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Research Project
What interest me is what are causes for antisocial personality disorder.
The second topic that I would like to explore is that, whether is there any association antisocial personality disorder and alcohol consumption.
I selected the NESARC data set.
Frequency Distribution for the GapMinder Dataset (Part 2)
This post is the concluding part of Frequency Distribution for the GapMinder Data set (Part 1)
Frequency Distributions for each Variable contd.
3. Employ Rate (employrate) observations
The output shows the frequency distribution table for the ‘employrate’ variable.
The variable is grouped by the 140 unique observations.
These values are continuous with most of the values occurring only once (Frequency = 1, Percentage = 0.469484%).
Missing data, denoted by an empty string is the mode of the distribution (Frequency = 35, Percentage = 16.431925%).
The Cumulative Frequency sums to the total observations (213) and Cumulative Percentage sums to 100%.
4. Residential Electricity per Person (relectricperperson) observations
The output shows the frequency distribution table for the ‘relectricperperson’ variable.
The variable is grouped by the 133 unique observations.
These values are continuous with most of the values occurring only once (Frequency = 1, Percentage = 0.469484%).
Missing data, denoted by an empty string is the mode of the distribution (Frequency = 77, Percentage = 36.150235%).
The Cumulative Frequency sums to the total observations (213) and Cumulative Percentage sums to 100%.
Part 1 and 2 contain all the Frequency Distribution information about the data set and variables under study in this research
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.
Data Management and Visualization: Week 2 Assignment
Running the Program:Python and SAS
Python: Code
import pandas #importing Pandas and NumPy libraries import numpy #Loading the Mars Crater dataset DATA = pandas.read_csv('marscrater_pds.csv',low_memory=False) #Printing the number of rows and coloums in the dataset print(len(DATA)) print(len(DATA.columns)) #Converting the value of the object into numeric DATA['LATITUDE_CIRCLE_IMAGE']= pandas.to_numeric(DATA['LATITUDE_CIRCLE_IMAGE']) #subsetting to obtain the specific data required ie the data coming in the range of latitude -25 to +25 EQTR=DATA[(DATA['LATITUDE_CIRCLE_IMAGE']>= -25)& (DATA['LATITUDE_CIRCLE_IMAGE']<=25)] EQTR2=EQTR.copy() #Converting the value of the object into numeric EQTR2['DIAM_CIRCLE_IMAGE']= pandas.to_numeric(EQTR2['DIAM_CIRCLE_IMAGE']) EQTR2['DEPTH_RIMFLOOR_TOPOG']= pandas.to_numeric(EQTR2['DEPTH_RIMFLOOR_TOPOG']) EQTR2['NUMBER_LAYERS']= pandas.to_numeric(EQTR2['NUMBER_LAYERS']) #Labelling, finding printing the frequency and percentage of the Diameters of the craters in the equatorial region print("Frequency of Diameters of craters in the equatorial region in kms") FREQ1= EQTR2["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print (FREQ1) print("Percentages of Diameters of craters in the equatorial region") PRE1=EQTR2["DIAM_CIRCLE_IMAGE"].value_counts(sort=True, normalize=True) print (PRE1) #Labelling, finding printing the frequency and percentage of the Depth of the craters in the equatorial region print("Frequency of Depth of craters in the equatorial region in kms") FREQ2= EQTR2["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort=True) print (FREQ2) print("Percentages of Depth of craters in the equatorial region") PRE2=EQTR2["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort=True, normalize=True) print (PRE2) #Labelling, finding and printing the frequency and percentage of the number of layers of ejecta in the equatorial region print("Frequency of number of ejecta layers of craters in the equatorial region") FREQ3= EQTR2["NUMBER_LAYERS"].value_counts(sort=True) print (FREQ3) print("Percentages of number of ejecta layers of craters in the equatorial region") PRE3=EQTR2["NUMBER_LAYERS"].value_counts(sort=True, normalize=True) print (PRE3)
Output
_
_
SAS:Code
/Reading in the Mars crater dataset/ LIBNAME data "/courses/d1406ae5ba27fe300" ACCESS = readonly; /Setting the Mars Crater dataset to new name DATA/ DATA new; SET data.marscrater_pds; /Labelling the variables into readable names/ LABEL DIAM_CIRCLE_IMAGE = "Diameter of the Craters at equatorial region" DEPTH_RIMFLOOR_TOPOG = "Depth of the Craters at the equatorial region" NUMBER_LAYERS ="Number of ejecta layers on the craters at the equatorial region"; /Subsetting to obtain the specific data required/ IF LATITUDE_CIRCLE_IMAGE LE 25; IF LATITUDE_CIRCLE_IMAGE GE -25; /Sorting according to the unique Crater id/ PROC SORT; BY CRATER_ID; /Finding and Printing the frequency and percentages of the three variables/ PROC FREQ; TABLES DIAM_CIRCLE_IMAGE DEPTH_RIMFLOOR_TOPOG NUMBER_LAYERS; RUN;
Output
The following conclusions can be arrived at from the above program results:
Out of 384343 observations 189672 of them comes within the latitude range of = -25 and +25. i.e about 49.3% craters are at the equatorial region.
