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Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management.

In 2011, east Texas experienced the worst drought on record causing extensive tree mortality. Initial mortality estimates for 2012 varied among tree genera. A rapid damage assessment (RDA) estimated that 65.5 (± 7.3) million trees died as a result of the drought in this region one year post‐drought. However, this estimate was untested against established monitoring networks. Moreover, pests and physiological damage can elevate tree mortality multiple years beyond a drought event. Since the RDA was unable to quantify multi‐year trends, it remained unclear whether these drivers caused increased tree mortality in east Texas beyond one year post‐drought and how different species responded over time. To address these questions, we compared total 2012 standing dead tree (SDT) estimates (i.e., drought‐killed plus all other SDT excluding harvested or salvaged trees) derived from the RDA and U.S. Forest Service Forest Inventory and Analysis (FIA) data for east Texas. Total SDT estimates did not significantly differ between the RDA (120.5 ± 8.5 million) and FIA (108.4 ± 8.7 million). Furthermore, total SDT estimates for the four most common genera (Pinus, Quercus, Liquidambar, Ulmus), which comprised over 80% of all species, did not significantly differ between the RDA and FIA. Additionally, we used logistic regression and FIA data from east Texas for 2011 through four years post‐drought (2012–2015) to examine temporal trends in plot‐level drought‐ and pest‐driven tree mortality of seven key species (Pinus taeda, Pinus echinata, Quercus nigra, Quercus stellata, Quercus falcata, Liquidambar styraciflua, Ulmus alata) from the four most common genera. At the plot‐level, drought‐driven mortality was immediate for the three Quercus species (notably Q. falcata) and L. styraciflua which significantly increased in 2012 while P. taeda mortality was delayed, not increasing significantly until 2013. Pest‐driven mortality increased from 2013 to 2015 for all species, with the highest mortality observed in Q. falcata and lowest in P. taeda and U. alata. This study affirms the validity and value of independent sampling efforts to quantify mortality immediately following major disturbance and also demonstrates the need for longer‐term species‐level assessments beyond the initial year post‐drought to account for differential impacts from drought and pests.

Background Psychological distress is common in maintenance hemodialysis patients, and high psychological resilience can promote psychological well-being. The current research focuses on psychological resilience protective factors such as family resilience and social support. However, the trajectories of psychological resilience, family resilience, and social support over time and their longitudinal relationships in maintenance hemodialysis patients have not been fully explored yet. Therefore, this study aims to explore the longitudinal relationship between these factors. Methods Patients who received regular hemodialysis treatment for more than three months at dialysis centers of three tertiary hospitals in Zhejiang, China, were recruited from September to December 2020. A total of 252 patients who met the inclusion and exclusion criteria completed three follow-up surveys, including social support, family resilience, and psychological resilience assessments. A repeated measures ANOVA was used to explore differences in their respective scores at different time points. The cross-lagged analysis was performed in AMOS using the maximum likelihood method to examine the the reciprocal predictive relationships between these factors. Results Social support and psychological resilience remained relatively stable over time, whereas family resilience indicated a little increasing trend. According to the cross-lagged analysis, higher T1 social support predicted higher family resilience at T2 [β = 0.123, 95% CI (0.026–0.244)]. Further, the effects of T2 social support to T3 family resilience [β = 0.194, 95%CI (0.039–0.335)] and psychological resilience [β = 0.205, 95%CI (0.049–0.354)] were significant. Finally, the effects of T2 family resilience to T3 social support [β = 0.122, 95%CI (0.010–0.225)] and psychological resilience [β = 0.244, 95%CI (0.119–0.359)] were also significant. Conclusions The study showed that the directionality of the relationship appears to be from social support or family resilience to patients’ psychological resilience but not vice versa. This finding reminds healthcare professionals to emphasize the vital role of social and family resources in providing appropriate support and interventions for maintenance hemodialysis patients to promote psychological resilience and mental health development.

Hurricanes are devastating natural disasters which dramatically modify the physical landscape and alter the socio-physical and biochemical characteristics of the environment, thus exposing the affected communities to new environmental stressors, which persist for weeks to months after the hurricane. This paper has three aims. First, it conceptualizes potential direct and indirect health effects of hurricanes and provides an overview of factors that exacerbate the health effects of hurricanes. Second, it summarizes the literature on the health impact of hurricanes. Finally, it examines the time lag between the hurricane (landfall) and the occurrence of diseases. Two major findings emerge from this paper. Hurricanes are shown to cause and exacerbate multiple diseases, and most adverse health impacts peak within six months following hurricanes. However, chronic diseases, including cardiovascular disease and mental disorders, continue to occur for years following the hurricane impact.

