Variable coding Sample Clauses

Variable coding. City, township and region of location was simplified to rural versus urban based on urban and rural population percentages. Urban and rural population percentages were based on UNFPA’s Myanmar 2014 population & housing census (UNFPA, 2014). Subjects located in urban areas that reported not being registered in their current district were classified as being from a rural location in order to account for the high frequency of rural to urban migration in these areas. Education was re-coded into three education levels, combining elementary school education with having not completed school, middle school education with high school education, and combining higher education with professional training. Income source was collapsed into 6 occupation types for analysis; unemployed, office worker, shopkeeper/merchant, manual labor, service based and retired. Marital status was coded as married versus unmarried, with unmarried including single, divorced and widowed. For responses to perceived time taken to treat TB, responses were coded as “<6 months”, “6-12 months”, “12+ months’ and “Don’t Know”. Responses for reported location of last medical care visit were reclassified to combine “informal pharmacy” and “informal healer” due to their close similarities, in order to quantify the total visits to vendors who typically provide TCM. Subject’s preferred treatment type was reclassified to combine ‘combination therapy’ with ‘traditional therapy’ due to sparse data, under the assumption that subjects preferring to take a treatment combination of both western and traditional medicine aren’t completely certain about whether traditional herbal medicine can be used as an effective treatment. For our outcome variable “believes that TCM can cure TB”, participants who answered “Don’t Know” were combined with those that answered “Yes” due to sparse data issues following our preliminary analysis. This decision was justified by response profile similarities between the two groups and by the fact that subjects that answered ‘Don’t Know’ were more likely to seek traditional treatment options. For multivariable analysis, individual levels for multi-level variables were recoded to binary ‘yes/no’ outcomes in order to eliminate dummy variables and allow us to isolate the influence of sub-level factors predictive ability.
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