METHODS: Data were derived from the Global School-Based Student Health Survey (GSHS). Data from 71176 adolescents aged 12-15 years residing in 23 countries were analyzed. The Centers for Disease Control and Prevention (CDC) 2000 growth charts were used to identify underweight, normal weight, and overweight/ obesity. Weighted age- and gender-adjusted prevalence of weight categories and tobacco use was calculated. Multivariate logistic regression analysis was performed to estimate the association between weight categories and tobacco use for each country, controlling for covariates. Pooled odds ratios and confidence intervals were computed using random- or fixed-effects meta-analyses.
RESULTS: A significant association between weight categories and tobacco use was evident in only a few countries. Adolescents reporting tobacco use in French Polynesia, Suriname, and Indonesia, had 72% (95% CI: 0.15-0.56), 55% (95% CI: 0.24-0.84), and 24% (95% CI: 0.61-0.94) lower odds of being underweight, respectively. Adolescents reporting tobacco use in Uganda, Algeria, and Namibia, had 2.30 (95% CI: 1.04-5.09), 1.71 (95% CI: 1.25-2.34), and 1.45 (95% CI: 1.00-2.12) times greater odds of being overweight/obese, but those in Indonesia and Malaysia had 33% (95% CI: 0.50-0.91) and 16% (95% CI: 0.73-0.98) lower odds of being overweight/obese.
CONCLUSIONS: The association between tobacco use and BMI categories is likely to be different among adolescents versus adults. Associating tobacco use with being thin may be more myth than fact and should be emphasized in tobacco prevention programs targeting adolescents.
METHODS: Nasopharyngeal swabs (NPS) for RT-PCR and serologic testing for SARS-CoV-2 were performed on mortuary and cemetery workers in Qatar. Data on specific job duties, living conditions, contact history, and clinical course were gathered. Environmental sampling was carried out to explore any association with infection. Logistic regression analysis was used to determine the factors associated with infection.
RESULTS: Forty-seven mortuary workers provided an NPS and seven (14.9%) were PCR positive; 32 provided a blood sample and eight (25%) were antibody positive, six (75%) who were seropositive were also PCR positive. Among the 81 cemetery workers, 76 provided an NPS and five (6.6%) were PCR positive; 64 provided a blood sample and 22 (34.4%) were antibody positive, three (13.6%) who were seropositive were also PCR positive. Three (22.2%) and 20 (83.3%) of the infected mortuary and cemetery workers were asymptomatic, respectively. Age <30 years (OR 4.9, 95% CI 1.7-14.6), community exposure with a known case (OR 4.7, 95% CI 1.7-13.3), and presence of symptoms in the preceding 2 weeks (OR 9.0, 95% CI 1.9-42.0) were independently associated with an increased risk of infection (PCR or antibody positive). Of the 46 environmental and surface samples, all were negative or had a Ct value of >35.
CONCLUSION: A substantial proportion of mortuary and cemetery workers had evidence of SARS-CoV-2 infection, which was incidentally detected upon serologic testing. These data are most consistent with community acquisition rather than occupational acquisition.
PATIENTS AND METHODS: This study was carried out on 297 newborns recruited consecutively at Naradhiwas Rajanagarindra Hospital in the south of Thailand. The SAO was identified on blood smear examination and polymerase chain reaction analysis. Thalassemia genotypes were defined. Hematologic parameters and hemoglobin (Hb) profiles were recorded and analyzed.
RESULTS: Among 297 newborns, 15 (5.1%) carried SAO, whereas 70 (23.6%) had thalassemia with 15 different thalassemia genotypes. Abnormal Hb including Hb C, Hb Q-Thailand, and Hb D-Punjab were observed in 5 newborns. It was found in the nonthalassemic newborns that RBC count, Hb, and hematocrit of the nonthalassemic newborns with SAO were significantly lower than those without SAO. The same finding was also observed in the thalassemic newborns; RBC count, Hb, and hematocrit of the thalassemic newborns with SAO were significantly lower than those without SAO. However, the mean corpuscular volume, mean corpuscular Hb, and RBC distribution width of the SAO-newborns were significantly higher.
CONCLUSIONS: Both SAO and hemoglobinopathy genotypes are common in southern Thailand. One should take this into consideration when evaluating neonatal anemia and other hematologic abnormalities. Identification of both genetic defects and long-term monitoring on the clinical outcome of this genetic interaction should be essential to understand the pathogenesis of these common genetic disorders in the region.
Methods: Fifteen countries of SA and SEA categorized as HE and LE, represented by the representatives of the national nephrology societies, participated in this questionnaire and interview-based assessment of the impact of economic status on renal care.
Results: Average incidence and prevalence of end-stage kidney disease (ESKD) per million population (pmp) are 1.8 times and 3.3 times higher in HE. Hemodialysis is the main renal replacement therapy (RRT) (HE-68%, LE-63%). Funding of dialysis in HE is mainly by state (65%) or insurance bodies (30%); out of pocket expenses (OOPE) are high in LE (41%). Highest cost for hemodialysis is in Brunei and Singapore, and lowest in Myanmar and Nepal. Median number of dialysis machines/1000 ESKD population is 110 in HE and 53 in LE. Average number of machines/dialysis units in HE is 2.7 times higher than LE. The HE countries have 9 times more dialysis centers pmp (median HE-17, LE-02) and 16 times more nephrologist density (median HE-14.8 ppm, LE-0.94 ppm). Dialysis sessions >2/week is frequently followed in HE (84%) and <2/week in LE (64%). "On-demand" hemodialysis (<2 sessions/week) is prevalent in LE. Hemodialysis dropout rates at one year are lower in HE (12.3%; LE 53.4%), death being the major cause (HE-93.6%; LE-43.8%); renal transplants constitute 4% (Brunei) to 39% (Hong Kong) of the RRT in HE. ESKD burden is expected to increase >10% in all the HE countries except Taiwan, 10%-20% in the majority of LE countries.
Conclusion: Economic disparity in SA and SEA is reflected by poor dialysis infrastructure and penetration, inadequate manpower, higher OOPE, higher dialysis dropout rates, and lesser renal transplantations in LE countries. Utility of RRT can be improved by state funding and better insurance coverage.