Threshold Evaluation of Emergency Risk Communication for Health Risks Related to Hazardous Ambient Temperature

Impact of extreme temperatures on the performance evaluation of China's work-related injury insurance system

With the influence of climate change resulting in more extreme days, a rise in the number of work-related injuries could be expected. The literature has addressed the performance evaluation of a work-related injury insurance (WII) system via a two-stage structure with input/output correlation as well as the impact of extreme temperatures under different scenarios. This article thus evaluates the performance of a system comprised of operational and service sub-systems under three scenarios of extreme temperatures and proposes a hybrid two-stage dynamic data envelopment analysis (DEA) model with nondiscretionary variables for measuring integrated WII efficiency under the three scenarios. The results are as follows: (1) the poor performance of the operational and service sub-systems leads to the integrated WII system's low efficiency for 30 provinces in China during 2010-2019, except for Zhejiang, Hainan, and Qinghai. (2) Extreme temperatures must be considered when measuring WII efficiency and its stage efficiencies, or otherwise WII efficiency and operational efficiency will be underestimated in 19 provinces. (3) The negative impacts of extreme temperatures on the efficiency of the integrated WII system should be taken notice of, especially for Sichuan.

Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in US metropolitan areas

Many United States (US) cities are experiencing urban heat islands (UHIs) and climate change-driven temperature increases. Extreme heat increases cardiovascular disease (CVD) risk, yet little is known about how this association varies with UHI intensity (UHII) within and between cities. We aimed to identify the urban populations most at-risk of and burdened by heat-related CVD morbidity in UHI-affected areas compared to unaffected areas. ZIP code-level daily counts of CVD hospitalizations among Medicare enrollees, aged 65-114, were obtained for 120 US metropolitan statistical areas (MSAs) between 2000 and 2017. Mean ambient temperature exposure was estimated by interpolating daily weather station observations. ZIP codes were classified as low and high UHII using the first and fourth quartiles of an existing surface UHII metric, weighted to each have 25% of all CVD hospitalizations. MSA-specific associations between ambient temperature and CVD hospitalization were estimated using quasi-Poisson regression with distributed lag non-linear models and pooled via multivariate meta-analyses. Across the US, extreme heat (MSA-specific 99th percentile, on average 28.6 °C) increased the risk of CVD hospitalization by 1.5% (95% CI: 0.4%, 2.6%), with considerable variation among MSAs. Extreme heat-related CVD hospitalization risk in high UHII areas (2.4% [95% CI: 0.4%, 4.3%]) exceeded that in low UHII areas (1.0% [95% CI: -0.8%, 2.8%]), with upwards of a 10% difference in some MSAs. During the 18-year study period, there were an estimated 37,028 (95% CI: 35,741, 37,988) heat-attributable CVD admissions. High UHII areas accounted for 35% of the total heat-related CVD burden, while low UHII areas accounted for 4%. High UHII disproportionately impacted already heat-vulnerable populations; females, individuals aged 75-114, and those with chronic conditions living in high UHII areas experienced the largest heat-related CVD impacts. Overall, extreme heat increased cardiovascular morbidity risk and burden in older urban populations, with UHIs exacerbating these impacts among those with existing vulnerabilities.

Improving fire risk communication between authorities and micro-entrepreneurs: A mental models study of Ghanaian central market fires

This study conceptualizes how fire management authorities can empower nonexpert public to participate in fire risk communication processes and increase their own responsibilities for managing fire preventive, protective and recovery processes effectively. Drawing narratives from 10 disaster management experts working at government institutions and nine micro-entrepreneurs operating self-sustaining businesses in different merchandized lines in Ghana, we analyzed the data thematically and explored new insights on mental models to generate a two-way fire risk communication model. The findings suggest that fire management authorities planned fire disasters at the strategic level, collaborated with multiple stakeholders, disseminated information through many risk communication methods, and utilized their capabilities to manage fire at the various stages of fire risk communication, but the outcomes were poor. The micro-entrepreneurs sought to improve fire management outcomes through attitude change, law enforcement actions, strengthened security and better public trust building. The study has implications for policymakers, governments, and risk communication authorities of developing countries to strengthen their fire disaster policies to minimize commercial fire incidents and address the damaging effects of fire on people's livelihoods, businesses, properties, and environments. Our proposed two-way fire risk communication model is a new theoretical lens for experts and the nonexpert public to assess each other's beliefs about risk information and manage fire risk communication effectively at all stages.

