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Brief Report

An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends

1
Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
2
Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
3
Faculty of Business and Communications, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia
*
Author to whom correspondence should be addressed.
Data 2022, 7(11), 163; https://doi.org/10.3390/data7110163
Received: 14 August 2022 / Revised: 15 September 2022 / Accepted: 26 September 2022 / Published: 17 November 2022
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)

Abstract

:
Due to significant social and economic upheavals brought on by the COVID-19 pandemic, there is a great deal of psychological pain. Google Trends data have been seen as a corollary measure to assess population-wide trends via observing trends in search results. Judicious analysis of Google Trends data can have both analytical and predictive capacities. This study aimed to compare nation-wide and inter-state trends in mental health before and after the Malaysian Movement Control Order 3.0 (MCO 3.0) commencing 12 May 2021. This was through assessment of two terms, “stress” and “sleep” in both the Malay and English language. Google Trends daily data between March 6 and 31 May in both 2019 and 2021 was obtained, and both series were re-scaled to be comparable. Searches before and after MCO 3.0 in 2021 were compared to searches before and after the same date in 2019. This was carried out using the differences in difference (DiD) method. This ensured that seasonal variations between states were not the source of our findings. We found that DiD estimates, β_3 for “sleep” and “stress” were not significantly different from zero, implying that MCO 3.0 had no effect on psychological distress in all states. Johor was the only state where the DiD estimates β_3 were significantly different from zero for the search topic ‘Tidur’. For the topic ‘Tekanan’, there were two states with significant DiD estimates, β_3, namely Penang and Sarawak. This study hence demonstrates that there are particular state-level differences in Google Trend search terms, which gives an indicator as to states to prioritise interventions and increase surveillance for mental health. In conclusion, Google Trends is a powerful tool to examine larger population-based trends especially in monitoring public health parameters such as population-level psychological distress, which can facilitate interventions.

1. Introduction

The COVID-19 pandemic has taken a huge toll on the world economy since being identified 3 years ago [1]. Multiple lockdowns have been enforced globally accompanied by quarantines; this has resulted in high levels of distress globally due to economic, healthcare, and social consequences [2]. In Malaysia, national lockdowns were imposed abruptly on 18 March 2020 as part of a Movement Control Order (MCO) [3,4]. Large amounts of evidence have accrued from cross-sectional studies suggesting that depression, anxiety and stress has worsened during this pandemic, both in general adult, and child and adolescent groups [5,6,7]. Moreover, these psychopathologies have worsened as the pandemic goes by, suggesting that they are not merely reactive to the initial stressor [8]. However, the evidence is limited by the inability to capture larger population findings, and also convenience sampling techniques that may infer unwarranted conclusions from unrepresentative groups.
One innovative and auxiliary way of acquiring a handle on the temperature of larger-scale population sentiments throughout the pandemic is to observe search patterns on Google, the largest search engine by user figures. Language-independent search frequencies on Google for topics are compiled on Google Trends, which serves as a useful repository and insight of the concerns that the population is having. The utilisation of Google Trends data has the following specific benefits. Firstly, there is no problem with data being self-reported by a sub-sample of respondents; rather, what is accurately captured is how lockout has affected all Google Search users in a specific region. Secondly, the observer-expectation effect and interviewer bias are less likely to affect Google Trends data. Thirdly, small-sample bias is less likely to affect Google Trends data. [9]. Google Trends information has utility in formulation of public health strategies; previous research demonstrates its utility as an epidemiology surveillance tool in studies of influenza as well as being a real-time tracker for specific search data [10,11]. Data on Google searches also correlate well with traditional surveillance data and can even predict outbreaks before they occur, allowing proper planning of timing and location for appropriate risk communication strategies [12,13]. One of the most often employed methods in impact evaluation studies is Difference-in-Differences. The method, which is based on a series of before-and-after comparisons, has an intuitive appeal and has been widely employed in economics, public policy, and health research. [14]. The DiD model is also commonly used in health policy evaluations as they allow causal inference using observational data under the assumption that the trends are parallel [15].
Malaysia is a particularly unique case in point. As Malaysia consists of 13 federated states with various degrees of autonomy with individual state legislatures and executive bodies, different levels of lockdown can be applied in different states, which can contribute to different levels of psychological distress. Malaysia has been in various lockdowns since the first one began on 18 March 2020, but entered the strictest level of lockdown (MCO 3.0) on 12 May 2021, namely the “Full MCO” [6]. There has been burgeoning evidence suggesting increases in depression and anxiety amongst Malaysian populations throughout the various lockdowns [5,16,17]; however, this data is cross-sectional in nature and hence limited by the aforementioned difficulties. This study thus aims to further investigate whether the research evidence of worsening stress and sleep in Malaysians due to the implementation of MCO is reflected through evidence from Google Trends, stratified by state. The aim of this study was to observe how search trends for particular mental health-related keywords such as stress (tekanan) and sleep (tidur) were distributed, classified by state. This enables comparison of searches conducted before and after MCO 3.0 in 2021 and searches conducted before and after the same date in 2019. This was thus hoped to showcase if stress-related research evidence obtained through convenience or randomised sampling methods would be reflected by larger search trends on a population level.

