Validating health conditions in a clinical registry using administrative data algorithms

IntroductionClinical registries are a potentially valuable resource to study the effects of medical interventions on outcomes, particularly patient-reported outcomes like health-related quality of life, which are not included in administrative data. However, because clinical registries are primarily intended for patient management and not for research, their validity must be established. Objectives and ApproachOur objective was to validate patient self-reported health conditions in a clinical registry. Study data were from a population-based regional joint replacement registry in the Canadian province of Manitoba. The clinical registry data were linked to administrative health data. Validated administrative data algorithms for 12 conditions were defined using diagnosis codes in hospital and physician records and medication codes in prescription drug records for the period up to three years prior to the joint replacement surgery. Accuracy of the clinical registry data was estimated using Cohen’s kappa coefficient, sensitivity, specificity, and positive and negative predictive values (PPV; NPV); 95% confidence intervals were also estimated. Analyses were stratified by joint type, age group, and sex. ResultsThe study cohort included 20,592 individuals (average age 66.3 years; 58.4% female); 8,424 (40.9%) had a total hip replacement. Sensitivity of the clinical registry data ranged from 16% (anemia) to more than 70% (diabetes, high blood pressure, rheumatoid arthritis); specificity was greater than 92% for all conditions, except back pain and high blood pressure. PPV ranged from 19% (back pain) to 83% (diabetes). Chance-adjusted agreement was very good for diabetes (kappa: 0.74), moderate for heart disease and high blood pressure (kappa range: 0.41-0.53) and poor or fair for back pain, anemia, cancer, depression, kidney disease, liver disease, rheumatoid arthritis and stomach ulcers (kappa range: 0.14-0.37). Estimates varied by sex (i.e., generally higher agreement for females) and age (i.e., generally lower agreement for older age groups), but not joint type. Conclusion/ImplicationsSelf-reported health conditions in registry data had good validity for conditions with clear diagnostic criteria, but low validity for conditions that are difficult to diagnose or rare (e.g., cancer). Linked registry and administrative data is strongly recommended to ensure valid and accurate comorbidity measures when developiong risk prediction models and conducting inter-jurisdictional comparisons of patient-reported outcome measures.

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Cohort profile: the St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry, a pragmatic prospective database defining outcomes in total hip and knee replacement patients

BMJ Open ◽

10.1136/bmjopen-2020-040408 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040408

Author(s):

Daniel Gould ◽

Sharmala Thuraisingam ◽

Cade Shadbolt ◽

Josh Knight ◽

Jesse Young ◽

...

Keyword(s):

Registry Data ◽

Prescribing Patterns ◽

Audit And Feedback ◽

Economic Evaluations ◽

Tertiary Referral Hospital ◽

Clinical Registry ◽

Patient Reported ◽

Prospective Database ◽

Future Plans

PurposeThe St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry is an institutional clinical registry housed at a tertiary referral hospital in Australia. The SMART Registry is a pragmatic prospective database, which was established to capture a broad range of longitudinal clinical and patient-reported outcome data to facilitate collaborative research that will improve policy and practice relevant to arthroplasty surgery for people with advanced arthritis of the hip or knee. The purpose of this cohort profile paper is to describe the rationale for the SMART Registry’s creation, its methods, baseline data and future plans for the Registry. A full compilation of the data is provided as a reference point for future collaborators.ParticipantsThe SMART Registry cohort comprises over 13 000 consecutive arthroplasty procedures in more than 10 000 patients who underwent their procedure at St Vincent’s Hospital Melbourne, since January 1998. Participant recruitment, data collection and follow-up is ongoing and currently includes up to 20 years follow-up data.Findings to dateSMART Registry data are used for clinical audit and feedback, as well as for a broad range of research including epidemiological studies, predictive statistical modelling and health economic evaluations. At the time of writing, there were 46 publications from SMART Registry data, with contributions from more than 67 coauthors.Future plansWith the recent linking of the SMART Registry with Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data through the Australian Institute of Health and Welfare, research into prescribing patterns and health system utilisation is currently underway. The SMART Registry is also being updated with the Clavien-Dindo classification of surgical complications.

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333 Associations Between Insomnia and Health Correlates in Nurses

SLEEP ◽

10.1093/sleep/zsab072.332 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A133-A133

Author(s):

Samantha Nagy ◽

Jessica Dietch ◽

Danica Slavish ◽

Brett Messman ◽

Camilo Ruggero ◽

...

