Intra-urban risk assessment of occupational injuries and illnesses associated with current and projected climate: Evidence from three largest Australian cities.

BACKGROUND: Increased risk of occupational injuries and illnesses (OI) is associated with ambient temperature. However, most studies have reported the average impacts within cities, states, or provinces at broader scales. METHODS: We assessed the intra-urban risk of OI associated with ambient temperature in three Australian cities at statistical area level 3 (SA3). We collected daily workers' compensation claims data and gridded meteorological data from July 1, 2005, to June 30, 2018. Heat index was used as the primary temperature metric. We performed a two-stage time series analysis: we generated location-specific estimates using Distributed Lag Non-Linear Models (DLNM) and estimated the cumulative effects with multivariate meta-analysis. The risk was estimated at moderate heat (90th percentile) and extreme heat (99th percentile). Subgroup analyses were conducted to identify vulnerable groups of workers. Further, the OI risk in the future was estimated for two projected periods: 2016-2045 and 2036-2065. RESULTS: The cumulative risk of OI was 3.4% in Greater Brisbane, 9.5% in Greater Melbourne, and 8.9% in Greater Sydney at extreme heat. The western inland regions in Greater Brisbane (17.4%) and Greater Sydney (32.3%) had higher risk of OI for younger workers, workers in outdoor and indoor industries, and workers reporting injury claims. The urbanized SA3 regions posed a higher risk (19.3%) for workers in Greater Melbourne. The regions were generally at high risk for young workers and illness-related claims. The projected risk of OI increased with time in climate change scenarios. CONCLUSIONS: This study provides a comprehensive spatial profile of OI risk associated with hot weather conditions across three cities in Australia. Risk assessment at the intra-urban level revealed strong spatial patterns in OI risk distribution due to heat exposure. These findings provide much-needed scientific evidence for work, health, and safety regulators, industries, unions, and workers to design and implement location-specific preventative measures.

Integrated case-based clinical approach in understanding pathways, complexities, pitfalls and challenges in neurodegenerative disorders.

INTRODUCTION: This paper presents 5 cases of neurodegenerative disorders from our tertiary care rural hospital in south India. The purpose of this paper is to generate an emerging common theme by thematic analysis of clinical data from each of these patients. A theme emerged, we identified that there was a common clinical ground in patients with movement disorders and psychiatric symptoms. From this common theme, these patients eventually went on to develop different courses of illnesses. METHODOLOGY: Clinical analysis of a case series of 5 patients with neurodegenerative disorders attending the Medicine or Psychiatry services of our hospital. CONCLUSION: A clear & consistent association between movement disorders and psychiatric symptoms was found. Although our data is limited, we conclude that movement disorders can be early clinical markers of organic psychopathology. However, we are aware that this association can be confounded by substance abuse, stress, sleep disruption and even therapeutic interventions, and thus these factors were accounted for and yet we conclude that movement disorders can be early clinical indictors of organic psychopathology.

Outdoor ambient temperatures and occupational injuries and illnesses: Are there risk differences in various regions within a city?

Increased risk of occupational injuries and illnesses (OI) is associated with hot ambient temperatures. However, the existing evidence of risk estimation is limited to large regions at the city or provincial scales. For effective and localized occupational health risk management, spatio-temporal analysis should be carried out at the intra-city level to identify high-risk areas within cities. This study examined the exposure-response relationship between ambient temperatures and OI at the intra-city scale in Greater Adelaide, Australia. Vulnerable groups of workers, in terms of workers' characteristics, the nature of their work, and workplace characteristics were identified. Further, the projected risk of OI was quantified in various climate change scenarios. The temperature-OI association was estimated using a time-series study design combined with Distributed Lag Non-linear Models. Daily workers' compensation claims (2005-2018) were merged with 5 km gridded meteorological data of maximum temperature (°C) at Statistical Area Level 3 in Greater Adelaide. Region-wise subgroup analyses were conducted to identify vulnerable groups of workers. Future projections (2006-2100) were conducted using downscaled climate projections and the risk was quantified using log-linear extrapolation. The analyses were performed in R 4.1.0. The overall OI risk was 16.7% (95%CI: 10.8-23.0) at moderate heat (90th percentile) and increased to 25.0% (95%CI: 16.4-34.2) at extreme heat (99th percentile). Northern Adelaide had a higher risk of OI for all types of workers at moderate heat, while western regions had a high risk for indoor industries. Southern and eastern regions had a higher OI risk for males, older workers, and outdoor industries at extreme heat. The projected risk of OI is estimated to increase from 20.8% (95%CI: -0.2-46.3) in 2010s to 22.9% (95%CI: -8.0-64.1) by 2050s. Spatio-temporal risk assessment at the intra-city scale can help us identify high-risk areas, where targeted interventions can be efficiently employed to reduce the socio-economic burden of OI.

