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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. [ABSTRACT FROM AUTHOR]

Objective: A new method known as 'current-day dietary recall' (current-day recall) is based on an application for mobile phones called 'electronic 12 h dietary recall' (e-12HR). This new method was designed to rank participants into categories of habitual intake regarding a series of key food groups. The present study compared current-day recall against a previously validated short paper FFQ.Design: Participants recorded the consumption of selected food groups using e-12HR during twenty-eight consecutive days and then filled out a short paper FFQ at the end of the study period. To evaluate the association and agreement between both methods, Spearman's correlation coefficients (SCC), cross-classification analysis and weighted kappa statistics (κ w) were used.Setting: Andalusia, Spain, Southern Europe.Subjects: University students and employees over the age of 18 years.Results: One hundred and eighty-seven participants completed the study (64·2 % female, 35·8 % male). For all particpants, for all food group intakes, the mean SCC was 0·70 (SCC≥0·62 were observed for all strata); the mean percentage of participants cross-classified into categories of 'exact agreement+adjacent' was 90·1 % (percentages≥87·8 % were observed for all strata); and the mean κ w was 0·55 (κ w≥0·53 in ten of the twelve strata).Conclusions: For the whole sample and for all strata thereof, the current-day recall has good agreement with the previously validated short paper FFQ for assessing food group intakes, rendering it a useful method for ranking individuals. [ABSTRACT FROM AUTHOR]

Most epidemiological studies examine how risk factors relate to average difference in outcomes (linear regression) or odds of a binary outcome (logistic regression); they do not explicitly examine whether risk factors are associated differentially across the distribution of the health outcome investigated. This paper documents a phenomenon found repeatedly in the minority of epidemiological studies which do this (via quantile regression): associations between a range of established risk factors and body mass index (BMI) are progressively stronger in the upper ends of the BMI distribution. In this paper, we document this finding and provide illustrative evidence of it in the 1958 British birth cohort study. Associations of low childhood socio-economic position, high maternal weight, low childhood general cognition and adult physical inactivity with higher BMI are larger at the upper end of the BMI distribution, on both absolute and relative scales. For example, effect estimates for socio-economic position and childhood cognition were around three times larger at the 90th compared with 10th quantile, while effect estimates for physical inactivity were increasingly larger from the 50th to 90th quantiles, yet null at lower quantiles. We provide potential explanations for these findings and discuss implications. Risk factors may have larger causal effects among those in worse health, and these effects may not be discovered when health is only examined in average terms. In such scenarios, population-based approaches to intervention may have larger benefits than anticipated when assuming equivalent benefit across the population. Further research is needed to understand why effect estimates differ across the BMI outcome distribution and to investigate whether differential effects exist for other physical and mental health outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We explore the different types of causes that are commonly investigated by epidemiologists. We first distinguish between causes which are events (including actions) and causes which are states. Second, we distinguish between modifiable and non-modifiable states. This yields three types of causes: fixed states (non-modifiable), dynamic states (modifiable) and events (including actions). Different causes may have different characteristics: the methods available to study them, the types of possible biases, and therefore the types of evidence needed to infer causality, may differ according to the specific cause-effect relationship under study. Nevertheless, there are also substantial commonalities. This paper is intended to improve understanding of the different types of causes, and the different types of causality, that underpin epidemiological practice. [ABSTRACT FROM AUTHOR]

Abstract 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.

Characterizing the relations between exposures and diseases is the central tenet of epidemiology. Researchers may want to evaluate exposure-disease causation by assessing whether the disease under concern is induced by the various exposures - the so-called "attribution". In this paper, the authors propose a method to attribute diseases to multiple pathways based on the causal-pie model. The method can also be used to evaluate the potential impact of an intervention strategy and to allocate responsibility in tort-law liability issues. [ABSTRACT FROM AUTHOR]

