Estimates Of Populations Research Articles

The prevalence of pulmonary hypertension in post-tuberculosis and active tuberculosis populations: a systematic review and meta-analysis.

The prevalence of tuberculosis (TB)-associated pulmonary hypertension (PH) has not previously been quantified, resulting in an underappreciated burden of disease. We aimed to estimate the prevalence of PH in post-TB and active TB populations. In this systematic review and meta-analysis, we searched PubMed/Medline, Cochrane Library, EBSCOhost, Scopus, African Journals Online and Google Scholar, with no language restriction, for available literature published after 1950. Eligible studies described adult participants (≥16 years), with documented evidence of active or prior TB, diagnosed with PH. Study quality was assessed using a risk of bias tool specifically developed for prevalence studies. Aggregate prevalence estimates with 95% confidence intervals were synthesised using a random-effects meta-analysis model, incorporating the Freeman-Tukey transformation. Subgroup analysis was conducted to ascertain prevalence estimates in specific patient populations. We identified 1452 unique records, of which 34 met our inclusion criteria. 23 studies, with an acceptable risk of bias and where PH was diagnosed at right heart catheterisation or echocardiography, were included in the meta-analysis. In post-TB studies (14/23), the prevalence of PH was 67.0% (95% CI 50.8-81.4) in patients with chronic respiratory failure, 42.4% (95% CI 31.3-54.0) in hospitalised or symptomatic patients and 6.3% (95% CI 2.3-11.8) in nonhealthcare-seeking outpatients (I2=96%). There was a lower estimated prevalence of PH in studies of populations with active TB (9.4%, 95% CI 6.3-13.0), I2=84%). Our results highlight the significant burden of PH in post-TB and active TB populations. We emphasise the need for increased recognition of TB-associated PH and additional high-quality prevalence data.

Accounting for the evolution of sedentarism on minimum dietary energy requirements

Estimates of food-insecure populations are biased upwards, lacking adjustment for global increases in sedentary behavior in recent decades. We construct a household model to account for sedentary choices during leisure and work time decisions. The model rationalizes increasing sedentary behavior from the household by accounting for increasing returns to cognitive human capital vs physical capital, alongside increased productivity of more sedentary activities both at work and at home. The household model informs an empirical model applied to our unique international pseudo-panel data on sitting time, proxy-ing for sedentarism. We econometrically estimate a transfer function linking sedentarism to widely available covariates and make out-of-sample predictions. It is applicable to most countries. The estimated sedentary time can be used to adjust the physical activity level reflected in the minimum dietary energy requirement used to determine a cutoff for food insecurity.

Phytoplankton ecology in the early years of a boreal oil sands end pit lake

BackgroundBase Mine Lake (BML) is the first full-scale end pit lake for the oil sands mining industry in Canada. BML sequesters oil sands tailings under a freshwater cap and is intended to develop into a functional ecosystem that can be integrated into the local watershed. The first stage of successful reclamation requires the development of a phytoplankton community supporting a typical boreal lake food web. To assess the diversity and dynamics of the phytoplankton community in BML at this reclamation stage and to set a baseline for future monitoring, we examined the phytoplankton community in BML from 2016 through 2021 using molecular methods (targeting the 23S, 18S, and 16S rRNA genes) and microscopic methods. Nearby water bodies were used as controls for a freshwater environment and an active tailings pond.ResultsThe phytoplankton community was made up of diverse bacteria and eukaryotes typical of a boreal lake. Microscopy and molecular data both identified a phytoplankton community comparable at the phylum level to that of natural boreal lakes, dominated by Chlorophyta, Cryptophyta, and Cyanophyta, with some Bacillariophyta, Ochrophyta, and Euglenophyta. Although many of the same genera were prominent in both BML and the control freshwater reservoir, there were differences at the species or ASV level. Total diversity in BML was also consistently lower than the control freshwater site, but consistently higher than the control tailings pond. The phytoplankton community composition in BML changed over the 5-year study period. Some taxa present in 2016–2019 (e.g., Choricystis) were no longer detected in 2021, while some dinophytes and haptophytes became detectable in small quantities starting in 2019–2021. Different quantification methods (qPCR analysis of 23S rRNA genes, and microscopic estimates of populations and total biomass) did not show a consistent directional trend in total phytoplankton over the 5-year study, nor was there any consistent increase in phytoplankton species diversity. The 5-year period was likely an insufficient time frame for detecting community trends, as phytoplankton communities are highly variable at the genus and species level.ConclusionsBML supports a phytoplankton community composition somewhat unique from control sites (active tailings and freshwater lake) and is still changing over time. However, the most abundant genera are typical of natural boreal lakes and have the potential to support a complex aquatic food web, with many of its identified major phytoplankton constituents known to be primary producers in boreal lake environments.

