METHODS: Consensus-driven approach between authors from the six selected countries was applied. Country specific policy documents, official government media statements, mainstream news portals, global statistics databases and latest published literature available between January-October 2020 were utilised for information retrieval. Situational and epidemiological trend analyses were conducted. Country-specific interventions and challenges were described. Based on evidence appraised, a descriptive framework was considered through a consensus. The authors subsequently outlined the lessons learned, challenges ahead and interventions that needs to be in place to control the pandemic.
RESULTS: The total number of people infected with COVID-19 between 1 January and 16 November 2020 had reached 48,520 in Malaysia, 58,124 in Singapore, 3,875 in Thailand, 470,648 in Indonesia, 409,574 in Philippines and 70,161 in Myanmar. The total number of people infected with COVID- 19 in the six countries from January to 31 October 2020 were 936,866 cases and the mortality rate was 2.42%. Indonesia had 410,088 cases with a mortality rate of 3.38%, Philippines had 380,729 cases with a mortality rate of 1.90%, Myanmar had 52,706 cases with a mortality rate of 2.34%, Thailand had 3,780 cases with a mortality rate of 1.56%, Malaysia had 31,548 cases with a mortality rate of 0.79%, and Singapore had 58,015 cases with a mortality rate of 0.05% over the 10- month period. Each country response varied depending on its real-time situations based on the number of active cases and economic situation of the country.
CONCLUSION: The number of COVID-19 cases in these countries waxed and waned over the 10-month period, the number of cases may be coming down in one country, and vice versa in another. Each country, if acting alone, will not be able to control this pandemic. Sharing of information and resources across nations is the key to successful control of the pandemic. There is a need to reflect on how the pandemic affects individuals, families and the community as a whole. There are many people who cannot afford to be isolated from their families and daily wage workers who cannot afford to miss work. Are we as a medical community, only empathising with our patients or are we doing our utmost to uphold them during this time of crisis? Are there any other avenues which can curb the epidemic while reducing its impact on the health and socio-economic condition of the individual, community and the nation?
METHODS: COVID-19 data on cases, deaths, testing, and vaccinations were extracted from the Our World in Data (OWID) COVID-19 data repository for all the ten ASEAN countries. Comparative time-trends of the epidemiology of COVID-19 using the incidence rate, cumulative case fatality rate (CFR), delay-adjusted case fatality rate, cumulative mortality rate (MR), test positivity rate (TPR), cumulative testing rate (TR) and vaccination rate was carried out.
RESULTS: Over the study period, a total of 12,720,661 cases and 271,475 deaths was reported within the ASEAN region. Trends of daily per capita cases were observed to peak between July and September 2021 for the ASEAN region. The cumulative case fatality rate (CFR) in Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, was of 0.9% (N=68), 2.2% (N=2,610), 3.5% (N=142,889), 0.1% (N=36), 1.2% (N=27,700), 4.0% (N=18,297), 1.6% (N=40,424), 0.1% (N=215), 1.7% (N=18,123), and 2.6% (N=21,043), respectively. CFR was consistently highest between January-June 2020. The cumulative mortality rate (MR) was 9.5, 13.7, 51.4, 0.2, 80.3, 32.4, 34.5, 1.6, 23.9 and 19.7 per 100,000 population, respectively. The cumulative test positivity rate (TPR) was 8.4%, 16.9%, 4.6%, 7.5%, 11.1%, 12.9%, 0.5%, 11.7%, and 3.6%, with the cumulative testing rate (TR) at 25.0, 90.1, 27.4, 917.7, 75.8, 177.8, 3303.3, 195.2, and 224.9 tests per 1,000 population in Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, respectively. The percentage of population that completed vaccinations (VR) was 44.5%, 65.3%, 18.5%, 28.2%, 61.8%, 6.8%, 19.2%, 76.8%, 22.7%, and 10% in Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, respectively.
CONCLUSION: In 2020, most countries in ASEAN had higher case fatality rates but lower mortalities per population when compared to the third quarter of 2021 where higher mortalities per population were observed. Low testing rates have been one of the factors leading to high test positivity rates. Slow initiation of vaccination programs was found to be the key factor leading to high incidence and case fatality rate in most countries in ASEAN. Effective public health measures were able to interrupt the transmission of this novel virus to some extent. Increasing preparedness capacity within the ASEAN region is critical to ensure that any future similar outbreaks can be dealt with collectively.
METHODS: A majority of 151 responders (82.1%) were female and 58.3% had comorbid illnesses. Notably, 90.07% of respondents were non-adherence to their prescription, with significant differences by occupation and aids in medication. This study's machine learning models perform better with recursive feature elimination for feature selection. Key variables included occupation, presence of other diseases, religion, income, medication aid, marital status, and number of medications taken per day. These variables were used to build predictive models for medication adherence.
RESULTS: Results from machine learning algorithms showed varied performance. Support vector machine, gradient boosting, and random forest models performed best with AUC values of 0.907, 0.775, and 0.632 utilizing all variables. When using selected variables, random forest (AUC = 0.883), gradient boosting (AUC = 0.872), and Bagging (AUC = 0.860) performed best. Model interpretation using SHapley Additive exPlanations analysis identified occupation as the most important variable affecting medication adherence. The study also found that unemployment, concomitant disease, income, medication aid type, marital status, and daily medication count are connected with non-adherence.
CONCLUSION: The findings underscore the multifaceted nature of medication adherence in arthritis, highlighting the need for personalized approaches to improve adherence rates.