Modelling the occurrence and spatial distribution of screwworm species in Northern Pakistan.

We use binomial kriging to model the spatial distribution of myiasis by three species namely Chrysomya bezziana, Wohlfahrtia magnifica and Lucilia cuprina in the livestock of Khyber Pakhtunkhwa, Pakistan. Traditional species distribution models are usually based on assumption of independence of observations. Species data often come in presence-only form for which background points are generated based on some covariates using statistical and machine learning techniques such as MaxEnt. We assume a symmetric binomial distribution based on the principle of maximum entropy in order to decide the number of pseudo-absences. Our results showed that the spatial models fitted very well and prediction distributions were estimated with excellent accuracy. Moreover kriging maps were more accurate as most of the non-spatial variation has been picked up by external drift with higher values of the sensitivity focusing partial AUC for all the three species. Land-use-land-cover was a common factor significantly affecting spatial distribution of all the three species suggesting that for established species anthropogenic factors such as land use become a strong determinant of their spatial distribution. Our results also revealed that for invading species like W. magnifica elevation acts as a barrier to species dispersal and therefore is more limiting to distribution. Furthermore the higher overall prediction accuracy demonstrated that our models performed well in predicting the distributions of the three species, which would lead to better understanding and management of the larval infestation.

Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases.

BACKGROUND: The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. METHODOLOGY: This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), and random walk forecasts (RWF) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), respectively. FINDINGS: The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021. CONCLUSION: Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS, can help in anticipating the future outbreaks of SARS-CoV2.

Extreme heat and occupational injuries in different climate zones: A systematic review and meta-analysis of epidemiological evidence.