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning.

The overpopulation of feral pigeons in Hong Kong has significantly disrupted the urban ecosystem, highlighting the urgent need for effective strategies to control their population. In general, control measures should be implemented and re-evaluated periodically following accurate estimations of the feral pigeon population in the concerned regions, which, however, is very difficult in urban environments due to the concealment and mobility of pigeons within complex building structures. With the advances in deep learning, computer vision can be a promising tool for pigeon monitoring and population estimation but has not been well investigated so far. Therefore, we propose an improved deep learning model (Swin-Mask R-CNN with SAHI) for feral pigeon detection. Our model consists of three parts. Firstly, the Swin Transformer network (STN) extracts deep feature information. Secondly, the Feature Pyramid Network (FPN) fuses multi-scale features to learn at different scales. Lastly, the model's three head branches are responsible for classification, best bounding box prediction, and segmentation. During the prediction phase, we utilize a Slicing-Aided Hyper Inference (SAHI) tool to focus on the feature information of small feral pigeon targets. Experiments were conducted on a feral pigeon dataset to evaluate model performance. The results reveal that our model achieves excellent recognition performance for feral pigeons.

Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring.

Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for ambulatory diagnosis and monitoring applications of populations at risk of cardiovascular disease, generally due to a limited sample size. This paper introduces an algorithm for BP estimation solely reliant on photoplethysmography (PPG) signals and demographic features. It automatically obtains signal features and employs the Markov Blanket (MB) feature selection to discern informative and transmissible features, achieving a robust space adaptable to the population shift. This approach was validated with the Aurora-BP database, compromising ambulatory wearable cuffless BP measurements for over 500 individuals. After evaluating several machine-learning regression methods, Gradient Boosting emerged as the most effective. According to the MB feature selection, temporal, frequency, and demographic features ranked highest in importance, while statistical ones were deemed non-significant. A comparative assessment of a generic model (trained on unclassified BP data) and specialized models (tailored to each distinct BP population), demonstrated a consistent superiority of our proposed MB feature space with a mean absolute error of [Formula: see text] for systolic BP and [Formula: see text] for diastolic BP on the whole dataset. Moreover, we present a first comparison of in-clinic vs. ambulatory models, with performance significantly lower for the latter with a drop of [Formula: see text] in systolic ( ) and [Formula: see text] for diastolic ( ) estimation errors. This work contributes to the resilient understanding of BP estimation algorithms from PPG signals, providing causal features in the signal and quantifying the disparities between ambulatory and in-clinic measurements.

On bootstrap based variance estimation under fine stratification.

The primary focus of all sample surveys is on providing point estimates for the parameters of primary interest, and also estimating the variance associated with those point estimates to quantify the uncertainty. Larger samples and important measurement tools can help to reduce the point estimates' uncertainty. Numerous effective stratification criteria may be used in survey to reduce variance within stratum. In fine stratification design, the population is divided into numerous small strata, each containing a relatively small number of sampling units as one or two. This is done to ensure that certain characteristics or subgroups of the population are well-represented in the sample. But with many strata, the sample size within each stratum can become small, potentially resulting in higher errors and less stable estimates. The variance estimation process becomes difficult when we only have one unit per stratum. In that case, the collapsed stratum technique is the classical methods for estimating variance. This method, however, is biased and results in an overestimation of the variance. This paper proposes a bootstrap-based variance estimator for the total population under fine stratification, which overcomes the drawbacks of the previously explored estimation approach. Also, the estimator's properties were investigated. A simulation study and practical application on survey of mental health organizations data were done to investigate properties of the proposed estimators. The results show that the proposed estimator performs well.

