Thailand’s first wave of COVID-19 in March 2020 was triggered from boxing events and nightclubs in Bangkok, which spread to 68 provinces. The nation responded rapidly with strong public health and social measures on 26 March 2020. Contact tracing was performed by over 1000 surveillance and rapid response teams with support from 1.1 million village health volunteers to identify, isolate and quarantine cases. Thailand implemented social measures in April 2020 including a full-scale national lockdown, curfews and 14-day mandatory quarantine for international travellers. With a strong health system infrastructure, people’s adherence to social measures and a whole-of-government approach, the first wave recorded only 3042 cases and 57 deaths with 1.46% case fatality rate. Economic activities were resumed on 1 May 2020 until the end of the year. On 17 December 2020, a second wave was carried by undocumented migrants who were not captured by the quarantine system. As the total lockdown earlier led to serious negative economic impact, the government employed a targeted strategy, locking down specific areas and employing active case finding. Essential resources including case finding teams, clinicians and medicine were mobilised. With synergistic multisectoral efforts involving health, non-health and private sector, the outbreak was contained in February 2021. Total cases were seven times higher than the first wave, however, early admission and treatment resulted in 0.11% case fatality rate. In conclusion, experiences of responding to the first wave informed the second wave response with targeted locking down of affected localities and active case findings in affected sites.

The World Health Organization (WHO) and the International Labour Organization (ILO) are developing joint estimates of the work-related burden of disease and injury (WHO/ILO Joint Estimates), with contributions from a large number of individual experts. Evidence from human, animal and mechanistic data suggests that occupational exposure to dusts and/or fibres (silica, asbestos and coal dust) causes pneumoconiosis. In this paper, we present a systematic review and meta-analysis of the prevalences and levels of occupational exposure to silica, asbestos and coal dust. These estimates of prevalences and levels will serve as input data for estimating (if feasible) the number of deaths and disability-adjusted life years that are attributable to occupational exposure to silica, asbestos and coal dust, for the development of the WHO/ILO Joint Estimates. We aimed to systematically review and meta-analyse estimates of the prevalences and levels of occupational exposure to silica, asbestos and coal dust among working-age (≥ 15 years) workers. We searched electronic academic databases for potentially relevant records from published and unpublished studies, including Ovid Medline, PubMed, EMBASE, and CISDOC. We also searched electronic grey literature databases, Internet search engines and organizational websites; hand-searched reference lists of previous systematic reviews and included study records; and consulted additional experts. We included working-age (≥ 15 years) workers in the formal and informal economy in any WHO and/or ILO Member State but excluded children (< 15 years) and unpaid domestic workers. We included all study types with objective dust or fibre measurements, published between 1960 and 2018, that directly or indirectly reported an estimate of the prevalence and/or level of occupational exposure to silica, asbestos and/or coal dust. At least two review authors independently screened titles and abstracts against the eligibility criteria at a first stage and full texts of potentially eligible records at a second stage, then data were extracted from qualifying studies. We combined prevalence estimates by industrial sector (ISIC-4 2-digit level with additional merging within Mining, Manufacturing and Construction) using random-effects meta-analysis. Two or more review authors assessed the risk of bias and all available authors assessed the quality of evidence, using the ROB-SPEO tool and QoE-SPEO approach developed specifically for the WHO/ILO Joint Estimates. Eighty-eight studies (82 cross-sectional studies and 6 longitudinal studies) met the inclusion criteria, comprising > 2.4 million measurements covering 23 countries from all WHO regions (Africa, Americas, Eastern Mediterranean, South-East Asia, Europe, and Western Pacific). The target population in all 88 included studies was from major ISCO groups 3 (Technicians and Associate Professionals), 6 (Skilled Agricultural, Forestry and Fishery Workers), 7 (Craft and Related Trades Workers), 8 (Plant and Machine Operators and Assemblers), and 9 (Elementary Occupations), hereafter called manual workers. Most studies were performed in Construction, Manufacturing and Mining. For occupational exposure to silica, 65 studies (61 cross-sectional studies and 4 longitudinal studies) were included with > 2.3 million measurements collected in 22 countries in all six WHO regions. For occupational exposure to asbestos, 18 studies (17 cross-sectional studies and 1 longitudinal) were included with > 20,000 measurements collected in eight countries in five WHO regions (no data for Africa). For occupational exposure to coal dust, eight studies (all cross-sectional) were included comprising > 100,000 samples in six countries in five WHO regions (no data for Eastern Mediterranean). Occupational exposure to silica, asbestos and coal dust was assessed with personal or stationary active filter sampling; for silica and asbestos, gravimetric assessment was followed by technical analysis. Risk of bias profiles varied between the bodies of evidence looking at asbestos, silica and coal dust, as well as between industrial sectors. However, risk of bias was generally highest for the domain of selection of participants into the studies. The largest bodies of evidence for silica related to the industrial sectors of Construction (ISIC 41-43), Manufacturing (ISIC 20, 23-25, 27, 31-32) and Mining (ISIC 05, 07, 08). For Construction, the pooled prevalence estimate was 0.89 (95% CI 0.84 to 0.93, 17 studies, I² 91%, moderate quality of evidence) and the level estimate was rated as of very low quality of evidence. For Manufacturing, the pooled prevalence estimate was 0.85 (95% CI 0.78 to 0.91, 24 studies, I² 100%, moderate quality of evidence) and the pooled level estimate was rated as of very low quality of evidence. The pooled prevalence estimate for Mining was 0.75 (95% CI 0.68 to 0.82, 20 studies, I² 100%, moderate quality of evidence) and the pooled level estimate was 0.04 mg/m³ (95% CI 0.03 to 0.05, 17 studies, I² 100%, low quality of evidence). Smaller bodies of evidence were identified for Crop and animal production (ISIC 01; very low quality of evidence for both prevalence and level); Professional, scientific and technical activities (ISIC 71, 74; very low quality of evidence for both prevalence and level); and Electricity, gas, steam and air conditioning supply (ISIC 35; very low quality of evidence for both prevalence and level). For asbestos, the pooled prevalence estimate for Construction (ISIC 41, 43, 45,) was 0.77 (95% CI 0.65 to 0.87, six studies, I² 99%, low quality of evidence) and the level estimate was rated as of very low quality of evidence. For Manufacturing (ISIC 13, 23-24, 29-30), the pooled prevalence and level estimates were rated as being of very low quality of evidence. Smaller bodies of evidence were identified for Other mining and quarrying (ISIC 08; very low quality of evidence for both prevalence and level); Electricity, gas, steam and air conditioning supply (ISIC 35; very low quality of evidence for both prevalence and level); and Water supply, sewerage, waste management and remediation (ISIC 37; very low quality of evidence for levels). For coal dust, the pooled prevalence estimate for Mining of coal and lignite (ISIC 05), was 1.00 (95% CI 1.00 to 1.00, six studies, I² 16%, moderate quality of evidence) and the pooled level estimate was 0.77 mg/m³ (95% CI 0.68 to 0.86, three studies, I² 100%, low quality of evidence). A small body of evidence was identified for Electricity, gas, steam and air conditioning supply (ISIC 35); with very low quality of evidence for prevalence, and the pooled level estimate being 0.60 mg/m³ (95% CI -6.95 to 8.14, one study, low quality of evidence). Overall, we judged the bodies of evidence for occupational exposure to silica to vary by industrial sector between very low and moderate quality of evidence for prevalence, and very low and low for level. For occupational exposure to asbestos, the bodies of evidence varied by industrial sector between very low and low quality of evidence for prevalence and were of very low quality of evidence for level. For occupational exposure to coal dust, the bodies of evidence were of very low or moderate quality of evidence for prevalence, and low for level. None of the included studies were population-based studies (i.e., covered the entire workers' population in the industrial sector), which we judged to present serious concern for indirectness, except for occupational exposure to coal dust within the industrial sector of mining of coal and lignite. Selected estimates of the prevalences and levels of occupational exposure to silica by industrial sector are considered suitable as input data for the WHO/ILO Joint Estimates, and selected estimates of the prevalences and levels of occupational exposure to asbestos and coal dust may perhaps also be suitable for estimation purposes. Protocol identifier: https://doi.org/10.1016/j.envint.2018.06.005. PROSPERO registration number: CRD42018084131.

