1 Thinking Scientifically: Evidence-Based Practices Evidence-Based Public Health (MEDS 4053) Kelley A. Carameli, DrPH Week 3 2 What is Evidence? Defining Evidence • Evidence: The available facts or information on whether a belief or concept is true or valid. ▪ Law: expert/witness testimony, forensics ▪ Medicine: epidemiologic (quantitative), experts ▪ Public Health: epidemiologic (quantitative), program or policy evaluations, qualitative (key informants, document studies, clinical or public health practice) • Evidence is used in decision-making and priority setting. • Evidence is most useful and effective when: ▪ Based on systematic assessment or evaluation ▪ Data are timely or relevant to issue at hand ▪ Real-world application ▪ Triangulated: quantitative, qualitative, and cultural/geographic 3 Evidence: Informed by Science How do we obtain public health evidence? • By using science, or scientific methods. What is science, or the scientific method? • A way of systematic thought and investigation to obtain reliable information. Obtaining knowledge through evidence. Communicating knowledge to others. Using methods that can be replicated (checked, valid, objective). • A process of thinking and answering questions by formulating questions and using a set of rules for inquiry or answering questions. Posing questions to understand relationships. Testing the proposed relationship against reality. Determining if something happened. • Additional consideration in scientific inquiry: – Reasoned judgment: using the best available knowledge to inform decision-making in the absence of complete evidence. – Opinion: personal view of reality. Custom: social influence on reality. 4 Thinking Scientifically in Health Thinking Scientifically in Public Health • A way of systematic thought and investigation to obtain reliable information. • A process of thinking and answering questions by formulating questions and using a set of rules for inquiry or answering questions. We often start and stop here We also need to progress to here 5 Thinking Scientifically in Health When to Apply Evidence-Based Approaches • Use evidence-based (scientific) approaches in public health: – when conducting literature reviews for grant proposals – when evaluating the effectiveness and cost-benefit of health programs – when establishing new health programs – when policies are being implemented – when scientific evidence is important to support decision-making. Are there ever disadvantages to using an evidence-based approach? Case example: A public health department wants to be innovative in establishing new programs. It is particularly interested in developing a program quickly to address transgender bullying in its county even though evaluative information is not readily available on successful interventions for this group. Should it proceed without evidence? What are the advantages and/or disadvantages? 6 When to Balance Science + Action Balancing Evidence/Scientific Approaches + Public Health Actions • Be realistic…every decision may not have robust evidence. Instead: – Use and weight all available information • Magnitude of the problem, known risk determinants? • Stakeholder opinion, existing practices/traditions? – Balance program implementation against its fidelity (original design) vs. reinvention (new setting, local context) • Recognize trade-offs – urgent action may limit ability for evidence-based decisions, but poorly informed action may also be hard to change • Considerations for decision-makers: • Size/scope of problem • Intervention effectiveness • Intervention cost, value, alternatives • Equitable • Preventability • Benefits and Harms • Acceptability (culture, values) • Sustainable, Appropriate 7 When to Balance Science + Action Considerations in evidence-based decision making: Size/scope of problem Intervention effectiveness Intervention cost Equitable Preventability Benefits and Harms Acceptability Sustainable, Appropriate • • • • • • • • • • • • Is it important? What is the public health burden? Does it work in real-world settings? Is there better evidence for an alternative? Is it affordable? Does it distribute resources fairly? What is the efficacy? Can it work in an “ideal” circumstance? What are the consequences? Trade-offs? Is it consistent with community priorities, culture, values, the political situation? Are resources and incentives likely needed to support/maintain the intervention? Is it likely to work in this setting? Others? Anderson et al., 2005, American Journal of Preventive Medicine 8 Benefits + Limits of Using Evidence Benefits Limits • • Evidence shown for one setting or time may not translate to another – Social context/culture shapes behavior, as does new data – Pap/prostate screening, DARE • Some outcomes easier to measure – Standard: tobacco use, vaccines – Mixed: cultural competency, health literacy • Socio-political values shape public health and what we measure – Risk-reduction vs. abstinence (sexual health, substance use) – Natural experiments vs. randomized control groups • • Greater likelihood of implementing more effective policies/ programs – Better use of resources (staff, materials, time, etc.) – More informed (and productive) public health workforce Greater likelihood of impacting change in public health issues – Change informed by evidence – Change may be reproducible (systematic) Access to higher-quality information. Learn what works! 