Estimates Of Populations Research Articles

#6737 ESTIMATED GLOMERULAR FILTRATION RATE BY FIVE DIFFERING MEASURES, RACE, GENETIC RISK AND PREDICTION OF CARDIOVASCULAR DISEASE AND MORTALITY

Abstract Background and Aims Estimated glomerular filtration rate (eGFR), a significant measurement of chronic kidney disease (CKD), is associated with adverse health outcomes in the general population but whether its utility differs by race and genetic risk remains unclear. The current study aims to investigate the performance of CKD staging and risk prediction in cardiovascular disease and mortality based on five eGFR measures among people of different ethnic groups and genetic background. Method In this prospective cohort study, we included 431,126 participants from the UK Biobank, deriving hazard ratios and 95% confidence intervals from multivariable adjusted Cox proportional hazards models. C statistic and net reclassification improvement (NRI) were utilized for comparing the predictive benefit of eGFR measures when added to the traditional cardiovascular risk factors. Results Over a median of 13.4 (IQR 12.7-14.9) years of follow-up, 27,757 (6.4%) participants died from any cause, of which 3272 (0.8%) died primarily from CVD. 39,522 (9.2%) incident composite CVD hospitalization and 911 (0.2%) ESRD occurred. Two new eGFR equations refitted without race provided lower estimates in Black population but higher in other ethnic backgrounds than the corresponding old ones. The relative risk of adverse outcomes was generally greater in Blacks and participants with low genetic susceptibility to impaired kidney function. The superiority of cystatin C-based eGFR measures in risk prediction did not differ by race and genetic risk. Conclusion Eliminating the use of racial categories in eGFR calculation help enhance the access to specialist care and intervention at the early stage of CKD for Black population. Cystatin C-based eGFR measures can be extensively used in risk assessment in clinical practice.

#4028 INSIDE CKD: PROJECTING THE POPULATION LEVEL CLINICAL BURDEN OF CHRONIC KIDNEY DISEASE ACCORDING TO URINE ALBUMIN-TO-CREATININE RATIO CATEGORIES

Abstract Background and Aims Chronic kidney disease (CKD) and its associated complications constitute a major challenge for healthcare systems worldwide. Albuminuria, measured as albumin-to-creatinine ratio (uACR), is a critical marker of glomerular injury and endothelial dysfunction. Elevated uACR is an independent predictor of CKD progression and cardio-renal mortality [1,2]. However, there is a paucity of data translating the burden of CKD at the population level according to uACR categories, in order to promote evidence-based policies. This study aims to assess the future epidemiological and financial burden of CKD using the Inside CKD microsimulation [3]. Specifically, we report CKD population level projections for cardio-renal complications, progression to end stage kidney disease (ESKD), and death due to any cause according to uACR categories. Method The Inside CKD microsimulation was used to simulate virtual individuals from 28 countries and regions. Individuals were assigned baseline characteristics such as age or sex based on national statistics, and estimated glomerular filtration rate (eGFR), uACR, CKD stage, and cardio-renal complications based on data from national health surveys or epidemiological studies. The following cardio-renal complications were projected between 2022 and 2027 according to uACR categories (normo-, micro-, macro-albuminuria): myocardial infarction (MI), stroke, heart failure (HF), CKD transition from stage 3 to 4 and from 4 to 5 (defined as a change in either eGFR or uACR category), and death due to any cause. Results Projected estimates for the total CKD population (all stages) with macro-albuminuria varied by country and region for 2023 (mean = 9.8%, range = 1.6% – 40.2%). The lowest percentages of macro-albuminuria (< 5%) were in Romania, Belgium, and the UK, compared to the highest (≥ 27%) in Brazil, Philippines and Mexico. Macro-albuminuria is associated with a higher relative risk of cardio-renal outcomes on a per person basis, but according to these estimates only a small proportion of the population have macro-albuminuria. Hence, the predominant CKD population with normo- or micro-albuminuria would be expected to account for most of the clinical burden. Accordingly, most of the cardio-renal incident events projected to occur by 2027 will be in the population with normo- or micro-albuminuria in all countries and regions (Figure 1): MI (97.9%), stroke (96.5%), HF (98.0%), the transition from CKD stage 3 to 4 (94.9%) and from stage 4 to 5 (97.5%), and death due to any cause (95.6%) (percentages represent mean and included the combined normo- and micro-albuminuria populations). Conclusion Although, macro-albuminuria is associated with a higher relative risk of cardio-renal outcomes on a per patient basis, the total CKD population should be considered with regards to the clinical burden in absolute terms. The Inside CKD microsimulation supports early intervention in the total CKD population, including individuals with normo- or micro-albuminuria, to reduce cardio-renal outcomes, delay progression to ESKD, and therefore the requirement for costly interventions, including heart related hospitalisations, transplantation and dialysis.

