Magnitude of metabolic syndrome and its predictors among patients on second-generation antipsychotic drugs at six psychiatry clinics and mental hospitals, in Addis Ababa, Ethiopia, 2019; Multicenter cross-sectional study.

AIMS: Second-generation antipsychotics are an important component in the management of psychiatric conditions but their use can also lead to weight gain, diabetes, and dyslipidemia. Thus, this study aimed to assess the magnitude of metabolic syndrome and its predictors among patients who were on second-generation antipsychotic drugs at six psychiatry Hospitals, in Addis Ababa, Ethiopia, 2019. METHOD: This study was conducted using a Multicenter cross-sectional study design. Medical charts of 510 patients on second-generation antipsychotic drugs who were selected by convenience sampling method and complete patients' charts were included in this study. Data was gathered using a checklist from patients' medical charts. To identify the determinants of metabolic syndrome, a logistic regression model was used. RESULT: From a total of 510 patients' Medical charts on the second-generation antipsychotic drugs, 270 (52.9%) of them were females and 240 (47.1%) were males. The magnitude of metabolic syndrome among patients receiving second-generation antipsychotic drugs was found to be 88 (17.8%). Age, education, family history of cardiovascular disease, and type of second-generation antipsychotic drugs are found to be significantly associated factors with metabolic syndrome. CONCLUSIONS: Considerable proportion of patients receiving second-generation antipsychotic medications have metabolic syndrome.

Glycemic control and its determinants among patients with type 2 diabetes mellitus at Menelik II Referral Hospital, Ethiopia.

OBJECTIVE: The objective of the study was to explore the level of glycemic control and its determinants among patients with type 2 diabetes mellitus at Menelik II Referral Hospital, Addis Ababa, Ethiopia. METHODS: Cross-sectional study design was employed. The sample size was determined using a single proportion formula and 245 patients with type 2 diabetes mellitus were involved in this study. Systematic sampling method was used to select the study subjects. Standard questionnaire was used to collect patient's biographic data, economic data, self-care activities, and patient compliance to medications. Summary statistics of a given data were calculated. Logistic regression model was used to measure the relationship between the outcome and predictor variable. Direction and strength of association was expressed using odds ratio and 95% confidence interval. RESULT: More than three-fourth, 191 (80.3%) of diabetic patients had poor glycemic control. Poor glycemic control was found to be 7.03 times higher among diabetic patients with duration of 5-10 years (adjusted odds ratio = 7.03, 95% confidence interval = 2.7-18.6). Similarly, diabetic patients with a duration of above 10 years were poorly controlled their blood sugar level (adjusted odds ratio = 2.3, 95% confidence interval = 1.028-5.08) in comparison to diabetic patients with a duration of fewer than 5 years. It was also found that compliance with a specific diet was significantly associated with good control of blood sugar level (adjusted odds ratio = 3.7, 95% confidence interval = 1.24-11.13). CONCLUSION: The magnitude of patients with poor glycemic control was high. Duration of diabetes and non-compliance with diets high in fruits, vegetables, and diets low in fat and sugar were significantly related to uncontrolled blood glucose levels. Therefore, developing strategies targeted toward improving blood glucose control with special attention to diabetes mellitus (DM) patients with a duration of ⩾5 years and those who poorly comply with their diet was strongly recommended.

Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020.

BACKGROUND: Ethiopia has taken strict preventive measures against COVID-19 to control its spread, to protect citizens, and ensure their wellbeing. Employee's adherence to preventive measures is influenced by their knowledge, perceived susceptibility, severity, benefit, barrier, cues to action, and self-efficacy. Therefore, this study investigated the predictors of COVID-19 prevention practice using the Health Belief Model among employees in Addis Ababa, Ethiopia, 2020. METHODS: Multicentre cross-sectional study design was used. A total of 628 employees selected by systematic sampling method were included in this study. Data were collected using a pretested self-administered questionnaire. Summary statistics of a given data for each variable were calculated. Logistic regression model was used to measure the association between the outcome and the predictor variable. Statistical significance was declared at p-value<0.05. Direction and strength of association were expressed using OR and 95% CI. RESULTS: From a total of 628 respondents, 432 (68.8%) of them had poor COVID-19 prevention practice. Three hundred ninety-one (62.3%), 337 (53.7%), 312 (49.7), 497 (79.1%), 303 (48.2%) and 299 (52.4%) of the respondents had high perceived susceptibility, severity, benefit, barrier, cues to action and self-efficacy to COVID-19 prevention practice, respectively. Employees with a low level of perceived barriers were less likely to have a poor practice of COVID-19 prevention compared to employees with a high level of perceived barrier [AOR = 0.03, 95% CI (0.01,0.05)]. Similarly, employees with low cues to action and employees with a low level of self-efficacy were practiced COVID prevention measures to a lesser extent compared those with high cues to action and high level of self-efficacy [AOR = 0.05, 95% CI (0.026,0.10)] and [AOR = 0.08, 95% CI (0.04,0.14)], respectively. CONCLUSION: The proportion of employees with poor COVID-19 prevention was high. Income, perceived barrier, cues to action, and self-efficacy were significantly associated with COVID-19 prevention practice.