Disease Surveillance Networks Initiative Asia: Final Evaluation

The DSN Initiative was launched in 2007 under the new strategy of the Rockefeller Foundation. The initiative intends:[1] To improve human resources for disease surveillance in developing countries, thus bolstering national capacity to monitor, report, and respond to outbreaks;[2] To support regional networks to promote collaboration in disease surveillance and response across countries; and[3] To build bridges between regional and global monitoring effortsThe purpose of the DSN evaluation in the Mekong region was twofold:[1]To inform the work and strategy of the Foundation, its grantees, and the broader field of disease surveillance, based on the experience of DSN investments in the Mekong region. More specifically, the evaluation will inform future directions and strategies for current areas of DSN Initiative work, particularly in Asia, and will highlight potential new areas of work and strategy; and[2] To provide accountability to the Rockefeller Foundation's board, staff, and stakeholders for the DSN funds spent in the Mekong region

Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand

In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world’s malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible

Chloroquine pharmacokinetics in pregnant and nonpregnant women with vivax malaria

PURPOSE: We compared the pharmacokinetics of chloroquine in pregnant and nonpregnant women treated for Plasmodium vivax malaria. METHODS: Twelve pregnant women and 15 nonpregnant women of child-bearing age with acute P. vivax malaria were treated with 25 mg chloroquine base/kg over 3 days on the northwestern border of Thailand. Blood concentrations of chloroquine and desethylchloroquine were measured using hydrophilic interaction liquid chromatography coupled with fluorescence detection. Twenty-five women completed the pharmacokinetic study. RESULTS: Although increasing gestational age was associated with reduced chloroquine AUC0-->infinity, there was no significant difference overall in the pharmacokinetics of chloroquine between pregnant and nonpregnant women. Fever was associated with lower chloroquine AUC0-->infinity values. Desethylchloroquine area under the curve (AUC) values were not significantly affected by pregnancy. CONCLUSIONS: Pregnancy did not significantly affect blood concentrations of chloroquine or its metabolite, desethylchloroquine, in women with P. vivax malaria

Artemisinin resistance in Plasmodium falciparum malaria.

BACKGROUND: Artemisinin-based combination therapies are the recommended first-line treatments of falciparum malaria in all countries with endemic disease. There are recent concerns that the efficacy of such therapies has declined on the Thai-Cambodian border, historically a site of emerging antimalarial-drug resistance. METHODS: In two open-label, randomized trials, we compared the efficacies of two treatments for uncomplicated falciparum malaria in Pailin, western Cambodia, and Wang Pha, northwestern Thailand: oral artesunate given at a dose of 2 mg per kilogram of body weight per day, for 7 days, and artesunate given at a dose of 4 mg per kilogram per day, for 3 days, followed by mefloquine at two doses totaling 25 mg per kilogram. We assessed in vitro and in vivo Plasmodium falciparum susceptibility, artesunate pharmacokinetics, and molecular markers of resistance. RESULTS: We studied 40 patients in each of the two locations. The overall median parasite clearance times were 84 hours (interquartile range, 60 to 96) in Pailin and 48 hours (interquartile range, 36 to 66) in Wang Pha (P<0.001). Recrudescence confirmed by means of polymerase-chain-reaction assay occurred in 6 of 20 patients (30%) receiving artesunate monotherapy and 1 of 20 (5%) receiving artesunate-mefloquine therapy in Pailin, as compared with 2 of 20 (10%) and 1 of 20 (5%), respectively, in Wang Pha (P=0.31). These markedly different parasitologic responses were not explained by differences in age, artesunate or dihydroartemisinin pharmacokinetics, results of isotopic in vitro sensitivity tests, or putative molecular correlates of P. falciparum drug resistance (mutations or amplifications of the gene encoding a multidrug resistance protein [PfMDR1] or mutations in the gene encoding sarco-endoplasmic reticulum calcium ATPase6 [PfSERCA]). Adverse events were mild and did not differ significantly between the two treatment groups. CONCLUSIONS: P. falciparum has reduced in vivo susceptibility to artesunate in western Cambodia as compared with northwestern Thailand. Resistance is characterized by slow parasite clearance in vivo without corresponding reductions on conventional in vitro susceptibility testing. Containment measures are urgently needed. (ClinicalTrials.gov number, NCT00493363, and Current Controlled Trials number, ISRCTN64835265.

