METHODS: A decision-analytic Markov model was developed to simulate the impact of S. suis infection and its major complications: death, meningitis and infective endocarditis among Thai people in 2019 with starting age of 51 years. Transition probabilities, and inputs pertaining to costs, utilities and productivity impairment associated with long-term complications were derived from published sources. A lifetime time horizon with follow-up until death or age 100 years was adopted. The simulation was repeated assuming that the cohort had not been infected with S.suis. The differences between the two set of model outputs in years of life, QALYs, and PALYs lived reflected the impact of S.suis infection. An annual discount rate of 3% was applied to both costs and outcomes. One-way sensitivity analyses and Monte Carlo simulation modeling technique using 10,000 iterations were performed to assess the impact of uncertainty in the model.

KEY RESULTS: This cohort incurred 769 (95% uncertainty interval [UI]: 695 to 841) years of life lost (14% of predicted years of life lived if infection had not occurred), 826 (95% UI: 588 to 1,098) QALYs lost (21%) and 793 (95%UI: 717 to 867) PALYs (15%) lost. These equated to an average of 2.46 years of life, 2.64 QALYs and 2.54 PALYs lost per person. The loss in PALYs was associated with a loss of 346 (95% UI: 240 to 461) million Thai baht (US$11.3 million) in GDP, which equated to 1.1 million Thai baht (US$ 36,033) lost per person.

METHODS: A retrospective analysis of the 14-year data from 2005-2018 of confirmed S.suis patients admitted at Chiang Mai University Hospital (CMUH) was conducted. Descriptive statistics were used to summarize the data of patients' characteristics, healthcare utilization and costs. The multiple imputation with predictive mean matching strategy was employed to deal with missing Glasgow Coma Scale (GCS) data. Generalized linear models (GLMs) were used to forecast costs model and identify determinants of costs associated with S.suis treatment. The modified Park test was adopted to determine the appropriate family. All costs were inflated applying the consumer price index for medical care and presented to the year 2019.

RESULTS: Among 130 S.suis patients, the average total direct medical cost was 12,4675 Thai baht (THB) (US$ 4,016), of which the majority of expenses were from the "others" category (room charges, staff services and medical devices). Infective endocarditis (IE), GCS, length of stay, and bicarbonate level were significant predictors associated with high total treatment costs. Overall, marginal increases in IE and length of stay were significantly associated with increases in the total costs (standard error) by 132,443 THB (39,638 THB) and 5,490 THB (1,715 THB), respectively. In contrast, increases in GCS and bicarbonate levels were associated with decreases in the total costs (standard error) by 13,118 THB (5,026 THB) and 7,497 THB (3,430 THB), respectively.

OBJECTIVES: Our study used data from the most recent network meta-analysis (NMA) and local parameters to assess the cost effectiveness of non-statin agents in statin-treated patients with a history of CVD.

METHODS: A published Markov model was adopted to investigate lifetime outcomes: (1) number of recurrent CVD events prevented, (2) quality-adjusted life-years (QALYs) gained, (3) costs and (4) incremental cost-effectiveness ratios (ICERs) of proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) and ezetimibe added to statin therapy. Event rates and effectiveness inputs were obtained from the NMA. Cost and utility data were gathered from published studies conducted in Thailand. A series of sensitivity analyses were performed.

RESULTS: Patients receiving PCSK9i and ezetimibe experienced fewer recurrent CVD events (number needed to treat [NNT] 17 and 30) and more QALYs (0.168 and 0.096 QALYs gained per person). However, under the societal perspective and at current acquisition costs in 2018, ICERs of both agents were $US1,223,995 and 27,361 per QALY gained, respectively. Based on threshold analyses, the costs need to be reduced by 97 and 85%, respectively, for PCSK9i and ezetimibe to be cost-effective.

OBJECTIVES: This study aimed to quantify the potential lifetime productivity loss due to pneumococcal diseases among the pediatric population in Thailand using productivity-adjusted life years (PALYs).

METHODS: A decision analytic model was used to estimate the burden of pneumococcal diseases among the current Thai population aged 0-5 years and followed up until aged 99 years or death. Base-case analysis compared years of life and PALYs lost to pneumococcal diseases. Scenario analyses investigated the benefits of prevention with pneumococcal conjugated vaccine 13 (PCV 13). All health outcomes were discounted at 3% per annum.

RESULTS: The base-case analysis estimated that 453,401 years of life and 457,598 PALYs would be lost to pneumococcal diseases, equating to a loss of US$5586 (95% CI 3338-10,302) million. Vaccination with PCV13 at birth was estimated to save 82,609 years of life and 93,759 PALYs, which equated to US$1144 (95% CI 367-2591) million in economic benefits. The incidence of pneumonia in those aged 0-4 years, vaccine efficacy, and the assumed period of protection were key determinants of the health economic outputs.