MSR31 Applying Multi-Level Network Meta-Regression (ML-NMR) to a Case Study in Triple-Class Exposed (TCE) Relapsed/Refractory Multiple Myeloma (RRMM)

BACKGROUND: ML-NMR provides a framework for defining an individual-level regression model, which is integrated over aggregate populations to form an aggregate-level model. ML-NMR avoids aggregation bias and produces estimates in any target population. ML-NMR has been published and applied to binary outcomes, but not time-to-event outcomes.

Adverse drug reactions and their fatal outcomes in clozapine patients in VigiBase: Comparing the top four reporting countries (US, UK, Canada and Australia)

BackgroundPharmacovigilance findings and box warnings in the clozapine package inserts have marked the history of clozapine. ObjectiveThis is the largest review of clozapine adverse drug reactions (ADRs) and their associated fatal outcomes. Reports to the World Health Organization's global pharmacovigilance database, VigiBase™, were analyzed, extending from clozapine's introduction to December 31, 2022. MethodsThe analysis focused on the top four reporting countries: United States (US), United Kingdom (UK), Canada and Australia (83 % of fatal outcomes worldwide). Attempts were made to control for population and clozapine prescription in each country. ResultsClozapine ADRs worldwide accounted for 191,557 reports, with the highest number (53,505) in “blood and lymphatic system disorder”. Of the 22,596 fatal outcomes reported in clozapine patients, 9587 were from the US, 6567 from the UK, 3623 from Canada and 1484 from Australia. The top category worldwide in fatal outcomes was nonspecifically labeled “death” with 46 % (range 22–62 %). “Pneumonia” was second with 30 % (range 17–45 %). Agranulocytosis was numerically only the 35th top clozapine ADR associated with fatal outcomes. On average, 2.3 clozapine ADRs were reported per fatal outcome. Infections were associated with 24.2 % of the UK fatal outcomes (9.4 %–11.9 % in the 3 other countries). ConclusionsThe four countries appeared to report clozapine ADRs in different ways, making comparisons difficult. We estimated higher fatal outcomes in the UK and Canada after controlling for cross-sectional estimations of population and published clozapine use. This last hypothesis is limited by the lack of precise estimation of accumulated clozapine use in each country.

0432 Estimating the Prevalence of Obesity Hypoventilation Syndrome in a Rural Appalachian Inpatient Cohort

Abstract Introduction Patients with Obstructive Sleep Apnea (OSA) and concomitant Obesity Hypoventilation Syndrome (OHS) have been shown to have excess morbidity and mortality and higher healthcare expenditure when compared with patients with OSA only. Despite this, OHS is typically underdiagnosed and untreated. These effects may be more pronounced in rural Appalachian patients in whom rates of obesity, cardiovascular disease, metabolic disease, smoking, and COPD exceed the national average, yet poverty and challenging geography undermine access to healthcare. Methods Eligible patients from Sept 2019-Jan 2022 were evaluated for sleep disordered breathing under our hospital sleep medicine program. These patients were screened using high-resolution pulse oximetry, or a portable Type III sleep testing device, or both. Their data was recorded onto our program registry and retrospectively analyzed. Results Of 1676 hospitalized patients evaluated through our hospital sleep medicine program, we determined that 1345 (80.2%) of patients had a BMI >/=30 kg/m2. Of these patients, 823 (49.1%) had an arterial or venous blood gas collected during screening. Excluding patients with other potential causes for hypercapnia, 178 (10.6%) met criteria for OHS. 119 (66.8% of this subgroup) met criteria for OSA/OHS, while 33% did not have OSA (AHI/ ODI< 5). Patients with OSA/OHS overlap in our cohort typically had mild OSA. Conclusion When compared estimates in urban populations, our cohort is underrepresented. As such, many more patients with potential OHS and OSA/OHS overlap are discharged home without any treatment or plan for follow-up. One possible solution is suggested by the fact that only 49.1% of our initial cohort had an arterial or venous blood gas (ABG/VBG) collected during the admission. We recommend additional screening with serum bicarbonate and subsequent blood gases as part of an inpatient screening program for OSA/OHS. Early identification of these patients may facilitate early treatment while in-hospital and help reduce health disparity. Support (if any)