9 Looking at the Scientific Evidence Scientific Evidence and Public Health Action • The higher-quality information needed for public health action comes from research studies or program evaluations to learn what ‘works’: 1. 2. 3. 4. 5. Understand etiologic links between behaviors and health. Develop testable methods (valid, reliable) for measuring behavior. Identify the factors that influence the behavior. Determine whether public health interventions are successful. Translate/Disseminate research findings into practice. Most public health actions (programs, policies) are based on the presumption that the behavior-health relationships (i.e., etiological links) are causal. Type 1 Evidence 10 Looking at the Scientific Evidence Levels of Evidence-Based Data Type 1: Something should be done. Most common: Medicine/Epi. Type 2: Specifically, this should be done. Public health practitioners have most interest here. Type 3: Context for how an intervention is done. From Brownson RC, et al. Evidence-Based Public Health: A Fundamental Concept for Public Health Practice, 2009. 11 Looking at the Scientific Evidence Scientific Evidence and Public Health Action • The higher-quality information needed for public health action comes from research studies or program evaluations to learn what ‘works’: 1. 2. 3. 4. 5. Understand etiologic links between behaviors and health. Develop testable methods (valid, reliable) for measuring behavior. Identify the factors that influence the behavior. Determine whether public health interventions are successful. Translate/Disseminate research findings into practice. Most public health actions (programs, policies) are based on the presumption that the behavior-health relationships (i.e., etiological links) are causal. Type 1 Evidence 1 2 3 12 Looking at the Scientific Evidence Levels of Evidence-Based Data Type 1 “Something should be done” • Size, strength, or causal relationship between the behavior (or risk factor/determinant) and health (or disease) – Magnitude of issue: number, incidence, prevalence – Severity: morbidity, mortality, disability – Preventability: deaths averted, effectiveness, economic impact • Clinical designs (randomized, experimental) focused on evidence of causality: – Consistency: association is observed in different settings, populations, methods. – Strength: size of the relative risk estimate. – Temporality: time relationship between risk factor onset and disease onset. – Dose-response: dose of the exposure and magnitude of relative risk estimate. – Biologic plausibility: biological mechanism between risk factor and disease outcome. – Experimental evidence: findings from a prevention trial; random assignment. 13 Looking at the Scientific Evidence Levels of Evidence-Based Data Type 1 “Something should be done” • Size, strength, or causal relationship between the behavior (or risk factor/determinant) and health (or disease) – Magnitude of issue: number, incidence, prevalence – Severity: morbidity, mortality, disability – Preventability: deaths averted, effectiveness, economic impact From Brownson RC, et al. Evidence-Based Public Health. New York: Oxford University Press, 2003. 14 Looking at the Scientific Evidence Levels of Evidence-Based Data Type 2 “This should be done” (specific intervention) • Relative effectiveness of intervention on the risk factors. • Population designs (non-experimental) to show evidence of intervention effectiveness (intervention → increases childhood vaccination) – Evidence-based (peer-reviewed, systematic, external validity) – Efficacious (peer review, research-tested, external validity) – Promising (formative or summative evaluation, theory-consistent, lacks peer review) – Emerging (ongoing or in-progress evaluation, theory-consistent) • Explores which intervention option (or combination) is more effective and/or cost-effective – Client reminder, Community edu., Health insurance, Vaccines in schools 15 Looking at the Scientific Evidence Levels of Evidence-Based Data Type 3 “How to take specific action” (context of intervention) • Adapt/translate evidence into population-level intervention or policy. • Program/policy may work in one context, but not another. Consider contextual domains (→). • New programs/policies (i.e., innovations) may incur unintended consequences of action. – Political, social, or structural domains – E.g., school vaccine policy → reduces measles rates) 16 Sources of Scientific Evidence Scientific evidence is relative to the time, culture, and context. – Public health decisions may be based on the ‘best possible’ (reasoned judgement) and not always the ‘best available’ evidence. – Important to consider triangulated evidence (e.g., mixed-methods). – Looking for an intervention’s ‘active ingredients’ – transferability by context. Quantitative Evidence • • Shows how variables are related; large sample sizes Surveys, surveillance: If X, then Y and Z Qualitative Evidence • • Explores why relationships exist; smaller sample sizes Interviews, case studies: Why Y? Why Z? What makes them similar/different? 