Introduction: Asthma and allergies are complex diseases affected by genetic and environmental factors, such as occupational and psychosocial factors, as well as interactions between them. Although childhood is a critical phase in the development of asthma and allergies, few cohort studies on occupational outcomes followed up participants from childhood onwards. We present design, methods, and initial data analysis for the third follow-up of SOLAR (Study on Occupational Allergy Risks), a prospective and population-based German asthma and allergy cohort.Methods: The SOLAR cohort was initially recruited in 1995–1996 for Phase II of the German branch of the International Study of Asthma and Allergies in Childhood (ISAAC II) and followed up three times since, in 2002–2003, 2007–2009, and 2017–2018. During the third follow-up (SOLAR III), participants were between 29 and 34 years old. Since SOLAR focuses on occupational exposures, follow-ups were conducted at important points in time of the development of participants' career. To evaluate the potential of selection bias, responders and non-responders were compared based on variables from earlier study phases. In responders, frequency and pattern of missing values were examined and compared within the subsets of paper and online versions of the used questionnaires.Results: In total, 1,359 participants completed the questionnaire of the third follow-up (47.3% of eligible participants). Initially, the cohort started with 6,399 participants from the ISAAC II questionnaire study. A selection process led to a study population that is more female, higher educated, smokes less and has a higher proportion of certain asthma and allergy symptoms (also in their parents) than the initial cohort. Pattern and frequency of missing values were different for paper and online questionnaires.Discussion: The third follow-up of the SOLAR cohort offers the opportunity to analyze the course of asthma and allergies and their associations to environmental, occupational and psychosocial risk factors over more than 20 years from childhood to adulthood. Selection processes within the cohort might lead to bias that needs to be considered in future analyses.

Risk and rate advancement periods (RAP) measure the impact of an exposure on the relation of age to disease. Specifically, they quantify the time by which the risk or rate of a disease is advanced among exposed subjects conditional on disease-free survival to a certain baseline age. The fact that these measures incorporate timing of disease occurrence makes them appealing for risk communication, and their use has increased over recent years. Unfortunately, their misinterpretations have also increased. In particular, RAP is often misinterpreted as the difference in mean survival time, when in fact it is a distinct concept and a profoundly different quantity. Other misinterpretations include interpreting RAP as the time by which a survival curve is shifted between exposed and unexposed, and equating RAP to a simple function of relative risks. In this paper we review RAP and show why common misinterpretations are flawed. We also show how RAP is profoundly sensitive to the specification of disease dependence on age. We conclude that whereas RAP remains a potentially useful measure, it requires multiple cautions beyond those needed for traditional measures of association, especially when computed for rates. [ABSTRACT FROM AUTHOR]

Background: The Danish National Patient Registry (DNPR) is one of the world's oldest nationwide hospital registries and is used extensively for research. Many studies have validated algorithms for identifying health events in the DNPR, but the reports are fragmented and no overview exists. Objectives: To review the content, data quality, and research potential of the DNPR. Methods: We examined the setting, history, aims, content, and classification systems of the DNPR. We searched PubMed and the Danish Medical Journal to create a bibliography of validation studies. We included also studies that were referenced in retrieved papers or known to us beforehand. Methodological considerations related to DNPR data were reviewed. Results: During 1977-2012, the DNPR registered 8,085,603 persons, accounting for 7,268,857 inpatient, 5,953,405 outpatient, and 5,097,300 emergency department contacts. The DNPR provides nationwide longitudinal registration of detailed administrative and clinical data. It has recorded information on all patients discharged from Danish nonpsychiatric hospitals since 1977 and on psychiatric inpatients and emergency department and outpatient specialty clinic contacts since 1995. For each patient contact, one primary and optional secondary diagnoses are recorded according to the International Classification of Diseases. The DNPR provides a data source to identify diseases, examinations, certain in-hospital medical treatments, and surgical procedures. Long-term temporal trends in hospitalization and treatment rates can be studied. The positive predictive values of diseases and treatments vary widely (,15%-100%). The DNPR data are linkable at the patient level with data from other Danish administrative registries, clinical registries, randomized controlled trials, population surveys, and epidemiologic field studies - enabling researchers to reconstruct individual life and health trajectories for an entire population. Conclusion: The DNPR is a valuable tool for epidemiological research. However, both its strengths and limitations must be considered when interpreting research results, and continuous validation of its clinical data is essential. [ABSTRACT FROM AUTHOR]

