Evidence Report/Technology Assessment

Number 222

Data Linkage 

Strategies To 

Advance Youth 

Suicide Prevention

Evidence-Based

Practice
Evidence Report/Technology Assessment

Number 222

Data Linkage Strategies To Advance Youth 

Suicide Prevention

Prepared for: 

Agency for Healthcare Research and Quality

U.S. Department of Health and Human Services

5600 Fishers Lane 

Rockville, MD 20857 

www.ahrq.gov 

Contract No. 290-2012-00007-I 

Prepared by:

Johns Hopkins University Evidence-based Practice Center

Baltimore, MD

Investigators: 

Holly C. Wilcox, Ph.D. 

Lawrence Wissow, M.D., M.P.H. 

Hadi Kharrazi, M.H.I., M.D., Ph.D. 

Renee F. Wilson, M.S. 

Rashelle J. Musci, Ph.D. 

Allen Zhang, B.S. 

Karen A. Robinson, Ph.D. 

AHRQ Publication No. 16(17)-E001-EF 

October 2016 
This report is based on research conducted by the Johns Hopkins University Evidence-based 

Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality 

(AHRQ), Rockville, MD (Contract No. 290-2012-00007-I). The findings and conclusions in this 

document are those of the authors, who are responsible for its contents; the findings and 

conclusions do not necessarily represent the views of AHRQ. Therefore, no statement in this 

report should be construed as an official position of AHRQ or of the U.S. Department of Health 

and Human Services. 

None of the investigators have any affiliations or financial involvement that conflicts with 

the material presented in this report.

The information in this report is intended to help health care decisionmakers—patients and 

clinicians, health system leaders, and policymakers, among others—make well-informed 

decisions and thereby improve the quality of health care services. This report is not intended to 

be a substitute for the application of clinical judgment. Anyone who makes decisions concerning 

the provision of clinical care should consider this report in the same way as any medical 

reference and in conjunction with all other pertinent information, i.e., in the context of available 

resources and circumstances presented by individual patients. 

This report is made available to the public under the terms of a licensing agreement between the 

author and the Agency for Healthcare Research and Quality. This report may be used and 

reprinted without permission, except those copyrighted materials that are clearly noted in the 

report. Further reproduction of those copyrighted materials is prohibited without the express 

permission of copyright holders. 

AHRQ or U.S. Department of Health and Human Services endorsement of any derivative 

products that may be developed from this report, such as clinical practice guidelines, other 

quality enhancement tools, or reimbursement or coverage policies, may not be stated or implied.

This report may periodically be assessed for the currency of conclusions. If an assessment is 

done, the resulting surveillance report describing the methodology and findings will be found on 

the Effective Health Care Program Web site at www.effectivehealthcare.ahrq.gov. Search on the 

title of the report.

Persons using assistive technology may not be able to fully access information in this report. For 

assistance, contact EffectiveHealthCare@ahrq.hhs.gov. 

Suggested citation: Wilcox HC, Wissow L, Kharrazi H, Wilson RF, Musci RJ, Zhang A, 

Robinson KA. Data Linkage Strategies To Advance Youth Suicide Prevention. Evidence 

Report/Technology Assessment No. 222. (Prepared by the Johns Hopkins University Evidencebased Practice Center under Contract No. 290-2012-00007-I.) AHRQ Publication No. 16(17)-

E001-EF. Rockville, MD: Agency for Healthcare Research and Quality; October 2016. 

www.effectivehealthcare.ahrq.gov/reports/final.cfm. DOI: https://doi.org/10.23970/

AHRQEPCERTA222.

ii
Preface

The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-based 

Practice Centers (EPCs), sponsors the development of systematic reviews to assist public- and 

private-sector organizations in their efforts to improve the quality of health care in the United 

States. These reviews provide comprehensive, science-based information on common, costly 

medical conditions, and new health care technologies and strategies. The Office of Disease 

Prevention of the National Institutes of Health (NIH) requested this report from the EPC 

Program at AHRQ. AHRQ assigned this report to the Johns Hopkins University EPC (Contract 

No. 290-2012-00007-I). The report was presented March 29–30, 2016, at an NIH public 

Pathways to Prevention Workshop, Advancing Research To Prevent Youth Suicide.

Systematic reviews are the building blocks underlying evidence-based practice; they focus 

attention on the strength and limits of evidence from research studies about the effectiveness and 

safety of a clinical intervention. In the context of developing recommendations for practice, 

systematic reviews can help clarify whether assertions about the value of the intervention are 

based on strong evidence from clinical studies. For more information about AHRQ EPC 

systematic reviews, see www.effectivehealthcare.ahrq.gov/reference/purpose.cfm.

AHRQ expects that these systematic reviews will be helpful to health plans, providers, 

purchasers, government programs, and the health care system as a whole. Transparency and 

stakeholder input are essential to the Effective Health Care Program. Please visit the Web site 

(www.effectivehealthcare.ahrq.gov) to see draft research questions and reports or to join an 

email list to learn about new program products and opportunities for input. 

 If you have comments on this systematic review, they may be sent by mail to the Task 

Order Officer named below at: Agency for Healthcare Research and Quality, 5600 Fishers Lane, 

Rockville, MD 20857, or by email to epc@ahrq.hhs.gov. 

Sharon B. Arnold, Ph.D. Arlene S. Bierman, M.D., M.S.

Acting Director Director

Agency for Healthcare Research and Quality Center for Evidence and Practice 

Improvement

Agency for Healthcare Research and Quality

Stephanie Chang, M.D., M.P.H. Kim Wittenberg, M.A

Director Task Order Officer

Evidence-based Practice Center Program Center for Evidence and Practice

Center for Evidence and Practice Improvement Improvement

Agency for Healthcare Research and Quality Agency for Healthcare Research and Quality

iii
Acknowledgments 

The authors gratefully acknowledge the following individuals for their contributions to this 

project: Ryoko Susukida and Fardad Gharghabi. We also thank Jeanette Edelstein for her 

assistance with this report.

Technical Expert Panel

In designing the study questions and methodology at the outset of this report, the EPC 

consulted several technical and content experts. Broad expertise and perspectives were 

sought. Divergent and conflicted opinions are common and perceived as healthy scientific 

discourse that results in a thoughtful, relevant systematic review. Therefore, in the end, study 

questions, design, methodologic approaches, and/or conclusions do not necessarily represent the 

views of individual technical and content experts.

Technical Experts must disclose any financial conflicts of interest greater than $10,000 and any 

other relevant business or professional conflicts of interest. Because of their unique clinical or 

content expertise, individuals with potential conflicts may be retained. The TOO and the EPC 

work to balance, manage, or mitigate any potential conflicts of interest identified.

The list of Technical Experts who provided input to this report follows:

Julie Cerel, Ph.D.*

University of Kentucky

Lexington, KY

Nicole Gibson, M.S.W.

American Foundation for Suicide Prevention

New York, NY

Jill Harkavy-Friedman, Ph.D.*

American Foundation for Suicide Prevention

New York, NY

Katherine Masyn, Ph.D.

Georgia State University School of Public Health

Atlanta, GA

Victor Schwartz, M.D.*

The Jed Foundation

New York, NY

Daniel Stein, B.A., M.B.A.

Stewards of Change

Huntington, NY

iv
Sharon Strouse, M.A., A.T.R.-B.C, L.C.P.A.T.*

The Kristin Rita Strouse Foundation

Baltimore, MD

Gary Wynn, M.D.*

Uniformed Services University of the Health Sciences

Bethesda, MD

*Provided input on Draft Report.

Peer Reviewers

Prior to publication of the final evidence report, EPCs sought input from independent Peer 

Reviewers without financial conflicts of interest. However, the conclusions and synthesis of the 

scientific literature presented in this report do not necessarily represent the views of individual 

reviewers.

Peer Reviewers must disclose any financial conflicts of interest greater than $10,000 and any 

other relevant business or professional conflicts of interest. Because of their unique clinical or 

content expertise, individuals with potential nonfinancial conflicts may be retained. The TOO 

and the EPC work to balance, manage, or mitigate any potential nonfinancial conflicts of interest 

identified.

The list of Peer Reviewers follows:

Jeff Bridge, Ph.D

The Research Institute at Nationwide Children’s Hospital

Columbus, OH

Nan Laird, Ph.D

Harvard University

Boston, MA

Richard McKeon, Ph.D., M.P.H.

Substance Abuse and Mental Health Services Administration

Rockville, MD

Robert Ursano, M.D.

United States Uniformed Health Services

Bethesda, MD

v
Data Linkage Strategies To Advance Youth

Suicide Prevention

Structured Abstract

Objectives. Linking national, State, and community data systems, such as those used for medical 

service billing, to existing data from suicide prevention efforts could facilitate the assessment of 

longer term outcomes. Our objective was to identify and describe data systems that can be linked 

to data from studies of youth suicide prevention interventions and to identify analytic approaches 

to advance youth suicide prevention research.

Data sources. We conducted a systematic review to identify studies of suicide prevention 

interventions and three types of searches to identify data systems providing suicide-related 

outcomes: (1) a literature search, (2) an environmental scan of gray literature, and (3) a targeted 

search, through contact with relevant individuals, in six States, two cities, and one tribal 

community. 

Review methods. Two independent reviewers screened all results. Studies and data systems had 

to be based in the United States; include individuals between 0 and 25 years of age; and include 

suicide, suicide attempt, or suicide ideation as an outcome. 

Results. Of the 47 studies (described in 59 articles) of suicide prevention interventions identified 

in our systematic review, only 6 studied outcomes by linking to external data systems and only 

12 explored treatment heterogeneity through the effects of moderators such as gender or 

race/ethnicity. We identified 153 unique and potentially linkable external data systems, 66 of 

which we classified as “fairly accessible” with data dictionaries available.

Conclusions. There is potential for linking existing data systems with suicide prevention efforts 

to assess the broader and extended impact of suicide prevention interventions. However, sparse 

availability of data dictionaries and lack of adherence to standard data elements limit the 

potential utility of linking prevention efforts with data systems.

vi
Contents

Introduction....................................................................................................................................1

Scope of the Project ...................................................................................................................2

PICOTS......................................................................................................................................3

Analytic Framework ..................................................................................................................3

Methods ..........................................................................................................................................5

Suicide Prevention Studies: Systematic Review........................................................................5

Data Systems..............................................................................................................................6

Phase 1: Systematic Literature Search.................................................................................7

Phase 2: Environmental Scan...............................................................................................7

Phase 3: Targeted Search.....................................................................................................8

Results ..........................................................................................................................................10

Suicide Prevention Studies ......................................................................................................10

Data Systems............................................................................................................................10

Key Question 1. What national, State, and community data systems can be linked to existing 

data from suicide prevention interventions in order to add possible value for stakeholders, 

and what methods are available to link the data systems?....................................................15

Key Question 2. Which statistical methods are reliable and valid for analyzing linked 

national, State, and community data systems and suicide prevention data to avoid 

misleading conclusions?

a. What are potential sources of bias for these statistical methods?

b. What are the advantages and disadvantages of these different methods? ........................16

Key Question 3. Which statistical methods are reliable and valid for understanding possible 

moderators in suicide prevention programs to improve targeting interventions to 

populations? ..........................................................................................................................18

Discussion .....................................................................................................................................20

Key Findings............................................................................................................................20

Findings in Relationship to What Is Already Known..............................................................21

Limitations of the Systematic Review Process........................................................................22

Limitations of the Data Systems..............................................................................................22

Future Research Needs and Opportunities...............................................................................23

 Potential Benefit of Data Linkage..................................................................................23

 Barriers to Data Linkage................................................................................................24

Suggested Next Steps....................................................................................................25

Conclusions..............................................................................................................................25

References.....................................................................................................................................26

Tables

Table 1. PICOTS description of inclusion criteria for suicide prevention studies and 

data systems.....................................................................................................................................3

Table 2. Overall search methodology to address Key Questions 1, 2, and 3...................................5

Table 3. Summary of intervention characteristics .........................................................................11

Table 4. Summary of data systems identified for Key Question 1 ................................................16

vii
Table 5. Summary of analytic methods, data linkage, and data systems used in the prevention 

studies that linked to external data systems (n=6) ......................................................................17

Table 6. Summary of moderator variables and analytic methods in studies that assessed effects of 

moderators (n=11).......................................................................................................................19

Figures

Figure 1. Analytic framework for suicide prevention......................................................................4

Figure 2. Suicide rates among persons aged 10–24 years by State – United States, 2012–13........9

Figure 3. Results of the search for data systems............................................................................14

Appendixes

Appendix A. Acronyms

Appendix B. Search Strategies

Appendix C. Environmental Scan Coding Scheme

Appendix D. Results of the Literature Search

Appendix E. Evidence Tables

Appendix F. Included Data Systems

viii
Introduction

Despite advances in the diagnosis and treatment of major mental disorders and increases in 

funding for suicide prevention, annual suicide rates in the United States for those in the 10-14, 

15-19, and 20-24 age groups have increased since 2007.

1 In 2014, suicide was the second leading 

cause of death among people between the ages of 15 to 19 and 20 to 29.2 According to estimates 

from the national Youth Risk Behavior Surveillance System at the Centers for Disease Control 

and Prevention (CDC), each year well over one million high school students are treated by a 

nurse or doctor for a suicide attempt.3

The National Action Alliance for Suicide Prevention (NAASP) (see Appendix A for a list of 

acronyms), the public-private partnership advancing the National Strategy for Suicide 

Prevention, developed a suicide prevention research agenda focusing on interventions with the 

potential to reduce morbidity (attempts) and mortality (deaths) by at least 20 percent in 5 years 

and at least 40 percent in 10 years. Specifically, the NAASP Task Force prioritized the 

prevention of “the emergence of suicidal behavior by developing and delivering the most 

effective prevention programs to build resilience and reduce risk in broad-based populations.”3

However, identifying the most effective prevention interventions is challenging. Several 

issues contribute to make the determination of the impact of suicide prevention interventions

difficult: (1) suicide is a rare outcome, requiring large studies to demonstrate an intervention 

effect; (2) misclassification and under-reporting of suicide and suicide attempts occur, owing to 

stigma and other issues; (3) there is no single, comprehensive, national system to document the 

scope of non-fatal suicide events; (4) interventions are often complex or “bundled” making it 

difficult to know which components are responsible for outcomes; and (5) the nature of 

populations at risk and available interventions require the use of quasi-experimental designs and 

“natural experiments” to evaluate prevention efforts. In addition, under the current funding 

structure of 3- to 5-year grant cycles, it is difficult to evaluate the longer-term impact of 

prevention interventions, especially those directed as primary prevention efforts, which can 

occur years before the peak period of risk for suicidal behaviors in young adulthood. 

Thus, several unanswered questions remain regarding the effectiveness of youth suicide 

prevention efforts. Leveraging existing data from suicide prevention interventions may help to 

address these questions. National, State, and community data systems could be linked to existing 

data from suicide prevention efforts in order to study the longer-term and broader intervention 

impact. Data systems (a system that includes the collection of data, such as in a database, as well 

as the information technology infrastructure to maintain and operate the system) exist for 

purposes of surveillance of suicidal behaviors, for medical service billing, and for administrative 

purposes. The linkage of existing prevention intervention data to suicidal behavior outcomes 

could add value for stakeholders. The rapid expansion of electronic health record (EHR) systems 

and patient registries has created an exceptional opportunity for data linkage.4 The emergence of 

State All Payer Claims Databases (APCD) could also significantly facilitate data linkages. 

