Molecular Pathology of Myotonic Dystrophy Type 1 in Iceland.

BACKGROUND: Myotonic Dystrophy type 1 (DM1) is an autosomal dominant disease with anticipation due to increased number of CTG repeats in the DMPK gene. METHODS: This retrospective, cohort study in Iceland assessed prevalence of DM1, molecular pathology, and patient ascertainment. Data was collected from all major hospitals in Iceland, Medical Director of Health, and independent clinics. Cohort criteria were diagnosis of DM1 on January 1, 2021, or time of death. Population-based Icelandic Genealogy Database of the Genetical Committee at the University of Iceland was used for genealogy. RESULTS: In Iceland, 221 individuals, including 19 obligate carriers, had been diagnosed with DM1 of which 144 were alive giving a point prevalence of 39 per 100,000 (four times the world average of 9.3). Genealogy analysis identified 45 first-degree families. Age-adjusted prevalence ranged between 11 and 66 per 100,000. Average potential years of life lost were 20.5 per person. Where information was available, 63% of ascertainment was based on family history in cascade testing. CONCLUSION: The differences in age-adjusted prevalence suggest that the overall point prevalence is an underestimation due to underdiagnosis in younger age groups and lethality in oldest age group. Our data supports use of cascade testing to improve DM1 ascertainment.

Lyme Disease Under-Ascertainment During the COVID-19 Pandemic in the United States: Retrospective Study.

Background: The COVID-19 pandemic resulted in a massive disruption in access to care and thus passive, hospital- and clinic-based surveillance programs. In 2020, the reported cases of Lyme disease were the lowest both across the United States and North Carolina in recent years. During this period, human contact patterns began to shift with higher rates of greenspace utilization and outdoor activities, putting more people into contact with potential vectors and associated vector-borne diseases. Lyme disease reporting relies on passive surveillance systems, which were likely disrupted by changes in health care-seeking behavior during the pandemic. Objective: This study aimed to quantify the likely under-ascertainment of cases of Lyme disease during the COVID-19 pandemic in the United States and North Carolina. Methods: We fitted publicly available, reported Lyme disease cases for both the United States and North Carolina prior to the year 2020 to predict the number of anticipated Lyme disease cases in the absence of the pandemic using a Bayesian modeling approach. We then compared the ratio of reported cases divided by the predicted cases to quantify the number of likely under-ascertained cases. We then fitted geospatial models to further quantify the spatial distribution of the likely under-ascertained cases and characterize spatial dynamics at local scales. Results: Reported cases of Lyme Disease were lower in 2020 in both the United States and North Carolina than prior years. Our findings suggest that roughly 14,200 cases may have gone undetected given historical trends prior to the pandemic. Furthermore, we estimate that only 40% to 80% of Lyme diseases cases were detected in North Carolina between August 2020 and February 2021, the peak months of the COVID-19 pandemic in both the United States and North Carolina, with prior ascertainment rates returning to normal levels after this period. Our models suggest both strong temporal effects with higher numbers of cases reported in the summer months as well as strong geographic effects. Conclusions: Ascertainment rates of Lyme disease were highly variable during the pandemic period both at national and subnational scales. Our findings suggest that there may have been a substantial number of unreported Lyme disease cases despite an apparent increase in greenspace utilization. The use of counterfactual modeling using spatial and historical trends can provide insight into the likely numbers of missed cases. Variable ascertainment of cases has implications for passive surveillance programs, especially in the trending of disease morbidity and outbreak detection, suggesting that other methods may be appropriate for outbreak detection during disturbances to these passive surveillance systems.

European Board and College of Obstetrics and Gynaecology position statement on maternal mortality surveillance in Europe.

Maternal mortality data and review are important indicators of the effectiveness of maternity healthcare systems and an impetus for action. Recently, a rising incidence of maternal mortality in high income countries has been reported. Various publications have raised concern about data collection methods at country level, as this usually relies mainly on national vital statistics. It is therefore essential that the collected data are complete and accurate and conform to international definitions and disease classification. Accurate data and review can only be truly available when an Enhanced Obstetric Surveillance System is in place. EBCOG calls for action by national societies to work closely with their respective ministries of health to ensure that high quality surveillance systems are in place.

Reweighting UK Biobank corrects for pervasive selection bias due to volunteering.

BACKGROUND: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. METHODS: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. RESULTS: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. CONCLUSIONS: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available.

Rejoinder to the discussion on "Bayesian meta-analysis of penetrance for cancer risk".

