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COVID19 First 100 Cases in New Mexico: Was There a Gender Difference?

The COVID-19 First 100 Cases in New Mexico: Was There a Gender Difference?

Executive Summary

COVID-19 has become a global pandemic infecting almost every country in the world. The first case in New Mexico was found in early March this year. The distribution of the disease was, however, unpredictable because of various factors like community transmission and local transmissions (Wise, 2020). However, from the first 100 cases, the highest number was from people over the age of 40. The data was collected following the DOH update in New Mexico. However, due to various factors, the department is yet to define the exact number of positive individuals. The average age of infected people is about 47.1. In the research process, I used qualitative analysis in prone locations. To find the results, I used two statistical tests and analysis; the independent sample t-test and Chi-square analysis. In the Chi analysis, I was interested in finding if the distribution of females and males was equal. The calculated mean age is 47.1. This means that the 95% confidence level of most of the cases is between 43 and 51. From the analysis, we can say that COVID-19 does not discriminate on gender or age. This implies that everyone is vulnerable to the disease, nevertheless of the health conditions of an individual.

 

The COVID-19 First 100 Cases in New Mexico: Was There a Gender Difference?

Introduction

            COVID-19 has become a global pandemic infecting almost every country in the world. The first case in New Mexico was found in early March this year. The disease is prone to every gender and age. Based on the fact that older people are the ones who are mostly infected, people tend to think that the disease affects only the seniors (Prince et al., 2020). However, people with pre-existing health problems like heart disease, diabetes, and asthma are more vulnerable to be seriously ill from the virus. Thereby all genders and age groups must take precautions and adhere to the protective measure. However, the exposure of the virus in New Mexico varied with gender. Males had the highest number of infections compared to that of females. This is much because of exposure and day to day activities.

From the research, males are (precisely those over 40 years) prone to crowded places as compared to women. However, the difference was so significant because the number was almost alike. The distribution of the disease was, however, unpredictable because of various factors like community transmission and local transmissions. However, from the first 100 cases, the highest number was from people over the age of 40. This is more because of the modes of interaction. Places such as bars and restaurants could be more vulnerable places for infections (Qin et al., 2020). The majority of old individuals spend most of their time with friends in these places, thus making them more vulnerable to the disease.

These are some of the exceptional reasons why the first 100 cases hit mostly older adults in New Mexico. However, by people learning about the modes of transmission of the disease, the age has now become an insignificant variable. From the first 100 cases, there are males under the age of 20s who were infected. Much of the exposure was based on the habits of the people. The disease is only transmitted by close interaction with an infected individual (Schatz and Bashroush, 2018). This means that the first people to acquire the disease had such significant interactions. Females under the age of 30 have fewer interactions with people hence making them safe in some circumstances. In this research, the independent variable is gender, whereas the dependent variable is age. This paper focuses on finding the number of males to females in the first 100 cases in New Mexico. The paper also examines facts about the infection and showing the age that is much prone to the infection. The objective of the report is to evaluate the significance of the infection while looking at certain variables, like gender and age, which might either be dependent or independent.

Data Description

The data was collected following the DOH update from New Mexico. The update is based on the location of the infected, age, and gender. However, my concern is the age and gender of the infected. The DOH provides the daily updates and announcement of the new positive cases in New Mexico. There are also some of the restrictions information that helps people to moderate the rates of infections and, at times, transmission. The investigations done by the department of health include contacts tracing and finding people who may have had any contact with a reported case (Nott, 2020).

However, due to some factors, the department is yet to define the exact number of positive individuals. The average age of infected people is about 47.1. This shows a dramatic significance on the older adults most precisely because of their habits. From the data then it is evident that the most exposed people are senior adults. Again from the gender perspective, infected males are more compared to those of infected females. The ratio of infected females to males is 2:3. This shows that for every two infected females, there are three males. The age varies from the location. Some counties have almost only older adults with the infection, while others have mixed ratios.

Methodology

In the research process, I used qualitative analysis in prone locations. I first started by estimating the cumulative number of infected cases in New Mexico after the first case was reported. However, in the data collection and analysis process, I focused on two variables: age and gender. This means that I had to accumulate all the findings and compile them to get the exact values. In the research process, I used the number of infected people and traveling individuals to have a cumulative probability of their distribution in New Mexico. The other method I used was to compile the data from DOH. Some of the findings I acquired were that among the first 100 cases, there were missing data. From their database, the department failed to report the gender on March 19th. This implies that my research lacks seven genders, thereby making the number to be 93. Additionally, in the age, the seven missing genders and one more from the 93 were missing. This makes the age samples to be N-92. We also removed the ID 71 case because it was a false positive.

