Measuring the impact of information and awareness on the life expectancy and the quality of life of the men population

Published: 17 March 2022| Version 2 | DOI: 10.17632/s24fbz7kr5.2
Contributor:
Lior Eli PhD

Description

LE (life expectancy) of the general and the male population is steadily rising. Over the last centuries and especially in recent decadesת there has been a dramatic change according to the evolution and progress of humanity and its derivatives (Upadhyaya, Kepplinger, 2014; Lutz, Qiang, 2002; Macrotrends, 2021). Similarly, LE for men in Israel has risen dramatically, being among the highest in the world (United Nations, 2019) except for a small decline recently observed due to the Covid-19 pandemic (Weinreb, 2021). The many effects that can be attributed to these revolutions can also be attributed to the phenomenal vector of change in QOL (quality of life) and LS (lifestyle) (Clarfield, 2018; Catillon, Cutler, Getzen, 2018). This has also led to exposure to new harmful factors affecting health and LS. Despite this, LE has risen and continues to so, and the assumption is that there are other significant variables beyond revolutions, progress, science and research, budgets, services, etc. that affect the trend like, genetics, socioeconomic status, quality and availability of health services etc. (Clarfield, 2018; OECD, 2019). These can be linked to other components in the social, behavioral, and perceptual personality, divided into categories of BIO (biography), MED (medical), and LS, assuming that these also have a significant impact. It is assumed that information and awareness have an positive impact on LE and QOL. Examining the effect of it on men's LE and QOL, we focused the study on two key questions - Is there an effect of independent variables in the BIO, MED and LS categories on the dependent parameters of LE and QOL for the male population participating in the study? What are the variables with the most influential information and awareness exposure for QOL and LE from the categories examined - BIO, MED, LS for the male population participating in the study? There is a positive relationship between awareness and the desire to change the characteristics of education as well as the characteristics of employment if these affect LE and QOL. Marital status was found to be one of the most significant variables affecting LE and QOL. Marriage has a positive effect on many sub-variables and especially in the effect on men, and these together contribute to the improvement of LE and QOL. In order to influence LE, there is a link between information and awareness, and a desire to make a change in weight and carry out vaccinations (including among those who take medication regularly and may be at higher risk for the disease). The high awareness of the effect of smoking is noticeable and this is reflected in the low percentage of smokers (in the population of participants). Apparently a small and measured consumption of alcohol on a weekly basis causes a positive effect on LE. Calculation by linear and exponential functions for understanding the future LE trend among men in Israel showed a high correlation to constant increase and high longevity.

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This study collected a sample of hundreds of participants who after a validation process were reduced to over 200 people, with the target population being men aged 60 and over. This population filled out questionnaires and even conducted interviews with them and / or their relatives. The entire study population lives in Israel. The information of the group of respondents was classified according to three main categories for which a comprehensive collection and analysis was performed which covered the main topics relevant to the study in each category - BIO, MED, LS and which were defined as independent variables. For these variables, matching was tested against the categories defined as dependent variables - LE, QOL. The questionnaire, distributed to the target population and covered the categories for research, was designed in the Google Forms tool and included features pertaining to each of the categories. The tools of Excel and JASP were used by us for descriptive statistical analysis while the JASP tool was also used to measure correlation between the variables. The calculation of the study population size is done using the MonkeySurvey tool. A process of examining, reviewing and verifying the data collected was performed to verify their accuracy and correctness. Manual adjustment of the data was done only in case of discrepancies. Also, a nominal scale is converted to a numerical scale. All of the above is done before beginning the data analysis process. A statistical analysis of the data was performed which included key measurements including mean, frequency and median. A statistical analysis of the range of data was also performed and included standard deviation, standard distribution, variance and quarterly information. In the relationship analysis, an attempt was made to find strong relationships between the independent variables BIO, MED, LS, and the dependent variables - LE, QOL. To measure the relationships and correlations between the variables, the following methods were used: the Spearman correlation, the Pearson correlation, and the Kendall correlation. Scatter plot - After the measurement phase, scatter plots were used for the variable pairs compared to finding the ratio. Finding Reasons for Strongly Related Variables - To find the reasons for noting high correlations, the top ten variables were collected and researched. After the process, conclusions and suggestions were formulated. Based on LE data of men in Israel for the years 1950 to 2000, as well as from LE data in the world (men and women) for the years 1770 to 2019, we performed a statistical analysis of LE for men in Israel up to 2100 using linear and exponential functions in order to understand the LE trend among men In Israel and what is LE's appreciation for men in Israel in the near and distant future.

Categories

Genetics, Education, Life-Span Study, Life Sciences, Risk Management, Business Administration, Alcohol Consumption, Medical Care, Socioeconomic Factor in Health, Socioeconomic Status, Biology of Gender, Self Awareness, Life-Span, Business Analytics, Life Style, Business Informatics, Life Expectancy, Information, Marital Status, Biography

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