Evaluation of the early weigh-in policy for mixed martial arts events adopted by North American athletic commissions.

OBJECTIVE: To characterize the epidemiology of overweight athletes before and after the introduction of the Early Weigh-In Policy (EWIP). METHODS: A retrospective cohort study examined the weigh-in results for professional mixed martial arts (MMA) events over a 2-year period around the introduction of the new EWIP between 2014 and 2018. Descriptive statistics were used to characterize the study populations. Risk ratios were used to identify differences in the study populations before and after the introduction of the EWIP. RESULTS: After the introduction of the EWIP, the number of overweight athletes increased from 5.7% to 8.4% and the average overweight mass increased from 1.3 kg (2.9 lbs) to 1.8 kg (3.9 lbs) [difference, 0.5 kg (1.0 lb), p = 4.35 × 10^(-5)]. The proportion of athletes is not distributed similarly across the different overweight mass categories when comparing the pre- and post-EWIP time frames (p = 0.006). More athletes in the pre-EWIP period were overweight by smaller amount, while in the post-EWIP period athletes were overweight by larger amount. Of the athletes who were overweight before the regulation change, 28.7% were over the weight limit by greater than 1.8 kg (4 lbs), compared to 39.5% after the new EWIP introduction. On average, the ratio of overweight athletes per events by commission was 1.2 before the introduction of the EWIP and 2.1 after. CONCLUSION: These results appear to indicate that the EWIP has not altered weight-cutting culture in MMA in a positive manner. This study casts doubt on the benefits of an EWIP and raised the possibility of utilizing the longitudinal weight monitoring approach to mitigate rapid weight-cycling behavior. However, before additional changes are made by any athletic commission, further research is needed to examine the efficacy of the abovementioned longitudinal weight monitoring approach or any other strategy.

Peoples' understanding, acceptance, and perceived challenges of vaccination against COVID-19: A cross-sectional study in Bangladesh.

In order to eliminate COVID-19, many countries provided vaccinations. However, success depends on peoples' knowledge levels and rates of acceptance. But, previous research on this topic is currently lacking in Bangladesh. This cross-sectional study aimed at to investigate Bangladeshi peoples' knowledge, acceptance, and perception of challenges regarding COVID-19 vaccines. Quantitative data were collected using an online survey (n = 1975) and face-to-face interviews (n = 2200) with a pre-tested structured questionnaire. In addition, seven open-ended interviews were conducted with health experts regarding challenges of vaccination. Binary logistic regression analyses were conducted to assess the association between explanatory and dependent variables. Effect size was estimated to understand the magnitude of relationship between two variables. Of 4175 respondents, 92.6% knew about COVID-19 vaccines, while only 37.4% believed vaccines to be effective in controlling COVID-19. Nearly 46% of respondents believed that COVID-19 vaccines have side-effects, and 16.4% of respondents believed that side-effects could be life-threatening. Only 60.5% of respondents indicated that they would receive the COVID-19 vaccine. Out of 1650 respondents (39.5%) who did not intend to receive the vaccine, 948 (57.4%) believed that they would be naturally protected. Regressions results indicated that men had higher rates of knowledge regarding the vaccine. In addition, rural respondents demonstrated lower knowledge regarding the vaccine. Furthermore, education had a significant association with knowledge of COVID-19 vaccines. Respondents with university education had more knowledge regarding the vaccine (Odds ratio, OR = 29.99; 95% confidence interval, CI 11.40-78.90, effect size 1.88; p = 0.01) and correct dosage (OR 27.34; 95% CI 15.25-49.00, effect size 1.83; p = 0.01). However, women (OR 1.16; 95% CI 0.96-1.40, effect size 0.08) and rural (OR 1.24; 95% CI 1.07-1.44, effect size 0.12; p = 0.01) respondents were more enthusiastic regarding receiving the COVID-19 vaccine. Higher educated respondents showed higher probability of receiving the vaccine. Those who believed in the effectiveness of the COVID-19 vaccine were 11.57 times more interested (OR 11.57; 95% CI 8.92-15.01, effect size 1.35; p = 0.01) in receiving the vaccine. Open-ended interviews identified several challenges toward successful COVID-19 vaccination. Mass awareness creation, uninterrupted supply, equitable distribution, and sectoral coordination were suggested to achieve at least 70% immunization across the country.

Community-Driven Prioritization of Primary Health Care Access Issues by Bangladeshi-Canadians to Guide Program of Research and Practice.

Research around probable solutions to immigrants accessing health care in Canada is not extensive, and the perspective of immigrant communities on priorities and potential solutions has not been captured effectively. The purpose of this article is to describe a research initiative that involved grassroots community members as producers of research priorities on primary care access issues. This study aimed to seek input from an immigrant community in Calgary, Canada. Members of the Bangladeshi community of Calgary were asked through a survey to rank 10 predefined primary care access topics as to what they felt constituted priorities for solution-oriented research (1, highest; 10, lowest). We used frequencies and percentages to describe the participant demographics. Ratings of preferred research themes were analyzed on the basis of relative weighted priority rank. We received 432 responses: 51.2% female; 58.9% aged 36 to 55 years; 90.5% had university-level education; 46.2% immigrated to Canada between 10 and 19 years ago; 82.5% employed full/part-time or self-employed. Lack of resources, lack of knowledge, health care cost, and workplace-related barriers were among the top-ranked topics identified as solution-oriented research priorities. Through partnerships and reciprocal learning, public input can increase insider perspectives to help develop interventions that align with the needs of community members.