About 1.67% of craters have 1.01 km diameter, which is the highest at the equatorial region.
About 80% of craters in this region have unknown depth, denoted by the value 0. This can be considered as missing value in the dataset.
About 94% of craters in this region have unknown number of ejecta_ denoted by value 0. Again this can also be considered as missing value in the dataset.
Data Management and Visualization : Week 1 Assignment
Being a Physics majors from college, as soon as I saw that there is an astrophysics related dataset available for research I was very much excited as I decided I’m going to select the MARS CRATER dataset.
A quick overview of the dataset gave a brief idea and an interesting topic popped in my head and it is the morphology of craters. I don’t know whether the dataset can answer the question related to this or not. Whatever the case is I selected some variables of my interest and made my own personal codebook.
The variables of interest are:-
• CRATER_ID – crater ID for internal sue, based upon the region of the planet (1/16ths), the “pass” under which the crate was identified, ad the order in which it was identified
• DIAM_CIRCLE_IMAGE – diameter from a non-linear least squares circle fit to the vertices selected to manually identify the crater rim (units are km)
• DEPTH_RIMFLOOR_TOPOG – average elevation of each of the manually determined N points along (or inside) the crater rim(units are km) o Depth Rim - Points are selected as relative topographic highs under the assumption they are the least eroded so most original points along the rim o Depth Floor – Points were chosen as the lowest elevation that did not include visible embedded craters
To be more specific about the research question, I selected one more topic after going through the codebook so that I can narrow down my research question. The topic is the location of the craters formed. I added a couple of more variables to my personal codebook
LATITUDE_CIRCLE_IMAGE – latitude from the derived center of a non-linear least-squares circle fit to the vertices selected to manually identify the crater rim (units are decimal degrees North)
LONGITUDE_CIRCLE_IMAGE – longitude from the derived center of a non-linear least-squares circle fit to the vertices selected to manually identify the crater rim (units are decimal degrees East)
NUMBER_LAYERS – the maximum number of cohesive layers in any azimuthal direction that could be reliably identified
The research question I arrived at is “whether there is any difference in morphologies of craters (larger or smaller or no. of layers of ejecta) formed at the equatorial region as compared to the rest of the mars surface?”
I did an elaborate literature review to find out whether anyone had ventured out to find the answer to the same question as I asked above and arrived at a hypothesis; the first stage of any scientific research. The search terms used for this are : impact craters at the equator of mars, Mars craters at equator, equatorial craters on mars.
Article 1: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2005GL022758
Small rampart craters in an equatorial region on Mars: Implications for near‐surface water or ice
D. Reiss , E. Hauber, G. Michael, R. Jaumann , G. Neukum and the HRSC Co‐Investigator Team
First published: 24 May 2005
Abstract: Small onset diameters (1 km) of rampart craters were identified in High Resolution Stereo Camera (HRSC) imagery in the equatorial region of the Valles Marineris plateaus. This is in contrast to previous global studies based on Viking imagery, which showed onset diameters in the range of 4 km to 7 km in equatorial regions. The observed small rampart craters show morphologically eroded ejecta blankets. This indicates a formation in the early Martian history, when erosion rates were higher. Volatile rich target material might have been available shortly after the formation of the Hesperian–aged plateaus surrounding Valles Marineris, and this is in agreement with the reported Hesperian fluvial activity in this region. The lack of fresh small rampart craters indicates a lowering of the ground ice table with time.
