Population Model Research Articles

Integrated Behavioral Health Care in Pediatric Practices: The Dollars Don’t Add Up

Abstract Given the prevalence of behavioral health disorders in children and adolescents, and ongoing access gaps, clinicians and policymakers have pushed to expand integrated care models in pediatric primary care settings. Despite the evidence surrounding the efficacy of integrated behavioral health models for pediatric populations, uptake has been slow. Practices report many implementation barriers, including stand-up costs, training needs, and inadequate administrative support. In this Commentary, we argue that perhaps even more fundamentally, ongoing financial challenges are restricting model adoption, scale, and sustainability, particularly for independent and smaller pediatric group practices. Two real-world case studies illustrate several key financial challenges and opportunity costs for such practices, including administrative barriers and lag times in contracting and credentialing behavioral health providers; reimbursement rates that fail to cover the costs of care delivery; opportunity costs for practice revenue; and persistent coding and billing restrictions. Policies aiming to fulfill the clinical promise of integrated behavioral health care must account for these fiscal realities, prioritizing billing and payment alignment with pediatric practices’ bottom dollar.

Multirhythmicity, Synchronization, and Noise-Induced Dynamic Diversity in a Discrete Population Model with Competition

The problem of mathematical modeling and analysis of stochastic phenomena in population systems with competition is considered. This problem is investigated based on a discrete system of two populations modeled by the Ricker map. We study the dependence of the joint dynamic behavior on the parameters of the growth rate and competition intensity. It is shown that, due to multistability, random perturbations can transfer the population system from one attractor to another, generating stochastic P-bifurcations and transformations of synchronization modes. The effectiveness of a mathematical approach, based on the stochastic sensitivity technique and the confidence domain method, in the parametric analysis of these stochastic effects is demonstrated. For monostability zones, the phenomenon of stochastic generation of the phantom attractor is found, in which the system enters the trigger mode with alternating transitions between states of almost complete extinction of one or the other population. It is shown that the noise-induced effects are accompanied by stochastic D-bifurcations with transitions from order to chaos.

Stem and progenitor cells in the synovial joint as targets for regenerative therapy.

Damage to articular cartilage, tendons, ligaments and entheses as a result of trauma, degeneration or inflammation in rheumatic diseases is prevalent. Regenerative medicine offers promising strategies for repairing damaged tissues, with the aim of restoring both their structure and function. While these strategies have traditionally relied on tissue engineering approaches using exogenous cells, interventions based on the activation of endogenous repair mechanisms are an attractive alternative. Key to advancing such approaches is a comprehensive understanding of the diversity of the stem and progenitor cells that reside in the adult synovial joint and how they function to repair damaged tissues. Advances in developmental biology have provided a lens through which to understand the origins, identities and functions of these cells, and insights into the roles of stem and progenitor cells in joint tissue repair, as well as their complex relationship with fibroblasts, have emerged. Integration of knowledge obtained through studies using advanced single-cell technologies will be crucial to establishing unified models of cell populations, lineage hierarchies and their molecular regulation. Ultimately, a more complete understanding of how cells repair tissues in adult life will guide the development of innovative pro-regenerative drugs, which are poised to enter clinical practice in musculoskeletal medicine.

Decarbonizing the textiles and clothing industry in South Africa: the role of renewable energy

Purpose South Africa’s textiles and clothing sector is positioned by the government to support economic growth and development. However, its expansion can increase carbon dioxide (CO2) emissions because of the high energy consumption and natural resource requirements. A proposed option to make the sector environmentally friendly is the adoption of renewable energy. This study aims to assess whether the CO2 emissions effect of the textiles and clothing sector can be reduced by adopting renewable energy. Design/methodology/approach CO2 emission function is formulated within the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) Model. Data for the analysis ranged from 1990 to 2022. Regression analysis is performed using the autoregressive distributed lag, fully modified ordinary least squares, canonical cointegrating regressions and dynamic ordinary least squares methods. Findings The textile and clothing sector positively affects CO2 emissions. Although renewable energy consumption has a direct positive long-run effect, it reduces the effect of the textiles and clothing sector on CO2 emissions. Practical implications The implications from the study include the formulation of policies that will hasten the adoption of renewable energy by the textiles and clothing sector for their activities. Originality/value This study contributes to the literature by assessing the carbon emissions effect of the textiles and clothing sector within the STIRPAT model. It also analyses the moderation effect of renewable energy on the textiles and clothing sector–carbon emissions nexus.

Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis

Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. However, no comprehensive review or meta-analysis has assessed the performance of these models across different sociocultural contexts. Therefore, this review systematically examines the performance of lung cancer risk prediction models in Western and Asian populations. PubMed and EMBASE were searched from inception through January 2023. Studies published in English that proposed a validated model on human populations with well-defined predictive performances were included. Two reviewers independently screened the titles and abstracts, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess study quality. A random-effects meta-analysis was performed, and a 95% confidence interval (CI) for model performance was reported. Between-study heterogeneity was adjusted for using the Hartung-Knapp-Sidik-Honkman test. A total of 54 studies were included, with 42 from Western countries and 12 from Asian countries. Most Western studies focused on ever-smokers (19/42; 45.2%) and the general population (17/42; 40.5%), and only two Asian studies developed models exclusively for never-smokers. Across both Western and Asian prediction models, the three most consistently included risk factors were age, sex, and family cancer history. In 45.2% (19/42) of Western and 50.0% (6/12) of Asian studies, models incorporated both traditional risk factors and biomarkers. In addition, 14.8% (8/54) of the studies directly compared biomarker-based models with those incorporating only traditional risk factors, demonstrating improved discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. External validation of PLCOM2012 (AUC = 0.748; 95% CI: 0.719–0.777) outperformed other prediction models, such as Bach (AUC = 0.710; 95% CI: 0.674–0.745) and Spitz models (AUC = 0.698; 95% CI: 0.640–0.755). Despite showing promising results, the majority of Asian risk models in our study lack external validation. Our review also highlights a significant gap in prediction models for never-smokers. Future research should focus on externally validating existing Asian models or incorporating relevant Asian risk factors into widely used Western models (PLCOM2012) to better account for unique risk profiles and lung cancer progression patterns in Asian populations.

The influence of seasonality and diapausing stages in patchy models for tick populations: a global attraction analysis

Abstract Lyme borreliosis, tick-borne encephalitis, and human granulocytic anaplasmosis are remarkable examples of tick-borne diseases. Motivated by their notable epidemiological role, this paper explores the interplay among seasonality, diapause, and diffusion on the population dynamics of ticks. To reach this goal, we analyze a metapopulation model in a fragmented ecosystem of m-patches, with ticks as a motivating example. Specifically, we offer a new methodology to derive criteria of global attraction in nonautonomous metapopulation models with delay (without assuming monotonicity requirements). A strength of our approach is that the results apply to metapopulations with any number of patches and topology. From a practical point of view, our theoretical analysis allows us to corroborate some previous experimental work. Another important conclusion of this paper is that the presence of sinks can benefit the tick population, simplifying the dynamical behavior of the whole metapopulation and increasing the total population size. To assess the real repercussions of the results, we analyze our model with parameters derived from real observations.

Lagrangians and Newtonian analogs for biological systems

This study investigates the potential for biological systems to be governed by a variational principle, suggesting that such systems may evolve to minimize or optimize specific quantities. To explore this idea, we focus on identifying Lagrange functions that can effectively model the dynamics of selected population systems. These functions provide a deeper understanding of population evolution by framing their behavior in terms of energy-like variables. We present an algorithm for generating Lagrangian functions applicable to a family of population dynamics models and demonstrate the equivalence between two-dimensional population models and a one-dimensional Newtonian mechanical analog. Furthermore, we explore the existence of conservation laws for these models, utilizing Noether’s theorems to investigate their implications.

