Dataset Information

Economic Burden of Dengue Virus Infection at the Household Level Among Residents of Puerto Maldonado, Peru.

ABSTRACT: Dengue virus (DENV) was reintroduced to Peru in the 1990s and has been reported in Puerto Maldonado (population ~65,000) in the Peruvian southern Amazon basin since 2000. This region also has the highest human migration rate in the country, mainly from areas not endemic for DENV. The objective of this study was to assess the proportion of household income that is diverted to costs incurred because of dengue illness and to compare these expenses between recent migrants (RMs) and long-term residents (LTRs). We administered a standardized questionnaire to persons diagnosed with dengue illness at Hospital Santa Rosa in Puerto Maldonado from December 2012 to March 2013. We compared direct and indirect medical costs between RMs and LTRs. A total of 80 participants completed the survey, of whom 28 (35%) were RMs and 52 (65%) were LTRs. Each dengue illness episode cost the household an average of US$105 (standard deviation [SD] = 107), representing 24% of their monthly income. Indirect costs were the greatest expense (US$56, SD = 87), especially lost wages. The proportion of household income diverted to dengue illness did not differ significantly between RM and LTR households. The study highlights the significant financial burden incurred by households when a family member suffers dengue illness.

SUBMITTER: Salmon-Mulanovich G

PROVIDER: S-EPMC4596582 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Economic Burden of Dengue Virus Infection at the Household Level Among Residents of Puerto Maldonado, Peru.

Salmon-Mulanovich Gabriela G Blazes David L DL Lescano Andres G AG Bausch Daniel G DG Montgomery Joel M JM Pan William K WK

The American journal of tropical medicine and hygiene 20150727 4

Dengue virus (DENV) was reintroduced to Peru in the 1990s and has been reported in Puerto Maldonado (population ~65,000) in the Peruvian southern Amazon basin since 2000. This region also has the highest human migration rate in the country, mainly from areas not endemic for DENV. The objective of this study was to assess the proportion of household income that is diverted to costs incurred because of dengue illness and to compare these expenses between recent migrants (RMs) and long-term residen ...[more]

PMID: 26217040

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Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Project description:BackgroundDengue imposes a substantial economic and disease burden in most tropical and subtropical countries. Dengue incidence and severity have dramatically increased in Mexico during the past decades. Having objective and comparable estimates of the economic burden of dengue is essential to inform health policy, increase disease awareness, and assess the impact of dengue prevention and control technologies.Methods and findingsWe estimated the annual economic and disease burden of dengue in Mexico for the years 2010-2011. We merged multiple data sources, including a prospective cohort study; patient interviews and macro-costing from major hospitals; surveillance, budget, and health data from the Ministry of Health; WHO cost estimates; and available literature. We conducted a probabilistic sensitivity analysis using Monte Carlo simulations to derive 95% certainty levels (CL) for our estimates. Results suggest that Mexico had about 139,000 (95%CL: 128,000-253,000) symptomatic and 119 (95%CL: 75-171) fatal dengue episodes annually on average (2010-2011), compared to an average of 30,941 symptomatic and 59 fatal dengue episodes reported. The annual cost, including surveillance and vector control, was US$170 (95%CL: 151-292) million, or $1.56 (95%CL: 1.38-2.68) per capita, comparable to other countries in the region. Of this, $87 (95%CL: 87-209) million or $0.80 per capita (95%CL: 0.62-1.12) corresponds to illness. Annual disease burden averaged 65 (95%CL: 36-99) disability-adjusted life years (DALYs) per million population. Inclusion of long-term sequelae, co-morbidities, impact on tourism, and health system disruption during outbreaks would further increase estimated economic and disease burden.ConclusionWith this study, Mexico joins Panama, Puerto Rico, Nicaragua, and Thailand as the only countries or areas worldwide with comprehensive (illness and preventive) empirical estimates of dengue burden. Burden varies annually; during an outbreak, dengue burden may be significantly higher than that of the pre-vaccine level of rotavirus diarrhea. In sum, Mexico's potential economic benefits from dengue control would be substantial.

Dataset Information

Economic Burden of Dengue Virus Infection at the Household Level Among Residents of Puerto Maldonado, Peru.

Publications

Economic Burden of Dengue Virus Infection at the Household Level Among Residents of Puerto Maldonado, Peru.

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