Obesity and pre-hypertension in family medicine: Implications for quality improvement

<p>Abstract</p> <p>Background.</p> <p>Prevention of pre-hypertension is an important goal for primary care patients. Obesity is a risk factor for hypertension, but has not been addressed for pre-hypertension in primary care populations. The objective of this study was to assess the degree to which obesity independently is associated with risk for pre-hypertension in family medicine patients.</p> <p>Methods.</p> <p>This study was a retrospective analysis of information abstracted from medical records of 707 adult patients. Multivariable logistic regression was used to test the relationship between body mass index (BMI) and pre-hypertension, after adjustment for comorbidity and demographic characteristics. Pre-hypertension was defined as systolic pressure between 120 and 139 mm Hg or diastolic pressure between 80 and 89 mm Hg.</p> <p>Results.</p> <p>In our sample, 42.9% of patients were pre-hypertensive. Logistic regression analysis revealed that, in comparison to patients with normal body mass, patients with BMI > 35 had higher adjusted odds of being pre-hypertensive (OR = 4.5, CI 2.55–8.11, p < .01). BMI between 30 and 35 also was significant (OR = 2.7, CI 1.61–4.63, p < 0.01) as was overweight (OR = 1.8, CI 1.14–2.92, p = 0.01).</p> <p>Conclusion.</p> <p>In our sample of family medicine patients, elevated BMI is a risk factor for pre-hypertension, especially BMI > 35. This relationship appears to be independent of age, gender, marital status and comorbidity. Weight loss intervention for obese patients, including patient education or referral to weight loss programs, might be effective for prevention of pre-hypertension and thus should be considered as a potential quality indicator.</p

Documentation of body mass index and control of associated risk factors in a large primary care network

<p>Abstract</p> <p>Background</p> <p>Body mass index (BMI) will be a reportable health measure in the United States (US) through implementation of Healthcare Effectiveness Data and Information Set (HEDIS) guidelines. We evaluated current documentation of BMI, and documentation and control of associated risk factors by BMI category, based on electronic health records from a 12-clinic primary care network.</p> <p>Methods</p> <p>We conducted a cross-sectional analysis of 79,947 active network patients greater than 18 years of age seen between 7/05 - 12/06. We defined BMI category as normal weight (NW, 18-24.9 kg/m²), overweight (OW, 25-29.9), and obese (OB, ≥ 30). We measured documentation (yes/no) and control (above/below) of the following three risk factors: blood pressure (BP) ≤130/≤85 mmHg, low-density lipoprotein (LDL) ≤130 mg/dL (3.367 mmol/L), and fasting glucose <100 mg/dL (5.55 mmol/L) or casual glucose <200 mg/dL (11.1 mmol/L).</p> <p>Results</p> <p>BMI was documented in 48,376 patients (61%, range 34-94%), distributed as 30% OB, 34% OW, and 36% NW. Documentation of all three risk factors was higher in obesity (OB = 58%, OW = 54%, NW = 41%, p for trend <0.0001), but control of all three was lower (OB = 44%, OW = 49%, NW = 62%, p = 0.0001). The presence of cardiovascular disease (CVD) or diabetes modified some associations with obesity, and OB patients with CVD or diabetes had low rates of control of all three risk factors (CVD: OB = 49%, OW = 50%, NW = 56%; diabetes: OB = 42%, OW = 47%, NW = 48%, p < 0.0001 for adiposity-CVD or diabetes interaction).</p> <p>Conclusions</p> <p>In a large primary care network BMI documentation has been incomplete and for patients with BMI measured, risk factor control has been poorer in obese patients compared with NW, even in those with obesity and CVD or diabetes. Better knowledge of BMI could provide an opportunity for improved quality in obesity care.</p

Cleveland: “Where rock began to roll”?

Focused on Cleveland, Ohio, this chapter asks how ‘music cities’ make their claims-to-fame. What underscores Cleveland’s assertion as the “birthplace” of rock ‘n’ roll, and since 1995, the site of the Rock and Roll Hall of Fame? Drawing from archival research, the chapter explores a micro-historical case study of the city’s popular music heritage. Cleveland claims several notable “firsts”, including the “first” rock ‘n’ roll concert—the Moondog Coronation Ball on 21 March 1952. The chapter also recounts the story of local record store, Record Rendezvous, where legend has it that the phrase “rock ‘n’ roll” was invented. Finally, the chapter recounts how these legacies were mobilized and mythologized, especially during the 1980s when Cleveland successfully positioned itself as a “city of origin” and a serious contender in the campaign to become the future site of the Rock and Roll Hall of Fame

Methods to identify the target population: implications for prescribing quality indicators

Background: Information on prescribing quality is increasingly used by policy makers, insurance companies and health care providers. For reliable assessment of prescribing quality it is important to correctly identify the patients eligible for recommended treatment. Often either diagnostic codes or clinical measurements are used to identify such patients. We compared these two approaches regarding the outcome of the prescribing quality assessment and their ability to identify treated and undertreated patients. Methods: The approaches were compared using electronic health records for 3214 diabetes patients from 70 general practitioners. We selected three existing prescribing quality indicators (PQI) assessing different aspects of treatment in patients with hypertension or who were overweight. We compared population level prescribing quality scores and proportions of identified patients using definitions of hypertension or being overweight based on diagnostic codes, clinical measurements or both. Results: The prescribing quality score for prescribing any antihypertensive treatment was 93% (95% confidence interval 90-95%) using the diagnostic code-based approach, and 81% (78-83%) using the measurement-based approach. Patients receiving antihypertensive treatment had a better registration of their diagnosis compared to hypertensive patients in whom such treatment was not initiated. Scores on the other two PQI were similar for the different approaches, ranging from 64 to 66%. For all PQI, the clinical measurement -based approach identified higher proportions of both well treated and undertreated patients compared to the diagnostic code -based approach. Conclusions: The use of clinical measurements is recommended when PQI are used to identify undertreated patients. Using diagnostic codes or clinical measurement values has little impact on the outcomes of proportion-based PQI when both numerator and denominator are equally affected. In situations when a diagnosis is better registered for treated than untreated patients, as we observed for hypertension, the diagnostic code-based approach results in overestimation of provided treatment