The five discussions of our paper provide several modeling alternatives, extensions, and generalizations that can potentially guide future research in meta-analysis. In this rejoinder, we briefly summarize and comment on some of those points.

Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials - A case study.

BACKGROUND: Variable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach. METHODS: We propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites). RESULTS: The site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site. CONCLUSIONS: We suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials. ORIGINAL TRIAL REGISTRATION NUMBER: NCT02475850.

Robustness in population-structure and demographic-inference results derived from the Aedes aegypti genotyping chip and whole-genome sequencing data.

The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches.

Ascertaining the Francophone population in Ontario: validating the language variable in health data.

BACKGROUND: Language barriers can impact health care and outcomes. Valid and reliable language data is central to studying health inequalities in linguistic minorities. In Canada, language variables are available in administrative health databases; however, the validity of these variables has not been studied. This study assessed concordance between language variables from administrative health databases and language variables from the Canadian Community Health Survey (CCHS) to identify Francophones in Ontario. METHODS: An Ontario combined sample of CCHS cycles from 2000 to 2012 (from participants who consented to link their data) was individually linked to three administrative databases (home care, long-term care [LTC], and mental health admissions). In total, 27,111 respondents had at least one encounter in one of the three databases. Language spoken at home (LOSH) and first official language spoken (FOLS) from CCHS were used as reference standards to assess their concordance with the language variables in administrative health databases, using the Cohen kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV). RESULTS: Language variables from home care and LTC databases had the highest agreement with LOSH (kappa = 0.76 [95%CI, 0.735-0.793] and 0.75 [95%CI, 0.70-0.80], respectively) and FOLS (kappa = 0.66 for both). Sensitivity was higher with LOSH as the reference standard (75.5% [95%CI, 71.6-79.0] and 74.2% [95%CI, 67.3-80.1] for home care and LTC, respectively). With FOLS as the reference standard, the language variables in both data sources had modest sensitivity (53.1% [95%CI, 49.8-56.4] and 54.1% [95%CI, 48.3-59.7] in home care and LTC, respectively) but very high specificity (99.8% [95%CI, 99.7-99.9] and 99.6% [95%CI, 99.4-99.8]) and predictive values. The language variable from mental health admissions had poor agreement with all language variables in the CCHS. CONCLUSIONS: Language variables in home care and LTC health databases were most consistent with the language often spoken at home. Studies using language variables from administrative data can use the sensitivity and specificity reported from this study to gauge the level of mis-ascertainment error and the resulting bias.

Validation of case-ascertainment algorithms using health administrative data to identify people who inject drugs in Ontario, Canada.

OBJECTIVES: Health administrative data can be used to improve the health of people who inject drugs by informing public health surveillance and program planning, monitoring, and evaluation. However, methodological gaps in the use of these data persist due to challenges in accurately identifying injection drug use (IDU) at the population level. In this study, we validated case-ascertainment algorithms for identifying people who inject drugs using health administrative data in Ontario, Canada. STUDY DESIGN AND SETTING: Data from cohorts of people with recent (past 12 months) IDU, including those participating in community-based research studies or seeking drug treatment, were linked to health administrative data in Ontario from 1992 to 2020. We assessed the validity of algorithms to identify IDU over varying look-back periods (ie, all years of data [1992 onwards] or within the past 1-5 years), including inpatient and outpatient physician billing claims for drug use, emergency department (ED) visits or hospitalizations for drug use or injection-related infections, and opioid agonist treatment (OAT). RESULTS: Algorithms were validated using data from 15,241 people with recent IDU (918 in community cohorts and 14,323 seeking drug treatment). An algorithm consisting of ≥1 physician visit, ED visit, or hospitalization for drug use, or OAT record could effectively identify IDU history (91.6% sensitivity and 94.2% specificity) and recent IDU (using 3-year look back: 80.4% sensitivity, 99% specificity) among community cohorts. Algorithms were generally more sensitive among people who inject drugs seeking drug treatment. CONCLUSION: Validated algorithms using health administrative data performed well in identifying people who inject drugs. Despite their high sensitivity and specificity, the positive predictive value of these algorithms will vary depending on the underlying prevalence of IDU in the population in which they are applied.

Sex differences and driving impairment related to psychoactive substances.