Updated prevalence of latent prostate cancer in Chinese population and comparison of biopsy results: An autopsy-based study

Prostate cancer detected by autopsy is named latent prostate cancer. As the repertoire of clinical prostate cancer, latent cancer may better reflect the disease burden. Unlike clinical prostate specimens, which are obtained exclusively from biopsy-positive cases, prostate specimens obtained through autopsy provide information on biopsy-negative cases, helping calculate the true sensitivity of prostate biopsy. From 2014 to 2021, we collected autopsy specimens of the prostate from body donors in China and performed transperineal and transrectal biopsies on specimens before step-sectioning and pathological measurements. We found that the crude prevalence of latent prostate cancer in middle-aged and elderly men was 35.1% (81/231), which was higher than previous estimates for Chinese populations. The overall per-patient sensitivities of transperineal and transrectal biopsies were not significantly different (33.3% vs. 32.1%, p= 0.82), but the two approaches differed in preferential sampling area along the proximal-distal axis of the prostate. Transperineal biopsy had a higher sensitivity for detecting clinically significant lesions in the distal third (34.7% vs. 16.3%, p= 0.02) and distal half (30.6% vs. 18.1%, p= 0.04), while transrectal biopsy had a higher sensitivity for lesions in the proximal half (25.0% vs. 13.9%, p= 0.046). Both transperineal and transrectal methods of biopsy missed most small lesions (<0.1mL) and 35.3% (6/17) of large lesions (>0.5mL). In conclusion, the prevalence of latent prostate cancer in China has increased over the past 2 decades. Systematic transperineal and transrectal methods of biopsy had comparable sensitivities but had different preferential sampling areas. Both approaches miss one-third of large lesions.

Experience in assessing the abundance and spatial distribution of geese on the Taimyr Peninsula using the aerial survey method

The Taimyr Peninsula is a key region for reproduction of waterfowl of European and Asian wintering populations. The uniqueness of Taimyr is that birds migrate there from both the west and the east, following five out of eight global flyways: East Atlantic, Black Sea/Mediterranean, West Asian/East African, Central Asian, and East Asian/Australasian. The study develops the topic of conservation ornithology and sustainable use of biological resources raised at the First All-Russian Ornithological Congress by Dr. Evgeny Syroechkovsky. Russia lacks the system of state monitoring and assessment of waterfowl populations, in particular with the use of aerial survey methods. In 2019, counts of geese in the tundra zone of the Taimyr Peninsula were carried out from the board of an ultralight aircraft specially designed for aerial surveys. New data on the abundance and spatial distribution of geese were obtained. We substantiated the methodology for the survey flight routing with no transects and the use of GIS, remote sensing, and generalized additive models (GAM) for interpreting the results and extrapolating estimates of goose abundance. A database of the results of aerial surveys was compiled (http://rggsurveys.ru). According to our estimate, for the first time made by statistical modeling for the tundra zone of the Taimyr Peninsula, more than 1.5 million White-fronted geese, about 450000 Bean geese, 78400 Red-breasted geese, and 43000 Lesser White-fronted geese nest and moult on Taimyr. Comparison of these figures with modern estimates of the world populations of these species demonstrates the key role of the Taimyr Peninsula in maintaining populations of rare and economically important Anseriformes.

ASSESSMENT OF A COMMUNITY MANAGEMENT PROGRAM OF RIVER TURTLES (PODOCNEMIDIDAE: Podocnemis) IN ZÁBALO, ECUADORIAN AMAZON

In Zábalo community within the Cuyabeno Fauna Production Reserve, a 26-year local initiative aimed at river turtle management was initiated following a significant decline in Podocnemis unifilis and P. expansa populations. Despite the program's lengthy history, weak monitoring and validation procedures prompted a comprehensive evaluation of management efficacy to strengthen the communitarian program. This assessment encompasses population estimates derived from nesting data (density, abundance, hatching success, hatchling production) for P. unifilis between 1994 and 2019. Some available data were also compiled for P. expansa which has not been the main focus of the communitarian program. Results highlight a positive temporal trend in P. unifilis population estimators, and a consistent high hatching success (81%), underscoring the program's contribution to the conservation of the species. Nonetheless, further research is imperative to assess age-specific population trends and enhance monitoring and management strategies for the relatively rare P. expansa.