BACKGROUND: The link between heat exposure and adverse health outcomes in workers is well documented and a growing body of epidemiological evidence from various countries suggests that extreme heat may also contribute to increased risk of occupational injuries (OI). Previously, there have been no comparative reviews assessing the risk of OI due to extreme heat within a wide range of global climate zones. The present review therefore aims to summarise the existing epidemiological evidence on the impact of extreme heat (hot temperatures and heatwaves (HW)) on OI in different climate zones and to assess the individual risk factors associated with workers and workplace that contribute to heat-associated OI risks. METHODS: A systematic review of published peer-reviewed articles that assessed the effects of extreme heat on OI among non-military workers was undertaken using three databases (PubMed, Embase and Scopus) without temporal or geographical limits from database inception until July 2020. Extreme heat exposure was assessed in terms of hot temperatures and HW periods. For hot temperatures, the effect estimates were converted to relative risks (RR) associated with 1 °C increase in temperature above reference values, while for HW, effect estimates were RR comparing heatwave with non-heatwave periods. The patterns of heat associated OI risk were investigated in different climate zones (according to Köppen Geiger classification) based on the study locations and were estimated using random-effects meta-analysis models. Subgroup analyses according to workers' characteristics (e.g. gender, age group, experience), nature of work (e.g. physical demands, location of work i.e. indoor/outdoor) and workplace characteristics (e.g. industries, business size) were also conducted. RESULTS: A total of 24 studies published between 2005 and 2020 were included in the review. Among these, 22 studies met the eligibility criteria, representing almost 22 million OI across six countries (Australia, Canada, China, Italy, Spain, and USA) and were included in the meta-analysis. The pooled results suggested that the overall risk of OI increased by 1% (RR 1.010, 95% CI: 1.009-1.011) for 1 °C increase in temperature above reference values and 17.4% (RR 1.174, 95% CI: 1.057-1.291) during HW. Among different climate zones, the highest risk of OI during hot temperatures was identified in Humid Subtropical Climates (RR 1.017, 95% CI: 1.014-1.020) followed by Oceanic (RR 1.010, 95% CI: 1.008-1.012) and Hot Mediterranean Climates (RR 1.009, 95% CI: 1.008-1.011). Similarly, Oceanic (RR 1.218, 95% CI: 1.093-1.343) and Humid Subtropical Climates (RR 1.213, 95% CI: 0.995-1.431) had the highest risk of OI during HW periods. No studies assessing the risk of OI in Tropical regions were found. The effects of hot temperatures on the risk of OI were acute with a lag effect of 1-2 days in all climate zones. Young workers (age < 35 years), male workers and workers in agriculture, forestry or fishing, construction and manufacturing industries were at high risk of OI during hot temperatures. Further young workers (age < 35 years), male workers and those working in electricity, gas and water and manufacturing industries were found to be at high risk of OI during HW. CONCLUSIONS: This review strengthens the evidence on the risk of heat-associated OI in different climate zones. The risk of OI associated with extreme heat is not evenly distributed and is dependent on underlying climatic conditions, workers' attributes, the nature of work and workplace characteristics. The differences in the risk of OI across different climate zones and worker subgroups warrant further investigation along with the development of climate and work-specific intervention strategies.

Bioaccumulation of Heavy Metals in Water, Sediments, and Tissues and Their Histopathological Effects on Anodonta cygnea (Linea, 1876) in Kabul River, Khyber Pakhtunkhwa, Pakistan.

The present investigation aimed to assess the concentrations of selected heavy metals in water and sediments and their bioaccumulation in tissues of freshwater mussels and their histopathological effects on the digestive gland, gills, and gonads of Anodonta cygnea. Water, sediments, and freshwater mussel samples were collected at four sites, that is, reference and polluted sites, along the Kabul River, Khyber Pakhtunkhwa. The polluted sites were receiving effluents from the industrial, agricultural, municipal, and domestic sources. The order of metals in the water was Zn > Pb > Ni > Cu > Mn > Fe > Cr > Cd, in sediments the order was Fe > Zn > Cr > Ni > Mn > Pb > Cu > Cd, and in the soft tissues the order was Fe > Zn > Mn > Pb > Cu > Cr > Ni > Cd. Histopathological alterations observed in polluted sites of Kabul River were inflammation, hydropic vacuolation, and lipofuscin pigments (in digestive gland), gill lamellar fusion, dilated hemolymphatic sinus, clumping, and generation of cilia and hemocytic infiltration (in gills), and atresia, necrosis, granulocytoma, hemocytic infiltration, and lipofuscin pigments (in gonads). The histopathological alterations in the organs of Anodonta cygnea can be considered as reliable biomarkers in biomonitoring of heavy metal pollution in aquatic ecosystems.

Rat-bites of an epidemic proportion in Peshawar vale; a GIS based approach in risk assessment.