Article 2: Martian dichotomy: From Wikipedia, the free encyclopedia
The most conspicuous feature of Mars is a sharp contrast, known as the Martian dichotomy, between the Southern hemisphere and the Northern. The two hemispheres' geography differ in elevation by 1 to 3 km. The average thickness of the Martian crust is 45 km, with 32 km in the northern lowlands region, and 58 km in the southern highlands.
The boundary between the two regions is quite complex in places. One distinctive type of topography is called fretted terrain. It contains mesas, knobs, and flat-floored valleys having walls about a mile high. Around many of the mesas and knobs are lobate debris aprons that have been shown to be rock glaciers.
Many large valleys formed by the lava erupted from the volcanoes of Mars cut through the dichotomy.
The Martian dichotomy boundary includes the regions called Deuteronilus Mensae, Protonilus Mensae, and Nilosyrtis Mensae. All three regions have been studied extensively because they contain landforms believed to have been produced by the movement of ice or paleoshorelines questioned as formed by volcanic erosion.
The northern lowlands comprise about one-third of the surface of Mars and are relatively flat, with as many impact craters as the southern hemisphere.The other two-thirds of the Martian surface are the highlands of the southern hemisphere. The difference in elevation between the hemispheres is dramatic. Three major hypotheses have been proposed for the origin of the crustal dichotomy: endogenic (by mantle processes), single impact, or multiple impacts. Both impact-related hypotheses involve processes that could have occurred before the end of the primordial bombardment, implying that the crustal dichotomy has its origins early in the history of Mars.
Hypothesis: The craters formed at the equatorial region (LATITUDE_CIRCLE_IMAGE = -250 to +250) have smaller morphologies. Diameters ( DIAM_CIRCLE_IMAGE ) of the craters are smaller and most of them have unknown depth ( DEPTH_RIMFLOOR_TOPOG ) and also most of the craters have unknown number of ejecta ( NUMBER_LAYERS ) layers.
Assignment: Running Your First Program
1. My program
import pandas as pd import numpy as nu
# bug fix for display formats to avoid run time errors - put after code for loading data above #pd.set_option('display.float_format', lambda x:'%f'%x)
data = pd.read_csv('nesarc_pds.csv', low_memory=False)
#upper-case all DataFrame column names data.columns = map(str.upper, data.columns)
print (len(data)) #number of observations (rows) print (len(data.columns)) # number of variables (columns)
#setting variables you will be working with to numeric
data['S1Q2D'] = data['S1Q2D'].convert_objects(convert_numeric=True) data['S1Q2E'] = data['S1Q2E'].convert_objects(convert_numeric=True) data['S6Q1'] = data['S6Q1'].convert_objects(convert_numeric=True)
#counts and percentages (i.e. frequency distributions) for each variable print ('COUNTS FOR BIOLOGICAL OR ADOPTIVE PARENTS GET DIVORCED OR PERMANENTLY STOP LIVING TOGETHER') cdiv = data['S1Q2D'].value_counts(sort=False) print (cdiv)
print ('PERCENTAGES FOR BIOLOGICAL OR ADOPTIVE PARENTS GET DIVORCED OR PERMANENTLY STOP LIVING TOGETHER') pdiv = data['S1Q2D'].value_counts(sort=False, normalize=True) print (pdiv)
print ('COUNTS FOR HAD PANIC ATTACK') cpan = data['S6Q1'].value_counts(sort=False) print (cpan)
print ('PERCENTAGES FOR HAD PANIC ATTACK') ppan = data['S6Q1'].value_counts(sort=False, normalize=True) print (ppan)
#subset data to kids who had BIOLOGICAL OR ADOPTIVE PARENTS GOT DIVORCED OR PERMANENTLY STOP LIVING TOGETHER WHEN RESPONDENT WAS age 3 to 12 subdiv=data[(data['S1Q2E']>=3) & (data['S1Q2E']<=12) & (data['S1Q2D']==1)]