Recent methodological innovation is giving rise to an increasing number of applied papers in medical and epidemiological journals in which natural direct and indirect effects are estimated. However, there is a longstanding debate on whether such effects are relevant targets of inference in population health. In light of the repeated calls for a more pragmatic and consequential epidemiology, we review three issues often raised in this debate: (i) the use of composite cross-world counterfactuals and the need for cross-world independence assumptions; (ii) interventional vs non-interventional identifiability; and (iii) the interpretational ambiguity of natural direct and indirect effect estimates. We use potential outcomes notation and directed acyclic graphs to explain ‘cross-world’ assumptions, illustrate implications of this assumption via regression models and discuss ensuing issues of interpretation. We argue that the debate on the relevance of natural direct and indirect effects rests on whether one takes as a target of inference the mathematical object per se, or the change in the world that the mathematical object represents. We further note that public health questions may be better served by estimating controlled direct effects. [ABSTRACT FROM AUTHOR]

Preliminary evidence suggests that neighborhood environments, such as socioeconomic disadvantage, pedestrian and physical activity infrastructure, and availability of neighborhood destinations (e.g., parks), may be associated with late-life cognitive functioning and risk of Alzheimer's disease and related disorders (ADRD). The supposition is that these neighborhood characteristics are associated with factors such as mental health, environmental exposures, health behaviors, and social determinants of health that in turn promote or diminish cognitive reserve and resilience in later life. However, observed associations may be biased by self-selection or reverse causation, such as when individuals with better cognition move to denser neighborhoods because they prefer many destinations within walking distance of home, or when individuals with deteriorating health choose residences offering health services in neighborhoods in rural or suburban areas (e.g., assisted living). Research on neighborhood environments and ADRD has typically focused on late-life brain health outcomes, which makes it difficult to disentangle true associations from associations that result from reverse causality. In this paper, we review study designs and methods to help reduce bias due to reverse causality and self-selection, while drawing attention to the unique aspects of these approaches when conducting research on neighborhoods and brain aging.

Background: During the last decades, sex and gender biases have been identified in various areas of biomedical and public health research, leading to compromised validity of research findings. As a response, methodological requirements were developed but these are rarely translated into research practice. The aim of this study is to provide good practice examples of sex/gender sensitive health research.Methods: We conducted a systematic search of research articles published in JECH between 2006 and 2014. An instrument was constructed to evaluate sex/gender sensitivity in four stages of the research process (background, study design, statistical analysis, discussion).Results: In total, 37 articles covering diverse topics were included. Thereof, 22 were evaluated as good practice example in at least one stage; two articles achieved highest ratings across all stages. Good examples of the background referred to available knowledge on sex/gender differences and sex/gender informed theoretical frameworks. Related to the study design, good examples calculated sample sizes to be able to detect sex/gender differences, selected sex/gender sensitive outcome/exposure indicators, or chose different cut-off values for male and female participants. Good examples of statistical analyses used interaction terms with sex/gender or different shapes of the estimated relationship for men and women. Examples of good discussions interpreted their findings related to social and biological explanatory models or questioned the statistical methods used to detect sex/gender differences.Conclusions: The identified good practice examples may inspire researchers to critically reflect on the relevance of sex/gender issues of their studies and help them to translate methodological recommendations of sex/gender sensitivity into research practice. [ABSTRACT FROM AUTHOR]

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.70 – 80.9%). • Next generation matrices can be used to estimate the proportion of infections that are not detected during an outbreak. • Our method is parameterised by linking case and contact tracing data, which should be routine in responding to outbreaks. • Our method is parameterised by less data than traditional methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.70 – 80.9%).