The main goal of the project was to focus on the exact age of the infected people. However, the government only reported the age in brackets of 10 years. For us to get the exact estimate of the age of the individuals, I took the midpoint of every ten year age group and used it as an approximate. For instance, an individual who was coded to be 50 years old was taken as 55. In cases where the data describes the age, like for instance, when a man died and was said to be in his late 70s, then he was coded as being 78. However, there were two cases where the age was exact. I then calculated a mean value of the age in the samples and a 95% confidence interval around the mean. I preferred using this methodology because it was precise on the visual (Gould, 2020). This implied that I was 95% confident that the ideal population means between the upper bound and the lower bound of the 95% confidence interval.

Some of the things I did were like running the independent samples tests between gender and age. This information in the analysis is crucial because I am comparing the mean ages of males and females in the first 100 cases in New Mexico. The primary interest in the research is checking if there is an average difference between the two variables. However, the tests would be precise in cases where we have the exact number of age. The analysis is thereby appropriate test if the mean age of males and females is different or the same (Beeching, Fletcher and Fowler, 2020). I later ran the data in SPSS and got this outcome. Most of the data was excel that was imported into SPSS for the analysis.

Results

In this part, I used two statistical tests and analysis; the independent sample t-test and Chi-square analysis. In the Chi analysis, I was interested in finding if the distribution of females and males was equal. The Chi analysis is mostly used in cases where the data is categorical or discrete. The primary use is thereby examining if the two variables are independent of each other or not. To be independent means that the two factors are not related. When the independence of the two variables is ruled out, then the Chi-square is used to outlook if the variables are certainly dependent or not. The Chi-square begins with several assumptions, hypotheses, and a sampling distribution (Khan, Qamar, and Bashir, 2016). Most of the assumptions here should be straight forward, and the data must be randomly selected. Hypothesis in Chi analyses has the research hypotheses and general null. In the null hypothesis, there is no relationship between age and gender, while in the research hypothesis, age and gender are correlated.

The independent sample t-test evaluates and compares the relationship between two different groups under ideal conditions. Like in this instance, we are trying to identify the first 100 cases based on gender and age, which is our dependent variable. However, the test also can help us understand whether the difference is based on gender. Yet, an independent sample t-test can only be done when the data follow the assumptions to get a different result (Shepherd, 2017). These assumptions include; the dependent variable (age) should be measured on a continuous scale. The age is estimated in years, thus making it on a constant range. The second assumption is that the independent variable should be of two categories. In this case, gender can be categorized to males or females, thus making it fit in the assumption. The third assumption is based on the observation of independence. This means that there should be no correlation between males and females. The other assumption is that there should be no significant outliers. This means that the data collected should follow a specific pattern. The age should also be normally approximately distributed for the males and females. The last assumption is that there is a need to be homogeneity of variances. This implies that our data follows all the assumptions, which means that the data could undergo an independent sample t-test.

After having the two tests, we found that the mean age was 47.15. This means that 95% confidence interval for the mean age of this data is 43-51.3. The number represented was 93 genders (both females and males). Line 101 is the Case ID that belongs to the 78-year-old male who was the fatality of COVID-19.

Histogram (Diagram 1)

There are gaps from the in the chart. This is because of the estimate made. For instance, the age of 20 was taken as 25, and age 40 was chosen as 45. This provided a systematic approximate of the data. Additionally, from the assumptions in the t-test, dependent variables are typically distributed reasonably bell-shaped based on the limitations discussed.

Bar Graph (Diagram 2)

            In the above illustration, there are possibly 9 cases that were represented to be 75 years old. However, there is 1 case at 78 years old and five more cases at 85 years old. This represents cases that are over 70 years of age. This means that the death rate in New Mexico is 1/15 for people over the age of 70 years in the first 100 cases, which is 0.066. From the statistics, there is no death case under the age of 70; thus, the death rate is 0. Therefore by calculating the case mortality rate, then 1/15 is equal to 6.67% case fatality for the people. This implies that from the analysis, the mortality rate by age is 6.5 times greater for the group.

