Introduction - Prevalence and Risk Factors: Ages 25-60
The number of cases of Type 2 diabetes has prominently increased day by day in densely populated areas. So this study has concentrated on this topic which is based on the area of Dhaka of the country of Bangladesh. This research will help to identify the problem and the requirement to solve the issue and the government's Ministry of country has launched the NGO that surveyed the selected population to proceed with the study. This survey is related to the socio-demographic features of that particular region which is performed in the area of Shah Ali Thana a region of the capital of the country.
Background of the study
The incidence of Type 2 diabetes has increased in Bangladesh because of specific factors including developing urbanization, deficiency of physical activities and the alteration in dietary habits. Understanding the frequencies of the cases and associated variables is crucial to getting knowledgeable policies and information. So this research is significant because it can provide the condition of Diabetes of type 2 by focusing on the age group of 25 to 60.
Research questions
What are the frequencies of type 2 diabetes in the age group of 25 to 60?
What are the frequencies of this issue amongst the variety of subpopulations of the participants of the region: age group of 25 to 40 and 41 to 60 for males, age group of 25 to 40 and 41 to 60 for females?
What are the health-related factors with the socio-demographic features that are responsible for T2 Diabetes in Dhaka?
Research aim
The primary aim of the study is to determine the rate of the frequency of these health issues in this region. It also concentrates on recognizing the characteristics of health-related and sociodemographic are responsible for the risk of this type of diabetes. The survey has aimed to offer the Ministry of Health to design the prevention and the intervention of the specific strategies to overcome the issue.
Methods
Design of the study
This segmented household survey was conducted between January and April in Dhaka, Bangladesh to the prevalence and risk factors of type 2 diabetes by April 2024. The selected participants completed a questionnaire on sociodemographic and health information. Blood samples were collected for measurement of HbA1c. The questionnaire measured age, sex, education, family history, waist circumference, BMI, blood pressure, physical activity, and smoking status (Murphy et al.2021). The study will estimate the overall prevalence and by age and sex, and use regression modelling to identify socio-demographic and health factors associated with changes in HbA1c levels.
The method of sampling
This study used a two-stage cluster sampling method to randomly select participants aged 25-60 years from Shah Ali police station area of Dhaka, Bangladesh. In the first phase, each of the 50 households was divided into 100 groups based on survey data (Grasselli et al. 2020). In the second phase, 10 households were randomly selected from each of the 30 clusters, totalling 300 households. The data collection team visited each selected household and appointed one qualified official who randomized, simulated all eligible members and obtained consent. Eligibility criteria were no previously diagnosed diabetes, 25-60 years of age, and community residence for at least one year. If one refused, another was chosen at random.
Relating to the research questions of this study the team at the NGO has employed the cross-sectional study which surveyed the region of Dhaka. This survey focused on the age group of 25 to 60 which gathered information on the HbA1c, with the associated factors of sociodemographic and health-related. For the press, the team directed the interview to gather the measurement of HbA1c (Ciardullo et al. 2021). This survey consisted of the male and the female groups based on the two types of age classification; those are 25 to 40 and 41 to 60. This statistical procedure of the survey will employ the descriptive analysis which is based on the characteristics of the participants and their frequencies to find out the values of the standard deviation and mean of the targeted variables (Denova et al. 2020). The dataset of this process has involved different variables including the sex, age of the participants, and the family of participants who have or have not diabetes, their measurement of waist with the blood pressure. The information in the survey also included the level of exercise of their physical and the history of smoking.
Dealing with missing data
If any type of data is missing in SPSS then it needs to be careful monitoring for continuing the reliability and the strength of the analysis. The missing data from the HbA1c variables can be missed for limited sources so it needs to maintain the mechanism in the assessment of missing data. This is probably the tendency in the deficiency of information for HbA1c meanwhile which is caused by malfunction of any equipment rather than by a random participant's non-response. The reason for the data not being present depends on unobserved values so this type of scenario can be known as the missing not at the random situation or MNAR. So the techniques that should be acquired to deal with this type of scenario like listwise deletion can be the better idea in this which can be related to the data with the outcomes of the results. The sensitivity analysis can be helpful in this case for discovering the various assumptions of missing data. The reason for the missing data would be the reliable for further misinterpretation (Tatulashvili et al. 2020). So carefully monitoring the missing data and applying suitable techniques in SPSS can minimize the bias of the study.