APCDs are large-scale databases that systematically collect medical claims, pharmacy claims, 

and eligibility and provider files from private and public payers. In addition, several States now 

have established Health Information Exchanges (HIE) that merge health care data from multiple

health care systems. As mentioned in NOT-MH-14-015 [Data Sharing Expectations for National 

Institute of Mental Health (NIMH)-funded Clinical Trials], the widespread data sharing by 

research communities adds significant value to research and accelerates the pace of discovery.5

In the field of suicide prevention, the linking of existing data has not yet been standardized 

across studies. This is likely due to a number of issues such as cost, feasibility of accessing data 

1
systems, interoperability challenges, and issues of sharing protected health information (PHI). 

Direct data linkage on an individual-person level may require identifying information which is 

protected by the Health Insurance Portability and Accountability Act (HIPAA) and also, 

therefore, explicit informed consent.

Scope of the Project

The aim of the project was to provide an objective description of the state of the science on 

data linkage strategies and analytic approaches in suicide prevention research, as well as a 

systematic summary of ongoing research limitations, barriers, challenges, gaps, and opportunities

for future data linkage approaches to enhance suicide prevention efforts to serve as the 

foundation for a National Institutes of Health (NIH) Pathway to Prevention workshop. This 

project studied data linkage and systems but did not evaluate or compare effectiveness of suicide 

prevention interventions. We focused on children, youth, and young adults ages 0-25, which

spans the age range from when suicide is relatively rare, but when primary preventive efforts 

may be effective, through adolescence and young adulthood, when suicide rates precipitously 

increase. We considered all socio-ecologic levels of interventions (individual, community, and 

policy-level), primary promotion of mental wellness as well as interventions targeting suicidal 

ideation, attempts, and suicide. 

Our project sought to address the following Key Questions (KQs):

KQ1. What national, State, and community data systems can be linked to 

existing data from suicide prevention interventions in order to add 

possible value for stakeholders, and what methods are available to 

link the data systems?

KQ2. Which statistical methods are reliable and valid for analyzing linked -

data to avoid misleading conclusions?

a. What are potential sources of bias for these statistical methods?

b. What are the advantages and disadvantages of these different 

methods?

KQ3. Which statistical methods are reliable and valid for understanding 

possible moderators in suicide prevention programs to improve 

targeting interventions to populations?

KQ4. Given the current state of research, what types of 

methodological/analytic advances would promote further evaluation 

of youth suicide prevention efforts (e.g., new approaches to data 

linkage, increased use of common data elements, approaches to 

intervention harmonization) and facilitate intervention selection and 

implementation decisions by local community and State-level 

policymakers?

2
PICOTS

Table 1 describes the inclusion criteria (PICOTS: Populations, Interventions, Comparators, 

Outcomes, Timing, Setting) applied to identify studies of suicide prevention interventions. We 

used the same criteria to identify data systems providing suicide-related outcomes, except that a 

specific intervention was not required (i.e., I and C not required for data systems).

Table 1. PICOTS description of inclusion criteria for suicide prevention studies and data systems

Population(s) Received intervention: ages 0-25; (longitudinal follow-up past age 25 is acceptable).

Intervention(s)

(used for studies)

Behavioral, community, clinical/medical, policy level including studies promoting wellness 

targeting suicide ideation, suicide attempt, and suicide, or any combination of these 

interventions and outcomes.

Comparison(s)

(used for studies)

Any intervention (including usual care). 

Outcome(s)

(the primary and 

intermediate 

outcomes)

Primary outcome of interest:

● Suicide ideation,* reported within 12 months of data collection 

● Suicide attempt, any time point post intervention

● Suicide, any time point post intervention

Intermediate outcomes (if primary outcome is present), at any time point post intervention: 

● Psychiatric and substance abuse disorders

● Service use (e.g. emergency department visit) for psychiatric/substance abuse 

disorders

● Graduation rates

● Incarceration rates

● Violence (both perpetrator and victim)

● Social support and connectedness

● Access to lethal means

All outcomes were limited to standardized measures.

Timing Publication date: 1990 or later. 

Setting Studies taking place in the United States only: Schools, home, primary care, emergency 

department, military bases, Indian reservations, juvenile justice systems, child welfare 

systems, suicide hotlines, other community settings.

* Suicide ideation reported over the last 12 months reduces the potential for recall bias and provides more relevant information 

for current prevention and intervention.6, 7

Analytic Framework

Figure 1 depicts the Key Questions illustrating how data, in populations of people from 0 to 

25 years of age, from suicide prevention interventions (including behavioral, community, 

clinical/medical, and policy level) can be linked with national, community, or State data systems.

Through statistical methods, these linked data can be analyzed to study the broader or longerterm impact of suicide prevention interventions. The study and identification of moderators of 

intervention impact (e.g., gender, race and ethnicity, LGBTQ (lesbian, gay, bi-sexual, 

transgender, queer) status, psychopathology status, trauma history, social connectedness, or

social support) could suggest which individuals or subpopulations would most benefit from 

specific suicide prevention interventions. Studying moderators in linked data may be an efficient 

and cost-effective way of developing more personalized or community-informed prevention 

efforts.

3
Figure 1. Analytic framework for suicide prevention

ED = Emergency Department; KQ = Key Question; LGBTQ: Lesbian, Gay, Bisexual, Transgender, and Queer

4
Methods

We identified suicide prevention intervention studies and relevant data systems on suicide 

prevention (see Table 2) in three parallel phases. In the first phase, we completed a systematic 

review of published literature to identify suicide prevention studies and existing data systems.

The results of this phase informed Key Questions 1, 2, and 3. In the second phase, we completed 

an environmental scan to identify suicide data systems not reported in published literature. This 

phase further informed Key Question 1. In the last phase, we performed a targeted search to 

identify data systems used in selected States, cities, or communities. This phase expanded the 

results for Key Question 1. 

Table 2. Overall search methodology to address Key Questions 1, 2, and 3*

Phase†

Key 

Questio

n

Search 

Criteria‡

Search 

Engines or 

Data 

Sources

Number 

of 

Raters/ 

Coders

Suicide 

Prevention 

Studies

Identification

Data 

System 

Identification

Statistical 

Method 

Identification

Moderator 

Identification

1. 

Systematic 

Literature 

Review

KQ1 PICOTS§ • PubMed

• Cochrane

• Campbell

• CINAHL

• PsycINFO

• ERIC

2 Yes Yes - -

KQ2 2 Yes - Yes -

KQ3 2 Yes - - Yes

2. Environmental Scan 

(Web)

KQ1 PICOTS§ • Google

• Yahoo

• Bing

• SPRC

• AFSP

• AAS

1-2 - Yes - -

3. Targeted 

Search║ KQ1 PICOTS§+ 

Location

Selected 

State, city, 

and local 

governmen

t personnel

1-2 - Yes - -

* KQ4 is not included in this table because it reflects a synthesis of opportunities and challenges based on the results of KQ1-3. ‡ Inclusion and exclusion criteria are the same for all searches. 

† All phases of the search method required that included studies and data systems included suicide prevention interventions. 

§ See Table 1 for the full PICOTS description. ║ Six States, two cities, and one local community were selected based on suicide rates, geographic location, and the presence of

active SAMHSA (Substance Abuse and Mental Health Services Administration) or GLS (Garrett Lee Smith Memorial Suicide 

Prevention Program) support. Terms representing these States, cities, or local communities were added to the PICOTS terms.

AAS=American Association of Suicidology; AFSP=American Foundation for Suicide Prevention; KQ=Key Question; 

PICOTS=Population, Interventions, Comparators, Outcomes, Timing, Setting; SPRC=Suicide Prevention Resource Center

Suicide Prevention Studies: Systematic Review

We searched PubMed, The Cochrane Library, Campbell Collaboration Library of Systematic 

Reviews, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO and 

Education Resources Information Center (ERIC) from January 1, 1990 to December 21, 2015. 

5
We developed a search strategy for PubMed based on medical subject headings (MeSH) and title 

and abstract terms from eligible articles we identified a priori. We used this search to develop 

strategies for the other search engines (see Appendix B). 

We uploaded the articles into DistillerSR (Evidence Partners, Ottawa, Ontario, Canada), a 

Web-based service for systematic review and data management. We used DistillerSR to track the 

screening process and results.

The inclusion and exclusion criteria for the systematic review were derived from the PICOTS 

(see Table 1). Specific exclusion criteria for title and abstract screening and full-text screening 

were the following: not a study of humans only, does not include data on individuals between the 

ages of 0 and 25 years, does not include an intervention of interest, does not include a primary 

outcome of interest, takes place outside of the United States, meeting abstract only, and no 

original data. We did not limit study inclusion by study population size or study design. Studies 

published prior to 1990 were not included because, according to the NAASP, suicide became a 

central issue in the United States in the mid-1990s with the Surgeon General’s Call to Action to 

Prevent Suicide, published in 1999.8 The amount of abstractable data is significantly limited 

prior to 1990.

Two reviewers independently screened titles and abstracts first, and then full-text articles. 

Disagreements about article eligibility that could not be resolved by the two reviewers were 

resolved by the domain experts on the team. We abstracted data on study characteristics, 

participant characteristics, intervention characteristics, and suicide outcomes to Microsoft Excel 

tables. As applicable, we also abstracted the primary analytic method used by the study to link 

data systems, statistical tests used in the study, and analyses performed. We abstracted variables 

used in the analyses, as well as those controlled for as covariates. Additionally, we identified and 

abstracted data system information (e.g., location of database) and how the data were linked to 

other sources.

A trained research assistant abstracted the data and another team member checked it. In the 

case of a disagreement, yet another team member reviewed the abstracted data, and the issue was 

resolved by consensus. If consensus was not attainable for a specific case, it was discussed by the 

full review team and resolved by majority vote. The same process was used for all data 

abstraction that was a part of this project. Abstracted data will be uploaded to the Systematic 

Review Data RepositoryTM (SRDR), a Web-based data repository, at completion of the project.

Data Systems

We conducted three phases of searches for data systems (described below). For results from 

these phases, two independent reviewers determined whether the identified data systems (i) met 

the PICOTS elements and (ii) fulfilled the minimum requirements of a data system that can be 

useful for linkage to suicide prevention studies. We excluded data systems that did not meet all 

of these requirements:

(a) Data system is still in existence and underlying data are available and accessible in 

digital format (e.g., datasets can be downloaded from a current website with or 

without permission or are available in another format); 

(b) Data system can be shared and acquired by others for research purposes (e.g., it has a 

public or transferable license that allows the data to be used for research purposes);

(c) Data system collects and contains at least one of the primary outcomes; and,

(d) Data system is not a duplicate of another data system already included in the project. 

6
The process of data abstraction for the data systems was the same as for the systematic 

review. We abstracted data about the PICOTS (see Table 1), such as study and participant 

characteristics when they were present, as well as details about the data systems and statistical 

methods. The coding and classification schema for the data systems was adapted and modified

from a framework previously developed to evaluate the quality of community-based data 

sources.9 The coding schema can be found in Appendix C. The process was limited to the 

information that could be extracted from information found through online searches. Acquiring 

data dictionaries or downloading and analyzing the data sources within each of these data 

systems, if they were not available on the Internet, was considered to be out of the scope of this 

project.

Phase 1: Systematic Literature Search

We scanned all eligible suicide prevention studies identified in the search for data systems. 

We also checked articles excluded during the full-text screen that had been tagged as having a

database or data systems. These databases were then re-reviewed for inclusion during Phase 2 of 

the methodology. We sought additional information about the data systems identified from the 

systematic review using the following methods: searching the Internet for additional information 

about the data systems; finding the data dictionaries associated with the data system, if available; 

downloading a sample data set from the data system, if available; reviewing the data sources 

used in the data system; and, searching for additional reports that may have described the data 

system in more detail.

Phase 2: Environmental Scan

To identify additional data systems on suicide prevention that may not be reported in the 

published literature, we conducted an environmental scan of grey literature on suicide prevention 

programs among youth. We searched the Web for pre-prints, preliminary progress and advanced 

reports, technical reports, statistical reports, memoranda, state-of-the-art reports, market research 

reports, theses, conference proceedings, technical specifications and standards, non-commercial 

translations, bibliographies, technical and commercial documentation, and official documents not 

published commercially (primarily government reports and documents).10

We did not limit the environmental scan to a specific type of document retrieved from the 

Web (i.e., the document could be an HTML webpage or a document in a downloadable format.) 

We used the PICOTS (Table 1) to determine whether a specific page/document was applicable. 

We required the presence of the population of interest (P), one of the primary outcomes (O), 

timing (T), and setting (S); however, Intervention and Comparison elements of PICOTS were not 

required.

The search terms and eligibility criteria used in the systematic review were also used for the 

environmental scan (see Appendix B). We used the advanced search functions of three search 

engines [Google, Yahoo, and Bing (Microsoft)] to execute the search. These search engines 

collectively represent 97 percent of the U.S. online search market.11 Owing to the extensive list 

of results returned by these search engines, only the first 100 retrieved documents from each 

database were considered. This limitation was also based on the finding that the precision of 

search engine results often reduces considerably after the first few dozen retrievals.12 To ensure a 

high recall or precision rate, the review team examined every 10 results ranked between the 100th

to the 300th results of these search engines. If at least one-third of these results were determined 

to be relevant to this review, we expanded the environmental scan to include the first 300 results 

7
from all three search engines. We excluded from the review duplicate results across the search 

engines and data systems that were already identified by the systematic review.

We also conducted an exploration of specific government, foundation, and professional 

association Web sites (national/Federal, State-level, and local/city/community-level): American 

Foundation for Suicide Prevention (AFSP), the American Association of Suicidology (AAS), 

and the Suicide Prevention Resource Center (SPRC) to identify data systems which could be 

linked to prevention programs.

Phase 3: Targeted Search

We conducted a targeted search for data systems providing information on suicide prevention 

in six States (CA, DE, OR, IL, MD and WI), two cities (Baltimore, MD and Wilmington, DE), 

and one tribal community (The Menominee Reservation in Wisconsin). It was beyond the scope 

of this project to complete a more comprehensive review across all States or major cities. We 

sought data systems that provided information about our primary outcomes (i.e., suicide ideation, 

suicide attempt, and suicide) (see Figure 2) and were maintained by a State, city, or communitylevel entity. 

The six States were selected based on the following criteria: (1) the State has an active 

Substance Abuse and Mental Health Services Administration (SAMHSA) Garrett Lee Smith

Memorial Suicide Prevention Program State youth suicide prevention grant that should facilitate 

the acquisition of information about suicide data sources (as SAMHSA has provided resources to 

facilitate and encourage State grantees to use data systems in their local evaluation procedures so 

data systems including suicide ideation, suicide attempt and suicide should be familiar to the

State’s contacts); (2) geographic proximity; matched pairs of States with a suicide rate lower and 

higher than the national crude average rate of suicide in 2010-2014 (8.4 per 100,000 population 

among those 10-25 years of age1

); and, to some extent, (3) familiarity of the research team 

experts with the data systems of those States. All of the six States match the first and second 

criteria (see Figure 2). In regards to the second criterion, in the Pacific region, Oregon has a 

higher suicide rate (10.9/100k) while California has a lower suicide rate (5.9/100k); in the 

Midwest region, Wisconsin has a higher suicide rate (10.4/100k) while Illinois has a lower

suicide rate (6.9/100k); and, finally, in the Mid-Atlantic region, Delaware has a higher suicide 

rate (8.6/100k) while Maryland has a lower suicide rate (6.7/100k). 