Outcome of a nationwide screening programme in young individuals

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Cardiac Risk in the Young. Background Sudden cardiac death (SCD) seemingly affects young, healthy individuals and is therefore recognised as an important public health concern for athletes and non-athletes. However, systematic population screening is not recommended in the United Kingdom (UK). Concerns include the low reported incidence of SCD in young individuals, the efficacy of the electrocardiogram (ECG) and the lack of robust evidence on cardiac screening. We sought to determine the diagnostic yield of a nationwide cardiac screening programme in young individuals. Importantly, we sought to report on the frequency of false-negative screen-test results, which is underrecognized in the literature, and the incidence of sudden cardiac arrest (SCA) and SCD amongst individuals who underwent screening. Methods From 2007 through 2018, 104,369 consecutive individuals, aged 14 to 35 years, underwent voluntary cardiac screening (62% were male, 91% were non-athletes). Initial evaluation consisted of a health questionnaire (HQ), ECG, and clinical consultation. Selective on-site echocardiography was available at the discretion of the consulting physician. Clinical outcomes were sourced from the Office for National Statistics-Hospital-Episode-Statistics database and an online HQ. Results During screening 280 (0.27%) individuals were found to have a cardiac condition associated with SCA/SCD and 115 received potentially life-saving treatments. A further 166 (0.16%) with congenital or valvular abnormalities were identified. During a mean follow-up of 6.2 ± 2.5 years, an additional 86 individuals, considered to have normal screening, were found to have a cardiac condition associated with SCA/SCD.After screening there were 86 deaths. Cardiac disorders accounted for 20 deaths (23%), all of which were sudden. In addition, 15 individuals survived a SCA. Primary electrical disorders accounted for 21 (60%) of the 35 cases of SCA/SCD, followed by cardiomyopathies (n=6), and congenital or acquired coronary artery disease (n=5). On the basis of a total of 626,550 person-years (PY), the combined incidence of SCA/SCD was 1:17,901 PY (5.6 per 100,000 PY). Males were at 3.5-fold higher risk than females (1:12,488 PY vs 1:44,067 PY, p < 0.01). For prevention of SCA/SCD the programme’s sensitivity was 19.4% with a specificity of 97.5%. For identifying conditions associated with SCD, the programme’s sensitivity was 76.5% with a specificity was 97.8%. Conclusion Diseases that are associated with SCD were identified in 0.27% of young individuals who underwent cardiac screening. A further 86 (0.08%) conditions were identified after screening. The incidence of SCA and SCD was 5.6 per 100,000 person-years, which is considerable higher than previous estimates in the general population. Most of these events were due to confirmed or presumed primary electrical disorders that had not been detected on screening.

Demographic Determinants and Geographical Variability of COVID-19 Vaccine Hesitancy in Underserved Communities.

