Controversy and debate on dengue vaccine series-paper 3: final response to review of a licensed dengue vaccine: inappropriate subgroup analyses and selective reporting may cause harm in mass vaccination programs

A multi-country study of the economic burden of dengue fever based on patient-specific field surveys in Burkina Faso, Kenya, and Cambodia

BACKGROUND

Dengue fever is a rapidly growing public health problem in many parts of the tropics and sub-tropics in the world. While there are existing studies on the economic burden of dengue fever in some of dengue-endemic countries, cost components are often not standardized, making cross-country comparisons challenging. Furthermore, no such studies have been available in Africa.

METHODS/PRINCIPAL FINDINGS

A patient-specific survey questionnaire was developed and applied in Burkina Faso, Kenya, and Cambodia in a standardized format. Multiple interviews were carried out in order to capture the entire cost incurred during the period of dengue illness. Both private (patient's out-of-pocket) and public (non-private) expenditure were accessed to understand how the economic burden of dengue is distributed between private and non-private payers. A substantial number of dengue-confirmed patients were identified in all three countries: 414 in Burkina Faso, 149 in Kenya, and 254 in Cambodia. The average cost of illness for dengue fever was $26 (95% CI $23-$29) and $134 (95% CI $119-$152) per inpatient in Burkina Faso and Cambodia, respectively. In the case of outpatients, the average economic burden per episode was $13 (95% CI $23-$29) in Burkina Faso and $23 (95% CI $19-$28) in Kenya. Compared to Cambodia, public contributions were trivial in Burkina Faso and Kenya, reflecting that a majority of medical costs had to be directly borne by patients in the two countries.

CONCLUSIONS/SIGNIFICANCE

The cost of illness for dengue fever is significant in the three countries. In particular, the current study sheds light on the potential economic burden of the disease in Burkina Faso and Kenya where existing evidence is sparse in the context of dengue fever, and underscores the need to achieve Universal Health Coverage. Given the availability of the current (CYD-TDV) and second-generation dengue vaccines in the near future, our study outcomes can be used to guide decision makers in setting health policy priorities.

Risk prediction system for dengue transmission based on high resolution weather data

BACKGROUND

Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction.

METHODS AND FINDINGS

We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach.

CONCLUSIONS

We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.

Targeting vaccinations for the licensed dengue vaccine: Considerations for serosurvey design

BACKGROUND

The CYD-TDV vaccine was unusual in that the recommended target population for vaccination was originally defined not only by age, but also by transmission setting as defined by seroprevalence. WHO originally recommended countries consider vaccination against dengue with CYD-TDV vaccine in geographic settings only where prior infection with any dengue serotype, as measured by seroprevalence, was >170% in the target age group. Vaccine was not recommended in settings where seroprevalence was <50%. Test-and-vaccinate strategies suggested following new analysis by Sanofi will still require age-stratified seroprevalence surveys to optimise age-group targeting. Here we address considerations for serosurvey design in the context of vaccination program planning.

METHODS

To explore how the design of seroprevalence surveys affects estimates of transmission intensity, 100 age-specific seroprevalence surveys were simulated using a beta-binomial distribution and a simple catalytic model for different combinations of age-range, survey size, transmission setting, and test sensitivity/specificity. We then used a Metropolis-Hastings Markov Chain Monte-Carlo algorithm to estimate the force of infection from each simulated dataset.

RESULTS

Sampling from a wide age-range led to more accurate estimates than merely increasing sample size in a narrow age-range. This finding was consistent across all transmission settings. The optimum test sensitivity and specificity given an imperfect test differed by setting with high sensitivity being important in high transmission settings and high specificity important in low transmission settings.

CONCLUSIONS

When assessing vaccination suitability by seroprevalence surveys, countries should ensure an appropriate age-range is sampled, considering epidemiological evidence about the local burden of disease.