TARGETING UNDERREPRESENTED POPULATIONS IN PRECISION MEDICINE: A FEDERATED TRANSFER LEARNING APPROACH.

The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research poses a significant barrier to translating precision medicine research into practice. Prediction models are likely to underperform in underrepresented populations due to heterogeneity across populations, thereby exacerbating known health disparities. To address this issue, we propose FETA, a two-way data integration method that leverages a federated transfer learning approach to integrate heterogeneous data from diverse populations and multiple healthcare institutions, with a focus on a target population of interest having limited sample sizes. We show that FETA achieves performance comparable to the pooled analysis, where individual-level data is shared across institutions, with only a small number of communications across participating sites. Our theoretical analysis and simulation study demonstrate how FETA's estimation accuracy is influenced by communication budgets, privacy restrictions, and heterogeneity across populations. We apply FETA to multisite data from the electronic Medical Records and Genomics (eMERGE) Network to construct genetic risk prediction models for extreme obesity. Compared to models trained using target data only, source data only, and all data without accounting for population-level differences, FETA shows superior predictive performance. FETA has the potential to improve estimation and prediction accuracy in underrepresented populations and reduce the gap in model performance across populations.

PP97 How Health Technology Agencies Estimate Target Population Size For Medical Devices: The Example Of Spinal Cord Stimulation

IntroductionSpinal cord stimulation (SCS) is an effective and safe option for patients with refractory neuropathic pain, with positive health technology assessment (HTA) recommendations across Western Europe, yet SCS uptake remains low. Estimating target patient populations within HTAs may impact upon medical device uptake, pricing and access. However, there is a dearth of information on how this is typically conducted. This study aimed to compare the approaches of Western Europe HTA agencies for estimating the target population for SCS.MethodsA survey was conducted among members of the Health Economics and Reimbursement function from Western Europe to collect country-specific information on how HTA agencies assess the target population of medical devices (MD). The estimations of the target population for SCS were extracted from HTA publications and compared.ResultsEight Health Economics and Reimbursement (HER) Analysts from France, Germany, the United Kingdom (UK), Belgium, Spain, Italy, Sweden and Norway completed the survey. HTA Agencies in France, UK and Belgium routinely ask for epidemiological data in the manufacturer submission, whereas in Germany, Sweden and Norway the request is dependent on the type of HTA submission. All HTA agencies, except NICE (UK), perform an independent estimation of the target population. HTA agencies in Germany and UK typically use epidemiological data from industry. In all countries, the estimation of the target population may indirectly impact the price of the MD, especially when budget impact analysis indicates a potential high use of healthcare resources. Only France, Belgium and UK have published HTA recommendations about SCS, however the estimated target population, nor the number of patients with refractory neuropathic pain, is not always included. Only the French and UK HTA agencies publish an approximation of the target population for SCS using the number of patients implanted every year.ConclusionsThis study showed there is a lack of harmonization between Western European HTA agencies’ guidelines on the inclusion and estimation of target patient populations. The new EU HTA Regulation should help to address this situation.

Estimates of diagnosed dementia prevalence and incidence among diverse beneficiaries in Traditional Medicare and Medicare Advantage

