A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics.

By collecting various control policies taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 2nd, 2020, we evaluate their impacts on the epidemic dynamics quantitatively through a combination of the multiple linear regression, neural-network-based nonlinear regression and sensitivity analysis. Remarkable differences in the public health policies are observed across these countries, which affect the spreading rate and infected population size to a great extent. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, which thereby confirms their dramatic impacts. Most importantly, we perform the mediation analysis on the SEIR-QD model, a representative of general compartment models, by using the structure equation modeling for multiple mediators operating in parallel. This, to the best of our knowledge, is the first of its kind in the field of epidemiology. The infection rate and the protection rate of the SEIR-QD model are confirmed to exhibit a statistically significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four dynamical features through the infection rate, highlights the crucial role of nucleic acid testing and suspected cases tracing in containing the spread of the epidemic. Our data-driven analysis offers a deeper insight into the inherent correlations between the effectiveness of public health policies and the dynamic features of COVID-19 epidemics.

An information-theoretic analysis of targeted regressions during reading.

Regressions, or backward saccades, are common during reading, accounting for between 5% and 20% of all saccades. And yet, relatively little is known about what causes them. We provide an information-theoretic operationalization for two previous qualitative hypotheses about regressions, which we dub reactivation and reanalysis. We argue that these hypotheses make different predictions about the pointwise mutual information or pmi between a regression's source and target. Intuitively, the pmi between two words measures how much more (or less) likely one word is to be present given the other. On one hand, the reactivation hypothesis predicts that regressions occur between words that are associated, implying high positive values of pmi. On the other hand, the reanalysis hypothesis predicts that regressions should occur between words that are not associated with each other, implying negative, low values of pmi. As a second theoretical contribution, we expand on previous theories by considering not only pmi but also expected values of pmi, E[pmi], where the expectation is taken over all possible realizations of the regression's target. The rationale for this is that language processing involves making inferences under uncertainty, and readers may be uncertain about what they have read, especially if a previous word was skipped. To test both theories, we use contemporary language models to estimate pmi-based statistics over word pairs in three corpora of eye tracking data in English, as well as in six languages across three language families (Indo-European, Uralic, and Turkic). Our results are consistent across languages and models tested: Positive values of pmi and E[pmi] consistently help to predict the patterns of regressions during reading, whereas negative values of pmi and E[pmi] do not. Our information-theoretic interpretation increases the predictive scope of both theories and our studies present the first systematic crosslinguistic analysis of regressions in the literature. Our results support the reactivation hypothesis and, more broadly, they expand the number of language processing behaviors that can be linked to information-theoretic principles.

The Transition of Sociodemographic and Substance Abuse Characteristics, Pairwise Co-occurrences and Factors Associated with Polysubstance Use Among US Adolescents and Young Adults.

Background: Substance abuse by adolescents and young adults is a major public health issue. This study aimed to (i) show the transition of sociodemographic and substance abuse characteristics from 1992 to 2017 among US adolescents and young adults, (ii) evaluate the likelihood of co-occurrence of substances, and (iii) identify significant sociodemographic characteristics in association with polysubstance abuse. Methods: This study extracted data for adolescents and young adults from 1992 and 2017 Treatment Episode Data Set-Admission (TEDS-A) datasets. The extracted sample included 337858 admissions in 1992 and 333322 in 2017. Findings: Both years experienced significant admissions. A significant transition in 2017 compared to 1992 was evident in education, living status, and ethnicity. Substance-specific transition showed alcohol was dominant in 1992, while marijuana/ hashish was dominant in 2017. Also, heroin, other opiates/synthetics, and methamphetamine experienced an increase, while cocaine/crack decreased. The pairwise co-occurrences exhibited a considerable variation in the likelihood of using one substance given another one. The odds ratios (ORs) obtained from generalized ordered logit models showed significantly higher odds of one or more substances with age, while education showed the opposite scenario. A mixed effect of gender was evident in 1992, whereas females were significantly less likely with one or more substances than males in 2017. Other significant vulnerable groups were those not in the labor force, homeless, white, and Mexican Americans. Conclusion: The findings may help to understand the overall changes between 1992 and 2017 and take necessary measures to reduce the burden of this public health problem.

Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies, which motivates the use of unsupervised learning. A mixture of regressions is an unsupervised method that can simultaneously identify clusters from multiple biomarkers while learning within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.

Parent-adolescent discrepancies in educational expectations, relationship quality, and study engagement: a multi-informant study using response surface analysis.

Whether parental educational expectations for adolescents serve as a source of motivation or stress depends on the extent to which adolescents hold expectations for themselves. Previous research on the discrepancies between parental and adolescent educational expectations and their impact on learning engagement has been limited by traditional statistical tests, and lacking an examination of the internal mediating mechanism of parent-child relational quality from both parental and adolescent perspectives. This cross-sectional study, utilizing a multi-informant design, examined the association between discrepancies in parents' and adolescents' reports of expectations, and adolescents' study engagement, as well as the mediating role of parent-child relational qualities perceived by both parties. The sample for this study consisted of 455 adolescents and their parents from 10 classes in a junior high school in Wuhan, Hubei Province, China. The adolescents had an average age of 12.8 years, and 51.6% of them were boys. Both parents and adolescents reported on their expectations and perceived relational quality, while adolescents also filled out questionnaires assessing their learning engagement. Data were analyzed using polynomial regressions with response surface analysis. The results revealed that when adolescents reported high expectations, regardless of whether their parents reported high or low expectations, adolescents reported satisfied relationships and high learning engagement. In contrast, parents reported satisfied relationships when both parties reported high expectations, or when parents reported higher expectations than adolescents. Lastly, the association between discrepancies in expectations and learning engagement was significantly mediated by adolescent-reported relationships but not parent-reported ones. These findings highlight the importance of considering multiple perspectives when studying the association between expectations and adolescent study engagement. This research advances our comprehension of the dynamics between parent-adolescent educational expectation discrepancies and adolescent learning engagement, offering insights for more nuanced and effective parenting strategies tailored to foster optimal educational outcomes.

Objective and subjective measures of air pollution and self-rated health: the evidence from Chile.

PURPOSE: The literature exploring individual differences in self-rated health has grown fast in recent years. Self-rated health (SRH) is a good indicator of general health status. This empirical study explores the association between outdoor air pollution and SRH in Chile. This type of analysis is infrequent in Latin America. METHODS: We used objective and subjective air pollution measures. The first corresponds to PM2.5, and the latter to the perception of a high level of air pollution. Drawing on data from two independent and repeated nationwide surveys over the period 2006-2017 at the individual level in Chile, we performed repeated cross-sectional analyses for each year of survey application. Ordered Logit (OL) and Logit (L) multivariate models were used to investigate the association between SRH and air pollution measures, considering other socioeconomic and demographic covariates. RESULTS: We found that the higher is the level of air pollution, the lower the SRH in Chile, regardless of whether air pollution is physically measured or perceived by respondents. The results were consistent over the years in the sign and significance of regression coefficients using two surveys and two forms of the outcome variable. CONCLUSIONS: Our findings add evidence that air pollution is a relevant determinant of SRH. In addition, they show that subjective measures of air pollution can be as reliable as physical measures in the analysis of the association between air pollution and human health.

Geopolitical risks and climate change stocks.

This paper aims to examine the impact of geopolitical risk (GPR), threats (GPT) and acts (GPA) on returns and volatilities of regional climate change stocks under different market conditions, employing quantile regressions. Our main results suggest that climate change stock returns positively (negatively) respond to GPR in bullish (bearish) market states, however the effect is not uniform across the regions. The volatilities mainly show a positive response to geopolitical tensions; geopolitical acts appear to have a more pronounced impact on volatilities than geopolitical threats. We further find that GPR leads to higher volatility during the Russia-Ukraine war, creating heightened uncertainty. Overall, the results reveal that geopolitical risks have an asymmetric and heterogenous impact on climate change stocks. The results provide significant insights and implications for financial market participants and policy makers.

