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Introduction There remains inconclusive evidence on potential changes in smoking status and behaviors during the coronavirus disease 2019 (COVID-9) pandemic, especially in developing countries. Aims and Methods This study explores the direction of changes in smoking status and behaviors after 10 months of the COVID-19 pandemic in Indonesia as well as examining the association between economic shocks and changes in smoking behaviors. Primary data were gathered through a phone survey targeting productive-age mobile-phone users in Indonesia (n = 1082). Descriptive analysis was employed to determine changes in smoking status and behaviors 10 months into the pandemic, while logistic regression analysis was used to investigate how employment shocks, financial strain, COVID-19-related indicators, and demographic characteristics were associated with smoking behaviors of people who continue smoking. Results Respondents experiencing changes in smoking status were dominated by people who persistently smoked during the pandemic, while those who quit, relapsed, and started smoking, was extremely small. Nevertheless, a considerable portion of people who continue smoking adjusted their smoking behaviors: 40.3% reduced smoking intensity and 25.3% switched to lower-price cigarettes. Multivariable regression analysis revealed that, among people who continue smoking, those who experienced financial strain during the pandemic had higher odds of reducing smoking intensity, while those who switched to lower job status had higher odds of switching to cheaper cigarettes. Conclusions The research has shown that smoking status and behaviors of people who continue smoking mostly remained unchanged after 10 months of the COVID-19 pandemic. Changes in employment and financial conditions during the pandemic were associated with modified smoking behaviors. Implications This study is the first to determine the direction and analyze the factors of changes in smoking during the COVID-19 pandemic in Indonesia. This new understanding should help improve predicting the trends in smoking in future crises or pandemics in developing countries, specifically Indonesia. The discovered patterns on smokers’ reaction to an exogenous shock may provide evidence to support tobacco control policies in Indonesia.

This study ( N = 1306) investigated the role behavioural immune system and specific beliefs in COVID-19 misinformation in predicting COVID-19 protective behaviours. By analysing the data from an online survey, we found that germ avoidance significantly predicted social distancing and was negatively correlated to transportation use. Belief in conspiracy theories and non-psychological cures and prevention methods were negatively associated to social distancing, while the latter also predicted lower preventive commodities purchasing. These findings suggested that germ aversion should be incorporated in the promotion of COVID-19 prevention behaviours and that any misinformation related to COVID-19 should be minimized to improve people’s protective behaviours.

BackgroundA significant tobacco tax increase has long been advocated to reduce Indonesia’s high smoking prevalence. However, implementing such a policy remains challenging due to the tobacco industry’s argument that it would negatively impact the economy.ObjectiveThis study aims to provide a comprehensive estimate of the net impact of tobacco taxation on Indonesia’s economy.MethodThe impact of the tax hike on the economy is simulated through a change in cigarette demand and reallocation of household’s budget and allocation government spending from additional tobacco tax revenue. Input-output analysis is employed to estimate the net effect of the tobacco tax rise on the total economic output, income and employment in Indonesia.FindingIncreasing the tobacco tax would generate a net positive impact on the economy as it would increase economic output, household income and employment. The positive impact is mainly driven by government spending from additional revenue from increased tobacco taxes. Spending tax revenue using the current structure of government spending has the potential to generate the optimal economic effect. Increasing tobacco tax by 45% from the 2019 tax level would increase economic output, household income and employment by Rp84.2 trillion, Rp24.1 trillion and 400.3 thousand jobs, respectively.

