Wheeze, asthma diagnosis and medication use : a national adult survey in a developing country

Background As relatively little is known about adult wheeze and asthma in developing countries, this study aimed to determine the predictors of wheeze, asthma diagnosis, and current treatment in a national survey of South African adults. Methods A stratified national probability sample of households was drawn and all adults (>14 years) in the selected households were interviewed. Outcomes of interest were recent wheeze, asthma diagnosis, and current use of asthma medication. Predictors of interest were sex, age, household asset index, education, racial group, urban residence, medical insurance, domestic exposure to smoky fuels, occupational exposure, smoking, body mass index, and past tuberculosis. Results A total of 5671 men and 8155 women were studied. Although recent wheeze was reported by 14.4% of men and 17.6% of women and asthma diagnosis by 3.7% of men and 3.8% of women, women were less likely than men to be on current treatment (OR 0.6; 95% confidence interval (CI) 0.5 to 0.8). A history of tuberculosis was an independent predictor of both recent wheeze (OR 3.4; 95% CI 2.5 to 4.7) and asthma diagnosis (OR 2.2; 95% CI 1.5 to 3.2), as was occupational exposure (wheeze: OR 1.8; 95% CI 1.5 to 2.0; asthma diagnosis: OR 1.9; 95% CI 1.4 to 2.4). Smoking was associated with wheeze but not asthma diagnosis. Obesity showed an association with wheeze only in younger women. Both wheeze and asthma diagnosis were more prevalent in those with less education but had no association with the asset index. Independently, having medical insurance was associated with a higher prevalence of diagnosis. Conclusions Some of the findings may be to due to reporting bias and heterogeneity of the categories wheeze and asthma diagnosis, which may overlap with post tuberculous airways obstruction and chronic obstructive pulmonary disease due to smoking and occupational exposures. The results underline the importance of controlling tuberculosis and occupational exposures as well as smoking in reducing chronic respiratory morbidity. Validation of the asthma questionnaire in this setting and research into the pathophysiology of post tuberculous airways obstruction are also needed.

Integrating field survey data with satellite image data to improve shallow water seagrass maps : the role of AUV and snorkeller surveys

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (< 5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass is often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous Underwater Vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (< 5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photos of the seagrass were collected along transects via snorkelling or an AUV. Photos from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller collected field data sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison to snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5 % of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programs.

Household food insecurity, diet, and weight status in a disadvantaged district of Ho Chi Minh City, Vietnam: A cross-sectional study

Background Food security exists when all people, at all times, have physical, economic and socially acceptable access to safe, sufficient, and adequately nutritious food in order to meet their dietary needs for an active and healthy life. For high income countries and those experiencing the nutrition transition, food security is not only about the quantity of available food but also the nutritional quality as related to over- and under-nutrition. Vietnam is currently undergoing this nutrition transition, and as a result the relationship between food insecurity, socio-demographic factors and weight status is complex. The primary objective of this study was to therefore measure the prevalence of household food insecurity in a disadvantaged urban district in Ho Chi Minh City (HCMC) in Vietnam using a more comprehensive tool. This study also aims to examine the relationships between food insecurity and socio-demographic factors, weight status, and food intakes. Methods A cross-sectional study was conducted using multi-stage sampling. Adults who were mainly responsible for cooking were interviewed in 250 households. Data was collected on socioeconomic and demographic factors using previously validated tools. Food security was assessed using the Latin American and Caribbean Household Food Security Scale (ELCSA) tool and households were categorized as food secure or mildly, moderately or severely food insecure. Questions regarding food intake were based on routinely used and validated questions in HCMC, weight status was self-reported. Results Cronbach’s alpha coefficient was 0.87, showing the ELCSA had a good internal reliability. Approximately 34.4% of households were food insecure. Food insecurity was inversely related to total household income (OR = 0.09, 95% CI = 0.04 - 0.22) and fruit intakes (OR = 2.2, 95% CI 1.31 - 4.22). There was no association between weight and food security status. Conclusions Despite rapid industrialization and modernization, food insecurity remains an important public health issue in large urban areas of HCMC, suggesting that strategies to address food insecurity should be implemented in urban settings, and not just rural locations. Fruit consumption among food insecure households may be compromised because of financial difficulties, which may lead to poorer health outcomes particularly related to non-communicable disease prevention and management.