COVID-19 hospitalizations and deaths disproportionately affect underserved and minority populations, emphasizing that vaccine hesitancy can be an especially important public health risk factor in these populations. To characterize COVID-19 vaccine hesitancy in underserved diverse populations. The Minority and Rural Coronavirus Insights Study recruited a convenience sample of adults (ages ≥18, n=3,735) from Federally Qualified Health Centers in California, Midwest (Illinois/Ohio), Florida and Louisiana and collected baseline data in November 2020-April 2021. Vaccine hesitancy status was defined as responses "no" or "undecided" to the question "Would you get a coronavirus vaccine, if it was available?" ("yes" categorized as not hesitant). The cross-sectional descriptive analyses and logistic regression models examined vaccine hesitancy prevalence by age, gender, race/ethnicity, and geography. The expected vaccine hesitancy estimates for the general population were calculated for the study counties using published county-level data. Crude associations with demographic characteristics within each region were assessed by the chi-squared test. The main effect model included age, gender, race/ethnicity, and geographical region to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Interactions between geography and each demographic characteristic were evaluated in separate models. The strongest vaccine hesitancy variability was by geographic region: in California 28.3% (26.5-31.1), the Midwest 36.1 % (32.1-40.2), Louisiana 59.1% (56.0-62.1), Florida 67.9% (65.0-70.8). The expected estimates for the general population were lower: 9.7% (California), 15.2 % (Midwest), 18.2% (Florida), and 27.0% (Louisiana). The demographic patterns also varied by geography. An inverted U-shape age pattern was found, with the highest prevalence among ages 25-34 in the Midwest (39.3%), Florida (79.5%,) and Louisiana (79.4%) (p <0.05). Females were more hesitant than males in the Midwest (36.5% vs 23.9%), Florida (71.6% vs 59.4%), and Louisiana (66.5% vs. 46.4%) (p<0.05). Racial/ethnic differences were found in California with the highest prevalence among non-Hispanic Black (45.8%) and in Florida with the highest among Hispanic (69.3%) participants (p<0.05) but not in the Midwest and Louisiana. The main effect model confirmed the U-shape association with age: strongest association with age 25-34, OR=2.28 (1.74, 2.99). Statistical interactions of gender and race/ethnicity with the region were significant, following the pattern found by the crude analysis. The associations with the female gender were strongest in Florida and Louisiana: ORs were 7.83 (5.94, 10.33) and 6.04 (4.52, 8.06) compared to males in California, respectively. Compared to non-Hispanic White participants in California, the strongest associations were found with being Hispanic in Florida and Black in Louisiana: ORs were 11.18 (7.01, 17.85) and 8.94 (5.53, 14.47), respectively. However, the strongest race/ethnicity variability was observed within California and Florida: ORs varied 4.7- and 2-fold between racial/ethnic groups in these regions, respectively. These findings highlight the role of local contextual factors in driving vaccine hesitancy and its demographic patterns.

Generalizability of 3D CNN models for age estimation in diverse youth populations using structural MRI

Recently, several studies have investigated the neurodevelopment of psychiatric disorders using brain data acquired via structural magnetic resonance imaging (sMRI). These analyses have shown the potential of sMRI data to provide a relatively precise characterization of brain structural biomarkers. Despite these advances, a relatively unexplored question is how reliable and consistent a model is when assessing subjects from other independent datasets. In this study, we investigate the performance and generalizability of the same model architecture trained from distinct datasets comprising youths in diverse stages of neurodevelopment and with different mental health conditions. We employed models with the same 3D convolutional neural network (CNN) architecture to assess autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), brain age, and a measure of dimensional psychopathology, the Child Behavior Checklist (CBCL) total score. The investigated datasets include the Autism Brain Imaging Data Exchange II (ABIDE-II, N = 580), Attention Deficit Hyperactivity Disorder (ADHD-200, N = 922), Brazilian High-Risk Cohort Study (BHRCS, N = 737), and Adolescent Brain Cognitive Development (ABCD, N = 11,031). Models’ performance and interpretability were assessed within each dataset (for diagnosis tasks) and inter-datasets (for age estimation). Despite the demographic and phenotypic differences of the subjects, all models presented significant estimations for age (p value < 0.001) within and between datasets. In addition, most models showed a moderate to high correlation in age estimation. The results, including the models' brain regions of interest (ROI), were analyzed and discussed in light of the youth neurodevelopmental structural changes. Among other interesting discoveries, we found that less confounded training datasets produce models with higher generalization capacity.

SNP-Based Heritability of Osteochondrosis Dissecans in Hanoverian Warmblood Horses.