OBJECTIVE: The objective of this study was to compare the health care of FH in countries of the Asia-Pacific region and Southern Hemisphere.

METHODS: A series of questionnaires were completed by key opinion leaders from selected specialist centers in 12 countries concerning aspects of the care of FH, including screening, diagnosis, risk assessment, treatment, teaching/training, and research; the United Kingdom (UK) was used as the international benchmark.

METHODS: We generated updated estimates of child mortality in early neonatal (age 0-6 days), late neonatal (7-28 days), postneonatal (29-364 days), childhood (1-4 years), and under-5 (0-4 years) age groups for 188 countries from 1970 to 2013, with more than 29,000 survey, census, vital registration, and sample registration datapoints. We used Gaussian process regression with adjustments for bias and non-sampling error to synthesise the data for under-5 mortality for each country, and a separate model to estimate mortality for more detailed age groups. We used explanatory mixed effects regression models to assess the association between under-5 mortality and income per person, maternal education, HIV child death rates, secular shifts, and other factors. To quantify the contribution of these different factors and birth numbers to the change in numbers of deaths in under-5 age groups from 1990 to 2013, we used Shapley decomposition. We used estimated rates of change between 2000 and 2013 to construct under-5 mortality rate scenarios out to 2030.

FINDINGS: We estimated that 6·3 million (95% UI 6·0-6·6) children under-5 died in 2013, a 64% reduction from 17·6 million (17·1-18·1) in 1970. In 2013, child mortality rates ranged from 152·5 per 1000 livebirths (130·6-177·4) in Guinea-Bissau to 2·3 (1·8-2·9) per 1000 in Singapore. The annualised rates of change from 1990 to 2013 ranged from -6·8% to 0·1%. 99 of 188 countries, including 43 of 48 countries in sub-Saharan Africa, had faster decreases in child mortality during 2000-13 than during 1990-2000. In 2013, neonatal deaths accounted for 41·6% of under-5 deaths compared with 37·4% in 1990. Compared with 1990, in 2013, rising numbers of births, especially in sub-Saharan Africa, led to 1·4 million more child deaths, and rising income per person and maternal education led to 0·9 million and 2·2 million fewer deaths, respectively. Changes in secular trends led to 4·2 million fewer deaths. Unexplained factors accounted for only -1% of the change in child deaths. In 30 developing countries, decreases since 2000 have been faster than predicted attributable to income, education, and secular shift alone.

INTERPRETATION: Only 27 developing countries are expected to achieve MDG 4. Decreases since 2000 in under-5 mortality rates are accelerating in many developing countries, especially in sub-Saharan Africa. The Millennium Declaration and increased development assistance for health might have been a factor in faster decreases in some developing countries. Without further accelerated progress, many countries in west and central Africa will still have high levels of under-5 mortality in 2030.

METHODS: We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to analyse a database of data for 7065 site-years and estimate the number of maternal deaths from all causes in 188 countries between 1990 and 2013. We estimated the number of pregnancy-related deaths caused by HIV on the basis of a systematic review of the relative risk of dying during pregnancy for HIV-positive women compared with HIV-negative women. We also estimated the fraction of these deaths aggravated by pregnancy on the basis of a systematic review. To estimate the numbers of maternal deaths due to nine different causes, we identified 61 sources from a systematic review and 943 site-years of vital registration data. We also did a systematic review of reports about the timing of maternal death, identifying 142 sources to use in our analysis. We developed estimates for each country for 1990-2013 using Bayesian meta-regression. We estimated 95% uncertainty intervals (UIs) for all values.

FINDINGS: 292,982 (95% UI 261,017-327,792) maternal deaths occurred in 2013, compared with 376,034 (343,483-407,574) in 1990. The global annual rate of change in the MMR was -0·3% (-1·1 to 0·6) from 1990 to 2003, and -2·7% (-3·9 to -1·5) from 2003 to 2013, with evidence of continued acceleration. MMRs reduced consistently in south, east, and southeast Asia between 1990 and 2013, but maternal deaths increased in much of sub-Saharan Africa during the 1990s. 2070 (1290-2866) maternal deaths were related to HIV in 2013, 0·4% (0·2-0·6) of the global total. MMR was highest in the oldest age groups in both 1990 and 2013. In 2013, most deaths occurred intrapartum or postpartum. Causes varied by region and between 1990 and 2013. We recorded substantial variation in the MMR by country in 2013, from 956·8 (685·1-1262·8) in South Sudan to 2·4 (1·6-3·6) in Iceland.

INTERPRETATION: Global rates of change suggest that only 16 countries will achieve the MDG 5 target by 2015. Accelerated reductions since the Millennium Declaration in 2000 coincide with increased development assistance for maternal, newborn, and child health. Setting of targets and associated interventions for after 2015 will need careful consideration of regions that are making slow progress, such as west and central Africa.