Background: Medication non-adherence in ambulatory care has received substantial attention in the literature, but less so as it affects acute care. Accordingly, we aimed to estimate the frequency with which non-adherence to medication contributes to hospital admissions. Methods: We searched the Cochrane Library, EMBASE, Cumulative Index to Nursing and Allied Health Literature, International Pharmaceutical Abstracts and PubMed (until December 2017) to identify prospective observational studies that examined prevalence rates of hospital admissions associated with medication non-adherence. A quality assessment was performed using an expanded Crombie checklist. Data extraction covered patterns, circumstances, and patient and other key characteristics of non-adherence. Pooled estimates were obtained using a random-effect model. Results: Of 24 included studies, 8 were undertaken in North America, 7 from Europe, 6 from Asia and 3 from Australia. Most studies (79%) were rated as low risk of bias. All but three studies used combination measures to detect non-adherence, but approaches to assess preventability varied considerably. Across the studies, there was high heterogeneity among prevalence estimates (χ²=548, df 23, p<0.001, I²=95.8%). The median prevalence rate of hospital admissions associated with non-adherence was 4.29% (IQR 3.22%–7.49%), with prevalence rates ranging from 0.72% to 10.79%. By definition, almost all of these admissions were considered preventable. The underlying causes contributing to these admissions included medication cost and side effects, and non-adherence most often involved cardiovascular medicines. Conclusions: Hospital admissions associated with non-adherence to medication are a common problem. This systematic review highlights important targets for intervention. Greater attention could be focused on adherence to medication during the hospital stay as part of an enhanced medication reconciliation process. Standardisation in study methods and definitions is needed to allow future comparisons among settings; future studies should also encompass emerging economies.

Background: After the spread of the coronavirus disease 2019 (COVID-19) globally, upgraded quarantine and physical distancing strategy, strict infection measures, and government's strict lockdown have been abided to confront the spread of the COVID-19 in Thailand. During the COVID-19 pandemic, concerns about the mental health and psychosocial problems among health care workers and the general population are now arising. Yet, information on mental health and psychosocial problems among health care workers and the general population have not been comprehensively reported in Thailand. As such, we conduct a cross-sectional study, a national online survey to describe the short- and long-term consequences of the COVID-19 pandemic on mental health and psychosocial problems among health care workers and the general population in Thailand. Methods: This study is a repeated cross-sectional study, an open online voluntary national-based survey during the wave I (April 21–May 4, 2020) follow-up in the wave II (August 3–16, 2020), wave III (November 15–28, 2020), and a 1-year follow-up survey (wave IV: April 21–May 4, 2021) in Thailand. Health care workers at the hospitals and the adult general population will be invited to participate in the online survey via the SurveyMonkey that limits one-time participation per unique internet protocol address. The target sample size of at least 1182 health care workers and 1310 general populations will be required to complete the online survey for each wave of the survey. Sociodemographic characteristics and a set of measurement tools for mental and psychosocial problems for each subcohort including depression, anxiety, stress, resilient copings, neuroticism, perceived social support, wellbeing, somatic symptoms, insomnia, burnout (for healthcare workers), and public stigma toward COVID-19 infection (for the general population) will be collected. For all estimates of prevalence, we will weigh data for all wave analyses under the complex design of the survey. Subgroup analyses stratified by key characteristics will also be done to analyze the proportion differences. For the repeated cross-sectional survey, we will combine the data from the wave I to wave IV survey to analyze changes in the mental health status. We will perform multilevel logistic regression models with random intercepts to explore associations with individual-level and region-level/hospital-level predictors. We also plan to perform an ancillary systematic review and meta-analysis by incorporating data from our findings to all available evidence. Results: Our findings will provide information on the short- and long-term mental health status as well as the psychosocial responses to the COVID-19 outbreak in a national sample of health care workers and the general population in Thailand. Conclusion: This prospective, nationally based, a repeated cross-sectional study will describe the mental health status and psychosocial problems among health care workers and the general population in Thailand during the COVID-19 pandemic. Ethics and dissemination: Ethical approval for the study was obtained from the Faculty of Public Health and Faculty of Pharmacy, Chiang Mai University. The findings will be disseminated through public, scientific, and professional meetings, and publications in peer-reviewed journals. Thai Clinical Trials Registry (TCTR) registration number: TCTR20200425001.