We contacted (via email and phone) individuals in each selected State, city, and community to 

request information on State- and community-level data sources/systems that include suicide

ideation, suicide attempt, and suicide among those under 26 years of age. The individuals were 

the designated contacts provided for State suicide prevention efforts, such as the Garrett Lee 

Smith Suicide Prevention program, and members of State, city, and community suicide 

prevention commissions or coalitions. 

8
Figure 2. Suicide rates among persons aged 10–24 years by State -- United States, 2012-13

Note: All races, ethnicities, sexes, and ages; Annualized crude rate for U.S is 11.59; Map generated by CDC Web-based Injury Statistics Query and Reporting 

System (WISQARS) .Source: CDC vital statistics—WISQARS.

9
Results

Suicide Prevention Studies 

The literature search identified 4,198 unique citations, of which 59 were eligible (Appendix 

D). Details on study characteristics are available in Appendix E. The 59 articles reported 47 

studies. Study population size was highly variable: randomized controlled trials (RCTs) ranged 

in size from 32 to 2,100 people. Twenty-nine of the studies (62%) had 500 or fewer study 

participants. Very few (34.0%) of the prevention intervention studies we identified reported on 

outcomes longer than one year post-intervention. The percentage of female participants ranged 

from 15.4 percent13 to 100 percent.14 Race was reported in most (95%) of the studies. Reporting 

on race was often limited to only percentages of white participants and black participants,

ranging from 8.1 percent white to 85 percent white (where reported). Reporting on education 

level was rare and, when included, referenced grade levels of participants during the study. Ten

studies (21.3%) focused on special populations: military personnel,

15-18 incarcerated 

individuals,

13,14,19 victims of sexual trauma,20 people with major depressive disorder,21 and highrisk youth22 (Table 3; Appendix E). The 47 studies used a variety of interventions and 

approaches to prevention; many studies applied more than one approach: behavioral and skill 

building (19), medication or pharmaceutical (7), psychotherapy (4), education or education/skill 

building (12), policy (5), screening (5), other (9) (Table 3; Appendix E).

Data Systems 

Our literature search, environmental scan, and targeted geographical searches identified 153

unique data systems (Figure 3; Appendix F). Our literature search identified seven unique data 

systems from the eligible articles of suicide prevention interventions and an additional 43 data 

systems from the articles screened at the full-text level.

The environmental scan identified 80 unique data systems. Overall, 71 percent of the State 

and tribal community entities contacted replied with 23 unique data systems identified.

10
Table 3. Summary of intervention characteristics

Program or Study Name (If Applicable)

Author, Year

Intervention Type/Purpose Setting

Adolescent Suicide Risk Screening

King, 201223 Screening Emergency department

Attachment-Based Family Therapy

Diamond, 201024 Psychotherapy, behavioral/skill building Emergency department, primary care 

Diamond, 201220

CAMS‡

Jobes, 2012 15 Psychotherapy Primary care, emergency department, military 

base

Jobes, 201215

C-CARE/CAST

Eggert, 200225 Behavioral/skill building School

Randell, 200126 School

Thompson, 200122 School and home

Hooven, 201027 Behavioral/Sskill building, education 

Communities That Care

Oesterle, 201528 Policy/legislation Community-based 

GLS

Garraza, 201519 Education/training/screening/infrastructure/crisis 

hotline/community partnerships (Garrett Lee Smith Memorial 

Suicide Prevention Program) 

School/juvenile justice/communitybased/primary care

Walrath, 201529 Screening, behavioral, policy changes, outreach, referral to 

mental health professionals, means restriction

NR*

Good Behavior Game

Wilcox, 200830 Behavioral/skill building, educational School

HOPE Family Program/ HOPE Health 

Education Program

Lynn, 201431 Behavioral/skill building (HOPE Family Program) Family housing shelters

National Treatment Improvement 

Evaluation Study

Ilgen, 200732 Substance abuse treatment Primary care (outpatient and residential)

PGC

Thompson, 200033 Behavioral/skill building School

Project Chrysalis

Brown, 2001 34 Behavioral/skill building, education School

Promoting Care

Hooven, 201235 Behavioral/skill building, education School and home

SAFETY Program

Asarnow, 201536 Educational/skill building (SAFETY Cognitive-Behavioral Family 

Treatment Program)

Emergency department

11
Table 3. Summary of intervention characteristics (continued)

Program or Study Name (if applicable)

Author, Year

Intervention Type/Purpose Setting

Seattle Social Development Program

Hawkins, 200537 Educational/skill building (Seattle Social Development Project 

intervention)

School

SOS 

Aseltine, 200438 Educational School

Aseltine, 200739

Student Assistance Program

Biddle, 201440 Multiple strategies School

Surviving the Teens Suicide Prevention 

and Depression Program

King, 201141 Behavioral/skill building, Education School

Systemic Crisis Intervention Program

Gutstein, 199042 Behavioral/skill building Primary care (outpatient)

TADS

March, 200743 Medication or pharmaceutical/Behavioral/Skill building

(fluoxetine hydrochloride therapy and CBT)

Primary care/community-based

TADS, 200944

Vitiello, 200945

TADS- SOFTAD 

Curry, 201146 Medication or pharmaceutical Primary care/community-based

TASA

Brent, 200947 Medication or pharmaceutical/behavioral/skill building 

(psychotherapy and medication management)

Primary care

Vitiello, 200948

The Coping Cat Program

Wolk, 2015 49 Behavioral/skill building (CBT) Primary care

TORDIA

Asarnow, 201150 Medication or pharmaceutical (medication switch and CBT) Primary care

Emslie, 201051

Shamseddeen, 201152

Woldu, 201153

US Air Force Suicide Prevention 

Knox, 200318 Educational Military base

YST-1

King, 200654 Educational/skill building (psychoeducation targeted for support 

persons)

Primary care (inpatient)

12
Table 3. Summary of intervention characteristics (continued)

Program or Study Name (if applicable)

Author, Year

Intervention Type/Purpose Setting

YST-2

King, 200955 Educational/skill building (psychoeducation targeted for support 

persons)

Primary care (inpatient)

No named prevention program or trial

Anestis, 201556 Policy/Legislation NR* (50 U.S. States)

Asarnow, 201157 Behavioral/skill building (Family Intervention for Suicide 

Prevention)

Emergency department

Brown, 200558 Cognitive therapy Emergency department

Collins, 2008 59 Medication or pharmaceutical Emergency department/primary care

Cooper, 2006 60 Safety-net of mental health services NR* (acute care hospitals)

Farmer, 199613 Policy/legislation Emergency department/prison

Fleegler, 201361 Policy/legislation NR* (50 U.S. States)

Gardner, 201062 Screening Primary care

Huey, 200463 Behavioral/skill building (multisystemic therapy) Emergency department

Kaminer, 200664 Aftercare NR* (outpatient) 

Kennard, 201421 Medication or pharmaceutical and psychotherapy (medication 

management and CBT)

Primary care (outpatient)

Kerr, 201414 Behavioral/skill building (Multidimensional Treatment Foster 

Care)

Juvenile justice

Olfson, 200365 Medication and pharmaceutical NR* (county-level suicide rates)

Rathus, 200266 Psychotherapy and behavioral/skill building (Dialectical 

Behavior Therapy)

Primary care (outpatient)

Rotheram-Borus, 200067 Specialized emergency department (ED) care intervention Emergency department

Rudd, 1996 68 Behavioral/Skill building Primary care (outpatient)

Segal, 199569 None provided (recommendation for continued intensive 

impatient treatment)

Emergency department

Spirito, 199270 Followup interviews Emergency department/home

Warner, 201116 Screening Primary care/military base

Wharff, 2012 71 Behavioral/skill building (family-based crisis intervention) Emergency department

Wingate, 200517 Behavioral/skill building Primary care (inpatient and 

outpatient)/emergency department/military

*Study was not specific in regard to program setting, incorporating county, State, or regional data.

CAMS=The Collaborative Assessment and Management of Suicidality ; CARE=Care, Assess, Respond, Empower; CAST=Coping and Support Training; CATCH-IT=Competent 

Adulthood Transition With Cognitive Behavioral And Interpersonal Training; C-CARE=Counselors: Care, Assess, Respond, Empower; CBT=cognitive behavioral therapy; 

ED=emergency department; GLS=The Garrett Lee Smith Memorial Suicide Prevention Program; NASY=National Annenberg Survey of Youth; NR=not reported; PGC=Personal 

Growth Class; RCT=randomized controlled trial; SOFTADS=Survey of Outcomes Following Treatment for Adolescent Depression; SOS=Signs Of Suicide; TADS=Treatment for 

Adolescents with Depression Study; TASA=Treatment of Adolescent Suicide Attempters; TORDIA=Treatment of Resistant Depression in Adolescents; YST-1=Youth-Nominated 

Support Team for Suicidal Adolescents (Version 1); YST-2=Youth-Nominated Support Team for Suicidal Adolescents (Version 2)

13
Figure 3. Results of the search for data systems

Note: Reasons for exclusion: duplicate data system, did not meet one or more of the PICOTS (populations, 

interventions, comparisons, outcomes, timing, setting) elements (i.e., did not report outcome information)

14
Key Question 1. What national, State, and community data systems can be 

linked to existing data from suicide prevention interventions in order to add 

possible value for stakeholders, and what methods are available to link the 

data systems?

Of the 153 data systems we identified, we found that 89.5 percent can be readily acquired for 

free or for a fee and 74.5 percent can be downloaded from the Internet in an aggregated and 

anonymized format. Of the data systems available on Web sites, only 1.3 percent permitted an 

automated registration process to obtain the data while 79.1 percent required confirmation by a 

data manager. Data dictionaries were accessible for 47.7 percent of the data systems. 

Of the data systems identified, 19.0 percent are used primarily for research, 11.1 percent for 

clinical care or operations, 29.4 percent for administrative services (such as for billing), and 52.3

percent for surveillance (some systems have multiple types of use). The geographic coverage of 

the data systems is as follows: 36.6 percent provide national-level data, 12.4 percent regionallevel, 63.4 percent State-level, and 41.2 percent smaller than State-level (i.e., communities by zip 

code, county, census block, tribal boundaries, territory, or island) (some data systems provide 

coverage for more than one geographical unit) (Table 4). A few data systems were identified that 

are specifically designed with a focus on subgroups of interest, such as tribal communities 

(0.7%), LGBTQ populations (2%), active duty military service members (6.5%), incarcerated 

populations (3.3%), and primary school, secondary school, and university students (18.3%). 

In regard to data granularity, patient-level data exists allowing for potential individual-level 

linkage for 96.7 percent of data systems identified; 2.6 percent of data systems only included 

aggregated data, allowing for ecological linkage to suicide prevention programs by demographic 

characteristics (59.5%), geographic region (56.2%), clinical specifications (42.5%), and entity 

type, such as an insurance company, health care provider, educational institution or employer 

(17.6%). Data sampling was used by 15.7 percent of the data systems identified. 

Of the data systems identified, 29.4 percent included data on suicide ideation, 54.2 percent 

included data on suicide attempts, and 70.6 percent included data on suicide completion. The 

coverage by the identified data systems for intermediate and secondary outcomes of interest was 

as follows: 28.1 percent included psychiatric and substance abuse disorders, 38.6 percent 

included service utilization for psychiatric and substance abuse disorders, 11.8 percent included 

graduation rates, 5.9 percent included incarceration and arrests, 44.4 percent included violence 

perpetration and victimization, 10.5 percent included social support and social connectedness, 

and 39.2 percent included access to lethal means. The types of data systems identified were as 

follows: 18.3 percent were designed specifically to capture information about suicide, 41.8

percent death records, 43.8 percent health care provider records (EMRs), 47.7 percent 

population-based surveys, and 5.2 percent health insurance claims data. Of those data systems 

identified, 94.1 percent are ongoing with prospective data collection, whereas only 5.2 percent 

have partially stopped data collection. The average year of the start of data collection is 1994. 

We classified the 153 data systems into three tiers to identify those that can be linked to 

existing data from suicide prevention interventions as “fairly accessible,” “potentially 

accessible,” and “more information needed.” These classifications were based on whether

'individual level data' or 'aggregated data' is offered through one of the following models: (1) the 

data are freely available on the Web site to download (e.g., public use file); (2) data access 

15
Table 4. Summary of data systems identified for Key Question 1

Measure Yes No N/A

Geographic 

Coverage

United States – national 36.6% 63.4% 0.0%

United States – regional (e.g., east coast) 12.4% 87.6% 0.0%

United States – State level (e.g., Maryland) 63.4% 36.6% 0.0%

United States – smaller than State level 41.2% 58.8% 0.0%

County 32.0% 67.3% 0.7%

Zip code 15.0% 84.3% 0.7%

Census block 2.0% 98.0% 0.0%

Tribal 1.3% 98.7% 0.0%

Territory 0.7% 99.3% 0.0%

Islands 0.7% 99.3% 0.0%

Other 0.0% 0.0% 0.0%

Level of 

Information 

Available for This 

Data System

Data exist and can be acquired (free or for a fee) 89.5% 0.0% 10.5%

Data are publicly available and can be downloaded (e.g., PUF) 74.5% 21.6% 3.9%

Data can be acquired but requires an automated registration 1.3% 97.4% 1.3%

Data can be acquired if confirmed by a person (e.g., needs email 

communication)

79.1% 0.0% 20.9%

Data dictionary or code book is accessible 47.7% 52.3% 0.0%

Data System's 

Primary Use

Research 19.0% 81.0% 0.0%

Clinical care/operations 11.1% 88.9% 0.0%

Administrative services (e.g., census) 29.4% 70.6% 0.0%

Public health (e.g., surveillance) 52.3% 47.7% 0.0%

Suicide 

Outcome(s) 

Included

Suicide ideation 29.4% 64.1% 6.5%

Suicide attempt 54.2% 38.6% 7.2%

Suicide completion 70.6% 25.5% 3.9%

N/A=not enough information based on what was found to make a yes/no decision; PUF = publicly uploadable file

requires a registration process (often with no additional checks); (3) request is reviewed 

manually by a person/committee before access to the data is granted; and, (4) whether a data 

dictionary is available. Based on this classification, 123 data systems were identified as “fairly 

accessible” (66 of these have a data dictionary available), 20 data systems as “potentially 

accessible,” and “more information is needed” for 12 data systems (Appendix F).

Key Question 2. Which statistical methods are reliable and valid for 

analyzing linked national, State, and community data systems and suicide 

prevention data to avoid misleading conclusions?

a. What are potential sources of bias for these statistical methods?

b. What are the advantages and disadvantages of these different methods?

Six studies reviewed linked to outcome data from external national, State, or community data 

systems (see Table 5). Three of these studies linked data at the individual level, using an 

individual linking variable such as social security number. Three of these studies linked data at 

an ecological level, using a variable such as zip code or county. 

