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lifedissenter-blog · 4 years ago

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ASSIGNMENT-4

#CODE

import pandas as pd

import numpy as np

import seaborn as sb

import matplotlib.pyplot as plt

# GapMinder dataset

data = pd.read_csv('gapminder.csv',low_memory=False)

# To lower the case all data frame column names

data.columns = map(str.lower, data.columns)

# To fix the bugs for displaying formats to avoid run time errors

pd.set_option('display.float_format', lambda x:'%f'%x)

# For setting up the variables to be numeric

data['suicideper100th'] = data['suicideper100th'].convert_objects(convert_numeric=True)

data['breastcancerper100th'] = data['breastcancerper100th'].convert_objects(convert_numeric=True)

data['hivrate'] = data['hivrate'].convert_objects(convert_numeric=True)

data['employrate'] = data['employrate'].convert_objects(convert_numeric=True)

# To display the summary statistics about the data

print("Statistics for a Suicide Rate")

print(data['suicideper100th'].describe())

# The subset data for a high suicide rate based on summary statistics

sub = data[(data['suicideper100th']>12)]

#make a copy of my new subsetted data

sub_copy = sub.copy()

# Univariate graph for breast cancer rate for people with a high suicide rate

sb.distplot(sub_copy["breastcancerper100th"].dropna(),kde=False)

plt.xlabel('Breast Cancer Rate')

plt.ylabel('Frequency')

plt.title('Breast Cancer Rate for People with a High Suicide Rate')

# Univariate graph for HIV rate for people with a high suicide rate

sb.distplot(sub_copy["hivrate"].dropna(),kde=False)

plt.xlabel('HIV Rate')

plt.ylabel('Frequency')

plt.title('HIV Rate for People with a High Suicide Rate')

# Univariate graph for employment rate for people with a high suicide rate

sb.distplot(sub_copy["employrate"].dropna(),kde=False)

plt.xlabel('Employment Rate')

plt.ylabel('Frequency')

plt.title('Employment Rate for People with a High Suicide Rate')

# Bivariate graph for association of breast cancer rate with HIV rate for people with a high suicide rate)

sb.regplot(x="hivrate",y="breastcancerper100th",fit_reg=False,data=sub_copy)

plt.xlabel('HIV Rate')

plt.ylabel('Breast Cancer Rate')

plt.title('Breast Cancer Rate vs. HIV Rate for People with a High Suicide Rate')

******************************************************************************************

OUTPUT

Univariate graph for breast cancer rate for people with a high suicide rate

SUMMARY OF THE GRAPH

This graph is univariate, with its highest pick at 0-20% of breast cancer rate. It seems to be skewed to the right as there are higher frequencies in lower categories than the higher categories.

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Univariate graph for HIV rate for people with a high suicide rate

SUMMARY OF THE GRAPH

This graph is univariate, with its highest pick at 0-1% of HIV rate. It seems to be skewed to the right as there are higher frequencies in lower categories than the higher categories.

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Univariate graph for employment rate for people with a high suicide rate

SUMMARY OF THE GRAPH

This graph is univariate, with its highest pick at the median of 55-60% employment rate. It seems to be a symmetric distribution as there are lower frequencies in lower and higher categories.

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Bivariate graph for association of breast cancer rate with HIV rate for people with a high suicide rate

SUMMARY OF THE GRAPH

This graph is bivariate, it plots the breast cancer rate vs. HIV rate for people with a high suicide rate. It shows that people with breast cancer are not infected with HIV.

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lifedissenter-blog · 4 years ago

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ASSIGNMENT-3

#CODE

import pandas as pd

# GapMinder dataset

data = pd.read_csv('gapminder.csv',low_memory=False)

# To lower-case all DataFrame column names

data.columns = map(str.lower, data.columns)

# To fix the bugs for display formats to avoid run time errors

pd.set_option('display.float_format', lambda x:'%f'%x)

#Here setting up the variables to be numeric

data['suicideper100th'] = data['suicideper100th'].convert_objects(convert_numeric=True)

data['breastcancerper100th'] = data['breastcancerper100th'].convert_objects(convert_numeric=True)

data['hivrate'] = data['hivrate'].convert_objects(convert_numeric=True)

data['employrate'] = data['employrate'].convert_objects(convert_numeric=True)

# Displaying summary of statistics about the data

print("Statistics for a Suicide Rate")

print(data['suicideper100th'].describe())

# Here is the subset data for a high suicide rate based on summary statistics

sub = data[(data['suicideper100th']>12)]