OBJECTIVE: The first aim of the study was to identify sex differences in the use of psychoactive substances among subjects with a previous driving under the influence (DUI) episode. The secondary objective was to propose specific strategies for medico-legal improvements. METHODS: This was a retrospective observational study that took place between June 1, 2019, and August 31, 2023. It was conducted on DUI subjects examined for reinstatement of their driver's license using an integrated medico-legal and toxicological approach. Ethyl glucuronide (EtG) and illicit psychoactive substances were determined from hair samples. We performed descriptive statistical analyses for the entire sample as well as separately by sex. Additionally, we conducted binary logistic regression analyses separately for males and females to identify protective/risk factors associated with previous road accidents and judgments of unfitness to drive due to excessive alcohol consumption (EtG ≥ 30 pg/mg). RESULTS: The study included 2,221 subjects, comprising 1,970 men and 251 women. Men exhibited a higher prevalence of tobacco, alcohol, and illicit psychoactive substance use. Women were more frequently co-users of alcohol and psychoactive substances and involved in road accidents at the time of DUI. Among the men, being married or having a partner was found to be a protective factor concerning past traffic accidents. For both sexes, a DUI episode with a blood alcohol concentration (BAC) exceeding 1.5 g/L or the co-ingestion of alcohol and drugs was identified as a risk factor for road accident involvement. For men, smoking more than 20 cigarettes per day and, for women, having a DUI episode with a BAC over 1.5 g/L were the main factors indicating unfitness to drive, as determined through high hair EtG levels (> 30 pg/mg). Women with a previous history of road accidents were less likely to have EtG levels of 30 pg/mg or more. CONCLUSIONS: The study confirmed sex differences in subjects with a previous DUI episode. A BAC exceeding 1.5 g/L or the simultaneous use of alcohol and drugs at the time of DUI necessitate careful assessment of both men and women seeking driver's license reinstatement. In women, a BAC exceeding 1.5 g/L is considered a risk factor for a subsequent judgment of unfitness to drive. The medico-legal assessment should also involve a thorough investigation of smoking habits in men, as these habits could be related to an increased risk of excessive alcohol consumption.

Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis.

BACKGROUND: Ascertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against "gold standard" (GS) methods to study the feasibility of using such methods in clinical trials. METHODS: Studies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves. RESULTS: A total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5-98.3), but lacked sensitivity (63.5%, 95% CI 51.3-74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies. CONCLUSIONS: RCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.

Strategies for secondary use of real-world clinical and administrative data for outcome ascertainment in pragmatic clinical trials.

BACKGROUND: Pragmatic trials are gaining popularity as a cost-effective way to examine treatment effectiveness and generate timely comparative evidence. Incorporating supplementary real-world data is recommended for robust outcome monitoring. However, detailed operational guidelines are needed to inform effective use and integration of heterogeneous databases. OBJECTIVE: Lessons learned from the Veterans Affairs (VA) Diuretic Comparison Project (DCP) are reviewed, providing adaptable recommendations to capture clinical outcomes from real-world data. METHODS: Non-cancer deaths and major cardiovascular (CV) outcomes were determined using VA, Medicare, and National Death Index (NDI) data. Multiple ascertainment strategies were applied, including claims-based algorithms, natural language processing, and systematic chart review. RESULTS: During a mean follow-up of 2.4 (SD = 1.4) years, 907 CV events were identified within the VA healthcare system. Slight delays (∼1 year) were expected in obtaining Medicare data. An additional 298 patients were found having a CV event outside of the VA in 2016 - 2021, increasing the CV event rate from 3.5 % to 5.7 % (770 of 13,523 randomized). NDI data required âˆ¼2 years waiting period. Such inclusion did not increase the number of deaths identified (all 894 deaths were captured by VA data) but enhanced the accuracy in determining cause of death. CONCLUSION: Our experience supports the recommendation of integrating multiple data sources to improve clinical outcome ascertainment. While this approach is promising, hierarchical data aggregation is required when facing different acquisition timelines, information availability/completeness, coding practice, and system configurations. It may not be feasible to implement comparable applications and solutions to studies conducted under different constraints and practice. The recommendations provide guidance and possible action plans for researchers who are interested in applying cross-source data to ascertain all study outcomes.

The hidden impact of alcohol on young victims: an analysis of alcohol-related police offences resulting in hospitalisation.

