Dataset Information

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia.

ABSTRACT: Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations.

SUBMITTER: Okami S

PROVIDER: S-EPMC5627027 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia.

Okami Suguru S Kohtake Naohiko N

Frontiers in public health 20170926

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resour ...[more]

PMID: 29034229

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Project description:Background There has been a decline in the stroke incidence across high income countries but such knowledge exists at Country or State rather than areal unit level such local government area (LGA). In this disease mapping study, we evaluate if there are local hot spots or temporal trends in TIA rate. Such knowledge will be of help in planning healthcare service delivery across regions. Methods Linked hospital discharge data (Victorian Admitted Episodes Dataset or VAED) was used to collect TIA (defined by ICD-10-AM codes G450-G459) cases from 2001 to 2011. The State of Victoria is the second most populous state in Australia, with a population of 6.7 million and can be divided into 79 administrative units or LGA. The data is anonymized and contains residence of the patient in terms of LGA but not exact location. The date of the TIA event when the patient is admitted to hospital is provided in the dataset. The number of TIAs per year was aggregated for each LGA. Standardized TIA ratios were calculated by dividing actual over expected cases for each LGA per year. We used Integrated Nested Laplace Approximation (INLA) to perform spatial and spatiotemporal regression, adjusting for hypertension, sex and population, age (≥60), and socio-economic status (SES) decile within the LGA. The final model was chosen based on the lowest the Deviance Information Criterion (DIC) and Watanabe-Akaike information criteria (WAIC). Results Choropleth maps showed a higher standardized TIA ratios in North-West rural region. Compared to the baseline model (DIC 13,159, WAIC 13,261), adding in a spatial random effect significantly improved the model (DIC 6,463, WAIC 6,667). However, adding a temporal component did not lead to a significant improvement (DIC 6,483, WAIC 6,707). Conclusion Our finding suggests a statically significant spatial component to TIA rate over regional areas but no temporal changes or yearly trends. We propose that such exploratory method should be followed by evaluation of reasons for regional variations and which in turn can identify opportunities in primary prevention of stroke, and stroke care.

Project description:IntroductionAlzheimer's disease (AD) is characterized by the accumulation of β-amyloid (Aβ) associated with brain atrophy and cognitive decline. The functional form to model the association between Aβ and regional brain atrophy has not been well defined. To determine the relationship between Aβ and atrophy, we compared the performance of the usual dichotomization of cerebrospinal fluid (CSF) Aβ to identify subjects as Aβ+ and Aβ- with a trilinear spline model of CSF Aβ.MethodsOne hundred and eighty-three subjects with mild cognitive impairment and 108 cognitively normal controls with baseline CSF Aβ and up to 4 years of longitudinal magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative were analyzed using mixed-effects regression. Piecewise-linear splines were used to evaluate the nonlinear nature of the association between CSF Aβ and regional atrophy and to identify points of acceleration of atrophy with respect to Aβ. Several parameterizations of CSF Aβ were compared using likelihood ratio tests and the Akaike information criterion. Periods of acceleration of atrophy in which subjects transition from CSF Aβ negativity to CSF Aβ positivity were estimated from the spline models and tested for significance.ResultsSpline models resulted in better fits for many temporal and parietal regions compared with the dichotomous models. The trilinear model showed that periods of acceleration of atrophy varied greatly by region with early changes seen in the insula, amygdala, precuneus, hippocampus, and other temporal regions, occurring before the clinical threshold for CSF Aβ positivity.DiscussionThe use of piecewise-linear splines provides an improved model of the nonlinear association between CSF Aβ and regional atrophy in regions implicated in the progression of AD. The important biological finding of this work is that some brain regions show periods of accelerated volume loss well before the CSF Aβ42 threshold. This implies that signs of brain atrophy develop before the current conventional definition of "preclinical AD".

Dataset Information

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia.

Publications

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia.

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