17 Sources of Scientific Evidence Analytic Tools for Obtaining Evidence Used in Public Health Systematic Reviews/Guides • • Synthesis of existing or state-of-the art research or literature. Translate evidence to local action Public Health Surveillance • • Ongoing, systematic data collection and analysis on disease / injury. Data tracking (e.g., tobacco sales) Economic Evaluation • Relative value, cost-benefit of action Health Impact Assessment • • Probable effect of public health policy/programs in nonhealth sectors Impact = “5 A-Day” on agri. production Participatory Approaches • Soliciting stakeholder (local) input 18 How to Think Scientifically Processes for “Thinking Scientifically” in Public Health 1. Define and quantify the issue – What is the size of the public health problem? 2. Gather evidence to inform public health action – When reviewing evidence consider: • What are the results? How precise? Similar across studies? • Are the results valid? Is the assessment reproducible? Was the methodological quality sound? • How can the results be applied to public health actions? Are the benefits worth the costs and potential risks? 3. Translate evidence into practice – – – Inputs Are there effective programs for addressing this problem? What information about the local context is needed? Is this particular policy or program worth doing? 4. Disseminate evidence-based findings and practices Process Outputs Outcome Impact 19 How to Think Scientifically Processes for “Thinking Scientifically” in Public Health Case Example: State Regulation/Firearm Homicide (Irvin et al., 2014) 20 How to Think Scientifically Processes in “Thinking Scientifically” in Public Health 1. Define and quantify the issue Inputs A. What is the health issue? (problem statement) – Develop a concise statement of the issue being considered ▪ What is the issue and why care? ▪ Who is the population(s) affected? ▪ What is the size / scope of the problem? ▪ What prevention opportunities currently exist? ▪ Who are the key stakeholders? B. Quantify the issue (counts, incidence, prevalence) – – Look to existing research for baseline data – descriptive data, vital statistics, surveillance systems, surveys/national studies What patterns exist? By person (gender, race/ethnicity, place (geography), or time (seasonal variation). C. Use the literature to shape the issue (logic model) 21 How to Think Scientifically Processes in “Thinking Scientifically” in Public Health 2. Gather evidence to inform health, program, and/or policy change – – – Look to existing research literature (peer-reviewed, testable) Initiate own research or evaluation Does this practice help alleviate the health issue? How? Why? Inputs A. What factors affect the health issue? (hypothesis) – – Descriptive to understand why or how; single concept Relational to understand connections; multiple concepts B. How to measure this relationship? (operationalization) – – Concept / Construct (real, phenomena)  Metric / Variable (validity or ‘accuracy’, reliability or ‘consistency’; IV and DV) Look to existing research (and theory) for measurement options C. What is learned from the data? (analysis, interpretation) – – Data relationships or trends/patterns (significance) Critical review – method, measures, theory, field comparisons 22 How to Think Scientifically Processes in “Thinking Scientifically” in Public Health 3. Translate evidence into practice (evidence-based decision-making) A. How can it be applied? (translational research) • • • What are the “real world” applications learned from the literature? – Prioritize findings. Process – Identify barriers: resources, political, cultural. Blend what is known from medicine, public health, and other disciplines Incorporate input from community-based stakeholders (e.g., Outputs expert panels, policy makers, coalitions. B. Develop an action plan and implement intervention(s) • Consider short- and long-term goals or changes C. Evaluate program or policy • Apply quantitative and qualitative techniques Outcome Processes for “Thinking Scientifically” in Public Health Case Example: State Regulation/ Firearm Homicide (Irvin et al., 2014) 3. Translate evidence into practice • Any patterns? • Does this affect our logic model? 