In.To. COVID-19 socio-epidemiological co-causality

Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk-perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets, and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validated InTo for COVID-19 in New Delhi and Mumbai by inferring distinct socio-epidemiological risk-perception patterns. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately [Formula: see text]60[Formula: see text] of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest non-linear predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, healthcare capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.

Short-term Exposure to Air Pollution and Attributable Risk of Kidney Diseases: A Nationwide Time-series Study

BACKGROUND

Several studies have shown that long-term exposure to air pollution is associated with reduced kidney function. However, less is known about effects of short-term exposure to air pollution on kidney disease aggravation and resultant emergency room (ER) burden. This study aimed to estimate excess ER visits attributable to short-term air pollution and to provide evidence relevant to air pollution standards to protect kidney patients.

METHODS

We conducted time-series analysis using National Health Insurance data covering all persons in South Korea (2003-2013). We collected daily data for air pollutants (particulate matter ≤10 µm [PM10], ozone [O3], carbon monoxide [CO], and sulfur dioxide [SO2]) and ER visits for total kidney and urinary system disease, acute kidney injury (AKI), and chronic kidney disease (CKD). We performed a two-stage time-series analysis to estimate excess ER visits attributable to air pollution by first calculating estimates for each of 16 regions, and then generating an overall estimate.

RESULTS

For all kidney and urinary disease (902,043 cases), excess ER visits attributable to air pollution existed for all pollutants studied. For AKI (76,330 cases), we estimated the highest impact on excess ER visits from O3, while for CKD (210,929 cases), the impacts of CO and SO2 were the highest. The associations between air pollution and kidney ER visits existed for days with air pollution concentrations below current World Health Organization guidelines.

CONCLUSION

This study provides quantitative estimates of ER burdens attributable to air pollution. Results are consistent with the hypothesis that stricter air quality standards benefit kidney patients.

Associations between long term air pollution exposure and first hospital admission for kidney and total urinary system diseases in the US Medicare population: nationwide longitudinal cohort study

Objective

To estimate the associations between long term exposure to air pollution and the first hospital admission related to kidney and total urinary system diseases.

Design

Nationwide longitudinal cohort study.

Setting

Data were collected from the Medicare fee-for-service for beneficiaries living in 34 849 zip codes across the continental United States from 2000 to 2016. Exposure variables were annual averages of traffic related pollutants (fine particles (PM_2.5) and nitrogen dioxide (NO₂)) that were assigned according to the zip code of residence of each beneficiary with the use of validated and published hybrid ensemble prediction models.

Participants

All beneficiaries aged 65 years or older who were enrolled in Medicare part A fee-for-service (n=61 097 767).

Primary and secondary outcome measures

First hospital admission with diagnosis codes for total kidney and urinary system disease or chronic kidney disease (CKD), analyzed separately.

Results

The average annual concentrations of air pollution were 9.8 µg/m³ for PM_2.5 and 18.9 ppb for NO₂. The total number of first admissions related to total kidney and urinary system disease and CKD were around 19.0 million and 5.9 million, respectively (2000-16). For total kidney and urinary system disease, hazard ratios were 1.076 (95% confidence interval 1.071 to 1.081) for a 5 µg/m³ increase in PM_2.5 and 1.040 (1.036 to 1.043) for a 10 ppb increase in NO₂. For CKD, hazard ratios were 1.106 (1.097 to 1.115) for a 5 µg/m³ increase in PM_2.5 and 1.013 (1.008 to 1.019) for a 10 ppb increase in NO₂. These positive associations between PM_2.5 and kidney outcomes persisted at concentrations below national health based air quality standards.

Conclusions

The findings suggest that higher annual air pollution levels were associated with increased risk of first hospital admission related to diseases of the kidney and urinary system or CKD in the Medicare population.

Hot weather as a risk factor for kidney disease outcomes: A systematic review and meta-analysis of epidemiological evidence

BACKGROUND

The occurrence or exacerbation of kidney disease has been documented as a growing problem associated with hot weather. The implementation of effective prevention measures requires a better understanding of the risk factors that increase susceptibility. To fill gaps in knowledge, this study reviews the current literature on the effects of heat on kidney-disease outcomes (ICD-10 N00-N39), including morbidity and mortality.