Household financial systems

In this paper we discuss some preliminary results of an ethnographic study focused on the ways money and financial issues are collaboratively handled within families. Families develop ‘systems’ or methods through which they organize and manage their everyday financial activities. These systems not only organize everyday family finances, but represent and shape family relationships. Through analysis of our ethnographic field study data, we develop four types of financial systems that we observed in the field: banking arrangements, physical hubs, goal-oriented systems and spatio-temporal organization. In this paper, we discuss examples of these systems and their implications for designing tools to support household financial practices.

Clustering acoustic events in environmental recordings for species richness surveys

Environmental acoustic recordings can be used to perform avian species richness surveys, whereby a trained ornithologist can observe the species present by listening to the recording. This could be made more efficient by using computational methods for iteratively selecting the richest parts of a long recording for the human observer to listen to, a process known as “smart sampling”. This allows scaling up to much larger ecological datasets. In this paper we explore computational approaches based on information and diversity of selected samples. We propose to use an event detection algorithm to estimate the amount of information present in each sample. We further propose to cluster the detected events for a better estimate of this amount of information. Additionally, we present a time dispersal approach to estimating diversity between iteratively selected samples. Combinations of approaches were evaluated on seven 24-hour recordings that have been manually labeled by bird watchers. The results show that on average all the methods we have explored would allow annotators to observe more new species in fewer minutes compared to a baseline of random sampling at dawn.

Household carbon emission research: An analytical review of measurement, influencing factors and mitigation prospects

Greenhouse gas (GHG) emissions are simultaneously exhausting the world's supply of fossil fuels and threatening the global climate. In many developing countries, significant improvement in living standards in recent years due to the accelerating development of their economies has resulted in a disproportionate increase in household energy consumption. Therefore, a major reduction in household carbon emissions (HCEs) is essential if global carbon reduction targets are to be met. To do this, major Organisation for Economic Co-operation and Development (OECD) states have already implemented policies to alleviate the negative environmental effects of household behaviors and less carbon-intensive technologies are also proposed to promote energy efficiency and reduce carbon emissions. However, before any further remedial actions can be contemplated, though, it is important to fully understand the actual causes of such large HCEs and help researchers both gain deep insights into the development of the research domain and identify valuable research topics for future study. This paper reviews existing literature focusing on the domain of HCEs. This critical review provides a systematic understanding of current work in the field, describing the factors influencing HCEs under the themes of household income, household size, age, education level, location, gender and rebound effects. The main quantification methodologies of input–output models, life cycle assessment and emission coefficient methods are also presented, and the proposed measures to mitigate HCEs at the policy, technology and consumer levels. Finally, the limitations of work done to date and further research directions are identified for the benefit of future studies.

The return to work experiences of middle-aged Australian workers diagnosed with colorectal cancer: a matched cohort study

Background Few studies have been undertaken to understand the employment impact in patients with colorectal cancer and none in middle-aged individuals with cancer. This study described transitions in, and key factors influencing, work participation during the 12 months following a diagnosis of colorectal cancer. Methods We enrolled 239 adults during 2010 and 2011who were employed at the time of their colorectal cancer diagnosis and were prospectively followed over 12 months. They were compared to an age- and gender-matched general population group of 717 adults from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. Data were collected using telephone and postal surveys. Primary outcomes included work participation at 12 months, changes in hours worked and time to work re-entry. Multivariable logistic and Cox proportional hazards models were undertaken. Results A significantly higher proportion of participants with colorectal cancer (27%) had stopped working at 12 months than participants from the comparison group (8%) (p < 0.001). Participants with cancer who returned to work took a median of 91 days off work (25–75 percentiles: 14–183 days). For participants with cancer, predictors of not working at 12 months included: being older, lower BMI and lower physical well-being. Factors related to delayed work re-entry included not being university-educated, working for an employer with more than 20 employees in a non-professional or managerial role, longer hospital stay, poorer perceived financial status and having or had chemotherapy. Conclusions In middle-adulthood, those working and diagnosed with colorectal cancer can expect to take around three months off work. Individuals treated with chemotherapy, without a university degree and from large employers could be targeted for specific assistance for a more timely work entry.

