Forecasting invasion risk under future climate conditions is critical for the effective management of invasive species, and species distribution models (SDMs) are key tools for doing so. However, SDM-based forecasts are uncertain, especially when correlative statistical models extrapolate to nonanalog environmental domains, such as future climate conditions. Different assumptions about the functional form of the temperature-suitability relationship can impact predicted habitat suitability under novel conditions. Hence, methods to understand the sources of uncertainty are critical when applying SDMs. Here, we use high-resolution predictions of lake water temperatures to project changes in habitat suitability under future climate conditions for an invasive macrophyte (Myriophyllym spicatum). Future suitability was predicted using five global circulation models and three statistical models that assumed different species-temperature functional responses. The suitability of lakes for M. spicatum was overall predicted to increase under future climate conditions, but the magnitude and direction of change in suitability varied greatly among lakes. Variability was most pronounced for lakes under nonanalog temperature conditions, indicating that predictions for these lakes remained highly uncertain. Integrating predictions from SDMs that differ in their species-environment response function, while explicitly quantifying uncertainty across analog and nonanalog domains, can provide a more robust and useful approach to forecasting invasive species distribution under climate change.

BACKGROUND: Pursuant to studies, receiving the three key maternal health services (Antenatal Care, Skilled Delivery Service, and Postnatal Care) in a continuum could prevent 71% of global maternal deaths. Despite the Western African region being known for its high maternal death and poor access to maternal health services, there is a dearth of studies that delve into the spectrum of maternal health services uptake. Hence, this study aimed to assess the level and predictors of partial and adequate utilization of health services in a single analytical model using the most recent Demographic and Health Survey (DHS) data (2013-2021).
METHODS: This study was based on the appended women\'s (IR) file of twelve West African countries. STATA software version 16 was used to analyze a weighted sample of 89,504 women aged 15-49 years. A composite index of maternal health service utilization has been created by combining three key health services and categorizing them into \'no\', \'partial\', or \'adequate\' use. A multilevel multivariable multinomial logistic regression analysis was carried out to examine the effects of each predictor on the level of service utilization. The degree of association was reported using the adjusted relative risk ratio (aRRR) with a corresponding 95% confidence interval, and statistical significance was declared at p < 0.05.
RESULTS: 66.4% (95% CI: 64.9, 67.7) and 23.8% (95% CI: 23.3, 24.2) of women used maternal health services partially and adequately, respectively. Togo has the highest proportion of women getting adequate health care in the region, at 56.7%, while Nigeria has the lowest proportion, at 11%. Maternal education, residence, wealth index, parity, media exposure (to radio and television), enrolment in health insurance schemes, attitude towards wife beating, and autonomy in decision-making were identified as significant predictors of partial and adequate maternal health service uptake.
CONCLUSIONS: The uptake of adequate maternal health services in the region was found to be low. Stakeholders should plan for and implement interventions that increase women\'s autonomy. Program planners and healthcare providers should give due emphasis to those women with no formal education and from low-income families. The government and the private sectors need to collaborate to improve media access and increase public enrolment in health insurance schemes.

The construction of tourist towns is an important aspect of new-type urbanization construction. In this study, 155 tourist towns in Fujian Province were selected as samples to analyze spatiotemporal differentiation using the geographical concentration index, nearest neighbor index, and local correlation index. Then, a geographic detector model was used to detect the factors that influence the spatiotemporal differentiation of tourist towns and to analyze the explanatory power and interaction of these detection factors. Finally, the mechanisms underlying the detection factors were discussed. Factors affecting the spatiotemporal differentiation of tourist towns in Fujian Province were core factors of traffic network, level of urbanization and population distribution; important factors of industrial structure and socioeconomic basis; and a fundamental factor of policy guidance. These six factors interacted to jointly affect the spatiotemporal differentiation of tourist towns in Fujian Province. The results of this study can provide a basis for the development of tourist towns in other similar regions and have reference value for better optimizing the pattern of urban and town systems and coordinating the synergistic development of urban and rural areas.

The gun assault case fatality rate measures the fraction of shooting victims who die from their wounds. Considerable debate has surrounded whether gun assault case fatality rates have changed over time and what factors may be involved. We use crime event data from Los Angeles to examine the victim and situational correlates of gun assault case fatality rates over time. We estimated log binomial regression models for the probability of death in each year from 2005 to 2021, conditioned on situational and victim characteristics of the crime. Case fatality rates increased by around 1.3% per year between 2005 and 2021 from around 15.9 to 19.7%. Baseline case fatality rates differed systematically by most situational and victim but followed similar temporal trends. Only victim age significantly covaried with the temporal trend in case fatality rates. An individual shot in Los Angeles in 2021 was 23.7% more likely to die than the equivalent victim in 2005. The steady increase in case fatality rates suggests that there were around 394 excess fatalities over what would have occurred if case fatality rates remained at the 2005 level. Increases in the average age of victims over time may contribute to the general temporal trend. We hypothesize that older victims are more likely to be shot indoors where lethal close-range wounds are more likely.

Demographic biases in source datasets have been shown as one of the causes of unfairness and discrimination in the predictions of Machine Learning models. One of the most prominent types of demographic bias are statistical imbalances in the representation of demographic groups in the datasets. In this article, we study the measurement of these biases by reviewing the existing metrics, including those that can be borrowed from other disciplines. We develop a taxonomy for the classification of these metrics, providing a practical guide for the selection of appropriate metrics. To illustrate the utility of our framework, and to further understand the practical characteristics of the metrics, we conduct a case study of 20 datasets used in Facial Emotion Recognition (FER), analyzing the biases present in them. Our experimental results show that many metrics are redundant and that a reduced subset of metrics may be sufficient to measure the amount of demographic bias. The article provides valuable insights for researchers in AI and related fields to mitigate dataset bias and improve the fairness and accuracy of AI models.