2. Method

2.1. Sample Selection

These search data were gathered for states that had implemented Movement Control Order 3.0 as of 12 May 2021. This produced data on all states in Malaysia except Kelantan and Kuala Lumpur.

2.2. Difference-in-Differences Estimators of Lockdown Effects

We used a Difference-in-Differences (DiD) estimation to compare searches before and after the COVID-19 pandemic and associated MCO 3.0 in 2021 to searches before and after the same date in 2019, ensuring that seasonal changes within states were not behind our findings. In our analysis, the lockdown date is the date MCO 3.0 was implemented, which is 12 May 2021.
The difference-in-differences regression model for a topic Y is written as:
Y i t = α + β 1 T r e a t i + β 2 P o s t t + β 3 ( T r e a t × P o s t ) i t + ϵ i t
where T r e a t i equals one for days in 2021 (i.e., the treatment group) and is zero otherwise, and P o s t t equals one for the post-treatment period (i.e., 12 May until 31 May 2019, and 12 May until 31 May 2021) and is zero otherwise. The parameter β 3 then estimates the DiD.

2.3. Google Trends Data

An unfiltered sample of Google search requests is collected from Google Trends data. As a result, it offers an index for search intensity by topic throughout the required time period in a particular region [9]. This is calculated by dividing the number of daily searches for the given topic by the highest number of daily searches for this topic for the relevant time period in that geographic area. This is scaled from 0 to 100, where 100 represents the day with the most searches for that subject, and 0 denotes that a certain day did not have enough searches for the specified phrase. [18]. In contrast to a topic query, which includes related search terms, a search-term query on Google Trends returns searches for an exact search term (in any language). For our research, we used Google Trends to submit the four following psychopathology-related topic search terms: sleep, tidur (the Malay word for sleep), stress, and tekanan (Malay Language of Stress). These four search terms were chosen as we were examining the prevalence of general levels of psychological distress in Google Trends; sleep is a common denominator of all psychological disorders [19], whereas stress is a common pathway that underlied psychopathology [20]. Sleep disturbances have been demonstrated to be a cause of stress, resulting in a cyclical effect that deteriorates mental health [21]. Additionally, both stress and sleep disorders have a bidirectional connection that can have an impact on the central nervous system [22]. Furthermore, we did not specify in which categories of web queries Google Trend was included. This search takes a sample of all web-based search queries and determines the region from which the majority of queries originated.
According to the National Health and Morbidity Survey, 500,000 Malaysians suffered from depression in 2019, and the COVID-19 pandemic and MCO made the situation far worse [23]. Therefore, we retrieved daily data between 6 March and 31 May in both 2019 and 2021 to determine daily search trends between those dates. We rescaled the two series to make them comparable because the daily data for 2019 came from a different request than the daily data for 2021.