Keyword(s):

Blood Pressure ◽

Sleep Apnea ◽

High Blood Pressure ◽

Sleep Disturbances ◽

Sleep Problems ◽

Health Conditions ◽

Future Research ◽

Evidence Based ◽

Insufficient Sleep ◽

Gastrointestinal Problems

Abstract Introduction Insomnia, shiftwork (i.e., circadian rhythm disruptions) and insufficient sleep are common among nurses and healthcare workers. Each of these sleep problems can contribute to physical (e.g., inflammation, musculoskeletal pain, cardiovascular disease and heart rate variability, indigestion, and menstrual cycle irregularity) and mental (e.g., depression, anxiety, suicidality) health problems as well as daytime fatigue and sleepiness among nurses and may contribute to burnout and job change. Methods Participants (N=458) were nurses recruited for a parent study, “Sleep and Vaccine Response in Nurses (SAV-RN)” (Taylor & Kelly: R01AI128359-01). Most identified as female (90.5%), White/Caucasian (77.2%), and non-Hispanic (88.6%) with an average age of 39.03 (SD = 11.07). Participants completed baseline measures online via Qualtrics survey. The Sleep Condition Indicator (SCI; Espie et al., 2014) was used to identify a probable diagnosis of insomnia (score of ≤16 = Insomnia; endorsement of each of the primary DSM-5 criteria on the measure). In addition, a checklist of current major health conditions (high blood pressure, sleep apnea, GI issues, HIV/AIDS, cancer, etc.) was also completed. A Chi square test of Independence was conducted using SPSS to determine if insomnia detected by the SCI was associated with reported health conditions. Results At baseline, 25.4% of nurses had a probable insomnia diagnosis. Insomnia was associated with a greater likelihood of diagnosed sleep apnea, cancer (all types), high blood pressure, chronic pain, gastrointestinal problems, an autoimmune disease, and/or an endocrine problem at Month 11 of the study (all ps <.05). Data cleaning is ongoing, but similar analyses will be presented examining shift work sleep disorder and insufficient sleep (i.e., average < 6hrs per night) as individual and simultaneous predictors of physical and mental health at baseline and change from baseline to Month 11 (if available). Conclusion These results help to identify associations between insomnia and health conditions in nurses and may contribute to future research that supports evidence-based intervention and prevention strategies for this population. While evidence-based interventions for sleep disturbances and insomnia exist (CBT-I), accessibility and feasibility of scaling such interventions to reach the nursing community at large remains challenging. Support (if any):

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PMS76 WHAT LOW BACK PAIN PATIENTS REALLY CARE ABOUT: REAL WORLD EVIDENCE ON PATIENT REPORTED HEALTH OUTCOMES IN AUSTRALIA.

Value in Health ◽

10.1016/j.jval.2019.09.1617 ◽

2019 ◽

Vol 22 ◽

pp. S707

Author(s):

K.J. Markus ◽

L. Strachan

Keyword(s):

Low Back Pain ◽

Back Pain ◽

Health Outcomes ◽

Real World ◽

Low Back ◽

Real World Evidence ◽

Patient Reported ◽

Reported Health ◽

Pain Patients

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Does high blood pressure reduce the risk of chronic low back pain? The Nord-Trøndelag Health Study

European Journal of Pain ◽

10.1002/j.1532-2149.2013.00398.x ◽

2013 ◽

Vol 18 (4) ◽

pp. 590-598 ◽

Cited By ~ 13

Author(s):

I. Heuch ◽

K. Hagen ◽

J.A. Zwart

Keyword(s):

Blood Pressure ◽

Low Back Pain ◽

Back Pain ◽

High Blood Pressure ◽

Chronic Low Back Pain ◽

Health Study ◽

Low Back

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SOCIODEMOGRAPHIC AND HEALTH FACTORS ASSOCIATED WITH FUNCTIONAL FITNESS IN OLDER WOMEN FROM A PHYSICAL ACTIVITY PROGRAM IN SOUTHERN BRAZIL

Estudos Interdisciplinares sobre o Envelhecimento ◽

10.22456/2316-2171.63719 ◽

2017 ◽

Vol 22 (1) ◽

Author(s):

Gislaine Cristina Vagetti ◽

Oldemar Mazzardo ◽

Valter Cordeiro Barbosa Filho ◽

Valdomiro De Oliveira ◽

Antônio Carlos Gomes ◽

...

