MATERIAL AND METHODS: We performed literature search using 4 databases from Medline, Cinahl, PubMed and Scopus from inception up to March 15, 2021 and selected relevant cross-sectional studies. Publication bias was assessed using funnel plot. Random effects model was used to estimate the pooled prevalence while risk factors were reported in odds ratio (OR) with 95% CI.

RESULTS: We included 148 studies with 159,194 HCPs and the pooled prevalence for depression was 37.5% (95%CI: 33.8-41.3), anxiety 39.7(95%CI: 34.3-45.1), stress 36.4% (95%CI: 23.2-49.7), fear 71.3% (95%CI: 54.6-88.0), burnout 68.3% (95%CI: 54.0-82.5), and low resilience was 16.1% (95%CI: 12.8-19.4), respectively. The heterogeneity was high (I2>99.4%). Meta-analysis reported that both females (OR = 1.48; 95% CI = 1.30-1.68) and nurses (OR = 1.21; 95%CI = 1.02-1.45) were at increased risk of having depression and anxiety [(Female: OR = 1.66; 95% CI = 1.49-1.85), (Nurse: OR = 1.36; 95%CI = 1.16-1.58)]. Females were at increased risk of getting stress (OR = 1.59; 95%CI = 1.28-1.97).

METHODS: A web-based cross-sectional study was conducted among 1280 healthcare providers aged 18 years and older from 30 primary care clinics in Selangor, Malaysia. In this study, the Copenhagen Burnout Inventory was used to assess burnout. The results were analyzed using multiple logistic regression.

RESULTS: The prevalence of personal burnout was 41.7%, followed by work-related burnout (32.2%) and client-related burnout (14.5%). The determinants for personal burnout in this study were younger age, being a doctor, higher COVID-19 exposure risk, do not know where to seek help, inability to handle stress, poorer sleep quality score, higher total COVID-19 fear score, higher total stress score, and lower total BRS score. The determinants of work-related burnout were younger age, being a doctor, longer years of working, higher COVID-19 exposure risk, do not know where to seek help, lower altruistic score, poorer sleep quality score, higher total stress score, and lower total brief resilience score (BRS) score. The determinants of client-related burnout were doctor, single/divorced, more than one attachment site, and higher satisfaction toward the infection control, inability to handle stress, higher total depression score, and lower total BRS score.

METHODS: Retrospective data of patients treated for NF were collected from two tertiary care hospitals in Central Malaysia between January 2014 and December 2018.

RESULTS: A total of 469 NF patients were identified. More than half of the NF patients were males (n = 278; 59.28%). The highest number of cases was found among age groups between 30 and 79, with mean age of 56.17. The majority of the NF cases (n = 402; 85.72%) were monomicrobial. Streptococcus spp. (n = 89; 18.98%), Pseudomonas aeruginosa (n = 63; 13.44%) and Staphylococcus spp. (n = 61; 13.01%) were identified as the top three microorganisms isolated. Among the 469 NF cases, 173 (36.8%) were amputated or dead while 296 (63.1%) recovered. Proteus spp. (n = 19; 12.93%), Klebsiella pneumoniae (n = 18; 12.24%) and Escherichia coli (n = 14; 9.52%) were associated with all types of amputations. The most common antibiotic prescribed was unasyn (n = 284; 60.56%), followed by clindamycin (n = 56; 11.94%) and ceftazidime (n = 41; 8.74%). A total of 239 (61.8%) recovered while 148 (38.2%) were either amputated or dead when managed with the unasyn, clindamycin or ceftazidime.

METHODS: We recruited eligible adults from the Klang Asthma Cohort registry in primary care for a 3-month mixed-method study plus a 2-month extended observation. We collected baseline data on socio-demography, health literacy and asthma control level. The outcomes of the intervention were assessed at 1- and 3-month: i) adoption (app download and usage), ii) adherence (app usage), iii) retention (app usage in the observation period), iv) health outcomes (e.g., severe asthma attacks) and v) process outcomes (e.g., ownership and use of action plans). At 1-month, participants were purposively sampled for in-depth interviews, which were audio-recorded, transcribed verbatim, and analysed deductively.

RESULTS: We recruited 48 participants; 35 participants (23 Female; median age = 43 years; median HLS score = 28) completed the 3 months study. Of these, 14 participants (10 Female; median age = 48 years; median HLS score = 28) provided interviews. Thirty-seven (77%) participants adopted the app (downloaded and used it in the first month of the study). The main factor reported as influencing adoption was the ease of using the app. A total of 950 app usage were captured during the 3-month feasibility study. App usage increased gradually, peaking at month 2 (355 total log-ins) accounting for 78% of users. In month 5, 51.4% of the participants used the app at least once. The main factors influencing continued use included adherence features (e.g., prompts and reminders), familiarity with app function and support from family members.

METHOD: This online-based cross-sectional study was conducted among 1280 healthcare providers aged ≥18 years from 30 primary care clinics in the state of Selangor, Malaysia. The Fear of COVID-19 Scale was used to assess the level of fear, and the results were analysed using multiple linear regression.

RESULTS: The mean age of the respondents was 36 years, and the mean working experience was 11 years. The majority of the respondents were women (82.4%) and Malays (82.3%). The factors that were significantly correlated with higher levels of fear were underlying chronic disease (ß=1.12, P=0.002, 95% confidence interval [CI]=0.08, 3.15), concern about mortality from COVID-19 (ß=3.3, P<0.001, 95% CI=0.19, 7.22), higher risk of exposure (ß=0.8, P<0.001, 95% CI=0.14, 5.91), concern for self at work (ß=2.8, P=0.002, 95% CI=0.08, 3.10) and work as a nurse (ß=3.6, P<0.001, 95% CI=0.30, 7.52), medical laboratory worker (ß=3.0, P<0.001, 95% CI=0.12, 4.27) and healthcare assistant (ß=3.9, P<0.001, 95% CI=0.17, 5.73). The level of fear was inversely correlated with a higher work-related stress management score (ß=-0.9, P<0.001, 95% CI=-0.14, -5.07) and a higher sleep quality score (ß=-1.8, P<0.001, 95% CI=-0.28, -10.41).