AbstractBackgroundApproximately half of Medicare beneficiaries are enrolled in Medicare Advantage (MA) ‐ a private plan alternative to Traditional Medicare (TM). Yet little is known about diagnosed dementia rates among Medicare Advantage enrollees, limiting population estimates.Methods100% claims of Medicare beneficiaries using Encounter data for MA and claims for TM were used to quantify diagnosed dementia prevalence and incidence rates in MA, compare rates to TM, and provide estimates for the entire Medicare population and for different racial/ethnic populations.ResultDementia incidence and prevalence among MA beneficiaries was 2.54% (95%CI 2.53‐2.55) and 7.04% (95%CI 7.03‐7.06). Comparison to TM adjusted for sociodemographic and health differences among beneficiaries in MA and TM; the prevalence of diagnosed dementia among beneficiaries in MA was lower (7.1%; 95%CI 7.12‐7.13) than in TM (8.7%; 95%CI 8.71‐8.72). Diagnosed dementia incidence rate was also lower in MA (2.50%; 95%CI 2.50‐2.50) compared to TM (2.99%; 95%CI 2.99‐2.99). There were lower rates in MA compared to TM for men and women and White, Black, Hispanic, Asian, American Indian/Alaska Native persons. Diagnosed dementia prevalence and incidence for the entire Medicare population was 7.9% (95%CI: 7.91‐7.93) and 2.8% (95%CI: 2.77‐2.78).ConclusionLower diagnosed dementia rates in MA compared to TM may exacerbate racial/ethnic disparities in diagnosed dementia. Rates tracked over time will provide understanding of impact on dementia diagnosis of 2020 MA risk adjustment for dementia.

Multidimensional foot measurements-based sex estimation for Taiwanese population in foot length overlapping interval

ABSTRACT Estimating the sex from unknown bodies or fragmented human remains is essential for forensic identification. For sex estimation from foot features, previous studies highly depend on the foot length, resulting in a single-dimensional threshold and thusly poor performance for data falling into the foot length overlapping interval. Moreover, available information about the sex estimation from foot dimensions among Taiwan population is lacking. Hence, this study proposes a novel method to achieve multidimensional foot measurements-based sex estimation for the human falling into the overlapping foot interval. In total, 780 men and 670 women with same foot lengths ranging from 23.0 to 25.5 cm were recruited. A three-dimensional foot scanner was used to measure 10 dimensions of both feet. Bilateral differences were detected in several foot dimensions under the same foot length categories. The support vector machine method provided better results in terms of accuracy (86.05%), recall (86.44%), specificity (85.59%), and precision (83.77%). This study indicates the importance of other variables including foot length, ball girth, instep girth, ball of foot length, and outside ball of foot length in sex estimation. The findings will provide helpful information for forensic practice for Taiwanese and extend the application range of machine learning methods.

Survival Outcomes of Patients with Diffuse Large B-Cell Lymphoma Who Received Autologous Stem Cell Transplantation in Germany - Real World Evidence from an Administrative Database

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma with increasing incidence. Autologous stem cell transplantation (ASCT) is a treatment option for patients who relapse or progress after up-front chemoimmunotherapy if they suitable for ASCT. However, patients may not qualify for this treatment because of age and/or comorbidity or because they are non-responder to second-line chemoimmunotherapy. Limited real-world data of the ASCT patient population is available for Germany. Therefore, this retrospective database analysis was conducted to describe real-world survival outcomes of patients with DLBCL who received ASCT in Germany. Patients and Methods: Using a large claims database of the German statutory health insurance with 6.7 million enrollees between 2010 and 2019, we identified patients who were newly diagnosed with DLBCL, had no other cancer co-morbidity, and received ASCT (index date). Pre-defined treatment lines were used to identify the utilization of ASCT. Unadjusted overall survival (OS) from index was plotted by means of the Kaplan-Meier estimator for the overall population and for those patients who had/had not a subsequent treatment line after ASCT. Furthermore, we performed a Cox proportional hazard survival regression with the following co-variates: age, sex, Charlson Comorbidity Index (CCI), time from start of first line treatment to ASCT, and presence of subsequent treatment after ASCT. Results: 112patients received ASCT. Among them, 39.3% received subsequent treatment after ASCT. Time to ASCT was on average 11.7 months for the entire cohort. Median OS estimated by Kaplan-Meier was 83.4 months for the entire cohort and 27.8 months for patients who received subsequent treatment. Median OS was not estimated for the group without subsequent treatment, for whom the Kaplan-Meier curve reached a plateau at just under 90%, assuming a potential cure. Finally, the only variable significantly reducing the OS was the presence of subsequent treatment after ASCT in a time-dependent model. Conclusion: Given the high survival outcomes, ASCT is a valuable treatment option for relapsed/refractory DLBCL. However, only a small subset of patients is fit enough to benefit from this treatment and patients who fail ASCT have a poor prognosis. Therefore, novel therapeutic approaches are required to address these unmet medical needs.

