Search

Background: The COVID-19 pandemic has revealed gaps in global health systems, especially in the low- and middle-income countries (LMICs). Evidence shows that patients with non-communicable diseases (NCDs) are at higher risk of contracting COVID-19 and suffering direct and indirect health consequences. Considering the future challenges such as environmental disasters and pandemics to the LMICs health systems, digital health interventions (DHI) are well poised to strengthen health care resilience. This study aims to implement and evaluate a comprehensive package of DHIs of integrated COVID-NCD care to manage NCDs in primary care facilities in rural Pakistan. Methods The study is designed as a pragmatic, parallel two-arm, multi-centre, cluster randomised controlled trial. We will randomise 30 primary care facilities in three districts of Punjab, where basic hypertension and diabetes diagnosis and treatment is provided, with a ratio of 1:1 between intervention and control. In each facility, we will recruit 50 patients who have uncontrolled hypertension. The intervention arm will receive training on an integrated COVID-NCD guideline, and will use a smartphone app-based telemedicine platform where patients can communicate with health providers and peer-supporters, along with a remote training and supervision system. Usual care will be provided in the control arm. Patients will be followed up for 10 months. Our primary indicator is systolic blood pressure measured at 10 months. A process evaluation guided by implementation science frameworks will be conducted to explore implementation questions. A cost-effectiveness evaluation will be conducted to inform future scale up in Pakistan and other LMICs. Discussion: Our study is one of the first randomised controlled trials to evaluate the effectiveness of DHIs to manage NCDs to strengthen health system resilience in LMICs. We will also evaluate the implementation process and cost-effectiveness to inform future scale-up in similar resource constrained settings.

Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

RationaleEvidence for the association between fine particulate matter (PM2.5) and mortality among patients with tuberculosis (TB) is limited. Whether greenness protects air pollution-related mortality among patients with multidrug-resistant tuberculosis (MDR-TB) is completely unknown.Methods2305 patients reported in Zhejiang and Ningxia were followed up from MDR-TB diagnosis until death, loss to follow-up or end of the study (31 December 2019), with an average follow-up of 1724 days per patient. 16-day averages of contemporaneous Normalised Difference Vegetation Index (NDVI) in the 500 m buffer of patient’s residence, annual average PM2.5and estimated oxidant capacity Oxwere assigned to patients regarding their geocoded home addresses. Cox proportional hazards regression models were used to estimate HRs per 10 μg/m3exposure to PM2.5and all-cause mortality among the cohort and individuals across the three tertiles, adjusting for potential covariates.ResultsHRs of 1.702 (95% CI 1.680 to 1.725) and 1.169 (1.162 to 1.175) were observed for PM2.5associated with mortality for the full cohort and individuals with the greatest tertile of NDVI. Exposures to PM2.5were stronger in association with mortality for younger patients (HR 2.434 (2.432 to 2.435)), female (2.209 (1.874 to 2.845)), patients in rural (1.780 (1.731 to 1.829)) and from Ningxia (1.221 (1.078 to 1.385)). Cumulative exposures increased the HRs of PM2.5-related mortality, while greater greenness flattened the risk with HRs reduced in 0.188–0.194 on average.ConclusionsIndividuals with MDR-TB could benefit from greenness by having attenuated associations between PM2.5and mortality. Improving greener space and air quality may contribute to lower the risk of mortality from TB/MDR-TB and other diseases.

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Studies examining long-term effects of ambient air pollution exposure, measured as annual averages, on pulmonary tuberculosis (TB) incidence are scarce, particularly in endemic, rural settings. We performed a small-area study in Ningxia Hui Autonomous Region (NHAR), a high TB-burden area in rural China, using township-level (n = 358 non-overlapping townships) annual TB notification data (2005-2017). We aimed to determine if annual average concentrations of ambient air pollution (particulate matter2·5 μm [PM

This is the accepted manuscript version of the work published in its final form as Erjia Ge; Jianhui Gao; Xiaolin Wei; Zhoupeng Ren; Jing Wei; Xin Liu; Xiaomeng Wang; Jieming Zhong; Jingru Lu; Xiaomei Tian; Fangrong Fei; Bin Chen; Xiaolin Wang; Ying Peng; Ming Luo; Juan Lei. BMJ Journals.http://dx.doi.org/10.1136/thoraxjnl-2020-216819 Deposited byshareyourpaper.organdopenaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailinghelp@openaccessbutton.org.

