Behavioral Risk Factor Surveillance System (BRFSS)

analyze the behavioral risk factor surveillance system (brfss) with r and monetdb experimental. the behavioral risk factor surveillance system (brfss) aggregates behavioral health data from 400,000 adults via telephone every year. it's um *clears throat* the largest telephone survey in the world and it's gotta lotta uses, here's a list neato. state health departments perform the actual d ata collection (according to a nationally-standardized protocol and a core set of questions), then forward all responses to the centers for disease control and prevention (cdc) office of surveillance, epidemiology, and laboratory services (osels) where the nationwide, annual data set gets constructed. independent administration by each state allows them to tack on their own questions that other states might not care about. that way, florida could exempt itself from all t he risky frostbite behavior questions. in addition to providing the most comprehensive behavioral health data set in the united states, brfss also eeks out my worst acronym in the federal government award - onchit a close second. annual brfss data sets have grown rapidly over the past half-decade: the 1984 data set contained only 12,258 respondents from 15 states, all states were participating by 1994, and the 2011 file has surpassed half a million interviews. if you're examining trends over time, do your homework and review the brfss technical documents for the years you're looking at (plus any years in between). what might you find? well for starters, the cdc switched to sampling cellphones in their 2011 methodology. unlike many u.s. government surveys, brfss is not conducted for each resident at a sampled household (phone number). only one respondent per phone number gets interviewed. did i miss anything? well if your next question is frequently asked, you're in luck. all brfss files are available in sas transport format so if you're sittin' pretty on 16 gb of ram, you could potentially read.xport a single year and create a taylor-series survey object using the sur vey package. cool. but hear me out: the download and importation script builds an ultra-fast monet database (click here for speed tests, installation instructions) on your local hard drive. after that, these scripts are shovel-ready. consider importing all brfss files my way - let it run overnight - and during your actual analyses, code will run a lot faster. the brfss generalizes to the u.s. adult (18+ ) (non-institutionalized) population, but if you don't have a phone, you're probably out of scope. this new github repository contains four scripts: 1984 - 2011 download all microdata.R create the batch (.bat) file needed to initiate the monet database in the future download, unzip, and import each year specified by the user create and save the taylor-series linearization complex sample designs create a well-documented block of code to re-initiate the monetdb server in the future 2011 single-year - analysis examples.R run the well-d ocumented block of code to re-initiate the monetdb server load the r data file (.rda) containing the taylor-series linearization design for the single-year 2011 file perform the standard repertoire of analysis examples, only this time using sqlsurvey functions 2010 single-year - variable recode example.R run the well-documented block of code to re-initiate the monetdb server copy the single-year 2010 table to maintain the pristine original add a new drinks per month category variable by hand re-create then save the sqlsurvey taylor-series linearization complex sample design on this new table close everything, then load everything back up in a fresh instance of r replicate statistics from this table , pulled from the cdc's web-enabled analysis tool replicate cdc weat - 2010.R run the well-documented block of code to re-initiate the monetdb server load the r data file (.rda) containing the taylor-series linearization design for the single-year 2010 file replicate statistics from this table, pulled