Systems Biology Approaches to Understanding COVID-19 Spread in the Population.

In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system's dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions.

Evaluación métrica de caninos permanentes para la estimación sexual en la Colección “Prof. Dr. Rómulo Lambre”, Buenos Aires, Argentina / Metric evaluation of permanent canines for sexual estimation in the Collection “Prof. Dr. Romulo Lambre”, Buenos Aires, Argentina

Introducción la estimación del sexo es un aspecto fundamental de la labor forense, ya que constituye un paso obligatorio para la identificación de restos humanos de procedencia desconocida. El análisis metodológico de la dentición como estimador sexual reviste importancia debido al elevado grado de preservación de los dientes. Considerando la necesidad de contar con información concreta respecto del potencial de la dentición en la estimación del sexo en casos locales de Argentina, el objetivo del presente estudio es evaluar la propuesta previamente desarrollada por Luna (2019) en una muestra local de restos esqueléticos humanos. Materiales y métodos se seleccionó una muestra de 152 caninos permanentes pertenecientes a 98 individuos de ambos sexos que forman parte de la colección osteológica Profesor Dr. Rómulo Lambre (La Plata, Argentina). Posteriormente se aplicó la propuesta de Luna (2019) para la estimación del sexo a partir de la métrica de la corona y del cuello de los caninos, la cual considera las medidas directas y los diferentes tipos de funciones discriminantes y regresiones logísticas. Resultados de las medidas directas consideradas, solo el diámetro mesiodistal cervical ofreció resultados aceptables (>75%) para la estimación sexual. Asimismo, únicamente la función discriminante 1 presentó probabilidades a posteriori de clasificaciones correctas superiores a 0,75 y las regresiones logísticas 1 y 3 exhibieron resultados generales satisfactorios. Conclusiones esta propuesta basada en el estudio métrico de caninos permanentes constituye una alternativa metodológica adecuada en situaciones en las cuales los elementos óseos diagnósticos del sexo se encuentran deteriorados o ausentes. (AU)

Introduction Sex estimation is a fundamental aspect of forensic work as a mandatory step for the identification of human remains of unknown origin. The methodological analysis of the dentition as a sexual estimator is important due to its high degree of preservation. Considering the need for specific information regarding the potential of dentition for sex estimation in forensic cases from Argentina, the aim of this study is to evaluate the proposal previously developed by Luna (2019) in a local sample of human skeletal remains. Materials and methods A sample of 152 permanent canines belonging to 98 individuals of both sexes was selected- The individuals belong to the Prof. Dr. Rómulo Lambre osteological collection (La Plata, Argentina). Luna's proposal (2019) was applied to estimate sex from canine crown and neck metrics, which considers direct measurements and different types of discriminant functions and logistic regressions. Results Only the cervical mesiodistal diameter showed acceptable results (>75%) for sex estimation. Moreover, discriminant function 1 showed a posteriori probabilities of correct classifications greater than 0.75 and logistic regressions 1 and 3 offered acceptable overall results. Conclusions This proposal based on the metric recording of permanent canines constitutes an adequate methodological alternative in situations in which the diagnostic bone elements of sex are deteriorated or absent. (AU)

Differing field methods and site conditions lead to varying bias in suspended sediment concentrations in the Lower Mississippi and Atchafalaya Rivers.

