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  1. The purpose of the research conducted by Ataey et al (2020) was to examine the nature of the interaction between the cases of obesity, and overweight, alongside the human development index among nations classified under the Eastern Mediterranean region and under the radar of the World Health Organization.  
  2. The research utilized quantitative data obtained from the World Health Organization’s as well as the United Nation’s websites. Specifically, the researcher’s leveraged the world health organization website to obtain publications on the overweight prevalence among individuals above the age of 18 years organized alongside their gender. The United Nation’s platform provided information concerning the body mass index.  Overweight was classified as having a body mass index equal to or exceeding 25.0 kg/m2 whereas the cases of obesity were classified as body mass index which is either identical to or exceeding the 30.0 kg/m2 threshold. The data collected was collected across 22 nations within the Eastern Mediterranean region also referred to as EMR nations. Conversely, the data were analyzed using the statistical package for social scientists version 24 to conduct both descriptive and inferential analysis. The descriptive analysis was used to find the mean and standard deviation of the study parameters. The inferential analysis entailed the correlation analysis and regression analysis to obtain the nature of the association between the body mass index, obesity, as well as overweight.
  3. The correlation among the three factors is deemed to be between moderate and strong. Overweight and obesity are both positively correlated with BMI and HDI, meaning that as one increases, so does the other. This is especially true for women, whose correlation between overweight/obesity and BMI/HDI is stronger than that of men.
  4. For the data presented in table 2, factorial ANOVA was used. This was appropriate since there was more than one dependent factor. The table illustrates that obesity and overweight are associated with lower human development index scores.
  5. The study concluded that the human development index and obesity were positively and significantly correlated. Additionally, the policymakers should be aware of potential cases of overweight as well as obesity brought about by the development agenda.
  1. The research design of this study is a multicohort study. The study looks at data from multiple cohorts in order to study the relationship between obesity and loss of disease-free years attributable to primary non-communicable illnesses.
  2. Nyberg et al. (2018) considered the following demographic characteristics for the people in the sample: gender. The descriptive statistics were analyzed alongside the various groups that are according to socioeconomic position, smoking status, as well as the physical activity of the participants.
  3. Considering figures 2, 4, and 5, the outcome variable in all three figures is the “number of disease-free years lost due to obesity.” The statistic is the percentage of “disease-free years lost due to obesity.”
  4. The researchers found that participants with a BMI of 40 or higher had an average of fewer disease-free years with 40 years as the reference age than participants with a BMI of 25 or lower.
  5. Figure 3 is a bar graph illustrating the distribution of the prevalence of obesity, smoking, as well as physical inactivity analysed as per the socioeconomic status of the participants. Therefore, the variables in this graph include the prevalence of obesity, smoking, as well as physical inactivity grouped as per the socioeconomic status. The descriptive statistic illustrated by figure 3 is therefore cumulative frequency of the stated variables.
  1. The aim of the study is to examine type 1 and type 2 diabetes in relation to cases of COIVD-19 deaths in England. The null hypothesis is that there is no significant correlation between type 1 and type 2 diabetes and cases of COIVD-19 deaths. The alternative hypothesis is that there exists a significant correlation between cases of COVID-19 mortality and the two types of diabetes.
  2. The statistical analysis used in this study was multivariable logistic regression analysis. This type of analysis is used to examine the effect of one or more independent variables on a binary dependent variable, in this case, in-hospital death with COVID-19. The logistic regression analysis is appropriate in this case because it can handle dichotomous dependent variables and because it is not affected by outliers in the data.
  3. For those with type 1 diabetes, the odds ratio of in-hospital mortality related to the COVID 19 pandemic was forecasted to have an average of 3.51 which was significantly higher than the odds ratio for those with type 2 diabetes which was averaged at 2.03. The odds ratio for both type 1 and type 2 diabetes were found to be substantially higher compared to odds that of people without diabetes. The odds ratio were arrived after adjusting for the following factors; age, gender, ethnical background, deprivation as well as geographical area. A subsequent adjustment with regards to hospital admissions associated with coronary heart complications, heart failure, or cerebrovascular illness resulted in attenuated odds ratio of 2.86 for the type 1 diabetes whereas type 2 diabetes had an attenuated average odds ratio of 1.80.
