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Data Management and Visualization: Week 4 Assignment

PYTHON CODE

#import libraries import numpy import pandas import matplotlib.pyplot as plt

#read file data=pandas.read_csv("dataset.csv")          

#print number of columns and rows, respectively print("NO. OF VARIABLES:")                       print(len(data.columns))   print("NO. OF OBSERVATIONS:") print(len(data.index))        

#convert string to numeric data['alcconsumption']=data['alcconsumption'].apply(pandas.to_numeric,errors='coerce')

#create subdata of the data set with the following condition sub1=data[(data['alcconsumption']>1.79)&(data['alcconsumption']<11.57)]

#print number of rows from the subset print("NO. OF OBSERVATIONS IN THE SUBSET:") print(len(sub1.index))            

#print observations from subdata print("COUNTRY NAME: ") c1=sub1["country"].value_counts(sort=False) print(c1)

print("ALCOHOL CONSUMPTION: ") c2=sub1["alcconsumption"].value_counts(sort=False) print(c2)

print("PERCENTAGE-WISE ALCOHOL CONSUMTPION: ") p2=sub1["alcconsumption"].value_counts(sort=False, normalize=True, dropna=False) print(p2)

print("BREAST CANCER: ") c3=sub1["breastcancerper100th"].value_counts(sort=False) print(c3)

print("PERCENTAGE-WISE BREAST CANCER: ") p3=sub1["breastcancerper100th"].value_counts(sort=False, normalize=True, dropna=False) print(p3)

print("HIV RATE: ") c4=sub1["hivrate"].value_counts(sort=False) print(c4)

print("PERCENTAGE-WISE HIV RATE: ") p4=sub1["hivrate"].value_counts(sort=False, normalize=True, dropna=False) print(p4)

print("LIFE EXPECTANCY: ") c5=sub1["lifeexpectancy"].value_counts(sort=False) print(c5)

print("PERCENTAGE-WISE LIFE EXPECTANCY: ") p5=sub1["lifeexpectancy"].value_counts(sort=False, normalize=True, dropna=False) print(p5)

CONCLUSION

The graph shows the trends in the variables used for this study: alcconsumption, HIVrate, breastcancerper100TH, lifeexpectancy. 

Thus, the research question proposed in the beginning, “ Whether life expectancy rate gets affected by greater consumption of alcohol that concurrently induces the risk of breast cancer and HIV rate” holds true that life expectancy rate does get affected by greater alcohol consumption. 

[thenyctophile-blog]

Data Management and Visualization: Week 3 Assignment

PYTHON CODE

#import libraries import numpy import pandas

#read file data=pandas.read_csv(“dataset.csv”)          

#print number of columns and rows, respectively print(“NO. OF VARIABLES:”)                       print(len(data.columns))   print(“NO. OF OBSERVATIONS:”) print(len(data.index))        

#convert string to numeric data[‘alcconsumption’]=data['alcconsumption’].apply(pandas.to_numeric,errors='coerce’)

#create subdata of the data set with the following condition sub1=data[(data['alcconsumption’]>1.79)&(data['alcconsumption’]<11.57)]      

#print number of rows from the subset print(“NO. OF OBSERVATIONS IN THE SUBSET:”) print(len(sub1.index))            

#print observations from subdata print(“COUNTRY NAME: ”) c1=sub1[“country”].value_counts(sort=False) print(c1)

print(“ALCOHOL CONSUMPTION: ”) c2=sub1[“alcconsumption”].value_counts(sort=False) print(c2)

print(“PERCENTAGE-WISE ALCOHOL CONSUMTPION: ”) p2=sub1[“alcconsumption”].value_counts(sort=False, normalize=True, dropna=False) print(p2)

print(“BREAST CANCER: ”) c3=sub1[“breastcancerper100th”].value_counts(sort=False) print(c3)

print(“PERCENTAGE-WISE BREAST CANCER: ”) p3=sub1[“breastcancerper100th”].value_counts(sort=False, normalize=True, dropna=False) print(p3)

print(“HIV RATE: ”) c4=sub1[“hivrate”].value_counts(sort=False) print(c4)

print(“PERCENTAGE-WISE HIV RATE: ”) p4=sub1[“hivrate”].value_counts(sort=False, normalize=True, dropna=False) print(p4)

print(“LIFE EXPECTANCY: ”) c5=sub1[“lifeexpectancy”].value_counts(sort=False) print(c5)

print(“PERCENTAGE-WISE LIFE EXPECTANCY: ”) p5=sub1[“lifeexpectancy”].value_counts(sort=False, normalize=True, dropna=False) print(p5)

OUTPUT

Please refer tho the following link for the output:

https://drive.google.com/file/d/1_Ko5iSczcR2hMfRFB4G2xrxoRaLfaOPw/view?usp=sharing

The following can be concluded from the above program results:

Out of 213 observations in the dataset, subset ‘sub1′ was created with 117 observations. These observations have alcohol consumption (alcconsumption) within the range of 1.79 and 11.57.

