Aronrag Meeyai

Cost-utility analysis of 10- and 13-valent pneumococcal conjugate vaccines: protection at what price in the Thai context?

OBJECTIVE This study aims to evaluate the costs and outcomes of offering the 10-valent pneumococcal conjugate vaccine (PCV10) and 13-valent pneumococcal conjugate vaccine (PCV13) in Thailand compared to the current situation of no PCV vaccination. METHODS Two vaccination schedules were considered: two-dose primary series plus a booster dose (2+1) and three-dose primary series plus a booster dose (3+1). A cost-utility analysis was conducted using a societal perspective. A Markov simulation model was used to estimate the relevant costs and health outcomes for a lifetime horizon. Costs were collected and values were calculated for the year 2010. The results were reported as incremental cost-effectiveness ratios (ICERs) in Thai Baht (THB) per quality adjusted life year (QALY) gained, with future costs and outcomes being discounted at 3% per annum. One-way sensitivity analysis and probabilistic sensitivity analysis using a Monte Carlo simulation were performed to assess parameter uncertainty. RESULTS Under the base case-scenario of 2+1 dose schedule and a five-year protection, without indirect vaccine effects, the ICER for PCV10 and PCV13 were THB 1,368,072 and THB 1,490,305 per QALY gained, respectively. With indirect vaccine effects, the ICER of PCV10 was THB 519,399, and for PCV13 was THB 527,378. The model was sensitive to discount rate, the change in duration of vaccine protection and the incidence of pneumonia for all age groups. CONCLUSIONS At current prices, PCV10 and PCV13 are not cost-effective in Thailand. Inclusion of indirect vaccine effects substantially reduced the ICERs for both vaccines, but did not result in cost effectiveness.

Seasonal influenza vaccination for children in Thailand: a cost-effectiveness analysis.

Background Seasonal influenza is a major cause of mortality worldwide. Routine immunization of children has the potential to reduce this mortality through both direct and indirect protection, but has not been adopted by any low- or middle-income countries. We developed a framework to evaluate the cost-effectiveness of influenza vaccination policies in developing countries and used it to consider annual vaccination of school- and preschool-aged children with either trivalent inactivated influenza vaccine (TIV) or trivalent live-attenuated influenza vaccine (LAIV) in Thailand. We also compared these approaches with a policy of expanding TIV coverage in the elderly. Methods and Findings We developed an age-structured model to evaluate the cost-effectiveness of eight vaccination policies parameterized using country-level data from Thailand. For policies using LAIV, we considered five different age groups of children to vaccinate. We adopted a Bayesian evidence-synthesis framework, expressing uncertainty in parameters through probability distributions derived by fitting the model to prospectively collected laboratory-confirmed influenza data from 2005-2009, by meta-analysis of clinical trial data, and by using prior probability distributions derived from literature review and elicitation of expert opinion. We performed sensitivity analyses using alternative assumptions about prior immunity, contact patterns between age groups, the proportion of infections that are symptomatic, cost per unit vaccine, and vaccine effectiveness. Vaccination of children with LAIV was found to be highly cost-effective, with incremental cost-effectiveness ratios between about 2,000 and 5,000 international dollars per disability-adjusted life year averted, and was consistently preferred to TIV-based policies. These findings were robust to extensive sensitivity analyses. The optimal age group to vaccinate with LAIV, however, was sensitive both to the willingness to pay for health benefits and to assumptions about contact patterns between age groups. Conclusions Vaccinating school-aged children with LAIV is likely to be cost-effective in Thailand in the short term, though the long-term consequences of such a policy cannot be reliably predicted given current knowledge of influenza epidemiology and immunology. Our work provides a coherent framework that can be used for similar analyses in other low- and middle-income countries.

Mortality Attributable to Seasonal Influenza A and B Infections in Thailand, 2005–2009: A Longitudinal Study

Influenza epidemiology differs substantially in tropical and temperate zones, but estimates of seasonal influenza mortality in developing countries in the tropics are lacking. We aimed to quantify mortality due to seasonal influenza in Thailand, a tropical middle-income country. Time series of polymerase chain reaction–confirmed influenza infections between 2005 and 2009 were constructed from a sentinel surveillance network. These were combined with influenza-like illness data to derive measures of influenza activity and relationships to mortality by using a Bayesian regression framework. We estimated 6.1 (95% credible interval: 0.5, 12.4) annual deaths per 100,000 population attributable to influenza A and B, predominantly in those aged ≥60 years, with the largest contribution from influenza A(H1N1) in 3 out of 4 years. For A(H3N2), the relationship between influenza activity and mortality varied over time. Influenza was associated with increases in deaths classified as resulting from respiratory disease (posterior probability of positive association, 99.8%), cancer (98.6%), renal disease (98.0%), and liver disease (99.2%). No association with circulatory disease mortality was found. Seasonal influenza infections are associated with substantial mortality in Thailand, but evidence for the strong relationship between influenza activity and circulatory disease mortality reported in temperate countries is lacking.

