Lost to Follow-Up among Tuberculosis Patients during the Public-Private Mix Era in Rural Area of Indonesia.

Background: Indonesia's national Tuberculosis (TB) strategy is public-private mix (PPM). The PPM aims to treat patients who have lost sight during TB treatment as these patients are TB carriers and at risk of transmitting TB. The purpose of this study was to identify predictive factors for loss to follow-up (LFTU) among TB patients receiving treatment when the PPM was at place in Indonesia. Methods: The design of this study was a retrospective cohort study. The data used in this study was sourced from the Tuberculosis Information System (SITB) of Semarang which was recorded routinely during 2020-2021. Univariate analysis, crosstabulation, and logistic regression were performed on 3434 TB patients meeting the minimum variables. Results: The participation of health facilities in reporting TB during the PPM era in Semarang reached 97.6% consisting of 37 primary healthcare center (100%), 8 public hospitals (100%), 19 private hospitals (90.5%), and a community-based pulmonary health center (100%). The regression analysis reveal that the predictive factors of LTFU-TB during the PPM are the year of diagnosis (AOR=1.541; p-value=<0.001; 95% CI=1.228-1.934), referral status (AOR=1.562, p-value=0.007; 95% CI=1.130-2160), healthcare and social security insurance ownership (AOR=1.638; p-value=<0.001; 95% CI=1.263-2.124), drugs source (AOR=4.667; p-value=0.035; 95% CI=1.117-19.489). Conclusions: The PPM strategy in dealing with LTFU patients should focus on TB patients without Healthcare and Social Security Insurance and who receive TB treatment rather than program drugs.

Academic Performance in Medical Education During the COVID-19 Pandemic: A Scoping Review.

Backgrounds: Several academic performance studies during the COVID-19 pandemic on education outside medicine showed varying results. This scoping review aims to identify research trends in medical education that focus on the academic performance of medical students during the COVID-19 pandemic to enable available research to be mapped and summarized, and gaps in research results can be identified. Methods: The authors applied the Arksey and O'Malley framework to conduct the scoping review. This review was conducted from January to 30 May 2022. Comprehensive article searches were conducted on six databases (PubMed, ProQuest, EBSCOhost, ERIC, Science Direct, Google Scholar) using keywords of COVID-19, academic performance, academic achievement, medical education, and medical students. Results: A literature search identified 24 publications eligible for analysis. The cohort is the most chosen research design. The publications were only taken from three continents; those were from Asia, America, and Europe. Most of the publications came from the Asian continent, and most of the participants involved in the studies were undergraduates. Eleven out of 24 publications reported on the impact of research before and during the COVID-19 pandemic on academic performance. Six out of 11 studies showed differences in results. Three studies indicated that students achieved lower grades during the COVID-19 pandemic. In contrast, three other studies reported that students got higher grades during the COVID-19 pandemic. Twenty studies reported the influencing factors of the academic performance. Conclusion: The literatures reported differences in medical students' academic performance before and during the COVID-19 pandemic. Twelve variables affected medical students' academic performance during the COVID-19 pandemic.

Prevalence and determinants of obesity among individuals with diabetes in Indonesia.

