<|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:"Human mobility and factors associated with malaria importation in Lusaka district, Zambia: a descriptive cross sectional study."
Abstract:"BackgroundMalaria is a major public health problem in Zambia with an estimated 4 million confirmed cases and 2389 deaths reported in 2015. Efforts to reduce the incidence of malaria are often undermined by a number of factors such as human mobility which may lead to introduction of imported infections. The aim of this study was to establish the burden of malaria attributed to human mobility in Lusaka district and identify factors associated with malaria importation among residents of Lusaka district.MethodsA cross sectional study was conducted in five randomly selected health facilities in Lusaka district from November 2015 to February 2016. Data was collected from 260 patients who presented with malaria and whose status was confirmed by rapid diagnostic test or microscopy. Each confirmed malaria case was interviewed using a structured questionnaire to establish their demographic characteristics, travel history and preventive measures. Travel history was used as a proxy to classify cases as either imported or local. Residency was also used as a secondary proxy for importation to compare characteristics of residents vs non-residents in relation to malaria importation. Logistic regression was used to determine factors associated with malaria importation among residents of Lusaka district.ResultsOut of 260 cases, 94.2% were classified as imported cases based on participants' travel history. There were 131 (50.4%) males and 129 (49.6%) females. Age distribution ranged from 0 to 68years with a median age of 15years (IQR 8-27). Imported cases came from all the ten provinces of Zambia with the Copperbelt Province being the highest contributor (41%). Of all imported cases, use of prophylaxis was found to be highly protective [AOR=0.22 (95% CI 0.06-0.82); p-value=0.024]. Other factors that significantly influence malaria transmission and importation by residents include duration of stay in a highly endemic region [AOR=1.25 (95% CI 1.09-1.44); p-value=0.001] and frequency of travel [AOR=3.71 (95% CI 1.26-10.84); p-value=0.017].ConclusionHuman mobility has influenced malaria transmission in Lusaka district through a number of factors by importing infections. This leads to onward transmission and poses a challenge to malaria elimination and control. However, taking of prophylaxis is highly protective and must be highly recommended.."
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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: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
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Human mobility and factors associated with malaria importation in Lusaka district, Zambia: a descriptive cross sectional study
BackgroundMalaria is a major public health problem in Zambia with an estimated 4 million confirmed cases and 2389 deaths reported in 2015. Efforts to reduce the incidence of malaria are often undermined by a number of factors such as human mobility which may lead to introduction of imported infections. The aim of this study was to establish the burden of malaria attributed to human mobility in Lusaka district and identify factors associated with malaria importation among residents of Lusaka district.MethodsA cross sectional study was conducted in five randomly selected health facilities in Lusaka district from November 2015 to February 2016. Data was collected from 260 patients who presented with malaria and whose status was confirmed by rapid diagnostic test or microscopy. Each confirmed malaria case was interviewed using a structured questionnaire to establish their demographic characteristics, travel history and preventive measures. Travel history was used as a proxy to classify cases as either imported or local. Residency was also used as a secondary proxy for importation to compare characteristics of residents vs non-residents in relation to malaria importation. Logistic regression was used to determine factors associated with malaria importation among residents of Lusaka district.ResultsOut of 260 cases, 94.2% were classified as imported cases based on participants' travel history. There were 131 (50.4%) males and 129 (49.6%) females. Age distribution ranged from 0 to 68years with a median age of 15years (IQR 8-27). Imported cases came from all the ten provinces of Zambia with the Copperbelt Province being the highest contributor (41%). Of all imported cases, use of prophylaxis was found to be highly protective [AOR=0.22 (95% CI 0.06-0.82); p-value=0.024]. Other factors that significantly influence malaria transmission and importation by residents include duration of stay in a highly endemic region [AOR=1.25 (95% CI 1.09-1.44); p-value=0.001] and frequency of travel [AOR=3.71 (95% CI 1.26-10.84); p-value=0.017].ConclusionHuman mobility has influenced malaria transmission in Lusaka district through a number of factors by importing infections. This leads to onward transmission and poses a challenge to malaria elimination and control. However, taking of prophylaxis is highly protective and must be highly recommended.