#make a copy of my new subsetted data subdiv2 = subdiv.copy()
#frequency distritions on new data frames print ('COUNTS FOR AGE 3 TO 12 y.o.') cage = subdiv2['S1Q2E'].value_counts(sort=False) print (cage)
print ('PERCENTAGES FOR AGE 3 TO 12 y.o.') page = subdiv2['S1Q2E'].value_counts(sort=False, normalize=True) print (page)
print ('COUNTS FOR DIVORCE') cdiv2 = subdiv2['S1Q2D'].value_counts(sort=False) print (cdiv2)
print ('PERCENTAGES FOR DIVORCE') pdiv2 = subdiv2['S1Q2D'].value_counts(sort=False, normalize=True) print (pdiv2)
2. The output that displays three of my variables as frequency tables + description
COUNTS FOR BIOLOGICAL OR ADOPTIVE PARENTS GET DIVORCED OR PERMANENTLY STOP LIVING TOGETHER BEFORE RESPONDENT WAS 18 1 6914 2 30261 9 65 Name: S1Q2D, dtype: int64 PERCENTAGES FOR BIOLOGICAL OR ADOPTIVE PARENTS GET DIVORCED OR PERMANENTLY STOP LIVING TOGETHER
1 0.160444 2 0.702225 9 0.001508 Name: S1Q2D, dtype: float64
Description:
6914 or 0.160444 (16%) of the respondents whose caregivers got divorced before respondent was 18,
30261 or 0.702225 (70.2%) of the respondents had not and 65 or 0.001508 (0.15%) of the respondents did not answer.
COUNTS FOR HAD PANIC ATTACK 1 5278 2 36429 9 1386 Name: S6Q1, dtype: int64
PERCENTAGES FOR HAD PANIC ATTACK 1 0.122479 2 0.845358 9 0.032163 Name: S6Q1, dtype: float64
Description:
5278 or 0.122479 (12.2%) of the respondents had a panic attack or an anxiety episode at least ones in their life, 36429 or 0.845358 (84.5%) of the respondents had not, and 1386 or 0.032163 (0.32%) of the respondents did not answer.
COUNTS FOR AGE 3 TO 12 y.o.
3 420 4 374 5 562 6 406 7 388 8 422 9 335 10 478 11 332 12 538 Name: S1Q2E, dtype: int64
Description: Here is the amount of 3 to 12 years old respondents whose caregivers got divorced (for each age group).
PERCENTAGES FOR AGE 3 TO 12 y.o. 3 0.098707 4 0.087897 5 0.132080 6 0.095417 7 0.091187 8 0.099177 9 0.078731 10 0.112338 11 0.078026 12 0.126439 Name: S1Q2E, dtype: float64
Description: Here is the distribution (percentages) of 3 to 12 years old respondents whose caregivers got divorced (for each age group).
COUNTS FOR DIVORCE 1 4255 Name: S1Q2D, dtype: int64
Description: Total amount of 3 to 12 years old respondents whose caregivers got divorced.
PERCENTAGES FOR DIVORCE 1 1 Name: S1Q2D, dtype: float64
Description: It shows that 100% of our sample are respondents whose caregivers got divorced.
Assignment: Getting Your Research Project Started
Data Management and Visualisation
After looking through the codebook for the NESARC study, I guess it would be interesting to look at the relationship between panic disorder/anxiety and childhood events and circumstances, such as lost of a parent or divorce.
Data set: NESARC
Literature review:
The study of Takeshi Otowa and colleagues (http://www.psy-journal.com/article/S0165-1781(14)00632-5/pdf) suggests that parental separation was associated with a wide range of adult psychopathology, whereas parental death was specifically associated with phobia and alcohol dependence. Maternal and paternal separations were almost equally associated with most forms of psychopathology. Structural equation modeling suggested that parental loss accounted for about 10% of the variance of adult psychopathology, of which parental separation had the strongest impacts on risk for depression and drug abuse/dependence (11% of the total variance). Their findings suggest that early parental separation has stronger and wider effects on adult psychopathology than parental death.