23 24 How to Think Scientifically Processes in “Thinking Scientifically” in Public Health 4. Disseminate evidence-based findings and practices – – – – Peer-review journals, conferences/meetings Media, local interactions, word-ofmouth Policies, programs Consider these elements when sharing findings to enhance stakeholder decisionmaking → Outputs Outcome Impact RESEARCH AND PRACTICE Evaluating the Effect of State Regulation of Federally Licensed Firearm Dealers on Firearm Homicide Nathan Irvin, MD, MS, Karin Rhodes, MD, MS, Rose Cheney, PhD, and Douglas Wiebe, PhD Effective federal regulation of firearm dealers has proven difficult. Consequently, many states choose to implement their own regulations. We examined the impact of state-required licensing, record keeping of sales, allowable inspections, and mandatory theft reporting on firearm homicide from 1995 to 2010. We found that lower homicide rates were associated with states that required licensing and inspections. We concluded that firearm dealer regulations might be an effective harm reduction strategy for firearm homicide. (Am J Public Health. 2014;104:1384–1386. doi: 10.2105/AJPH.2014.301999) Current federal regulations and enforcement practices limit the federal government’s ability to effectively deter illegal firearm sales by federally licensed firearm dealers.1—4 Several states have enacted their own firearm laws or require an additional layer of oversight, but evidence in support of these laws as injury reduction strategies vary.5– 7 Firearm dealer regulations aimed at decreasing trafficking have been successful, yet little data exist regarding the effect of these regulations on firearm homicides.8 In this study, we examined state licensing and other lawful sales promoting dealer regulations, and hypothesized that they decrease firearm homicide. METHODS We conducted a state-level panel study to examine how regulation of federally licensed firearm dealers related to firearm homicide during 1995 to 2010. We used data from the Centers for Disease Control and Prevention’s Web-based Injury Statistics Query and Reporting Systems and Multiple Cause of Death files to identify statelevel firearm homicide totals from 1995 to 2010. Homicide rates were subsequently calculated for each state. We used published peer-reviewed research that cited the laws regulating firearm dealers, and characterized the regulatory status of each state during the study.9 LexisNexis was used for confirmation. We performed multivariable Poisson regression analyses controlling for sociodemographic characteristics from the US Census, burglary and drug arrest rates from the FBI’s Uniform Crime Report, state firearm regulation scores from the Traveler’s Guide to Firearm Laws of the Fifty States, and a validated firearm ownership proxy measure.10—12 Models were analyzed using state licensing, theft reporting, allowable inspections, and mandatory record keeping as categorical independent variables and homicide rates as the dependent variable. We also constructed models evaluating interactions between key variables. In addition, a model using an overall strength variable, which equaled the sum of the 4 regulations, was constructed and analyzed. All analyses controlled for clustering at the state level. RESULTS The characterization of each state’s dealer regulations are listed in Table 1. Over the years examined, 195 932 people died by firearm homicide. The median annual homicide rate per 100 000 people was 3.66 (interquartile range = 1.80—5.39). Lower homicide rates were associated with states that required licensing and allowed inspections (Table 2). Theft reporting was not associated with lower homicide rates (incidence rate ratio [IRR] = 1.04; 99% confidence interval [CI] = 0.95, 1.14), and record maintenance was associated with higher homicide rates (Table 2). The protective effect was stronger in states that required both licensing and inspections (IRR = 0.49; 99% CI = 0.42, 0.58). Lower homicide rates were associated with states that had 3 or more laws regulating 1384 | Research and Practice | Peer Reviewed | Irvin et al. firearm dealers (IRR = 0.76; 99% CI = 0.67, 0.86 [3 laws] and IRR = 0.75; 99% CI = 0.65, 0.86 [4 laws]). DISCUSSION Our national study adds to the literature through a rigorous examination of the effect of state regulation of firearm dealers on firearm homicide. Our findings suggest that firearm dealer licensing and allowable inspections might save lives. Although limited to association by the observational design and absence of policy change during the study period, these findings are promising and warrant further investigation. Similar to previous studies, our results varied based on the type of regulation. State licensing and authorized inspections were associated with lower homicide rates, whereas record keeping was associated with increased homicides. Furthermore, having both licensing and inspections