Before the genomics era, heritability estimates were performed using pedigree data. Data collection for pedigree analysis is time consuming and holds the risk of incorrect or incomplete data. With the availability of SNP-based arrays, heritability can now be estimated based on genotyping data. We used SNP array and 1.6 million imputed genotype data with different minor allele frequency restrictions to estimate heritabilities for osteochondrosis dissecans in the fetlock, hock and stifle joints of 446 Hanoverian warmblood horses. SNP-based heritabilities were estimated using a genomic restricted maximum likelihood (GREML) method and accounting for patterns of regional linkage disequilibrium in the equine genome. In addition, we employed GREML for family data to account for different degrees of relatedness in the study population. Our results indicate that we were able to capture a larger proportion of additive genetic variance compared to pedigree-based estimates in the same population of Hanoverian horses. Heritability estimates on the linear scale for fetlock-, hock- and stifle-osteochondrosis dissecans were 0.41-0.43, 0.62-0.63, and 0.23-0.25, respectively, with standard errors of 0.11-0.14. Accounting for linkage disequilibrium patterns had an upward effect on the imputed data and a downward impact on the SNP array genotype data. GREML for family data resulted in higher heritability estimates for fetlock-osteochondrosis dissecans and slightly higher estimates for hock-osteochondrosis dissecans, but had no effect on stifle-osteochondrosis dissecans. The largest and most consistent heritability estimates were obtained when we employed GREML for family data with genomic relationship matrices weighted through patterns of regional linkage disequilibrium. Estimation of SNP-based heritability should be recommended for traits that can only be phenotyped in smaller samples or are cost-effective.

How many days of continuous physical activity monitoring reliably represent time in different intensities in cancer survivors.

Physical activity (PA) monitoring is applied in a growing number of studies within cancer research. However, no consensus exists on how many days PA should be monitored to obtain reliable estimates in the cancer population. The objective of the present study was to determine the minimum number of monitoring days required for reliable estimates of different PA intensities in cancer survivors when using a six-days protocol. Furthermore, reliability of monitoring days was assessed stratified on sex, age, cancer type, weight status, and educational level. Data was obtained from two studies where PA was monitored for seven days using the SenseWear Armband Mini in a total of 984 cancer survivors diagnosed with breast, colorectal or prostate cancer. Participants with ≥22 hours monitor wear-time for six days were included in the reliability analysis (n = 736). The intra-class correlation coefficient (ICC) and the Spearman Brown prophecy formula were used to assess the reliability of different number of monitoring days. For time in light PA, two monitoring days resulted in reliable estimates (ICC >0.80). Participants with BMI ≥25, low-medium education, colorectal cancer, or age ≥60 years required one additional monitoring day. For moderate and moderate-to-vigorous PA, three monitoring days yielded reliable estimates. Participants with BMI ≥25 or breast cancer required one additional monitoring day. Vigorous PA showed the largest within subject variations and reliable estimates were not obtained for the sample as a whole. However, reliable estimates were obtained for breast cancer survivors (4 days), females, BMI ≥30, and age <60 years (6 days). Shorter monitoring periods may provide reliable estimates of PA levels in cancer survivors when monitored continuously with a wearable device. This could potentially lower the participant burden and allow for less exclusion of participants not adhering to longer protocols.

The validity of causal claims with repeated measures designs: A within-study comparison evaluation of differences-in-differences and the comparative interrupted time series.

Modern policies are commonly evaluated not with randomized experiments but with repeated measures designs like difference-in-differences (DID) and the comparative interrupted time series (CITS). The key benefit of these designs is that they control for unobserved confounders that are fixed over time. However, DID and CITS designs only result in unbiased impact estimates when the model assumptions are consistent with the data at hand. In this paper, we empirically test whether the assumptions of repeated measures designs are met in field settings. Using a within-study comparison design, we compare experimental estimates of the impact of patient-directed care on medical expenditures to non-experimental DID and CITS estimates for the same target population and outcome. Our data come from a multi-site experiment that includes participants receiving Medicaid in Arkansas, Florida, and New Jersey. We present summary measures of repeated measures bias across three states, four comparison groups, two model specifications, and two outcomes. We find that, on average, bias resulting from repeated measures designs are very close to zero (less than 0.01 standard deviations; SDs). Further, we find that comparison groups which have pre-treatment trends that are visibly parallel to the treatment group result in less bias than those with visibly divergent trends. However, CITS models that control for baseline trends produced slightly more bias and were less precise than DID models that only control for baseline means. Overall, we offer optimistic evidence in favor of repeated measures designs when randomization is not feasible.

Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population

Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10-6). This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Conditional cross-design synthesis estimators for generalizability in Medicaid.

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases-lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in thissetting.

Widespread latitudinal asymmetry in the performance of marginal populations: A meta‐analysis

AbstractAimRange shifts are expected to occur when populations at one range margin perform better than those at the other margin, yet no global trend in population performances at range margins has been demonstrated empirically across a wide range of taxa and biomes. Here we test the prediction that, if impacts of ongoing climate change on performance in marginal populations are widespread, then populations from the high‐latitude margin (HLM) should perform as well as or better than central populations, whereas low‐latitude margin (LLM) populations should perform worse.LocationGlobal.Time period1995–2019.Major taxa studiedPlants and animals.MethodsTo test our prediction, we used a meta‐analysis to quantify empirical support for asymmetry in the performance of high‐ and low‐latitude margin populations compared to central populations. Performance estimates (survival, reproduction, or lifetime fitness) for populations occurring in their natural environment were derived from 51 papers involving 113 margin‐centre comparisons from 54 species and 705 populations from the Americas, Europe, Africa and Australia. We then related these performance differences to climatic differences among populations. We also tested whether patterns are consistent across taxonomic kingdoms (plants vs animals) and across realms (marine vs terrestrial).ResultsPopulations at margins performed significantly worse than central populations, and this trend was primarily driven by the low‐latitude margin. Although the difference was of small magnitude, it was largely consistent across biological kingdoms and realms. Differences in performance were weakly (p = .08) related to the difference in average temperatures between central and marginal populations.Main conclusionsThe observed asymmetry in performance in marginal populations is consistent with predictions about the effects of global climate change, though further research is needed to confirm the effect of climate. It indicates that changes in demographic rates in marginal populations can serve as early‐warning signals of impending range shifts.

Food Demand and Its Differential Effects on Household Food Access in Nigeria

This paper aims to examine how urban households’ food demand pattern differs with the extent of urbanization using a cross-sectional survey in southwest Nigeria. Using principal component analysis, sampled households were categorized by urbanicity index. A quadratic almost ideal demand system (QUAIDS) was employed to estimate expenditure and price elasticities. From the result, all seven food groups were normal goods across the urban categories, with the meat group being more expenditure-elastic than the others (1.16–2.42). The own-price elasticities differ among food commodities, with the meat and roots/tubers groups being most elastic at the low (−1.35) and middle (−1.05) urban categories, respectively. Findings suggest that changes in urbanicity levels have a substantial impact on the composition of household food demand and, by extension, the extent of food access in Nigeria. Therefore, the use of aggregate demand estimates for the entire urban population might underestimate the effect of expected changes in the urbanization level on food demand. JEL Classifications: C31, D12, Q18

On the use of antibiotics in plasticity research: Gastropod shells unveil a tale of caution.

Through phenotypic plasticity, individual genotypes can produce multiple phenotypes dependent on the environment. In the modern world, anthropogenic influences such as man-made pharmaceuticals are increasingly prevalent. They might alter observable patterns of plasticity and distort our conclusions regarding the adaptive potential of natural populations. Antibiotics are nowadays nearly ubiquitous in aquatic environments and prophylactic antibiotic use is also becoming more common to optimize animal survival and reproductive output in artificial settings. In the well-studied plasticity model system Physella acuta, prophylactic erythromycin treatment acts against gram-positive bacteria and thereby reduces mortality. Here, we study its consequences for inducible defence formation in the same species. In a 2 × 2 split-clutch design, we reared 635 P. acuta in either the presence or absence of this antibiotic, followed by 28-day exposure to either high or low predation risk as perceived through conspecific alarm cues. Under antibiotic treatment, risk-induced increases in shell thickness, a well-known plastic response in this model system, were larger and consistently detectable. Antibiotic treatment reduced shell thickness in low-risk individuals, suggesting that in controls, undiscovered pathogen infection increased shell thickness under low risk. Family variation in risk-induced plasticity was low, but the large variation in responses to antibiotics among families suggests different pathogen susceptibility between genotypes. Lastly, individuals that developed thicker shells had reduced total mass, which highlights resource trade-offs. Antibiotics thus have the potential to uncover a larger extent of plasticity, but might counterintuitively distort plasticity estimates for natural populations where pathogens are a part of natural ecology.