This range was determined by taking standard deviation into consideration.

In the percentage-wise distribution, the blank entries are also considered.

It is observed that alcohol consumption does not effect HIV rate as significantly as it effects Breast Cancer per 100th. 

Considering all three factors- Alcohol consumption, HIV rate, Breast Cancer Rate these effect the Life Expectancy Rate for a country. 

Hence, life expectancy rate gets affected by greater consumption of alcohol that concurrently induces the risk of breast cancer and HIV rate.

[thenyctophile-blog]

Data Management and Visualization: Week 2 Assignment

PYTHON CODE

#import libraries import numpy import pandas

#read file data=pandas.read_csv("dataset.csv")          

#print number of columns and rows, respectively print("NO. OF VARIABLES:")                       print(len(data.columns))   print("NO. OF OBSERVATIONS:") print(len(data.index))        

#convert string to numeric data['alcconsumption']=data['alcconsumption'].apply(pandas.to_numeric,errors='coerce')

#create subdata of the data set with the following condition sub1=data[(data['alcconsumption']>1.79)&(data['alcconsumption']<11.57)]      

#print number of rows from the subset print("NO. OF OBSERVATIONS IN THE SUBSET:") print(len(sub1.index))            

#print observations from subdata print("COUNTRY NAME: ") c1=sub1["country"].value_counts(sort=False) print(c1)

print("ALCOHOL CONSUMPTION: ") c2=sub1["alcconsumption"].value_counts(sort=False) print(c2)

print("PERCENTAGE-WISE ALCOHOL CONSUMTPION: ") p2=sub1["alcconsumption"].value_counts(sort=False, normalize=True, dropna=False) print(p2)

print("BREAST CANCER: ") c3=sub1["breastcancerper100th"].value_counts(sort=False) print(c3)

print("PERCENTAGE-WISE BREAST CANCER: ") p3=sub1["breastcancerper100th"].value_counts(sort=False, normalize=True, dropna=False) print(p3)

print("HIV RATE: ") c4=sub1["hivrate"].value_counts(sort=False) print(c4)

print("PERCENTAGE-WISE HIV RATE: ") p4=sub1["hivrate"].value_counts(sort=False, normalize=True, dropna=False) print(p4)

print("LIFE EXPECTANCY: ") c5=sub1["lifeexpectancy"].value_counts(sort=False) print(c5)

print("PERCENTAGE-WISE LIFE EXPECTANCY: ") p5=sub1["lifeexpectancy"].value_counts(sort=False, normalize=True, dropna=False) print(p5)

OUTPUT

Please refer tho the following link for the output:

https://drive.google.com/file/d/1_Ko5iSczcR2hMfRFB4G2xrxoRaLfaOPw/view?usp=sharing

The following can be concluded from the above program results:

Out of 213 observations in the dataset, subset ‘sub1′ was created with 117 observations. These observations have alcohol consumption (alcconsumption) within the range of 1.79 and 11.57.

This range was determined by taking standard deviation into consideration.

In the percentage-wise distribution, the blank entries are also considered.

[thenyctophile-blog]

DATA MANAGEMENT AND VISUALISATION: WEEK-1 ASSIGNMENT

Amongst the code books suggested in the curriculum, I have chosen GapMinder that includes numerous country-level indicators of health, wealth, and development. It includes over 200 indicators like GDP, employment rate, HIV prevalence, etc. of about 192 UN members and 24 other areas. 

The provided GapMinder dataset consists of over 15 variables, namely Income per person, Alcohol consumption, Armed force rate, etc. 

An overview of the dataset gave me a clear insight into the topic and helped me choose the variables of my interest.

The variables that I have chosen are: 

alcconsumption – Recorded and estimated average alcohol consumption (in liters) of adults (15+) per capita.

breastcancerper100TH  – Number of new cases of breast cancer in 100,000 female residents during a certain year.

HIVrate – Estimated number of people living with HIV per 100 population of age group 15-49. 

lifeexpectancy – The average number of years a new born child would live if current mortality patterns were to stay the same.

 These variables made me arrive at the research question, “How does life expectancy rate get affected by greater consumption of alcohol that concurrently induces the risk of breast cancer and HIV?”