Background: Obesity and diabetes mellitus (DM), both individually or simultaneously, increase the risk of morbidity and mortality. The present study aimed to determine the prevalence and determinants of obesity among diabetic individuals in Indonesia. Methods: Data were extracted based on 2018 Indonesian Basic Health Survey (Riset Kesehatan Dasar=RISKESDAS). This study involved all individuals with DM and categorized obesity based on body mass index. After data clearing, this study analyzed 3911 DM subjects of the 33.905 subjects acquired from the 2018 RISKESDAS. The study also observed demographic data, diabetes control parameters, history of hypertension, lipid profiles, and food consumption patterns. These variables were involved in a Chi-square test, and related variables were then involved in the Binary logistic regression to define the independent determinants of obesity among DM subjects. Results: Of the 3911 DM subjects included, the study found an obesity prevalence of 32.9%. This study found that female (prevalence odds ratio [POR]=2.15; 95% CI: 1.76-2.62), age 15-44 years (POR=2.46; 95% CI: 1.83-3.33), urban residence (POR=1.49; 95% CI: 1.25-1.77), history of hypertension (POR=1.25; 95% CI: 1.04-1.51), high diastolic blood pressure (POR=1.90; 95% CI: 1.58-2.29), high LDL (POR=1.44; 95% CI: 1.13-1.84), high HDL (POR=0.60; 95% CI: 0.46-0.78, and high triglycerides (POR=1.27; 95% CI: 1.07-1.50) were the risk factor of obesity  among DM subjects; while higher education (POR=0.64; 95% CI: 0.53-0.78) and married (POR=0.73; 95% CI: 0.59-0.90) were protective factors of obesity among DM subjects. Conclusions: The study concluded that almost one-third of DM subjects in Indonesia were obese. Female, age, urban residence, education level, history of hypertension, diastolic blood pressure, and lipid profiles were all associated with obesity among DM subjects in Indonesia. These findings suggest that monitoring and controlling of related determinants is needed to prevent complications caused by the doubled burden of diabetes and obesity.

Prevalence of Mental Health Disorders among Elderly Diabetics and Associated Risk Factors in Indonesia.

This cross-sectional study aimed to explore mental health disorders (MHD) prevalence among elderly diabetics in Indonesia. Data were extracted from the 2018 national basic health survey in Indonesia (abbreviated as RISKESDAS). The survey involved households randomly selected from 34 provinces, 416 districts, and 98 cities in Indonesia, with 1,017,290 respondents. The number of subjects selected in this study was 2818 elderly diabetic subjects. MHD was determined by self-reporting assessment. Secondary data acquired from RISKESDAS 2018 data involved age, sex, urban-rural residence status, marital status, educational level, employment status, obesity, hypertension, heart disease, stroke, family history of MHD, and DM duration. Binary logistic regression with a backward stepwise method was used to analyze the risk factors related to MHD. MHD prevalence among elderly diabetics in Indonesia was 19.3%. Factors associated with MHD among elderly diabetics were being female (prevalence odds ratio (POR) = 1.64; 95% CI: 1.126-2.394), married (POR = 0.05; 95% CI: 0.031-0.084), less education (POR = 3.37; 95% CI: 1.598-10.355), and stroke (POR = 1.61; 95% CI: 1.183-2.269). MHD prevalence among elderly diabetics in Indonesia was 19.3%, suggesting that screening for psychological problems and educating elderly diabetic patients is essential. Unmarried female elderly diabetics with less education and stroke were altogether more likely to experience MHD.

Recurrent SARS-CoV-2 RNA positivity after COVID-19: a systematic review and meta-analysis.

Present study aimed to estimate the incidence of recurrent SARS-CoV-2 RNA positivity after recovery from COVID-19 and to determine the factors associated with recurrent positivity. We searched the PubMed, MedRxiv, BioRxiv, the Cochrane Library, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry for studies published to June 12, 2020. Studies were reviewed to determine the risk of bias. A random-effects model was used to pool results. Heterogeneity was assessed using I2. Fourteen studies of 2568 individuals were included. The incidence of recurrent SARS-CoV-2 positivity was 14.8% (95% confidence interval [CI] 11.44-18.19%). The pooled estimate of the interval from disease onset to recurrence was 35.4 days (95% CI 32.65-38.24 days), and from the last negative to the recurrent positive result was 9.8 days (95% CI 7.31-12.22 days). Patients with younger age and a longer initial illness were more likely to experience recurrent SARS-CoV-2 positivity, while patients with diabetes, severe disease, and a low lymphocyte count were less likely to experience. Present study concluded that the incidence of recurrent SARS-CoV-2 positivity was 14.8% suggesting further studies must be conducted to elucidate the possibility of infectious individuals with prolonged or recurrent RNA positivity.