Human-perceived wind chill describes the combined effects of wind velocity and low temperature, strongly related to human health and natural environment. Although long-term trends in the air or ambient temperature over China under global warming have been well studied in the literature, the changes in human-perceived wind chill conditions, especially under possible urbanization effects, are still not completely known. This paper investigates the changes of wind chill over China and quantifies the associated urbanization effect by examining nearly 2000 meteorological stations during 1961–2014 using the generalized additive model (GAM). Results show that the winter wind chill temperature (WCT) in China exhibits more prominent raising trends than the air temperature, i.e., 0.623 and 0.349 °C per decade, respectively. The wind speed (V) and wind chill days (WCD) decreased by 0.149 m/s and 1.970 days per decade, respectively. These trends become more substantial in densely populated and highly urbanized areas such as the North China Plain. The expansion of urban built-up area induces additional warming (reducing) to the increase (decrease) in WCT (WCD). On average, an increase from 0% to 100% in the urban fraction induced 0.290 ± 0.067 °C higher WCT (± denotes the 95% confidence interval), along with a reduction in V and WCD by 0.052 ± 0.014 m/s and 3.513 ± 0.387 days, respectively; whereas, the presence of the grassland and forest significantly diminishes the WCT and increases the WCD and surface V. It is expected that wind chill over China tends to be weakened under glocal warming and local urbanization in the near future. Our results have important implications for climate change mitigation, urban planning, landscape design, and air pollution abatement.

BackgroundThe novel coronavirus disease 2019 (COVID-19) has infected 1.9% of the world population by May 2, 2021. Since most previous studies that examined risk factors for mortality and severity were based on hospitalized individuals, population-based cohort studies are called for to provide evidence that can be extrapolated to the general population. Therefore, we aimed to examine the associations of comorbidities with mortality and disease severity in individuals with COVID-19 diagnosed in 2020 in Ontario, Canada.Methods and findingsWe conducted a retrospective cohort study of all individuals with COVID-19 in Ontario, Canada diagnosed between January 15 and December 31, 2020. Cases were linked to health administrative databases maintained in the ICES which covers all residents in Ontario. The primary outcome is all-cause 30-day mortality after the first COVID-19 diagnosis, and the secondary outcome is a composite severity index containing death and hospitalization. To examine the risk factors for the outcomes, we employed Cox proportional hazards regression models and logistic regression models to adjust for demographic, socio-economic variables and comorbidities. Results were also stratified by age groups. A total of 167,500 individuals were diagnosed of COVID-19 in 2020 and included in the study. About half (43.8%, n = 73,378) had at least one comorbidity. The median follow-up period were 30 days. The most common comorbidities were hypertension (24%, n = 40,154), asthma (16%, n = 26,814), and diabetes (14.7%, n = 24,662). Individuals with comorbidity had higher risk of mortality compared to those without (HR = 2.80, 95%CI 2.35–3.34; pConclusionWe found that solid organ transplant, dementia, chronic kidney disease, severe mental illness, CVD, hypertension, COPD, cancer, diabetes, rheumatoid arthritis, HIV, and asthma were associated with mortality or severity. Our study highlights that the number of comorbidities was a strong risk factor for deaths and severe outcomes among younger individuals with COVID-19. Our findings suggest that in addition of prioritizing by age, vaccination priority groups should also include younger population with multiple comorbidities.

Background While temperature changes have been confirmed as one of the contributory factors affecting human health, the association between high-frequency temperature variability (HFTV, i.e., temperature variation at short time scales such as 1, 2, and 5 days) and the hospitalization of chronic obstructive pulmonary disease (COPD) was rarely reported. Objectives To evaluate the associations between high-frequency temperature variabilities (i.e., at 1, 2, and 5-day scales) and daily COPD hospitalization. Methods We collected daily records of COPD hospitalization and meteorological variables from 2013 to 2017 in 21 cities of Guangdong Province, South China. A quasi-Poisson regression with a distributed lag nonlinear model was first employed to quantify the effects of two HFTV measures, i.e., the day-to-day (DTD) temperature change and the intraday-interday temperature variability (IITV), on COPD morbidity for each city. Second, we used multivariate meta-analysis to pool the city-specific estimates, and stratified analyses were performed by age and sex to identify vulnerable groups. Then, the meta-regression with city-level characteristics was employed to detect the potential sources of the differences among 21 cities. Results A monotonic increasing curve of the overall exposure-response association was observed, suggesting that positive HFTV (i.e., increased DTD and IITV) will significantly increase the risk of COPD admission. Negative DTD was associated with reduced COPD morbidity while positive DTD elevated the COPD risk. An interquartile range (IQR) increase in DTD was associated with a 24% (95% CI: 12–38%) increase in COPD admissions. An IQR increase in IITV0-1 was associated with 18% (95% CI: 7–27%) increase in COPD admissions. Males and people aged 0–64 years appeared to be more vulnerable to the DTD effect than others. Potential sources of the disparity among different cities include urbanization level, sex structure, industry structure, gross domestic product (GDP), health care services, and air quality. Conclusions The increases of DTD and IITV have significant adverse impacts on COPD hospitalization. As climate change intensifies, precautions need to be taken to mitigate the impacts of high-frequency temperature changes.