Keyword(s):

Blood Pressure ◽

Older Women ◽

High Blood Pressure ◽

Educational Level ◽

Skin Color ◽

Health Problems ◽

Health Perception ◽

Functional Fitness ◽

Independent Variables ◽

Reported Health

Aim: The purpose of the present study was to investigate the association of sociodemographic (skin color, socioeconomic level, educational level, occupational and marital status) and health (high blood pressure, self-reported health problems, use of medicines and health perception), with the functional fitness in older women. Methods: This cross-sectional study investigates sociodemographic and health variables assessed by questionnaires and the association with functional fitness measured with the "Senior Fitness Test". Statistical analysis used the Kruskal -Wallis test to check for differences between age groups, chi-square tests and logistic regression analyses to investigate associations between each component of functional fitness and independent variables. Results: The final sample consisted of 1,806 older women, mean age 68.93 years (SD 6.6). Sociodemographic (skin color, socioeconomic class and educational level) and health variables (High blood pressure, self-reported health problems and health perception) were associated with different components of functional fitness and the overall score of functional capacity. Conclusion: Among all the independent variables, educational level and health perception were those most correlated to functional fitness.

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Multimorbidity patterns among COVID-19 deaths: proposal for the construction of etiological models

Revista Panamericana de Salud Pública ◽

10.26633/rpsp.2020.166 ◽

2020 ◽

Vol 44 ◽

pp. 1

Author(s):

Julián A. Fernández-Niño ◽

John A. Guerra-Gómez ◽

Alvaro J. Idrovo

Keyword(s):

Blood Pressure ◽

Cardiovascular Disease ◽

Respiratory Disease ◽

High Blood Pressure ◽

Health Conditions ◽

Older Individuals ◽

Age Related ◽

Probability Of Death ◽

Polynomial Regressions

Objectives. To describe patterns of multimorbidity among fatal cases of COVID-19, and to propose a classification of patients based on age and multimorbidity patterns to begin the construction of etiological models. Methods. Data of Colombian confirmed deaths of COVID-19 until June 11, 2020, were included in this analysis (n=1488 deaths). Relationships between COVID-19, combinations of health conditions and age were explored using locally weighted polynomial regressions. Results. The most frequent health conditions were high blood pressure, respiratory disease, diabetes, cardiovascular disease, and kidney disease. Dyads more frequents were high blood pressure with diabetes, cardiovascular disease or respiratory disease. Some multimorbidity patterns increase probability of death among older individuals, whereas other patterns are not age-related, or decrease the probability of death among older people. Not all multimorbidity increases with age, as is commonly thought. Obesity, alone or with other diseases, was associated with a higher risk of severity among young people, while the risk of the high blood pressure/diabetes dyad tends to have an inverted U distribution in relation with age. Conclusions. Classification of individuals according to multimorbidity in the medical management of COVID-19 patients is important to determine the possible etiological models and to define patient triage for hospitalization. Moreover, identification of non-infected individuals with high-risk ages and multimorbidity patterns serves to define possible interventions of selective confinement or special management.

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Health and Functional Limitations Predict Depression Scores in the Health and Retirement Study

Californian Journal of Health Promotion ◽

10.32398/cjhp.v11i1.1522 ◽

2013 ◽

Vol 11 (1) ◽

pp. 97-108 ◽

Cited By ~ 6

Author(s):

Robert Kennison ◽

John Cox

Keyword(s):

Blood Pressure ◽

Heart Disease ◽

Back Pain ◽

Lung Disease ◽

High Blood Pressure ◽

Functional Limitations ◽

Depression Score ◽

Multivariate Adaptive Regression Splines ◽

Health And Retirement Study ◽

Retirement Study

Objective: To examine the effects of chronic health conditions and functional status limitations on depression scores in a large representative sample of Americans. Method: The data included 27,461 respondents ages 50 to 90 who completed up to eight test occasions from the Health and Retirement Study. Multivariate adaptive regression splines (MARS) modeling was applied. Possible covariates of depression included arthritis, lung disease, back pain, diabetes, heart disease, high blood pressure, cancer, 28 pairwise combinations of the aforementioned conditions, ADL functional limitations, age, education and being female, being white, and being Hispanic. Results: The best fitting model had a GRSq of 0.18 (comparable to R2 ) and included 12 of 42 covariates. Depression score was predicted by: 1) ADL limitations, 2) education, 3) back pain, 4) lung disease, 5) being female, 6) being Hispanic, 7) heart disease, 8) being white, 9) high blood pressure plus stroke, 10) age, 11) back pain plus arthritis, and 12) back pain plus diabetes. Conclusions: Functional limitations was the strongest predictor of depression; reporting one limitation increased depression scores by nearly double the increase associated with two or more limitations. Back pain and lung disease were the strongest chronic disease predictors of depression; both are associated with considerable discomfort.

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