The studies conducted data analyses with individually-linked data systems using the 

following methods: logistic regression, Cox proportional hazards models, and simple descriptive 

statistics. These methods are generally considered appropriate for use with this type of data. The 

16
studies that conducted data analyses with ecologically-linked data used simple regression models 

(both logistic and multivariate) and t-tests. Regression methods can be appropriate for use with 

ecological data if they account for clustering with mixed effects models. The studies that used 

regression models did account for clustering within their models.

Table 5. Summary of analytic methods, data linkage, and data systems used in the prevention 

studies that linked to external data systems (n=6)

Author, Year Analytic Method Linking Level Brief Results Data System

Walrath, 201529 Sequential propensity 

scores; simple 

regression model

County Counties that implemented 

training had significantly 

lower youth (10 to 24 years

of age) suicide rates as 

compared with counties that 

did not obtain training.

SAMHSA-Funded 

Garrett Lee Smith 

Dataset; U.S. Census 

Bureau; Bureau of 

Labor Statistics

Gardner, 201062 Logistic regression Individual 14% of youth reported a 

suicidal thought in the 

previous month. 

Nationwide children’s 

hospital EHRs

Collins, 200859 Cox proportional 

hazards models

Individual In comparison of mood 

stabilizers, divalproex was 

the most common mood 

stabilizer and demonstrated 

an increased risk for suicide 

attempts compared with 

lithium.

Oregon Medicaid 

data, Oregon vital 

statistics

Olfson, 200365 Adjusted linear 

regression models, ttests

Region Regional increase in the 

adolescent use of 

antidepressants was 

associated with a decrease 

in adolescent suicide 

attempts.

Pharmacy benefit 

management 

organization, CDC’s 

national suicide 

mortality rates, U.S.

Census Bureau, Area 

resource file

Farmer, 199613 Descriptive statistics Individual After a suicide prevention 

program was implemented, 

only one suicide was 

reported; 13 cases of 

suicide attempt required 

emergency medical 

attention.

Galveston County Jail 

records

Cooper, 200660 Logistic regression County Living in a county that 

offered a minimum safetynet of mental health 

services significantly 

reduced suicide risk.

Colorado Trauma 

Registry, U.S. Census 

Bureau

CDC=Center for Disease Control and Prevention; SAMSHA =Substance Abuse and Mental Health Services Administration; 

EHR=electronic health record

17
Key Question 3. Which statistical methods are reliable and valid for 

understanding possible moderators in suicide prevention programs to 

improve targeting interventions to populations?

None of the six studies that linked to external data systems (Table 5) conducted statistical 

analyses to understand moderators of intervention impact. Twelve studies explored the effect of 

moderators in suicide prevention efforts; all moderation relationships were stated as a-priori 

hypotheses. Ten of these 12 studies used a form of regression, either linear or logistic, depending 

on the outcome of interest, and interaction terms to explore moderation (Table 6). This is an 

effective analytic technique to explore the effects of moderators on suicide prevention 

interventions. Researchers typically look for a significant interaction term followed by probes to 

determine whether the effect of the intervention significantly differs for chosen conditional 

values of the moderator.72 For example, Rotheram-Borus, et al.67 were interested in the 

interaction between the intervention program of interest and baseline psychiatric symptoms. 

Psychiatric symptoms were coded into a three-category variable: low, moderate, and high. An 

interaction term was created between symptom severity category and intervention status and 

included in the linear mixed regression model. This methodology is appropriate for the 

exploration of moderators in prevention or intervention effectiveness. 

Only 12 studies explored moderation effects; much of the research included is primarily 

focused on the main effects of interventions, and treatment effect heterogeneity is not assessed.

18
Table 6. Summary of moderator variables and analytic methods in studies that assessed effects of 

moderators (n=11)

Author, Year Moderator Variables Brief Results Analytic Methods

Diamond, 201220 Sexual trauma History of sexual trauma did not moderate 

treatment outcome for attachment-based 

family therapy.

Hierarchical 

generalized linear 

model

King, 201223 Receipt of public 

assistance, gender

Adolescents whose families did not receive 

public assistance had higher levels of suicide 

ideation if assigned to in-person followup.

ANOVAs and logistic 

regressions

King, 200955 History of multiple 

suicide attempts, 

gender

Youth-nominated support team intervention 

effects were moderated by a history of 

multiple suicide attempts, demonstrating 

more rapid decrease in ideation for those with 

multiple attempts. 

Mixed effects models

Kaminer, 200664 Internalizing 

disorders, 

externalizing 

disorders, substance 

use disorders, suicide 

ideation

Results of the intervention did not significantly 

differ by gender, DISC internalizing disorders, 

DISC substance use disorders, and baseline 

SIQ scores.

General linear models

King, 200654 Gender Adolescent girls who received the Youthnominated support team intervention

demonstrated greater decreases in selfreported suicide ideation as compared with 

girls who did not get the intervention.

Chi-square, Fisher’s 

exact test

Wingate, 200517 Problem solving 

appraisal

Participants with poor problem solving 

appraisal at baseline responded better than 

participants with good problem solving 

appraisal. 

Hierarchical multiple 

regression

Huey, 200463 Gender, age, ethnicity Multisystemic therapy was more effective at 

reducing suicide attempts as compared with 

emergency hospitalization, this relationship 

varied by ethnicity, gender, and age.

Mixed effects growth 

models

Thompson, 

200033

Gender Personal control mediated the relationship 

between support resources and suicide risk 

behavior.

Hierarchical linear 

growth modeling

Rotheram-Borus, 

200067

Psychiatric symptoms The intervention had the greatest impact on 

maternal emotional distress and family 

cohesion among suicide attempters who were 

highly symptomatic.

Linear mixed effects 

regression model

Curry, 201146 Family income, 

depressive 

symptoms, cognitive 

distortions

Family income, depressive symptoms, and 

cognitive distortions all moderated the acute 

outcome of the TADS study. 

Logistic regression

Shamseddeen, 

201152

School difficulties Lowest rates of response occurred among 

adolescents having school difficulties and 

ending treatment during the active school 

year.

Multivariate regression

Hawkins, 200537 Sex, poverty, ethnicity Intervention effects significantly differ by 

gender but not by childhood poverty. White 

participants in the full intervention group 

showed more constructive engagement 

compared with those in the control group.

MANOVA

ANOVA=analysis of variance; DISC=The Diagnostoc Interview Scale for Children; MANOVA=Multivariate Analysis of 

Variance; SIQ: Suicidal Ideation Questionnaire

19
Discussion

Key Findings

● A minority of community, State, and national datasets are currently usable for linkage: 121 of 

the 153 data systems we identified are fairly accessible, but only 66 of these have a data 

dictionary available.

● Most linkable datasets have outcomes related to suicide; fewer datasets include suicide 

ideation and suicide attempts.

● Most of the potentially linkable datasets offer the possibility of linkage at the individual 

level, but ethical or legal barriers may prohibit linkage at this level.

● Few datasets allow for the detailed study of particular high-risk populations, though variables 

indicating membership in one of those populations are widely available.

● Lack of readily available codebooks and data dictionaries for datasets limits ready access and

the conclusions that can be drawn about the potential utility of linkage.

● Lack of adherence to a standard set of data elements in suicide data systems and prevention 

studies is a barrier that reduces the potential utility of linkage.

● Of the six studies that linked prevention intervention data with external data systems, three 

used ecological-level linking and three used individual-level linking. The methods used for 

data analysis were appropriate.

● Of the six studies that linked prevention intervention data with external data systems, all 

focused on intervention main effects. None assessed for treatment effect heterogeneity or the 

impact of moderators. Only 12 studies explored moderation effects.

Data systems are highly variable in terms of geographic reach and the inclusion of data about 

subpopulations. Of the 153 data systems identified, 36.6 percent are national and 41.2 percent 

have geographic coverage of areas smaller than a State. All 50 U.S. States are represented by at 

least one data system, usually the State’s vital statistics data. Some States have multiple data 

systems for possible linkage to prevention programs. For the majority of these sources (89.5%), 

the data are potentially available for free or for a fee; however, of those, a data dictionary or 

codebook is readily accessible online for only 47.7 percent. Coverage of special populations at 

higher risk for suicide attempt and suicide is limited: only one of the systems identified is 

focused on Native American populations and only 2 percent are specifically focused on LGBTQ

populations. Although few data systems allow for the comprehensive study of subpopulations at 

increased risk for suicide, variables indicating membership in one of those populations are 

available in 37.9 percent of the data systems.

Some data systems may be more useful to linkage efforts than others, depending on what

variables the systems track. Most linkable data systems include suicide as an outcome; however,

very large prevention studies are needed for sufficient statistical power to detect a difference in 

suicide death between intervention groups or over time. Many of the prevention studies 

identified had small sample sizes. Data systems including suicide ideation and attempts are less 

common, although the use of systems that include them may be more useful for prevention 

studies, as these variables are more prevalent than suicide and also have actionable outcomes 

(i.e. intervention could be initiated if someone at high risk for suicide were identified.) More than 

94 percent of the data systems we identified appear to be updated on an ongoing basis, increasing 

the likelihood that linkage would be useful; however, information on data lags or completeness 

within the sources we located is not available.

20
Linking to external data systems could allow for longer-term assessment of suicide 

prevention interventions yet data system linkage is under-utilized in suicide prevention studies. 

While we identified 153 unique data systems, we found only six studies that included assessment 

of outcomes by linking their prevention data to external data systems at the individual or 

ecological level. The majority of the prevention studies identified have the capability to link to 

medical record data or mortality data, with patient-level consent. Because hospitals routinely 

track people who were treated for suicidal behaviors, the expansion of the electronic medical 

record could make accessing those data much easier. One study we identified29 showed that 

suicide prevention programs within geographic boundaries (e.g., counties) can be linked in 

aggregate or at an ecological level to suicide mortality data to study suicide prevention program 

impact before and after implementation. In this study, the authors compared data in counties with 

SAMHSA GLS youth suicide prevention programs to counties without SAMHSA GLS youth 

suicide prevention programs by linking to suicide mortality data at the county level. Their goal 

was to examine whether a reduction in youth suicide mortality that could be reasonably 

attributed to a GLS program occurred between 2007 and 2010. Several existing prevention 

programs could be enhanced, at low cost, by this type of linkage to data systems.

Of the six studies that linked with external data systems, three used ecological linking 

methodology and three used individual linking methodology. The analysis methods appropriately 

handled the different types of data. The bulk of the studies focused on the main effects of 

interventions and did not assess treatment effect heterogeneity, as only 11 of the 47 studies

explored moderation effects.

Findings in Relationship to What Is Already Known

Recently, a resource was published by the Data and Surveillance Task Force (DSTF) of the 

NAASP, which had been charged with making recommendations for improving national data 

systems on suicidal behaviors.73 The DSTF report listed suicide data systems, identifying 28 

such systems (26 of which were eligible for inclusion in our project). The DSTF also made

several recommendations to improve existing data systems:

1. Coding manuals and national surveys should adopt the use of standard language and 

definitions. For example, public and private organizations should adopt and promote the 

use of standard definitions such as those described in the CDC’s Self-Directed Violence 

Surveillance Uniform Definitions and Recommended Data Elements;

74

2. Suicide data systems should add missing key variables or data elements (e.g., sociodemographics, mechanism of injury) to existing nonfatal data systems to enhance their 

usefulness;

3. Medical records should include external cause coding (a data element needed to identify 

suicide attempts) as a requirement for reimbursement by insurance carriers; 

4. Systems should capture “real-time” information on hospital emergency visits to improve 

the monitoring of trends in suicidal behavior; 

5. All States should be encouraged to include nonfatal suicidal behavior (suicide attempts) 

by youth 12 to 17 years of age as a health condition to be reported to the State health 

department.

Our findings confirm the importance of these five points in addressing the need to make 

suicide data systems more useful. Our work builds on this, adding information on linkage of 

suicide data systems to suicide prevention intervention data to study the extended value of the 

21
suicide prevention efforts. To date, this has been a missed opportunity in the field of suicide 

prevention.

Limitations of the Systematic Review Process

Our ability to determine which prevention studies might be linked to data systems was 

limited by the lack of available codebooks or data dictionaries outlining specifics about the 

available data sets. Some of the data dictionaries are available online (48%); however, when we 

contacted named contact people for each of the other data systems to request information about a 

data dictionary or codebook, we received no responses.

We were not able to assess data quality for the data systems identified. Only about half of the 

identified data systems are suicide-specific or had as their primary purpose the collection of data 

about suicide. The rest of the systems we identified were developed for other purposes (for 

example, hospital discharge data that happens to include suicide as an admitting or discharge 

diagnosis). We did not include some data systems, including Behavioral Risk Factors 

Surveillance System (BRFSS) and crime reports, because they do not have primary suicide 

outcomes; however, these might be used by other researchers to assess impact on behaviors that 

are highly correlated with suicidal behavior, including drug overdoses.

Limitations of the Data Systems

Although linkage is possible, serious inadequacies exist in the quality of available suicide 

mortality and morbidity data: (1) the definitions of suicidal behavior vary greatly; (2) suicide is a 

rare outcome, so it is challenging to detect an intervention impact on suicide mortality without 

large studies (up to a million person years); (3) misclassification and under-reporting of suicide 

and suicide attempt as outcomes due to stigma, lack of information to confirm suicide intent was 

present, and other issues; (4) there is no single, comprehensive, national system to document the 

scope of non-fatal suicide attempts. There is regional variation in how suicide is investigated and 

coded and how people making a determination of suicide are trained. Rockett et al.75 note that 

variation in coding in the United States appears to be partially an artifact of geographic region 

and partially a degree of toxicological assessment in the case ascertainment process. Variation in 

classification could be driven by sociocultural or political factors (i.e., stigma), economic factors, 

or forensic factors (i.e., lack of training of providers to elicit the information needed). However, 

as pointed out by the CDC, the quality of the data on nonfatal suicidal behavior is even more 

problematic than that of suicides.74

According to the DSTF of the NAASP,

76 the data systems currently used to estimate trends in 

suicidal behavior were not designed solely to address this subject. In these data systems, 

questions specific to suicidal behavior are often limited and the data systems may have lesser or 

variable quality in terms of ascertaining suicide morbidity and mortality as outcomes.

The CDC document Self-directed Violence Surveillance: Uniform Definitions and 

Recommended Data Elements addresses definitional inconsistencies as well as common data 

elements to promote and improve consistency of surveillance.74 Although the lack of readily 

available codebooks and data dictionaries limits our ability to say which databases are useful, 

only the National Violent Death Reporting System (NVDRS) and individual State versions of it 

adhere to the CDC common data dictionary for suicide databases.74 The lack of adherence to a 

standard set of data elements in suicide registries and studies is a barrier to linkage and reduces 

the potential utility of linkage.

22
Hospital and International Classification of Diseases (ICD) codes for self-harm are not 

necessarily suicide attempts; this is an important limitation. Diagnoses may be underreported 

during hospitalizations or physician visits. The same underlying condition may also be coded in 

different ways, depending on the clinical circumstances. Definitions of suicide-related constructs 

should have validity and reliability, but not all behaviors in the CDC Self-Directed Violence 

Surveillance have demonstrated reliability, validity, and utility. Suicidal behaviors recorded in 

hospital visits and suicide deaths reflected in mortality data typically reflect only the ‘tip of the 

iceberg’ of suicidal events.

