National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
Topics
- Blood Thinners (1)
- Cardiovascular Conditions (1)
- Data (1)
- Evidence-Based Practice (1)
- Guidelines (1)
- Health Services Research (HSR) (1)
- Infectious Diseases (1)
- Maternal Care (1)
- Medication (1)
- Outcomes (1)
- Patient-Centered Outcomes Research (1)
- Pregnancy (1)
- Primary Care (1)
- (-) Research Methodologies (10)
- (-) Risk (10)
- Social Determinants of Health (2)
- Stroke (1)
- U.S. Preventive Services Task Force (USPSTF) (1)
- Women (1)
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 10 of 10 Research Studies DisplayedColey RY, Liao Q, Simon N
Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction.
Clinical prediction models for uncommon outcomes, such as suicide, psychiatric hospitalizations, and opioid overdose, are garnering increased attention. Precise model validation is essential for choosing the appropriate model and deciding on its application. Split-sample estimation and validation of clinical prediction models, where data are divided into training and testing sets, may decrease predictive accuracy and precision. Utilizing the entire dataset for estimation and validation improves the sample size for both processes, but overfitting or optimism must be accounted for. The researchers compared split-sample and whole-sample approaches for estimating and validating a suicide prediction model. The study found that both the split-sample and whole-sample prediction models demonstrated similar prospective performance. Performance estimates assessed in the testing set for the split-sample model and through cross-validation for the whole-sample model correctly represented prospective performance. Validation of the whole-sample model using bootstrap optimism correction overestimated prospective performance. The researchers concluded that although previous studies have validated the bootstrap optimism correction for parametric models in small samples, this method did not accurately validate the performance of a rare-event prediction model estimated with random forests in a large clinical dataset. Cross-validation of prediction models estimated using all available data offers precise independent validation while maximizing sample size.
AHRQ-funded; HS026369.
Citation: Coley RY, Liao Q, Simon N .
Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction.
BMC Med Res Methodol 2023 Feb 1; 23(1):33. doi: 10.1186/s12874-023-01844-5..
Keywords: Research Methodologies, Risk
Ellis RP, Hsu HE, Siracuse JJ
Development and assessment of a new framework for disease surveillance, prediction, and risk adjustment: the diagnostic items classification system.
The purpose of this study was to develop an updated classification framework for predicting diverse health care payment, quality, and performance outcomes, based on the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). All ICD-10-CM diagnoses were mapped into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as timing and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. The primary outcome was annual health care spending top-coded at $250 000, and the researchers predicted 14 different outcomes, including: hospital days and admissions; emergency department visits; enrollee out-of-pocket spending; spending for 6 types of services; and overall and plan-paid health care spending. The researchers created 3223 DXIs: 2435 main effects, 772 modifiers, and 16 scaled items. The study found that relative to HHS-HCCs, the use of DXIs reduced underpayment for enrollees with rare diagnoses by 83%. The researchers concluded that in this study, for all spending and utilization outcomes considered, the new DXI classification system demonstrated improved predictions over current diagnostic classification systems.
AHRQ-funded; HS026485
Citation: Ellis RP, Hsu HE, Siracuse JJ .
Development and assessment of a new framework for disease surveillance, prediction, and risk adjustment: the diagnostic items classification system.
JAMA Health Forum 2022 Mar;3(3):e220276. doi: 10.1001/jamahealthforum.2022.0276..
.
.
Keywords: Risk, Research Methodologies
Davidson KW, Krist AH, Tseng CW
AHRQ Author: Mills J, Borsky A
Incorporation of social risk in US Preventive Services Task Force Recommendations and identification of key challenges for primary care.
The authors assessed how social risks have been considered in USPSTF recommendation statements and identified current gaps in evidence needed to expand the systematic inclusion of social risks in future recommendations. They concluded that their report serves as a benchmark and foundation for ongoing work to advance the goal of ensuring that health equity and social risks are incorporated into USPSTF methods and recommendations.
AHRQ-authored.
Citation: Davidson KW, Krist AH, Tseng CW .
Incorporation of social risk in US Preventive Services Task Force Recommendations and identification of key challenges for primary care.
JAMA 2021 Oct 12;326(14):1410-15. doi: 10.1001/jama.2021.12833..
Keywords: U.S. Preventive Services Task Force (USPSTF), Primary Care, Social Determinants of Health, Risk, Evidence-Based Practice, Research Methodologies, Guidelines
Predmore Z, Hatef E, Weiner JP
Integrating social and behavioral determinants of health into population health analytics: a conceptual framework and suggested road map.
There is growing recognition that social and behavioral risk factors impact population health outcomes. Interventions that target these risk factors can improve health outcomes. This study presents a review of existing literature and proposes a conceptual framework for the integration of social and behavioral data into population health analytics platforms. The authors describe several use cases for these platforms at the patient, health system, and community levels, and align these use cases with the different types of prevention identified by the Centers for Disease Control and Prevention.
AHRQ-funded; HS000029.
Citation: Predmore Z, Hatef E, Weiner JP .
Integrating social and behavioral determinants of health into population health analytics: a conceptual framework and suggested road map.