2.4. Scaling Procedure

Let’s use the numbers Y i s 2019 and Y i s 2021 to represent the number of Google daily searches for a topic on day i in state s from 6 March to 31 May 2019 and from 6 March to 31 May 2021, respectively. However, since their denominator (the maximum number of searches during one day in the period) differs, we were unable to directly compare the numbers from 2019 and 2021. For the purpose of comparing these numbers, we rescaled the daily data for each period by the corresponding week search interest weights that we determined using weekly data that was continuously available throughout the study period.
For each week between 6 March 2019, and 31 May 2021, we first calculated the corresponding weekly search interest weights. We calculated the weekly average searches for the topic in states over this time period by averaging the daily data from 6 March to 31 May: Y i s 2019 ¯ . We then repeated the procedure for the time period of 6 March 2021, to 31 May 2021: Y i s 2021 ¯ . We also observed from the weekly data downloaded over the entire period: Y i s 2019 2021 ¯ . We calculated the weekly search interest weights using the information provided above, w s 2019 and w s 2021 :
w s 2019 = Y i s 2019 2021 ¯ Y i s 2019 ¯   and   w s 2021 = Y i s 2019 2021 ¯ Y i s 2021 ¯ .
We could now rescale the daily data for each distinct period using these weekly search interest weights by multiplying Y i s 2019 by w s 2019 in 2019, and Y i s 2021 by w s 2021 in 2021. There was no normalization applied to obtain figures between 0 and 100.

3. Results

3.1. Difference-in-Differences Estimation Results

We begin our investigation by comparing the raw data searches conducted in 2021 before and after MCO 3.0 to those conducted in 2019 before and after the same date, as reported by the state. As shown in Table 1, all DiD estimates, β 3 for topic Sleep and Stress were not significantly different from zero which implied that the MCO 3.0 had no effect on psychological distress in Malaysians (i.e., Sleep and Stress) in all states. Johor was the only state where the DiD estimates β 3 were significantly different from zero for the search topic ‘Tidur’. For the topic ‘Tekanan’, there were two states with significant DiD estimates, β 3 , namely Penang and Sarawak.

3.2. Graphical Analysis

The daily search activity for four of our search topics—sleep, “tidur”, stress, and “tekanan”—is plotted in Figure 1 and Figure 2. As the Table 1 indicates, there are no significant differences in DID for the remaining states, with the exception of Johor, Penang, and Sarawak; therefore, we have omitted unnecessary images and graphical data of these states from the paper.

4. Discussion

The results of the above study are fascinating because they illustrate variations in the frequency of particular Google search terms (stress and sleep) across Malaysian states. This supports the notion that geographic differences may mirror variations in the public’s interest in stress and sleep and, based on this interpretation, raises the possibility of a greater impact of MCO 3.0 on mental health related to stress and sleep in varying ways across states. This may be attributed to the fact that Malaysia has been placed under multiple lockdowns, each of which has been extended multiple times and has seen numerous rule changes, all of which may have desensitised the population [24]. Johor state on the other hand, had a significant difference between pre- and post-MCO 3.0 searches for ‘tidur’ (sleep). Sarawak and Penang both have significant pre- and post- MCO 3.0 differences on searches for ‘tekanan’ (stress). This could be potentially explained as Sarawak went through a particularly high case load and led case figures in the country during the third wave of COVID-19 in April to May 2021 [4]. Sarawak state recorded the greatest number of deaths in a single day due to COVID-19 on May 31, which was 2 weeks after the initiation of the MCO 3.0 (The full Movement Control Order which Malaysian Government imposed for the third time). The