appeared to be more strongly protective against homicide than either alone. It makes intuitive sense for the effect to be stronger for having both mandatory licensing and allowable inspections because it is important to have a mechanism by which to evaluate and enforce compliance with the licensing. The association between record keeping and increased homicides is less clear. Perhaps this finding exists because states that have problems with firearm diversion, and consequently, increased access to guns that might be used in homicides, have chosen to enact these laws to attempt to address these problems. These findings highlight the complex nature of these associations and suggest that the findings might also be influenced by other unmeasured covariates, such as enforcement of the law or other unmeasured laws or variables not included within our models.13 Our findings are compelling, but have limitations. In addition to the study design caveats mentioned previously, it is important to acknowledge that, as evidenced by the nonlinear association between increasing laws and decreased firearm injury, all laws are not equivalent, and further research is necessary to identify the combination of laws that might best prevent homicide. Furthermore, we were unable to quantify enforcement in our models, which evidence suggested is an important factor in determining the effect of laws.8,14,15 American Journal of Public Health | August 2014, Vol 104, No. 8 RESEARCH AND PRACTICE TABLE 1—Mean Annual Homicide Rates by State and State Laws Regulating Federally Licensed Firearm Dealers: United States, 1995–2010 Firearm homicide is a persistent threat to societal well-being. Our study highlights regulatory approaches states could take to potentially decrease firearm homicide. Through tougher regulation of firearm dealers, it might be possible to prevent firearm-related deaths. j Mean Homicide Rate License Records Inspections Thefts 7.27 4.46 Yes No Yes Yes No No No No Arizona 6.18 No No No No About the Authors Arkansas 5.82 No No No No California 5.06 Yes Yes Yes Yes Colorado 2.84 No Yes Yes No Connecticut 2.4 Yes Yes Yes No At the time of this study, Nathan Irvin was with the Department of Emergency Medicine, University of Pennsylvania School of Medicine, Philadelphia. Karin Rhodes is with the Department of Emergency Medicine, University of Pennsylvania School of Medicine. Rose Cheney and Douglas Wiebe are with the Firearm Injury Center at Penn (FICAP), University of Pennsylvania School of Medicine. Correspondence should be sent to Nathan Irvin, Johns Hopkins Department of Emergency Medicine, 5801 Smith Ave, Baltimore MD, 21209 (e-mail: nirvin1@jhmi.edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted March 24, 2014. State Alabama Alaska Delaware 3.49 Yes Yes Yes No District of Columbia Florida 29.01 4.44 Yes No Yes No No No No No Georgia 5.62 Yes Yes Yes No Hawaii 1.09 Yes No Yes No Idaho 1.98 No No No No Illinois 5.37 No Yes Yes No Indiana 4.41 Yes No No No Iowa 1.25 No No No No Kansas Kentucky 3.36 3.78 No No No No No No No No Louisiana 10.5 No No No No Maine 1.31 No Yes Yes No Maryland 6.79 Yes Yes Yes No Massachusetts 1.49 Yes Yes Yes Yes Acknowledgments Michigan 5.22 No Yes Yes Yes Minnesota 1.72 No No Yes No Mississippi Missouri 7.76 5.3 No No Yes No Yes No No No Montana 2.64 No No No No We would like to acknowledge the Robert Wood Johnson Foundation Clinical Scholars Program at the University of Pennsylvania for their support and assistance with this project. We would also like to thank Kelly Chen for helping with the legal research involved in this study. Nebraska 2.29 No No No No Nevada 5.7 No No No No New Hampshire 0.99 Yes No No No New Jersey 2.6 Yes Yes Yes Yes New Mexico 5.07 No No No No References New York North Carolina 3.09 5.43 Yes No Yes Yes Yes Yes Yes No North Dakota 1.07 No No No No 1. USDOJ, Office of the Inspector General. Review of ATF’s Federal Firearms Licensee inspection program. April 2013. Available at: http://www.justice.gov/oig/ reports/atf.htm. Accessed January 10, 2014. Ohio 3.21 No No No Yes Oklahoma 4.4 No No No No Oregon 2.23 No Yes Yes No Pennsylvania 4.2 Yes Yes No No Rhode Island 1.89 Yes Yes Yes No South Carolina South Dakota 5.58 1.03 Yes No Yes No Yes No No No Tennessee 6.07 No Yes Yes No Texas 4.53 No No No No Continued August 2014, Vol 104, No. 8 | American Journal of Public Health Contributors N. Irvin helped conceptualize the idea, gathered the data, analyzed the data, and helped write each version of the article. K. Rhodes and R. Cheney helped develop the project idea and assisted in writing and revising the article. D. Wiebe helped develop the idea, conduct the analyses, and write and revise all versions of the article. Human Participant Protection This study was deemed exempt by the University of Pennsylvania institutional review board. 2. Wintemute G. Firearm retailers’ willingness to participate in an illegal gun purchase. J Urban Health. 