Thailand was hit by the second wave of Coronavirus Disease 2019 (COVID-19) in a densely migrant-populated province (Samut Sakhon). COVID-19 vaccines were known to be effective; however, the supply was limited. Therefore, this study aimed to predict the effectiveness of Thailand’s COVID-19 vaccination strategy. We obtained most of the data from the Ministry of Public Health. Deterministic system dynamics and compartmental models were utilized. The reproduction number (R) between Thais and migrants was estimated at 1.25 and 2.5, respectively. Vaccine effectiveness (VE) to prevent infection was assumed at 50%. In Samut Sakhon, there were 500,000 resident Thais and 360,000 resident migrants. The contribution of migrants to the province’s gross domestic product was estimated at 20%. Different policy scenarios were analyzed. The migrant-centric vaccination policy scenario received the lowest incremental cost per one case or one death averted compared with the other scenarios. The Thai-centric policy scenario yielded an incremental cost of 27,191 Baht per one life saved, while the migrant-centric policy scenario produced a comparable incremental cost of 3782 Baht. Sensitivity analysis also demonstrated that the migrant-centric scenario presented the most cost-effective outcome even when VE diminished to 20%. A migrant-centric policy yielded the smallest volume of cumulative infections and deaths and was the most cost-effective scenario, independent of R and VE values. Further studies should address political feasibility and social acceptability of migrant vaccine prioritization.