increase in searches for ‘tidur’ (sleep) and ‘tekanan’ (stress) for Johor and Penang, respectively, may be associated with the fact that during the 3rd MCO, interstate and inter-district travel was not permitted. This posed unique travel restrictions for both areas; Penang is a state with half its population on the mainland and half its population on a separate island which is a separate district. Johor state on the other hand borders Singapore, which is a different sovereign nation, and a large proportion of residents in the capital of Johor, Johor Bahru City, commute to work in Singapore pre-pandemic. Hence, this increase in searches for stress and sleep might be reflective of the particular difficulty imposed upon Penangites and Johorians as the majority of residents in these two states need to travel to the mainland and Singapore, respectively, for work [25]. When the MCO became stricter, this was precluded, and there would be large socioeconomic ramifications due to loss of income and loss of ability to access the usual coping strategies such as family or friends who could be on the other side of the district or national boundaries.
Our results are similar to other existing studies in which they are able to identify drops in searches of ‘stress’. This is likely due to the presence of multiple mental health care services that have been initiated and expanded widely by the non-government sector, such as Befrienders and various other community-run mental health hotlines, in order to help the people of Malaysia cope with their 3rd lockdown [26]. Our results for ‘sleep’, on the other hand, shows disparity with current studies (except with Europe). This may be owing to more and more companies implementing a ‘Work from Home’ approach for their workers as employers gain experience in employee management across multiple lockdowns. This however potentially results in large disturbances of sleep-wake cycles due to concurrent childcare requirements at home and large shifts in education online leaving parents unable to work during standard hours [27]. This results in disrupted productivity, potentially exacerbating the search figures for stress or “tekanan”.
Nonetheless, it is evident that Google Trends data has significant limitations. First, Google Trends is limited to only comparing keywords and does not offer any objective indicator as to how popular a phrase actually is. On Google Trends, we are only able to figure out an approximate number of individuals that looked for a certain phrase over a given time period because Google only provides a relative number of searches. There are no absolute statistics that can be accessed using Google Trends, notwithstanding the user’s input; nonetheless, relative data can provide a promising insight into the interests of the general public [28]. Second, Google Trends offers anonymous data, limiting the ability to analyse age groups and socioeconomic variables [29]. When compared to people of older generations, younger generations are more likely to make use of Google Search [30]. Third, it is to be anticipated that the majority of rural areas in developing countries such as Malaysia will not have access to an adequate amount of internet, which will limit the amount of data that is available from Google Trends [31]. Lastly, in order to maintain the exact meaning of each search phrase, we limited our use of search phrases to English and Malay. According to [32], the definition of search keywords is an extremely important step in the process of retrieving information from search databases. Although Google’s search engine is the most widely used, and English words and phrases continue to be the most common official language in a great number of countries around the world, other languages and cultures could have varying terms regarding sleep and stress. Due to the fact that Malaysia is characterised by its multilingualism [33], the difficulty that comes with conducting an analysis in a number of different languages may have an effect on our findings.