Ambient air pollution is the leading environmental risk factor for disease globally. Air pollutants can increase the risk of some respiratory infections, but their effects on tuberculosis (TB) are unclear. In this systematic literature review, we aimed to assess epidemiological studies on the association between outdoor air pollutants and TB incidence, hospital admissions and death (collectively referred to here as ‘TB outcomes’). We sought to consolidate available evidence on this topic and propose recommendations for future studies. Following PRISMA guidelines, we searched PubMed, Web of Science, Google Scholar, and Scopus with no restrictions imposed on year of publication. A total of 11 epidemiological studies, performed in Asia, Europe and North America, met our inclusion criteria (combined sample size: 215,337 people). We extracted key study characteristics from each eligible publication, including design, exposure assessment, analytical approaches and effect estimates. The studies were assessed for overall quality and risk of bias using standard criteria. The pollutant most frequently associated with statistically significant effects on TB outcomes was fine particulate matter ( The existing epidemiological evidence is limited and shows mixed results. However, it is plausible that exposure to air pollutants, particularly PM2.5, may suppress important immune defence mechanisms, increasing an individual's susceptibility to development of active TB and TB-related mortality. Considering the small number of studies relative to the demonstrably large global health burdens of air pollution and TB, further research is required to corroborate the findings in the current literature. Based on a critical assessment of existing evidence, we conclude with methodological suggestions for future studies.

Objectives: Healthcare workers (HCWs) are at high risk of latent tuberculosis infection (LTBI), and the baseline prevalence of LTBI among frontline TB HCWs in southeastern China remains unknown. The aim of this study was to assess the prevalence of LTBI among TB HCWs and to analyze factors associated with LTBI at both the individual and institutional level. Methods: Based on a cross-sectional study design, 31 out of 89 TB-designated hospitals in Zhejiang Province of China were selected. Information on TB infection control measures was collected through field visits to each of the selected hospitals. All TB HCWs from the selected hospitals were recruited to answer a questionnaire and to undergo LTBI testing by TB interferon gamma release assay. Univariate analyses and a generalized linear mixed model were applied to analyze factors associated with LTBI at both the individual and hospital level. Results: A total of 487 TB HCWs were recruited at the 31 TB-designated hospitals; 33.9% of them tested positive for LTBI. At the institutional level, a low TB epidemic level, regular infection control training for HCWs, and regular maintenance of ultraviolet disinfection equipment were found to be significantly associated with a lower LTBI rate among HCWs. At the individual level, alcohol use, a greater number of years working on TB, and a longer weekly duration of contact with TB patients were identified as associated factors for LTBI among HCWs. Conclusions: The LTBI rate among frontline TB HCWs was found to be high in southeastern China. Factors at the institutional and individual level could both affect the prevalence of LTBI among HCWs. Keywords: Tuberculosis, Latent tuberculosis infection, Healthcare workers

While the changes in air temperature under climate change and urbanization have been extensively examined in the literature, little is known about how human-perceived temperatures change. This study investigates the long-term changes in human-perceived temperatures (e.g., summer heat stress and winter wind chill conditions) over South China and quantifies the possible effects of urbanization. Daily observations at 86 stations during the period of 1961–2014 are collected and analyzed. Analysis results show that heat stress and wind chill temperature increase faster than air temperatures (i.e., daily mean, maximum, and minimum temperatures), and these increases are stronger in winter than in summer. The probabilities of hot extremes increase throughout the study period, whereas those of cold extremes decrease. Compared with those in non-urban areas, the warming trend in both seasons and the increase (decrease) in hot (cold) extremes are even more substantial in highly developed urban areas, such as the Pearl River Delta, indicating a remarkable effect of urbanization. It is estimated that urbanization contributes as much as half of the warming trend in summer and around one-third of the trend in winter. These results are helpful for mitigating climate change and reducing the impacts of extreme events.