Kathryn H. Howell and colleagues research (http://www.tandfonline.com/doi/full/10.1080/07481187.2014.951497) examined factors theorized to contribute to adaptive functioning in 56 parentally bereaved children (age 7–13) who had lost their caregiver within the previous 6 months. Linear mixed modeling revealed that children in the adaptive functioning group had lower mean scores on avoidant coping and higher mean scores on coping efficacy, religiosity, parental positive reinforcement, and parental empathy. Findings suggest that adaptive functioning following parental loss is related to both child-intrinsic factors and child-extrinsic factors.
Mc Elroy S and Hevey D study (http://www.ncbi.nlm.nih.gov/pubmed/24011494) examined a relationship between adverse early experiences, stressors, psychosocial resources and wellbeing. They suggest that all early experiences, except physical abuse and death of a parent in childhood, were significantly correlated with increased number of, stressors and lower wellbeing scores.
The literature research helped me clarify my research question to formulate the hypothesis.
Hypothesis: There might be a grater correlation between panic disorder/anxiety and separation with a parent in early age due to divorce than the same disorder and separation in early age due to paren’s death.
Variables from the NESARC dataset that I will use:
S1Q2D - DID BIOLOGICAL OR ADOPTIVE PARENTS GET DIVORCED OR PERMANENTLY STOP LIVING TOGETHER BEFORE RESPONDENT WAS 18
S1Q2E - AGE WHEN BIOLOGICAL/ADOPTIVE PARENTS STOPPED LIVING TOGETHER
S6Q1 - HAD PANIC ATTACK, SUDDENLY FELT FRIGHTENED/OVERWHELMED/NERVOUS AS IF IN GREAT DANGER BUT WERE NOT
S1Q2K - DID BIOLOGICAL OR ADOPTIVE PARENT DIE BEFORE RESPONDENT WAS 18
S1Q2L - AGE AT DEATH OF BIOLOGICAL OR ADOPTIVE PARENT
Data Management and Visualization - Assignment 1.
With the purpose of learning and excelling the skills in researching the given data I have enrolled in the Data Management and Visualization course offered by Wesleyan University on Coursera platform.
Choice of Data set - GapMinder
Looking at the code books provided in the course I can state that I am particularly interested in studying Gapminder dataset. GApminder data set studies 214 countries along 15 factors.
Topic of Interest - Health issues along various socio economic levels among countries.
Using GapMinder data set I want to study specifically the health issues based on income per person, urban rate , employment rate , and female employment rate. Health issues that will be studied are : breast cancer , HIV rate, life expectancy, suicide rate , co2 emissions , and alcohol consumption.
Research question :
“What health issues are associated with various socio economic levels among countries ??”
Hypothesis statements :
Is high income per person associated with high alcohol consumption ?
Is suicide rate higher in countries with high urban rate ?
Is life expectancy associated with income levels of countries ?
How Co2 emissions are associated with income levels and urban rate ?
Are breast cancer and HIV rates associated with urban rate, income per person and employment rate?
Is breast cancer associated with female employment rate ?
Is alcohol consumption associated with suicide rates ?
Literature review
Google Scholar have been the primary source for reviewing the following literature and references used are provided as an HTML link with respective conclusion.
The following search terms were used during literature review :
socio economic status and health.
alcohol consumption and income.
breast cancer and female employment rate.
urban rate and co2 emissions.
life expectancy and income levels
Summary
By the literature review done it can be concluded that there have been a marked increase in research on socioeconomic status (SES) and health. There are multiple pathways by which SES determines health; a comprehensive analysis must include macroeconomic contexts and social factors as well as more immediate social environments, individual psychological and behavioral factors, and biological predispositions and processes. (source)
There have been no study finding association of breast cancer and female employment in general however some specific job types have been studied e.g. radium dial workers, flight attendants etc.
The association of urban rate and co2 emissions have also been restricted to country particular. (there was a study on China.)
Life Expectancy and Income levels have been widely studied. It has been shown that Life expectancy varies directly with the income levels however there lso have been high contradictions.(source)
Other topics like association of alcohol consumption and socio economic satus have been studied.Consumption of wine, beer, hard liquor, and all combined showed positive associations with various health issues. High income showed positive associations with cancers of the breast, thyroid gland, uterine corpus, and melanomas in males. (source)
Most of the research papers were paid thus were not reviewed. Summary have been made based on the abstract provided.
