BackgroundDespite intensive vector control efforts, dengue epidemics continue to occur throughout Southeast Asia in multi-annual cycles. Weather is considered an important factor in these cycles, but the extent to which the El Niño-Southern Oscillation (ENSO) is a driving force behind dengue epidemics remains unclear.MethodsWe examined the temporal relationship between El Niño and the occurrence of dengue epidemics, and constructed Poisson autoregressive models for incidences of dengue cases. Global ENSO records, dengue surveillance data, and local meteorological data in two geographically diverse regions in Thailand (the tropical southern coastal region and the northern inland mountainous region) were analyzed.ResultsThe strength of El Niño was consistently a predictor for the occurrence of dengue epidemics throughout time lags from 1 to 11 months in the two selected regions of Thailand. Up to 22% (in 8 northern inland mountainous provinces) and 15% (in 5 southern tropical coastal provinces) of the variation in the monthly incidence of dengue cases were attributable to global ENSO cycles. Province-level predictive models were fitted using 1996-2004 data and validated with out-of-fit data from 2005. The multivariate ENSO index was an independent predictor in 10 of the 13 studied provinces.ConclusionEl Niño is one of the important driving forces for dengue epidemics across the geographically diverse regions of Thailand; however, spatial heterogeneity in the effect exists. The effects of El Niño should be taken into account in future epidemic forecasting for public health preparedness.

Understanding infection dynamics of respiratory diseases requires the identification and quantification of behavioural, social and environmental factors that permit the transmission of these infections between humans. Little empirical information is available about contact patterns within real-world social networks, let alone on differences in these contact networks between populations that differ considerably on a socio-cultural level. Here we compared contact network data that were collected in the Netherlands and Thailand using a similar online respondent-driven method. By asking participants to recruit contact persons we studied network links relevant for the transmission of respiratory infections. We studied correlations between recruiter and recruited contacts to investigate mixing patterns in the observed social network components. In both countries, mixing patterns were assortative by demographic variables and random by total numbers of contacts. However, in Thailand participants reported overall more contacts which resulted in higher effective contact rates. Our findings provide new insights on numbers of contacts and mixing patterns in two different populations. These data could be used to improve parameterisation of mathematical models used to design control strategies. Although the spread of infections through populations depends on more factors, found similarities suggest that spread may be similar in the Netherlands and Thailand.

BackgroundInformation on social interactions is needed to understand the spread of airborne infections through a population. Previous studies mostly collected egocentric information of independent respondents with self-reported information about contacts. Respondent-driven sampling (RDS) is a sampling technique allowing respondents to recruit contacts from their social network. We explored the feasibility of webRDS for studying contact patterns relevant for the spread of respiratory pathogens.Materials and MethodsWe developed a webRDS system for facilitating and tracking recruitment by Facebook and email. One-day diary surveys were conducted by applying webRDS among a convenience sample of Thai students. Students were asked to record numbers of contacts at different settings and self-reported influenza-like-illness symptoms, and to recruit four contacts whom they had met in the previous week. Contacts were asked to do the same to create a network tree of socially connected individuals. Correlations between linked individuals were analysed to investigate assortativity within networks.ResultsWe reached up to 6 waves of contacts of initial respondents, using only non-material incentives. Forty-four (23.0%) of the initially approached students recruited one or more contacts. In total 257 persons participated, of which 168 (65.4%) were recruited by others. Facebook was the most popular recruitment option (45.1%). Strong assortative mixing was seen by age, gender and education, indicating a tendency of respondents to connect to contacts with similar characteristics. Random mixing was seen by reported number of daily contacts.ConclusionsDespite methodological challenges (e.g. clustering among respondents and their contacts), applying RDS provides new insights in mixing patterns relevant for close-contact infections in real-world networks. Such information increases our knowledge of the transmission of respiratory infections within populations and can be used to improve existing modelling approaches. It is worthwhile to further develop and explore webRDS for the detection of clusters of respiratory symptoms in social networks.

More than 11 million Thai people (38%) work in agriculture, but since most are in the informal sector, government enforcement and support are very limited. As a result, working conditions on Thai farms vary greatly, putting the health of many agricultural workers at risk. A cross-sectional study in three Thai provinces collected information on the work activities and conditions of 424 farmers representing five farm types: rice, vegetable, flower, rice/vegetable, and flower/vegetable. The agricultural workers were mainly women (60%); their average age was 53 but ranged from 18 to 87 years. More than 64% worked more than 5 days/week. Seventy-four percent of them had only primary school education. A number of the health and hazardous working conditions surveyed were significantly different by farm type. Rice farmers were found to have the highest prevalence of allergies, nasal congestion, wheezing, and acute symptoms after pesticide use, while flower farmers had the lowest prevalence of these health outcomes. Rice farmers reported the highest prevalence of hazardous working conditions including high noise levels, working on slippery surfaces, sitting or standing on a vibrating machine, spills of chemicals/pesticides, and sharp injuries. The lowest prevalence of these working conditions (except noise) was reported by flower farmers. Vegetable farmers reported the highest prevalence knee problems, while rice farmers had the lowest prevalence. Among these farmers, more than 27 different types of pesticides were reported in use during the past year, with the majority reporting use once a month. The flower/vegetable farming group reported the highest frequency of good exposure prevention practices during pesticide use. They were the most likely to report using cotton or rubber gloves or a disposable paper masks during insecticide spraying. Those farmers who only grew vegetables had the lowest frequency of good exposure prevention practices, including use of personal protective equipment. The economic cost of work-related injuries and illnesses among informal sector agricultural workers in Thailand is unknown and in need of study. Gaps in the regulations covering pesticide sales allow farmers to purchase pesticides without adequate training in their safe use. Training targeted to farm type regarding safe pesticide use and the prevention of accidents and musculoskeletal disorders is needed. Studies of chronic health effects among Thai farmers are needed, with special emphasis on respiratory, metabolic disease and cancer.

Abstract. Focusing on the socio-geographical factors that influence local vulnerability to dengue at the village level, spatial regression methods were applied to analyse, over a 5-year period, the village-specific, cumulative incidence of all reported dengue cases among 437 villages in Prachuap Khiri Khan, a semi-urban province of Thailand. The K-order nearest neighbour method was used to define the range of neighbourhoods. Analysis showed a significant neighbourhood effect (ρ = 0.405, P <0.001), which implies that villages with geographical proximity shared a similar level of vulnerability to dengue. The two independent social factors, associated with a higher incidence of dengue, were a shorter distance to the nearest urban area (β = -0.133, P <0.05) and a smaller average family size (β = -0.102, P <0.05). These results indicate that the trend of increasing dengue occurrence in rural Thailand arose in areas under stronger urban influence rather than in remote rural areas.