OBJECTIVE: To examine ethnic differences in participation in medical check-ups among the elderly.
DESIGN: A nationally representative data set was employed. Multiple logistic regressions were utilised to examine the relationship between ethnicity and the likelihood of undergoing medical check-ups. The regressions were stratified by age, income, marital status, gender, household location, insurance access and health status. These variables were also controlled for in the regressions (including stratified regressions).
PARTICIPANTS: The respondents were required to be residents of Malaysia and not be institutionalised. Overall, 30,806 individuals were selected to be interviewed, but only 28,650 were actually interviewed, equivalent to a 93% response rate. Of those, only 2248 were used in the analyses, because 26,402 were others or below aged 60.
MAIN MEASURES: The dependent variable was participation in a medical check-up. The main independent variables were the three major ethnic groups in Malaysia (Malay, Chinese, Indian).
KEY RESULTS: Among the elderly aged 70-79 years, Chinese (aOR 1.89; 95% CI 1.28, 2.81) and Indians (aOR 2.39; 95% CI 1.20, 4.74) were more likely to undergo medical check-ups than Malays. Among the elderly with monthly incomes of ≤ RM999, Chinese (aOR 1.44; 95% CI 1.12, 1.85) and Indians (aOR 1.50; 95% CI 0.99, 2.28) were more likely to undergo medical check-ups than Malays. Indian males were more likely to undergo medical check-ups than Malay males (aOR 2.32; 95% CI 1.15, 4.67). Chinese with hypercholesterolaemia (aOR 1.45; 95% CI 1.07, 1.98) and hypertension (aOR 1.32; 95% CI 1.02, 1.72) were more likely to undergo medical check-ups than Malays.
CONCLUSIONS: There were ethnic differences in participation in medical check-ups among the elderly. These ethnic differences varied across age, income, marital status, gender, household location, insurance access and health status.
PURPOSE: This study aimed to determine the prevalence of LI and lactose malabsorption (LM) in Malay and Chinese children and examine its relationship with calcium intake (CI) and BHS.
METHODS: A total of 400 children participated in this study. The prevalence of lactose tolerance (LT) was assessed using hydrogen breath test, LT test, and visual analogue scales. Assessment of CI was performed using a 24-h dietary recall interview (24-h DR) and food frequency questionnaire (FFQ). Calcaneal broadband ultrasound attenuation (BUA) was measured using a quantitative ultrasonometer.
RESULTS: The prevalence of LI among Chinese children (37%) was significantly higher (p = 0.002) than among Malay children (22.5%). However, 61.5% of Malay and 54.5% of Chinese children were found to have LM. CI of the children fulfilled 30.5% and 33.9% of the Malaysian recommended CI (1300 mg/day) for 24-h DR and FFQ, respectively. The BUA score of Malay children was significantly higher (p 0.05, respectively).
CONCLUSIONS: LI was diagnosed among Malay and Chinese children. However, the higher prevalence of LM is rather worrying as it could develop to LI. The prevalence possibly has been worsened by insufficient CI. Thus, effective approaches to increase CI are highly needed as bone development occurs rapidly at this age and is important for the attainment of the optimum peak bone mass during late adolescence.
METHODS: We used data spanning 2010-2018 from children aged 2-12 years within the Chicago Area Patient-Centered Outcomes Research Network-an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample.
RESULTS: Using data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample.
CONCLUSIONS: Stratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.
OBJECTIVES: Investigate urinary levels of OPFRs and OPFR metabolites in Taiwanese infants, young children, schoolchildren, and adolescents within the general population.
METHODS: Different age groups of subjects (n=136) were recruited from southern Taiwan to detect 10 OPFR metabolites in urine samples. Associations between urinary OPFRs and their corresponding metabolites and potential health status were also examined.
RESULTS: The mean level of urinary Σ10 OPFR in this broad-spectrum young population is 2.25 μg/L (standard deviation (SD) of 1.91 μg/L). Σ10 OPFR metabolites in urine are 3.25 ± 2.84, 3.06 ± 2.21, 1.75 ± 1.10, and 2.32 ± 2.29 μg/L in the age groups comprising of newborns, 1-5 year-olds, 6-10 year-olds, and 11-18 year-olds, respectively, and borderline significant differences were found in the different age groups (p=0.125). The OPFR metabolites of TCEP, BCEP, DPHP, TBEP, DBEP, and BDCPP predominate in urine and comprise more than 90% of the total. TBEP was highly correlated with DBEP in this population (r=0.845, p<0.001). The estimated daily intake (EDI) of Σ5OPFRs (TDCPP, TCEP, TBEP, TNBP, and TPHP) was 2,230, 461, 130, and 184 ng/kg bw/day for newborns, 1-5 yr children, 6-10 yr children, and 11-17 yr adolescents, respectively. The EDI of Σ5OPFRs for newborns was 4.83-17.2 times higher than the other age groups. Urinary OPFR metabolites are significantly correlated with birth length and chest circumference in newborns.
CONCLUSION: To our knowledge, this is the first investigation of urinary OPFR metabolite levels in a broad-spectrum young population. There tended to be higher exposure rates in both newborns and pre-schoolers, though little is known about their exposure levels or factors leading to exposure in the young population. Further studies should clarify the exposure levels and factor relationships.
METHODS: We followed the Joanna Briggs Institute guideline for the conduct of this scoping review. We searched MEDLINE, Embase, LILACS and study registers from inception to 14 March 2022. We included cross-sectional and cohort studies in populations representing a geographically-defined unit (urban or rural) in LMICs, and with data on CVH metrics i.e. all health or clinical factors (cholesterol, blood pressure, glycemia and body mass index) and at least one health behavior (smoking, diet or physical activity). We report findings following the PRISMA-Scr extension for scoping reviews.
RESULTS: We included 251 studies; 85% were cross-sectional. Most studies (70.9%) came from just ten countries. Only 6.8% included children younger than 12 years old. Only 34.7% reported seven metrics; 25.1%, six. Health behaviors were mostly self-reported; 45.0% of studies assessed diet, 58.6% physical activity, and 90.0% smoking status.
CONCLUSIONS: We identified a substantial and heterogeneous body of research presenting CVH metrics in LMICs. Few studies assessed all components of CVH, especially in children and in low-income settings. This review will facilitate the design of future studies to bridge the evidence gap. This scoping review protocol was previously registered on OSF: https://osf.io/sajnh.
METHODS: Our literature search of peer-reviewed English language primary source articles published between 1991 and 2018 was conducted across six databases (Embase, PubMed, Web of Sciences, CINAHL, PsychINFO, Academic Search Complete) and Google Scholar, yielding 3844 articles. After duplicate removal, we independently screened 3413 studies to determine whether they met inclusion criteria. Seventy-six studies were identified for inclusion in this review. Data were extracted on study characteristics, content, and findings.
FINDINGS: Seventy-six studies met the inclusion criteria. The most represented subgroups were Chinese (n = 74), Japanese (n = 60), and Filipino (n = 60), while Indonesian (n = 1), Malaysian (n = 1), and Burmese (n = 1) were included in only one or two studies. Several Asian American subgroups listed in the 2010 U.S. Census were not represented in any of the studies. Overall, the most studied health conditions were cancer (n = 29), diabetes (n = 13), maternal and infant health (n = 10), and cardiovascular disease (n = 9). Studies showed that health outcomes varied greatly across subgroups.
CONCLUSIONS: More research is required to focus on smaller-sized subgroup populations to obtain accurate results and address health disparities for all groups.