Setting Shanghai is a mega city where 39% of the population comprises internal migrants. Objective To examine the different roles played by migrants and permanent residents in the transmission of multidrug-resistant tuberculosis (MDR-TB). Design We conducted a population-based cohort study to assess MDR-TB transmission in Shanghai between 1 January 2009 and 31 December 2012 using genotyping and geospatial analyses. Results A total of 367 MDR-TB cases formed the study cohort. Significant differences between MDR-TB cases who were internal migrants and those who were permanent residents were found with regard to age, sex, region, genetic characteristics and treatment outcomes. Permanent residents had a higher transmission rate than internal migrants (OR 3.36, 95%CI 1.86-6.09). Permanent residents and genotypic clustering cases had similar clusters in central downtown and some parts of suburban areas. Most of the clusters of internal migrants were found in rural areas bordering suburban areas. Clusters of genotypic non-clustering cases showed patterns that closely matched those of internal migrants, suggesting acquired drug resistance in migrants. Conclusion In Shanghai, permanent residents were significantly associated with recent transmission of MDR-TB in central downtown areas. Clustered cases of internal migrants in rural areas were most likely to have contracted MDR-TB through acquired resistance.

Heat waves (HWs) exert increasingly profound effects on public health and ecosystems under global warming and regional urbanization. This study examines the changes in the frequency, duration, magnitude, and timing of HWs in Fujian, Southeast China during 1971–2014 and quantifies the possible effects of urbanization using a dynamical classification scheme for urban and rural stations. Considering that some rural stations could have been converted into urban types during the urbanization process, this scheme dynamically classifies urban and rural stations based on time-varying land use/cover maps in different subperiods, thus giving a better estimation of urbanization effects. Our results show that HWs in Fujian are commencing earlier and ending later, and becoming more frequent, more intense, longer lasting. Particularly, severe (i.e., stronger and longer-lived) HW events have even stronger intensifying trends than regular ones. Moreover, we notice that the intensifying HW activities are especially prominent in densely populated and highly urbanized areas of Fujian. It is estimated that urbanization contributes to approximately half of the increasing HWs. Urbanization induces an additional increase of 0.15 events (3.78 days) per decade to HW frequency (participating days), accounting for 40.3% (57.5%) of the total trend. Meanwhile, 50.4% (60.8%) of the prolonging trend in the average (longest) duration and 27.0% (65.0%) of the intensifying trend in the average (strongest) magnitude are attributable to urbanization, i.e., 1.71 (1.19) days per decade and 0.18 (0.08) °C per decade, respectively. Moreover, urbanization induces further advancing (delaying) trend of 1.37 (1.39) days per decade for the onset (ending) date of HW in the calendar year. The findings presented here uncover the multi-faceted behaviors of HW and advance the existing research by providing a broader picture of this extreme event. It is also recommended to consider these various HW aspects in the research of climate change and extreme climate events.

Evidence for the association between fine particulate matter (PM2305 patients reported in Zhejiang and Ningxia were followed up from MDR-TB diagnosis until death, loss to follow-up or end of the study (31 December 2019), with an average follow-up of 1724 days per patient. 16-day averages of contemporaneous Normalised Difference Vegetation Index (NDVI) in the 500 m buffer of patient's residence, annual average PMHRs of 1.702 (95% CI 1.680 to 1.725) and 1.169 (1.162 to 1.175) were observed for PMIndividuals with MDR-TB could benefit from greenness by having attenuated associations between PM

Background Living closer to greenness were thought to benefit various health outcomes. We aimed to assess the association between residential greenness and mortality among patients undergoing multidrug resistant tuberculosis (MDR-TB) treatment. Methods We enrolled all local MDR-TB patients reported in Zhejiang, China from 2009 to 2017 and followed them throughout the treatment. We calculated the contemporaneous normalized difference vegetation index (NDVI) in the 250 and 500 m radius around patient’s residence. Cox proportional hazards regression models with time-varying NDVI were used to assess the impact of greenness exposure on all-cause mortality during MDR-TB treatment, adjusting for potential individual and contextual covariates. Results We ascertained 1,621 active MDR-TB cases, which contributed 3036 person-years at risk with an average follow-up of 684 days (s.d. 149 days) per patient. Among them, there were 163 deaths during follow-up, representing a crude mortality rate of 537 deaths per 10,000 person-years. Patients exposed to the second quintile (Q2) of greenness within the 500 m buffer had around 64% reduced mortality risk over the lowest quintile of greenness with hazard ratio (HR) = 0.364 (95% CI: 0.109–1.22). In lower nighttime light (NTL) areas, the hazard ratios (HR) per quintile increase in NDVI within the 500 m buffer were Q2: 0.35 (95% CI: 0.10–1.18), Q3: 0.24 (95% CI: 0.09–0.66), Q4: 0.26 (95% CI: 0.10–0.69), and Q5: 0.26 (95% CI: 0.10–0.71) relevant to the lowest quintile Q1, with a trend of p-value ≤0.01. Patients who were female, younger ( Conclusion Increasing greenness exposure along with medical treatment reduces all-cause mortality among patients living in lower NTL areas.

