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Detecting and understanding disturbance is a challenge in ecology that has grown more critical with global environmental change and the emergence of research on social–ecological systems. We identify three areas of research need: developing a flexible framework that incorporates feedback loops between social and ecological systems, anticipating whether a disturbance will change vulnerability to other environmental drivers, and incorporating changes in system sensitivity to disturbance in the face of global changes in environmental drivers. In the present article, we review how discoveries from the US Long Term Ecological Research (LTER) Network have influenced theoretical paradigms in disturbance ecology, and we refine a framework for describing social–ecological disturbance that addresses these three challenges. By operationalizing this framework for seven LTER sites spanning distinct biomes, we show how disturbance can maintain or alter ecosystem state, drive spatial patterns at landscape scales, influence social–ecological interactions, and cause divergent outcomes depending on other environmental changes.

Ecological studies have suggested fewer COVID-19 morbidities and mortalities in Bacillus Calmette–Guérin (BCG)-vaccinated countries than BCG-non-vaccinated countries. However, these studies obtained data during the early phase of the pandemic and did not adjust for potential confounders, including PCR-test numbers per population (PCR-tests). Currently—more than four months after declaration of the pandemic—the BCG-hypothesis needs reexamining. An ecological study was conducted by obtaining data of 61 factors in 173 countries, including BCG vaccine coverage (%), using morbidity and mortality as outcomes, obtained from open resources. ‘Urban population (%)’ and ‘insufficient physical activity (%)’ in each country was positively associated with morbidity, but not mortality, after adjustment for PCR-tests. On the other hand, recent BCG vaccine coverage (%) was negatively associated with mortality, but not morbidity, even with adjustment for percentage of the population ≥ 60 years of age, morbidity, PCR-tests and other factors. The results of this study generated a hypothesis that a national BCG vaccination program seems to be associated with reduced mortality of COVID-19, although this needs to be further examined and proved by randomized clinical trials.

Background Starting in August 2015, there was an increase in the number of cases of neonatal microcephaly in Northeast Brazil. These findings were identified as being an epidemic of microcephaly related to Zika virus (ZIKV) infection. The present study aims to analyse the spatial distribution of microcephaly cases in Recife (2015–2016), which is in Northeast Brazil, and its association with the living conditions in this city. Methods This was an ecological study that used data from reported cases of microcephaly from the State Health Department of Pernambuco (August 2015 to July 2016). The basic spatial unit of analysis was the 94 districts of Recife. The case definition of microcephaly was: neonates with a head circumference of less than the cut-off point of −2 standard deviations below the mean value from the established Fenton growth curve. As an indicator of the living conditions of the 94 districts, the percentage of heads of households with an income of less than twice the minimum wage was calculated. The districts were classified into four homogeneous strata using the K-means clustering algorithm. We plotted the locations of each microcephaly case over a layer of living conditions. Results During the study period, 347 microcephaly cases were reported, of which 142 (40.9%) fulfilled the definition of a microcephaly case. Stratification of the 94 districts resulted in the identification of four strata. The highest stratum in relation to the living conditions presented the lowest prevalence rate of microcephaly, and the overall difference between this rate and the rates of the other strata was statistically significant. The results of the Kruskal-Wallis test demonstrated that there was a strong association between a higher prevalence of microcephaly and poor living conditions. After the first 6 months of the study period, there were no microcephaly cases recorded within the population living in the richest socio-economic strata. Conclusion This study showed that those residing in areas with precarious living conditions had a higher prevalence of microcephaly compared with populations with better living conditions.

Background The coronavirus disease 2019 (COVID-19) pandemic has posed a major challenge to health, economic and political systems around the world. Understanding the socioeconomic, demographic and health determinants affecting the pandemic is of interest to stakeholders. The purpose of this ecological study is to analyse the effect of the different socioeconomic, demographic and healthcare determinants on the mortality rate and estimated cumulative incidence of COVID-19 first wave in the Spanish regions. Methods From the available data of the 17 Spanish regions (Autonomous Communities), we have carried out an ecological study through multivariate linear regression using ordinary least squares. To do this, we conducted an analysis using two distinct dependent variables: the logarithm of mortality rate per 1,000,000 inhabitants and the estimated cumulative incidence. The study has 12 explanatory variables. Results After applying the backward stepwise multivariate analysis, we obtained a model with nine significant variables at different levels for mortality rate and a model with seven significant variables for estimated cumulative incidence. Among them, six variables are statistically significant and of the same sign in both models: “Nursing homes beds”, “Proportion of care homes over 100 beds”, “Log GDP per capita”, “Aeroplane passengers”, “Proportion of urban people”, and the dummy variable “Island region”. Conclusions The different socioeconomic, demographic and healthcare determinants of each region have a significant effect on the mortality rate and estimated cumulative incidence of COVID-19 in territories where the measures initially adopted to control the pandemic have been identical.