Spatio-temporal effects of estimated pollutants released from an industrial estate on the occurrence of respiratory disease in Maptaphut Municipality, Thailand

BACKGROUND: Maptaphut Industrial Estate (MIE) was established with a single factory in 1988, increasing to 50 by 1998. This development has resulted in undesirable impacts on the environment and the health of the people in the surrounding areas, evidenced by frequent complaints of bad odours making the people living there ill. In 1999, the Bureau of Environmental Health, Department of Health, Ministry of Public Health, conducted a study of the health status of people in Rayong Province and found a marked increase in respiratory diseases over the period 1993–1996, higher than the overall prevalence of such diseases in Thailand. However, the relationship between the pollutants and the respiratory diseases of the people in the surrounding area has still not been quantified. Therefore, this study aimed to determine the spatial distribution of respiratory disease, to estimate pollutants released from the industrial estates, and to quantify the relationship between estimated pollutants and respiratory disease in the Maptaphut Municipality. RESULTS: Disease mapping showed a much higher risk of respiratory disease in communities adjacent to the Maptaphut Industrial Estate. Disease occurrence formed significant clusters centred on communities near the estate, relative to the weighted mean centre of chimney stacks. Analysis of the rates of respiratory disease in the communities, categorized by different concentrations of estimated pollutants, found a dose-response effect. Spatial regression analysis found that the distance between community and health providers decreased the rate of respiratory disease (p < 0.05). However, after taking into account distance, total pollutant (p < 0.05), SO(2 )(p < 0.05) and NO(x )(p < 0.05) played a role in adverse health effects during the summer. Total pollutant (p < 0.05) and NO(x )(p < 0.05) played a role in adverse health effects during the rainy season after taking into account distance, but during winter there was no observed relationship between pollutants and rates of respiratory disease after taking into account distance. A 12-month time-series analysis of six communities selected from the disease clusters and the areas impacted most by pollutant dispersion, found significant effects for SO(2 )(p < 0.05), NO(x )(p < 0.05), and TSP (p < 0.05) after taking into account rainfall. CONCLUSION: This study employed disease mapping to present the spatial distribution of disease. Excessive risk of respiratory disease, and disease clusters, were found among communities near Maptaphut Industrial Estate. Study of the relationship between estimated pollutants and the occurrence of respiratory disease found significant relationships between estimated SO(2), NO(x), and TSP, and the rate of respiratory disease

Application of mobile-technology for disease and treatment monitoring of malaria in the "Better Border Healthcare Programme"

<p>Abstract</p> <p>Background</p> <p>The main objective of this study was to assess the effectiveness of integrating the use of cell-phones into a routine malaria prevention and control programme, to improve the management of malaria cases among an under-served population in a border area. The module for disease and treatment monitoring of malaria (DTMM) consisted of case investigation and case follow-up for treatment compliance and patients' symptoms.</p> <p>Methods</p> <p>The module combining web-based and mobile technologies was developed as a proof of concept, in an attempt to replace the existing manual, paper-based activities that malaria staff used in treating and caring for malaria patients in the villages for which they were responsible. After a patient was detected and registered onto the system, case-investigation and treatment details were recorded into the malaria database. A follow-up schedule was generated, and the patient's status was updated when the malaria staff conducted their routine home visits, using mobile phones loaded with the follow-up application module. The module also generated text and graph messages for a summary of malaria cases and basic statistics, and automatically fed to predetermined malaria personnel for situation analysis. Following standard public-health practices, access to the patient database was strictly limited to authorized personnel in charge of patient case management.</p> <p>Results</p> <p>The DTMM module was developed and implemented at the trial site in late November 2008, and was fully functioning in 2009. The system captured 534 malaria patients in 2009. Compared to paper-based data in 2004-2008, the mobile-phone-based case follow-up rates by malaria staff improved significantly. The follow-up rates for both Thai and migrant patients were about 94-99% on Day 7 <it>(Plasmodium falciparum) </it>and Day 14 <it>(Plasmodium vivax) </it>and maintained at 84-93% on Day 90. Adherence to anti-malarial drug therapy, based on self-reporting, showed high completion rate for <it>P. falciparum</it>-infected cases, but lower rate for <it>P. vivax </it>cases. Patients' symptoms were captured onto the mobile phone during each follow-up visit, either during the home visit or at Malaria Clinic; most patients had headache, muscle pain, and fatigue, and some had fever within the first follow-up day (day7/14) after the first anti-malarial drug dose.</p> <p>Conclusions</p> <p>The module was successfully integrated and functioned as part of the malaria prevention and control programme. Despite the bias inherent in sensitizing malaria workers to perform active case follow-up using the mobile device, the study proved for its feasibility and the extent to which community healthcare personnel in the low resource settings could potentially utilize it efficiently to perform routine duties, even in remote areas. The DTMM has been modified and is currently functioning in seven provinces in a project supported by the WHO and the Bill & Melinda Gates Foundation, to contain multi-drug resistant malaria on the Thai-Cambodian border.</p

