Trends in Time to Confirmation and Recognition of New-Onset Hypertension, 2002–2006

Selby, Joe V.; Lee, Janelle; Swain, Bix E.; Tavel, Heather M.; Ho, P. Michael; Margolis, Karen L.; O’Connor, Patrick J.; Fine, Lawrence J.; Schmittdiel, Julie A.; Magid, David J.

doi:10.1161/hypertensionaha.110.153528

Cited by 30 publications

(29 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CVRN Hypertension Registry includes all adult patients with hypertension at three large integrated healthcare delivery systems, Health Partners of Minnesota, Kaiser Permanente Colorado, and Kaiser Permanente Northern California. The algorithm for entry into the cohort has been described previously [8]. Briefly, patients entered into the registry upon meeting one or more of the following criteria: at least two consecutive elevated BP measurements (i.e.…”

Section: Methodsmentioning

confidence: 99%

Detection and recognition of hypertension in anxious and depressed patients

Byrd¹,

Powers²,

Magid³

et al. 2012

Journal of Hypertension

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Detection and recognition of hypertension in anxious and depressed patients

Byrd¹,

Powers²,

Magid³

et al. 2012

Journal of Hypertension

Self Cite

View full text Add to dashboard Cite

show abstract

“…This definition was adapted from the Hypertension Registry of the Cardiovascular Disease Research Network. 25 Diabetes mellitus was defined as having an ICD-9 diagnosis code of 250.XX. Lipid values were considered unfavorable if high-density lipoprotein cholesterol was ≤40 mg/dL, triglycerides were ≥200 mg/dL, or fasting glucose was ≥110 mg/dL.…”

Section: Questionnaire Datamentioning

confidence: 99%

Effects of Physical Activity and Sedentary Time on the Risk of Heart Failure

Young

Reynolds

Sidell

et al. 2014

Circ: Heart Failure

105

View full text Add to dashboard Cite

Background— Although the benefits of physical activity for risk of coronary heart disease are well established, less is known about its effects on heart failure (HF). The risk of prolonged sedentary behavior on HF is unknown. Methods and Results— The study cohort included 82 695 men aged ≥45 years from the California Men’s Health Study without prevalent HF who were followed up for 10 years. Physical activity, sedentary time, and behavioral covariates were obtained from questionnaires, and clinical covariates were determined from electronic medical records. Incident HF was identified through International Classification of Diseases, Ninth Revision codes recorded in electronic records. During a mean follow-up of 7.8 years (646 989 person-years), 3473 men were diagnosed with HF. Controlling for sedentary time, sociodemographics, hypertension, diabetes mellitus, unfavorable lipid levels, body mass index, smoking, and diet, the hazard ratio (95% confidence interval [CI]) of HF in the lowest physical activity category compared with those in the highest category was 1.52 (95% CI, 1.39–1.68). Those in the medium physical activity category were also at increased risk (hazard ratio, 1.17 [95% CI, 1.06–1.29]). Controlling for the same covariates and physical activity, the hazard ratio (95% CI) of HF in the highest sedentary category compared with the lowest was 1.34 (95% CI, 1.21–1.48). Medium sedentary time also conveyed risk (hazard ratio, 1.13 [95% CI, 1.04–1.24]). Results showed similar trends across white and Hispanic subgroups, body mass index categories, baseline hypertension status, and prevalent coronary heart disease. Conclusions— Both physical activity and sedentary time may be appropriate intervention targets for preventing HF.

show abstract

“…All electronic BP records were obtained for outpatient primary care visits during the observation period. 41,42 To ensure appropriate connection to the clinician, BP was analyzed only after the patient's first visit with the clinician in that period. If more than one BP was recorded in a single day, the average for that day was used.…”

Section: Conceptual Model Guiding Patient Measuresmentioning

confidence: 99%

An Investigation of Associations Between Clinicians’ Ethnic or Racial Bias and Hypertension Treatment, Medication Adherence and Blood Pressure Control

et al. 2014

View full text Add to dashboard Cite

BACKGROUND: Few studies have directly investigated the association of clinicians' implicit (unconscious) bias with health care disparities in clinical settings. OBJECTIVE: To determine if clinicians' implicit ethnic or racial bias is associated with processes and outcomes of treatment for hypertension among black and Latino patients, relative to white patients. RESEARCH DESIGN AND PARTICIPANTS: Primary care clinicians completed Implicit Association Tests of ethnic and racial bias. Electronic medical records were queried for a stratified, random sample of the clinicians' black, Latino and white patients to assess treatment intensification, adherence and control of hypertension. Multilevel random coefficient models assessed the associations between clinicians' implicit biases and ethnic or racial differences in hypertension care and outcomes. MAIN MEASURES: Standard measures of treatment intensification and medication adherence were calculated from pharmacy refills. Hypertension control was assessed by the percentage of time that patients met blood pressure goals recorded during primary care visits. KEY RESULTS: One hundred and thirty-eight primary care clinicians and 4,794 patients with hypertension participated. Black patients received equivalent treatment intensification, but had lower medication adherence and worse hypertension control than white patients; Latino patients received equivalent treatment intensification and had similar hypertension control, but lower medication adherence than white patients. Differences in treatment intensification, medication adherence and hypertension control were unrelated to clinician implicit bias for black patients (P=0.85, P= 0.06 and P=0.31, respectively) and for Latino patients (P=0.55, P=0.40 and P=0.79, respectively). An increase in clinician bias from average to strong was associated with a relative change of less than 5 % in all outcomes for black and Latino patients. CONCLUSIONS: Implicit bias did not affect clinicians' provision of care to their minority patients, nor did it affect the patients' outcomes. The identification of health care contexts in which bias does not impact outcomes can assist both patients and clinicians in their efforts to build trust and partnership.KEY WORDS: hypertension; healthcare disparities; discrimination; implicit bias; race/ethnicity; quality. J Gen Intern Med 29(7):987-95

show abstract

Trends in Time to Confirmation and Recognition of New-Onset Hypertension, 2002–2006

Abstract: Abstract-Achieving full benefits of blood pressure control in populations requires prompt recognition of previously undetected hypertension.

Cited by 30 publications

References 26 publications

Detection and recognition of hypertension in anxious and depressed patients

Detection and recognition of hypertension in anxious and depressed patients

Effects of Physical Activity and Sedentary Time on the Risk of Heart Failure

An Investigation of Associations Between Clinicians’ Ethnic or Racial Bias and Hypertension Treatment, Medication Adherence and Blood Pressure Control

Contact Info

Product

Resources

About