Dataset Information

Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings.

ABSTRACT:

Background

Malaria incidence is in decline in many parts of SE Asia leading to a decreasing proportion of febrile illness that is attributable to malaria. However in the absence of rapid, affordable and accurate diagnostic tests, the non-malaria causes of these illnesses cannot be reliably identified. Studies on the aetiology of febrile illness have indicated that the causes are likely to vary by geographical location within countries (i.e. be spatially heterogeneous) and that national empirical treatment policies based on the aetiology measured in a single location could lead to inappropriate treatment.

Methods

Using data from Vientiane as a reference for the incidence of major febrile illnesses in the Lao People's Democratic Republic (Laos) and estimated incidences, plausible incidence in other Lao provinces were generated using a mathematical model for a range of national and local scale variations. For a range of treatment protocols, the mean number of appropriate treatments was predicted and the potential impact of a spatially explicit national empirical treatment protocol assessed.

Findings

The model predicted a negative correlation between number of appropriate treatments and the level of spatial heterogeneity. A spatially explicit national treatment protocol was predicted to increase the number of appropriate treatments by 50% for intermediate levels of spatial heterogeneity.

Conclusions

The results suggest that given even only moderate spatial variation, a spatially explicit treatment algorithm will result in a significant improvement in the outcome of undifferentiated fevers in Laos and other similar resource poor settings.

SUBMITTER: White LJ

PROVIDER: S-EPMC3448597 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings.

White Lisa J LJ Newton Paul N PN Maude Richard J RJ Pan-ngum Wirichada W Fried Jessica R JR Mayxay Mayfong M Maude Rapeephan R RR Day Nicholas P J NP

PloS one 20120921 9

<h4>Background</h4>Malaria incidence is in decline in many parts of SE Asia leading to a decreasing proportion of febrile illness that is attributable to malaria. However in the absence of rapid, affordable and accurate diagnostic tests, the non-malaria causes of these illnesses cannot be reliably identified. Studies on the aetiology of febrile illness have indicated that the causes are likely to vary by geographical location within countries (i.e. be spatially heterogeneous) and that national e ...[more]

PMID: 23028559

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Project description:Background: Across Southeast Asia, declining malaria incidence poses a challenge for healthcare providers, in how best to manage the vast majority of patients with febrile illnesses who have a negative malaria test. In rural regions, where the majority of the population reside, empirical treatment guidelines derived from central urban hospitals are often of limited relevance. In these settings, health workers with limited training deliver care, often without any laboratory diagnostic support. In this paper, we model the impact of point-of-care C-reactive protein testing to inform the decision to prescribe antibiotics and regional surveillance data to inform antibiotic selection, and then simulate the subsequent impact on mortality from febrile illnesses, rooted in the real-world context of rural Savannakhet province, southern Laos. Methods: Our model simulates 100 scenarios with varying quarterly incidence of six key pathogens known to be prevalent in rural Laos. In the simulations, community health workers either prescribe antibiotics in-line with current practice as documented in health facilities in rural Laos, or with the aid of the two interventions. We provide cost-effectiveness estimates for each strategy alone and then for an integrated approach using both interventions. Results: We find that each strategy is predicted to be highly cost-effective, and that the combined approach is predicted to result in the biggest reduction in mortality (averting a predicted 510 deaths per year in rural Savannakhet, a 28% reduction compared to standard practice) and is highly cost-effective, with an incremental cost-effectiveness ratio of just $66 per disability-adjusted life year averted. Conclusions: Substantial seasonal variation in the predicted optimal empirical antibiotic treatment for febrile illness highlights the benefits of up-to-date information on regional causes of fever. In this modelling analysis, an integrated system incorporating point-of-care host biomarker testing and regional surveillance data appears highly cost-effective, and may warrant piloting in a real-life setting.

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Dataset Information

Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings.

Background

Methods

Findings

Conclusions

Publications

Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings.

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