#make a copy of my new subsetted data

sub_copy = sub.copy()

# BREAST CANCER RATE

# The frequency and percentage distribution for a number of breast cancer cases with a high suicide rate

# It includes the count of missing data and group the variables in 4 groups by number of

# breast cancer cases (1-23, 24-46, 47-69, 70-92)

bc_max=sub_copy['breastcancerper100th'].max() # maximum of breast cancer cases

#Here we group the data in 4 groups by number of breast cancer cases and record it into new variable bc group4

sub_copy['bcgroup4']=pd.cut(sub_copy.breastcancerper100th,[0*bc_max,0.25*bc_max,0.5*bc_max,0.75*bc_max,1*bc_max])

#The frequency for 4 groups of breast cancer cases with a high suicide rate

bc=sub_copy['bcgroup4'].value_counts(sort=False,dropna=False)

# The percentage for 4 groups of breast cancer cases with a high suicide rate

pbc=sub_copy['bcgroup4'].value_counts(sort=False,dropna=False,normalize=True)*100

# The Cumulative frequency and Cumulative percentage for 4 groups of breast cancer cases with a high suicide rate

bc1=[] # Cumulative Frequency

pbc1=[] # Cumulative Percentage

cf=0

cp=0

for freq in bc:

cf=cf+freq

bc1.append(cf)

pf=cf*100/len(sub_copy)

pbc1.append(pf)

print('Number of Breast Cancer Cases with a High Suicide Rate')

fmt1 = '%10s %9s %9s %12s %13s'

fmt2 = '%9s %9.d %10.2f %9.d %13.2f'

print(fmt1 % ('# of Cases','Freq.','Percent','Cum. Freq.','Cum. Percent'))

for i, (key, var1, var2, var3, var4) in enumerate(zip(bc.keys(),bc,pbc,bc1,pbc1)):

print(fmt2 % (key, var1, var2, var3, var4))

# HIV RATE

# The frequency and percentage distribution for HIV rate with a high suicide rate

#It includes the count of missing data and group the variables in 4 groups by quartile function

#It groups the data in 4 groups and record it into new variable hc group4

sub_copy['hcgroup4']=pd.qcut(sub_copy.hivrate,4,labels=["0% tile","25% tile","50% tile","75% tile"])

# Thefrequency for 4 groups of HIV rate with a high suicide rate

hc = sub_copy['hcgroup4'].value_counts(sort=False,dropna=False)

# percentage for 4 groups of HIV rate with a high suicide rate

phc = sub_copy['hcgroup4'].value_counts(sort=False,dropna=False,normalize=True)*100

# The Cumulative frequency and Cumulative percentage for 4 groups of HIV rate with a high suicide rate

hc1=[] # Cumulative Frequency

phc1=[] # Cumulative Percentage

cf=0

cp=0

for freq in hc:

cf=cf+freq

hc1.append(cf)

pf=cf*100/len(sub_copy)

phc1.append(pf)

print('HIV Rate with a High Suicide Rate')

print(fmt1 % ('Rate','Freq.','Percent','Cum. Freq.','Cum. Percent'))

for i, (key, var1, var2, var3, var4) in enumerate(zip(hc.keys(),hc,phc,hc1,phc1)):

print(fmt2 % (key, var1, var2, var3, var4))

# EMPLOYMENT RATE

# The frequency and percentage distritions for employment rate with a high suicide rate

# It includes the count of missing data and group the variables in 5 groups by

# It groups the data in 5 groups and record it into new variable ecgroup4

def ecgroup4 (row):

if row['employrate'] >= 32 and row['employrate'] < 51:

return 1

elif row['employrate'] >= 51 and row['employrate'] < 59:

return 2

elif row['employrate'] >= 59 and row['employrate'] < 65:

return 3

elif row['employrate'] >= 65 and row['employrate'] < 84:

return 4

else:

return 5 # record for NAN values

sub_copy['ecgroup4'] = sub_copy.apply(lambda row: ecgroup4 (row), axis=1)

# The frequency for 5 groups of employment rate with a high suicide rate

ec = sub_copy['ecgroup4'].value_counts(sort=False,dropna=False)

# percentage for 5 groups of employment rate with a high suicide rate

pec = sub_copy['ecgroup4'].value_counts(sort=False,dropna=False,normalize=True)*100

#The Cumulative frequency and Cumulative percentage for 5 groups of employment rate with a high suicide rate

ec1=[] # Cumulative Frequency

pec1=[] # Cumulative Percentage

cf=0

cp=0

for freq in ec:

cf=cf+freq

ec1.append(cf)

pf=cf*100/len(sub_copy)

pec1.append(pf)

print('Employment Rate with a High Suicide Rate')

print(fmt1 % ('Rate','Freq.','Percent','Cum. Freq.','Cum. Percent'))

for i, (key, var1, var2, var3, var4) in enumerate(zip(ec.keys(),ec,pec,ec1,pec1)):

print(fmt2 % (key, var1, var2, var3, var4))

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OUTPUT

Output with Frequency Tables at High Suicide Rate for Breast Cancer Rate, HIV Rate and Employment Rate Variables.