Results
Descriptive results from the data
Figure 1: Statistics of hba1c
The above figure displays the descriptive statistics of hba1c. This figure represents the description of the dataset (Birkeland et al. 2020). It defines the mean, median, mode, std error, variance skewness, range, minimum, maximum, to sum, etc. All the described values are displayed
The above figure displays the graph of the descriptive statistics of hba1c (Salter et al. 2021). This graph is based on the descriptive value. The graph shows the frequency of the dataset of Hba1c.
Figure 3: Statistical analysis of factors of diabetes
The figure is the statistics of diabetes cases. Which displays the valid and missing cases: 262 are valid and 38 are missing (Zahra et al. 2022). It also shows the mean value, std. Deviation value, Minimum value and maximum value.
Figure 4: Prevalence of T2 diabetes among adults
The above figure displays the prevalence of T2 diabetes among adults. This figure represents the calculation table of the case processing summary and diabetes constant cross-tabulation. The case processing summary shows the cases of diabetes (Jankar et al. 2020). The cases were decided in 2 ways: the valid cases, and the missing cases. So the number of valid cases is 262 and the percentage is 87.3. And the number of missing cases is 38 and the percentage is 12.7. Total 300 cases. The constant distribution table shows the constant value of diabetes which is 262. And the percentage cons is 100%. Total also shows 100%.
Figure 5: The Summary of the scenario
The summary of the frequencies of the diabetes cases shows that the cases of the females in the age group of 41 to 60 have shown the maximum result here which is 100%. Females of the group of 25 to 40 have shown 52.7% of the cases along with 47.3% for the cases of males (Sadriddinovich, 2023). The last group of data is 41 to 60 males representing 41.2% of the prevalence of diabetes cases.
Figure 6: Variety of Subpopulation
The descriptive analysis has represented the frequencies of diabetes among males and females based on the specific age group (Mustika et al. 2022). From that one age group is 25 to 40 and the other one is 41 to 60. This analysis showed that in the case of males whose age group is 41 to 60, it offers 58 out of the cases of 141. For the cases of females, it has been found that the frequencies are 138 out of the cases 262 among the age group of 25 to 40. The prevalence of diabetes has been discovered as 1 out of one case among the age group of 41 to 60 for females in this analysis.
Figure 7: Chi-square test
The above figure displays the chi-square test. This table represents which people are addicted to diabetes. Their person chi-square value is 38.746, likelihood ratio value is 45.255, linear by linear association value is 7.173 and N of valid cases is 262 (Purwanto, 2021). The total person chi-square value is 38.746, the likelihood ratio value is 45.255, the linear by linear association value is 7.173 and the N of valid cases is 262. And person chi square Asymp value is .226, the likelihood ratio Asymp value is .076, linear by linear association Asymp value is .007
Figure 8: The statistical analysis of the predictor variables
The above figure displays the result of the logistic regression. The above figures represent a model summary (Barua et al. 2021). The model summary defines the R-value which is 707, the R-squared value as 500, the adjusted r-square value as 482 and std. The error of the estimated value is 5438.
Figure 9: ANOVA test of the predictors
The figure table shows the Anova test which represents the sum of squares value which is 7452.856 of the regression model, df value is 9, means square value is 828.095, and F value 27.998. The residual model sum of squares value is 7453.469, the Df value is 252, the mean square value is 29.577 and the f value is 27998 (Islam et al. 2020). So the total value of the sum of squares is 14906.324 and the df value is 262
Figure 10: Coefficient of Logistic regression
The above figure displaces the coefficient of logistic regression (Mannan et al. 2021). This figure table displays the models, unstandardized coefficient standardized coefficient. In the unstandardized coefficient B value and Std error value are shown in the model and in the standardized coefficient Beta values are displayed. Also, values of t and values of sig are shown for the model.