Popul Health Manag 2019 Dec;22(6):488-94. doi: 10.1089/pop.2018.0151..
Keywords: Social Determinants of Health, Risk, Research Methodologies
Vanderlaan J, Dunlop A, Rochat R
Methodology for sampling women at high maternal risk in administrative data.
This study compared the net benefits of using the Obstetric Comorbidity Index (OCI) to identify women at high maternal risk compared to conventional risk identification methods. Hospitalization discharge and vital records data for women experience singleton births in George from 2008 to 2012 was used. Results found there was a small but positive net benefit in using the OCI and conventional risk identification methods actually performed worse than using no risk identification methods at all. The researchers suggest that using OCI helps reduce misclassification.
AHRQ-funded; HS024655.
Citation: Vanderlaan J, Dunlop A, Rochat R .
Methodology for sampling women at high maternal risk in administrative data.
BMC Pregnancy Childbirth 2019 Oct 21;19(1):364. doi: 10.1186/s12884-019-2500-7..
Keywords: Research Methodologies, Health Services Research (HSR), Pregnancy, Maternal Care, Risk, Women
Goodman KE, Lessler J, Harris AD
A methodological comparison of risk scores versus decision trees for predicting drug-resistant infections: a case study using extended-spectrum beta-lactamase (ESBL) bacteremia.
Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. The investigators previously reported on a decision tree for predicting extended-spectrum beta-lactamase bloodstream (ESBL) infections. Their objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.
AHRQ-funded; HS025089.
Citation: Goodman KE, Lessler J, Harris AD .
A methodological comparison of risk scores versus decision trees for predicting drug-resistant infections: a case study using extended-spectrum beta-lactamase (ESBL) bacteremia.
Infect Control Hosp Epidemiol 2019 Apr;40(4):400-07. doi: 10.1017/ice.2019.17..
Keywords: Research Methodologies, Risk, Infectious Diseases
Desai RJ, Wyss R, Jin Y
Extension of disease risk score-based confounding adjustments for multiple outcomes of interest: an empirical evaluation.
Use of disease risk score (DRS)-based confounding adjustment when estimating treatment effects on multiple outcomes is not well studied. In this empirical cohort study, the investigators compared dabigatran initiators and warfarin initiators with respect to risks of ischemic stroke and major bleeding in 12 sequential monitoring periods (90 days each), using data from the Truven Marketscan database (Truven Health Analytics, Ann Arbor, Michigan).
AHRQ-funded; HS022193.
Citation: Desai RJ, Wyss R, Jin Y .
Extension of disease risk score-based confounding adjustments for multiple outcomes of interest: an empirical evaluation.
Am J Epidemiol 2018 Nov;187(11):2439-48. doi: 10.1093/aje/kwy130.
.
.
Keywords: Blood Thinners, Cardiovascular Conditions, Medication, Outcomes, Research Methodologies, Risk, Stroke
Desai RJ, Glynn RJ, Wang S
Performance of disease risk score matching in nested case-control studies: a simulation study.
The authors investigate whether or not matching on a disease risk score (DRS), which includes many confounders, results in greater precision than matching on only a few confounders. Their results suggest that DRS matching might increase the statistical efficiency of case-control studies, particularly when the outcome is rare.
AHRQ-funded; HS022193.
Citation: Desai RJ, Glynn RJ, Wang S .
Performance of disease risk score matching in nested case-control studies: a simulation study.
Am J Epidemiol 2016 May 15;183(10):949-57. doi: 10.1093/aje/kwv269.
.
.
Keywords: Research Methodologies, Risk, Patient-Centered Outcomes Research
Bennette CS, Ramsey SD, McDermott CL
Predicting low accrual in the National Cancer Institute's cooperative group clinical trials.
The study’s objective was to evaluate the empirical relationship and predictive properties of putative risk factors for low accrual in the National Cancer Institute's (NCI's) Cooperative Group Program, now the National Clinical Trials Network (NCTN). It identified multiple characteristics of NCTN-sponsored trials associated with low accrual and developed a prediction model that can provide a useful estimate of accrual risk based on these factors.
AHRQ-funded; HS023340.
Citation: Bennette CS, Ramsey SD, McDermott CL .
Predicting low accrual in the National Cancer Institute's cooperative group clinical trials.
J Natl Cancer Inst 2016 Feb;108(2). doi: 10.1093/jnci/djv324.
.
.
Keywords: Research Methodologies, Risk
Haukoos JS, Lewis RJ
The propensity score.
The authors discuss studies by Rozé et al and Huybrechts et al that used propensity score matching and propensity score stratification, respectively. They argue that although both methods are more valid in terms of balancing study groups than simple matching or stratification based on baseline characteristics, they vary in their ability to minimize bias. In general, propensity score matching minimizes bias to a greater extent than propensity score stratification.
AHRQ-funded; HS021749.
Citation: Haukoos JS, Lewis RJ .
The propensity score.
JAMA 2015 Oct 20;314(15):1637-8. doi: 10.1001/jama.2015.13480..
Keywords: Research Methodologies, Data, Risk