Statistics for a Suicide Rate

count 191.000000

mean 9.640839

std 6.300178

min 0.201449

25% 4.988449

50% 8.262893

75% 12.328551

max 35.752872

Number of Breast Cancer Cases with a High Suicide Rate

# of Cases Freq. Percent Cum. Freq. Cum. Percent

(1, 23] 18 33.96 18 33.96

(23, 46] 15 28.30 33 62.26

(46, 69] 10 18.87 43 81.13

(69, 92] 8 15.09 51 96.23

NA 2 3.77 53 100.00

HIV Rate with a High Suicide Rate

Rate Freq. Percent Cum. Freq. Cum. Percent

0% tile 18 33.96 18 33.96

25% tile 8 15.09 26 49.06

50% tile 11 20.75 37 69.81

75% tile 12 22.64 49 92.45

NA 4 7.55 53 100.00

Employment Rate with a High Suicide Rate

Rate Freq. Percent Cum. Freq. Cum. Percent

1 10 18.87 10 18.87

2 24 45.28 34 64.15

3 5 9.43 39 73.58

4 13 24.53 52 98.11

5 1 1.89 53 100.00

Summary of Frequency Distributions

Here, I tried to group the breast cancer rate, HIV rate and employment rate variables to create three new variables:

bcgroup4, hcgroup4 and ecgroup4 using three different methods while coding.

The grouped data also includes the count for missing data represented as NA.

Some of the data is missing as only few cases are registered at the desk of concerned authorities due to which no proper data is analysed which results in incomplete solutions for the problem or cause.

1) For the breast cancer rate, I grouped the data into 4 groups by number of breast cancer cases (1-23, 24-46, 47-69, 70-92) using pandas.cut function.

People with lower breast cancer rate experience a high suicide rate.

2) For the HIV rate, I grouped the data into 4 groups by quartile pandas.qcut function.

People with lower HIV rate experience a high suicide rate.

3) For the employment rate, I grouped the data into 5 categorical groups using def and apply functions: (1:32-50, 2:51-58, 3:59-64, 4:65-83, 5:NA).

The employment rate is between 51%-58% for people with a high suicide rate.

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lifedissenter-blog · 4 years ago

Text

#CODE…. Assingment-2

# GapMinder Data set

import pandas as pd

import numpy as np

data = pd.read_csv('gapminder.csv',low_memory=False)

# DataFrame column names

data.columns = map(str.lower, data.columns)

pd.set_option('display.float_format', lambda x:'%f'%x)

# For setting the variables to be numeric

data['suicideper100th'] = data['suicideper100th'].convert_objects(convert_numeric=True)

data['breastcancerper100th'] = data['breastcancerper100th'].convert_objects(convert_numeric=True)

data['hivrate'] = data['hivrate'].convert_objects(convert_numeric=True)

data['employrate'] = data['employrate'].convert_objects(convert_numeric=True)

# To display summary statistics about the data

print("Statistics for a Suicide Rate")

print(data['suicideper100th'].describe())

# Subset data for a high suicide rate based on summary statistics

sub = data[(data['suicideper100th']>12)]

#make a copy of my new subsetted data

sub_copy = sub.copy()

# DISPLAY OF BREAST CANCER RATE

# The display of frequency and percentage distribution for a number of breast cancer cases with a high suicide rate is as follows:-

print('frequency for a number of breast cancer cases with a high suicide rate')

bc = sub_copy['breastcancerper100th'].value_counts(sort=False,bins=10)

print(bc)

print('percentage for a number of breast cancer cases with a high suicide rate')

pbc = sub_copy['breastcancerper100th'].value_counts(sort=False,bins=10,normalize=True)*100

print(pbc)

