Bayesian spatiotemporal analysis of malaria infection along an international border: Hlaingbwe Township in Myanmar and Tha-Song-Yang District in Thailand

Spatiotemporally Comparative Analysis of HIV, Pulmonary Tuberculosis, HIV-Pulmonary Tuberculosis Coinfection in Jiangsu Province, China

Purpose

Pulmonary tuberculosis (PTB) is a severe chronic communicable disease that causes a heavy disease burden in China. Human Immunodeficiency Virus (HIV) and PTB coinfection dramatically increases the risk of death. This study analyzes the spatiotemporal dynamics of HIV, PTB and HIV-PTB coinfection in Jiangsu Province, China, and explores the impact of socioeconomic determinants.

Patients and Methods

The data on all notified HIV, PTB and HIV-PTB coinfection cases were extracted from Jiangsu Provincial Center for Disease Control and Prevention. We applied the seasonal index to identify high-risk periods of the disease. Time trend, spatial autocorrelation and SaTScan were used to analyze temporal trends, hotspots and spatiotemporal clusters of diseases. The Bayesian space-time model was conducted to examine the socioeconomic determinants.

Results

The case notification rate (CNR) of PTB decreased from 2011 to 2019 in Jiangsu Province, but the CNR of HIV and HIV-PTB coinfection had an upward trend. The seasonal index of PTB was the highest in March, and its hotspots were mainly distributed in the central and northern parts, such as Xuzhou, Suqian, Lianyungang and Taizhou. HIV had the highest seasonal index in July and HIV-PTB coinfection had the highest seasonal index in June, with their hotspots mainly distributed in southern Jiangsu, involving Nanjing, Suzhou, Wuxi and Changzhou. The Bayesian space-time interaction model showed that socioeconomic factor and population density were negatively correlated with the CNR of PTB, and positively associated with the CNR of HIV and HIV-PTB coinfection.

Conclusion

The spatial heterogeneity and spatiotemporal clusters of PTB, HIV and HIV-PTB coinfection are exhibited obviously in Jiangsu. More comprehensive interventions should be applied to target TB in the northern part. While in southern Jiangsu, where the economic level is well-developed and the population density is high, we should strengthen the prevention and control of HIV and HIV-PTB coinfection.

Predicting the risk of malaria re-introduction in countries certified malaria-free: a systematic review

BACKGROUND

Predicting the risk of malaria in countries certified malaria-free is crucial for the prevention of re-introduction. This review aimed to identify and describe existing prediction models for malaria re-introduction risk in eliminated settings.

METHODS

A systematic literature search following the PRISMA guidelines was carried out. Studies that developed or validated a malaria risk prediction model in eliminated settings were included. At least two authors independently extracted data using a pre-defined checklist developed by experts in the field. The risk of bias was assessed using both the prediction model risk of bias assessment tool (PROBAST) and the adapted Newcastle-Ottawa Scale (aNOS).

RESULTS

A total 10,075 references were screened and 10 articles describing 11 malaria re-introduction risk prediction models in 6 countries certified malaria free. Three-fifths of the included prediction models were developed for the European region. Identified parameters predicting malaria re-introduction risk included environmental and meteorological, vectorial, population migration, and surveillance and response related factors. Substantial heterogeneity in predictors was observed among the models. All studies were rated at a high risk of bias by PROBAST, mostly because of a lack of internal and external validation of the models. Some studies were rated at a low risk of bias by the aNOS scale.

CONCLUSIONS

Malaria re-introduction risk remains substantial in many countries that have eliminated malaria. Multiple factors were identified which could predict malaria risk in eliminated settings. Although the population movement is well acknowledged as a risk factor associated with the malaria re-introduction risk in eliminated settings, it is not frequently incorporated in the risk prediction models. This review indicated that the proposed models were generally poorly validated. Therefore, future emphasis should be first placed on the validation of existing models.

Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models

Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predict Plasmodium falciparum and Plasmodium vivax malaria incidences at the provincial level. We first describe the available data, explain the hierarchical spatiotemporal framework underlying the analysis, and then display the results of fitting various space-time formulations to the malaria data with the different model selection metrics. The Bayesian model selection process assessed the sensitivity of different specifications to obtain the optimal models. To assess whether malaria could be eliminated by 2024 per Thailand's National Malaria Elimination Strategy, 2017-2026, we used the best-fitted model to project the estimated cases for 2022-2028. The study results based on the models revealed different predicted estimates between both species. The model for P. falciparum suggested that zero P. falciparum cases might be possible by 2024, in contrast to the model for P. vivax, wherein zero P. vivax cases might not be reached. Innovative approaches in the P. vivax-specific control and elimination plans must be implemented to reach zero P. vivax and consequently declare Thailand as a malaria-free country.

Modelling malaria in southernmost provinces of Thailand: a two-step process for analysis of highly right-skewed data with a large proportion of zeros

Background

Malaria remains a serious health problem in the southern border provinces of Thailand. The issue areas can be identified using an appropriate statistical model. This study aimed to investigate malaria for its spatial occurrence and incidence rate in the southernmost provinces of Thailand.

Methods

The Thai Office of Disease Prevention and Control, Ministry of Public Health, provided total hospital admissions of malaria cases from 2008 to 2020, which were classified by age, gender, and sub-district of residence. Sixty-two sub-districts were excluded since they had no malaria cases. A logistic model was used to identify spatial occurrence patterns of malaria, and a log-linear regression model was employed to model the incidence rate after eliminating records with zero cases.

Results

The overall occurrence rate was 9.8% and the overall median incidence rate was 4.3 cases per 1,000 population. Malaria occurence peaked at young adults aged 20–29, and subsequently fell with age for both sexes, whereas incidence rate increased with age for both sexes. Malaria occurrence and incidence rates fluctuated; they appeared to be on the decline. The area with the highest malaria occurrence and incidence rate was remarkably similar to the area with the highest number of malaria cases, which were mostly in Yala province's sub-districts bordering Malaysia.

Conclusions

Malaria is a serious problem in forest-covered border areas. The correct policies and strategies should be concentrated in these areas, in order to address this condition.

Genotyping of ABO and Duffy blood groups among malaria patients in Thailand

ABO blood groups have been proposed to influence malaria parasite infection and disease severity in individuals residing in different geographical areas. In Thailand, genetic polymorphisms of blood groups and susceptibility to malaria infection have rarely been investigated. The aim of this study was to assess the genotype frequencies of ABO and Duffy blood groups and susceptibility to malaria infection in two populations residing in malaria-endemic areas of Thailand. 1100 malaria samples and an identical number of samples from healthy subjects were collected from Thai-Malaysian and Thai-Myanmar areas. Genotyping of ABO and Duffy blood groups was performed by sequence specific primer-polymerase chain reaction. The distribution of ABO and Duffy blood groups was similar in malaria-positive and negative subjects. Blood group O was prevalent in both populations followed by blood group B (BO genotype) and A (AO genotype), respectively. In Plasmodium falciparum infections, blood group A frequency was significantly higher in Thai-Malaysian samples (P = 0.042) whereas blood group B frequency was significantly higher in Thai-Myanmar samples (P = 0.022). FY*A/*A frequency was significantly higher in Plasmodium vivax infection (P = 0.036) while FY*A/*B frequency was significantly higher in healthy subjects (P = 0.005). The different ABO blood group frequencies in the two populations may contribute to susceptibility to P. falciparum infection and the high prevalence of FY*A/*A can confer a risk of P. vivax infection. Further research in various ethnic groups is needed to clarify the association between blood groups and pathogenesis of malaria.

Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

Background

In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases.

Methods

In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand.

Results

From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence.

Conclusions

A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

mHealth Technology Translation in a Limited Resources Community-Process, Challenges, and Lessons Learned From a Limited Resources Community of Chiang Mai Province, Thailand

This report aims to provide practical advice about the implementation of a public health monitoring system using both geographic information system technology and mobile health, a term used for healthcare delivery via mobile devices. application amongst household residents and community stakeholders in the limited resource community. A public health monitoring system was implemented in a semi-rural district in Thailand. The challenges encountered during implementation were documented qualitatively in a series of monthly focus group discussions, several community hearings, and many targeted interviews. In addition, lessons learned from the expansion of the program to 75 other districts throughout Thailand were also considered. All challenges proved solvable yielding several key pieces of advice for future project implementation teams. Specifically, communication between team members, anticipating technological challenges, and involvement of community members are critical. The problems encountered in our project were mainly related to the capabilities of the data collectors and technical issues of mobile devices, internet coverage, and the GIS application itself. During the implementation phase, progressive changes needed to be made to the system promptly, in parallel with community team building in order to get the highest public health impact.