Contemporary studies demonstrate that rodent bites do not occur frequently. However, a huge number of cases were reported from Peshawar vale, Pakistan during 2016. Two species, the local black rat Rattus rattus (Linnaeus, 1758) and the invasive brown rat Rattus norvegicus (Berkenhout, 1769) might be the suspected cause. Several studies indicated the invasion of brown rats into Pakistan presumably via port city of Karachi. In this study, we modeled geospatial distribution of rodent bites for risk assessment in the region. Bite cases reported to tertiary care lady reading hospital were monitored from January 1 to August 31, 2016. Among 1747 cases, statistically informative data (n = 1295) was used for analyses. MaxEnt algorithm was employed for geospatial modeling, taking into account various environmental variables (temperature, precipitation, humidity, and elevation) and anthropogenic factors (human population density, distance from roads, distance from water channels, and land use/land cover). MaxEnt results revealed that urban slums (84.5%) are at highest risk followed by croplands (10.9%) and shrublands (2.7%). Anthropogenic factors affecting incidence of rodent bites included host density (contribution: 34.7), distance from water channels (3.2), land use/land cover (2.8), and distance from roads (2). Most of the cases occurred within a radius of 0.3 km from roads and 5 km from water channels. Rodent bite incidence is currently at its peak in Peshawar vale. Factors significantly affecting rodents' bite activity and their distribution and dispersal include urbanization, distance from roads, and water channels. Further studies are needed to determine the impact of invasion by brown rat on bite incidence.

Spatial modeling of rat bites and prediction of rat infestation in Peshawar valley using binomial kriging with logistic regression.

In this study, we propose to develop a geostatistical computational framework to model the distribution of rat bite infestation of epidemic proportion in Peshawar valley, Pakistan. Two species Rattus norvegicus and Rattus rattus are suspected to spread the infestation. The framework combines strengths of maximum entropy algorithm and binomial kriging with logistic regression to spatially model the distribution of infestation and to determine the individual role of environmental predictors in modeling the distribution trends. Our results demonstrate the significance of a number of social and environmental factors in rat infestations such as (I) high human population density; (II) greater dispersal ability of rodents due to the availability of better connectivity routes such as roads, and (III) temperature and precipitation influencing rodent fecundity and life cycle.

Environmental risk modelling and potential sand fly vectors of cutaneous leishmaniasis in Chitral district: a leishmanial focal point of mount Tirich Mir, Pakistan.

OBJECTIVE: To provide baseline information about suspected vectors and the incidence, distribution and an active zone of transmission for cutaneous leishmaniasis (CL) in Chitral, Pakistan, using GIS tools; and to investigate the role of environmental factors in the disease dynamics. METHOD: Two surveys in 2014 and 2016 as a basis for choropleth and environmental risk mapping. RESULTS: A total of 769 captured specimens yielded 14 Phlebotomus and six Sergentomyia species including two potential vectors of CL, i.e. Phlebotomus papatasi and Phlebotomus sergenti. P. papatasi (71%) was dominant, followed by P. sergenti (18%). A choropleth map generated in Arcmap 10.1 based on 1560 CL case reports displayed maximum prevalence (0.92-2.5%) in Ayun, Broz, Charun, Chitral 1 and 2 and Darosh 1 and 2 union councils. An environmental risk map constructed by MaxEnt 3.3.3 defined an active zone of transmission based on leishmaniasis occurrence records (n = 315). The analysis of variable contribution in MaxEnt indicates significance of elevation (54.4%), population density (23.3%) and land use/land cover (6.6%) in CL disease dynamics. CONCLUSION: The probability of CL increases (0.6-1 on logistic scale) in severely deforested areas, in lowland valleys and in regions with high-population density.

Dataset of traumatic myiasis observed for three dominant screw worm species in North West Pakistan with first report of Wohlfahrtia magnifica (Schiner).

Regional surveys were carried out in different parts of North West Pakistan among domestic animals (N=57,921) including pets and livestock identifying cases of traumatic myiasis (n=1037). A total of four surveys focused general livestock population during Eid ul Adha (Eid surveys; incidence=1.21%) while another four surveys (Miscellaneous surveys; incidence=7.34%) targeted animal population brought to veterinary hospitals and dispensaries. Timeframe spanned four years from 2012 to 2015. Maggots were sampled and location of the wound was recorded for each host. Taxonomic identification used light and electron microscopic techniques. Our dataset shows three species as principle agents of myiasis (n=882) including Chrysomya bezziana Villeneuve (n=394), Wohlfahrtia magnifica (n=244) and Lucilia cuprina Wiedemann (n=244). Others (n=155) including Chrysomya megacephala (Fabricius), Chrysomya rufifacies (Macquart), Lucilia sericata (Meigen), Lucilia illustris (Meigen), Lucilia porphyrina (Walker), Hemipyrellia ligguriens (Wiedemann), Calliphora vicina (Robineau-Desvoidy), Sarcophaga crassipalpalis (Macquart) and Sarcophaga species were identified as species of minor importance. The obligatory screwworm species W. magnifica is a first report from Pakistan. The results based on this dataset are presented in a recent publication "Distribution Modeling of three screwworm species in the ecologically diverse landscape of North West Pakistan" (Zaidi et al., 2016) [1].