# The Cumulative Frequency and Cumulative Percentage for a number of breast cancer cases with a high suicide rate

bc1=[] # Cumulative Frequency

pbc1=[] # Cumulative Percentage

cf=0

cp=0

for freq in bc:

cf=cf+freq

bc1.append(cf)

pf=cf*100/len(sub_copy)

pbc1.append(pf)

print('cumulative frequency for a number of breast cancer cases with a high suicide rate')

print(bc1)

print('cumulative percentage for a number of breast cancer cases with a high suicide rate')

print(pbc1)

print('Number of Breast Cancer Cases with a High Suicide Rate')

fmt1 = '%s %7s %9s %12s %12s'

fmt2 = '%5.2f %10.d %10.2f %10.d %12.2f'

print(fmt1 % ('# of Cases','Freq.','Percent','Cum. Freq.','Cum. Percent'))

for i, (key, var1, var2, var3, var4) in enumerate(zip(bc.keys(),bc,pbc,bc1,pbc1)):

print(fmt2 % (key, var1, var2, var3, var4))

fmt3 = '%5s %10s %10s %10s %12s'

print(fmt3 % ('NA', '2', '3.77', '53', '100.00'))

# DISPLAY OF HIV RATE

# The frequency and percentage distribution for HIV rate with a high suicide rate.

print('frequency for HIV rate with a high suicide rate')

hc = sub_copy['hivrate'].value_counts(sort=False,bins=7)

#print(hc)

print('percentage for HIV rate with a high suicide rate')

phc = sub_copy['hivrate'].value_counts(sort=False,bins=7,normalize=True)*100

print(phc)

# The Cumulative frequency and Cumulative percentage for HIV rate with a high suicide rate.

hc1=[] # Cumulative Frequency

phc1=[] # Cumulative Percentage

cf=0

cp=0

for freq in bc:

cf=cf+freq

hc1.append(cf)

pf=cf*100/len(sub_copy)

phc1.append(pf)

print('cumulative frequency for HIV rate with a high suicide rate')

print(hc1)

print('cumulative percentage for HIV rate with a high suicide rate')

print(phc1)

print('HIV Rate with a High Suicide Rate')

fmt1 = '%5s %12s %9s %12s %12s'

fmt2 = '%5.2f %10.d %10.2f %10.d %12.2f'

print(fmt1 % ('Rate','Freq.','Percent','Cum. Freq.','Cum. Percent'))

for i, (key, var1, var2, var3, var4) in enumerate(zip(hc.keys(),hc,phc,hc1,phc1)):

print(fmt2 % (key, var1, var2, var3, var4))

fmt3 = '%5s %10s %10s %10s %12s'

print(fmt3 % ('NA', '2', '3.77', '53', '100.00'))

# DISPLAY OF EMPLOYMENT RATE

# THE frequency and percentage distribution for employment rate with a high suicide rate.

print('frequency for employment rate with a high suicide rate')

ec = sub_copy['employrate'].value_counts(sort=False,bins=10)

print(ec)

print('percentage for employment rate with a high suicide rate')

pec = sub_copy['employrate'].value_counts(sort=False,bins=10,normalize=True)*100

print(pec)

# The Cumulative frequency and Cumulative percentage for employment rate with a high suicide rate.

ec1=[] # Cumulative Frequency

pec1=[] # Cumulative Percentage

cf=0

cp=0

for freq in bc:

cf=cf+freq

ec1.append(cf)

pf=cf*100/len(sub_copy)

pec1.append(pf)

print('cumulative frequency for employment rate with a high suicide rate')

print(ec1)

print('cumulative percentage for employment rate with a high suicide rate')

print(pec1)

print('Employment Rate with a High Suicide Rate')

fmt1 = '%5s %12s %9s %12s %12s'

fmt2 = '%5.2f %10.d %10.2f %10.d %12.2f'

print(fmt1 % ('Rate','Freq.','Percent','Cum. Freq.','Cum. Percent'))

for i, (key, var1, var2, var3, var4) in enumerate(zip(ec.keys(),ec,pec,ec1,pec1)):

print(fmt2 % (key, var1, var2, var3, var4))

fmt3 = '%5s %10s %10s %10s %12s'

print(fmt3 % ('NA', '2', '3.77', '53', '100.00'))

-------------------------------------------------------------------------------------------------------------------------