Development of formulae for dental age estimation using digital radiographs in the Mongolian population

This study aimed to examine the applicability of the pulp/tooth ratio (PTR) method for age estimation in Mongolian populations using panoramic radiographs and derive new regression formulae. Moreover, we aimed to assess the accuracy of these formulae in other subjects from the Mongolian population and compare them with the other formulae derived from different Asian populations.The total sample size of the study was 381. The formulae were derived from the examination of panoramic radiographs of 271 individuals aged 15–62 years. Following Cameriere’s method, PTR was calculated for the upper and lower canine teeth. Linear regression analyses were performed between the actual age and that obtained from upper-lower canine PTR and established formulae for age estimation. To verify the formulae, two types of test samples were collected: 73 panoramic radiographs and 37 periapical radiographs. The estimated age was calculated using our new formulae and three other formulae derived from Asian populations.The correlation coefficient between the actual age and that obtained by PTR was significantly negative for both canines. According to our new regression formulae, the differences between the estimated age and actual age showed a bell-shaped curve distribution in both test groups. While using the other formulae derived from the Asian population, the distribution patterns obtained were significantly different in the Mongolian population.This study was the first to examine the relationship between actual age and PTR in Mongolian population, and these results advance the field of forensic science in Mongolia.

Cardiovascular risk estimation in rural psychiatric inpatient populations: Cross-sectional preliminary study.

Cardiovascular risk was evaluated in patients admitted to rural inpatient psychiatric services over a one-year period in a sparsely populated region of the United Kingdom. Care records were analysed for risk factor recording, and cardiovascular risk estimated using the QRISK3 calculator, which estimates 10-year risk of myocardial infarction or stroke. Of eligible patients, risk factor recording as part of routine care was completed in 86% of possible QRISK3 inputs, enabling QIRSK3 estimation in all eligible patients. QRISK3 for this group was significantly raised relative to an age, sex and ethnicity-matched population, and high risk of cardiovascular disease (QRISK3 score >10%) was detected in 28% of patients. The results demonstrate that there is significant unmet need in rural patients for cardiovascular risk reduction that could be identified as part of routine care. An opportunity exists to integrate mental and physical healthcare by routinely assessing cardiovascular risk in rural psychiatric inpatients. Resources and training are needed to produce this risk information and act on it.

Uncertainties in measuring the dark matter signal from Milky Way satellites using Cherenkov telescopes

In this work, we present a modelling of the galactic sub-clumps based on statistical estimations of the full Milky Way satellite population. We introduce 10 substructure modellings (SM i , i ∈ {1, … , 10}) with the following varying parameters: a) subhalos inner profile, b) spatial distribution of subhalos, c) mass distribution of subhalos, d) total number of subhalos and e) concentration parameter. The sensitivity curves of CTA for sources in each model are calculated for the τ + τ - and bb̅ decay channels. With both detection of a signal (5σ) with the CTA and no signal observation, no model was effective in accessing the thermal values of 〈σv〉. We analyse the systematic effect introduced by the substructures models.

Comparing tools for automation of formant estimation in diverse pediatric populations

Pediatric vowel productions are notoriously hard to reliably quantify. Listener judgments and transcription are subjective, show low intra- and interrater reliability, and are influenced by listener bias. Formant estimation may offer more objectivity, but child speech is fraught with acoustic challenges. Children have less refined articulatory control and they make more production errors. They have high fundamental frequencies, wide formant bandwidths, more variable formant values, and increased subglottal coupling relative to adult speech. Historically, estimation of pediatric formants has been done manually, which is laborious and time-consuming. In recent years, automation tools have been developed to speed up the process. However, these tools have not been widely tested on pediatric speech samples. Even more critically, these tools have not been tested on diverse children. This study uses speech samples from children of color, children with disabilities, and children with both of these identities to compare three automation tools: SpeechMark® (Boyce etal., 2012), Fast Track™ (Barreda, 2021), and a custom Praat® (Boersma &amp; Weenink, 2021) script written by the first author. Outcomes of each tool will be compared and contrasted. The discussion will review the considerations, benefits, and tradeoffs of each automation tool when working with diverse pediatric speech samples.