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses:a case study in endemic districts of Bhutan

BACKGROUND: Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX.METHODS: This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month.RESULTS: It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts.CONCLUSIONS: The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan.</p

Spatio-temporal patterns of malaria infection in Bhutan:a country embarking on malaria elimination

BACKGROUND: At the verge of elimination of malaria in Bhutan, this study was carried out to analyse the trend of malaria in the endemic districts of Bhutan and to identify malaria clusters at the sub-districts. The findings would aid in implementing the control activities. Poisson regression was performed to study the trend of malaria incidences at district level from 1994 to 2008. Spatial Empirical Bayesian smoothing was deployed to identify clusters of malaria at the sub-district level from 2004 to 2008.RESULTS: Trend of the overall districts and most of the endemic districts have decreased except Pemagatshel, which has an increase in the trend. Spatial cluster-outlier analysis showed that malaria clusters were mostly concentrated in the central and eastern Bhutan in three districts of Dagana, Samdrup Jongkhar and Sarpang. The disease clusters were reported throughout the year. Clusters extended to the non-transmission areas in the eastern Bhutan.CONCLUSIONS: There is significant decrease in the trend of malaria with the elimination at the sight. The decrease in the trend can be attributed to the success of the control and preventive measures. In order to realize the target of elimination of malaria, the control measure needs to be prioritized in these high-risk clusters of malaria.</p

Chikungunya virus was isolated in Thailand, 2010

Chikungunya fever (CHIKF) is an acute febrile illness caused by a mosquito-borne alphavirus, chikungunya virus (CHIKV). This disease re-emerged in Kenya in 2004, and spread to the countries in and around the Indian Ocean. The re-emerging epidemics rapidly spread to regions like India and Southeast Asia, and it was subsequently identified in Europe in 2007, probably as a result of importation of chikungunya cases. On the one hand, chikungunya is one of the neglected diseases and has only attracted strong attention during large outbreaks. In 2008–2009, there was a major outbreak of chikungunya fever in Thailand, resulting in the highest number of infections in any country in the region. However, no update of CHIKV circulating in Thailand has been published since 2009. In this study, we examined the viral growth kinetics and sequences of the structural genes derived from CHIKV clinical isolates obtained from the serum specimens of CHIKF-suspected patients in Central Thailand in 2010. We identified the CHIKV harboring two mutations E1-A226V and E2-I211T, indicating that the East, Central, and South African lineage of CHIKV was continuously circulating as an indigenous population in Thailand. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11262-014-1105-5) contains supplementary material, which is available to authorized users

In vitro activity of ferroquine (SSR 97193) against Plasmodium falciparum isolates from the Thai-Burmese border

Abstract Background On the borders of Thailand, Plasmodium falciparum has become resistant to nearly all available drugs, and there is an urgent need to find new antimalarial drugs or drug combinations. Ferroquine (SSR97193) is a new 4-aminoquinoline antimalarial active against chloroquine resistant and sensitive P. falciparum strains in vivo and in vitro. This antimalarial organic iron complex (a ferrocenyl group has been associated with chloroquine) is meant to use the affinity of Plasmodium for iron to increase the probability for encountering the anti-malarial molecule. The aim of the present study was to investigate the activity of ferroquine against P. falciparum isolates from an area with a known high multi-drug resistance rate. Methods Parasite isolates were obtained from patients with acute falciparum malaria attending the clinics of SMRU. In vitro cultures of these isolates were set-up in the SMRU-laboratory on pre-dosed drug plates, and grown in culture for 42 hours. Parasite growth was assessed by the double-site enzyme-linked pLDH immunodetection (DELI) assay. Results Sixty-five P. falciparum isolates were successfully grown in culture. The ferroquine mean IC50 (95% CI) was 9.3 nM (95% C.I.: 8.7 – 10.0). The mean IC50 value for the principal metabolite of ferroquin, SR97213A, was 37.0 nM (95% C.I.: 34.3 – 39.9), which is four times less active than ferroquine. The isolates in this study were highly multi-drug resistant but ferroquine was more active than chloroquine, quinine, mefloquine and piperaquine. Only artesunate was more active than ferroquine. Weak but significant correlations were found between ferroquine and its principal metabolite (r2 = 0.4288), chloroquine (r2 = 0.1107) and lumefantrine (r2 = 0.2364). Conclusion The results presented in this study demonstrate that the new ferroquine compound SSR97193 has high anti-malarial activity in vitro against multi-drug resistant P. falciparum.</p