OUTPUT

Statistics for a Suicide Rate

count 191.000000

mean 9.640839

std 6.300178

min 0.201449

25% 4.988449

50% 8.262893

75% 12.328551

max 35.752872

Number of Breast Cancer Cases with a High Suicide Rate

# of Cases Freq. Percent Cum. Freq. Cum. Percent

6.51 6 11.32 6 11.32

15.14 14 26.42 20 37.74

23.68 5 9.43 25 47.17

32.22 7 13.21 32 60.38

40.76 2 3.77 34 64.15

49.30 4 7.55 38 71.70

57.84 5 9.43 43 81.13

66.38 1 1.89 44 83.02

74.92 3 5.66 47 88.68

83.46 4 7.55 51 96.23

NA 2 3.77 53 100.00

HIV Rate with a High Suicide Rate

Rate Freq. Percent Cum. Freq. Cum. Percent

0.03 39 73.58 6 11.32

2.64 4 7.55 20 37.74

5.23 2 3.77 25 47.17

7.81 0 0.00 32 60.38

10.40 0 0.00 34 64.15

12.98 2 3.77 38 71.70

15.56 1 1.89 43 81.13

18.15 0 0.00 44 83.02

20.73 0 0.00 47 88.68

23.32 1 1.89 51 96.23

NA 2 3.77 53 100.00

Employment Rate with a High Suicide Rate

Rate Freq. Percent Cum. Freq. Cum. Percent

37.35 2 3.77 6 11.32

41.98 2 3.77 20 37.74

46.56 7 13.21 25 47.17

51.14 8 15.09 32 60.38

55.72 16 30.19 34 64.15

60.30 4 7.55 38 71.70

64.88 5 9.43 43 81.13

69.46 2 3.77 44 83.02

74.04 3 5.66 47 88.68

78.62 3 5.66 51 96.23

NA 2 3.77 53 100.00

Analysis of Frequency Distributions

Q 1) What is a number of breast cancer cases associated with a high suicide rate?

A 1) The high suicide rate is associated with the low number of breast cancer cases.

Q 2) How HIV rate is associated with a high suicide rate?

A 2) The high suicide rate is associated with the low HIV rate.

Q 3) How employment rate is associated with a high suicide rate?

A 3) The high suicide rate occurs at 55% of employment rate.

SUMMARY ON FREQUENCY DISTRIBUTION.

· Somehow employment rates, breast cancer rates and HIV rates are leading to increase in the suicide rate. Which is affecting the nations growth directly or indirectly.

· Some of the data is missing as only few cases are registered at the desk of concerned authorities due to which no proper data is analysed which results in incomplete solutions for the problem or cause.

· The high suicide rate occurs at 0.03% of HIV rate.

·The high suicide rate occurs at 15.14% of breast cancer rate.

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lifedissenter-blog · 4 years ago

Text

ASSINGMENT -1

DATA MANAGEMENT AND VISUALIZATION : ASSINGMENT -1

GapMinder

I chose “GapMinder” to explore. Due to the following reasons:

i) To know about the gross domestic production, employment rate, HIV prevalence etc.

ii) To understand the stastics about the social, economic and environmental development at local, national and global level.

I am going to focus HIV Rate and Life Expectancy how the increasing HIV cases are affecting Life expectancy rate in the society. How the HIV is taking away the innocent and valuable lives.

THE VARIABLES USED FOR THE RESEARCH ARE AS FOLLOWS:-

1) HIVrate :- It represents about the HIV cases.

2) lifeexpectancy :- It represents the average number of years a newborn child would live if current mortality patterns were to stay the same.

So here is the research question:-

1) Is there any association between HIV rate and Life expectancy rate?

There is one more additional question for the research:-

1) How gross domestic product affects the growth of a country?

Based on the literature review (below mentioned) , my hypothesis is as follows:

HIVrate shows direct association with lifeexpectancy at local level, national level, global level and on cultural background or context and may be associated with the perception of health condition world-wide.

LITERATURE REVIEW

Case Study[1]

Projected life expectancy of people with HIV according to timing of diagnosis

Nakagawa, Fumiyoa; Lodwick, Rebecca K.a; Smith, Colette J.a; Smith, Ruthb; Cambiano, Valentinaa; Lundgren, Jens D.c,d; Delpech, Valerieb; Phillips, Andrew N.a

AIDS: January 28th, 2012 - Volume 26 - Issue 3 - p 335-343

doi: 10.1097/QAD.0b013e32834dcec9

Reference:- https://journals.lww.com/aidsonline/Fulltext/2012/01280/Projected_life_expectancy_of_people_with_HIV.9.aspx

Effective antiretroviral therapy (ART) has contributed greatly toward survival for people with HIV (variable HIVrate (It represents about the HIV cases.)), yet many remain undiagnosed until very late. Our aims were to estimate the life expectancy (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ) of an HIV-infected MSM living in a developed country with extensive access to ART and healthcare, and to assess the effect of late diagnosis on life expectancy.