The spatial-temporal variations of the ground-based and satellite-derived PM2.5 are crucial for studying air quality, human health, and climate change. However, the existing ground-based PM2.5 monitoring network has sparsely-distributed sites and satellite cannot give 24-h PM2.5, which make it difficult to grasp the spatial and sub-daily variation characteristics of PM2.5. This study aims to fill that gap by establishing a virtual network of hourly PM2.5 concentration using the LightGBM model, based on the high-density ground meteorological observations at ~2400 sites across China. The virtual network shows a desirable performance of hourly PM2.5 estimation across China, with R2 of 0.86, root-mean-square error values of 14.99 μg/m3, and mean absolute error of 9.48 μg/m3 (the results of Cross-Validation). It also exhibits high spatial-temporal consistencies with the observed PM2.5. Spatially, the heaviest PM2.5 pollution is mainly distributed in eastern China (especially the Beijing-Tianjin-Hebei, the Yangtze and Pearl river deltas, and the Sichuan-Chongqing areas). Temporarily, PM2.5 exhibits remarkable seasonal and diurnal changes characterized by higher concentration in winter and nighttime and lower in summer and daytime. Meanwhile, we found that visibility can be used as the primary predictor in the machine learning model to enhance the accuracy of estimated PM2.5. The established virtual hourly PM2.5 network (~2400 stations) provides a more intuitive and detailed PM2.5 data for us to understand the diurnal variation of PM2.5 and monitor inter-regional transport of haze over China. It thus is of benefit to the study of air pollution control and related diseases.

While previous studies largely focus on extreme events, little is known about the behaviors of mild weather, a positive and pleasant condition occurring frequently, directly associated with outdoor activities, and highly relatable to the public. Here we examine the climatological characteristics and long-term trends of mild weather over China during 1971–2014, as well as the possible linkage with the El Niño−Southern Oscillation. It is found that, on average, China experiences 94.5 days (25.4% of all days) of mild weather in a year, and the annual number of mild days increased by 1.02% per decade (3.73 days per decade) during 1971–2014, especially in summer (1.54% per decade), spring (1.49% per decade), and autumn (1.03% per decade). We also find that most parts of China have been experiencing increasing mild weather in 1971–1998 but decreasing in 1998–2014. Clustering analysis reveals six subregions that exhibit distinct mild weather behaviors. In particular, harsh seasons (i.e., summer of southern China and winter of northern China) are becoming even less pleasant. Besides these secular trends, it is also noticed that El Niño event in the preceding winter is followed by less pleasant spring and summer and more pleasant autumn and winter in most areas of China. The results reported here have significant implications for urban planners and governmental policymakers.

ObjectivesPrevious studies on geographical disparities in healthcare access have been limited by not accounting for the healthcare provider’s capacity, a key determinant of supply and demand relationships.DesignThis study proposed a spatial coverage modelling approach to evaluate disparities in hospital care access using Canadian Institute for Health Information data in 2007.SettingThis study focusses on accessibility of inpatient and emergency cares at both levels of individual hospital and the administrative regions of Local Health Integration Network (LHIN) levels.MeasuresWe integrated a set of traffic and geographical data to precisely estimate travel time as a measure of the level of accessibility to the nearest hospital by three scenarios: walking, driving and a combination of the both. We estimated population coverage rates, using hospital capacities and population in the catchments, as a measure of the level of the healthcare availability. Hospital capacities were calculated based on numbers of medical staff and beds, occupation rates and annual working hours of healthcare providers.ResultsWe observed significant disparities in hospital capacity, travel time and population coverage rate across the LHINs. This study included 25 teaching and 148 community hospitals. The teaching hospitals had stronger capacities with 489 209 inpatient and 130 773 emergency patients served in the year, while the population served in community hospitals were 2.64 times higher. Compared with north Ontario, more locations in the south could reach to hospitals within 30 min irrespective of the travel mode. Additionally, Northern Ontario has higher population coverage rates, for example, with 42.6~46.9% for inpatient and 15.7~44% for emergency cares, compared with 2.4~34.7% and 0.35~14.6% in Southern Ontario, within a 30 min catchment by driving.ConclusionCreating a comprehensive, flexible and integrated healthcare system should be considered as an effective approach to improve equity in access to care.

Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.

It is known that rapid expansion in urban area exerts prominent effects on surface or near-surface temperature and the urban heat island effect. However, whether and how urban land expansion influences atmospheric humidity, a key indicator closely relating to human comfort and urban environment, warrant further investigations. By taking Guangdong Province as an example, in this study we examined the change in atmospheric moisture over South China during 1961–2014 and evaluated the urban expansion effects. We found a prominent drying tendency over Guangdong, as characterized by decreasing relative and specific humidities and increasing vapor pressure deficit. The drying trend has a positive correlation with urban expansion speed and exhibited regional differences, with particularly stronger trends in more urbanized and populated areas such as the Pearl River Delta. Using a dynamic classification of urban / rural stations based on time-varying high-resolution land use / land cover maps, we estimated that urbanization contributed to approximately 50% of the total drying trend in the urban areas of Guangdong. Possible mechanisms and processes underlying the urban expansion effect were also discussed. We suggested that the changes in atmospheric moisture and the urban expansion effect should be considered in future climate change mitigation, urban planning, and landscape design to support sustainable development in urban areas.

