in people with severe TBI was 49% in people aged 60-69 years and 81% in people aged 70-79 years. CONCLUSION: The overall incidence and mortality rates for msTBI in Central Norway were low but increased from age 70 years, and among those ≥80 years of age with severe TBI, nearly all died. Overall estimates are strongly influenced by high incidence and mortality rates in the elderly, and studies should therefore report age-specific estimates, for better comparison of incidence and mortality rates.

Germina: herramienta para la detección automática de agrupaciones de casos de COVID-19 / Germina: tool for automatic detection of clusters of COVID-19 cases

La pandemia de COVID-19 evidenció que la vigilancia epidemiológica no disponía de recursos para responder a los aumentos de casos ni a los brotes. La alta transmisibilidad comunitaria entre la población escolar en la ciudad de Barcelona al inicio de la sexta ola tensionó la unidad de vigilancia de COVID-19 de la ciudad. Mediante metodología SCRUM se desarrolló e implementó Germina, una herramienta informática capaz de capturar, armonizar, integrar, almacenar, analizar y visualizar diariamente datos de múltiples fuentes de información. Germina permite identificar agrupaciones de tres o más casos escolares y calcular indicadores epidemiológicos. La implementación de Germina facilitó la respuesta epidemiológica a la sexta ola de COVID-19 en el ámbito escolar en Barcelona. Esta herramienta es aplicable a otros ámbitos de exposición y a otras enfermedades transmisibles. El uso de herramientas informáticas automatizadas, como Germina, mejora los sistemas de vigilancia epidemiológica y apoya la toma de decisiones basada en la evidencia.(AU)

The COVID-19 pandemic showed that epidemiological surveillance was under-resourced to respond to increases in cases and outbreaks. The high community transmissibility among the school population in the city of Barcelona at the beginning of the sixth wave strained the local COVID-19 surveillance unit. Using SCRUM methodology, Germina was developed and implemented as a software tool capable of capturing, harmonizing, integrating, storing, analysing and visualizing data from multiple information sources on a daily basis. Germina identifies clusters of three or more school cases and calculates epidemiological indicators. The implementation of Germina facilitated the epidemiological response to the sixth wave of COVID-19 in the school setting in the city of Barcelona. This tool is transferable to other exposure settings and communicable diseases. The use of automated informatics tools such, as Germina, improves epidemiological surveillance systems and supports evidence-based decision making.(AU)

Using trajectory modeling of spatio-temporal trends to illustrate disparities in COVID-19 death in flint and Genesee County, Michigan.

COVID-19's rapid onset left many public health entities scrambling. But establishing community-academic partnerships to digest data and create advocacy steps offers an opportunity to link research to action. Here we document disparities in COVID-19 death uncovered during a collaboration between a health department and university research center. We geocoded COVID-19 deaths in Genesee County, Michigan, to model clusters during two waves in spring and fall 2020. We then aggregated these deaths to census block groups, where group-based trajectory modeling identified latent patterns of change and continuity. Linking with socioeconomic data, we identified the most affected communities. We discovered a geographic and racial gap in COVID-19 deaths during the first wave, largely eliminated during the second. Our partnership generated added and immediate value for community partners, including around prevention, testing, treatment, and vaccination. Our identification of the aforementioned racial disparity helped our community nearly eliminate disparities during the second wave.

Validity of Algorithm for Classification of In- and Outpatient Hospital Contacts in the Danish National Patient Registry.

Purpose: Following the implementation of the 3rd version of the Danish National Patient Register (DNPR-3), information on whether hospitalizations were inpatient, outpatient, or emergency room (ER) contacts was no longer readily available. This study examined the positive predictive values (PPV) of a common algorithm to characterize hospitalizations as inpatient, outpatient, or emergency room (ER) contacts in both DNPR-2 and DNPR-3. Patients and Methods: All hospital contacts in North Denmark Region were identified in the DNPR within a 1-year window of the implementation of DNPR-3 in early 2019. An algorithm based upon proportion of overnight (±50%) and elective (±50%) contacts for each hospital department was developed. Next, PPVs of these categorizations were computed using manual characterization of all departments and clinics by two experienced clinicians as reference. Second, the reliability of various time intervals to join department contacts and subsequent categorization of overnight hospital stays as proxies for inpatient contacts was explored. Results: The algorithm yielded PPVs of 91% and 89% for hospital units and related contacts categorized as inpatient in DNPR-2 and 100% for both parameters in DNPR-3. In outpatient units, the PPVs were 99% in both DNPR-2 and DNPR-3, whereas the corresponding PPVs were 99.6% and 99% on the contact level. In contrast, the PPV for ERs was 33% in DNPR-2 and 56% in DNPR-3, primarily due to misclassification of outpatient clinics. Still, the proportion of correctly categorized ER contacts was 87% in DNPR-2 and 85% in DNPR-3. Using time intervals from 0 to 12 hours to join department contacts showed that overnight hospitalizations comprised inpatient contacts in 97% in DNPR-2 and 98% in DNPR-3. However, the sensitivity was moderate at 76-78% for all inpatient hospitalizations in DNPR-2 and DNPR-3. Conclusion: This algorithm accurately categorized hospitalizations as inpatient, outpatient, or ER contacts in both DNPR-2 and DNPR-3.

Modelling multiple time-scales with flexible parametric survival models.

BACKGROUND: There are situations when we need to model multiple time-scales in survival analysis. A usual approach in this setting would involve fitting Cox or Poisson models to a time-split dataset. However, this leads to large datasets and can be computationally intensive when model fitting, especially if interest lies in displaying how the estimated hazard rate or survival change along multiple time-scales continuously. METHODS: We propose to use flexible parametric survival models on the log hazard scale as an alternative method when modelling data with multiple time-scales. By choosing one of the time-scales as reference, and rewriting other time-scales as a function of this reference time-scale, users can avoid time-splitting of the data. RESULT: Through case-studies we demonstrate the usefulness of this method and provide examples of graphical representations of estimated hazard rates and survival proportions. The model gives nearly identical results to using a Poisson model, without requiring time-splitting. CONCLUSION: Flexible parametric survival models are a powerful tool for modelling multiple time-scales. This method does not require splitting the data into small time-intervals, and therefore saves time, helps avoid technological limitations and reduces room for error.