5. Conclusions

This study demonstrates an innovative methodology in looking at Google Trends as a proxy measure of the psychological distress in Malaysia, using difference in difference methods to examine the changes pre-COVID and post-COVID 19 lockdowns. It also stratifies data by state, allowing us to capture regional changes in temperature of the population, thus allowing more geographically focused interventions to take place. As Malaysia moves away from the pandemic towards a more endemic phase of COVID-19, there will still be ebbs and falls of trending searches in Google that relate to particular facets of psychological distress revolving around COVID-19. It is hence crucial that academia work closely with governments and public policy or health risk communication units to use publicly available big data judiciously to feel the pulse of the people, map it against the state of affairs prevalent in various states in Malaysia, in order to react more quickly and more presciently to population concerns. This thus allows us to use Google Trends as a powerful tool for population-level behaviour prediction.

Author Contributions

Conceptualization, N.T.P.P. and A.K.; methodology, N.T.P.P. and A.K.; software, A.K.; validation, N.T.P.P. and A.K.; formal analysis, A.K.; investigation, A.K.; resources, N.T.P.P. and A.K.; data curation, A.K.; writing—original draft preparation, N.T.P.P. and A.K.; writing—review and editing, C.M.H., W.W. and M.W.L.T.; visualization, N.T.P.P. and A.K.; supervision, N.T.P.P.; project administration, N.T.P.P. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by INTI International University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Nie, X.-D.; Wang, Q.; Wang, M.-N.; Zhao, S.; Liu, L.; Zhu, Y.-L.; Chen, H. Anxiety and depression and its correlates in patients with coronavirus disease 2019 in Wuhan. Int. J. Psychiatry Clin. Pract. 2021, 25, 109–114. [Google Scholar] [CrossRef] [PubMed]
  2. Talevi, D.; Socci, V.; Carai, M.; Carnaghi, G.; Faleri, S.; Trebbi, E.; di Bernardo, A.; Capelli, F.; Pacitti, F. Mental health outcomes of the CoViD-19 pandemic. Riv. Psichiatr. 2020, 55, 137–144. [Google Scholar] [PubMed]
  3. Ping, N.P.T.; Kamu, A.; Kassim, M.A.M.; Mun, H.C. Analyses of the effectiveness of Movement Control Order (MCO) in reducing the COVID-19 confirmed cases in Malaysia. J. Health Transl. Med. 2020, 24, 16–27. [Google Scholar] [CrossRef]
  4. Pang, N.T.P.; Kamu, A.; Kassim, M.A.M.; Ho, C.M. Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of COVID-19 in Malaysia: A generalized logistic growth modeling approach. Infect. Dis. Model. 2021, 6, 898–908. [Google Scholar] [CrossRef] [PubMed]
  5. Woon, L.S.-C.; Sidi, H.; Nik Jaafar, N.R.; Leong Bin Abdullah, M.F.I. Mental health status of university healthcare workers during the COVID-19 pandemic: A post–movement lockdown assessment. Int. J. Environ. Res. Public Health 2020, 17, 9155. [Google Scholar] [CrossRef]
  6. Wan Mohd Yunus, W.M.A.; Badri, S.K.Z.; Panatik, S.A.; Mukhtar, F. The unprecedented movement control order (lockdown) and factors associated with the negative emotional symptoms, happiness, and work-life balance of Malaysian University students during the coronavirus disease (COVID-19) pandemic. Front. Psychiatry 2021, 11, 566221. [Google Scholar] [CrossRef]
  7. Dubey, S.; Biswas, P.; Ghosh, R.; Chatterjee, S.; Dubey, M.J.; Chatterjee, S.; Lahiri, D.; Lavie, C.J. Psychosocial impact of COVID-19. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 779–788. [Google Scholar] [CrossRef]
  8. Sher, L. The impact of the COVID-19 pandemic on suicide rates. QJM Int. J. Med. 2020, 113, 707–712. [Google Scholar] [CrossRef]
  9. Brodeur, A.; Clark, A.E.; Fleche, S.; Powdthavee, N. COVID-19, lockdowns and well-being: Evidence from Google Trends. J. Public Econ. 2021, 193, 104346. [Google Scholar] [CrossRef]
  10. Kang, M.; Zhong, H.; He, J.; Rutherford, S.; Yang, F. Using google trends for influenza surveillance in South China. PLoS ONE 2013, 8, e55205. [Google Scholar] [CrossRef]