Trafficking in persons is one of the gravest of crimes, but the scope and extent of the crime as measured by the number of victims have scarcely been quantified. Generating data on the population of trafficked persons presents a unique challenge because it tends to be a very hidden population--much more hidden than other hidden populations, such as the homeless population or the drug-injecting population. Epidemiological methods to detect hidden populations have advanced in recent years, with a variety of research papers describing the use of the network scale-up method to detect key affected and other subpopulations. The approach relies on conducting a survey of the general population, in which questions are asked about the number of individuals of interest in the personal network of the respondent and a specific set of questions are devised to estimate the size of the respondent's network. Advantages include the detection of multiple populations of trafficked persons in one survey, the minimization of harm to respondents for divulging first-hand knowledge, and improved statistical accuracy achieved by averaging over a large sample. Disadvantages include limited knowledge of the covariates of the population being surveyed. [ABSTRACT FROM AUTHOR]

Objectives Previous and ongoing epidemiological surveillance of sports and recreation injuries (SRI) has been cross-sectional in nature, utilised a subset of injuries based on athletic trainer availability, or focused on elite and professional athletes. In the United States, surveillance is often prohibitively expensive and not well funded by national organisations or agencies, except for the case of some professional and elite sports. This paper details the methodology, barriers, and successes of using deterministic linkage to combine emergency department and hospitalisation data with a single identifier for use in surveilling sports injuries for persons aged 5 to 18 years. Design Data linkage of a population cohort. Methods We performed deterministic linkage of administrative emergency department and hospitalisation data from the state of Florida in the US. Data was acquired from the Florida Agency for Health Care Administration. With limited identifiers available due to privacy, we combined data across multiple years using a near universal identifier. We identified sport and recreation injuries using a modified External Cause of Injury Morbidity Matrix and ICD codes across all possible diagnoses. Finally, we obtained descriptive statistics of records that were successfully linked and those that were not to assess similarities between the groups. Results We found 384,731 visits for SRI over a seven-year period. We were able to link approximately 70% of the records using a single identifier. There were statistically significant differences by age, sex, payer, and race/ethnicity for the records that were linked compared to the records that were not linked. Conclusions This study is significant because while similar methods have been used to examine other conditions (e.g. asthma), few have linked multiple types of administrative data especially with nearly no identifiers to examine sports and recreation injuries. This method was found useful to identify injuries over time for the same individuals seeking care in emergency departments, or in hospital inpatient settings, though future work will need to address the limitations of this method. If we expect to move health surveillance forward as budgets for it become even more limited, we must develop and improve methods to do it with fewer resources, including using data that has great limitations. Keywords deterministic linkage; epidemiological methods; epidemiology; surveillance; public health; data cleaning; data linkage; reformatting; SAS; Python; injury surveillance; disease surveillance; health surveillance; biostatistics

In the early stages of response to the SARS-CoV-2 pandemic, it was imperative for researchers to rapidly determine what animal species may be susceptible to the virus, under low knowledge and high uncertainty conditions. In this scoping review, the animal species being evaluated for SARS-CoV-2 susceptibility, the methods used to evaluate susceptibility, and comparing the evaluations between different studies were conducted. Using the PRISMA-ScR methodology, publications and reports from peer-reviewed and gray literature sources were collected from databases, Google Scholar, the World Organization for Animal Health (OIE), snowballing, and recommendations from experts. Inclusion and relevance criteria were applied, and information was subsequently extracted, categorized, summarized, and analyzed. Ninety seven sources (publications and reports) were identified which investigated 649 animal species from eight different classes: Mammalia, Aves, Actinopterygii, Reptilia, Amphibia, Insecta, Chondrichthyes, and Coelacanthimorpha. Sources used four different methods to evaluate susceptibility, in silico, in vitro, in vivo, and epidemiological analysis. Along with the different methods, how each source described "susceptibility" and evaluated the susceptibility of different animal species to SARS-CoV-2 varied, with conflicting susceptibility evaluations evident between different sources. Early in the pandemic, in silico methods were used the most to predict animal species susceptibility to SARS-CoV-2 and helped guide more costly and intensive studies using in vivo or epidemiological analyses. However, the limitations of all methods must be recognized, and evaluations made by in silico and in vitro should be re-evaluated when more information becomes available, such as demonstrated susceptibility through in vivo and epidemiological analysis. [ABSTRACT FROM AUTHOR]