Mitigating the effect of the population model uncertainty on the strong lensing Bayes factor using nonparametric methods

Mitigating the effect of the population model uncertainty on the strong lensing Bayes factor using nonparametric methods

An upper limit on the frequency of short-period black hole companions to Sun-like stars

Stellar-mass black holes descend from high-mass stars, most of which had stellar binary companions. However, the number of those binary systems that survive the binary evolution and black hole formation is uncertain by multiple orders of magnitude. The survival rate is particularly uncertain for massive stars with low-mass companions, which are thought to be the progenitors of most black hole X-ray binaries. We present a search for close black hole companions (orbital period lesssim 3,days, equivalent to separation lesssim 20 R_⊙) to AFGK-type stars in TESS ; that is, the non-accreting counterparts to and progenitors of low-mass X-ray binaries. Such black holes can be detected by the tidally induced ellipsoidal deformation of the visible star, and the ensuing photometric light curve variations. From an initial sample of $4.7 TESS stars, we have selected 457 candidate ellipsoidal variables with large mass ratios. However, after spectroscopic follow-up of 250 of them, none so far are consistent with a close black hole companion. On the basis of this non-detection, we determine (with $2σ$ confidence) that fewer than one in $10^5$ solar-type stars in the solar neighbourhood hosts a short-period black hole companion. This upper limit is in tension with a number of `optimistic' population models in the literature that predict short-period black hole companions around one in ∼ 10^ stars. Our limit is still consistent with other models that predict only a few in ∼ 10^

The Baryonic Tully–Fisher Relation. II. Stellar Mass Models

Abstract We present new color–ϒ* (mass-to-light ratio) models to convert Wide-field Infrared Survey Explorer W1 fluxes into stellar masses. We outline a range of possible star formation histories and chemical evolution scenarios to explore the confidence limits of stellar population models on the value of ϒ*. We conclude that the greatest uncertainties (around 0.1 dex in ϒ*) occur for the bluest galaxies with the strongest variation in recent star formation. For high-mass galaxies, the greatest uncertainty arises from the proper treatment of bulge–disk separation in which to apply different ϒ* relations appropriate for those differing underlying stellar populations. We compare our deduced stellar masses with those deduced from Spitzer Space Telescope 3.6 μm fluxes and stellar mass estimates in the literature using optical photometry and different ϒ* modeling. We find the correspondence to be excellent, arguing that rest-frame near-IR photometry is still more advantageous than other wavelengths.

Long-term seasonal forecasting model for the trans-regional migration of brown planthopper in Eastern China.

The brown planthopper (BPH), Nilaparvata lugens (Stål), is the most important rice pest in China and other East Asian countries. Identifying their source areas and predicting their population dynamics are crucial for managing migratory pests. Northern South China (NSC) is one of the key regions for northward BPH migration and a direct source of BPH in the key rice-growing area of the Lower Yangtze River Valley (LYRV). Hence, this study aimed to explore the environmental drivers affecting the population dynamics of BPH in NSC, and develop models for predicting the immigration levels in the LYRV. Initially, the BPH immigrants in NSC were identified to have mostly originated from northern and north-central Vietnam, Laos, and northeastern Thailand (15°-22°N) in May by using a trajectory analysis approach. The population model showed that immigration size of BPH in NSC in May can be predicted by the temperature observed in February over these source areas combined with the probability of BPH from south-central Vietnam (their principal overwintering region) immigrating to these source areas in March. Subsequently, the immigration size of BPH in NSC in May combined with the onset time of the South China Sea Summer Monsoon (a sign of rain belt movement and arrival of the flood season in China), can be used to predict the immigration level of BPH in the LYRV in July. These 2 prediction models could forecast nearly 2months in advance, allowing time for effective control measures to be implemented.

