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.

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.

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.

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.

No abstract

BackgroundIncreased Disability Adjusted Life Year (DALY) of diabetes and hypertension draws policy attention to improve effective coverage. This study assesses effective coverage of the two conditions in Thailand between 2016 and 2019.MethodWe estimated total diabetes and hypertension cases using age and sex specific prevalence rates for respective populations. Individual data from public insurance databases (2016-2019) were retrieved to estimate three indicators: detected need (diagnosed/total estimated cases), crude coverage (received health services/total estimated cases) and effective coverage (controlled/total estimated cases). Controlled diabetes was defined as Haemoglobin A1C (HbA1C) below 7% and controlled hypertension as blood pressure below 140/90 mmHg. In-depth interview of 85 multi-stakeholder key informants was conducted to identify challenges to better effective coverage.FindingsIn 2016-2019, among Universal Coverage Scheme members residing outside Bangkok, estimated cases were around 3.1-3.2 million for diabetes and 8.7-9.2 million for hypertension. For diabetes services, all three indicators have shown slow increase over the four years (67.4%, 69.9%, 71.9%, and 74.7% for detected need; 38.7%, 43.1%, 45.1%, 49.8% for crude coverage; and 8.1%, 10.5%, 11.8%, 11.7% for effective coverage). For hypertension services, the performance was poorer for detection (48.9%, 50.3%, 51.8%, 53.3%) and crude coverage (22.3%, 24.7%, 26.5%, 29.2%) but was better for effective coverage (11.3%, 13.2%, 15.1%, 15.7%) than diabetes service. For both diseases, the estimates were higher for the females and older age groups than their counterparts. Complex interplays between supply and demand side barriers were a key challenge. Database challenges remain which hamper regular assessment of effective coverage.RecommendationsGiven the increased diabetes and hypertension prevalence, strategic recommendations cover long term actions for primary prevention of known risk factors as unhealthy diet and sedentary behaviour. Short term actions aim to improve effective coverage through the application of Chronic Care Model, increase attention to non-pharmacological intervention and patient empowerment.

ObjectivesThis study assesses effective coverage of diabetes and hypertension in Thailand during 2016–2019.DesignMixed method, analysis of National health insurance database 2016–2019 and in-depth interviews.SettingBeneficiaries of Universal Coverage Scheme residing outside Bangkok.ParticipantsQuantitative 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.OutcomesEstimate 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.ResultsEstimated 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.ConclusionEffective 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.

In Thailand, various preventive measures have been implemented to control and minimize the volume of road traffic accidents (RTAs). However, the country still faces varying degrees of success in curbing the incidence of RTAs, which has devastating consequences to the Thai economy. Since there are many factors related to the density of RTAs, this study examines the density according to three main causes-humans, the environment, and vehicles-relative to the provincial economic status in Thailand. A time-series cross-sectional design was employed. Descriptive statistics, Spearman’s rank correlation, and the concentration index (CI) were used to measure the degree of inequitable distribution of RTAs relative to the provincial economic levels. The cumulative proportion of accidents was measured against the cumulative proportion of provinces ranked by the gross provincial product per capita. A negative value from CI-RTAs indicated a disproportional accumulation of RTAs among the poor. In contrast, apositive value reflected a disproportional concentration of RTAs among the well-off populations. Since 2006, human causes of RTAs (such as driving while under the influence of alcohol and violation of traffic rules) were the main contribution to RTAs. The RTAs were concentrated among the well-off provinces during 2006-2013, as reflected by positive indices and positive CI-RTAs were stronger for human-caused RTAs. Since 2014, the total in cidence of RTAs was higher among the less well-off provinces (negative CI-RTAs). The shifting trend of CI-RTAs from among the affluent to the less affluent provinces provided insightful information for policymakers and implementation in the Thai public health arena. However, the statistical significance of the associations between RTAs and economic status was observed only in the early years of the investigation. The reasons behind the varying degree of statistical significance in the association should be investigated further. Additional studies on CIs and RTAs, which could help inform policymakers and academics regarding the prioritization and optimization of RTA preventive measures of each individual area, are recommended.