Hypercholesterolemia and Lifetime Risk of Coronary Heart Disease in the General Japanese Population: Results from the Suita Cohort Study.

AIM: Lifetime risk (LTR) is a measure of disease burden, which presents the probability of occurrence of a specific disease in the remaining lifetime of a group of people for a given index age. This measure is useful for presenting the risk dynamics of a disease at the population level, which constitutes important public health information toward prevention. To date, there have been no studies investigating the LTR for coronary heart diseases (CHDs) in relation to hypercholesterolemia in Asian populations. Therefore, we estimated the LTR of CHDs according to serum low-density lipoprotein cholesterol (LDL-C). METHODS: The participants included in this study were 2,559 men and 2,848 women, enrolled in the Suita Cohort Study of urban residents followed up from 1989 to 2007 for a total of 69,823 person-years. We estimated the sex- and index-age-specific LTR for the first CHD event among participants with or without hypercholesterolemia (LDL-C ≥ 160 mg/dL), accounting for the competing risk for mortality. RESULTS: For men with hypercholesterolemia, the LTR was 47.2% (95% confidence interval [CI]: 29.3-65.1%) and 44.5% (95% CI: 21.4-68.5%) for those aged 45 and 75, respectively. The LTRs of women with hypercholesterolemia were also higher than of those without hypercholesterolemia. However, their LTRs were lower for all index ages compared to men. These results did not differ for hypercholesterolemia defined by non-high-density lipoprotein cholesterol. CONCLUSIONS: The presence of hypercholesterolemia increases the LTR for CHDs in the Japanese population, especially in men. This estimate can be used in preventive knowledge translation efforts at the population level.

Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics.

OBJECTIVE: Stroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis. DESIGN: Systematic review and meta-analysis. DATA SOURCES: A detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models. ELIGIBILITY CRITERIA: All studies that developed stroke prediction models in populations with diabetes were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic. RESULTS: Our search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68. CONCLUSIONS: Overall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models' predictive ability and further external validation of the existing models in diverse population to improve generalisability.

Prognostic tools for cardiovascular disease in patients with type 2 diabetes: A systematic review and meta-analysis of C-statistics.

BACKGROUND: Diabetes is associated with an increased risk for cardiovascular diseases (CVD). Risk prediction models are tools widely used to identify individuals at particularly high-risk of adverse events. Many CVD risk prediction models have been developed but their accuracy and consistency vary. OBJECTIVE: This study reviews the literature on available CVD risk prediction models specifically developed or validated in patients with diabetes and performs a meta-analysis of C-statistics to assess and compare their predictive performance. METHODS: The online databases and manual reference checks of all identified relevant publications were searched. RESULTS: Fifteen CVD prediction models developed for patients with diabetes and 11 models developed in a general population but later validated in diabetes patients were identified. Meta-analysis of C-statistics showed an overall pooled C-statistic of 0.67 and 0.64 for validated models developed in diabetes patients and in general populations respectively. This small difference in the C-statistic suggests that CVD risk prediction for diabetes patients depends little on the population the model was developed in (p = 0.068). CONCLUSIONS: The discriminative ability of diabetes-specific CVD prediction models were modest. Improvements in the predictive ability of these models are required to understand both short and long-term risk before implementation into clinical practice.

Chronic kidney disease in type 2 diabetes: Does an abnormal urine albumin-to-creatinine ratio need to be retested?

OBJECTIVE: To determine the positive predictive value (PPV) of a single random abnormal urine albumin-to-creatinine ratio (ACR) compared with repeat test results in patients with type 2 diabetes to diagnose chronic kidney disease (CKD). DESIGN: Retrospective, longitudinal secondary data analysis using Calgary Laboratory Services data. SETTING: Calgary, Alta. PARTICIPANTS: Patients aged 21 and older with a new diagnosis of diabetes in the study period from January 2008 to December 2015 and with a first abnormal urine ACR followed by another ACR test completed within 120 days. MAIN OUTCOME MEASURES: The PPV of an abnormal urine ACR (2 to 20 mg/mmol) to diagnose CKD was calculated. A test result was considered a true positive if a subsequent positive test result (≥ 2 mg/mmol) was identified within 120 days of the first positive test result and a false positive if 2 subsequent negative test results were identified within the same time period. The relationship between the first and second urine ACR values to assess the probability of the second urine ACR being abnormal (≥ 2 mg/mmol) based on the values of the first abnormal urine ACR was also explored. RESULTS: The PPV of the first abnormal urine ACR between 2 and 20 mg/mmol to diagnose CKD was calculated at 96.80% (95% CI 95.37% to 98.21%). Additionally, there was increased predictive probability of the second urine ACR being abnormal at higher values of the first urine ACR (2 to 20 mg/mmol). The data were further analyzed to exclude test results with a new or changed prescription of angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker medications around the time of the first urine ACR test to focus results on screening and not treatment response. With these exclusions, the PPV for first urine ACR between 2 and 20 mg/mmol to diagnose CKD was calculated as 96.23% (95% CI 94.13% to 98.32%). CONCLUSION: The first random abnormal urine ACR has a good PPV for the diagnosis of CKD in patients with type 2 diabetes, so multiple random urine ACR tests might not be necessary to diagnose patients with type 2 diabetes as having persistent microalbuminuria and CKD. A simpler diagnostic model for diagnosing renal disease might improve patient compliance, efficiency of testing, and implementation of health interventions. Reduced testing would also be expected to result in reduced cost from a health care expenditure perspective.