Loneliness has become a major public health issue of the recent decades due to its severe impact on health and mortality. Little is known about the relation between loneliness and social anxiety. This study aimed (1) to explore levels of loneliness and social anxiety in the general population, and (2) to assess whether and how loneliness affects symptoms of social anxiety and vice versa over a period of five years. The study combined data from the baseline assessment and the five-year follow-up of the population-based Gutenberg Health Study. Data of = 15 010 participants at baseline ( = 55.01, s.d. = 11.10) were analyzed. Multiple regression analyses with loneliness and symptoms of social anxiety at follow-up including sociodemographic, physical illnesses, and mental health indicators at baseline were used to test relevant covariates. Effects of loneliness on symptoms of social anxiety over five years and vice versa were analyzed by autoregressive cross-lagged structural equation models. At baseline, 1076 participants (7.41%) showed symptoms of social anxiety and 1537 (10.48%) participants reported feelings of loneliness. Controlling for relevant covariates, symptoms of social anxiety had a small significant effect on loneliness five years later (standardized estimate of 0.164, < 0.001). Vice versa, there was no significant effect of loneliness on symptoms of social anxiety taking relevant covariates into account. Findings provided evidence that symptoms of social anxiety are predictive for loneliness. Thus, prevention and intervention efforts for loneliness need to address symptoms of social anxiety.

Background One of the effective ways to control hypertension is long-term self-management, which is difficult to maintain. Therefore, understanding how people engage in the process of self-management behaviour change is necessary. In this study, we aimed to examine the dynamic relationship between self-perceived disease control and self-management behaviours in Chinese middle-aged and older hypertensive patients, namely, medication use, self-monitoring, physical activity, tobacco and alcohol avoidance, and to explore the mediating role of subjective life expectancy (SLE) on this relationship. Methods Data were obtained from a nationally representative sample of 508 middle-aged and older hypertensive patients (aged 45+) from the 2013, 2015, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey. A cross-lagged panel model combined with mediation analysis was used to determine the dynamic relationship between self-perceived disease control and self-management behaviours and to clarify the mediating effect of SLE on this ascertained relationship. Results Good self-perceived disease control subsequently predicted good medication use, self-monitoring and physical activity, and vice versa. Subjective life expectancy (SLE) partially mediated the prospective reciprocal relationships between self-perceived disease control and these self-management behaviours, which accounted for 37.11, 25.88, and 19.39% of the total effect of self-perceived disease control on medication use, self-monitoring and physical activity, respectively. These self-management behaviours had a significant and positive feedback effect on self-perceived disease control. However, neither the direct and indirect effects (via SLE) of self-perceived disease control on tobacco and alcohol avoidance were revealed. Conclusions Positive feedback loops of present self-perceived disease control, future SLE and self-management behaviours (medication use, self-monitoring, and physical activity) help middle-aged and older hypertensive patients adhere to these behaviours but are useless for the avoidance of addictive behaviours. Interventions aimed at enhancing the effect perception of general self-management behaviours (e.g., medication use, self-monitoring and physical activity) on the present disease control perspective, and future lifespan perspective would be beneficial for the consistent self-management behaviours of middle-aged and older hypertensive patients. The utility of present disease control perception to these self-management behaviours was much higher than the utility of future expectations. Alternative stress relief strategies may be conducive to long-term changes in addictive behaviours.

Objective The bidirectional causality between frailty and depressive symptoms (DS) in older adults were investigated, while the potential psychological mechanism remains unclear. This study aimed to explore the mediation role of attitudes to aging (AA) in their reciprocal relationships. Methods Data was collected from a community-based prospective cohort in Shanghai, which included 4,082 participants aged 60 and above. Frailty, DS, and AA were assessed using the Chinese Frailty Screening Scale-10, the Patient Health Questionnaire-9, and the Attitudes to Aging Questionnaire-12, respectively. A cross-lagged model was employed to investigate the reciprocal relationship between frailty and DS, as well as to estimate the mediating effect of AA. Results The findings revealed significant correlations between frailty, DS, and AA at both measurement points. In the cross-lagged model, after controlling for covariates, we observed a bidirectional causality between frailty and DS. Specifically, greater frailty was associated with higher levels of DS ( β  = 0.142, P  < 0.001), and higher levels of DS similarly predicted increased frailty ( β  = 0.058, P  = 0.006). When AA were included in the cross-lagged model, results showed that older adults with more positive AA significantly predicted lower future levels of DS ( β =-0.051, P  < 0.001) and frailty ( β =-0.055, P  < 0.001). Mediation analyses indicated that AA had a significant mediating effect ( β  = 0.003, P  = 0.029) between DS and frailty, although this effect was not reciprocated ( P  = 0.242). Conclusion These findings underscored the importance of promoting positive AA as preventive measures against both frailty and DS. They also provided valuable insights into the mechanisms underlying the relationship between depression and frailty in older adults.