  11. Cho, S.; Sohn, C.H.; Jo, M.W.; Shin, S.-Y.; Lee, J.H.; Ryoo, S.M.; Kim, W.Y.; Seo, D.-W. Correlation between national influenza surveillance data and google trends in South Korea. PLoS ONE 2013, 8, e81422. [Google Scholar] [CrossRef] [PubMed]
  12. Zhang, Y.; Bambrick, H.; Mengersen, K.; Tong, S.; Hu, W. Using Google Trends and ambient temperature to predict seasonal influenza outbreaks. Environ. Int. 2018, 117, 284–291. [Google Scholar] [CrossRef] [PubMed]
  13. Ciaffi, J.; Meliconi, R.; Landini, M.P.; Ursini, F. Google trends and COVID-19 in Italy: Could we brace for impact? Intern. Emerg. Med. 2020, 15, 1555–1559. [Google Scholar] [CrossRef] [PubMed]
  14. Daw, J.R.; Hatfield, L.A. Matching and regression to the mean in difference-in-differences analysis. Health Serv. Res. 2018, 53, 4138–4156. [Google Scholar] [CrossRef] [PubMed]
  15. Dimick, J.B.; Ryan, A.M. Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA 2014, 312, 2401–2402. [Google Scholar] [CrossRef]
  16. Pang, N.T.P.; Nold Imon, G.; Johoniki, E.; Mohd Kassim, M.A.; Omar, A.; Syed Abdul Rahim, S.S.; Hayati, F.; Jeffree, M.S.; Ng, J.R. Fear of COVID-19 and COVID-19 stress and association with sociodemographic and psychological process factors in cases under surveillance in a frontline worker population in Borneo. Int. J. Environ. Res. Public Health 2021, 18, 7210. [Google Scholar] [CrossRef]
  17. Salvaraji, L.; Rahim, S.S.S.A.; Jeffree, M.S.; Omar, A.; Pang, N.T.P.; Ahmedy, F.; Hayati, F.; Yeap, B.T.; Giloi, N.; Saupin, S. The importance of high index of suspicion and immediate containment of suspected COVID-19 cases in institute of higher education Sabah, Malaysia Borneo. Malays. J. Public Health Med. 2020, 20, 74–83. [Google Scholar] [CrossRef]
  18. Timoneda, J.C.; Wibbels, E. Spikes and variance: Using Google Trends to detect and forecast protests. Political Anal. 2022, 30, 1–18. [Google Scholar] [CrossRef]
  19. Krystal, A.D. Psychiatric disorders and sleep. Neurol. Clin. 2012, 30, 1389–1413. [Google Scholar] [CrossRef]
  20. Kotera, Y.; Dosedlova, J.; Andrzejewski, D.; Kaluzeviciute, G.; Sakai, M. From stress to psychopathology: Relationship with self-reassurance and self-criticism in Czech university students. Int. J. Ment. Health Addict. 2022, 20, 2321–2332. [Google Scholar] [CrossRef]
  21. Merrill, R.M. Mental Health Conditions according to Stress and Sleep Disorders. Int. J. Environ. Res. Public Health 2022, 19, 7957. [Google Scholar] [CrossRef] [PubMed]
  22. Hirotsu, C.; Tufik, S.; Andersen, M.L. Interactions between sleep, stress, and metabolism: From physiological to pathological conditions. Sleep Sci. 2015, 8, 143–152. [Google Scholar] [CrossRef] [PubMed]
  23. Bernama. Almost 500,000 Msians Depressed; Nearly 500 Suicide Attempts This Year. Available online: https://www.nst.com.my/news/nation/2020/10/631154/almost-500000-msians-depressed-nearly-500-suicide-attempts-year (accessed on 13 September 2022).
  24. Woon, L.S.-C.; Mansor, N.S.; Mohamad, M.A.; Teoh, S.H.; Leong Bin Abdullah, M.F.I. Quality of life and its predictive factors among healthcare workers after the end of a movement lockdown: The salient roles of COVID-19 stressors, psychological experience, and social support. Front. Psychol. 2021, 12, 652326. [Google Scholar] [CrossRef]
  25. Rahman, S. Borderland without Business: The Economic Impact of COVID-19 on Peninsular Malaysia’s Southernmost State of Johor; ISEAS—Yusof Ishak Institute: Singapore, 2021. [Google Scholar]
  26. Ping, N.P.T.; Shoesmith, W.D.; James, S.; Hadi, N.M.N.; Yau, E.K.B.; Lin, L.J. Ultra brief psychological interventions for COVID-19 pandemic: Introduction of a locally-adapted brief intervention for mental health and psychosocial support service. Malays. J. Med. Sci. MJMS 2020, 27, 51. [Google Scholar]
  27. Subhas, N.; Pang, N.T.-P.; Chua, W.-C.; Kamu, A.; Ho, C.-M.; David, I.S.; Goh, W.W.-L.; Gunasegaran, Y.I.; Tan, K.-A. The Cross-Sectional Relations of COVID-19 Fear and Stress to Psychological Distress among Frontline Healthcare Workers in Selangor, Malaysia. Int. J. Environ. Res. Public Health 2021, 18, 10182. [Google Scholar] [CrossRef] [PubMed]