Background: In August 2021, we published in Environmental Health a Toolkit for detecting misused epidemiological methods with the goal of providing an organizational framework for transparently evaluating epidemiological studies, a body of evidence, and resultant conclusions. Tsuda et al., the first group to utilize the Toolkit in a systematic fashion, have offered suggestions for its modification.Main Body: Among the suggested modifications made by Tsuda et al., we agree that rearrangement of Part A of the Toolkit to reflect the sequence of the epidemiological study process would facilitate its usefulness. Expansion or adaptation of the Toolkit to other disciplines would be valuable but would require the input of discipline-specific expertise. We caution against using the sections of the Toolkit to produce a tally or cumulative score, because none of the items are weighted as to importance or impact. Rather, we suggest a visual representation of how a study meets the Toolkit items, such as the heat maps used to present risk of bias criteria for studies included in Cochrane reviews. We suggest that the Toolkit be incorporated in the sub-specialty known as "forensic epidemiology," as well as in graduate training curricula, continuing education programs, and conferences, with the recognition that it is an extension of widely accepted ethics guidelines for epidemiological research.Conclusion: We welcome feedback from the research community about ways to strengthen the Toolkit as it is applied to a broader assemblage of research studies and disciplines, contributing to its value as a living tool/instrument. The application of the Toolkit by Tsuda et al. exemplifies the usefulness of this framework for transparently evaluating, in a systematic way, epidemiological research, conclusions relating to causation, and policy decisions.Postscript: We note that our Toolkit has, most recently, inspired authors with discipline-specific expertise in the field of Conservation Biology to adapt it for use in the Biological Sciences. [ABSTRACT FROM AUTHOR]

The hypothalamus-pituitary-adrenal (HPA) axis plays an important role in primary aldosteronism. Aldosterone biosynthesis is regulated not only by angiotensin II in the renin-angiotensin-aldosterone system, but also by adrenocorticotropic hormone (ACTH), one of the key components of the HPA axis. Although previous studies have reported cortisol cosecretion in primary aldosteronism, particularly aldosterone-producing adenoma (APA), the clinical relevance of such aldosterone and cortisol cosecretion from APA and hypertension or other metabolic disorders has not been fully established. Several somatic mutations including KCNJ5 and CACNA1D are known to induce autonomous production of aldosterone in APA, and the aldosterone responsiveness to ACTH may vary according to each mutation. The ACTH stimulation test has been reported to be a useful tool to distinguish the subtypes of primary aldosteronism (e.g., unilateral vs bilateral) in some studies, but it has not been commonly applied in clinical practice due to limited evidence. Given the recent advancement of imaging, omics research, and computational approach, it is important to summarize the most updated evidence to disentangle the potential impact of cortisol excess in primary aldosteronism and whether the ACTH stimulation test needs to be considered during the diagnostic process of primary aldosteronism. In this article, we conducted a systematic review of epidemiological studies about (i) cortisol cosecretion in primary aldosteronism and (ii) the ACTH stimulation test for the diagnosis of primary aldosteronism (including subtype diagnosis). Then, we discussed potential biases (e.g., confounding bias, overadjustment, information bias, selection bias, and sampling bias) in the previous studies and introduced some advanced epidemiological/statistical methods to minimize these limitations. A better understanding of biases and epidemiological perspective on this topic would allow us to produce further robust evidence and balanced discussion about the causal mechanisms involving the HPA axis and clinical usefulness of the ACTH stimulation test among patients with primary aldosteronism. [ABSTRACT FROM AUTHOR]

Background: Most health surveys have experienced a decline in response rates. A structured approach to evaluate whether a decreasing - and potentially more selective - response over time biased estimated trends in health behaviours is lacking. We developed a framework to explore the role of differential non-response over time. This framework was applied to a repeated cross-sectional survey in which the response rate gradually declined.Methods: We used data from a survey conducted biannually between 1995 and 2017 in the city of Rotterdam, The Netherlands. Information on the sociodemographic determinants of age, sex, and ethnicity was available for respondents and non-respondents. The main outcome measures of prevalence of sport participation and watching TV were only available for respondents. The framework consisted of four steps: 1) investigating the sociodemographic determinants of responding to the survey and the difference in response over time between sociodemographic groups; 2) estimating variation in health behaviour over time; 3) comparing weighted and unweighted prevalence estimates of health behaviour over time; and 4) comparing associations between sociodemographic determinants and health behaviour over time.Results: The overall response rate per survey declined from 47% in 1995 to 15% in 2017. The probability of responding was higher among older people, females, and those with a Western background. The response rate declined in all subgroups, and a faster decline was observed among younger persons and those with a non-Western ethnicity as compared to older persons and those with a Western ethnicity. Variation in health behaviours remained constant. Prevalence estimates and associations did not follow the changes in response over time. On the contrary, the difference in probability of participating in sport gradually decreased between males and females, while no differential change in the response rate was observed.Conclusions: Providing insights on non-response patterns over time is essential to understand whether declines in response rates may have influenced estimated trends in health behaviours. The framework outlined in this study can be used for this purpose. In our example, in spite of a major decline in response rate, there was no evidence that the risk of non-response bias increased over time. [ABSTRACT FROM AUTHOR]