Background: The COVID-19 pandemic has had a negative impact on both the physical and mental health of individuals worldwide. Evidence regarding the association between mental health problems and information exposure among Thai citizens during the COVID-19 outbreak is limited. Objective: This study aimed to explore the relationship between information exposure and mental health problems during the COVID-19 pandemic in Thailand. Methods: Between April 21 and May 4, 2020, we conducted a cross-sectional, nationwide online survey of the general population in Thailand. We categorized the duration of exposure to COVID-19-related information as follows: <1 h/day (reference group), 1-2 h/day, and ≥3 h/day. Mental health outcomes were assessed using the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 scale, the Perceived Stress Scale-10, and the Insomnia Severity Index for symptoms of depression, anxiety, perceived stress, and insomnia, respectively. Multivariable logistic regression models were used to evaluate the relationship between information exposure and the risk of developing the aforementioned symptoms. An ancillary analysis using multivariable multinomial logistic regression models was also conducted to assess the possible dose-response relationship across the severity strata of mental health problems. Results: Of the 4322 eligible participants, 4004 (92.6%) completed the online survey. Of them, 1481 (37.0%), 1644 (41.1%), and 879 (22.0%) participants were exposed to COVID-19-related information for less than 1 hour per day, 1 to 2 hours per day, or 3 or more hours per day, respectively. The major source of information related to the COVID-19 pandemic was social media (95.3%), followed by traditional media (68.7%) and family members (34.9%). Those exposed to information for 3 or more hours per day had a higher risk of developing symptoms of depression (adjusted odds ratio [OR] 1.35, 95% CI 1.03-1.76; P=.03), anxiety (adjusted OR 1.88, 95% CI 1.43-2.46; P<.001), and insomnia (adjusted OR 1.52, 95% CI 1.17-1.97; P=.001) than people exposed to information for less than 1 hour per day. Meanwhile, people exposed to information for 1 to 2 hours per day were only at risk of developing symptoms of anxiety (adjusted OR 1.35, 95% CI 1.08-1.69; P=.008). However, no association was found between information exposure and the risk of perceived stress. In the ancillary analysis, a dose-response relationship was observed between information exposure of 3 or more hours per day and the severity of mental health problems. Conclusions: These findings suggest that social media is the main source of COVID-19-related information. Moreover, people who are exposed to information for 3 or more hours per day are more likely to develop psychological problems, including depression, anxiety, and insomnia. Longitudinal studies investigating the long-term effects of COVID-19-related information exposure on mental health are warranted.

Objective: To evaluate the association between antipsychotic use in pregnancy and the risk of congenital malformations in children.Data sources: Searches of PubMed, EMBASE, PsycINFO and Cochrane Library were conducted from inception to 06 January 2020 using keywords: antipsychotics, pregnancy, pregnancy complication and congenital abnormalities.Study selection: Of 38 reports initially identified as being of potential interest, 13 studies met our inclusion criteria: English observational studies that examined the association between gestational antipsychotic use and congenital malformations in children.Data extraction: Data were extracted independently by 2 investigators including the publication year, study site, study period, data source, study design, sample size, medication exposure, exposure period and pregnancy definition, exposure as well as outcome ascertainment, selection of study and comparison group, confounding adjustment, statistical analysis, and method of linkage between mother and children. Risk estimates were pooled using a random‐effect model and the I² statistic was used to evaluate the degree of heterogeneity.Results: Thirteen studies met our systematic review inclusion criteria. Six studies with a total of 2 515 272 pregnancy episodes were included in our meta‐analysis, which provided a pooled adjusted risk ratio of 1.23, 95% confidence interval: 0.96–1.58. The I² result showed moderate heterogeneity between studies (I² = 35.2%, P = .173).Conclusion: We did not find strong evidence of an association between prenatal exposure to antipsychotic medications and the risk of congenital malformations in children. We recommend further studies investigate this association, focusing on specific medication classes and dose responses, which would help clinicians decide whether to prescribe certain antipsychotics during pregnancy.