The research was carried out to determine the link between heavy drinking and dying earlier age. The study was carried out at an appropriate time and thus the best findings were encountered. Information obtained entailed death for any reason and death due to heavy drinking among the participants. The research was performed in the form of a prospective cohort study. It was stated clearly that drinking alcoholic drinks heavily is part of the cause of premature deaths.
The population of interest is adults aged eighteen to thirty-six years. Since this age is more vulnerable to heavy alcoholic drinking which leads to reduced lifespan. People within this age bracket enjoy more freedom and may misuse it by engaging in drinking alcohol (Shin et al., 2020). The majority of the population claims that taking alcoholic drinks tends to relieve them from stress and hence feel more relaxed. This is not usually the case as there have been a lot of premature deaths mainly resulting from the high intake of alcoholic drinks.
The sample would entail the population of interest as it will represent the entire population within the study area. When research is carried out using representatives, the findings will be generalized to the entire population (Shin et al., 2020). This will enable easier understanding of information such as the minimum number of people who drink alcohol heavily per a thousand people. The sample is effective as it minimizes the time wasted in reaching the entire population.
The outcome variable refers to the death irrespective of the cause and the number of people who died can be estimated by counting the death certificates. Death of any cause is usually analyzed thus enabling the researcher to know the number of people who die from any cause (Shin et al., 2020). Out of the total population of people who die due to different reasons, it enables the researcher to identify people who die due to heavy drinking.
Key variables to be considered include heavy drinking and age. Years help to determine the age factor thus the two variables focus on the investigation (Schabath & Cote, 2019). Confounding variables refer to health, social economic status, and lifestyle characteristics. These factors are used because they have a link between heavy drinking and early death. These variables will also form the basis of the investigation as they have a prominent level of research.
Sources of bias include self-reporting of alcohol consumption and incorrect designation of the cause of death. Incorrect classification of the cause of death results in over-reporting that alcoholic drinkings are the major cause of death (Shin et al., 2020). Self-reporting of alcohol intake results in under-reporting thus resulting in biasness. The approach of data collection has been done by conducting interviews and medical records. Self-reported alcohol consumption information was done by interview whereas the cause of death was done through medical records.
The recollection bias was the great limit encountered since the participant may fail to recall the amount of alcohol that they consumed due to memory lapse. They may also decide to be dishonest about the amount of alcohol consumed leading to the collection of wrong data. Confounding factors are the other limits that lead to biased information especially if the link between heavy drinking and early death was altered by other variables.
In conclusion, drinking heavily has been a major cause of earlier death. Due to the weakening of the immune system by excessive alcohol intake, the body can not fight against disease-causing microorganisms hence becoming vulnerable to attack by diseases. Policies such as an increase in taxes on alcoholic drinks should be embraced to ensure their consumption is limited. This will help to increase the lifespan of the people.
References
Schabath, M. B., & Cote, M. L. (2019). Cancer progress and priorities: lung cancer.Cancer epidemiology, biomarkers & prevention, 28(10), 1563-1579. Web.
Shin, H., Oh, S., Hong, S., Kang, M., Kang, D., Ji, Y. G., Choi, Y. (2020). Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes.ACS nano, 14(5), 5435-5444. Web.