Background: Net survival is commonly quantified as relative survival (observed survival among lung cancer patients versus expected survival among the general population) and cause-specific survival (lung cancer–specific survival among lung cancer patients). These approaches have drastically different assumptions; hence, failure to distinguish between them results in significant implications for study findings. We quantified the differences between relative and cause-specific survival when reporting net survival of patients with non-small cell lung cancer (NSCLC). Methods: Cases of NSCLC diagnosed between 2004 and 2014 were extracted from the Surveillance, Epidemiology, and End Results database. The net survival of each stage-by-age stratum was expressed as cause-specific survival (Kaplan-Meier approach) and relative survival (Ederer II approach); percentage-point (pp) differences between the survival estimates were quantified up to 10 years postdiagnosis. Results: Analyses included 263,894 cases. Cause-specific survival estimates were higher than relative survival estimates across all strata. Although the differences were negligible at 1 year postdiagnosis, they increased with increasing years of follow-up, up to 9.3 pp at 10 years (eg, aged 60–74 with stage I disease: 53.0% vs 43.7%). Differences in survival estimates between the methods also increased by increasing age groups (eg, at 10 years postdiagnosis: 5.1 pp for ages 18–44, 8.8 pp for ages 45–59, and 9.3 pp for ages 60–74) but decreased drastically for those aged ≥75 (3.1 pp). Conclusion: Relative survival and cause-specific survival are not interchangeable. The type of survival estimate used in cancer studies should be specified, particularly for long-term survival. [ABSTRACT FROM AUTHOR]

Background: Household surveys are an essential tool for vaccine coverage monitoring in developing countries, and the World Health Organization (WHO) Expanded Program on Immunization (EPI) cluster survey design has been a default choice for decades. In response to methodological limitations of the traditional EPI sampling, alternative methods have been proposed, based on modern statistical and geographical techniques. This study compared the coverage estimates and the time efficiency of the EPI sampling design and two alternative methods: the compact segment sampling and innovative grid-based geographical information system (GIS) sampling.Methods: We conducted a series of equal-sized concurrent prospective vaccine coverage surveys in Karachi, Pakistan, from January to December 2016, using traditional EPI, compact segment and grid-based GIS sampling methods.Results: No differences in vaccine coverage estimates were identified across sampling methods in the peri-urban setting; however, due to stronger clustering effects and correct incorporation of sampling weights, the compact segment [design effect (DEFF) = 2.03] and the grid-based GIS surveys (DEFF = 1.72) had higher design effects and, therefore, appeared to have lower statistical precision than the traditional EPI surveys (DEFF = 1.57). To achieve the same level of apparent precision, data collection activities in the compact segment surveys would require more than twice the implementation time needed compared with the traditional EPI surveys.Conclusions: The precision of the EPI surveys appeared higher than that of the alternative methods because, under a questionable self-weighting assumption, the estimated design effect did not account for variable sampling weights. The compact segment and grid-based GIS methods were designed to improve randomness and representativeness of sampling households. Although these alternative methods did not result in coverage estimates that differed from the EPI survey results in the peri-urban setting, they have a lower risk of selection bias and therefore may be preferred. [ABSTRACT FROM AUTHOR]