BACKGROUND The pathogenesis and pathophysiology of eosinophilia-related chronic cough such as non-asthmatic eosinophilic bronchitis and cough variant asthma are still not clear. OBJECTIVE This study is to examine the potential role of traffic-related air pollution (TRAP) in eosinophilic inflammation and cough responses. METHODS Non-sensitized guinea-pigs were exposed to TRAP in an urban traffic tunnel or kept in a filtered air environment for 7 or 14 days. Reflexive cough was measured using citric acid and allyl isothiocyanate (AITC) challenges, respectively. Spontaneous cough counting was determined using audio recording and a waveform analysis. Airway inflammation was evaluated using differential cells in bronchoalveolar lavage fluid (BALF) and lung histopathology. To further elucidate the relationship between airway inflammation and cough hypersensitivity, a subgroup of those exposed for 14 days received a dexamethasone treatment. RESULTS Compared to reflexive cough count (mean (95% confidence interval) in 10 minutes) provoked by the AITC challenge for the unexposed animals (3.1 (1.7-4.5)), those were increased significantly following both the 7-day (12.0 (6.8-17.2), P

Background Studies differentiating the cardiorespiratory morbidity effects of PM2.5, PM10, and PM2.5∼10 (i.e. coarse PM or PMc) are still limited and inconsistent. Objective To estimate the acute, cumulative, and harvesting effects of exposure to the three size-specific PM on cardiorespiratory morbidity, and their concentration-response relations. Methods A total of 6,727,439 emergency department (ED) visits were collected from 16 public teaching hospitals in Guangzhou, from January 1st 2012 to December 31st 2015, among which over 2.1 million were asthma, COPD, pneumonia, respiratory tract infection (RTI), hypertension, stroke, and coronary heart disease (CHD). Distributed lag non-linear models (DLNM) was used to estimate the associations between the three size-specific PM and ED visits for the cardiovascular diseases. Long-term trends, seasonality, influenza epidemics, meteorological factors, and other gas pollutants, including SO2, NO2, and O3, were adjusted. We stratified the analyses by gender and age. Results Elevated PM2.5 and PM10 were significantly associated with increased ED visits for pneumonia, RTI, and CHD at both lag0 and lag0-3. A 10 μg/m3 increment of PMc (at lag0-14) was estimated to increase ED visits for pneumonia by 6.32% (95% CI, 4.19, 8.49) and for RTI by 4.72% (95% CI, 3.81, 5.63), respectively. PMc showed stronger cumulative effects on asthma in children than elderly. We observed significant harvesting effects (i.e. morbidity displacements) of the three size-specific PM on respiratory but very little on cardiovascular ED visits. The concentration-response curves suggested non-linear relations between exposures to the three different sizes of PM and respiratory morbidity. Conclusions Overall, the three size-specific PM demonstrated distinct acute and cumulative effects on the cardiorespiratory diseases. PM2.5 and PMc would have significant effects on pneumonia and RTI. Strategies should be considered to further reduce levels of ambient PM2.5 and PMc.

Human mobility has played a major role in the spread of infectious diseases such as tuberculosis (TB) through transportation; however, its pattern and mechanism have remained unclear. This study used transport networks as a proxy for human mobility to generate the spatial process of TB incidence. It examined the association between TB incidence and four types of transport networks at the provincial level: provincial roads, national roads, highways, and railways. Geographical information systems and geospatial analysis were used to examine the spatial distribution of 2217 smear-positive TB cases reported between 2009 and 2011 in the Shandong province. The study involved factors such as population density and elevation difference in conjunction with the types of transport networks to predict the disease occurrence in space. It identified spatial clusters of TB incidence linked not only with transport networks of the regions but also differentiated by elevation. Our research findings provide evidence of targeting populous regions with well-connected transport networks for effective surveillance and control of TB transmission in Shandong.