The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.DEN4 introduces a multilevel noise separation module that effectively distinguishes between signal and noise, thereby improving the signal-to-noise ratio (SNR) and reducing the error.The experimental results demonstrate that DEN4 significantly outperforms traditional denoising methods in several key metrics, including mean square error (MSE), SNR, Hausdorff distance, and structural similarity index (SSIM).In the 60 sample dataset, DEN4 achieved the best mean and standard deviation on all metrics: Specifically, the MSE mean was found to be 0.0094, with a standard deviation of 0.0008, the SNR mean was 149.1570, with a standard deviation of 0.5628, the Hausdorff mean was 0.8503, with a standard deviation of 0.0947, and the SSIM mean was 0.8399, with a standard deviation of 0.0054. For instance, in the S10 dataset, DEN4 attained a 70.2% diminution in MSE and a 37.8% augmentation in SNR in comparison with PTD.The findings demonstrate the efficacy of DEN4 in multiple forest datasets, its ability to maintain geometric integrity, and its enhanced stability without the necessity for pre-labelled data. The algorithm's superior performance and robustness in diverse forest environments underscores its potential application in single tree segmentation and forest resource management.

Estimation of population attributable fraction (PAF) requires unbiased relative risk (RR) by using either Levin’s or Miettinen’s formula, on which decision depends on the available exposure information in reference group, not the types of studies. For ecological studies and studies with aggregated outcomes, once having unbiased RRs, Levin’s and Miettinen’s formulae would provide identical PAF estimates. PAF could also be applied to compare relative burdens of disease between countries across time, which is an additional information in consideration of country-level policies.

BackgroundStrong variations in injury rates have been documented cross-nationally. Historically, these have been attributed to contextual determinants, both social and physical. We explored an alternative, yet understudied, explanation for variations in adolescent injury reporting—that varying access to medical care is, in part, responsible for cross-national differences.MethodsAge-specific and gender-specific rates of medically treated injury (any, serious, by type) were estimated by country using the 2013/2014 Health Behaviour in School-aged Children study (n=209 223). Available indicators of access to medical care included: (1) the Healthcare Access and Quality Index (HAQ; 39 countries); (2) the Universal Health Service Coverage Index (UHC; 37 countries) and (3) hospitals per 100 000 (30 countries) then physicians per 100 000 (36 countries). Ecological analyses were used to relate injury rates and indicators of access to medical care, and the proportion of between-country variation in reported injuries attributable to each indicator.ResultsAdolescent injury risks were substantial and varied by country and sociodemographically. There was little correlation observed between national level injury rates and the HAQ and UHC indices, but modest associations between serious injury and physicians and hospitals per 100 000. Individual indicators explained up to 9.1% of the total intercountry variation in medically treated injuries and 24.6% of the variation in serious injuries.ConclusionsCross-national variations in reported adolescent serious injury may, in part, be attributable to national differences in access to healthcare services. Interpretation of cross-national patterns of injury and their potential aetiology should therefore consider access to medical care as a plausible explanation.