Monitoring illicit drug use through wastewater analysis in south east Queensland, Australia

Introduction and Aims: Wastewater analysis has become a useful technique for monitoring illicit drug use in communities. Findings have been reported from different countries in Europe and North America. We applied this technique to gauge the illicit drug consumption in an urban catchment from South East Queensland, Australia. Design and Methods: The sampling campaigns were conducted in 2009 (21st November – 2nd December) and 2010 (19th – 25th November). We collected daily composite wastewater samples from the inlet of the sewage treatment plant using continuous flow-proportional sampling. Ten illicit drug residues (parent compounds and key metabolites) in the samples were measured using liquid chromatography coupled to tandem mass spectrometer. Results: Seven compounds were quantified in all the samples. Our data indicated higher drug consumption on weekends. Cannabis was the highest used drug in both sampling periods. Compared to the first sampling campaign which indicated that cocaine and methamphetamine use exceeded ecstasy usage, the second sampling campaign suggested the use of methamphetamine exceeded that of ecstasy which in turn exceeded cocaine use. Discussion and Conclusions: The observed weekly trend of drug use in our study is in agreement with findings in other studies. The variation between two sampling periods in the prevalence of drug use may relate to the availability and prices of the drugs on markets. The cocaine use we estimated in 2009 was much greater than estimations obtained through the national household survey [1], implying under- reporting of cocaine use in surveys. Future work is underway to tackle methodological challenges for more accurate estimation.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Assistive classification for improving the efficiency of avian species richness surveys

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Frequency and precision of aerial surveys for kangaroo management

The appropriate frequency and precision for surveys of wildlife populations represent a trade-off between survey cost and the risk of making suboptimal management decisions because of poor survey data. The commercial harvest of kangaroos is primarily regulated through annual quotas set as proportions of absolute estimates of population size. Stochastic models were used to explore the effects of varying precision, survey frequency and harvest rate on the risk of quasiextinction for an arid-zone and a more mesic-zone kangaroo population. Quasiextinction probability increases in a sigmoidal fashion as survey frequency is reduced. The risk is greater in more arid regions and is highly sensitive to harvest rate. An appropriate management regime involves regular surveys in the major harvest areas where harvest rate can be set close to the maximum sustained yield. Outside these areas, survey frequency can be reduced in relatively mesic areas and reduced in arid regions when combined with lowered harvest rates. Relative to other factors, quasiextinction risk is only affected by survey precision (standard error/mean × 100) when it is >50%, partly reflecting the safety of the strategy of harvesting a proportion of a population estimate.

The influence of animal mobility on the assumption of uniform distances in aerial line-transect surveys

Line-transect distance sampling is a widely used method for estimating animal density from aerial surveys. Analysis of line-transect distance data usually relies on a requirement that the statistical distribution of distances of animal groups from the transect line is uniform. We show that this requirement is satisfied by the survey design if all other assumptions of distance sampling hold, but it can be violated by consistent survey problems such as responsive movement of the animals towards or away from the observer. We hypothesise that problems with the uniform requirement are unlikely to be encountered for immobile taxa, but might become substantial for species of high mobility. We test evidence for non-uniformity using double-observer distance data from two aerial surveys of five species with a spectrum of mobility capabilities and tendencies. No clear evidence against uniformity was found for crabeater seals or emperor penguins on the pack-ice in East Antarctica, while minor non-uniformity consistent with responsive movement up to 30 m was found for Adelie penguins. Strong evidence of either non-uniformity or a failure of the capture-recapture validating method was found for eastern grey kangaroos and red kangaroos in Queensland.

A comparison of mark-recapture distance-sampling methods applied to aerial surveys of eastern grey kangaroos

Aerial surveys of kangaroos (Macropus spp.) in Queensland are used to make economically important judgements on the levels of viable commercial harvest. Previous analysis methods for aerial kangaroo surveys have used both mark-recapture methodologies and conventional distance-sampling analyses. Conventional distance sampling has the disadvantage that detection is assumed to be perfect on the transect line, while mark-recapture methods are notoriously sensitive to problems with unmodelled heterogeneity in capture probabilities. We introduce three methodologies for combining together mark-recapture and distance-sampling data, aimed at exploiting the strengths of both methodologies and overcoming the weaknesses. Of these methods, two are based on the assumption of full independence between observers in the mark-recapture component, and this appears to introduce more bias in density estimation than it resolves through allowing uncertain trackline detection. Both of these methods give lower density estimates than conventional distance sampling, indicating a clear failure of the independence assumption. The third method, termed point independence, appears to perform very well, giving credible density estimates and good properties in terms of goodness-of-fit and percentage coefficient of variation. Estimated densities of eastern grey kangaroos range from 21 to 36 individuals km-2, with estimated coefficients of variation between 11% and 14% and estimated trackline detection probabilities primarily between 0.7 and 0.9.