BACKGROUND: Alcohol-related harm (ARH) is a significant public health concern affecting young individuals, particularly those involved in alcohol-related police incidents resulting in hospitalisation. However, the impact of alcohol on young victims remains under researched. This study aimed to identify the characteristics of offenders and victims involved in these incidents, analyse the types of offences, and understand the under-ascertainment of ARH in hospital records. METHODS: A retrospective longitudinal study of 12-24-year-olds born between 1980 and 2005 was conducted using linked data from hospital admissions, emergency department presentations, and police incident records. Alcohol-related incidents were identified based on the attending officers' opinions in the Western Australia Police's Incident Management System (IMS). Logistic and log-binomial regression were utilised to analyse the factors associated with victimisation and under-ascertainment of ARH. RESULTS: Our study included 22,747 individuals (11,433 victims and 11,314 offenders) involved in alcohol-related police incidents, with a small majority of victims being female (53%, n = 6,074) and a large majority of offenders being male (84.3%, n = 9,532). Most victims did not receive a diagnosis of ARH (71%, n = 760). Women were 10 times more likely to have been a victim in ARH police incidents and 2 times more likely to have an undiagnosed alcohol-related hospital admission than men. Victims and offenders predominantly came from disadvantaged areas and major cities. Aboriginal individuals were overrepresented as both offenders and victims. A significant proportion of individuals experienced emergency department presentations or hospital admissions, with head injuries being the most common. Assault causing bodily harm was the most prevalent offence resulting in hospitalisation (66%, n = 2,018). CONCLUSIONS: There is a noteworthy disparity between the quantity of hospital admissions attributed to alcohol-related incidents and the number of cases that are formally classified as ARH in the hospital system. This disparity highlights a more profound issue of substantial under-ascertainment or inadequate identification of ARH than previously acknowledged. Our findings justify the prioritisation of prevention strategies, beyond improvement in the documentation of alcohol-related hospitalisation. Considering the scale of the problem, and the underestimation of the burden of alcohol-related hospitalisation, a proportional increase in investment is necessary to achieve population-level reductions in ARH.

Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.

AIM: To harmonize two ascertainment and severity rating instruments commonly used for the clinical high risk syndrome for psychosis (CHR-P): the Structured Interview for Psychosis-risk Syndromes (SIPS) and the Comprehensive Assessment of At-Risk Mental States (CAARMS). METHODS: The initial workshop is described in the companion report from Addington et al. After the workshop, lead experts for each instrument continued harmonizing attenuated positive symptoms and criteria for psychosis and CHR-P through an intensive series of joint videoconferences. RESULTS: Full harmonization was achieved for attenuated positive symptom ratings and psychosis criteria, and modest harmonization for CHR-P criteria. The semi-structured interview, named Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS (PSYCHS), generates CHR-P criteria and severity scores for both CAARMS and SIPS. CONCLUSIONS: Using the PSYCHS for CHR-P ascertainment, conversion determination, and attenuated positive symptom severity rating will help in comparing findings across studies and in meta-analyses.

A case of hyperlysinemia identified by urine newborn screening.

Hyperlysinemia is a rare autosomal recessive deficiency of 2-aminoadipic semialdehyde synthase (AASS) affecting the initial step in lysine degradation. It is thought to be a benign biochemical abnormality, but reports on cases remain scarce. The description of additional cases, in particular, those identified without ascertainment bias, may help counseling of new cases in the future. It may also help to establish the risks associated with pharmacological inhibition of AASS, a potential therapeutic strategy that is under investigation for other inborn errors of lysine degradation. We describe the identification of a hyperlysinemia case identified in the Provincial Neonatal Urine Screening Program in Sherbrooke, Quebec. This case presented with a profile of cystinuria but with a very high increase in urinary lysine. A diagnosis of hyperlysinemia was confirmed through biochemical testing and the identification of biallelic variants in AASS. The p.R146W and p.T371I variants are novel and affect the folding of the lysine-2-oxoglutarate domain of AASS. The 11-month-old boy is currently doing well without any therapeutic interventions. The identification of this case through newborn urine screening further establishes that hyperlysinemia is a biochemical abnormality with limited clinical consequences and may not require any intervention.

Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.

OBJECTIVES: Chart review as the current gold standard for phenotype evaluation cannot support observational research on electronic health records and claims data sources at scale. We aimed to evaluate the ability of structured data to support efficient and interpretable phenotype evaluation as an alternative to chart review. MATERIALS AND METHODS: We developed Knowledge-Enhanced Electronic Profile Review (KEEPER) as a phenotype evaluation tool that extracts patient's structured data elements relevant to a phenotype and presents them in a standardized fashion following clinical reasoning principles. We evaluated its performance (interrater agreement, intermethod agreement, accuracy, and review time) compared to manual chart review for 4 conditions using randomized 2-period, 2-sequence crossover design. RESULTS: Case ascertainment with KEEPER was twice as fast compared to manual chart review. 88.1% of the patients were classified concordantly using charts and KEEPER, but agreement varied depending on the condition. Missing data and differences in interpretation accounted for most of the discrepancies. Pairs of clinicians agreed in case ascertainment in 91.2% of the cases when using KEEPER compared to 76.3% when using charts. Patient classification aligned with the gold standard in 88.1% and 86.9% of the cases respectively. CONCLUSION: Structured data can be used for efficient and interpretable phenotype evaluation if they are limited to relevant subset and organized according to the clinical reasoning principles. A system that implements these principles can achieve noninferior performance compared to chart review at a fraction of time.

Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa.

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. Owing to inadequate public awareness, unestablished monitoring practices, limited testing and stigmas, there might exist extensive under-ascertainment of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (s.d. 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria). The mean overall report rate was estimated to be 5.37% (s.d. 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in São Tomé and Príncipe. An average of 5.46% (s.d. 6.4%) of all infected cases were severe cases and 66.74% (s.d. 17.28%) were asymptomatic ones. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance systems in these countries.

Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity and fatality.

Background: The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death. Methods: We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants. Results: We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%-61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22-44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17-0.36), IIR of 0.015% (95%CI: 0.011-0.023), and IFR of 0.05% (95%CI: 0.04-0.08). Conclusions: Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.

Comparing Amyotrophic lateral sclerosis (ALS) patient characteristics from the National ALS Registry and the Massachusetts ALS Registry, data through 2015.

OBJECTIVE: To compare, for completeness, ALS patients identified in the National ALS Registry (National Registry) from MA to those in the Massachusetts ALS Registry (MA Registry) through 2015. METHODS: Sensitivity analyses were conducted to determine the completeness among patients reported in both registries. Patients were matched on first and last name, month and year of birth, sex, as well as Soundex name matching. Demographics for matching and nonmatching ALS patients were also examined using bivariate analyses and logistic regression. RESULTS: There were 1,042 ALS patients in the MA Registry, and 642 patients matched (61.6%) in the National Registry. Sensitivity analyses found the National Registry had a sensitivity of 87.7% and specificity of 60%. For these matched patients, 522 (81.2%) came from Medicare. Of the 400 patients in the MA Registry not matched to the National Registry, 11.1% were nonwhite, compared to 6.0% in the matched group) (p = 0.0091) and 59.2% were diagnosed before age 60, compared to 28.6% in the matched group (p < 0.0001). Multivariate logistic regression analysis showed being an ALS case (p < 0.0001) and having an ALS diagnosis at age 60 or later (p < 0.0001) were associated with being more likely to match between the two registries. CONCLUSIONS: These findings show that ALS's non-notifiable condition status at the national level continues to pose a challenge in identifying all ALS patients. This analysis also showed missing cases at the state level even with a reporting statute. Additional strategies are needed for better patient-ascertainment to quantify all ALS patients in the U.S.

Anxiety and Depression in People with Eczema or Psoriasis: A Comparison of Associations in UK Biobank and Linked Primary Care Data.

Introduction: Previous research has shown associations between eczema and psoriasis and anxiety and depression. We investigated whether associations are consistent across different settings of ascertainment for depression and anxiety, including interview and survey responses from UK Biobank (a large longitudinal cohort recruiting individuals aged 40-69 years between 2006-2010), and linked primary care data, with the aim of drawing more reliable conclusions through triangulation. Methods: In cross-sectional studies, we estimated associations between eczema or psoriasis and anxiety or depression, defining anxiety or depression as 1) self-reported previous diagnosis at UK Biobank recruitment interview; 2) PHQ-9/GAD-7 score indicating depression or anxiety from a UK Biobank mental health follow-up survey in 2016; and 3) diagnosis in linked primary care electronic health record data. Results: We analysed 230,047 people with linked Biobank and primary care data. We found poor agreement between the data sources for eczema, psoriasis, anxiety, and depression. Eg, 9474 had a previous eczema diagnosis in primary care data, 4069 self-reported previous eczema diagnosis at the UK biobank interview, and 1536 had eczema in both data sources (for depression 40,455; 13,320; and 9588 respectively). Having eczema or psoriasis (recorded in primary care or baseline interview) was associated with higher odds of anxiety and depression. Eg, the adjusted odds ratio for depression comparing those with eczema to those without was greater than 1 when defining the outcome from 1) the recruitment interview (1.36, 95% confidence interval 1.27-1.45); 2) the follow-up survey (1.24, 1.09-1.39), and 3) primary care records (1.56, 1.50-1.62). Discussion: Our findings support increased prevalence of mental illness in people with psoriasis and eczema across multiple data sources, which should be considered in planning of mental health services. However, we found poor agreement in disease ascertainment between settings, with implications for data interpretation in electronic health records.