Fall and rise of a threatened raptor: Unraveling long-term population dynamics with spatially explicit integrated models.

Population dynamics are governed by the so-called four BIDE processes: birth, immigration, death, and emigration. However, most population models fail to explicitly consider all four processes, which may hinder a comprehensive understanding of how and why populations change over time. The advent of Integrated Population Models (IPMs) and recent developments in spatial mark-recapture models have enabled deeper insights into demography and dispersal. In this study, we merged both kinds of models into a spatially explicit IPM. By integrating count, reproduction, mark-recapture, and dispersal data, this framework permitted the separate modeling of all BIDE processes, which subsequently allowed (1) a fine-scale estimation of population dynamics and (2) the estimation of central population parameters and stages that have traditionally been elusive in demographic studies but are key to applied conservation, such as the long-term dynamics of floaters (sexually mature non-breeders), sink-source status, and dispersal processes. Using this approach, we carried out a fine-scale assessment of the long-term dynamics and demographic drivers of one long-lived Bonelli's eagle population from Western Europe (1986-2020). Our results illustrated a considerable population decline and subsequent recovery alongside multiple demographic insights scarcely documented to date in long-lived species. First, we reported a decrease and subsequent increase in floater numbers probably associated with parallel changes in the breeding population, hence contributing to the scarce empirical knowledge available about the role and dynamics of floaters. Second, we detected a change in average population functioning from a sink to a neutral contributor, thus shedding light on the flexibility and drivers of sink-source dynamics. Third, we underscored the central role of non-breeder survival for population recovery, suggesting that long-lived species conservation action should not only focus on adult or breeding populations, as is typically the case. Fourth, we quantified the magnitudes and variations of local and dispersal processes in the long term and discussed their potential implications in terms of management implementation. Overall, our study highlights the potential of spatially explicit IPMs to build more complete assessments of population dynamics, contribute to better-informed conservation action, and help fill knowledge gaps in ecological sciences.

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features

ObjectivesSarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an explainable prediction model of SO using easily available clinical characteristics.Setting and participantsA preliminary cohort of 1,431 participants from three community regions in Ziyang city, China, was used for model development and internal validation. For external validation, we utilized data from 832 residents of multi-center nursing homes.MeasurementsThe diagnosis of SO was based on the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) criteria. Five machine learning models (support vector machine, logistic regression, random forest, light gradient boosting machine, and extreme gradient boosting) were used to predict SO. The performance of these models was assessed by the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) approach was used for model interpretation.ResultsAfter feature reduction, an 8-feature model demonstrated good predictive ability. Among the five models tested, the support vector machine (SVM) model performed best in SO prediction in both internal (AUC = 0.862) and external (AUC = 0.785) validation sets. The eight key predictors identified were BMI, gender, neck circumference, waist circumference, thigh circumference, time to full tandem standing, time to five-times sit-to-stand, and age. SHAP analysis revealed BMI and gender as the most influential predictors. To facilitate the utilization of the SVM model in clinical setting, we developed a web application (https://svcpredictapp.streamlit.app/).ConclusionsWe developed an explainable machine learning model to predict SO in aging community and nursing populations. This model offers a novel, accessible, and interpretable approach to SO prediction with potential to enhance early detection and intervention strategies. Further studies are warranted to validate our model in diverse populations and evaluate its impact on patient outcomes when integrated into comprehensive geriatric assessments.

Economic Implications of Off-Road Cycle Lanes to Increase Physical Activity and Reduce Sex and Gender Differences in the Risk of Dementia.