The COVID-19 pandemic will not be the last of its kind. As the world charts a way towards an equitable and resilient recovery, Public Health and Social Measures (PHSMs) that were implemented since the beginning of the pandemic need to be made a permanent feature of health systems that can be activated and readily deployed to tackle sudden surges in infections going forward. Although PHSMs aim to blunt the spread of the virus, and in turn protect lives and preserve health system capacity, there are also unintended consequences attributed to them. Importantly, the interactions between PHSMs and their accompanying key indicators that influence the strength and duration of PHSMs are elements that require in-depth exploration. This research employs case studies from six Asian countries, namely Indonesia, Singapore, South Korea, Thailand, the Philippines and Vietnam, to paint a comprehensive picture of PHSMs that protect the lives and livelihoods of populations. Nine typologies of PHSMs that emerged are as follows: (1) physical distancing, (2) border controls, (3) personal protective equipment requirements, (4) transmission monitoring, (5) surge health infrastructure capacity, (6) surge medical supplies, (7) surge human resources, (8) vaccine availability and roll-out and (9) social and economic support measures. The key indicators that influence the strength and duration of PHSMs are as follows: (1) size of community transmission, (2) number of severe cases and mortality, (3) health system capacity, (4) vaccine coverage, (5) fiscal space and (6) technology. Interactions between PHSMs can be synergistic or inhibiting, depending on various contextual factors. Fundamentally, PHSMs do not operate in silos, and a suite of PHSMs that are complementary is required to ensure that lives and livelihoods are safeguarded with an equity lens. For that to be achieved, strong governance structures and community engagement are also required at all levels of the health system.

In mid-2021, Thailand faced a fourth wave of Coronavirus Disease 2019 (COVID-19) predominantly fueled by the Delta and Alpha variants. The number of cases and deaths rose exponentially, alongside a sharp increase in hospitalizations and intubated patients. The Thai Government then implemented a lockdown to mitigate the outbreak magnitude and prevent cases from overwhelming the healthcare system. This study aimed to model the severity of the outbreak over the following months by different levels of lockdown effectiveness. Secondary analysis was performed on data primarily obtained from the Ministry of Health; the data were analyzed using both the deterministic compartmental model and the system dynamics model. The model was calibrated against the number of daily cases in Greater Bangkok during June–July 2021. We then assessed the outcomes (daily cases, daily deaths, and intubated patients) according to hypothetical lockdowns of varying effectiveness and duration. The findings revealed that lockdown measures could reduce and delay the peak of COVID-19 cases and deaths. A two-month lockdown with 60% effectiveness in the reduction in reproduction number caused the lowest number of cases, deaths, and intubated patients, with a peak about one-fifth of the size of a no-lockdown peak. The two-month lockdown policy also delayed the peak until after December, while in the context of a one-month lockdown, cases peaked during the end of September to early December (depending on the varying degrees of lockdown effectiveness in the reduction in reproduction number). In other words, the implementation of a lockdown policy did not mean the end of the outbreak, but it helped delay the peak. In this sense, implementing a lockdown helped to buy time for the healthcare system to recover and better prepare for any future outbreaks. We recommend further studies that explore the impact of lockdown measures at a sub-provincial level, and examine the impact of lockdowns on parameters not directly related to the spread of disease, such as quality of life and economic implications for individuals and society.

Objectives This study assesses effective coverage of diabetes and hypertension in Thailand during 2016–2019. Design Mixed method, analysis of National health insurance database 2016–2019 and in-depth interviews. Setting Beneficiaries of Universal Coverage Scheme residing outside Bangkok. Participants Quantitative analysis was performed by acquiring individual patient data of diabetes and hypertension cases in the Universal Coverage Scheme residing outside bangkok in 2016-2019. Qualitative analysis was conducted by in-depth interview of 85 multi-stakeholder key informants to identify challenges. Outcomes Estimate three indicators: detected need (diagnosed/total estimated cases), crude coverage (received health services/total estimated cases) and effective coverage (controlled/total estimated cases) were compared. Controlled diabetes was defined as haemoglobin A1C (HbA1C) below 7% and controlled hypertension as blood pressure below 140/90 mm Hg. Results Estimated cases were 3.1–3.2 million for diabetes and 8.7–9.2 million for hypertension. For diabetes, all indicators have shown slow improvement between 2016 and 2019 (67.4%, 69.9%, 71.9% and 74.7% for detected need; 38.7%, 43.1%, 45.1% and 49.8% for crude coverage and 8.1%, 10.5%, 11.8% and 11.7% for effective coverage). For hypertension, the performance was poorer for detection (48.9%, 50.3%, 51.8% and 53.3%) and crude coverage (22.3%, 24.7%, 26.5% and 29.2%) but was better for effective coverage (11.3%, 13.2%, 15.1% and 15.7%) than diabetes. Results were better for the women and older age groups in both diseases. Complex interplays between supply and demand side were a key challenge. Database challenges also hamper regular assessment of effective coverage. Sensitivity analysis when using at least three annual visits shows slight improvement of effective coverage. Conclusion Effective coverage was low for both diseases, though improving in 2016–2019, especially among men and ัyounger populations. The increasing rate of effective coverage was significantly smaller than crude coverage. Health information systems limitation is a major barrier to comprehensive measurement. To maximise effective coverage, long-term actions should address primary prevention of non-communicable disease risk factors, while short-term actions focus on improving Chronic Care Model.