Future Research Needs and Opportunities

Potential Benefit of Data Linkage

There are challenges in the United States related to financing the infrastructure required to 

sustain large-scale, coordinated suicide prevention efforts. Suicide prevention programs have 

limited ability to study long-term outcomes under the current funding structure. Our results show 

that longer-term results could be obtained by linkage of prevention efforts to existing data 

systems. 

Linkage to accessible, current surveillance data could help to address the lack of studies 

testing the impact of early intervention on risk for suicide attempt and suicide.

76 Primary 

prevention approaches delivered in early development require extended followup periods to track 

populations through the period of risk onset for suicidal thoughts and behaviors. Randomized 

trials of prevention programs conducted in early childhood have reported reduced occurrence and 

severity of mental, emotional, and behavioral problems that increase risk for suicidal behavior 

later in life (e.g., aggression, depression, substance use, and deviant peer associations);77,78,79

however, with the exception of Wilcox, et al., (2008)30 the impact of these programs on reducing 

suicidal behaviors is unknown at present because evaluators of these interventions have rarely 

followed their cohorts into the peak age of risk for suicide attempt and suicide and often did not 

include suicidal behavior in their outcome measures. Also, these interventions were often 

directed at early development, many years before suicidal thoughts and behaviors typically 

occur. The Wilcox, et al. (2008)30 study allowed for decades of longitudinal followup which may 

no longer be possible in the current research funding climate.

Aside from individual studies of interventions, there are several ongoing national initiatives 

in the United States targeting key risk factors for suicide, such as the National Child Traumatic 

Stress Network and Project LAUNCH (Linking Actions for Unmet Needs in Children’s Health). 

These initiatives, and others, represent large investments by the Federal Government with broad 

national reach and the potential to impact suicide morbidity and mortality, which could be 

studied by data linkage. Linkage of these prevention data to systems, such as State HIE and the 

restricted-access NVDRS, would provide the ability to access individual-level quantitative data,

as well as incident narrative reports on all suicide decedents. Currently, the NVDRS is available 

in only 32 States, but it could be expanded to all States. These data are potentially available for 

linking with other external data systems, if coordinated with State public health departments. 

23
Barriers to Data Linkage

There are several barriers or limitations suicide prevention scientists could face regarding the 

linkage of prevention data to data systems: the adequate ascertainment of those affected by 

suicide ideation and attempts; costs associated with access to the National Death Index (NDI)

and other data systems; sizeable interoperability challenges on a national level, even for routine 

sharing of clinical data; the lack of adequate access to data dictionaries; and, the possibility that 

one data system may not have all the outcomes of interest to preventive studies and the 

consequent need to link to multiple data systems for a more complete picture of outcomes.

Suicide prevention efforts could be sustained by communities after grant or contract funding 

for suicide prevention programs end; however, there are few resources to sustain the assessment 

of study outcomes. With minimal resources, existing data systems could be accessed by service 

systems or public health agencies to assess the impact of suicide prevention activities. Ideally, 

this would be a bi-directional process whereby those agencies could also use data systems to 

proactively identify individuals, communities, or subgroups at risk for suicide to whom they 

could direct outreach and interventions.

There was a lack of studies identified in the literature review addressing the reduction of 

access to lethal means as a suicide prevention strategy, although many other countries have 

reduced their suicide rates this way.80 Means restriction approaches include firearm safety, 

construction of barriers at jumping sites, detoxification of domestic gas, improvements in the use 

of catalytic converters in motor vehicles, restrictions on pesticides, reduction of lethality or 

toxicity of prescriptions, use of lower-toxicity antidepressants, change in packaging of 

medications to blister packs, and restriction of sales of lethal hypnotics (i.e. barbiturates). Means 

restriction is the universal approach with the strongest and most consistent evidence of 

preventing suicide.56, 61, 81 This approach typically involves linkage to mortality data to study the 

impact of changes in legislation at the State level.

Because many of the prevention studies identified in the review had small sample sizes, and 

large sample sizes are needed to find an intervention impact on suicide attempt or suicide, 

intervention harmonization approaches could be beneficial. Intervention harmonization could 

help to identify who could benefit from different youth suicide prevention options across 

subpopulations and provide for personalized and contextualized interventions. Finally, the 

linkage of suicide morbidity and mortality data to the Collaborative Data Synthesis for 

Adolescent Depression Trials (R01MH040859), which has combined de-identified data from 

over 30 prevention and depression treatment trials, could provide great value for a very small 

investment. Many of the academic medical centers which were funded to recruit for this trial 

have EMR access. Also, NDI searches could be conducted on each member of the trials, if the 

proper permissions were obtained.

Data linkage approaches need to protect the privacy and security of individual information on 

suicidal behaviors and suicide-related risk factors. Because suicidal behaviors are relatively rare 

events, if a data system includes certain geographic identifiers such as county or school, it might 

be possible to identify a specific individual. Those carrying out linkage could use processes 

which ensure that individuals cannot be identified and that identifying data (e.g., name, date of 

birth, address) is not transferred between data sets. Data linkage procedures could be approved 

by an International Review Board and subject to data use policies and agreements. The legal 

feasibility of linkage depends on the applicability to the specific purpose of the data linkage of 

Federal and State legal protections for the confidentiality of health information and participation 

24
in human research, and also on any specific permissions obtained from individual patients for the 

use of their health information. Detailed guidelines on the technical and legal aspects of data 

linkage could be developed to facilitate work in this area. Data sharing agreements are needed. 

There is a national discussion about integrating data from health care delivery systems and 

health insurance systems to create a national health research data infrastructure (e.g., PCORnet,

FDA Sentinel, National Institutes of Health Precision Medicine Initiative (NIH PMI)). This type 

of national resource could advance linkage opportunities to suicide prevention data. Data linkage 

in suicide prevention needs to be a part of this national discussion.

Suggested Next Steps

● Intervention harmonization: Approaches to advance intervention harmonization might allow 

for smaller studies of like interventions to be combined to increase statistical power. 

Biostatistical methods are needed for intervention harmonization across prevention studies 

aimed at youth suicide, as well as interventions directed at conditions in the causal pathway 

to suicide. This work could also advance the identification and study of mediators and 

moderators, which could allow for the targeting of specific interventions at high-risk groups. 

These new methodologic approaches are necessary because nearly all of the suicide 

prevention studies are underpowered to address mediation or moderation within their own 

studies.

● A National suicide outcomes data repository: Such a repository could be created to combine

data from several sources to aim to establish better coverage of suicide ideation, suicide 

attempt, and suicide on a national level.

● Guidelines on data linkage methods and procedures: Such guidelines could be developed to 

facilitate the linkage of prevention data with external data systems.

● A Technical support center: A support center could be established to assist researchers, 

prevention scientists, health systems, States, and others with methods and procedures for data 

linkage.

Conclusions

We identified only six studies that linked suicide prevention efforts with data systems, and none 

of these explored the effects of moderators. We identified 153 unique data systems, 66 of which 

we classified as “fairly accessible” with data dictionaries available. There is potential for linking 

existing data systems with existing suicide prevention efforts to assess the broader and extended 

impact of suicide prevention programs; however, limited availability of data dictionaries and 

lack of adherence to standard data elements limits the potential utility of linking prevention 

efforts with data systems.

25
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al. The 18-month impact of an emergency 

room intervention for adolescent female 

suicide attempters. J Consult Clin Psychol. 

2000 Dec;68(6):1081-93. PMID: 11142542.

68. Rudd MD, et al. Effectiveness of an Outpatient 

Intervention Targeting Suicidal Young 

Adults: Preliminary Results. J Consult Clin 

Psychol. 1996;64(1):179-90. PMID: 

8907098.

69. Segal HG, King CA, Naylor MW. Psychosocial 

functioning of severely disturbed 

adolescents after short-term hospitalization. 

Psychiatr Serv. 1995 Mar;46(3):287-9. 

PMID: 7796221.

70. Spirito A, Plummer B, Gispert M, et al. 

Adolescent suicide attempts: outcomes at

follow-up. Am J Orthopsychiatry. 1992 

Jul;62(3):464-8. PMID: 1497112.

71. Wharff EA, Ginnis KM, Ross AM. Family-based 

crisis intervention with suicidal adolescents 

in the emergency room: a pilot 

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study. Soc Work. 2012 Apr;57(2):133-43. PMID: 

23038875.

72. Preacher K, Rucker D, Hayes A. Addressing 

Moderated Mediation Hypotheses: Theory, 

Methods, and Prescriptions. Multivariate 

Behav Res. 2007;42(1):185-227.

73. . Improving national data systems for 

surveillance of suicide-related events. Am J 

Prev Med. 2014 Sep;47(3 Suppl 2):S122-9. 

doi: 10.1016/j.amepre.2014.05.026. PMID: 

25145729.

74. Crosby A, Ortega L, Melanson C. Self-directed 

Violence Surveillance: Uniform Definitions 

and Recommended Data Elements, Version 

1.0 Centers for Disease Control and 

Prevention, National Center for Injury 

Prevention and Control. Atlanta, GA: 2011. 

75. Rockett IR, Hobbs GR, Wu D, et al. Variable 

Classification of Drug-Intoxication Suicides 

across US States: A Partial Artifact of 

Forensics? PLoS One. 

2015;10(8):e0135296. doi: 

10.1371/journal.pone.0135296. PMID: 

26295155.

76. National Action Alliance for Suicide Prevention: 

Research Prioritization Task Force. U.S. 

National Suicide Prevention Research 

Efforts: 2008–2013 Portfolio Analyses 

National Institute of Mental Health and the 

Research Prioritization Task Force. 

Rockville, MD: 2015. 

77. Botvin GJ, Griffin KW, Diaz T, et al. Preventing 

illicit drug use in adolescents: long-term 

follow-up data from a randomized control 

trial of a school population. Addict Behav. 

2000 Sep-Oct;25(5):769-74. PMID: 

11023017.

78. Hawkins JD, Oesterle S, Brown EC, et al. 

Results of a type 2 translational research 

trial to prevent adolescent drug use and 

delinquency: a test of Communities That 

Care. Arch Pediatr Adolesc Med. 2009 

Sep;163(9):789-98. doi: 

10.1001/archpediatrics.2009.141. PMID: 

19736331.

79. Spoth RL, Redmond C, Shin C. Randomized trial 

of brief family interventions for general 

populations: adolescent substance use 

outcomes 4 years following baseline. J 

Consult Clin Psychol. 2001 Aug;69(4):627-

42. PMID: 11550729.

80. Preventing Suicide: A Global Imperative. 

Geneva, Switzerland: World Health 

Organization; 2014. 

http://www.who.int/mental_health/suicideprevention/world_report_2014/en/. 

Accessed on December 2, 2015.

81. Crifasi CK, Meyers JS, Vernick JS, et al. Effects 

of changes in permit-to-purchase handgun 

laws in Connecticut and Missouri on suicide 

rates. Prev Med. 2015 Oct;79:43-9. doi: 

10.1016/j.ypmed.2015.07.013. PMID: 

26212633.

31
Appendix A. Acronyms

Acronym Definition

AAS American Association of Suicidology

AFSP American Foundation for Suicide Prevention

AHRQ Agency for Healthcare Research and Quality

ANOVA Analysis of variance

APCD All Payer Claims Databases

BRFSS Behavioral Risk Factors Surveillance System

CAMS The Collaborative Assessment and Management of Suicidality 

CARE Care, Assess, Respond, Empower

CAST Coping and Support Training

CATCH-IT Competent Adulthood Transition With Cognitive Behavioral And Interpersonal Training

C-CARE Counselors: Care, Assess, Respond, Empower

CDC Centers for Disease Control and Prevention

CINAHL Cumulative Index to Nursing and Allied Health Literature

DSTF Data and Surveillance Task Force

EMR healthcare provider records (electronic medical records)

EPC Evidence-base Practice Center

ER Emergency room

ED Emergency department

ERIC Education Resources Information Center 

FDA Food and Drug Administration

GLS The Garrett Lee Smith Memorial Suicide Prevention Program

HIE Health Information Exchanges

HIPAA Health Insurance Portability and Accountability Act

KQ Key Question

LAUNCH Linking Actions for Unmet Needs in Children’s Health

LGBTQ Lesbian, gay, bi-sexual, transgender, queer

MeSH Medical Subject Heading

NAASP National Action Alliance for Suicide Prevention

NASY+ National Annenberg Survey of Youth

NIH National Institutes of Health

NR not reported

NREPP National Registry of Evidence-based Programs and Practices

NVDRS National Violent Death Reporting

PGC Personal Growth Class

PHI Protected Health Information

PICOTS Populations, Interventions, Comparators, Outcomes, Timing, Setting

PUF Participant user files

RCT Randomized controlled trial

SAMHSA Substance Abuse and Mental Health Services Administration

SOFTADS Survey of Outcomes Following Treatment for Adolescent Depression

SOS Signs Of Suicide

SPRC Suicide Prevention Resource Center

SRDR Systematic Review Data RepositoryTM

TADS Treatment for Adolescents with Depression Study

TASA Treatment of Adolescent Suicide Attempters

TORDIA Treatment of Resistant Depression in Adolescents

YST-1 Youth-Nominated Support Team for Suicidal Adolescents (Version 1)

YST-2 Youth-Nominated Support Team for Suicidal Adolescents (Version 2)

WISQARS Web-based Injury Statistics Query and Reporting

A-1
Appendix B. Search Strategies

Table B-1. PubMed search strategy

Table B-2. Cochrane Library search strategy

#1 MeSH descriptor: [Suicide] explode all trees and with qualifier(s): [Prevention & control - PC]

 #2 suicid*:ti,ab,kw (Word variations have been searched)

 #3 prevent:ti,ab,kw (Word variations have been searched)

 #4 "prevention":ti,ab,kw (Word variations have been searched)

 #5 #1 or (#2 and (#3 or #4)

Publication Year 

from 1990 to 

2015 

# Search

1 suicide/prevention[mh]

2 Suicide, Attempted/prevention[mh]

3 suicid*[tiab] AND (prevent[tiab] OR prevention[tiab])

4 1 OR 2 OR 3 

5 clinical trial[pt]

6 “Non-randomized”[tiab]

7 Nonrandomized[tiab]

8 cohort[tiab]

9 “next study”[tiab]

10 observational[tiab]

11 “Case-control”[tiab]

12 “cohort studies”[mh]

13 cross-over studies[mh]

14 prospectiv*[tiab]

15 registr*[tiab]

16 restrospectiv*[tiab]

17 "Comparative Study" [pt]

18 “propensity score”[tiab]

19 “propensity Score”[mh]

20 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14 OR 15 OR 16 OR 17 OR 18 OR 19

21 4 AND 20

22 1990:2016[dp] 