Background During the COVID-19 pandemic, Florida reported some of the highest numbers of cases and deaths in the US; however, county-level variation in COVID-19 outcomes has yet to be comprehensively investigated. The present ecological study aimed to assess correlates of COVID-19 outcomes among Florida counties that explain variation in case rate, mortality rate, and case fatality rate (CFR) across pandemic waves. Method We obtained county-level administrative data and COVID-19 case reports from public repositories. We tested spatial autocorrelation to assess geographic clustering in COVID-19 case rate, mortality rate, and CFR. Stepwise linear regression was employed to investigate the association between COVID-19 outcomes and 17 demographic, socioeconomic, and health-related county-level predictors. Results We found mortality rate and CFR were significantly higher in rural counties compared to urban counties, among which significant differences in vaccination coverage were also observed. Multivariate analysis found that the percentage of the population aged over 65 years, the percentage of obese people, and the percentage of the rural population were significant predictors of COVID-19 case rate. Median age, vaccination coverage, percentage of people who smoke, and percentage of the population with diabetes were significant influencing factors for CFR. Importantly, higher vaccination coverage was significantly associated with a reduction in case rate ( R = -0.26, p  = 0.03) and mortality ( R = -0.51, p  < 0.001). Last, we found that spatial dependencies play a role in explaining variations in COVID-19 CFR among Florida counties. Conclusion Our findings emphasize the need for targeted, equitable public health strategies to reduce disparities and enhance population resilience during public health crises.

Abstract BackgroundNew-onset substance use disorder (SUD) following bariatric surgery is a significant concern that is likely multi-factorial, although the etiologies are unclear. Previous studies have identified variable rates of SUD along with utilizing different methods and measures. The objective of this study is to evaluate new-onset SUD diagnoses among adults following bariatric surgery and compare these rates to those in the general population as well as those diagnosed with overweight or obesity.MethodsData was extracted from TriNetX Research Platform and used to build three cohorts of adults: those who had bariatric surgery (bariatric surgery cohort), those diagnosed with obesity or overweight, and a general population cohort. Rates of incident SUD were compared among these three groups. Initial encounters for all individuals were from January 1, 2018, to June 30, 2019.ResultsThe incidence rate of SUD in patients with a history of bariatric surgery was 6.55% (n = 2523). When compared to the general population, persons who had any type of bariatric procedure had a decreased risk of new-onset SUD with an overall odds ratio (OR) [95% confidence limits (CL)] of 0.89 [0.86, 0.93]. When compared to persons with overweight or obesity, bariatric patients were less likely to develop any form of SUD (OR: 0.65 [0.62, 0.67]).ConclusionWhile overall rates of new-onset SUD are lower among those who had bariatric surgery, they also vary by surgery and substance type. Efforts should still be made to address new-onset SUD in order to optimize the post-surgical care of patients.

Background The incidence of thyroid cancer as the most common type of endocrine gland malignancy has risen more significantly than any malignancies in recent years. Estimated new cases of thyroid cancer in the United States in 2024 were 12,500 and 31,520 for men and women, respectively, and estimated deaths were 1,180 for women and 990 for men. Indices of socio-economic have been commonly used to measure the development of countries. Therefore, this study aimed to examine the correlation between indices of socioeconomic status and epidemiological indices of thyroid cancer throughout the world. In addition, this study has compared two indices of human development and a socio-demographic index. Method This worldwide ecological study used data on thyroid cancer incidence, mortality, human development index (HDI), and sociodemographic index (SDI) between 1990 and 2019 from the Global Burden of Disease (GBD). We evaluated the correlation between incidence and mortality rates with socioeconomic indices by using Pearson’s correlation coefficient. Furthermore, for the first time, the generalized additive model (GAM) was employed for modeling. The statistical software R, version 4.2.2, was used to conduct all statistical analyses. Results The correlation between the incidence of thyroid cancer and the HDI was significant and positive ( r  = 0.47, p-value < 0.001). While the correlation between thyroid cancer mortality and HDI was not statistically significant ( r  = 0.01, p-value = 0.076). Besides, the incidence of thyroid cancer was significantly positively correlated with SDI ( r  = 0.48, p-value < 0.001). The multiple GAM showed that for one unit increase in HDI, the risk of thyroid cancer was increased by 2.1 times (RR = 2.1, 95%CI = 2.04 to 2.19), and for one unit increase in SDI, the risk of thyroid cancer was shown to increase by 2.2 times. (RR = 2.2, 95%CI = 2.19 to 2.35). Conclusion It has been evident that countries with higher incidence of thyroid cancer display higher socioeconomic indices. While, countries with higher socioeconomic indices, report lower mortality rates. However, based on the modeling results, it can be concluded that the SDI is slightly more useful in this regard. Therefore, examining the epidemiological indices of thyroid cancer by socio-economic indices can be useful to reflect a clear image of the distribution of this cancer in each country, and can be used for planning cancer prevention strategies.