Discussion

The calculated mean age is 47.1. This means that the 95% confidence level of most of the cases is between 43 and 51. However, from the statistical point of view, there is no significant difference between these. By combining the statistics in New Mexico regardless of the gender, then we can say that the most frequent victim target is those with the age of 47. The modes support the notion that Senior Citizens are not the only ones who are vulnerable to infections (Lancet, 2020). Moreover, the calculation of the mode is 35 in New Mexico.

From the government and DOH, the disease is vulnerable to all ages. Most of the data given out in New Mexico shows that the seniors have the most cases. The macro-level, in this instance, looks at the sociology of change and stability. From the government and DOH databases, the infection is increasing. The macro-level sociology thereby outlooks on small-scale interaction between people most precisely in group dynamics and conversations. The government and DOH tend to find modes of attraction to locate positive individuals.

The mezzo in the community state department of health shows how the community works directly with much smaller groups and family members (Moran and Valle, 2016). However, they assert that the infection is paramount and that it does not segregate the age. This is because age is a dependent variable. From micro levels instances, the virus is predominant to all ages, but the modes of infection state the difference. They assert that older people are vulnerable than younger people. The disease has, however, impacted all the systems because it has significantly changed the modes of operations. Everyone starting from the health centers to the government offices have to minimize their interaction with their colleagues. Ideally, there is a strong claim that older people are the only ones getting this disease.

Limitations

There are several limitations to the research process. Being able to understand the underlying conditions people were dealing with would have helped build a more cogent argument against the victims and COVID-19. This is because we could not tell other possible infections that could have led to the 78-year-old man dying. The data showed one mortality but did not provide any significant data about the history of the individual. This means that there is no specific evidence that the disease could be deadly. Additionally, from the HIPAA law, a patient’s data should not be revealed to the public at any condition (Katuska, 2018). This implies that most of the findings were generalized based on COVID-19 and no other health problem.

Conclusion

From the above analysis, we can say that COVID-19 does not discriminate on gender or age. This implies that everyone is vulnerable to the disease, nevertheless of the health conditions of an individual. Very healthy individuals are contracting the disease. However, the government has isolated individuals with the disease to prevent further interaction. Isolation has some psychological impacts on individuals due to limited freedom of movement (Brooks et al., 2020). To some extent, it may also lead to stigmatization. From the findings and the results, I can thereby conclude that the disease can infect everyone despite their gender or age. Older people are not the only people prone to infections. The data in New Mexico shows that the first 100 cases of COVID-19 are people under the age of 50. The younger group should, however, be educated on how they can avoid being infected by the virus. Issues such as social distancing and self-isolation should be well explained to foster their benefits. Moreover, the gender distribution is almost equal, and there is no significant disparity by gender (Xu and Li, 2020).

Recommendation

From the information given out by the DOH, the disease seems to be vulnerable to only older people. The mortality rate is meager, which signifies that the disease is not very paramount. However, the disease is deadly as it is respiratory based. In some other instances, the media has been providing information that was not exceptional. From the statistics, the seniors are prone, but in reality, everyone is vulnerable. The media is putting much information about the spread of the disease. This can be influential in the overall health outcomes of the country. Misinformation can sway individual perceptions of health issues. This can make some individuals overestimate the risk of various health problems and underestimate others (Savoia et al., 2017).

Media can be used to foster positive outcomes of the overall well-being of the people. The media can be used to influence the physical and psychological impacts of people in isolation. This is because of some of the campaigns aimed at increasing people’s awareness, maybe contended. The media giving out misinformation about the disease and how it spreads may only bring much panic and inconvenience in the country. This implies that the media should be convenient when releasing any information. Assuring people that the virus only infects seniors will only leave juniors at high risks. This is because most of them will not adhere to the safety precautions given out by the government.

Moreover, the spread of the virus is a critical factor in this disease. It spreads much easier because it is airborne. For people to minimize the spread and risks of attaining the disease, then they have to avoid overcrowded places and maintain general hygiene. The disease has got several facts that make it strange and easily transmitted. Adhering to these precautions and routines may reduce the rate of infection. The media should also focus on giving the exact information rather than manipulating them. People take in what they hear, thus providing wrong information about the disease will only leave them exposed.

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