Augmentation of Real-World Mortality Data in the Electronic Medical Record: Assessing the Impact on Overall Survival Estimates in Multiple Myeloma

Introduction: Utilization of real-world data (RWD) has progressively grown as a valuable supplement to randomized clinical trials in the context of drug development. Overall survival (OS) is a critical measure for assessing treatment effectiveness. Nevertheless, due to the frequent incompleteness in mortality data in the real world, there is a potential for overestimating OS. RWD has facilitated substantial research on multiple myeloma (MM), the second most prevalent hematologic malignancy. Given the paramount importance of OS as a clinical outcome in hematology and oncology settings, ensuring the completeness of mortality data is of utmost significance. The purpose of this study was to assess how different types of RWD mortality data sources affect OS estimates among patients with MM. Methods: This retrospective study included patients aged ≥18 years with initial diagnosis of MM between January 1, 2011, and December 31, 2022. Excluded populations consisted of those with an initial diagnosis of one or more primary malignancies other than MM, non-melanoma skin cancer, and/or cervical carcinoma in situ, and follow-up time &amp;lt;30 days. The study population was derived from ConcertAI real-world electronic medical record (EMR)-based oncology data (ConcertAI RWD360 dataset), representing approximately 7.6 million cancer patients from community and academic oncology practices throughout the United States. Death information is available as captured in the EMR as well as through patient-level linkage from external sources including the Social Security Death Index (SSDI), obituary, and open claims. Patients were categorized based on their source of death date. One composite death date based on the hierarchical order of availability from EMR, SSDI, obituary, and open claims was generated. OS was defined from the time of initial diagnosis date of MM to date of death. Patients were censored at the last recorded clinical activity date. Kaplan-Meier (KM) methods and Log-Rank tests were used to estimate OS across different mortality source categories. Results: The study population included 49,541 patients (53.1% male, 61.0% white) with a median age of 68 years (range: 18, 88) at initial MM diagnosis. Median follow-up from diagnosis to death or censored date was 2.4 years (range: 0.1, 12.5). Distribution of deceased patients by year of diagnosis was similar between the four sources. The number and percentage of patients with death event and date in each source were: 12,599 (25.4%) from obituary, 11,734 (23.7%) from EMR, 5,453 (11.0%) from SSDI, and 4,972 (10.0%) from open claims. Upon hierarchical integration of death date from the individual sources, 19,909 (40.2%) patients had an event. Figure 1 and Table 1 present the KM OS results by the death sources. Conclusion: This study evaluated the completeness of death date from multiple sources and its impact on OS estimates for an EMR-selected MM patient population. Among the sources evaluated, EMR and obituary had the highest level of death information captured relative to SSDI and open claims. Highest completion of death date was achieved when EMR death date was integrated with external data from obituary, SSDI, and open claims, resulting in OS estimates that are consistent with previous knowledge on MM prognosis. Completeness in capture of mortality data is essential to generate reliable estimates of OS in real-world studies, and integration of death information from multiple sources is critical in achieving this augmentation.

Modeling ocean distributions and abundances of natural- and hatchery-origin Chinook salmon stocks with integrated genetic and tagging data.

Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations. We introduce and fit a comprehensive joint model that integrates data from CWT recoveries and GSI sampling to inform the marine life history of Chinook salmon stocks at spatial and temporal scales relevant to ongoing fisheries management efforts. In a departure from similar models based primarily on CWT recoveries, modeled stocks in the new framework encompass both hatchery- and natural-origin fish. We specifically model the spatial distribution and marine abundance of four distinct stocks with spawning locations in California and southern Oregon, one of which is listed under the U.S. Endangered Species Act. Using the joint model, we generated the most comprehensive estimates of marine distribution to date for all modeled Chinook salmon stocks, including historically data poor and low abundance stocks. Estimated marine distributions from the joint model were broadly similar to estimates from a simpler, CWT-only model but did suggest some differences in distribution in select seasons. Model output also included novel stock-, year-, and season-specific estimates of marine abundance. We observed and partially addressed several challenges in model convergence with the use of supplemental data sources and model constraints; similar difficulties are not unexpected with integrated modeling. We identify several options for improved data collection that could address issues in convergence and increase confidence in model estimates of abundance. We expect these model advances and results provide management-relevant biological insights, with the potential to inform future mixed-stock fisheries management efforts, as well as a foundation for more expansive and comprehensive analyses to follow.

