Adherence to Antimalarial Drug Therapy among Vivax Malaria Patients in Northern Thailand

Vivax malaria is a significant cause of morbidity due to malaria in northern Thailand, accounting for approximately 50% of all malaria cases. The objective of this study was to determine the behavioural factors associated with adherence to the standard 14-day course of chloroquine and primaquine, prescribed from malaria clinics, among patients with vivax malaria. A retrospective study was conducted among 206 patients living in Muang and Mae Sa Riang districts of Mae Hon Son province in northern Thailand. Data on adherence and potential behavioural factors relating to adherence were collected using a structured interviewer-administered questionnaire and supplemented with qualitative data from focus-group interviews. The results indicated that 76.21% of the 206 patients with vivax malaria did not complete the medication course. The adherence of the patients was associated with knowledge scores of malaria (adjusted odds ratio [AOR]=2.2, 95% confidence interval [CI] 1.1-4.5) and accessing drug prescription scores (AOR=5.6, 95% CI 2.13-15.3). Therefore, further effort is needed to educate patients with vivax malaria on knowledge of malaria and its treatment with simple health messages and encourage them to adhere to their treatment

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

Adherence to Antimalarial Drug Therapy among Vivax Malaria Patients in Northern Thailand

Vivax malaria is a significant cause of morbidity due to malaria in northern Thailand, accounting for approximately 50% of all malaria cases. The objective of this study was to determine the behavioural factors associated with adherence to the standard 14-day course of chloroquine and primaquine, prescribed from malaria clinics, among patients with vivax malaria. A retrospective study was conducted among 206 patients living in Muang and Mae Sa Riang districts of Mae Hon Son province in northern Thailand. Data on adherence and potential behavioural factors relating to adherence were collected using a structured interviewer- administered questionnaire and supplemented with qualitative data from focus-group interviews. The results indicated that 76.21% of the 206 patients with vivax malaria did not complete the medication course. The adherence of the patients was associated with knowledge scores of malaria (adjusted odds ratio [AOR]=2.2, 95% confidence interval [CI] 1.1-4.5) and accessing drug prescription scores (AOR=5.6, 95% CI 2.13-15.3). Therefore, further effort is needed to educate patients with vivax malaria on knowledge of malaria and its treatment with simple health messages and encourage them to adhere to their treatment

Deployment of Early Diagnosis and Mefloquine- Artesunate Treatment of Falciparum Malaria in Thailand: The Tak Malaria Initiative

BACKGROUND: Early diagnosis and treatment with artesunate-mefloquine combination therapy (MAS) have reduced the transmission of falciparum malaria dramatically and halted the progression of mefloquine resistance in camps for displaced persons along the Thai-Burmese border, an area of low and seasonal transmission of multidrug-resistant Plasmodium falciparum. We extended the same combination drug strategy to all other communities (estimated population 450,000) living in five border districts of Tak province in northwestern Thailand. METHODS AND FINDINGS: Existing health structures were reinforced. Village volunteers were trained to use rapid diagnostic tests and to treat positive cases with MAS. Cases of malaria, hospitalizations, and malaria-related deaths were recorded in the 6 y before, during, and after the Tak Malaria Initiative (TMI) intervention. Cross-sectional surveys were conducted before and during the TMI period. P. falciparum malaria cases fell by 34% (95% confidence interval [CI], 33.5–34.4) and hospitalisations for falciparum malaria fell by 39% (95% CI, 37.0–39.9) during the TMI period, while hospitalisations for P. vivax malaria remained constant. There were 32 deaths attributed to malaria during, and 22 after the TMI, a 51.5% (95% CI, 39.0–63.9) reduction compared to the average of the previous 3 y. Cross-sectional surveys indicated that P. vivax had become the predominant species in Thai villages, but not in populations living on the Myanmar side of the border. In the displaced persons population, where the original deployment took place 7 y before the TMI, the transmission of P. falciparum continued to be suppressed, the incidence of falciparum malaria remained low, and the in vivo efficacy of the 3-d MAS remained high. CONCLUSIONS: In the remote malarious north western border area of Thailand, the early detection of malaria by trained village volunteers, using rapid diagnostic tests and treatment with mefloquine-artesunate was feasible and reduced the morbidity and mortality of multidrug-resistant P. falciparum

Comparative studies on the biology and filarial susceptibility of selected blood-feeding and autogenous Aedes togoi sub-colonies

Blood-feeding and autogenous sub-colonies were selected from a laboratory, stock colony of Aedes togoi, which was originally collected from Koh Nom Sao, Chanthaburi province, Southeast Thailand. Comparative biology and filarial susceptibility between the two sub-colonies (blood-feeding: F11, F13; autogeny: F38, F40) were investigated to evaluate their viability and vectorial capacity. The results of comparison on biology revealed intraspecific differences, i.e., the average egg deposition/gravid female (F11/F38; F13/F40), embryonation rate (F13/F40), hatchability rate (F11/F38; F13/F40), egg width (F11/F38), wing length of females (F13/F40), and wing length and width of males (F11/F38) in the blood-feeding sub-colony were significantly greater than that in the autogenous sub-colony; and egg length (F11/F38) and width (F13/F40), and mean longevity of adult females (F11/F38) and males (F13/F40) in the blood-feeding sub-colony were significantly less than that in the autogenous sub-colony. The results of comparison on filarial susceptibility demonstrated that both sub-colonies yielded similar susceptibilities to Brugia malayi [blood-feeding/autogeny = 56.7% (F11)/53.3%(F38), 60%(F13)/83.3%(F40)] and Dirofilaria immitis [blood-feeding/autogeny = 85.7%(F11)/75%(F38), 45%(F13)/29.4%(F40)], suggesting autogenous Ae. togoi sub-colony was an efficient laboratory vector in study of filariasis

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

Map of Tak Province

<p>The study area is shown in grey.</p