<|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:"What protects against pre-diabetes progressing to diabetes? Observational study of integrated health and social data."
Abstract:"Aims: To examine the incidence of type 2 diabetes in people with newly diagnosed prediabetes and the factors that protect against this progression. Methods: The study population was 14,043 adults with pre-diabetes enrolled in a primary health organization in the upper North Island of New Zealand. Glycated hemoglobin (HbA1c) and body mass index (BMI) were linked to government health, census and social datasets in the Statistics New Zealand Integrated Data Infrastructure. Adults with a first diagnosis of pre-diabetes between 2009 and 2017 (HbA1c in range 5.9-6.6% [41-49 mmol/mol]) were followed-up for type 2 diabetes incidence. Cox regression was used to examine protective factors and adjust for potential confounding. Results: Cumulative diabetes incidence was 5.0% after three years. Progression was greater in younger adults, men, people with higher HbA1c, greater BMI and a more recent diagnosis. Progression was lower in people treated with metformin, and Indigenous language speakers. Higher progression rates for Maori (Indigenous population) and Pacific peoples (migrants to New Zealand) were related to higher baseline HbA1c. Conclusions: This is the first study to identify Indigenous language as a protective factor against diabetes, and results confirm obesity as a key target for population prevention. People with identified risk factors should be prioritized for pre-diabetes interventions. (C) 2018 Elsevier B.V. All rights reserved.."
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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: 

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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
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What protects against pre-diabetes progressing to diabetes? Observational study of integrated health and social data
Aims: To examine the incidence of type 2 diabetes in people with newly diagnosed prediabetes and the factors that protect against this progression. Methods: The study population was 14,043 adults with pre-diabetes enrolled in a primary health organization in the upper North Island of New Zealand. Glycated hemoglobin (HbA1c) and body mass index (BMI) were linked to government health, census and social datasets in the Statistics New Zealand Integrated Data Infrastructure. Adults with a first diagnosis of pre-diabetes between 2009 and 2017 (HbA1c in range 5.9-6.6% [41-49 mmol/mol]) were followed-up for type 2 diabetes incidence. Cox regression was used to examine protective factors and adjust for potential confounding. Results: Cumulative diabetes incidence was 5.0% after three years. Progression was greater in younger adults, men, people with higher HbA1c, greater BMI and a more recent diagnosis. Progression was lower in people treated with metformin, and Indigenous language speakers. Higher progression rates for Maori (Indigenous population) and Pacific peoples (migrants to New Zealand) were related to higher baseline HbA1c. Conclusions: This is the first study to identify Indigenous language as a protective factor against diabetes, and results confirm obesity as a key target for population prevention. People with identified risk factors should be prioritized for pre-diabetes interventions. (C) 2018 Elsevier B.V. All rights reserved.