METHODS

Databases were systematically searched for relevant literature published between 1990 and 2020 and the quality of evidence evaluated. We performed random effects meta-analysis to calculate the pooled relative risks (RRs) of the association between high temperatures (and heatwaves) and kidney disease outcomes. We further evaluated vulnerability concerning contextual population characteristics.

RESULTS

Of 2739 studies identified, 91 were reviewed and 82 of these studies met the criteria for inclusion in a meta-analysis. Findings showed that with a 1 °C increase in temperature, the risk of kidney-related morbidity increased by 1% (RR 1.010; 95% CI: 1.009-1.011), with the greatest risk for urolithiasis. Heatwaves were also associated with increased morbidity with a trend observed with heatwave intensity. During low-intensity heatwaves, there was an increase of 5.9% in morbidity, while during high-intensity heatwaves there was a 7.7% increase. There were greater RRs for males, people aged ≤64 years, and those living in temperate climate zones. Similarly, for every 1 °C temperature increase, there was a 3% (RR 1.031; 95% CI: 1.018-1.045) increase in the risk of kidney-related mortality, which also increased during heatwaves.

CONCLUSIONS

High temperatures (and heatwaves) are associated with an elevated risk of kidney disease outcomes, particularly urolithiasis. Preventive measures that may minimize risks in vulnerable individuals during hot spells are discussed.

Impact of short-term exposure to extreme temperatures on diabetes mellitus morbidity and mortality? A systematic review and meta-analysis

The relationship between diabetes mellitus and short-term exposure to extreme temperatures remains controversial. A systematic review and meta-analysis were performed to assess the association between extreme temperatures and diabetes mellitus morbidity and mortality. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched since inception to January 1, 2019, and updated on November 17, 2020. The results were combined using random effects model and reported as relative risk (RR) with 95% confidence interval (CI). In total, 32 studies met the inclusion criteria. (1) Both heat and cold exposures have impact on diabetes. (2) For heat exposure, the subgroup analysis revealed that the effect on diabetes mortality (RR=1.139, 95% CI: 1.089-1.192) was higher than morbidity (RR=1.012, 95% CI: 1.004-1.019). (3) With the increase of definition threshold, the impact of heat exposure on diabetes rose. (4) A stronger association between heat exposure and diabetes was observed in the elderly (≥ 60 years old) (RR=1.040, 95% CI: 1.017-1.064). In conclusion, short-term exposure to both heat and cold temperatures has impact on diabetes. The elderly is the vulnerable population of diabetes exposure to heat temperature. Developing definitions of heatwaves at the regional level are suggested.

The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation

BACKGROUND

The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016.

METHODS

A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test.

RESULTS

Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB.

CONCLUSION

Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

Impact of Extreme Temperatures on Ambulance Dispatches Due to Cardiovascular Causes in North-West Spain

Introduction and objectives. The increase in mortality and hospital admissions associated with high and low temperatures is well established. However, less is known about the influence of extreme ambient temperature conditions on cardiovascular ambulance dispatches. This study seeks to evaluate the effects of minimum and maximum daily temperatures on cardiovascular morbidity in the cities of Vigo and A Coruña in North-West Spain, using emergency medical calls during the period 2005–2017. Methods. For the purposes of analysis, we employed a quasi-Poisson time series regression model, within a distributed non-linear lag model by exposure variable and city. The relative risks of cold- and heat-related calls were estimated for each city and temperature model. Results. A total of 70,537 calls were evaluated, most of which were associated with low maximum and minimum temperatures on cold days in both cities. At maximum temperatures, significant cold-related effects were observed at lags of 3–6 days in Vigo and 5–11 days in A Coruña. At minimum temperatures, cold-related effects registered a similar pattern in both cities, with significant relative risks at lags of 4 to 12 days in A Coruña. Heat-related effects did not display a clearly significant pattern. Conclusions. An increase in cardiovascular morbidity is observed with moderately low temperatures without extremes being required to establish an effect. Public health prevention plans and warning systems should consider including moderate temperature range in the prevention of cardiovascular morbidity.