Discussion
Description of the key findings
The descriptive analysis of the Hba1c test offers a broad elaboration of the levels of this test which include the values of the median, mean, variance, and the values of the maximum and minimum prevalence (Sadia et al. 2020). Hba1c is the test which helps to diagnose type 2 diabetes in the body. The major findings of the analysis of those results are explained in this section. The statistical analysis of the dataset is based on the frequencies of T2 diabetes amongst adults in the area of Dhaka. It has revealed that 87.3% were effective from all of the cases and 12.7% were the missing evidence. The results have represented the prevalence among a variety of subpopulations. From the results it has shown that the female responses with the highest prevalence of the cases. The chi-square analysis helps to discover the link between the factors and dependence on diabetes which can indicate the p value of less than 0.05. The logistic regression evaluates the variables to express the value of R squared which is 0.50. The importance of the coefficient is evaluated by the Anova test of the provided dataset.
Description of the weakness and strength points of the method
The strengths and the weaknesses of the analysis are emphasized with the cross-sectional study which aids the prevalence of type 2 diabetes (Mannan et al. 2021). The sociodemographic factors with the other factors which can affect the cases of diabetes are also strengths of the evaluation. The evaluation and the analysis are done in the Spss software to get better findings and graphs based on the survey. The missing cases of the data are another weak point of the analysis. The interpretation will be more effective if the number of cases is more valid here.
The importance of the result for public health policies
The outcomes of the result signify the position of the public health sector in the country of Bangladesh. There are required superior intervention policies for the females of age group 41 to 60 years. So there should be implementation of specific strategies to evaluate the study more prominently and recognise the responsible factors of this issue. Type 2 diabetes in countries of low to middle income is analyzed here with gender-specific involvements. Many variations may happen during the assessment according to the factors of the sociodemographic.
Analytical Strategy and Methods
Demonstration of suitable methods can make the study more informative by focusing on the aim of the study, and the design of the research which are justified to the adopted research questions. This section of this study helps to explore the description of the methods that are followed here to get the result consisting of the reason for the selection of those methods into this study.
Description of the Analytical Methods
The elected inferential logical techniques can justify the questions on which the study is focused, the strategies of the study, and the features of the gathered information (Barua et al. 2021). To recognize the frequencies of the incidence of this particular health issue surveyed the adults of the region of Bangladesh and also explored to differentiate the level between the various subpopulations (Mustika et al. 2022). Linear regression has been employed in this survey to evaluate the factors of health related to the risk of T2 diabetes. It can investigate the synchronized impact of factors like age, gender, education, family history of having diabetes, the circumference of the waist, body mass index or BMI, systolic blood pressure, physical activity, and smoking status on HbA1c levels because this method accommodates multiple interpreter variables.
Demonstration of choosing the method
The imitate variables suggested here the education, sex, the level of physical activity along the family history which can certify the regression analysis. The implication of those variables helps to explore the incorporation of the impact of the risk factors of the health issue in specific participants of the area. This study has been analyzed through the Spss software to get accurate results and a better understanding of the frequencies of the variables. The statistical analysis has helped to discover the elements responsible for the population of Dhaka.
Conclusion
In conclusion, it can be concluded that the study of the frequencies of type 2 diabetes can be effectively analyzed through the assessment by Spss software. There are a variety of evaluations based on the dataset of the shah Ali Thana of Dhaka evaluated through this analysis. This study has represented the Anova test, and Chi square test with regression to encompass the questions of the research on which this study is focused on. This analysis effectively evaluated the prevalence of diabetes and which group can give the higher prevalence it has also established through this study.
Overwhelmed by multiple assignments and don’t know where to start?
Let Professional Assignment Writers help you take control of your academic workload. Their strategic approach to research, planning, and writing means you receive high-quality, structured assignments without the last-minute stress. It’s the smart way to stay on top of your coursework and succeed academically.
References
Journals
Barua, L., Faruque, M., Chowdhury, H.A., Banik, P.C. and Ali, L., 2021. Health?related quality of life and its predictors among the type 2 diabetes population of Bangladesh: A nation?wide cross?sectional study. Journal of diabetes investigation, 12(2), pp.277-285.
Birkeland, K.I., Bodegard, J., Eriksson, J.W., Norhammar, A., Haller, H., Linssen, G.C., Banerjee, A., Thuresson, M., Okami, S., Garal?Pantaler, E. and Overbeek, J., 2020. Heart failure and chronic kidney disease manifestation and mortality risk associations in type 2 diabetes: a large multinational cohort study. Diabetes, obesity and metabolism, 22(9), pp.1607-1618.