Comparing approaches to determining poor oral health among older adults in a national survey.

To compare the clinical validity of the three approaches in residential care facility residents. In NZ residential care facilities, the interRAI assessment tool is used by trained registered nurses for assessing oral status when new residents are admitted, but its validity has been questioned. Although Locker's global oral health item has been used to measure oral health in surveys and health services research, it is not routinely used in care facilities, yet its clinical validity has been demonstrated in population-based samples. Self-perceived oral health need may also be useful. Using a secondary analysis of clinical and self-reported data from a national survey of nursing home residents (the 2012 New Zealand Older People's Oral Health Survey, or OPOHS), we compared the validity of Locker's item, the interRAI tool and self-reported treatment need for identifying three key clinical indicators of poor oral health among dentate older adults; those were coronal caries (3+ teeth affected), root caries (1+ teeth affected) and xerostomia. Analyses were conducted using STATA, and survey weighting was used to obtain estimates for a source population of 25-843 individuals. The prevalence of 3+ teeth with coronal caries was 28.7% (23.9, 34.0), the prevalence of 1+ teeth with root caries was 33.7% (28.7, 39.0), the prevalence of xerostomia was 23.1% (18.4, 28.3). Marked gradients in prevalence risk ratio were seen across different categories of Locker's global oral health item and the interRAI assessment tooth for coronal caries and xerostomia. Locker's global oral health item gave a better fitting model and was more discriminative in detecting coronal caries than the interRAI assessment tool (Lockers AIC = 0.76, interRAI AIC = 0.81). None of the approaches was particularly discriminative for root surface caries experience. Self-reported approaches are discriminative for poor oral health. Standardised assessment tools used in residential care facilities should consider including a self-assessment component such as Locker's global oral health item.

The first population size estimation of transgender and non-binary people in Georgia

Purpose Accurate data on the number of transgender and non-binary people are essential for the effectiveness of planning and implementation of transgender services, as well as for advocacy, but these data are insufficient. Our goal in this study was to estimate the number of transgender people in Georgia and compare it with estimations in other Eastern European and Central Asian (EECA) countries. Methods We used a variety of methodologies, including the mobile/web app multiplier, service multiplier, and network-based capture-recapture (Telefunken) methods, which all were included in a survey of 100 transgender people. A final estimation was obtained using the Anchored Multiplier method. We also review the existing research on estimating transgender and non-binary population sizes in EECA countries. Results The estimation final value of transgender and non-binary people in Georgia was 0.06 [0.05–0.07%] of the total adult (18–54 years) population of the country or 1,044 [870–1,219]. Conclusion Our transgender and non-binary population size estimates in Georgia are in line with those in other EECA countries. These estimates can offer useful information for planning and assessing trans specific services, including HIV prevention programs at the national level. Furthermore, we believe that our findings will close the information gap regarding regional estimates of transgender and non-binary populations.

Efficient multiply robust imputation in the presence of influential units in surveys

AbstractItem nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply robust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient version of multiply robust estimators based on the concept of conditional bias, a measure of influence. We present the results of a simulation study to show the benefits of our proposed method in terms of bias and efficiency.

Trends in Chlamydia trachomatis IgG seroprevalence in the general population of the Netherlands over 20 years

ObjectivesTo report sex and age-specific Chlamydia trachomatis (Ct) seroprevalence estimates in the general population of the Netherlands between 1996 and 2017 and identify risk factors associated with Ct seropositivity.MethodsParticipants (n=5158,...