  28. Sycińska-Dziarnowska, M.; Szyszka-Sommerfeld, L.; Kłoda, K.; Simeone, M.; Woźniak, K.; Spagnuolo, G. Mental Health Interest and Its Prediction during the COVID-19 Pandemic Using Google Trends. Int. J. Environ. Res. Public Health 2021, 18, 12369. [Google Scholar] [CrossRef] [PubMed]
  29. Barros, J.M.; Melia, R.; Francis, K.; Bogue, J.; O’Sullivan, M.; Young, K.; Bernert, R.A.; Rebholz-Schuhmann, D.; Duggan, J. The validity of Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland. Int. J. Environ. Res. Public Health 2019, 16, 3201. [Google Scholar] [CrossRef]
  30. Ohlheiser, A. Google Examines How Different Generations Handle Misinformation. Available online: https://www.technologyreview.com/2022/08/11/1057552/gen-z-misinformation/ (accessed on 13 September 2022).
  31. Pang, N.; Lee, G.; Tseu, M.; Joss, J.I.; Honey, H.A.; Shoesmith, W.; James, S.; Loo, J.L.; Lasimbang, H. Validation of the alcohol use disorders identification test (AUDIT)–Dusun version in alcohol users in Sabahan Borneo. Arch. Psychiatry Res. Int. J. Psychiatry Relat. Sci. 2020, 56, 129–142. [Google Scholar] [CrossRef]
  32. Arora, V.S.; McKee, M.; Stuckler, D. Google Trends: Opportunities and limitations in health and health policy research. Health Policy 2019, 123, 338–341. [Google Scholar] [CrossRef]
  33. Wider, W.; Suki, N.M.; Lott, M.L.; Nelson, L.J.; Low, S.K.; Cosmas, G. Examining Criteria for Adulthood among Young People in Sabah (East Malaysia). J. Adult Dev. 2021, 28, 194–206. [Google Scholar] [CrossRef]
Figure 1. Daily search activity for Sleep and ‘Tidur’ in three states in Malaysia: (a) Johor, (b) Penang, and (c) Sarawak.
Figure 1. Daily search activity for Sleep and ‘Tidur’ in three states in Malaysia: (a) Johor, (b) Penang, and (c) Sarawak.
Data 07 00163 g001
Figure 2. Daily search activity for Stress and ‘Tekanan’ in three states in Malaysia: (a) Johor, (b) Penang, and (c) Sarawak.
Figure 2. Daily search activity for Stress and ‘Tekanan’ in three states in Malaysia: (a) Johor, (b) Penang, and (c) Sarawak.
Data 07 00163 g002aData 07 00163 g002b
Table 1. The DiD estimates for four of the search topics.
Table 1. The DiD estimates for four of the search topics.
State DiD   Estimates ,   β 3
Sleep‘Tidur’Stress‘Tekanan’
B (t Value)B (t Value)B (t Value)B (t Value)
Johor10.564 (1.139)16.512 (2.278) *9.012 (1.162)−5.780 (−0.685)
Kedah12.186 (1.205)−8.802 (−0.963)−3.476 (−0.404)5.890 (1.610)
KelantanN/IN/IN/IN/I
Kuala LumpurN/IN/IN/IN/I
LabuanN/AN/A0.597 (0.123)N/A
Malacca2.084 (0.276)12.587 (1.423)4.740 (0.653)3.174 (0.533)
Negeri Sembilan−4.550 (−0.562)11.621 (1.420)9.169 (0.954)7.510 (1.155)
Pahang2.392 (0.303)−7.968 (−1.087)13.335 (1.649)−1.307 (−0.163)
Penang−2.272 (−0.244)13.128 (1.346)−3.636 (−0.380)17.065 (2.222) *
PerakN/A11.556 (1.501)11.735 (1.191)16.357 (1.461)
Perlis6.018 (0.698)−4.033 (−0.536)N/A3.087 (0.499)
Putrajaya−6.632 (−1.016)4.935 (0.777)4.797 (1.028)9.516 (0.492)
Sabah3.743 (0.485)9.808 (1.635)−4.916 (−0.655)−2.598 (−0.383)
Sarawak0.359 (0.047)16.891 (1.913)0.172 (0.022)15.203 (2.447) *
Selangor0.129 (0.018)5.108 (0.787)6.862 (0.950)−3.295 (−.343)
Terengganu1.813 (0.204)8.039 (0.843)7.348 (0.974)9.585 (1.076)
* Significance at α = 0.05 . N/I denotes that the state did not implement MCO 3.0 on 12 May 2021. N/A denotes no raw data searches.
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Pang, N.T.P.; Kamu, A.; Ho, C.M.; Wider, W.; Tseu, M.W.L. An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends. Data 2022, 7, 163. https://doi.org/10.3390/data7110163

AMA Style

Pang NTP, Kamu A, Ho CM, Wider W, Tseu MWL. An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends. Data. 2022; 7(11):163. https://doi.org/10.3390/data7110163

Chicago/Turabian Style

Pang, Nicholas Tze Ping, Assis Kamu, Chong Mun Ho, Walton Wider, and Mathias Wen Leh Tseu. 2022. "An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends" Data 7, no. 11: 163. https://doi.org/10.3390/data7110163

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