Distribution Modeling of three screwworm species in the ecologically diverse landscape of North West Pakistan.

North West Pakistan (NWP) is characterized by four eco-zones: Northern Montane Region, North Western Hills, Submontane Region and Indus Plains. Present study identified 1037 cases of traumatic myiasis in the region during 2012-2015. Screw worm larvae were classified as 12 species: Chrysomya bezziana (Villeneuve), Chryomya megacephala (Fabricius), Chrysomya rufifacies (Macquart), Lucilia cuprina (Wiedemann), Lucilia sericata (Meigen), Lucilia illustris (Meigen), Lucilia porphyrina (Walker), Hemipyrellia ligguriens (Wiedemann), Calliphora vicina (Robineau-Desvoidy), Wohlfahrtia magnifica (Schiner), Sarcophaga crassipalpalis (Macquart), Sarchophaga species. Among these C. bezziana, L. cuprina and W. magnifica with approximately 882 case reports were the principal agents of traumatic myiasis. The species W. magnifica is a first report from Pakistan. In order to investigate spatial distribution of these dominant species we used MaxEnt niche model. Our results revealed a well-established occurrence of C. bezziana and L. cuprina in the four eco-regions while W. magnifica is currently contained in the Submontane Region. Several hot spot areas of infestation were detected all characterized by high human population density showing synanthropic nature of these species. Wohlfahrtia magnifica was excluded from Northern Montane Region with severe winters and Southern Indus Plains with harsh summers revealing that invasive species are initially sensitive to extreme of temperatures. Presence of L. cuprina in the wet areas of North Humid Belt (Maximum annual precipitation: 1641mm) depicted a moisture preference of the species. In perspective of changing climate and future predictions of severe events such as droughts and flooding in NWP, W. magnifica can potentially alter the species composition. Considering these findings in an eco-geographically dynamic region of Pakistan we predict that two factors (1) Growing human population (2) Climatic conditions, equally contribute to range shift of synanthropic species.

Species Distribution Modelling of Aedes aegypti in two dengue-endemic regions of Pakistan.

OBJECTIVES: Statistical tools are effectively used to determine the distribution of mosquitoes and to make ecological inferences about the vector-borne disease dynamics. In this study, we utilised species distribution models to understand spatial patterns of Aedes aegypti in two dengue-prevalent regions of Pakistan, Lahore and Swat. Species distribution models can potentially indicate the probability of suitability of Ae. aegypti once introduced to new regions like Swat, where invasion of this species is a recent phenomenon. METHODS: The distribution of Ae. aegypti was determined by applying the MaxEnt algorithm on a set of potential environmental factors and species sample records. The ecological dependency of species on each environmental variable was analysed using response curves. We quantified the statistical performance of the models based on accuracy assessment and spatial predictions. RESULTS: Our results suggest that Ae. aegypti is widely distributed in Lahore. Human population density and urban infrastructure are primarily responsible for greater probability of mosquito occurrence in this region. In Swat, Ae. aegypti has clumped distribution, where urban patches provide refuge to the species in an otherwise hostile heterogeneous environment and road networks are assumed to have facilitated in passive-mediated dispersal of species. CONCLUSIONS: In Pakistan, Ae. aegypti is expanding its range northwards; this could be associated with rapid urbanisation, trade and travel. The main implication of this expansion is that more people are at risk of dengue fever in the northern highlands of Pakistan.