<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"ITALIAN ATLAS OF MORTALITY INEQUALITIES BY EDUCATION LEVEL."
Abstract:"OBJECTIVES To evaluate the geographical and socioeconomic differences in mortality and in life expectancy in Italy; to evaluate the proportion of mortality in the population attributable to a medium-low education level through the use of maps and indicators. DESIGN Longitudinal design of the population enrolled in the 2011 Italian Census, following the population over time and registering any exit due to death or emigration. SETTING AND PARTICIPANTS The study used the database of the Italian National Institute of Statistics (Istat) developed by linking the 2011 Census with the Italian National Register of Causes of Death (2012-2014) for 35 groups of causes of death. Age, sex, residence, and education level information were collected from the Census. MAIN OUTCOME MEASURES Life expectancy at birth was calculated by sex, Italian region, and education level. For the population aged 30-89 years, the following items were developed by sex: 1. provincial maps showing, fir each cause of death, the distribution in quintiles of smoothed standardized mortality ratio (SMR), adjusted for age and education level and estimated with Bayesian models fir small areas (spatial conditional autoregressive model); 2. regional maps of population attributable fraction (PAP) for low and medium education levels, calculated starting from age-standardized mortality ratios; 3. tables illustrating for each region standardized mortality rates and standardized years of life lost rate by age (standardized YLL rate), and mortality rate ratios standardized by age (MMRs). RESULTS Males with a lower education level throughout Italy show a life expectancy at birth that is 3 years less than those with higher education; residents in Southern Italy lose an additional year in life expectancy, regardless of education level. Social inequalities in mortality are present in all regions, but are more marked in the poorer regions of Southern Italy. Geographical differences, taking into account the different population distributions in terms of age and education level, produce mortality differences for all causes: from -15% to +30% in women and from -13% to +26% in men, compared to the national average. Among the main groups of causes, the geographical differences are greater for cardiovascular diseases, respiratory diseases, and accidents, and lower for many tumour sites. A clear mortality gradient with an excess in Southern Italy can be seen for cardiovascular diseases: there are some areas where mortality for people with higher education level is higher than that for residents in Northern Italy with low education level. The gradient for All tumours, instead, is from South to North, as it is for most single tumour sites. Population attributable fraction for low education level in Italy, taking into account the population distribution by age, is 13.4% in women and 18.3% in men. CONCLUSIONS The study highlighted important geographical differences in mortality, regardless of age and socioeconomic ."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

ITALIAN ATLAS OF MORTALITY INEQUALITIES BY EDUCATION LEVEL
OBJECTIVES To evaluate the geographical and socioeconomic differences in mortality and in life expectancy in Italy; to evaluate the proportion of mortality in the population attributable to a medium-low education level through the use of maps and indicators. DESIGN Longitudinal design of the population enrolled in the 2011 Italian Census, following the population over time and registering any exit due to death or emigration. SETTING AND PARTICIPANTS The study used the database of the Italian National Institute of Statistics (Istat) developed by linking the 2011 Census with the Italian National Register of Causes of Death (2012-2014) for 35 groups of causes of death. Age, sex, residence, and education level information were collected from the Census. MAIN OUTCOME MEASURES Life expectancy at birth was calculated by sex, Italian region, and education level. For the population aged 30-89 years, the following items were developed by sex: 1. provincial maps showing, fir each cause of death, the distribution in quintiles of smoothed standardized mortality ratio (SMR), adjusted for age and education level and estimated with Bayesian models fir small areas (spatial conditional autoregressive model); 2. regional maps of population attributable fraction (PAP) for low and medium education levels, calculated starting from age-standardized mortality ratios; 3. tables illustrating for each region standardized mortality rates and standardized years of life lost rate by age (standardized YLL rate), and mortality rate ratios standardized by age (MMRs). RESULTS Males with a lower education level throughout Italy show a life expectancy at birth that is 3 years less than those with higher education; residents in Southern Italy lose an additional year in life expectancy, regardless of education level. Social inequalities in mortality are present in all regions, but are more marked in the poorer regions of Southern Italy. Geographical differences, taking into account the different population distributions in terms of age and education level, produce mortality differences for all causes: from -15% to +30% in women and from -13% to +26% in men, compared to the national average. Among the main groups of causes, the geographical differences are greater for cardiovascular diseases, respiratory diseases, and accidents, and lower for many tumour sites. A clear mortality gradient with an excess in Southern Italy can be seen for cardiovascular diseases: there are some areas where mortality for people with higher education level is higher than that for residents in Northern Italy with low education level. The gradient for All tumours, instead, is from South to North, as it is for most single tumour sites. Population attributable fraction for low education level in Italy, taking into account the population distribution by age, is 13.4% in women and 18.3% in men. CONCLUSIONS The study highlighted important geographical differences in mortality, regardless of age and socioeconomic level, with a more significant impact in the poorer Southern regions, revealing a never-before-seen health advantage in the regions along the Adriatic coast. A lower education level explains a considerable proportion of mortality risk, although with differing effects by geographical area and cause of death. There are still mortality inequalities in Italy, therefore, representing a possible missed gain in health in our Country; these inequalities suggest a reassessment of priorities and definition of health targets. Forty years after the Italian National Health Service was instituted, the goal of health equity has not yet been filly achieved.