At sites that have been sampled for decades, changes in field and laboratory methods happen over time as instrumentation and protocols improve. Here, we compare the influence of depth- and point-integrated sampling on total, fine (< 0.0625 mm), and coarse (≥ 0.0625 mm) suspended sediment (SS) concentrations in the Lower Mississippi and Atchafalaya Rivers. Using historical field method information, we identified seven sites to test such differences. We found SS samples collected using point-integration tended to have higher concentrations than those collected using depth-integration. However, the presence and magnitude of the bias were inconsistent across sites. Bias was present at the site with less-than-ideal conditions (i.e., non-trapezoidal channel, non-uniform flow) and non-existent at the ideal site location, indicating the bias between sampling methods depends on site sampling conditions. When present, the bias is greater at higher concentrations and at moderate to high flows. At the less-than-ideal site, point-integrated samples can have 16% (total) and 34% (coarse) higher concentrations than depth-integrated samples. When flow effects are removed, this translates to a bias of 19, 9, and 8 mg per liter for total, fine, and coarse SS. When a change in field methods occurs, comparison samples and a rigorous evaluation of those samples are warranted to determine the proper course of action for a particular site. Often, the effect and solution will not be known until several years of comparison samples have been collected under a variety of hydrologic conditions.

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Application of Moyer's mixed dentition analysis and establishing probability tables in a sample of Pakistani population.

Objective: To assess the congruity of Moyers' dental analysis in Pakistani individuals and to make tables foreseeing the size of non-erupted permanent premolars and canines in children. Methods: A cross-sectional study was conducted at Orthodontics Department of Armed Forces Institute of Dentistry (AFID) Rawalpindi, Pakistan. This was conducted from January 2020 to December 2021 and included mixed dentition subjects' casts. Mesiodistal widths (MDW) of the permanent dentition were estimated and contrasted with the anticipated values that emerged from the Moyer's mixed dentition examination. Students t-test, simple linear regression and Pearson correlation coefficient examination were utilized for statistical correlation. All data was gathered utilizing an electronic digital caliper and further investigated through IBM SPSS Statistics 24. Results: Dental casts of the 258 subjects, 106 (41%) males and 152 (58.9%) females with the ages ranging 15.825±2.95 years were analyzed. Tooth size contrasts were significant (p <0.05) which was noticed in comparison to Moyer's predictive table. This demonstrates a bigger tooth size in young men than in young ladies. New probability tables were created through linear regression equations for our population. Conclusion: Moyer's prediction cannot be applied to the Pakistani population. Mandibular incisors in addition to maxillary first molars are superior in prediction for the size assessment of canines and premolars (cp) that are unerupted in males and upper arch only in females. Another noteworthy conclusion is that in our study sample, in lower arch of females, the width of mandibular molars with mandibular incisors predicts better.

Spontaneous regression of a metastatic carcinoma transmitted by a kidney graft.

Transmission of a malignancy from a donor's organ to the recipient of the graft is a rare event, though it is a severe complication that can result in a poor outcome. Usually, immunosuppressive therapy is discontinued and the allograft is removed. However, treatment of patients with the disseminated cancers implies that after the graft removal and cessation of the immunosuppression, radiotherapy, chemotherapy, or immunotherapy with alpha-interferon (INF-α) or interleukin-2 (IL-2) are required. The case report presents a clinical case of a transmitted kidney graft with multiple metastases (MTS) in a 31-year-old woman with the spontaneous regression of the metastatic cancer after transplantectomy and cancellation of the immunosuppressive therapy. Obviously, the determining factor is the recognition of the tumor by the effectors of the antitumor immunity due to the human leukocyte antigen (HLA) mismatch between the donor and the recipient. Therefore, cancellation of the immunosuppressive therapy in cases of transferal of a malignancy with a transplanted organ allows the effectors of the immune system to distinguish the tumor as a foreign tissue and effectively eliminate this neoplasm.

MODELING CELL POPULATIONS MEASURED BY FLOW CYTOMETRY WITH COVARIATES USING SPARSE MIXTURE OF REGRESSIONS.

The ocean is filled with microscopic microalgae, called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small- and large-scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the northeast Pacific in the spring of 2017.

The relationship between moderate to vigorous physical activity and metabolic syndrome: a Bayesian measurement error approach.

Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pressure, cholesterol, waist circumference, and fasting glucose. There are many articles in the literature exploring the relationship between physical activity and MetS, but most do not consider the measurement error in the physical activity measurements nor the correlations among the MetS risk factors. Furthermore, previous work has generally treated MetS as binary, rather than directly modeling the risk factors on their measured, continuous space. Using data from the National Health and Nutrition Examination Survey (NHANES), we explore the relationship between minutes of moderate to vigorous physical activity (MVPA) and MetS risk factors. We construct a measurement error model for the accelerometry data, and then model its relationship between MetS risk factors with nonlinear seemingly unrelated regressions, incorporating dependence among MetS risk factors. The novel features of this model give the medical research community a new way to understand relationships between MVPA and MetS. The results of this approach present the field with a different modeling perspective than previously taken and suggest future avenues of scientific discovery.

The effect of morphometric and geometric indices of the human calvarium on mechanical response.

BACKGROUND: When developing a surrogate model of the human skull, there is a multitude of morphometric and geometric properties to consider when constructing the model. To simplify this approach, it is important to identify only the properties that have a significant influence on the mechanical response of the skull. The objective of this study was to identify which morphometric and geometric properties of the calvarium were significant predictors of mechanical response. METHODS: Calvarium specimens (N = 24) were micro-computed tomography scanned to determine morphometric and geometric properties. The specimens were assumed to be Euler-Bernoulli beams and were subject to 4-point quasi-static bending to determine mechanical response. Univariate linear regressions were performed whereby the morphometric and geometric properties were independent or predictor variables and the mechanical responses were dependent or outcome variables. FINDINGS: Nine significant linear regression models were established (p < 0.05). In the diploë, trabecular bone pattern factor was a significant predictor of force and bending moment at fracture. The inner cortical table had more significant predictors (thickness, tissue mineral density, and porosity) of mechanical response compared to the outer cortical table and diploë. INTERPRETATION: Morphometric and geometric properties had a key influence on the calvarium's biomechanics. Trabecular bone pattern factor and the morphometry and geometry of the cortical tables must be considered when evaluating the mechanical response of the calvarium. These properties can aid the design of surrogate models of the skull that seek to mimic its mechanical response for head impact simulation.

The independent prognostic effect of marital status on non-small cell lung cancer patients: a population-based study.

Background: Previous studies had demonstrated that marital status was an independent prognostic factor in multiple cancers. However, the impact of marital status on non-small cell lung cancer (NSCLC) patients was still highly controversial. Method: All NSCLC patients diagnosed between 2010-2016 were selected from the Surveillance, Epidemiology and End Results (SEER) database. To control the confounding effect of related clinicopathological characteristics, propensity score matching (PSM) was conducted between married and unmarried groups. In addition, independent prognostic clinicopathological factors were evaluated via Cox proportional hazard regression. Moreover, nomograms were established based on the clinicopathological characteristics, and the predictive accuracy was assessed by calibration curves. Furthermore, decision curve analysis (DCA) was used to determine the clinical benefits. Results: In total, 58,424 NSCLC patients were enrolled according to the selection criteria. After PSM, 20,148 patients were selected into each group for further analysis. The married group consistently demonstrated significantly better OS and CSS compared to unmarried group [OS median survival (95% CI): 25 (24-26) vs. 22 (21-23) months, p < 0.001; CSS median survival (95% CI): 31 (30-32) vs. 27 (26-28) months, p < 0.001]. Moreover, single patients were associated with the worst OS [median survival (95% CI): 20 (19-22) months] and CSS [median survival (95%CI): 24 (23-25) months] among unmarried subgroups. Besides, unmarried patients had a significantly worse prognosis compared to married patients in both univariate and multivariate Cox proportional hazard regressions. Furthermore, married group was associated with better survival in most subgroups. To predict the 1-, 3- and 5-year OS and CSS probabilities, nomograms were established based on age, race, sex, gender, marital status, histology, grade, TNM stage. The C-index for OS and CSS were 0.759 and 0.779. And the calibration curves showed significant agreement between predictive risk and the observed probability. DCA indicated nomograms had consistently better predict performance. Conclusion: This study demonstrated that unmarried NSCLC patients were associated with significantly worse OS and CSS compared to married NSCLC patients. Therefore, unmarried patients need not only closer surveillance, but also more social and family support, which may improve patients' adherence and compliance, and eventually improve the survival.