Inapparent avian exposure was suspected for the sporadic infection of avian influenza A(H7N9) occurring in China. This type of exposure is usually unnoticed and difficult to model and measure. Infected poultry with avian influenza H7N9 virus typically remains asymptomatic, which may facilitate infection through inapparent poultry/bird exposure, especially in a country with widespread practice of backyard poultry. The present study proposed a novel approach that integrated ecological and case-control methods to quantify the risk of inapparent avian exposure on human H7N9 infection. Significant associations of the infection with chicken and goose densities, but not with duck density, were identified after adjusting for spatial clustering effects of the H7N9 cases across multiple geographic scales of neighborhood, community, district and city levels. These exposure risks varied geographically in association with proximity to rivers and lakes that were also proxies for inapparent exposure to avian-related environment. Males, elderly people, and farmers were high-risk subgroups for the virus infection. These findings enable health officials to target educational programs and awareness training in specific locations to reduce the risks of inapparent exposure.

Background Recent biochemical studies suggest that exogenous sulfur dioxide (SO2) at low concentrations may have been beneficial in inhibiting mycobacteria tuberculosis (TB) growth. However, there is a dearth of population-based studies. Objectives To examine the association of ambient SO2 levels and initial TB outpatient visits. Methods In Ningbo, China, we collected all daily initial outpatient visits for TB and routinely air pollution monitoring data between January 2009 and December 2013. A time-series study was conducted by using generalized additive regression (GAM) with log-linear Poisson models to estimate the associations between daily initial TB outpatient visits and daily average concentration of SO2. Other traffic-related co-pollutants were adjusted. Sensitivity analyses were conducted to examine the relationship when 1% extreme SO2 concentrations excluded or if related to the early onsets of TB symptoms. Results SO2 concentrations in Ningbo were low with a daily average of 25 μg/m3 (i.e. 0.0089 ppm). Negative associations were identified between ambient SO2 concentrations and daily initial TB outpatient visits. A 10 μg/m3 increase in SO2 at lag3 and lag0-3 days were associated with −2.0% (95%CI, −3.2, −0.8) and −4.6% (95%CI, −6.8, −2.4) changes, respectively, in initial TB outpatient visits according to single-pollutant models. The negative association became stronger when nitrogen dioxide (NO2) or particulate matter with aerodynamic diameter less than 10 μm (PM10) was adjusted in two-pollutant models. This association was higher in males vs. females and in middle-aged adults vs. the elderly. We found a stronger negative association between SO2 concentration and the initial symptom occurrence. Conclusion Short-term exposure to ambient SO2 was associated with reduced risk of initial TB outpatient visits, suggesting acute protective effects of low-level ambient SO2 exposure on bacteria-induced pulmonary infections.

The rise in global sea levels has been recognized by many scientists as an important global research issue. The process of sea-level change has demonstrated a complex scaling behavior in space and time. Large numbers of tide gauge stations have been built to measure sea-level change in the North Pacific Ocean, Indian Ocean, North Atlantic Ocean, and Antarctic Ocean. Extensive studies have been devoted to exploring sea-level variation in Asia concerning the Bohai Gulf (China), the Yellow Sea (China), the Mekong Delta (Thailand), and Singapore. Hong Kong, however, a mega city with a population of over 7 million situated in the mouth of the Pear River Estuary in the west and the South China Sea in the east, has yet to be studied, particularly in terms of the temporal scaling behavior of sea-level change. This article presents an approach to studying the temporal scaling behavior of sea-level change over multiple time scales by analyzing the time series of sea-level change in Tai Po Kou, Tsim Bei Tsui, and Quarry Bay from the periods of 1964–2010, 1974–2010, and 1986–2010, respectively. The detection of long-range correlation and multi-fractality of sea-level change seeks answers to the following questions: (1) Is the current sea-level rise associated with and responsible for the next rise over time? (2) Does the sea-level rise have specific temporal patterns manifested by multi-scaling behaviors? and (3) Is the sea-level rise is temporally heterogeneous in the different parts of Hong Kong? Multi-fractal temporally weighted de-trended fluctuation analysis (MF-TWDFA), an extension of multi-fractal de-trended fluctuation analysis (MF-DFA), has been applied in this study to identify long-range correlation and multi-scaling behavior of the sea-level rise in Hong Kong. The experimental results show that the sea-level rise is long-range correlated and multi-fractal. The temporal patterns are heterogeneous over space. This finding implies that mechanisms associated with the local ecological environment, hydrodynamic and morphodynamic processes, and human activities might have driven a distinct sea-level rise in Hong Kong.