23 Eng[la]

24 4 AND 20 AND 22 AND 23

B-1
Table B-3. CINAHL, PsycINFO and ERIC search strategy

S26 S7 AND S23 Limiters - Published Date: 19900101-

20151231 

Narrow by Language: - English 

Search modes - Boolean/Phrase 

S25 S7 AND S23 Narrow by Language: - English 

Search modes - Boolean/Phrase 

S24 S7 AND S23 Search modes - Boolean/Phrase 

S23 S8 OR S9 OR S10 OR S11 OR S12 OR S13 OR S14 OR S15 

OR S16 OR S17 OR S18 OR S19 OR S20 OR S21 OR S22

Search modes - Boolean/Phrase 

S22 TX propensity score Search modes - Boolean/Phrase 

S21 TX comparative study Search modes - Boolean/Phrase 

S20 TX comparative study Search modes - Boolean/Phrase 

S19 TX retrospective study Search modes - Boolean/Phrase 

S18 TX registry Search modes - Boolean/Phrase 

S17 TX prospectiv* Search modes - Boolean/Phrase 

S16 TX "cross over" Search modes - Boolean/Phrase 

S15 TX case control study Search modes - Boolean/Phrase 

S14 TX observational study Search modes - Boolean/Phrase 

S13 TX cohort study Search modes - Boolean/Phrase 

S12 TX "follow up" OR TX "follow-up" OR TX Followup Search modes - Boolean/Phrase 

S11 TX "next study" Search modes - Boolean/Phrase 

S10 TX "non-randomized" Search modes - Boolean/Phrase 

S9 TX nonrandomized Search modes - Boolean/Phrase 

S8 TX "clinical Trials" Search modes - Boolean/Phrase 

S7 S5 AND S6 Search modes - Boolean/Phrase 

S6 S3 OR S4 Search modes - Boolean/Phrase 

S5 S1 OR S2 Search modes - Boolean/Phrase 

S4 TX Prevention Search modes - Boolean/Phrase 

S3 TX Prevent Search modes - Boolean/Phrase 

S2 TX suicid* Search modes - Boolean/Phrase 

S1 SU Suicide Search modes - Boolean/Phrase 

Table B-4. The Campbell Collaboration Library of Systematic Reviews

suicide AND prevention

B-2
Appendix C. Environmental Scan Coding Scheme

Table C-1. Environmental scan coding schema

ID# Topics Level Type Must Code

1 Data System Specification and Meta Information 1 x

1 Review method and information source used to find it 2 x

1 Systematic review 3 Y/N/? x

1 IN 4 Y/N/? x

1 OUT 4 Y/N/? x

1 Environmental scan 3 Y/N/? x

1 Google 4 Y/N/? x

1 Yahoo 4 Y/N/? x

1 Bing 4 Y/N/? x

1 Appendix A: NREPP Database of Suicide Prevention Programs 4 Y/N/? x

1 Appendix D: Potential Data Systems 4 Y/N/? x

1 NAASP paper 4 Y/N/? x

1 AFSP/AAS/SPRC 4 Y/N/? x

1 Ad-hoc search 4 Y/N/? x

1 Targeted search 3 Y/N/? x

1 CA 4 Y/N/? x

1 OR 4 Y/N/? x

1 MD 4 Y/N/? x

1 DE 4 Y/N/? x

1 IL 4 Y/N/? x

1 WI 4 Y/N/? x

1 City 4 Y/N/? x

1 City name 4 text-name x

1 Community 4 Y/N/? x

1 Community name 4 text-name x

1 Name of the database / data system (if any) 2 text-name x

1 Data custodian 2 x

1 Name 3 text-name x

1 Contact email address 3 email optional

1 Contact phone number 3 phone optional

1 Web Link/URL 2 x

1 URL - generic / home page of the study or data custodian 3 URL x

1 URL - data dictionary and/or public use file 3 URL x

1 URL - additional useful links (e.g., reports) #1 3 URL x

1 URL - additional useful links (e.g., reports) #2 3 URL optional

C-1
Table C-1. Environmental scan coding schema (continued)

ID# Topics Level Type Must Code

1 URL - additional useful links (e.g., reports) #3 3 URL optional

1 URL - additional useful links (e.g., reports) #4 3 URL optional

1 URL - additional useful links (e.g., reports) #5 3 URL optional

1 Level of information avaiable for this data system 2 x

1 Data exists and can be acquired (free or for a fee) 3 Y/N/? x

1 Data is publicly available and can be downloaded (e.g., PUF) 4 Y/N/? x

1 Data can be acquired but requires an automated registration 4 Y/N/? x

1

Data can be acquired if confirmed by a person (e.g., needs email 

comm) 4 Y/N/? x

1 Data dictionary or code book is accessible 3 Y/N/? x

1 Formal data dictionary (e.g., xml, xls, pdf) 4 Y/N/? optional

1 Informal data dictionary (e.g., data intake survey) 4 Y/N/? optional

1

Proxy data dictionaries (e.g., reports) which may not have all 

variables 4 Y/N/? optional

1 Web page for this data system includes 3 Y/N/? optional

1 summary of what the data system is about 4 Y/N/? optional

1 some detailed information about the data system 4 Y/N/? optional

1 high level of details about the data system 4 Y/N/? optional

1 Data Use / Functions 2 x

1 Primary function of the data system / database 3 x

1 Reseach (e.g., academic, pharma) 4 Y/N/? x

1 Clinical care / operations 4 Y/N/? x

1 Administrative services (e.g., census) 4 Y/N/? x

1 Public health (e.g., surveillance) 4 Y/N/? x

1 Other 4 text x

1 Secondary function of the data system 3 text optional

2 Geographic Coverage 1 x

2 Countries 2 x

2 US 3 Y/N/? x

2 Other (non-US) 3 text-list x

2 US coverage 2 x

2 US - national 3 Y/N/? x

2 US - regional (e.g., east coast) 3 Y/N/? x

2 US - state level (e.g., Maryland) 3 Y/N/? x

2 US - smaller than state level 3 Y/N/? x

2 County 4 Y/N/? x

2 Zip code 4 Y/N/? x

2 Census block 4 Y/N/? x

2 Tribal 4 Y/N/? x

2 Teritory 4 Y/N/? x

C-2
Table C-1. Environmental scan coding schema (continued)

ID# Topics Level Type Must Code

2 Islands 4 Y/N/? x

2 Other 4 text x

2 US states 2 x

2 Alabama (AL) 3 Y/N/? x

2 Alaska (AK) 3 Y/N/? x

2 Arizona (AZ) 3 Y/N/? x

2 Arkansas (AR) 3 Y/N/? x

2 California (CA) 3 Y/N/? x

2 Colorado (CO) 3 Y/N/? x

2 Connecticut (CT) 3 Y/N/? x

2 Delaware (DE) 3 Y/N/? x

2 Florida (FL) 3 Y/N/? x

2 Georgia (GA) 3 Y/N/? x

2 Hawaii (HI) 3 Y/N/? x

2 Idaho (ID) 3 Y/N/? x

2 Illinois (IL) 3 Y/N/? x

2 Indiana (IN) 3 Y/N/? x

2 Iowa (IA) 3 Y/N/? x

2 Kansas (KS) 3 Y/N/? x

2 Kentucky (KY) 3 Y/N/? x

2 Louisiana (LA) 3 Y/N/? x

2 Maine (ME) 3 Y/N/? x

2 Maryland (MD) 3 Y/N/? x

2 Massachusetts (MA) 3 Y/N/? x

2 Michigan (MI) 3 Y/N/? x

2 Minnesota (MN) 3 Y/N/? x

2 Mississippi (MS) 3 Y/N/? x

2 Missouri (MO) 3 Y/N/? x

2 Montana (MT) 3 Y/N/? x

2 Nebraska (NE) 3 Y/N/? x

2 New Hampshire (NH) 3 Y/N/? x

2 New Jersey (NJ) 3 Y/N/? x

2 New Mexico (NM) 3 Y/N/? x

2 New York (NY) 3 Y/N/? x

2 North Carolina (NC) 3 Y/N/? x

2 North Dakota (ND) 3 Y/N/? x

2 Ohio (OH) 3 Y/N/? x

2 Oklahoma (OK) 3 Y/N/? x

C-3
Table C-1. Environmental scan coding schema (continued)

ID# Topics Level Type Must Code

2 Oregon (OR) 3 Y/N/? x

2 Pennsylvania (PA) 3 Y/N/? x

2 Rhode Island (RI) 3 Y/N/? x

2 South Carolina (SC) 3 Y/N/? x

2 South Dakota (SD) 3 Y/N/? x

2 Tennessee (TN) 3 Y/N/? x

2 Texas (TX) 3 Y/N/? x

2 Utah (UT) 3 Y/N/? x

2 Vermont (VT) 3 Y/N/? x

2 Virginia (VA) 3 Y/N/? x

2 Washington (WA) 3 Y/N/? x

2 West Virginia (WV) 3 Y/N/? x

2 Wisconsin (WI) 3 Y/N/? x

2 Wyoming (WY) 3 Y/N/? x

2 Other (e.g., islands, teritories...) 3 text-list x

3 Demographic Coverage 1 x

3 Target population 2 x

3 American Indian (Tribal) 3 Y/N/? x

3 LGBT 3 Y/N/? x

3 Military 3 Y/N/? x

3 Prison 3 Y/N/? x

3 Students (schools/campuses) 3 Y/N/? x

3 General population 3 Y/N/? x

3 Other 3 text x

4 Data Granularity 1 x

4 Patient-level data exists (patient-level linkage possible) 2 Y/N/? x

4 Aggregated on certain dimensions (ecological linkage possible) 2 Y/N/? x

4 Demographics 3 Y/N/? x

4 Geographic 3 Y/N/? x

4 Clinical specifications 3 Y/N/? x

4 Entity 3 Y/N/? x

4 Data Sampling 2 Y/N/? x

5 Variables (if data dictionary is available) 1 x

5 Dependent variables 2 x

5 Primary 3 Y/N/? x

5 Suicide completion 4 Y/N/? x

5 Suicide attempt 4 Y/N/? x

5 Suicide ideation 4 Y/N/? x

C-4
Table C-1. Environmental scan coding schema (continued)

ID# Topics Level Type Must Code

5 Secondary / Intermediate 3 Y/N/? optional

5 Psychiatric and substance abuse disorders 4 Y/N/? optional

5

Service use for psychiatric and substance abuse disorders (e.g. 

ER visit) 4 Y/N/? optional

5 Graduation rates (e.g., educational, training) 4 Y/N/? optional

5 Incarceration rates 4 Y/N/? optional

5 Violence (both perpetrator and victim) 4 Y/N/? optional

5 Social support and connectedness 4 Y/N/? optional

5 Access to lethal means 4 Y/N/? optional

5 Other 4 text-list optional

6 Data Scalability 1 x

6 Data types 2 x

6 Suicide specific 3 Y/N/? x

6 Death records 3 Y/N/? x

6 Healthcare provider records (e.g., EHRs) 3 Y/N/? x

6 Population-based surveys 3 Y/N/? x

6 Health insurance claims 3 Y/N/? x

6 Other 3 text x

6 Updates and Data Collection 2 x

6 Ongoing 3 Y/N/? x

6 Stopped 3 Y/N/? x

6 Year Data Collection Started 3

date 

(YYYY) x

6 Year Data Collection Stopped 3

date 

(YYYY) x

7 Data Governance 1 x

7 Data access 2 x

7 Unrestricted 3 Y/N/? x

7 PUF (public use file) URL 4 URL optional

7 requires registration 4 Y/N/? optional

7 Restricted 3 Y/N/? x

7 can be used for research 4 Y/N/? x

7 can be used for clinical care / operations 4 Y/N/? x

7 can be used for commercial purposes 4 Y/N/? x

7 Data commodity 2 x

7 free 3 Y/N/? x

7 commercial / has a fee 3 Y/N/? x

7 type of license 3 text x

8 Excldue PICOTS Reason 1 Y/N/? x

8 Population(s) 2 Y/N/? x

8 Explain 3 text x

C-5
Table C-1. Environmental scan coding schema (continued)

ID# Topics Level Type Must Code

8 Intervention(s) 2 Y/N/? x

8 Explain 3 text x

8 Comparison(s) 2 Y/N/? x

8 Explain 3 text x

8 Outcome(s) 2 Y/N/? x

8 Explain 3 text x

8 Timing 2 Y/N/? x

8 Explain 3 text x

8 Setting 2 Y/N/? x

8 Explain 3 text x

8 Other? 2 text x

C-6
Appendix D. Results of the Literature Search

Figure D-1. Results of the literature search 

D-1
Appendix E. Evidence Tables

Evidence Table E-1. Characteristics of studies identified in the literature search

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

Adolescent 

Suicide Risk 

Screening

King, 

20121

RCT 2009 to 

2010

Emergency 

department

MI Adolescent Suicide Risk 

Screening (ER)

245 53.4 15.32(1.37) White: 80; 

AfricanAmerican: 21.6; 

Asian 2.9; 

Hispanic: 5.7; 

Other: 13.1b

NR

AttachmentBased Family 

Therapy 

Diamond, 

20102

RCT 2005 to 

2007

Emergency 

department, 

Primary care 

PA Attachment-Based Family 

Therapy

66 83 15.1 (1.5) AfricanAmerican: 746

Diamond, 

20123c RCT NR Emergency 

department, 

Primary care 

Philadelphia, 

PA

Attachment-Based Family 

Therapy

66 83 15.1(1.5) AfricanAmerican: 746

CAMS: The 

Collaborative 

Assessment 

and 

Management 

of Suicidality 

Jobes ,

d

20124

Observational NR Primary care, 

Emergency 

department, 

Military base

NR US Airforce clinical records 55 (66) 29.1 (7.2) White: 47 NR

Jobes , d

20124

RCT NR Primary care, 

Emergency 

department, 

Military base

Seattle, WA NR 32 62 29.1 (7.2) White: 66 NR

E-1
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

C-CARE/CAST 

(CounselorsCARE and 

Coping and 

Support 

Training)

Eggert, 

20025

RCT NR School 341 44.6 range: 14 to 19 White: 39.9; 

AfricanAmerican: 12.3; 

Asian: 12.9; 

Hispanic: 7; 

Other: 25.8

9

Randell, 

20016

RCT NR School 341 Range: 

48-59

range: 14 to 19 White: 40 9

Thompson,

20017e RCT NR School and home Pacific 

Northwest 

urban school 

districts

Counselors-Child and 

Adolescent Risk Evaluation 

(C-CARE); Coping and 

Support Training (CAST)

460 52 range: 14 to 19 AfricanAmerican: 18; 

Euro-American: 

49; AsianAmerican: 4; API: 

18; 

Latino/Hispanic: 

1

1, 2.5, 9

Hooven, 

20108

RCT NR School and 

Home

615 60 15.95(1.08) White: 67 9

Communities 

That Care

Oesterle, 

20159

Community 

randomized 

trial

2005-2012 Policy/Legislation CO, IL, KS, ME, 

OR, UT, WA

Community Youth 

Development Study

2407 NR Range: 10-14 NR

GLS: The 

Garrett Lee 

Smith 

Memorial 

Suicide 

Prevention 

Program

Garraza, 

201510

Observational 2006 to 

2009

Education/Trainin

g/Screening/Infra

structure/Crisis

hotline/

Community 

Partnerships

466 Counties 

(exposed) and 

1161 Counties 

(not exposed) 

in the US

The Garrett Lee Smith 

Memorial Suicide 

Prevention Program

173,000 51.5-52.3 Range: 12->26 White, nonHispanic: 80; 