Campylobacteriosis causes a significant disease burden in humans worldwide and is the most common type of zoonotic gastroenteritis in Finland. To identify infection sources for domestic Campylobacter infections, we analyzed Campylobacter case data from the Finnish Infectious Disease Register (FIDR) in 2004-2021 and outbreak data from the National Food- and Waterborne Outbreak Register (FWO Register) in 2010-2021, and conducted a pilot case-control study (256 cases and 756 controls) with source attribution and patient sample analysis using whole-genome sequencing (WGS) in July-August 2022. In the FIDR, 41% of the cases lacked information on travel history. Based on the case-control study, we estimated that of all cases, 39% were of domestic origin. Using WGS, 22 clusters of two or more cases were observed among 185 domestic cases, none of which were reported to the FWO register. Based on this case-control study and source attribution, poultry is an important source of campylobacteriosis in Finland. More extensive sampling and comparison of patient, food, animal, and environmental isolates is needed to estimate the significance of other sources. In Finland, campylobacteriosis is more often of domestic origin than FIDR notifications indicate. To identify the domestic cases, travel information should be included in the FIDR notification, and to improve outbreak detection, all domestic patient isolates should be sequenced.

Long Covid-19 syndrome (LCS) manifests with a wide range of clinical symptoms, yet the factors associated with LCS remain poorly understood. The current study aimed to investigate the relationships that demographic characteristics, clinical history, laboratory indicators, and the frequency of HLA-I alleles have with the likelihood of developing LCS. We extracted the demographic characteristics and clinical histories from the medical records of 88 LCS cases (LCS+ group) and 96 individuals without LCS (LCS- group). Furthermore, we evaluated the clinical symptoms, serum levels of interleukin (IL)-6 and tumor necrosis factor-α, laboratory parameters, and the frequencies of HLA-I alleles. Following this we used multiple logistic regression to investigate the association these variables had with LCS. Subjects in the LCS+ group were more likely to have experienced severe Covid-19 symptoms and had higher body mass index (BMI), white blood cell, lymphocyte counts, C-reactive protein (CRP), and IL-6 levels than those in the LCS- group (for all: P < 0.05). Moreover, the frequencies of the HLA-A*11, -B*14, -B*38, -B*50, and -C*07 alleles were higher in the LCS+ group (for all: P < 0.05). After adjusting for the most important variables, the likelihood of suffering from LCS was significantly associated with BMI, CRP, IL-6, the HLA-A*11, and -C*07 alleles, as well as a positive history of severe Covid-19 (for all: P < 0.05). Our study showed that a history of severe Covid-19 during the acute phase of the disease, the HLA-A*11, and -C*07 alleles, higher BMI, as well as elevated serum CRP and IL-6 levels, were all associated with an increased likelihood of LCS.

For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 (\"Omicron\") and B.1.617.2 (\"Delta\"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures.

Many countries struggled with suppressing the incidence of COVID-19 caused by SARS-CoV-2 Omicron variant (B.1.1.529). As the epidemic size of COVID-19 in 2022 became bigger than earlier years in Japan, the present study aimed to estimate life expectancy at birth at the end of 2022, using provisional death datasets in Aichi and Fukui prefectures. We collected monthly death count from 2019 to the end of 2022, computing the period life table. While the life expectancy at birth in Aichi, 2019 was 84.6 years, it was very slightly extended to 84.7 years in 2020 and 2021, followed by a shortening for nearly 0.4 years in 2022. In Fukui, monotonous extension pattern was seen, i.e., 85.5 years in 2019, 85.6 in 2020, followed by 85.8 and 86.2 years in 2021 and 2022, respectively. Although decades-long trend of extending life expectancy at birth was partly discontinued from 2020 due to the pandemic at the national level, we have shown that the pandemic impact was still small in Japan by the end of 2022. First Omicron wave occurred shortly after primary series vaccination, and even real time booster program was underway during that wave. Different demographic consequences between Aichi and Fukui are explained by differential epidemic sizes prior to vaccination.

The Delta-Omicron wave of the COVID-19 pandemic (Wave 4) in the United States occurred in Fall of 2021 through Spring of 2022. Although vaccinations were widely available, this was the deadliest period to date in the U.S., and the toll was especially high in rural areas, exacerbating an existing rural mortality penalty. This paper uses county-level multilevel regression models and publicly available data for 47 U.S. states and the District of Columbia. We describe differences in COVID-19 case and mortality rates across the rural-urban continuum during Wave 4 of the COVID-19 pandemic. Using a progressive modeling approach, we evaluate the relative contribution of a range of explanatory factors for the rural disadvantage we observe, including: pre-pandemic population health composition, vaccination rates, political partisanship, socioeconomic composition, access to broadband internet rate, and primary care physicians per capita. Results show that rural counties had higher observed burdens of cases and deaths in Wave 4 compared to more urban counties. The most remote rural counties had Wave 4 COVID-19 mortality rates 52% higher than the most urban counties. Older age composition, worse pre-pandemic population health, lower vaccination rates, higher share of votes cast for Donald Trump in the 2020 Presidential election, and lower socioeconomic composition completely explained the rural disadvantage in reported COVID-19 case rates in Wave 4, and accounting for these factors reversed the observed rural disadvantage in COVID-19 mortality. In models of mortality rate, Trump vote share had the largest effect size, followed by the percentage of the population age 50 or older, the poverty rate, the pre-pandemic mortality rate, the share of residents with a 4-year college degree, and the vaccination rate. These findings add to a growing literature describing the disproportionate toll of the COVID-19 pandemic on rural America, highlighting the combined effect of multiple sources of rural disadvantage.