2010;87(5):865—878. 3. Wintemute GJ. Frequency of and responses to illegal activity related to commerce in firearms: findings from the Firearms Licensee Survey. Inj Prev. 2013;19(6):412–420. 4. Sorenson SB, Vittes KA. Buying a handgun for someone else: firearm dealer willingness to sell. Inj Prev. 2003;9(2):147—150. 5. Rosengart M, Cummings P, Nathens A, et al. An evaluation of state firearm regulations and homicide and suicide death rates. Inj Prev. 2005;11(2): 77—83. Irvin et al. | Peer Reviewed | Research and Practice | 1385 RESEARCH AND PRACTICE TABLE 1—Continued Utah 1.8 No No No No Vermont 1.31 No Yes Yes No Virginia 4.32 Yes Yes Yes No Washington 2.39 Yes Yes No No West Virginia 3.62 No No No No Wisconsin Wyoming 2.46 2.15 No No Yes Yes No Yes No No TABLE 2—Adjusted Effect of the State Regulations on Firearm Homicides: United States, 1995–2010 Outcome/Laws IRR (95% CI) Homicide rate 34.65 Licensing 0.74* (0.67, 0.81) Record keeping 1.45* (1.30, 1.61) Inspections 0.64* (0.59, 0.69) Theft reporting Licensing and inspections 1.04 (0.95, 1.14) 0.49* (0.42, 0.58) Strength 1 law AIC 34.65 1.10 (0.96, 1.26) 2 laws 0.94 (0.85, 1.05) 3 laws 0.76* (0.67, 0.86) 4 laws 0.75* (0.65, 0.86) Note. AIC = Akaike’s information criterion; CI = confidence interval; IRR = incident rate ratio. Covariates in the model included race, percent urban, percent living in poverty, percent male, percent younger than 24 years old, percent college educated, drug arrest rate, burglary rates,12 scores, and firearm ownership proxy. *P £ .001. 6. Conner KR, Zhong Y. State firearm laws and rates of suicide in men and women. Am J Prev Med. 2003;25 (4):320—324. 11. Wiebe DJ. Homicide and suicide risks associated with firearms in the home: a national case-control study. Ann Emerg Med. 2003;41(6):771—782. 7. Webster DW, Vernick JS, Zeoli AM, Manganello JA. Association between youth-focused firearm laws and youth suicides. JAMA. 2004;292(5):594—601. 12. Kappas S. Traveler’s Guide to the Firearm Laws of the Fifty States. Covington, KY: Traveler’s Guide Inc; 1997—2010. 8. Webster DW, Vernick JS, Bulzacchelli MT. Effects of state-level firearm seller accountability policies on firearm trafficking. J Urban Health. 2009;86(4):525–537. 13. Braga AA, Wintemute GJ, Pierce GL, Cook PJ, Ridgeway G. Interpreting the empirical evidence on illegal gun market dynamics. J Urban Health. 2012;89 (5):779—793. 9. Vernick JS, Webster DW, Bulzacchelli MT, Mair JS. Regulation of firearm dealers in the United States: an analysis of state law and opportunities for improvement. J Law Med Ethics. 2006;34(4):765—775. 14. Webster DW, Vernick JS, Bulzacchelli MT, Vittes KA. Temporal association between federal gun laws and the diversion of guns to criminals in Milwaukee. J Urban Health. 2012;89(1):87—97. 10. Miller M, Hemenway D, Azrael D. State-level homicide victimization rates in the US in relation to survey measures of household firearm ownership, 2001–2003. Soc Sci Med. 2007;64(3):656—664. 15. Webster DW, Bulzacchelli MT, Zeoli AM, Vernick JS. Effects of undercover police stings of gun dealers on the supply of new guns to criminals. Inj Prev. 2006;12 (4):225—230. 1386 | Research and Practice | Peer Reviewed | Tiwari et al. The Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER Chetan Tiwari, PhD, Kirsten Beyer, PhD, MPH, MS, and Gerard Rushton, PhD CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) is the nation’s primary data repository for health statistics. Before WONDER data are released to the public, data cells with fewer than 10 case counts are suppressed. We showed that maps produced from suppressed data have predictable geographic biases that can be removed by applying population data in the system and an algorithm that uses regional rates to estimate missing data. By using CDC WONDER heart disease mortality data, we demonstrated that effects of suppression could be largely overcome. (Am J Public Health. 2014;104:1386–1388. doi: 10.2105/AJPH.2014.301900) CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) provides county-level data on directly age-adjusted mortality rates, and age- and gender-stratified mortality and population counts.1 To protect against the potential disclosure of personal health information, WONDER suppresses any statistic (counts or rates) calculated using fewer than 10 observations.2 However, such suppression restricts the utility of WONDER data to compute and map reliable rates for areas with small populations, for short time periods, or for rare diseases.3,4 Furthermore, rates that are indirectly adjusted for age, which are currently not provided by WONDER, can only be calculated for those counties where count data are not suppressed.5,6 Using an example of heart disease mortality, we showed American Journal of Public Health | August 2014, Vol 104, No. 8
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