<|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:"A new method to estimate mortality in crisis-affected and resource-poor settings: validation study."
Abstract:"Background Data on mortality rates are crucial to guide health interventions in crisis-affected and resource-poor settings. The methods currently available to collect mortality data in such settings feature important methodological limitations. We developed and validated a new method to provide near real-time mortality estimates in such settings. Methods We selected four study sites: Kabul, Afghanistan; Mae La refugee camp, Thailand; Chiradzulu District, Malawi; and Lugufu and Mtabila refugee camps, Tanzania. We recorded information about all deaths in a 60-day period by asking key community informants and decedents' next of kin to refer interviewers to bereaved households. We used the total number of deaths and population estimates to calculate mortality rates for 60- and 30-day periods. For validation we compared these rates with a best estimate of mortality using capture-recapture analysis with two further independent lists of deaths. Results The population covered by the new method was 76 476 persons in Kabul, 43 794 in Mae La camp, 54 418 in Chiradzulu District and 80 136 in the Tanzania camps. The informant method showed moderate sensitivity (55.0% in Kabul, 64.0% in Mae La, 72.5% in Chiradzulu and 67.7% in Tanzania), but performed better than the active surveillance system in the Tanzania refugee camps. Conclusions The informant method currently features moderate sensitivity for accurately assessing mortality, but warrants further development, particularly considering its advantages over current options (ease of implementation and analysis and near-real estimates of mortality rates). Strategies should be tested to improve the performance of the informant method.."
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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. 

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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:
Q1 = [1];
Q2 = [4,5];
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A new method to estimate mortality in crisis-affected and resource-poor settings: validation study
Background Data on mortality rates are crucial to guide health interventions in crisis-affected and resource-poor settings. The methods currently available to collect mortality data in such settings feature important methodological limitations. We developed and validated a new method to provide near real-time mortality estimates in such settings. Methods We selected four study sites: Kabul, Afghanistan; Mae La refugee camp, Thailand; Chiradzulu District, Malawi; and Lugufu and Mtabila refugee camps, Tanzania. We recorded information about all deaths in a 60-day period by asking key community informants and decedents' next of kin to refer interviewers to bereaved households. We used the total number of deaths and population estimates to calculate mortality rates for 60- and 30-day periods. For validation we compared these rates with a best estimate of mortality using capture-recapture analysis with two further independent lists of deaths. Results The population covered by the new method was 76 476 persons in Kabul, 43 794 in Mae La camp, 54 418 in Chiradzulu District and 80 136 in the Tanzania camps. The informant method showed moderate sensitivity (55.0% in Kabul, 64.0% in Mae La, 72.5% in Chiradzulu and 67.7% in Tanzania), but performed better than the active surveillance system in the Tanzania refugee camps. Conclusions The informant method currently features moderate sensitivity for accurately assessing mortality, but warrants further development, particularly considering its advantages over current options (ease of implementation and analysis and near-real estimates of mortality rates). Strategies should be tested to improve the performance of the informant method.