Medical Expenditure Panel Survey (MEPS)

analyze the medical expenditure panel survey (meps) with r the meps household component leads the pack for examining individual-level medical expenditures by payor and type of service. total expenditures captured by the survey tend to be low, but unbiased across the board and can be adjusted to match the national health expenditure accounts. i wrote the wikipedia article , so it's data-oriented. if you vandalize it, i will revert your changes and t.p. your front yard. give it a read for more details about what's possible. the agency for healthcare research and quality (ahrq) produces meps and rhymes with shark. the medical expenditure panel survey - household component (meps-hc) contains data laid out a few different ways. the consolidated file has one-record-per-person with all the complex sample survey variables. start there. the eight event files contain one-record-per-person-per-event, and (except for the supplies/vision table) those events have some sort of dates. crikey. there are tables with one-record-per-person-per-medical-condition, one-record-per-job, even a one-record-per-person-per-interview-per-private-health-plan table for anyone who wants to spend less time with his or her family. if you merge anything to the consolidated file, make sure you understand the difference between setting the parameter all.x = TRUE versus all.x = FALSE -- some respondents have zero records in the non-consolidated files, others have multiple. hot tip: you probably want to aggregate non-consolidated files somehow. you might use tapply and aggregate, but i prefer aggregation using sql. everything can be read in as a sas transport file (.ssp) using read.xport, but if you like making things harder than they have to be (i.e. if you ride a fixie), you can also follow the example bu ried in the ?read.SAScii documentation. ahrq draws the meps sample from the national health interview survey, interviews about thirty-five thousand individuals per year, and keeps everyone in the panel for two years. half of the respondents are in their first of two years of interviews, half are in their second. capice? meps generalizes to the us non-institutional, non-active duty military population. this new github repository contains three scripts: 1996-2010 household component - download all microdata .R loop through every year and every file type, download, then rename according to a pattern save each file as an r data file (.rda) and (if specified by the user) sas transport (.ssp), comma-separated value (.csv), and s tata-readable (.dta) download the codebook and documentation, if available 2010 consolidated - analyze with brr.R load the r data file (.rda) created by the download script (above) set up the balanced repeated replication design outlined in this document perform a boatload of analysis examples (spoiler: there will be barplots ) 2010 consolidated - analyze with tsl.R load the r data file (.rda) created by the download script (above) set up a taylor-series linearization survey design outlined in this document perform the same boatload of analysis examples click here to view these three scripts for more detail about the medical expenditure panel survey - household component (meps-hc), visit: the agency for healthcare research and quality's medical expenditure panel survey homepage the meps insurance component homepage (microdata not publicly available) a younger version of myself giving an introduction to online query tools with mepsnet at slide ten notes: if you don't know which analysis method to use, choose the replicate weights. replicate weighting requires slightly more ram, but taylor-series designs don't allow the computation of a confidence interval around quantile statistics (like the median). this repository doesn't include a script to replicate the meps taylor-series linearization or replicate-w eighted methods of variance calculation, because i wrote the original journal article with meps. it's legit. if you just want a one-off statistic and can't bear to get your typing fingers dirty, try their fabulous table-building website mepsnet confidential to sas, spss, stata, sudaan users: why are you still making ca lls with two tin-cans and a string now that we've created cell phones? time to transition to r. :D

使用R语言开展医疗支出专项调查（Medical Expenditure Panel Survey, MEPS）数据分析：MEPS家庭组件（MEPS-Household Component, MEPS-HC）在按支付方与服务类型调研个体层面医疗支出领域处于领先地位。本次调查捕获的总支出水平整体偏低，但具备无偏性，且可通过调整以匹配国民健康支出账户数据。我曾撰写该主题的维基百科词条，因此本内容以数据为导向。若有人恶意篡改词条内容，我将回滚所有更改，并在前院草坪张贴厕纸进行恶作剧。如需了解更多可行分析方向，请参阅该维基词条。

医疗保健研究与质量局（Agency for Healthcare Research and Quality, AHRQ）负责编制MEPS，其发音与单词shark（鲨鱼）押韵。MEPS-HC的数据组织形式多样：合并文件采用一人一条记录的格式，包含所有复杂抽样调查变量，建议以此作为分析起点；8个事件文件采用一人一事件一条记录的格式，除供应/眼科表格外，其余事件文件均包含相关日期字段，值得注意。另有按一人一疾病、一人一工作岗位组织的表格，甚至包含一人一访谈一私人健康保险计划的表格——对于那些愿意减少陪伴家人时间的研究者而言颇为合适。

若需将数据与合并文件进行合并，请务必明确参数all.x = TRUE与all.x = FALSE的区别：部分受访者在非合并文件中无对应记录，而部分受访者则存在多条记录。实用技巧：通常需要对非合并文件进行聚合处理。可使用tapply与aggregate函数完成聚合，而我个人更倾向于使用结构化查询语言（Structured Query Language, SQL）进行聚合。所有数据均可通过read.xport函数读取为SAS传输文件（.ssp）；若您偏好自找麻烦（比如骑死飞单车的硬核玩家），也可参考?read.SAScii文档中的示例进行导入。

AHRQ从全国健康访谈调查中抽取MEPS样本，每年调研约35000名个体，并将所有受访者纳入为期两年的追踪面板。其中半数受访者处于两年调研周期的第一年，另一半处于第二年，清楚了吗？MEPS的样本可推广至美国非机构化、非现役军人人群。

本全新GitHub仓库包含三个脚本：
1. 1996-2010家庭组件-下载所有微观数据.R：遍历每一年与每一种文件类型，完成下载后按指定格式重命名，将每个文件保存为R数据文件（.rda），并可根据用户需求导出为SAS传输文件（.ssp）、逗号分隔值文件（.csv）以及Stata可读格式文件（.dta）；同时可按需下载代码本与相关文档（若有提供）。
2. 2010合并文件-使用平衡重复复制法（Balanced Repeated Replication, BRR）分析.R：加载前述下载脚本生成的R数据文件（.rda），按照本文档所述设置平衡重复复制抽样设计，执行大量分析示例（剧透：将包含条形图）。
3. 2010合并文件-使用泰勒级数线性化法（Taylor-Series Linearization, TSL）分析.R：加载前述下载脚本生成的R数据文件（.rda），按照本文档所述设置泰勒级数线性化抽样设计，执行完全相同的大量分析示例。

如需查看这三个脚本的更多细节，或了解MEPS-HC的更多信息，请访问：AHRQ的MEPS官方主页，以及MEPS保险组件官方主页（微观数据不对外公开）。一段早期教学视频中，年轻的我曾使用MEPSnet在线查询工具进行介绍，详见第10张幻灯片。

补充说明：若您不确定应选用何种分析方法，请选择重复加权法。重复加权法需要稍多的运行内存，但泰勒级数线性化设计无法针对分位数统计量（如中位数）计算置信区间。本仓库未包含用于复现MEPS泰勒级数线性化或重复加权方差计算方法的脚本，因为我曾在原创期刊文章中使用MEPS完成相关研究，该方法具备合法性。若您仅需单次统计量且不愿动手编写代码，可尝试他们出色的表格构建网站MEPSnet。

致SAS、SPSS、Stata、SUDAAN用户：既然我们已经有了智能手机，为何还要用两个易拉罐和一根绳子打电话？是时候转向R语言了。:D