The COVID-19 pandemic poses a heightened risk to health workers, especially in low- and middle-income countries such as Indonesia. Due to the limitations to implementing mass RT-PCR testing for health workers, high-performing and cost-effective methodologies must be developed to help identify COVID-19 positive health workers and protect the spearhead of the battle against the pandemic. This study aimed to investigate the application of machine learning classifiers to predict the risk of COVID-19 positivity (by RT-PCR) using data obtained from a survey specific to health workers. Machine learning tools can enhance COVID-19 screening capacity in high-risk populations such as health workers in environments where cost is a barrier to accessibility of adequate testing and screening supplies. We built two sets of COVID-19 Likelihood Meter (CLM) models: one trained on data from a broad population of health workers in Jakarta and Semarang (full model) and tested on the same, and one trained on health workers from Jakarta only (Jakarta model) and tested on an independent population of Semarang health workers. The area under the receiver-operating-characteristic curve (AUC), average precision (AP), and the Brier score (BS) were used to assess model performance. Shapley additive explanations (SHAP) were used to analyze feature importance. The final dataset for the study included 3979 health workers. For the full model, the random forest was selected as the algorithm of choice. It achieved cross-validation mean AUC of 0.818 ± 0.022 and AP of 0.449 ± 0.028 and was high performing during testing with AUC and AP of 0.831 and 0.428 respectively. The random forest model was well-calibrated with a low mean brier score of 0.122 ± 0.004. A random forest classifier was the best performing model during cross-validation for the Jakarta dataset, with AUC of 0.824 ± 0.008, AP of 0.397 ± 0.019, and BS of 0.102 ± 0.007, but the extra trees classifier was selected as the model of choice due to better generalizability to the test set. The performance of the extra trees model, when tested on the independent set of Semarang health workers, was AUC of 0.672 and AP of 0.508. Our models yielded high predictive performance and may have the potential to be utilized as both a COVID-19 screening tool and a method to identify health workers at greatest risk of COVID-19 positivity, and therefore most in need of testing.

(1) Background: because of close contacts with COVID-19 patients, hospital workers are among the highest risk groups for infection. This study examined the socioeconomic and behavioral correlates of COVID-19 infection among hospital workers in Indonesia, the country hardest-hit by the disease in the Southeast Asia region. (2) Methods: we conducted a cross-sectional study, which collected data from 1397 hospital staff from eight hospitals in the Greater Jakarta area during April–July 2020. The data was collected using an online self-administered questionnaire and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) tests. We employed descriptive statistics and adjusted and unadjusted logistic regressions to analyze the data of hospital workers as well as the subgroups of healthcare and non-healthcare workers. (3) Results: from a total of 1397 hospital staff in the study, 22 (1.6%) were infected. In terms of correlates, being a healthcare worker (adjusted odds ratio (AOR) = 8.31, 95% CI 1.27–54.54) and having a household size of more than five (AOR = 4.09, 1.02–16.43) were significantly associated with a higher risk of infection. On the other hand, those with middle- and upper-expenditure levels were shown to have a lower risk of infection (AOR = 0.06, 0.01–0.66). Behavioral factors associated with COVID-19 infection among healthcare and non-healthcare workers included knowledge of standard personal protective equipment (PPE) (AOR = 0.08, 0.01–0.54) and application of the six-step handwashing technique (AOR = 0.32, 0.12–0.83). (4) Conclusion: among hospital staff, correlates of COVID-19 infection included being a healthcare worker, household size, expenditure level, knowledge and use of PPE, and application of appropriate hand washing techniques.

There is an urgent need to provide an evidence-based study to support and mainstream PWD on the development agenda in Indonesia due to low prioritisation of poverty eradication of PWD. Thus, this study aims at examining the impacts of disability, types of disability and causes of disability on household’s poverty status and household’s poverty gap index. Applying Logistic and Tobit regressions, this study confirmed that disabled-headed households are more likely to become poor by 1.3 percentage points and have deeper poverty gap index by 2.6 percent. Household with a visually impaired household head is less likely to be poor compared to other disabled-headed households, while one that has a household head with self-care problem tends to have higher probability of falling into poverty. Moreover, a household in which the household head has congenital disability (disability at birth) has higher probability of being poor by 4.8 percentage points and has deeper poverty gap index of about 7.8 percent.