  4. The findings are not generalizable to the population because the study only looked at a specific group of people diagnosed with the two types of diabetes. In essence, it is unclear to what extent the findings are generalizable because of several limitations associated with the study. First, Barron et al (2020) were only able to include individuals who had type 1 or type 2 diabetes diagnosed by a healthcare professional. Secondly, the definition of death resulting from COVID-19 was based on clinical observations that might be subject to incomplete ascertainment and misclassification at both patient and clinician level (see 'Strengths and limitations of this study' section). Thirdly, crucial and feasible confounders for instance smoking, the body-mass index, and comorbidities were not available in primary care records included in CPRD. Fourthly, the researchers were unable to discriminate between COVID-19 death occurring at home and deaths occurring in hospital for both patients with and without diabetes. Additionally, the study also did not include patients that may have had COVID and recovered in the community and therefore their exclusion affected the generalizability of the findings.
  5. In this study, the main finding was that people with type 1 or type 2 diabetes have an elevated susceptibility to COVID-19-associated deaths. In principle, the study found that in England, the trends and magnitude of type 1 and type 2 diabetes-related mortality were similar to those elsewhere in Europe, but that the risk of disease-related death was higher among people with type 1 than type 2 diabetes.
  1. The research focussed on investigating the efficacy of lifestyle intervention on morbidity as well as mortality in persons suffering from impaired glucose tolerance. As such, the authors set to test the hypothesis that lifestyle intervention can prevent or delay type 2 diabetes, and its micro-vascular as well as macro-vascular challenges. The null hypothesis is that there is no difference in morbidity and mortality between the intervention and control groups. The alternative hypothesis is that the intervention group has lower morbidity and mortality than the control group.
  2. The four primary outcomes as shown in figure 2 include the following; the number of cases of diabetes, the number of cases of cardiovascular disease, all-cause mortality, and finally cancer mortality. The number of cases of cardiovascular disease was established in 195 correspondents who were in the intervention group alongside a cumulative incidence of 52.9 percent whereas, in the control group, the CDV was found in 135 participants alongside a 66.5 percent cumulative incidence. The all-cause mortality had a hazard ratio of 0.74, with a 0.67 hazard ratio for cardiovascular disease mortality. The all-cause mortality had a hazard ratio of 0.74 whereas the number of cases of diabetes had an HR of 0.61. with the significance value of the four outcomes being less than 0.05, the study found that the lifestyle intervention was correlated with a lower incidence of diabetes, a lower prevalence of cardiovascular disease, a lower all-cause mortality rate, and a lower diabetes-related mortality rate.
  3. The study found that lifestyle intervention significantly reduced the risk of developing diabetes and was associated with a hazard ratio of 0.61 (p<0.0001).
  4. The intervention which encompassed dietary change, exercise, and weight loss helped people with impaired glucose tolerance to improve their diet and lifestyle and as a result, their health improved and decreased the risk of contracting diabetes.
  5. The several limitations associated with the study include the small size of the sample, the authors failed to conduct systematic evaluations at pre-determined time-lapse on the correspondents, and there was a lack of data on the maintenance of acquired lifestyle as the trial intervention came to an end (Gong et al. 2019). The last limitation was the lack of generalizability of the findings since the researchers concentrated on type 2 diabetes prevention.

References

Ataey, A., Jafarvand, E., Adham, D., & Moradi-Asl, E. (2020). The relationship between obesity, overweight, and the human development index in world health organization eastern mediterranean region countries. Journal of Preventive Medicine and Public Health, 53(2), 98.

Barron, E., Bakhai, C., Kar, P., Weaver, A., Bradley, D., Ismail, H., & Valabhji, J. (2020). Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study. The lancet Diabetes & endocrinology, 8(10), 813-822.

Gong, Q., Zhang, P., Wang, J., Ma, J., An, Y., Chen, Y., & Roglic, G. (2019). Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. The lancet Diabetes & endocrinology, 7(6), 452-461.

Nyberg, S. T., Batty, G. D., Pentti, J., Virtanen, M., Alfredsson, L., Fransson, E. I., & Kivimäki, M. (2018). Obesity and loss of disease-free years owing to major non-communicable diseases: a multicohort study. The lancet Public health, 3(10), e490-e497.

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