Relationship Between Elevated Automatic Blood Pressure Readings and Manual Blood Pressure Readings in Adult Patients With Normal and High Body Mass Index.

CONTEXT: Discrepancies in ambulatory blood mercury sphygmomanometers pressure readings exist between automated blood pressure machines and manual mercury sphygmomanometers. For patients with elevated blood pressure, consistent blood pressure readings between these methods are important across all body mass index (BMI) levels. OBJECTIVE: To determine the relationship between automated and manual blood pressure readings and the effect of BMI in patients with an elevated automated blood pressure reading. METHODS: Automated and manual blood pressure readings (using the same arm) were collected from July 2014 to December 2016 across community-oriented primary care clinics in New York City for a retrospective medical record review. Automated systolic blood pressure (SBP) readings greater than 140 mm Hg or diastolic blood pressure (DBP) readings greater than 90 mm Hg were qualifying criteria for a manual blood pressure assessment. The difference in automated blood pressure readings relative to a manual blood pressure reading was assessed overall and for any relationship with BMI using linear regression and analysis of variance. RESULTS: Data from 281 patients (166 [59%] women, 115 [41%] men; mean [SD] age of 57 [12.6] years) were assessed. For SBP, automated readings had an overall mean that was 8.0% greater than manual readings (P<.001). This relative difference decreased linearly by 2.7 points (95% CI, 1.0-4.4) for each 10-unit increase in BMI (P=.002). For DBP, automated readings had an overall mean that was 4.5% greater than manual readings (P<.001). This relative difference followed a quadratic relationship with BMI (P=.01), where the downward curve peaked at 6.6% (95% CI, 4.5-8.7) for a BMI of 35. When BMI was grouped into 4 categories, (normal weight, overweight, obese, and morbidly obese), morbidly obese patients had a smaller mean percentage SBP difference (4.3% [95% CI, 1.5-7.1]) compared with the other 3 categories (8.6% [95% CI, 7.2-9.9], P=.007). No relative differences between automated and manual methods for DBP were found among the BMI categories (P=.11). CONCLUSIONS: The current study found significant differences between automated and manual blood pressure readings. The relationship of these differences with BMI was also statistically significant, but their clinical significance remains inconclusive. Because manual blood pressure readings may have clinical value when evaluating or treating a patient with elevated blood pressure, better adherence to proper technique may improve accuracy.

Community health center provider ability to identify, treat and account for the social determinants of health: a card study.

BACKGROUND: The social determinants of health (SDH) are conditions that shape the overall health of an individual on a continuous basis. As momentum for addressing social factors in primary care settings grows, provider ability to identify, treat and assess these factors remains unknown. Community health centers care for over 20-million of America's highest risk populations. This study at three centers evaluates provider ability to identify, treat and code for the SDH. METHODS: Investigators utilized a pre-study survey and a card study design to obtain evidence from the point of care. The survey assessed providers' perceptions of the SDH and their ability to address them. Then providers filled out one anonymous card per patient on four assigned days over a 4-week period, documenting social factors observed during encounters. The cards allowed providers to indicate if they were able to: provide counseling or other interventions, enter a diagnosis code and enter a billing code for identified factors. RESULTS: The results of the survey indicate providers were familiar with the SDH and were comfortable identifying social factors at the point of care. A total of 747 cards were completed. 1584 factors were identified and 31 % were reported as having a service provided. However, only 1.2 % of factors were associated with a billing code and 6.8 % received a diagnosis code. CONCLUSIONS: An obvious discrepancy exists between the number of identifiable social factors, provider ability to address them and documentation with billing and diagnosis codes. This disparity could be related to provider inability to code for social factors and bill for related time and services. Health care organizations should seek to implement procedures to document and monitor social factors and actions taken to address them. Results of this study suggest simple methods of identification may be sufficient. The addition of searchable codes and reimbursements may improve the way social factors are addressed for individuals and populations.