Mahidol Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Malar J. 2019 Jul 16;18(1):240. doi: 10.1186/s12936-019-2871-2.
Tak Province, at the Thai-Myanmar border, is one of three high malaria incidence areas in Thailand. This study aimed to describe and identify possible factors driving the spatiotemporal trends of disease incidence from 2012 to 2015.
Climate variables and forest cover were correlated with malaria incidence using Pearson's r. Statistically significant clusters of high (hot spots) and low (cold spots) annual parasite incidence per 1000 population (API) were identified using Getis-Ord Gi* statistic.
The total number of confirmed cases declined by 76% from 2012 to 2015 (Plasmodium falciparum by 81%, Plasmodium vivax by 73%). Incidence was highly seasonal with two main annual peaks. Most cases were male (62.75%), ≥ 15 years (56.07%), and of Myanmar (56.64%) or Thai (39.25%) nationality. Median temperature (1- and 2-month lags), average temperature (1- and 2-month lags) and average relative humidity (2- and 3-month lags) correlated positively with monthly total, P. falciparum and P. vivax API. Total rainfall in the same month correlated with API for total cases and P. vivax but not P. falciparum. At sub-district level, percentage forest cover had a low positive correlation with P. falciparum, P. vivax, and total API in most years. There was a decrease in API in most sub-districts for both P. falciparum and P. vivax. Sub-districts with the highest API were in the Tha Song Yang and Umphang Districts along the Thai-Myanmar border. Annual hot spots were mostly in the extreme north and south of the province.
There has been a large decline in reported clinical malaria from 2012 to 2015 in Tak Province. API was correlated with monthly climate and annual forest cover but these did not account for the trends over time. Ongoing elimination interventions on one or both sides of the border are more likely to have been the cause but it was not possible to assess this due to a lack of suitable data. Two main hot spot areas were identified that could be targeted for intensified elimination activities.
泰国达府位于泰缅边境，是该国三个疟疾高发地区之一。本研究旨在描述并确定 2012 年至 2015 年期间疾病发病率的时空趋势的可能驱动因素。
使用 Pearson's r 相关分析气候变量和森林覆盖与疟疾发病率的关系。使用 Getis-Ord Gi* 统计识别具有高（热点）和低（冷点）年寄生虫发病率每 1000 人口（API）的统计学显著聚类。
2012 年至 2015 年期间，确诊病例总数下降了 76%（间日疟原虫下降了 81%，卵形疟原虫下降了 73%）。发病率具有很强的季节性，有两个主要的年度高峰。大多数病例为男性（62.75%）、≥15 岁（56.07%）、缅甸（56.64%）或泰国（39.25%）国籍。中位数温度（1 个月和 2 个月滞后）、平均温度（1 个月和 2 个月滞后）和平均相对湿度（2 个月和 3 个月滞后）与每月总 API、间日疟原虫 API 和卵形疟原虫 API 呈正相关。同月总降雨量与总病例 API 以及卵形疟原虫 API 相关，但与间日疟原虫 API 不相关。在分区一级，大多数年份森林覆盖率的百分比与间日疟原虫、卵形疟原虫和总 API 呈低正相关。间日疟原虫和卵形疟原虫的 API 大多数分区都有所下降。API 最高的分区位于泰缅边境的 Tha Song Yang 和 Umphang 区。年度热点主要分布在该省的最北部和最南部。
2012 年至 2015 年期间，达府报告的临床疟疾病例大幅下降。API 与每月气候和年度森林覆盖相关，但这些因素并不能解释随时间的趋势。边界两侧正在进行的消除干预措施更有可能是导致这种情况的原因，但由于缺乏合适的数据，无法对此进行评估。确定了两个主要的热点地区，可以针对这些地区开展强化消除活动。