Analysis of 2009 pandemic influenza A/H1N1 outcomes in 19 European countries: association with completeness of national strategic plans

Objective To describe changes in reported influenza activity associated with the 2009 H1N1 pandemic in European countries and determine whether there is a correlation between these changes and completeness of national strategic pandemic preparedness. Design A retrospective correlational study. Setting Countries were included if their national strategic plans had previously been analysed and if weekly influenza-like illness (ILI) data from sentinel networks between week 21, 2006 and week 20, 2010 were more than 50% complete. Outcome measures For each country we calculated three outcomes: the percentage change in ILI peak height during the pandemic relative to the prepandemic mean; the timing of the ILI peak and the percentage change in total cases relative to the prepandemic mean. Correlations between these outcomes and completeness of a countrys national strategic pandemic preparedness plan were assessed using the Pearson product–moment correlation coefficient. Results Nineteen countries were included. The ILI peak occurred earlier than the mean seasonal peak in 17 countries. In 14 countries the pandemic peak was higher than the seasonal peak, though the difference was large only in Norway, the UK and Greece. Nine countries experienced more total ILI cases during the pandemic compared with the mean for prepandemic years. Five countries experienced two distinct pandemic peaks. There was no clear pattern of correlation between overall completeness of national strategic plans and pandemic influenza outcome measures and no evidence of association between these outcomes and components of pandemic plans that might plausibly affect influenza outcomes (public health interventions, vaccination, antiviral use, public communication). Amongst the 17 countries with a clear pandemic peak, only the correlation between planning for essential services and change in total ILI cases significantly differed from zero: correlation coefficient (95% CI) 0.50 (0.02, 0.79). Conclusions The diversity of pandemic influenza outcomes across Europe is not explained by the marked variation in the completeness of pandemic plans.

An evaluation of usage patterns, effectiveness and cost of the national smoking cessation quitline in Thailand

Background Telephone-based smoking cessation services (quitlines) offering counselling for smoking cessation without nicotine replacement therapy may be important components of tobacco control efforts in low and middle income countries, but evaluations in such resource-limited settings are lacking. We aimed to evaluate the usage, effectiveness and cost of the Thailand National Quitline (TNQ). Methods Analysis of retrospective data for callers to the TNQ between 2009 and 2012 and a follow-up survey in 1161 randomly selected callers. Results Between 2009 and 2012 there were 116 862 callers to the TNQ; 36 927 received counselling and at least one follow-up call. Compared with smokers in the general population, callers were younger, more highly educated, more likely to be students, and more likely to smoke cigarettes rather than roll-your-own tobacco. Continuous abstinence rates at 1, 3 and 6 months after calling were 49.9%, 38.0% and 33.1%. The predicted rate at 12 months was 19.54% (95% CI 14.55 to 26.24). Average cost per completed counselling was

Potential for greater coherence in EUROPEAN influenza surveillance.

31 and the average cost per quitter was

Pandemic influenza H1N1 2009 in Thailand

253. Assuming all (and two-thirds) TNQ callers who succeed in quitting would have failed to quit without the assistance of the TNQ, cumulative life years saved (LYS) for the 4-year period were 57 238 (36 733) giving a cost per LYS of

Does Thailand Need Its Own Mathematical Model of the Influenza A (H1N1-2009) Pandemic and the Following Seasonal Influenza?

32 (50) (about 7.93 LYS per quitter) and an estimated return on investment over 4 years of 9.01 (5.78). Conclusions A low-cost quitline without nicotine replacement therapy is a promising model for smoking cessation services and likely to offer good value for money in Thailand.

Tobacco consumption among youth in Thailand between 2004 and 2009

Ensuring coherence between national, regional and global surveillance systems poses substantial challenges. An influenza pandemic such as that experienced since 11 June 2009 places additional strain on surveillance systems. It is therefore in the interest of governments and public health for international surveillance networks to function effectively to make the best possible use of available resources, and provide robust data in a timely manner. Previously, influenza activity in Europe was monitored through the European Influenza Surveillance Scheme (EISS) and reported in weekly bulletins.1 In recent years, the EISS has been …

Developing an economic-epidemiological model system to allow economic evaluation of pandemic influenza preparedness policies

Background: Developing a quantitative understanding of pandemic influenza dynamics in South-East Asia is important for informing future pandemic planning. Hence, transmission dynamics of influenza A/H1N1 were determined across space and time in Thailand. Methods: Dates of symptom onset were obtained for all daily laboratory-confirmed cases of influenza A/H1N1pdm in Thailand from 3 May 2009 to 26 December 2010 for four different geographic regions (Central, North, North-East, and South). These data were analysed using a probabilistic epidemic reconstruction, and estimates of the effective reproduction number, R(t), were derived by region and over time. Results: Estimated R(t) values for the first wave peaked at 1.54 (95% CI: 1.42-1.71) in the Central region and 1.64 (95% CI: 1.38-1.92) in the North, whilst the corresponding values in the North-East and the South were 1.30 (95% CI: 1.17-1.46) and 1.39 (95% CI: 1.32-1.45) respectively. As the R(t) in the Central region fell below one, the value of R(t) in the rest of Thailand increased above one. R(t) was above one for 30 days continuously through the first wave in all regions of Thailand. During the second wave R(t) was only marginally above one in all regions except the South. Conclusions: In Thailand, the value of R(t) varied by region in the two pandemic waves. Higher R(t) estimates were found in Central and Northern regions in the first wave. Knowledge of regional variation in transmission potential is needed for predicting the course of future pandemics and for analysing the potential impact of control measures.