Analyzing NVDRS RAD Data Katie Fowler, Ph.D. Senior Scientist, NVDRS team NVDRS Surveillance Academy

Transcription

1 Analyzing NVDRS RAD Data Katie Fowler, Ph.D. Senior Scientist, NVDRS team NVDRS Surveillance Academy National Center for Injury Prevention and Control Division of Violence Prevention

2 Introduction This session is intended to give an overview of: NVDRS data structure Tips for setting up an analysis with NVDRS data

3 Data flow in NVDRS

4 Data processing We download and clean the national dataset once a year, each summer Each data year closeout is for 18 months prior States correct errors we send them Dataset represents a fixed point in time (the download) but the database is everchanging

5 Creation of the RAD dataset Create analytic SAS dataset from the csv files comprising the download. This process includes a series of complex many-to-one merges in SAS, so that victims, suspect information, weapon information, toxicology, and narratives are all packaged together in one dataset. This simplifies the data structure considerably for the end user, producing a flat file ready for analysis. Calculated variables are also created at this point to integrate information that is source-specific (e.g., circumstances) Create SAS formats catalog

6 Variable types in NVDRS Binary Categorical Continuous Free text Gateway variables

7 Top 10 Analysis Tips 1) Think carefully about years/states in your analysis 2) Use calculated abstractor death manner variable 3) Check availability of variables across years 4) Age filters require combination of 2 variables 5) Use calculated variables 6) Yes is yes, zero is not always no 7) NVDRS is not a survey 8) NVDRS does not use sampling methods 9) Use combined race/ethnicity variable 10) Be aware of victim-suspects

8 Tip 1: Think carefully about years/states

9 Tip 1: Think carefully about years/states May prefer to use 2005-onward because system was relatively more stable Stable set of states matters if: You are looking at trends There is a strong reason to think that some states would bias your sample But it does not necessarily matter if: You want to maximize your sample size and do not have the concerns above You want to report rates, as you can calculate an annualized average rate that adjusts for the addition of new states

10 Tip 2: Use the right manner of death variable NVDRS contains CME, LE, DC, and abstractor-assigned manner of death, as well as ICD codes for cause of death The abstractor-assigned manner of death is the gold standard for death manner because it reconciles manner across sources We strongly advise against using ICD codes to analyze manner of death: there are often errors The variable to use is AbstractorDeathmanner_C (not DeathMannerAbstractor) It is a calculated variable ( _C ) that mostly uses abstractor manner of death but also uses priority rules to substitute CME/DC/LE information if abstractor death manner is missing

11 Tip 3: Check availability of variables across years Variables were added in different years Several added in 2012/2013 data years when converted to web system The best way to check this is to run a crosstabs of variables by year If a number is very low (or 0) then increases suddenly it was probably added after the start of the system Year Walk by assault (n)

12 Tip 4: Age filters may require a combination of 2 variables Age is a continuous variable, no groupings and also can represent years or other units (e.g., months, weeks) Use variable AgeYears_C unless your questions involve infants under 1 year You would then filter by Age Unit NOT years and use the variable Age

13 Tip 5: Use calculated variables When the datasets are created, calculated variables are added These are indicated by _c at the end of the variable They include: AbstractorDeathmanner_c: Reconciles manner of death across sources AgeYears_c: Uses Age Unit as a filter to make continuous numeric age that represents ONLY years All the circumstance variables: Combines LE and CME to create one variable that indicates yes for a circumstance if EITHER indicates it E.g., if LE report indicates Financial problem and CME report does not, FinancialProblem_c = 1(yes)

14 Tip 6: Zero is not always no Circumstances are endorsed if they are present (1=yes) If a circumstance is not endorsed, this does not mean that the circumstance was definitely not present For example, you could interpret findings on suicide victims with financial problems like this: Over 50% of financial-related suicides were also precipitated by job problems But you should NOT interpret them like this: In around half of suicides precipitated by financial problems, victims were not experiencing job problems Other variables do have 0=no values (Yes, No, Unknown, Not applicable) Examples: Homeless, military, injured at work, injured at home

15 Tip 7: NVDRS is not a survey Circumstances are endorsed if they are present (1=yes), however Our coding guidance specifies that these are precipitating circumstances: That is, the investigators thought that they were related in some way to the V s death This differs from typical surveys, which usually simply record variables and then later employ statistical modeling to determine relation to outcomes Circumstances are already determined to be related to the outcome of interest (violent deaths) Therefore, statistical modeling of certain types is not useful with this data

16 Tip 8: NVDRS does not use sampling methods NVDRS is a census of all known violent deaths occurring in participating jurisdictions No sampling methodology is employed Therefore, it is not necessary (or interpretable) to report confidence intervals around counts of NVDRS data

17 Tip 9: Use the combined race/ethnicity variable Race and ethnicity are represented in various ways in the datasets: Yes/No to each of 5 race categories One combined race variable with all 5 categories One ethnicity variable capturing whether V was Hispanic For convenience, we have created a combined race/ethnicity variable that we recommend using, RaceEth Captures each race category, non-hispanic (e.g., non-hispanic black, non-hispanic white) Captures Hispanic, and takes each V in this category out of their race category count (e.g., Hispanic ethnicity, white race is now Hispanic)

18 Tip 10: Be aware of victim-suspects NVDRS contains information on victims AND victimsuspects (VSs) They are suspects in a violent death, not other crimes VSs have usually committed a homicide within the same incident. Can include: Homicide-suicides, where the VS committed one or more homicides then died by suicide Homicide-legal interventions, where the VS committed one or more homicides then was fatally injured by law enforcement Mutual homicides or shootouts, where two or more VSs kill each other PersonType =3 (Victim-Suspect) can be excluded from your analysis if inclusion is not appropriate

19 Statistical Analyses to Consider Descriptive Statistics Group Differences Testing (e.g., chi-sq, nonparametrics) Regression Latent class analysis

20 Qualitative Analysis NVDRS narratives or other data fields (e.g., occupation) have been used to code variables not traditionally captured in NVDRS, to identify a special type of case, or both. SAS and other statistical software have word search options Examples: Cancer-related suicides (case identification) Eviction-foreclosure related suicides (case identification; coding) Violent deaths among medical personnel (occupation coding)

21 Acknowledgments NVDRS Grantees Coroners/Medical Examiners and Law Enforcement staff in NVDRS States Surveillance Branch Alex Crosby, MD MPH, Surveillance Branch Chief Leroy Frazier Jr., MSPH CHES, Surveillance Branch Deputy Chief Shae Pope, MPH, Training Coordinator Mortality Surveillance Team Jamar Barnes MPH, Project Officer Jason Baumgardner MPH, Mathematical Statistician Craig Bryant, Computer Scientist Katherine Fowler, PhD, Senior Science Officer Shane Jack PhD, Science Officer Asha Ivey-Stephenson PhD, Science Officer Michele LaLand, Project Officer Bridget Lyons MPH, Science Officer Emiko Petrosky, MD MPH, Science Officer Rebecca Wilson MPH, Project Officer