Prevalence, characteristics and risk factors of imported and local malaria cases in North-Western Province, Zambia: a cross-sectional study

BACKGROUND

Imported malaria is a major challenge for countries that are in malaria elimination stage such as Zambia. Legitimate cross-border activities add to the risk of transmission, necessitating determination of prevalence, characteristics and risk factors of imported and local malaria.

METHODS

This cross-sectional study was conducted in 103 consented child and adult patients with clinical malaria symptoms, from selected health facilities in north-western Zambia. Patient demographic data and blood samples for malaria microscopy and full blood count were obtained. Chi-square and penalized logistic regression were performed to describe the characteristics and assess the risk factors of imported and local malaria in North-Western Province.

RESULTS

Overall, malaria prevalence was 78.6% with 93.8% Plasmodium falciparum and 6.2% other species. The local cases were 72 (88.9%) while the imported were 9 (11.1%) out of the 81 positive participants. About 98.6% of the local cases were P. falciparum compared to 55.6% (χ2 = 52.4; p < 0.01) P. falciparum among the imported cases. Among the imported cases, 44% were species other than P. falciparum (χ2 = 48; p < 0.01) while among the local cases only 1.4% were. Gametocytes were present in 44% of the imported malaria cases and only in 2.8% of the local cases (χ2 = 48; p < 0.01). About 48.6% of local participants had severe anaemia compared to 33.3% of participants from the two neighbouring countries who had (χ2 = 4.9; p = 0.03). In the final model, only country of residence related positively to presence of species other than P. falciparum (OR = 39.0, CI [5.9, 445.9]; p < 0.01) and presence of gametocytes (OR = 23.1, CI [4.2, 161.6]; p < 0.01).

CONCLUSION

Malaria prevalence in North-Western Province is high, with P. falciparum as the predominant species although importation of Plasmodium ovale and Plasmodium malariae is happening as well. Country of residence of patients is a major risk factor for malaria species and gametocyte presence. The need for enhanced malaria control with specific focus on border controls to detect and treat, for specific diagnosis and treatment according to species obtaining, for further research in the role of species and gametocytaemia in imported malaria, cannot be overemphasized.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

Spatiotemporal Distribution of Hand, Foot, and Mouth Disease in Guangdong Province, China and Potential Predictors, 2009⁻2012

Background: Hand, foot, and mouth disease (HFMD) is a common infectious disease among children. Guangdong Province is one of the most severely affected provinces in south China. This study aims to identify the spatiotemporal distribution characteristics and potential predictors of HFMD in Guangdong Province and provide a theoretical basis for the disease control and prevention. Methods: Case-based HFMD surveillance data from 2009 to 2012 was obtained from the China Center for Disease Control and Prevention (China CDC). The Bayesian spatiotemporal model was used to evaluate the spatiotemporal variations of HFMD and identify the potential association with meteorological and socioeconomic factors. Results: Spatially, areas with higher relative risk (RR) of HFMD tended to be clustered around the Pearl River Delta region (the mid-east of the province). Temporally, we observed that the risk of HFMD peaked from April to July and October to December each year and detected an upward trend between 2009 and 2012. There was positive nonlinear enhancement between spatial and temporal effects, and the distribution of relative risk in space was not fixed, which had an irregular fluctuating trend in each month. The risk of HFMD was significantly associated with monthly average relative humidity (RR: 1.015, 95% CI: 1.006–1.024), monthly average temperature (RR: 1.045, 95% CI: 1.021–1.069), and monthly average rainfall (RR: 1.004, 95% CI: 1.001–1.008), but not significantly associated with average GDP. Conclusions: The risk of HFMD in Guangdong showed significant spatiotemporal heterogeneity. There was spatiotemporal interaction in the relative risk of HFMD. Adding a spatiotemporal interaction term could well explain the change of spatial effect with time, thus increasing the goodness of fit of the model. Meteorological factors, such as monthly average relative humidity, monthly average temperature, and monthly average rainfall, might be the driving factors of HFMD.