I have done a literature review to find out more in this regard. Few of the articles that I read are described below: 

  Article 1: Links between alcohol consumption and breast cancer: a look at the evidence 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299758/

Ying Liu,1 Nhi Nguyen,1 and Graham A Colditz*,1,2 

Published: 1 Nov 2015

 Abstract

Alcohol consumption by adult women is consistently associated with risk of breast cancer. Several questions regarding alcohol and breast cancer need to be addressed. Menarche to first pregnancy represents a window of time when breast tissue is particularly susceptible to carcinogens. Youth alcohol consumption is common in the USA, largely in the form of binge drinking and heavy drinking. Whether alcohol intake acts early in the process of breast tumorigenesis is unclear. This review aims to focus on the influences of timing and patterns of alcohol consumption and the effect of alcohol on intermediate risk markers. We also review possible mechanisms underlying the alcohol-breast cancer association.

Alcohol is considered by the International Agency for Research on Cancer to be causally related to breast cancer risk [1], with a 7–10% increase in risk for each 10 g (~1 drink) alcohol consumed daily by adult women [2–4]. This association is observed in both premenopausal and postmenopausal women. Compared with other organs, breast appears to be more susceptible to carcinogenic effects of alcohol. The risk of breast cancer is significantly increased by 4–15% for light alcohol consumption (≤1 drink/day or ≤12.5 g/day) [2,5,6] which does not significantly increase cancer risk in other organs of women [7]. This raises a clinical and public health concern because nearly half of women of child-bearing age drink alcohol and 15% of drinkers at this age have four or more drinks at a time [8]. Approximately 4–10% of breast cancers in the USA are attributable to alcohol consumption [2,5,6], accounting for 9000–23,000 new invasive breast cancer cases each year. Therefore, better understanding of how alcohol consumption increases breast cancer risk is crucial for developing breast cancer prevention strategies. As previous meta-analyses and systemic reviews comprehensively summarized the association between adult alcohol consumption and breast cancer risk [3,5,9,10], here we reviewed the recent epidemiologic evidence, with special emphasis on timing and patterns of alcohol consumption and the effect of alcohol on intermediate markers. In addition, we discussed up-to-date mechanisms that have been proposed to explain the association and provide guidance for clinicians on preventive messages.

 Article 2: Alcohol’s Role in HIV Transmission and Disease Progression

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3860502/

Ivona Pandrea , M.D., Ph.D., Kyle I. Happel , M.D., Angela M. Amedee , Ph.D., 

Gregory J. Bagby , Ph.D., and Steve Nelson, M.D.

Published: 2010

  Abstract

Alcohol use has negative effects on HIV disease progression through several mechanisms, including transmission, viral replication, host immunity, and treatment efficacy. Research with animal models has explored the effect of alcohol intake on several aspects of simian immunodeficiency virus (SIV) disease progression. Data suggest that the increased SIV levels observed in alcohol-consuming animals may represent an increase in virus production as opposed to a decrease in host defense. Results also suggest that changes in nutritional balance and metabolism, as a possible consequence of a proinflammatory state, together with increased virus production in animals consuming alcohol, accelerate SIV and possibly HIV disease progression. Further studies using the animal model are necessary.

Human immunodeficiency virus (HIV) infection continues to be a major global health problem, with an estimated 33 million people infected as of 2007. In the United States, more than 1.1 million people are infected with HIV (Centers for Disease Control and Prevention 2009). Although effective treatment of HIV infection has substantially decreased HIV-related mortality, HIV still is the sixth leading cause of death for adults aged 25–44 in the United States.

  Article 3: Does Alcohol Abuse Your Life Expectancy? Published: November 20, 2019

https://www.turningpointtreatment.org/blog/alcohol-abuse-and-life-expectancy/

 In addition to causing physical and mental health problems, alcohol abuse can lower your life expectancy. If you’re ready to address an alcohol problem, reach out to Turning Point today.

Alcohol can lead to a shorter life expectancy in different ways. Drinking alcohol on a regular and long-term basis can increase the risk of developing life-threatening diseases or conditions. 

Some of these diseases and medical conditions include:

· stroke

· damage to the heart

· high blood pressure

· cirrhosis of the liver

· pancreatitis

· cancer of the liver, head, neck, colon, breast, and oesophagus

 I also went through a few other articles and came up with a Hypothesis.

 Hypothesis: 

Consumption of alcohol (alcconsumption) can induce the risk of breast cancer (breastcancerper100TH) and HIV (HIVrate) hence leading to a shorter life expectancy rate (lifeexpectancy).

Regular consumption of alcohol can weaken the immune system and damage the liver, as well as lead to risky behaviors that increase the chance of getting HIV or passing it on to others. Research has shown that people with HIV who regularly drink alcohol tend to have a higher viral load and lower overall CD4 count.

A pooled analysis of data from 53 studies found for each alcoholic drink consumed per day, the relative risk of breast cancer increased by about 7 percent.

Having between 200-350 grams of alcohol on a weekly basis was associated with a 1-2-year decrease in life expectancy. Having over 350 grams of alcohol each week was linked to a 4-5-year decrease in life expectancy. 

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You can't talk butterfly language with caterpillar people

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