Prediction models for groundwater quality parameters using a multiple linear regression (MLR): a case study of Kermanshah, Iran.

Groundwater is one of the major sources of exploitation in arid and semiarid regions. Spatial and temporal quality distribution is an important factor in groundwater management. Thus for protecting groundwater quality, data production on spatial and temporal distribution is essential. The present study has applied multiple linear regression (MLR) techniques to predict the fitness of groundwater quality in Kermanshah province, west of Iran. The parameters examined were Total dissolved solids (TDS), Total hardness (TH), Sodium adsorption ratio (SAR). the quality variables were modelled by MLR. Finally, the performance of the models was assessed using the coefficient of determination (R2). The relationship between parameters by MLR showed that TDS and water quality parameters in semi-deep wells and aquifers had a strong positive correlation (r = 0.94, r = 0.98) and there was a strong positive significant correlation between SAR and water quality parameters in deep wells and aquifers (r = 0.98, r = 0.99). Also, TH and water quality parameters in all water sources had a strong positive correlation (r = 1). The MLR model could serve as an alternative and cost-effective tool for groundwater quality prediction where there is limitation in laboratory facilities, trained expertise or time. Consequently, the usefulness of these linear regression equations in predicting the groundwater quality is an approach, which can be applied in any other locations.

Capitalisation of multiple ecosystems on property prices in Auckland, New Zealand.

Ecosystems and their associated services have become essential in policymaking as they often have implications for the welfare of those who live nearby. Coupled with the shift toward integrating green infrastructure, ecosystem services, and nature-based solutions in urban and environmental planning, ecosystems may capitalise on property prices. But a potential phenomenon in applications of hedonic prices models is the offsetting between amenities and disamenities values, which may result in insignificant marginal effects. Some strategies to mitigate this phenomenon involve introducing a greater categorisation of open spaces and housing submarkets typology. Hence, this paper explores the capitalisation patterns of 14 terrestrial and aquatic ecosystems across the housing market segments of Auckland, New Zealand's largest city. We used a dataset of 274 thousand sales transactions between January 2011 and December 2019. Models are estimated via OLS and unconditional quantile regressions (UQR) to account for heterogeneity, outliers and heavy-tailed distributions. UQR produce more accurate estimates of the implicit prices and lead to more effective policy recommendations. We find multiple capitalisation patterns across the prices distribution and confirm offsetting effects when ecosystems are not controlled as separate variables. This paper informs land-use decisions involving the preservation or creation of natural spaces in residential areas and improves our understanding of the interaction between their economic valuation and regional diversity.

Analysing outpatient care access for planning purposes: The Basilicata Region experience.

The delivery of healthcare services at regional level should be modeled on population needs, stemming from patients' consumption pattern and trying to include unexpressed needs and exclude excesses of demand due to both moral hazard behaviors and inducements from the supply side. We propose a model able to estimate the frequency of access in outpatient care (OC) based on the characteristics of the population. According to empirical evidence, among the determinants of outpatient access we include variables addressing health, socioeconomic status and place of residence, plus variables related to the supply of services. We run generalized linear models for counting data of the Poisson family with the aim of both identifying the determinants of OC utilization and quantifying the related effects. We use the regional administrative database of Basilicata region, year 2019. Results are consistent with literature findings and provide new insights into the analysis of OC, suggesting that our model could easily be implemented by regional policymakers to plan the supply of ambulatory services on population needs.