from the cdc's web-enabled analysis tool click here to view these four scripts for more detail about the behavioral risk factor surveillance system, visit: the centers for disease control and prevention beh avioral risk factor surveillance system homepage the behavioral risk factor surveillance system wikipedia entry notes: if you're just scroungin' around for a few statistics, the cdc's web-enabled analysis tool (weat) might be all your heart desires. in fact , on slides seven, eight, nine of my online query tools video, i demonstrate how to use this table creator. weat's more advanced than most web-based survey analysis - you can run a regression. but only seven (of eighteen) years can currently be queried online. since data types in sql are not as plentiful as they are in the r language, the definition of a monet database-backed complex design object requires a cutoff be specified between the categorical variables and the linear ones. that cut point gets defined using the check.factors argument in the sqlsurvey() and sqlrepsurvey() function calls. check.factors defaults to ten, but can be raised or lowered as needed. here's how it works: if the column would be a character string or factor inside an r data frame, the sql database stores it as a varchar column. if the column would be numeric or integer in an r data frame, but has fewer than eleven unique values, the sql database also stores it as a varchar column.< /li> if the column would be numeric or integer in an r data frame, but has at least eleven unique values, the sql database stores it as a double (that's sql-speak for numeric). confidential to sas, spss, stata, sudaan users: when statistical languages are plotted on cartesian coordinates, what-you-paid-for vs. what-you-get are best represented as y = 1/x. time to transition to r. :D

本实验将使用R与MonetDB对行为风险因素监测系统（Behavioral Risk Factor Surveillance System, BRFSS）展开分析。行为风险因素监测系统每年通过电话调研收集40万名成年人的行为健康数据，是全球规模最大的电话调查项目，应用场景十分广泛。该项目由各州卫生部门按照全国统一的标准化方案与核心问卷开展实际的数据收集工作，随后将所有调研结果报送至疾病控制与预防中心（Centers for Disease Control and Prevention, CDC）的监测、流行病学与实验室服务办公室（Office of Surveillance, Epidemiology, and Laboratory Services, OSELS），由该机构构建全国年度数据集。各州可独立开展调研，因此能够追加本州关注而其他州无需涉及的问卷题目——例如佛罗里达州即可豁免所有与冻伤风险行为相关的调研问题。
除了提供全美最全面的行为健康数据集外，BRFSS还曾获评联邦政府最糟糕缩写称号——Onchit紧随其后。BRFSS的年度数据集规模在过去五年间快速扩张：1984年的数据集仅包含来自15个州的12258份有效问卷；1994年实现全国各州全覆盖；2011年的调研样本量已突破50万份。
若您希望开展时序趋势分析，请务必查阅对应年份及间隔年份的BRFSS技术文档。可开展的分析方向举例如下：疾控中心于2011年的调研方法中改用手机抽样方案。与多数美国政府调研项目不同，BRFSS并非对抽样家庭（电话号码）中的每位居民进行访谈，每个电话号码仅可选取一名受访者参与调研。
若您有常见疑问，可查阅以下说明：所有BRFSS数据集均以SAS传输格式提供。若您的设备配备16GB以上内存，可通过`survey`包读取单年度.xport格式文件并构建泰勒级数调研对象。不过，本项目提供的下载与导入脚本可在本地硬盘上构建极速运行的Monet数据库（点击此处查看性能测试与安装指南），脚本就绪后即可直接使用。建议您直接采用本方案导入全部BRFSS数据集，可让任务在夜间自动运行；在正式分析阶段，代码执行速度将大幅提升。
BRFSS的调研结果可推广至美国18岁及以上非机构化成年人群，但未拥有电话的人群不在调研覆盖范围内。
本项目对应的GitHub仓库包含四个脚本文件：
1. `1984-2011_download_all_microdata.R`：生成用于后续创建Monet数据库的批量（.bat）文件，实现用户指定年份的所有微数据下载、解压与导入工作；创建并保存泰勒级数线性化复杂抽样设计代码；编写文档完备的代码块，用于未来重新启动MonetDB服务器。
2. `2011_single-year_analysis_examples.R`：运行文档完备的MonetDB服务器重启代码块；加载包含2011年单年度数据集泰勒级数线性化设计的R数据文件（.rda）；使用`sqlsurvey`包函数执行标准分析示例。
3. `2010_single-year_variable_recode_example.R`：运行MonetDB服务器重启代码块；复制2010年单年度数据集以保留原始未修改版本；手动新增“每月饮酒量”分类变量；基于新数据集重新创建并保存`sqlsurvey`泰勒级数线性化复杂抽样设计；关闭所有进程后在全新的R实例中重新加载所有内容；基于新数据集复现统计结果，该结果可从CDC的网页分析工具（Web-Enabled Analysis Tool, WEAT）获取。
4. `replicate_cdc_weat_2010.R`：运行MonetDB服务器重启代码块；加载包含2010年单年度数据集泰勒级数线性化设计的R数据文件（.rda）；基于该数据集复现从CDC网页分析工具获取的统计结果。
点击此处查看这四个脚本的详细说明。如需了解更多行为风险因素监测系统的相关信息，可访问：疾病控制与预防中心行为风险因素监测系统官方主页，以及行为风险因素监测系统维基百科条目。
备注：若您仅需获取少量统计数据，CDC的网页分析工具（WEAT）即可满足需求。事实上，在我在线查询工具视频的第7、8、9页幻灯片中，我演示了如何使用该表生成器。WEAT的功能优于多数基于网页的调研分析工具，支持运行回归分析，但目前仅支持在线查询18个年份中的7个。由于SQL支持的数据类型不如R语言丰富，在基于Monet数据库构建复杂抽样设计对象时，需要指定分类变量与连续变量的分界阈值。该分界阈值可通过`sqlsurvey()`与`sqlrepsurvey()`函数调用中的`check.factors`参数进行设置，其默认值为10，可根据需求调整。具体规则如下：
- 若某列在R数据框中为字符型或因子型，则SQL数据库会将其存储为`varchar`类型；
- 若某列在R数据框中为数值型或整数型，但唯一值数量少于11个，SQL数据库同样会将其存储为`varchar`类型；
- 若某列在R数据框中为数值型或整数型，且唯一值数量不少于11个，SQL数据库会将其存储为`double`类型（SQL中对应数值型）。
致SAS、SPSS、Stata、SUDAAN用户：若以笛卡尔坐标系绘制统计语言的“付费成本”与“所得价值”关系曲线，其关系可近似为y=1/x。是时候转向R语言了：D