Dementia represents one of the greatest global health challenges. There are known risk factors that might prevent or delay nearly 50% of the different types of dementia. There are substantial differences in risk factors and progression of dementia between women and men, including engagement in regular physical activity. The study aimed to quantify the impact of increasing women's participation in physical activity with off-road cycles and its effect on dementia incidence, relative to men and the associated health and social care costs. Our study employed a population model with secondary data analysis approach to investigate the potential economic effect of implementing off-cycle lanes in the UK. Data were drawn from published evidence on lifetime risk of dementia relative to physical activity in men and women for the UK population, levels of physical activity in the UK, evidence on the effectiveness of off-road cycle lanes in increasing the level of physical activity in men and women, lifetime costs of dementia in the UK. Two scenarios were compared, assuming an increase from the baseline levels of cycling of 40.3% and 114% for women and by 36.4% and 77% for men, respectively. Sensitivity analysis was conducted to account for variations in key variables. Current dementia related lifetime costs were estimated at £1090.1 billion, and total lifetime costs £3326.1 billion. Under Scenario 1, dementia related lifetime costs decreased by £4.7 billion, and total lifetime costs by £0.11 billion. In Scenario 2, dementia related lifetime costs decreased by £7.0 billion, and total lifetime costs by £1.9 billion. Off-road cycle lanes, especially for women, this type of structural and lifestyle intervention has the potential to bring health and economic benefits. Increased physical activity not only provides numerous health benefits, but also contributes to preventing the onset and lifetime costs of dementia.

Equilibrium analysis of discrete stochastic population models with gamma distribution.

Equilibrium analysis of discrete stochastic population models with gamma distribution.

Populational Model of Rhipicephalus microplus in Beef Cattle in the Southern Region of Paraná, Brazil

In order to describe the population dynamics of R. microplus in the southern region of the state of Paraná, Brazil, and to identify racial differences between hosts in the level of infestation by this parasite, the teleogins of approximately 70 males per year were counted every 14 days for 5 years. The herd comprised eight genetic groups, aged 12 to 24 months, in an area of 76 ha (60 ha of native fields and 16 ha of Hemarthria altíssima). The monthly averages of the counts per animal were analyzed using the GENMOD procedure, which accounts for the effects of genetic group, month, genetic group × month and cycle. Three generations of ticks of increasing infestation intensity were analyzed in December, February and May, respectively. Animals from Caracu and Canchim breeds as well as Charolais × Caracu and Aberdden Angus × Canchim cross-breeds had the lowest levels of infestation compared to Charolais and Aberdeen Angus. We conclude that by employing the aforementioned mixed breeds, in combination with a strategic control of initiating the application of acaricides in November, followed by two to three treatments every 21 days, this procedure represents the most effective method of controlling ticks in this region.

Adhesion and volume filling in one‐dimensional population dynamics under no‐flux boundary condition

AbstractWe study the (generalized) one‐dimensional population model developed by Anguige and Schmeiser [J. Math. Biol. 58 (3) (2009), 395–427], which reflects cell–cell adhesion and volume filling under no‐flux boundary condition. In this generalized model, depending on the adhesion and volume filling parameters , the resulting equation is classified into six types. Among these, we focus on the type exhibiting strong effects of both adhesion and volume filling, which results in a class of advection–diffusion equations of the forward–backward–forward type. For five distinct cases of initial maximum, minimum, and average population densities, we derive the corresponding patterns for the global behavior of weak solutions to the initial and no‐flux boundary value problem. Due to the presence of a negative diffusion regime, we indeed prove that the problem is ill‐posed and admits infinitely many global‐in‐time weak solutions, with the exception of one specific case of the initial datum. This nonuniqueness is inherent in the method of convex integration that we use to solve the Dirichlet problem of a partial differential inclusion arising from the ill‐posed problem.

Incorporating adult age into mosquito population models: implications for predicting abundances in changing climates.