Black nonHispanic: 9; 

AIAN: 1-2; Asian: 

1.2-1.7; 

multiracial: 1-1.2; 

Hispanic: 5.5-7.3

NA

Walrath, 

201511

Observational 2000 to 

2006

Screening, 

Behavioral, 

Policy Changes, 

Outreach, 

Referral to 

mental health 

professionals, 

Means restriction

NR The Garrett Lee Smith 

Memorial Suicide 

Prevention Program

2095 50.5 Range: 10 to 24 White: 79.5; 

AfricanAmerican: 9.5; 

Asian; 1.5; 

Hispanic: 6.1; 

Other: 3.4

36

E-2
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

Good Behavior 

Game

Wilcox, 

200812

RCT 1985 to 

2002

Behavioral/Skill 

building, 

Educational

Baltimore, MD 1196 51 range: 19-24 White: 34; 

AfricanAmerican: 66

24

HOPE Family 

Program/ 

HOPE Health 

Education 

Program

Lynn, 

201413

Observational NR Behavioral/Skill 

building 

New York, NY 28 45 12.8 (1.2) Latino/ Hispanic: 

40; Black: 43; 

Black/Hispanic/ra

cial mix: 17; 

NR

National 

Treatment 

Improvement 

Evaluation 

Study

Ilgen, 

200714

Observational NR Substance abuse 

treatment

NR National Treatment 

Improvement Evaluation 

Study

3733 36 32.7(7.8) NR NR

Personal 

Growth Class

Thompson

, 200015

Three 

group 

repeated 

measures 

design

1990 to 

1993

Behavioral/Skill 

building

Urban high 

schools (no 

state)

Personal Growth Class 106 58.5 NR NR 5, 10 

Project 

Chrysalis

Brown, 

200116

RCT and 

Interviews 

for the 

qualitative 

portion

1994 to 

1999

Behavioral/Skill 

building, 

Education

OR Project Chrysalis 1108 100 range: 15.13-

15.44

Non-white: 26-

43.1

12, 24

Promoting 

CARE

Hooven,f

201217

RCT NR Behavioral/Skill 

building, 

Education

Seattle, WA Promoting CARE 615 60 16 (NR) White: 66; 

AfricanAmerican: 4; 

Asian 8; Other: 

17

15

SAFETY 

Program

Asarnow, 

201518

Observational 2006-2010 Educational/Skill 

building

NR WISQARS, Law Center to 

Prevent Gun Violence

35 86 14.89 (1.6) White: 40; 

Hispanic: 34; 

Black: 11; AsianOther: 14

33

E-3
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

Seattle Social 

Development 

Project

Hawkins, 

200519

Non-RCT 

followup

NR Educational/Skill 

building

Seattle, WA 437-643 

(depend

ent on 

the 

outcom

e 

measur

ed)

NR 21 NR NA

SOS Suicide 

Prevention 

Program

Aseltine, 

200420

RCT 2001 to 

2002

Educational GA and CT 2100 51.4 NRg White: 15.5; 

AfricanAmerican: 25.4; 

Other: 5.7

3

Aseltine, 

200721

RCT 2001-2002 Educational GA, MA, and 

CT

4133 52.0 NRg White: 25.4; 

Black: 38.6; 

Hispanic: 35.3; 

Other: 0.7

3

Student 

Assistance 

Program

Biddle, 

201422

Observational 1997 to 

2006

Multiple 

Strategies

PA PA Department of 

Education (SAP Online)

2112 66.5 range: 13 to 21 White:81.7; 

AfricanAmerican: 7.9; 

Asian 2

NR

Surviving the 

Teens Suicide 

Prevention and 

Depression 

Program

King, 

201123

Pre-post NR Behavioral/Skill 

building, 

Education

OH NR 966 56.1 14.1(0.79) White:85; 

AfricanAmerican: 4.1; 

Asian1.7; 

Hispanic: 2.4; 

Other: 6.8

3

Systemic Crisis 

Intervention 

Program

Gutstein, 

1990 24

Observational NR Behavioral/Skill 

building

Houston, TX Systemic Crisis Intervention 

Program

47 25 14.4 (NR) White: 61.7; 

AfricanAmerican: 10.6; 

Asian: 2.1; Other: 

25.5

18

E-4
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

TADS March, 

200725

RCT NR Primary 

care/Communitybased

NR Treatment of Adolescents 

with Depression Study

327 45 14.6(1.5) White: 74; 

AfricanAmerican: 11.3; 

Other: 9.8

1.5, 3, 4.5, 

6, 8, 9 

TADS, 

200926

RCT NR Primary 

care/Communitybased

NR Treatment of Adolescents 

with Depression Study

327 55 14.6(1.5) White: 74; 

AfricanAmerican: 11.3; 

Hispanic: 9.8

3, 6, 9 

Vitiello, 

200927

RCT NR Primary 

care/Communitybased

NR Treatment of Adolescents 

with Depression Study

439 54 14.6 (1.5) NR

White: 74

3, 9

TADSSOFTAD Curry, 

201128

Observational NR Primary 

care/Communitybased

TADS- SOFTAD 196 56.1 18 (1.8); White: 79; 

AfricanAmerican: 8; 

Hispanic: 9; 

Other: 4

63

TASA: 

Treatment of 

Adolescent 

Suicide 

Attempters 

Brent, 

200929h RCT NR Medication or 

Pharmaceutical/B

ehavioral/Skill 

building 

NR Treatment of Adolescent 

Suicide Attempters Study

124 77.4 White: 66.9; 

AfricanAmerican: 12.9; 

Hispanic: 15.3; 

Other: 4.8

1.5, 3, 4.5, 

6

Vitiello, 

200930

Mixed:

Randomized 

or 

determined 

by study 

participant

2004 to 

2007

Medication or 

Pharmaceutical/B

ehavioral/Skill 

building 

MD, NY, NC, 

PA, TX

439 54 14.6(1.5) White: 74 6

The Coping 

Cat Program

Wolk, 

201531

RCT

Data drawn 

from 2 RCTs

NR Primary care PA NR 66 51.5 mean: 27.23 (at 

time of followup)7-14 (at time 

of intake)(3.54 

(at time of 

follow-up))

White: 84.8 84-228

E-5
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

TORDIA: 

Treatment of 

Resistant 

Depression in 

Adolescents

Asarnow, 

201132

RCT 2001 to 

2007

Medication or 

Pharmaceutical 

CA, OR, PA, RI, 

TX

327 69.7 range: 15.9 to 

18 (1.6)

White: 83.2 6

Emslie, 

201033

RCT 2001 to 

2007

Medication or 

Pharmaceutical 

CA, OR, PA, 

RI, TX

334 69.8 15.9 (1.6) White: 82.9 6

Shamsed

deen, 

201134

RCT 2001 to 

2007

Medication or 

Pharmaceutical 

CA, OR, PA, 

RI, TX

334 69.8 15.9(1.6) White: 82.9 6

Woldu, 

201135

RCT 2001 to 

2007

Medication or 

Pharmaceutical 

CA, OR, PA, 

RI, TX

334 NR Range:12 to 18 NR 6

US Air Force 

suicide 

prevention 

programme

Knox, 

200336i Quasiexperimental

1990 to 

2002

Educational US 5,260,2

92b NR NR NR 144

YouthNominated 

Support Team 

for Suicidal 

Adolescents 

(Version 1)

King, 

200637

RCT 1998 to 

2000

Educational/Skill 

building

MI 289 68.2 15.3(1.5) White: 82.4; 

AfricanAmerican: 10.2; 

Other: 7.4

6

YouthNominated 

Support Teamversion II

King, 

200938

RCT 2002 to 

2008

Educational/Skill 

building

MI 448 71.2 15.6(1.3) White: 84; 

AfricanAmerican: 6; 

Hispanic: 2; 

Other: 8

12

Single studies Anestis, 

201539

Observational 2013 to 

2013

Policy/Legislation National 

database

WISQARS NR NR NR NR NA

Asarnow, 

201140

RCT 2003 to 

2005

Behavioral/Skill 

building

CA 181 69.0 14.7 (2) White: 33; 

AfricanAmerican: 13; 

Other: 54

2

E-6
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

Single studies

(continued)

Brown, 

200541

RCT 1999 to 

2002

Cognitive therapy PA 

(Philadelphia)

120 60.8 range: 18 to 66 White: 35 18

Collins, 

200842

Observational 1998 to 

2003

Medication or 

Pharmaceutical

OR OR state Medicaid and 

mental health databases

12662 65.8 38.7(14.2) NR NA

Cooper, 

200643

Observational 1998 to 

2002

Safety-net of 

mental health 

services

CO CO Health and Hospital 

Association (note no 

findings via Google for this, 

did find CO Hospital 

Association CHA) 

discharge records

1317j 729 (55.4) range: 18 to 24 NR 12

Farmer, 

199644 k Case report 1989 to 

1994

Policy/Legislation TX 13 15.4 29 (NR) White: 53.8; 

AfricanAmerican: 30.7; 

Other: 15.4

60

Fleegler, 

201345l Observational 2007 to 

2010

Policy/Legislation All states Centers for Disease Control 

and Prevention WISQARS

121,084 NR NR NR 36

Gardner, 

201046

Non-RCT: 

treatment 

based on 

referral

2005 to 

2006

Screening OH 1503 58 range: 10 to 21 White: 34; 

AfricanAmerican: 57; 

Other: 9

6

Huey, 

200447

RCT NR Behavioral/Skill 

building

SC 156 35 12.9 (2.1) AfricanAmerican: 65; 

Asian; Other: 1

12

Kaminer, 

200648

RCT NR Aftercare CT 177 37 15.9 (1.2) White: 77; 

AfricanAmerican: 7; 

Hispanic: 12; 

Other: 4

3

Kennard, 

201449m RCT NR Medication or 

Pharmaceutical 

and 

psychotherapy 

TX NR 144 53.5 13.8(2.6) White: 81.9; 

African-American: 

10.4; Asian145; 

Hispanic: 29.9; 

Other: 6.3

7

E-7
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

Program or 

Study Name 

(if applicable)

Author, 

Year

Study 

design

Study 

start and 

end dates Setting Study location

Linked database/registry 

(if applicable) Na % female

Age, mean 

(SD) or 

otherwise 

specified

% Race/

ethnicity

FU 

(months)

Single studies

(continued)

Kerr, 

201450

RCT 1997 to 

2006

Behavioral/Skill 

building 

NW United 

States

166 100 15.3 (1.2) Caucasian: 68; 

Black: 1.8; 

Hispanic: 11.4; 

Native American: 

0.6; Asian: 0.6; 

multiracial: 16.9; 

Other: 0.6

24-36

Olfson, 

200351n Observational 1990 to 

2000

Medication and 

Pharmaceutical

See 

note

NR range: 10 to 19 NR 120

Rathus, 

200252

QuasiexperimentalNR Psychotherapy 

and 

Behavioral/Skill 

building

New York, NY Adolescent Depression and 

Suicide Program (ADSP)

111 NR NR White: 8.1; 

African-American: 

17.1; Asian 0.9; 

Hispanic: 67.6; 

Other: 6.3

3

Rothera

m-Borus, 

200053

Quasiexperimental1991 to 

1994

Specialized 

emergency room 

(ER) care 

NY 211 26.5 14.9(1.4) Hispanic: 87.1 18 

Rudd, 

199654

RCT NR Behavioral/Skill 

building

NR NR 264 17.9 22 (2.3) White: 60.6; 

African-American: 

25..8; Asian; 1.5; 

Hispanic: 10.6; 

Other: 1.5

1, 6, 12, 

18, 24 

Segal, 

199555

Observational NR NR MI NR 42 NR 14.5(1.4) NR 6 

Spirito, 

199256

Observational NR Follow-up 

interviews

NR NR 130 86.9 median: 15; 

range: 13 to 18

White: 73 Spirito, 

199256

Warner, 

201157

Observational 2007 to 

2008

Screening GA Surveillance of Combat and 

Operational Stress 

Reactions

21031 8.9 range: 18 to 

40+

NR Warner, 

201157

Wharff, 

201258

Case control 

study

2001 to 

2002

Behavioral/Skill 

building

Boston, MA Boston Children’s Hospital 

ER

250 74.8 15.6(1.5) White: 65; AfricanAmerican: 16; 

Asian 2; Other: 14

Wharff, 

201258

Wingate, 

200559

RCT NR Behavioral/Skill 

building

98 18.4 22(2.5) White: 62; AfricanAmerican: 23; 

Hispanic: 10; 

Other: 5

Wingate, 

200559

E-8
Evidence Table E-1. Characteristics of studies identified in the literature search (continued)

CAMS=The Collaborative Assessment and Management of Suicidality ; CARE=Care, Assess, Respond, Empower; CAST=Coping and Support Training; CATCH-IT=Competent Adulthood Transition With Cognitive Behavioral And 

Interpersonal Training; C-CARE=Counselors: Care, Assess, Respond, Empower; GLS=The Garrett Lee Smith Memorial Suicide Prevention Program; NASY+National Annenberg Survey of Youth; NR=not reported; PGC=Personal Growth 

Class; RCT=randomized controlled trial; SOFTADS=Survey of Outcomes Following Treatment for Adolescent Depression; SOS=Signs Of Suicide; TADS=Treatment for Adolescents with Depression Study; TASA=Treatment of 

Adolescent Suicide Attempters; TORDIA=Treatment of Resistant Depression in Adolescents; WISQARS = web-based injury statistics query and reporting YST-1=Youth-Nominated Support Team for Suicidal Adolescents (Version 1); 

YST-2=Youth-Nominated Support Team for Suicidal Adolescents (Version 2); SAFETY= Safe Alternatives for Teens & Youths 

a

At baseline

b Race: percentages total greater than 100% as participants were able to select multiple racial identities c

Experienced sexual trauma

d Single study where two slightly different interventions were applied; Military population

e High-risk youth f

Same population as Hooven, 2010—different followup periods g Takes place in high school: can assume ages 13-19 h Participant characteristics given not by arm, but by subsequent suicide attempts or events; Due to difficulty with recruitment the participants were either randomized of chose their preferred. i

Active-duty military personnel

j

This is the N of participants with suicidal behavior (attempters). 

k Prison population

l

This study looks at firearm related deaths over all 50 states. The N here is the number of deaths from 2007-2010. mYouth with major depressive disorder

n

Large dataset of 588 zip codes from 1990 and 2000

E-9
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E-12
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E-13
Appendix F. Included Data Systems

Table F-1. Data systems with fairly accessible information and data dictionaries

Organization Data System Name

AHRQ - Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS)

AHRQ - Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID)

AHRQ - Healthcare Cost and Utilization Project (HCUP)

Nationwide Emergency Department Sample 

(NEDS)

AHRQ - Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID)

AHRQ - Healthcare Cost and Utilization Project (HCUP)

State Ambulatory Surgery and Services 

Databases (SASD)

AHRQ - Healthcare Cost and Utilization Project (HCUP)

State Emergency Department Databases 

(SEDD)

American Association of Poison Control Centers (AAPCC) National Poison Data System (NPDS)

American College of Surgeons National Trauma Data Bank (NTDB)

Army

Army Study To Assess Risk and Resilience in 

Servicemembers (STARRS)

