Cost Effectiveness and Budget Impact Analyses of Influenza Vaccination for Prisoners in Thailand: An Application of System Dynamic Modelling

Influenza outbreaks in Thai prisons were increasing in number every year and to address this, the Thai Ministry of Public Health (MOPH) initiated a policy to promote vaccination for prisoners. The objective of this study was to assess the cost effectiveness and budget impact of the influenza vaccination policy for prisoners in Thailand. The study obtained data from the Division of Epidemiology, Department of Disease Control (DDC), MOPH. Deterministic system dynamic modelling was exercised to estimate the financial implication of the vaccination programme in comparison with routine outbreak control. The incremental cost-effectiveness ratio (ICER) was calculated via a DDC perspective. The reproductive number was estimated at 1.4. A total of 143 prisons across the country (375,763 prisoners) were analysed. In non-vaccination circumstances, the total healthcare cost amounted to 174.8 million Baht (US$5.6 million). Should all prisoners be vaccinated, the total healthcare cost would reduce to 90.9 million Baht (US$ 2.9 million), and 46.8 million Baht (US$1.5 million) of this is related to the vaccination. The ICER of vaccination (compared with routine outbreak control) varied between 39,738.0 to 61,688.3 Baht per disability-adjusted life year (DALY) averted (US$ 1281.9–1989.9). Should the vaccination cover 30% of the prisoners, the ICER would be equal to 46,866.8 Baht (US$1511.8) per DALY averted with the budget burden amounted to Baht (US$ 4.8 million). The vaccination programme would become more cost-effective if the routine outbreak control was intensified. In summary, the vaccination programme was a cost-effective measure to halt influenza outbreak amongst prisoners. Further primary studies that aim to assess the actual impact of the programme are recommended

Challenges and best practices in real-time prediction of infectious disease: a case study of dengue in Thailand

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create accurate and actionable real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created an operational and computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing naive seasonal models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making

Relative mean absolute error (MAE) comparing our prediction model vs. a model that predicts a seasonal median, by province and step forward (in biweeks).

<p>Results to the left of the dotted line signify more accurate predictions from our models when compared to the seasonal model, and results to the right indicate less accurate predictions.</p

Raw dengue hemorrhagic fever case counts for 77 provinces of Thailand across 47 years (1968–2014).

<p>Provinces are ordered by by population (larger populations on the top). Gray regions indicate periods of time when a province was not in existence.</p

Mean absolute error (MAE) of our prediction model by province and step forward (in biweeks).

<p>Provinces are sorted by population, with the most populous at the top of the figure.</p

Country-wide real-time predictions for incident dengue hemorrhagic fever.

<p>Red lines show predicted case counts, black bars show cases reported by the end of the 2014 reporting period. The three figures show (top to bottom) one-, two-, and three-biweek ahead predictions. So, for example, every dot on the top graph is a one-biweek ahead real-time prediction made from all available data at the time of analysis.</p

Comparison of province-level prediction accuracy between full-data and real-time predictions, by prediction horizon.

<p>These results were computed comparing predictions as if the full data was available at the analysis time with the real-time predictions that build in a 6-biweek (approximately 3 month) buffer to account for delayed case data. The table shows the 5th percentile (<i>Q</i>₅), 25th percentile (<i>Q</i>₂₅), median (<i>Q</i>₅₀), 75th percentile (<i>Q</i>₇₅), and 95th percentile (<i>Q</i>₉₅) value of the relative MAE from each province at the given horizon. The relative MAEs were calculated as the MAE from the real-time predictions divided by the MAE from the full-data predictions, i.e. values larger than 1 indicate that the real-time models showed more absolute error on average than the full-data models.</p

This figure illustrates three different methods used to create forecasts.

<p>Panel A shows predictions made using only data that was available at the analysis time, and ignoring the most recent six biweeks of reported cases. Panel B shows predictions that used fully observed data (including data that was not available at the analysis time) but still ignored cases from the six biweeks preceding the analysis time. Panel C shows predictions that could have been made at the analysis time if no reporting delays existed and all data that eventually was reported had been available in real-time.</p

Summary of real-time prediction accuracy, by prediction horizon.

<p>These results are aggregated across all provinces. The <i>R</i>² and 95% PI coverage columns present the overall correlation coefficient and prediction interval coverage. The relative MAE columns show five quantiles of the distribution of province-level relative MAEs comparing the real-time model at the given horizon to a seasonal baseline model at the given horizon: <i>Q</i>₅ (the 5<i>^th</i> percentile), <i>Q</i>₂₅ (25<i>^th</i> percentile), <i>Q</i>₅₀ (median), <i>Q</i>₇₅ (75<i>^th</i> percentile), and <i>Q</i>₉₅ (the 95th percentile). The relative MAEs were calculated as the MAE from the real-time predictions divided by the MAE from the seasonal average predictions, therefore values larger than 1 indicate that the real-time models showed more absolute error on average than the seasonal models.</p