Mosquito-borne diseases (MBDs) pose increasing threats under future climate change scenarios and an understanding of mosquito population dynamics is pivotal to predicting future risk of MBDs. Most models that describe mosquito population dynamics often assume that adult life-history is independent of adult age and yet mosquito senescence is known to affect mosquito mortality, fecundity and other key biological traits. Despite this, little is known about the effects of adult age at the level of the mosquito population, especially under varying temperature scenarios. We develop a stage-structured delayed differential equations (DDEs) model incorporating the effects of the abiotic environment and adult age to shed light on the complex interactions between age, temperature, and mosquito population dynamics. Taking Culex pipiens, a major vector of West Nile Virus, as our study species our results show that failing to consider mosquito senescence can lead to underestimates of future mosquito abundances predicted under climate change scenarios. We also find that the age-dependent mechanisms combined with the effects of density-dependent mortality on the immature stages can result in mosquito abundances decreasing at extreme temperatures. With our work, we underscore the need for more studies to consider the effects of mosquito age. Not accounting for senescence can compromise the accuracy of abundance estimates and has implications for predicting the risk of future MBD outbreaks.

Regional Disparities and Driving Factors of Residential Carbon Emissions: An Empirical Analysis Based on Samples from 270 Cities in China

Residential carbon emissions (RCEs) have become a major contributor to China’s overall carbon emission growth. A comprehensive analysis of the evolution characteristics of regional disparities in RCEs at the urban level, along with a thorough examination of the driving factors behind RCEs and the convergence, is crucial for achieving carbon reduction goals within regions. This study calculates the RCEs of 270 cities in China from 2011 to 2019 based on multiregional input–output tables and explores the regional differences and spatiotemporal evolution characteristics of RCEs using the Dagum Gini coefficient decomposition method and kernel density estimation. On this basis, we examine the driving factors of RCEs using an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) econometric model and further analyze the convergence of RCEs by introducing a β-convergence model. The results are as follows: (1) The regional disparity of RCEs in China generally shows a wave-like declining trend, with the primary source of this disparity being the differences between city tiers. (2) Kernel density estimation shows that the greater the urban rank, the larger the regional disparity; the RCE distribution in third- and lower-tier cities is more concentrated. (3) Population density, population aging, and education level significantly exert a negative influence on RCEs, whereas economic development level, number of researchers, and number of private cars are positively correlated with RCEs. (4) Each urban agglomeration’s RCEs exhibits significant β-convergence, but the driving factors of RCEs and their convergence differ significantly across the urban agglomerations. This study provides targeted policy recommendations for China to achieve its emission reduction goals effectively. Each city cluster should tailor its approach to strengthen regional collaborative governance, optimize urban layouts, and promote low-carbon lifestyles in order to facilitate the convergence of RCEs and low-carbon transformation.

Revolutionizing Patient Reported Outcomes Analysis for Oncology Drug Development Using Population Models.

Patient reported outcome (PRO) plays a crucial role as clinical endpoint in oncology trials. Traditional statistical methods, such as hypothesis testing, have been commonly used by pharmaceutical industry and regulators to evaluate treatment efficacy on PRO endpoints. However, the analysis of PRO data remains challenging due to high variability and missing data issues. Here, we will present examples where inappropriate statistical analyses of PRO data can confound treatment efficacy analyses. To overcome these challenges, we propose the application of individual participant data and population models. Population models have been extensively used in pharmacokinetics and pharmacodynamics analyses and are well accepted by regulators. However, their potential in PRO data analyses, particularly in the field of oncology, remains largely untapped. This perspective paper aims to highlight the value of population modeling approaches in PRO data analyses for oncology clinicians and researchers. Population models integrate individual participant data and can effectively handle the substantial variability in PRO measurements by incorporating covariates, between-subject variability, and accounting for measurement noise. By leveraging information from the population, this approach also provides accurate estimations for participants with missing data or sparse sampling. Moreover, these models could be applied to predict long-term PRO dynamics. If used appropriately, population modeling approaches could revolutionize the analysis of PRO data in oncology drug development, enabling a more comprehensive understanding of the impact of treatment on patients' lives. Our aim is to encourage stakeholders to consider population modeling as a standard and effective tool to enhance decision-making and ultimately improve patient care.