Multilingual abstracts in the six official working languages of the United Nations. (PDF 548 kb)

Fractal is employed in this paper as a scale-based method for the identification of the scaling behavior of time series. Many spatial and temporal processes exhibiting complex multi(mono)-scaling behaviors are fractals. One of the important concepts in fractals is crossover time scale(s) that separates distinct regimes having different fractal scaling behaviors. A common method is multifractal detrended fluctuation analysis (MF-DFA). The detection of crossover time scale(s) is, however, relatively subjective since it has been made without rigorous statistical procedures and has generally been determined by eye balling or subjective observation. Crossover time scales such determined may be spurious and problematic. It may not reflect the genuine underlying scaling behavior of a time series. The purpose of this paper is to propose a statistical procedure to model complex fractal scaling behaviors and reliably identify the crossover time scales under MF-DFA. The scaling-identification regression model, grounded on a solid statistical foundation, is first proposed to describe multi-scaling behaviors of fractals. Through the regression analysis and statistical inference, we can (1) identify the crossover time scales that cannot be detected by eye-balling observation, (2) determine the number and locations of the genuine crossover time scales, (3) give confidence intervals for the crossover time scales, and (4) establish the statistically significant regression model depicting the underlying scaling behavior of a time series. To substantive our argument, the regression model is applied to analyze the multi-scaling behaviors of avian-influenza outbreaks, water consumption, daily mean temperature, and rainfall of Hong Kong. Through the proposed model, we can have a deeper understanding of fractals in general and a statistical approach to identify multi-scaling behavior under MF-DFA in particular.

The highly pathogenic avian influenza A H5N1 has become a serious public health problem, fatal to poultry and humans all over the world. Since its detection in 2003, H5N1 viruses have exhibited considerable capability of transmission from wild birds to humans, spreading from southeastern Asia across Europe and into Africa. Avian influenza has been recognized as a potential pandemic by practitioners and scientists and has become an important research issue in virology, molecular biology, phylogeography, and spatial epidemiology. Although avian influenza has explicit spatial and temporal dimensions, rigorous geographical analysis, particular its temporal process, of the outbreaks is at its initial stage. The purpose of this article is to provide an approach to study the temporal behaviors of avian influenza A (H5N1) over multiple time scales by analyzing the global and continental outbreak time series from December 2003 to March 2009. The detection of long-range correlation and multifractality in the outbre...

Recent experimental and clinical studies suggest that exogenous carbon monoxide (CO) at lower concentrations may have beneficial effects under certain circumstances, whereas population-based epidemiologic studies of environmentally relevant CO exposure generated mixed findings.To examine the acute effects of ambient CO on respiratory tract infection (RTI) hospitalizations.A time series study was conducted. Daily emergency hospital admission and air pollution data in Hong Kong were collected from January 2001 to December 2007. Log-linear Poisson models were used to estimate the associations between daily hospital admissions for RTI and daily average concentrations of CO across three background air monitoring stations and three roadside stations, respectively, controlling for other traffic-related copollutants.CO concentrations were low during the study period with a daily average of 0.6 ppm in background stations and 1.0 ppm in roadside stations. Negative associations were found between ambient CO concentrations and daily hospital admissions for RTI. One ppm increase in background CO at lag 0-2 days was associated with -5.7% (95% confidence interval, -9.2 to -2.1) change in RTI admissions from the whole population according to single-pollutant model; the negative association became stronger when nitrogen dioxide or particulate matter with aerodynamic diameter less than 10 μm was adjusted for in two-pollutant models. The negative association seemed to be stronger in the adults than in the children and elderly.Short-term exposure to ambient CO was associated with decreased risk of hospital admissions for RTI, suggesting some acute protective effects of low ambient CO exposure on respiratory infection.

Highly pathogenic avian influenza (HPAI) H5N1, a disease associated with high rates of mortality in infected human populations, poses a serious threat to public health in many parts of the world. This article reports findings from a study aimed at improving our understanding of the spatial pattern of the highly pathogenic avian influenza, H5N1, risk in East-Southeast Asia where the disease is both persistent and devastating. Though many disciplines have made important contributions to our understanding of H5N1, it remains a challenge to integrate knowledge from different disciplines. This study applies genetic analysis that identifies the evolution of the H5N1 virus in space and time, epidemiological analysis that determines socio-ecological factors associated with H5N1 occurrence, and statistical analysis that identifies outbreak clusters, and then applies a methodology to formally integrate the findings of the three sets of methodologies. The present study is novel in two respects. First it makes the initiative attempt to use genetic sequences and space-time data to create a space-time phylogenetic tree to estimate and map the virus' ability to spread. Second, by integrating the results we are able to generate insights into the space-time occurrence and spread of H5N1 that we believe have a higher level of corroboration than is possible when analysis is based on only one methodology. Our research identifies links between the occurrence of H5N1 by area and a set of socio-ecological factors including altitude, population density, poultry density, and the shortest path distances to inland water, coastlines, migrating routes, railways, and roads. This study seeks to lay a solid foundation for the interdisciplinary study of this and other influenza outbreaks. It will provide substantive information for containing H5N1 outbreaks.