Few studies have explored temperature–mortality relationships in China, especially at the multi-large city level. This study was based on the data of seven typical, large Chinese cities to examine temperature-mortality relationships and optimum temperature of China. A generalized additive model (GAM) was applied to analyze the acute-effect of temperature on non-accidental mortality, and meta-analysis was used to merge data. Furthermore, the lagged effects of temperature up to 40 days on mortality and optimum temperature were analyzed using the distributed lag non-linear model (DLNM). We found that for all non-accidental mortality, high temperature could significantly increase the excess risk (ER) of death by 0.33% (95% confidence interval: 0.11%, 0.56%) with the temperature increase of 1 °C. Similar but non-significant ER of death was observed when temperature decreased. The lagged effect of temperature showed that the relative risk of non-accidental mortality was lowest at 21 °C. Our research suggests that high temperatures are more likely to cause an acute increase in mortality. There was a lagged effect of temperature on mortality, with an optimum temperature of 21 °C. Our results could provide a theoretical basis for climate-related public health policy.

The relation between climate change and human health has become one of the major worldwide public health issues. However, the evidence for low-latitude plateau regions is limited, where the climate is unique and diverse with a complex geography and topography. This study aimed to evaluate the effect of ambient temperature on the mortality burden of nonaccidental deaths in Yunnan Province and to further explore its spatial heterogeneity among different regions. We collected mortality and meteorological data from all 129 counties in Yunnan Province from 2014 to 2020, and 16 prefecture-level cities were analyzed as units. A distributed lagged nonlinear model was used to estimate the effect of temperature exposure on years of life lost (YLL) for nonaccidental deaths in each prefecture-level city. The attributable fraction of YLL due to ambient temperature was calculated. A multivariate meta-analysis was used to obtain an overall aggregated estimate of effects, and spatial heterogeneity among 16 prefecture-level cities was evaluated by adjusting the city-specific geographical characteristics, demographic characteristics, economic factors, and health resources factors. The temperature-YLL association was nonlinear and followed slide-shaped curves in all regions. The cumulative cold and heat effect estimates along lag 0-21 days on YLL for nonaccidental deaths were 403.16 (95% empirical confidence interval [eCI] 148.14-615.18) and 247.83 (95% eCI 45.73-418.85), respectively. The attributable fraction for nonaccidental mortality due to daily mean temperature was 7.45% (95% eCI 3.73%-10.38%). Cold temperature was responsible for most of the mortality burden (4.61%, 95% eCI 1.70-7.04), whereas the burden due to heat was 2.84% (95% eCI 0.58-4.83). The vulnerable subpopulations include male individuals, people aged <75 years, people with education below junior college level, farmers, nonmarried individuals, and ethnic minorities. In the cause-specific subgroup analysis, the total attributable fraction (%) for mean temperature was 13.97% (95% eCI 6.70-14.02) for heart disease, 11.12% (95% eCI 2.52-16.82) for respiratory disease, 10.85% (95% eCI 6.70-14.02) for cardiovascular disease, and 10.13% (95% eCI 6.03-13.18) for stroke. The attributable risk of cold effect for cardiovascular disease was higher than that for respiratory disease cause of death (9.71% vs 4.54%). Furthermore, we found 48.2% heterogeneity in the effect of mean temperature on YLL after considering the inherent characteristics of the 16 prefecture-level cities, with urbanization rate accounting for the highest proportion of heterogeneity (15.7%) among urban characteristics. This study suggests that the cold effect dominated the total effect of temperature on mortality burden in Yunnan Province, and its effect was heterogeneous among different regions, which provides a basis for spatial planning and health policy formulation for disease prevention.

ObjectiveTo quantify the lagged effects of mean temperature on deaths from cardiovascular diseases in Brisbane, Australia.DesignPolynomial distributed lag models were used to assess the percentage increase in mortality up to 30 days associated with an increase (or decrease) of 1°C above (or below) the threshold temperature.SettingBrisbane, Australia.Patients22 805 cardiovascular deaths registered between 1996 and 2004.Main outcome measuresDeaths from cardiovascular diseases.ResultsThe results show a longer lagged effect in cold days and a shorter lagged effect in hot days. For the hot effect, a statistically significant association was observed only for lag 0–1 days. The percentage increase in mortality was found to be 3.7% (95% CI 0.4% to 7.1%) for people aged ≥65 years and 3.5% (95% CI 0.4% to 6.7%) for all ages associated with an increase of 1°C above the threshold temperature of 24°C. For the cold effect, a significant effect of temperature was found for 10–15 lag days. The percentage estimates for older people and all ages were 3.1% (95% CI 0.7% to 5.7%) and 2.8% (95% CI 0.5% to 5.1%), respectively, with a decrease of 1°C below the threshold temperature of 24°C.ConclusionsThe lagged effects lasted longer for cold temperatures but were apparently shorter for hot temperatures. There was no substantial difference in the lag effect of temperature on mortality between all ages and those aged ≥65 years in Brisbane, Australia.