California Department of Public Health (CDPH) Portal: California 

EpiCenter

California Electronic Violent Death Reporting 

System (VDRS)

California Department of Public Health (CDPH)

California Health and Human Services Open Data Portal (CHHS 

Open Data)

California Department of Public Health Vital Statistics Advisory 

Committee (VSAC)

California Health and Human Services Agency's Committee for the 

Protection of Human Subjects (CPHS) California Death Data Files

CDC

National Survey of Prison Health Care 

(NSPHC)

CDC - National Center for Health Statistics (NCHS) National Death Index (NDI)

CDC - National Center for Health Statistics (NCHS)

National Health and Nutrition Examination 

Survey (NHANES) (up to 2004)

CDC - National Center for Health Statistics (NCHS)

National Ambulatory Medical Care Survey 

(NAMCS)

CDC - National Center for Health Statistics (NCHS)

National Hospital Ambulatory Medical Care 

Survey (NHAMCS)

CDC - National Center for Health Statistics (NCHS) Portal: National 

Vital Statistics System (NVSS) National Mortality Followback Survey (NMFS)

CDC - National Syndromic Surveillance Program (NSSP) BioSense Data

CDC Portal: Web-based Injury Statistics Query and Reporting 

System (WISQARS)

National Violent Death Reporting System 

(NVDRS)

Census National Longitudinal Mortality Study (NLMS)

Colorado Department of Public Health and Environment Portal: 

Colorado Health Information Dataset (CoHID) Colorado Death Certificates and Data

Colorado Department of Public Health and Environment Portal: 

Colorado Health Information Dataset (CoHID) Colorado Injury Hospital Data

Colorado Hospital Association (CHA) Colorado Hospital Association Discharge Data

Connecticut Department of Health Connecticut School Health Survey

Consumer Product Safety Commission

National Electronic Injury Surveillance System 

(NEISS)

DoD DoD Survey of Health Related Behaviors

DoD Armed Forces - Armed Forces Health Surveillance Branch Defense Medical Surveillance System (DMSS)

F-1
Table F-1. Data systems with fairly accessible information and data dictionaries (continued)

Organization Data System Name

Federal Transit Administration 

National Transit Database - Safety & Security Time 

Series Data

Florida Department of Health Florida Death Rate Query System

Florida Health Florida Injury Surveillance Data System 

Healthy Minds Network

Research on Adolescent and Young Adult Mental 

Health

Illinois Department of Human Services (IDHS) Illinois Youth Survey (IYS)

Illinois Department of Public Health (IDPH) Illinois Trauma Registry Database

Illinois Department of Public Health (IDPH) - Emergency 

Medical Services Illinois Prehospital Data Program

Illinois Department of Public Health (IDPH) Portal: EMS Data 

Reporting System Illinois Hospital Discharge Database

Maine Health Data Organization (MHDO) Maine All Payer Claims Database (APCD)

Maine Health Data Organization (MHDO) Maine Hospital Inpatient and Outpatient Data

Maryland Department of Health and Mental Hygiene (DHMH) Maryland Medicaid Claims

Maryland Department of Health and Mental Hygiene (MDHMH) Maryland Violent Death Reporting System (VDRS)

Michigan Department of Education Michigan Youth Risk Behavior System (YRBS)

Minnesota Department of Health Minnesota Injury Data Access System (MIDAS)

Missouri Department of Health &Senior Service Missouri Death Records

Missouri Department of Health &Senior Service

Missouri Patient Abstract System (Hospital 

Discharges)

National Center for Health Statistics (NCHS)

Portal: CDC WONDER (Wide-ranging Online Data for 

Epidemiologic Research)

Mortality Medical Data System (Multiple and 

Underlying Cause of Death Data Files)

National Center for the Review and Prevention of Child Deaths 

(NCRPCD)

National Child Death Review Case Reporting 

System

National Highway Traffic Safety Administration - EMS National EMS Information System (NEMSIS)

National Institute of Mental Health (NIMH)

Collaborative Psychiatric Epidemiology Surveys 

(CPES)

New Mexico Department of Health

New Mexico Violent Death Reporting System 

(VDRS)

New York State Department of Health

New York State Statewide Planning and Research 

Cooperative System (SPARCS)

NIH - NIAAA (National Institute on Alcohol Abuse and 

Alcoholism)

National Epidemiologic Survey on Alcohol and 

Related Conditions (NESARC)

NIH and University of North Carolina

The National Longitudinal Study of Adolescent to 

Adult Health (Add Health)

Oregon Health Authority

Oregon Adolescent Suicide Attempt Data System 

(ASADS)

Oregon Health Authority Oregon Healthy Teen Survey

Oregon Health Authority - Public Health Division Oregon Student Wellness Survey

PennState Center for Collegiate Mental Health (CCMH) CCMH Data Navigator

Pennsylvania Department of Education’s Safe Schools Office

Pennsylvania Network for Student Assistance 

Services

Substance Abuse and Mental Health Service Administration 

(SAMHSA)

Emergency Department Data / Drug Abuse 

Warning Network (DAWN)

Substance Abuse and Mental Health Service Administration 

(SAMHSA)

Population Data / National Survey on Drug Use 

and Health (NSDUH)

F-2
Table F-1. Data systems with fairly accessible information and data dictionaries (continued)

Organization Data System Name

Substance Abuse and Mental Health Service Administration 

(SAMHSA) - Mental Health Facilities Data National Mental Health Services Survey (N-MHSS)

Suicide Prevention Data Center (SPDC)

The Garrett Lee Smith Memorial Suicide 

Prevention Program

Veteran Affairs VA Behavioral Health Autopsy Program (BHAP) 

Virginia Health Information Virginia Hospital Discharges

Washington State Department of Health Washington Healthy Youth Survey

Washington State Department of Health

Washington State Vital Statistics and Population 

Data (Death Data)

Washington State Department of Health

Washington State Comprehensive Hospital 

Abstract Reporting System (CHARS)

Wisconsin Hospital Association (WHA) - WHA Information 

Center

Wisconsin WIpop Data - Inpatient and Outpatient 

Discharge Summaries

CCMH=Center for Collegiate Mental Health;CDC=Centers for Disease Control and Prevention;CHA=Colorado Hospital 

Association;DoD=Department of Defense;EMS=Emergency Medical Services;NIH=National Institutes of 

Health;PA=Pennsylvania;UNC=University of North Carolina;WHA=Wisconsin Hospital Association;WIpop=Wisconsin 

population

F-3
Table F-2. Data systems with fairly accessible information but no accessible data dictionary

Organization Data System Name

Alaska Department of Health and Social Services (ADHSS) -

Statewide Suicide Prevention Council Alaska Detailed Causes of Death

Arizona Department of Health Services; Office of Injury Prevention Arizona Child Fatality Review Program

Bureau of Justice Statistics - Suicide and Homicide in State Prisons 

and Local Jails

[National] Deaths in Custody Reporting 

Program (DCRP)

California - Los Angeles County Public Health Department - Vital 

Records Office Los Angeles County Mortality Dataset

California - Office of Statewide Health Planning and Development 

(OSHPD) California Hospital Discharge Data

California Department of Corrections and Rehabilitation (CDCR) California CDCR COMPSTAT

CDC

School-Associated Violent Death Study 

(SAVD)

CDC - National Center for Health Statistics (NCHS)

National Hospital Discharge Survey 

(NHDS)

CDC - National Center for Health Statistics (NCHS) National Hospital Care Survey (NHCS)

Colorado Department of Public Health and Environment Portal: 

Colorado Health Information Dataset (CoHID)

Colorado Violent Death Report System 

(VDRS)

Connecticut Department of Public Health - State Vital Records 

Office Connecticut Vital Records

Connecticut Department of Public Health - The Office of Injury 

Prevention

Connecticut Violent Death Reporting 

System (VDRS)

Connecticut Hospital Association & Connecticut Office of Health 

Care Access (OHCA) Connecticut Chime Data

Delaware Child Death, Near Death and Stillbirth Commission 

(CDNDSC) Delaware Child Death Review

Delaware Health and Social Services - Division of Public Health

Delaware Electronic Reporting and 

Syndromic Surveillance System 

(DERSS)

Delaware Health and Social Services - Division of Public Health -

Delaware Health Statistics Center Delaware Hospital Discharge Data

Georgia - Fulton County Medical Examiner

Georgia "Holds Our Medical Examiner 

Records" (HOMER)

Illinois Department of Public Health (IDPH)

Illinois Violent Death Reporting System 

(VDRS)

Illinois Department of Public Health (IDPH) - Center for Health 

Statistics Illinois Vital Statistics

Illinois Department of Public Health (IDPH) - Center for Health 

Statistics Portal: iQuery

Illinois Project for Local Assessment of 

Needs (IPLAN)

Kentucky - Division of Medical Examiner's Services Kentucky Medical Examiner Reports

Los Angeles County Public Health Department - The Injury & 

Violence Prevention Program (IVPP) Los Angeles Injury Data and Reports

Maine Department of Health and Human Services - Maine Suicide 

Prevention Program

Maine Data, Research and Vital 

Statistics

Maryland Department of Health and Mental Hygiene (DHMH)

Maryland Vital Statistics and Reports 

(Death Certificates)

Massachusetts - Executive Office of Health and Human Services 

(EOHHS)

Massachusetts Violent Death Reporting 

System (VDRS)

National Center on Safe Supportive Learning Environments 

(NCSSLE) School Climate Survey Compendia

Nebraska Department of Health and Human Services

Nebraska Child & Maternal Death 

Review Team

Nebraska Department of Health and Human Services Nebraska Death Certificates

New Mexico Department of Health

New Mexico Youth Resiliency & Risk 

Survey (YRRS)

New Mexico Department of Health New Mexico Vital Records

New York State Department of Health New York State Vital Statistics

F-4
Table F-2. Data systems with fairly accessible information but no accessible data dictionary 

(continued)

New York State Department of Health

New York State Suicide and SelfInflicted Injuries

NIDA Substance Abuse and Mental Health Data Archive 

(SAMHDA) 

Drug Abuse Treatment Outcome Survey 

(DATOS)

NIMH

Treatment for Adolescents with 

Depression Study (TADS)

NIMH and University of Pittsburgh

Treatment of SSRI-Resistant 

Depression In Adolescents (TORDIA)

Ohio Department of Health Ohio Death Data and Statistics

Oklahoma Office of the Chief Medical Examiner Oklahoma Medical Examiner Reports

Oklahoma State Department of Health (OSDH)

Oklahoma Violent Death Reporting 

System (VDRS)

Oregon Health Authority Oregon Injury and Fatality Data

Oregon Health Authority Oregon Death with Dignity Act Database

Oregon Health Authority Oregon Death Data

Oregon Health Authority - Public Health Division

Oregon Violent Death Reporting System 

(VDRS)

Oregon State Police - Medical Examiner Division Oregon Medical Examiner Reports

Texas Department of State Health Services - Center for Health 

Statistics Texas Health Data (Death Data)

Truven Health Analytics Truven MarketScan

UCLA Center for Health Policy Research

California Health Interview Survey 

(CHIS)

University of Pittsburgh - NIMH

Sequenced Treatment Alternatives to 

Relieve Depression

Utah Department of Health's Public Health Data Resource

Utah Public Health Indicator Based 

Information System (IBIS)

Virginia Department of Health

Virginia Violent Death Reporting System 

(VDRS)

Virginia Department of Health - Office of the Medical Examiner Virginia Medical Examiner Reports

Washington State Department of Health Washington State Injury Data Tables

Washington State Department of Health

Washington State Death with Dignity 

Data

Wisconsin Department of Health Services

Wisconsin Violent Death Reporting 

System (VDRS)

Wisconsin Department of Health Services Wisconsin Death Database

Wyoming Department of Health Wyoming Vital Records - Suicide Data

CDC=Centers for Disease Control and Prevention;CDCR= California Department of Corrections and 

Rehabilitation;COMPSTAT=Computer Statistics;LA=Los Angeles;NCHS=National Center for Health Statistics;NIDA=National 

Institute on Drug Abuse;NIMH=National Institute of Mental Health

F-5
Table F-3. Data systems with potentially accessible information

Organization Data System Name

American College Health Association National College Health Assessment (NCHA)

Association for University and College Counseling Center 

Directors (AUCCCD) Directors Surveys

Bureau of Justice Statistics Arrest-Related Deaths

Bureau of Justice Statistics National Corrections Reporting Program (NCRP)

California Department of Public Health (CDPH) California County Health Status Profiles

Delaware Health and Social Services - Delaware Health 

Statistics Center Delaware STIPDA Injury Data

DoD DoD Recruit Mortality Registry (RMR)

DoD

Department of Defense Suicide Event Report 

(DoDSER)

Los Angeles County Child & Adolescent Suicide Review 

Team

Los Angeles County Child & Adolescent Suicide 

Review

New Jersey Department of Health New Jersey Violent Death Reporting System (VDRS)

North Carolina Department of Health and Human Services North Carolina Surveillance and Vital Statistics Data

San Francisco Department of Public Health San Francisco Violent Injury Reporting System

Suicide Prevention Action Network (SPAN) of Idaho Idaho Youth Risk Behavior Survey (YRBS)

Tennessee Tennessee Medicaid data

Texas Department of State Health Services - Center for 

Health Statistics Texas Youth Risk Behavior Survey (YRBS) 

Texas Department of State Health Services - Center for 

Health Statistics Texas Injury Statistics

UChicago, Harvard, Columbia and NIJ

Project on Human Development in Chicago 

Neighborhoods (PHDCN)

Veteran Affairs VA Suicide Surveillance and Clinical Support System

Virginia Department of Health Virginia Syndromic Surveillance Data

WHO WHO Mortality Database

CDC=Centers for Disease Control and Prevention;NIJ=National Institute of Justice;STIPDA= State and Territorial Injury 

Prevention Directors Association;DOD=Department of Defense;WHO=World Health Organization

F-6
Table F-4. Data systems with no information available on specifications

Organization Data System Name

Annenberg Public Policy Center

National Annenberg Survey of Youth 

Datasets

Army

Army Behavioral Health Integrated 

Data Environment (ABHIDE)

CDC - Division of Adolescent and School Health (DASH)

Youth Risk Behavior Surveillance 

System (YRBSS)

Delaware Health and Social Services - Division of Public Health -

Office of Vital Statistics Delaware Birth and Death Records

Indian Health Services

IHS Behavioral Health Data Mart and 

Reporting System

Maryland Department of Health and Mental Hygiene (M-DHMH)

Maryland Assessment Tool for 

Community Health (MATCH)

National Center for Deaf Health Research (NCDHR) Deaf Health Survey

National Emergency Number Association (NENA 911) NENA [Database Name Unknown]

Tennessee Tennessee Medicaid data

University of Pittsburgh Pittsburgh Girls Study

VA - VISN 2 Center for Suicide Prevention VA MIRECC/CoE

Washington State Department of Health

Washington State Trauma Registry 

(WTR)

CDC=Centers for Disease Control and Prevention;CoE= centers of excellence;HIS=Indian Helath Services;MIRECC=Mental 

Illness Research, Education and Clinical Centers;VA=Veterans Health Administration;VISN=Veterans Integrated Service 

Networks

F-7