Ciardullo, S., Monti, T. and Perseghin, G., 2021. High prevalence of advanced liver fibrosis assessed by transient elastography among US adults with type 2 diabetes. Diabetes Care, 44(2), pp.519-525.
Denova?Gutiérrez, E., Lopez?Gatell, H., Alomia?Zegarra, J.L., López?Ridaura, R., Zaragoza?Jimenez, C.A., Dyer?Leal, D.D., Cortés?Alcala, R., Villa?Reyes, T., Gutiérrez?Vargas, R., Rodríguez?González, K. and Escondrillas?Maya, C., 2020. The association of obesity, type 2 diabetes, and hypertension with severe coronavirus disease 2019 on admission among Mexican patients. Obesity, 28(10), pp.1826-1832.
Grasselli, G., Greco, M., Zanella, A., Albano, G., Antonelli, M., Bellani, G., Bonanomi, E., Cabrini, L., Carlesso, E., Castelli, G. and Cattaneo, S., 2020. Risk factors associated with mortality among patients with COVID-19 in intensive care units in Lombardy, Italy. JAMA internal medicine, 180(10), pp.1345-1355.
Islam, S., Rahman, S., Haque, T., Sumon, A.H., Ahmed, A.M. and Ali, N., 2020. Prevalence of elevated liver enzymes and its association with type 2 diabetes: A cross?sectional study in Bangladeshi adults. Endocrinology, diabetes & metabolism, 3(2), p.e00116.
Jankar, J.S., Harley, K.N., Mohod, K.M. and Babar, V.Y., 2020. Association of urinary albumin with hba1c levels in subjects of type 2 diabetes mellitus in Central India. Journal of Evolution of Medical and Dental Sciences-JEMDS, 9(52), pp.3921-25.
Mannan, A., Hasan, M.M., Akter, F., Rana, M.M., Chowdhury, N.A., Rawal, L.B. and Biswas, T., 2021. Factors associated with low adherence to medication among patients with type 2 diabetes at different healthcare facilities in southern Bangladesh. Global health action, 14(1), p.1872895.
Murphy, H.R., Howgate, C., O'Keefe, J., Myers, J., Morgan, M., Coleman, M.A., Jolly, M., Valabhji, J., Scott, E.M., Knighton, P. and Young, B., 2021. Characteristics and outcomes of pregnant women with type 1 or type 2 diabetes: a 5-year national population-based cohort study. The lancet Diabetes & endocrinology, 9(3), pp.153-164.
Mustika, I., Nabella, S.D. and Mulyadi, M., 2022. DATA-PROCESSING TRAINING USES A SPSS APPLICATION FOR IBNU SINA UNIVERSITY MANAGEMENT STUDENTS. International Journal of Engagement and Empowerment, 2(2), pp.179-183.
Purwanto, A., 2021. Education research quantitative analysis for little respondents: comparing of Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. Jurnal Studi Guru Dan Pembelajaran, 4(2).
Sadia, L., Barua, L., Banik, P.C. and Faruque, M., 2020. Physical activity levels, its barriers, and associated factors among the patients with type 2 diabetes residing in the capital city of Bangladesh. Lifestyle Medicine, 1(2), p.e14.
Sadriddinovich, J.T., 2023. Capabilities of SPSS Software in High Volume Data Processing Testing. American Journal of Public Diplomacy and International Studies (2993-2157), 1(9), pp.82-86.
Salter, A., Fox, R.J., Newsome, S.D., Halper, J., Li, D.K., Kanellis, P., Costello, K., Bebo, B., Rammohan, K., Cutter, G.R. and Cross, A.H., 2021. Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis. JAMA neurology, 78(6), pp.699-708.
Tatulashvili, S., Fagherazzi, G., Dow, C., Cohen, R., Fosse, S. and Bihan, H., 2020. Socioeconomic inequalities and type 2 diabetes complications: A systematic review. Diabetes & metabolism, 46(2), pp.89-99.
Zahra, Z., Ramadhani, C.T., Mamfaluti, T., Pamungkas, S.R. and Firdausa, S., 2022. Association between depression and HbA1c levels in the elderly population with type 2 diabetes mellitus during COVID-19 pandemic. Narra J, 2(1).