Background: Propensity score adjustment is a popular approach for confounding control in observational studies. Reliable frameworks are needed to determine relative propensity score performance in large-scale studies, and to establish optimal propensity score model selection methods.Methods: We detail a propensity score evaluation framework that includes synthetic and real-world data experiments. Our synthetic experimental design extends the 'plasmode' framework and simulates survival data under known effect sizes, and our real-world experiments use a set of negative control outcomes with presumed null effect sizes. In reproductions of two published cohort studies, we compare two propensity score estimation methods that contrast in their model selection approach: L1-regularized regression that conducts a penalized likelihood regression, and the 'high-dimensional propensity score' (hdPS) that employs a univariate covariate screen. We evaluate methods on a range of outcome-dependent and outcome-independent metrics.Results: L1-regularization propensity score methods achieve superior model fit, covariate balance and negative control bias reduction compared with the hdPS. Simulation results are mixed and fluctuate with simulation parameters, revealing a limitation of simulation under the proportional hazards framework. Including regularization with the hdPS reduces commonly reported non-convergence issues but has little effect on propensity score performance.Conclusions: L1-regularization incorporates all covariates simultaneously into the propensity score model and offers propensity score performance superior to the hdPS marginal screen. [ABSTRACT FROM AUTHOR]

Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for ‘nowcasting’ (integrated detection and prediction) of influenza activity are warranted. [ABSTRACT FROM PUBLISHER]

Background: A standard parameter to compare the disease incidence of a cohort relative to the population is the standardized incidence ratio (SIR). For statistical inference is commonly assumed that the denominator, the expected number of cases, is fixed. If a disease registry is available, incident cases can sometimes be identified by linkage with the registry, however, registries may not contain information on migration or death from other causes. A complete follow-up with a population registry may not be possible. In that case, end-of-follow-up date and therefore, exact person-years of observation are unknown.Methods: We have developed a method to estimate the observation times and to derive the expected number of cases using population data on mortality and migration rates. We investigate the impact of the underlying assumptions with a sensitivity analysis.Results: The method provides a useful estimate of the SIR. We illustrate the method with a numerical example, a simulation study and with a study on standardized cancer incidence ratios in a cohort of migrants relative to the German population. We show that the additional variance induced by the estimation method is small, so that standard methods for inference can be applied.Conclusions: Estimation of the observation time is possible for cohort studies with incomplete follow-up. [ABSTRACT FROM AUTHOR]

Background: Long-term multicentre studies are subject to numerous factors that may affect the integrity of their conclusions. Quality control and standardization of data collection are crucial to minimise the biases induced by these factors. Nevertheless, tools implemented to manage biases are rarely described in publications about population-based cohorts. This report aims to describe the processes implemented to control biases in the Constances cohort taking lung function results as an example.Methods: Constances is a general-purpose population-based cohort of 200,000 participants. Volunteers attend physical examinations at baseline and then every 5 years at selected study sites. Medical device specifications and measurement methods have to comply with Standard Operating Procedures developed by experts. Protocol deviations are assessed by on-site inspections and database controls. In February 2016, more than 94,000 participants yielding around 30 million readings from physical exams, had been covered by our quality program.Results: Participating centres accepted to revise their practices in accordance with the study research specifications. Distributors of medical devices were asked to comply with international guidelines and Constances requirements. Close monitoring enhanced the quality of measurements and recordings of the physical exams. Regarding lung function testing, spirometry acceptability rates per operator doubled in some sites within a few months and global repeatability reached 96.7 % for 29,772 acceptable maneuvers.Conclusions: Despite Constances volunteers being followed in multiple sites with heterogeneous materials, the investment of significant resources to set up and maintain a continuous quality management process has proved effective in preventing drifts and improving accuracy of collected data. [ABSTRACT FROM AUTHOR]

Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar. We first introduce the concept of deterministic linkage and contrast this with probabilistic linkage. We illustrate each step of the process using a simple exemplar and describe the data structure required to perform a probabilistic linkage. We describe the process of calculating and interpreting matched weights and how to convert matched weights into posterior probabilities of a match using Bayes theorem. We conclude this article with a brief discussion of some of the computational demands of record linkage, how you might assess the quality of your linkage algorithm, and how epidemiologists can maximize the value of their record-linked research using robust record linkage methods. [ABSTRACT FROM AUTHOR]