A stochastic computer simulation model of HIV infection and the effect of ART was used to estimate life expectancy (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ) and determine the distribution of potential lifetime outcomes of an MSM, aged 30 years, who becomes HIV (variable HIVrate (It represents about the HIV cases.)), positive in 2010. The effect of altering the diagnosis rate was investigated.

Assuming a high rate of HIV (variable HIVrate (It represents about the HIV cases.)), diagnosis (median CD4 cell count at diagnosis, 432 cells/μl), projected median age at death (life expectancy) was 75.0 years. This implies 7.0 years of life were lost on average due to HIV. Cumulative risks of death by 5 and 10 years after infection were 2.3 and 5.2%, respectively. The 95% uncertainty bound for life expectancy was (68.0,77.3) years. When a low diagnosis rate was assumed (diagnosis only when symptomatic, median CD4 cell count 140 cells/μl), life expectancy (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ) was 71.5 years, implying an average 10.5 years of life lost due to HIV.

SUMMARY:-

If low rates of virologic failure observed in treated patients continue, predicted life expectancy (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ) is relatively high in people with HIV (variable HIVrate (It represents about the HIV cases.)) who can access a wide range of antiretrovirals. The greatest risk of excess mortality is due to delays in HIV diagnosis.

Case Study[2]

Smoking and life expectancy among HIV-infected individuals on antiretroviral therapy in Europe and North America

Marie Helleberg,a,b Margaret T. May,c Suzanne M. Ingle,c Francois Dabis,d Peter Reiss,e Gerd Fätkenheuer,f Dominique Costagliola,g,h Antonella d’Arminio,i Matthias Cavassini,j Colette Smith,k Amy C. Justice,l,m John Gill,n Jonathan A.C. Sterne,c and Niels Obela,b

PMCID: PMC4284008

PMID: 25426809

AIDS. 2015 Jan 14; 29(2): 221–229.

Published online 2015 Jan 7. doi: 10.1097/QAD.0000000000000540

Reference:- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4284008/

Cardiovascular disease and non-AIDS malignancies have become major causes of death among HIV-infected (variable HIVrate (It represents about the HIV cases.)) individuals. The relative impact of lifestyle and HIV-related factors are debated.

We estimated associations of smoking with mortality more than 1 year after antiretroviral therapy (ART) initiation among HIV-infected (variable HIVrate (It represents about the HIV cases.))individuals enrolled in European and North American cohorts. IDUs were excluded. Causes of death were assigned using standardized procedures. We used abridged life tables to estimate life expectancies (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ). Life-years lost to HIV (variable HIVrate (It represents about the HIV cases.)) were estimated by comparison with the French background population.

Among 17 995 HIV-infected (variable HIVrate (It represents about the HIV cases.)) individuals followed for 79 760 person-years, the proportion of smokers was 60%. The mortality rate ratio (MRR) comparing smokers with non-smokers was 1.94 [95% confidence interval (95% CI) 1.56–2.41]. The MRRs comparing current and previous smokers with never smokers were 1.70 (95% CI 1.23–2.34) and 0.92 (95% CI 0.64–1.34), respectively. Smokers had substantially higher mortality from cardiovascular disease, non-AIDS malignancies than non-smokers [MRR 6.28 (95% CI 2.19–18.0) and 2.67 (95% CI 1.60–4.46), respectively]. Among 35-year-old HIV-infected (variable HIVrate (It represents about the HIV cases.)) men, the loss of life-years associated with smoking and HIV was 7.9 (95% CI 7.1–8.7) and 5.9 (95% CI 4.9–6.9), respectively. The life expectancy (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ) of virally suppressed, never-smokers was 43.5 years (95% CI 41.7–45.3), compared with 44.4 years among 35-year-old men in the background population. Excess MRRs/1000 person-years associated with smoking increased from 0.6 (95% CI –1.3 to 2.6) at age 35 to 43.6 (95% CI 37.9–49.3) at age at least 65 years.

SUMMARY:-

Well treated HIV-infected (variable HIVrate (It represents about the HIV cases.)) individuals may lose more life expectancy (variable lifeexpectancy (It represents the average number of years a new born child would live if current mortality patterns were to stay the same.) ) through smoking than through HIV. Excess mortality associated with smoking increases markedly with age. Therefore, increases in smoking-related mortality can be expected as the treated HIV-infected population ages. Interventions for smoking cessation should be prioritized.

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