A linked population-based database is being created in Denmark for research on drug safety during pregnancy. It combines information from the Danish National Health Service Prescription Database (with information on all prescriptions reimbursed in Denmark since 2004), the Danish Fetal Medicine Database, the Danish National Registry of Patients, and the Medical Birth Registry. The new linked database will provide validated information on malformations diagnosed both prenatally and postnatally. The cohort from 2008 to 2014 will comprise 589,000 pregnancies with information on 424,000 pregnancies resulting in live-born children, ~420,000 pregnancies undergoing prenatal ultrasound scans, 65,000 miscarriages, and 92,000 terminations. It will be updated yearly with information on ~80,000 pregnancies. The cohort will enable identification of drug exposures associated with severe malformations, not only based on malformations diagnosed after birth but also including those having led to termination of pregnancy or miscarriage. Such combined data will provide a unique source of information for research on the safety of medications used during pregnancy. [ABSTRACT FROM AUTHOR]

Background: The UK population is ageing; improved understanding of risk factors for hospital admission is required. Linkage of the Hertfordshire Cohort Study (HCS) with Hospital Episode Statistics (HES) data has created a multiple-failure survival dataset detailing the characteristics of 2,997 individuals at baseline (1998-2004, average age 66 years) and their hospital admissions (regarded as 'failure events') over a 10 year follow-up. Analysis of risk factors using logistic regression or time to first event Cox modelling wastes information as an individual's admissions after their first are disregarded. Sophisticated analysis techniques are established to examine risk factors for admission in such datasets but are not commonly implemented.Methods: We review analysis techniques for multiple-failure survival datasets (logistic regression; time to first event Cox modelling; and the Andersen and Gill [AG] and Prentice, Williams and Peterson Total Time [PWP-TT] multiple-failure models), outline their implementation in Stata, and compare their results in an analysis of housing tenure (a marker of socioeconomic position) as a risk factor for different types of hospital admission (any; emergency; elective; >7 days). The AG and PWP-TT models include full admissions histories in the analysis of risk factors for admission and account for within-subject correlation of failure times. The PWP-TT model is also stratified on the number of previous failure events, allowing an individual's baseline risk of admission to increase with their number of previous admissions.Results: All models yielded broadly similar results: not owner-occupying one's home was associated with increased risk of hospital admission. Estimated effect sizes were smaller from the PWP-TT model in comparison with other models owing to it having accounted for an increase in risk of admission with number of previous admissions. For example, hazard ratios [HR] from time to first event Cox models were 1.67(95 % CI: 1.36,2.04) and 1.63(95 % CI:1.36,1.95) for not owner-occupying one's home in relation to risk of emergency admission or death among women and men respectively; corresponding HRs from the PWP-TT model were 1.34(95 % CI:1.15,1.56) for women and 1.23(95 % CI:1.07,1.41) for men.Conclusion: The PWP-TT model may be implemented using routine statistical software and is recommended for the analysis of multiple-failure survival datasets which detail repeated hospital admissions among older people. [ABSTRACT FROM AUTHOR]

Background: Recent years have witnessed a growing body of observational literature on the association between glucose-lowering treatments and cardiovascular disease. However, many of the studies are based on designs or analyses that inadequately address the methodological challenges involved. Methods: We reviewed recent observational literature on the association between glucose-lowering medications and cardiovascular outcomes and assessed the design and analysis methods used, with a focus on their ability to address specific methodological challenges. We describe and illustrate these methodological issues and their impact on observed associations, providing examples from the reviewed literature. We suggest approaches that may be employed to manage these methodological challenges. Results: From the evaluation of 81 publications of observational investigations assessing the association between glucose-lowering treatments and cardiovascular outcomes, we identified the following methodological challenges: 1) handling of temporality in administrative databases; 2) handling of risks that vary with time and treatment duration; 3) definitions of the exposure risk window; 4) handling of exposures that change over time; and 5) handling of confounding by indication. Most of these methodological challenges may be suitably addressed through application of appropriate methods. Conclusions/interpretation: Observational research plays an increasingly important role in the evaluation of the clinical effects of diabetes treatment. Implementation of appropriate research methods holds the promise of reducing the potential for spurious findings and the risk that the spurious findings will mislead the medical community about risks and benefits of diabetes medications. [ABSTRACT FROM AUTHOR]