scholarly journals Blood Pressure Trajectories Across the Life Course

2021 ◽  
Vol 34 (3) ◽  
pp. 234-241
Author(s):  
Norrina B Allen ◽  
Sadiya S Khan
Keyword(s):  
Blood Pressure ◽  
Measurement Errors ◽  
Clinical Care ◽  
Population Level ◽  
Cumulative Exposure ◽  
Older Adulthood ◽  
Individual Level ◽  
The Life Course ◽  
Level Group ◽  
Improve Health

Abstract High blood pressure (BP) is a strong modifiable risk factor for cardiovascular disease (CVD). Longitudinal BP patterns themselves may reflect the burden of risk and vascular damage due to prolonged cumulative exposure to high BP levels. Current studies have begun to characterize BP patterns as a trajectory over an individual’s lifetime. These BP trajectories take into account the absolute BP levels as well as the slope of BP changes throughout the lifetime thus incorporating longitudinal BP patterns into a single metric. Methodologic issues that need to be considered when examining BP trajectories include individual-level vs. population-level group-based modeling, use of distinct but complementary BP metrics (systolic, diastolic, mean arterial, mid, and pulse pressure), and potential for measurement errors related to varied settings, devices, and number of readings utilized. There appear to be very specific developmental periods during which divergent BP trajectories may emerge, specifically adolescence, the pregnancy period, and older adulthood. Lifetime BP trajectories are impacted by both individual-level and community-level factors and have been associated with incident hypertension, multimorbidity (CVD, renal disease, cognitive impairment), and overall life expectancy. Key unanswered questions remain around the additive predictive value of BP trajectories, intergenerational contributions to BP patterns (in utero BP exposure), and potential genetic drivers of BP patterns. The next phase in understanding BP trajectories needs to focus on how best to incorporate this knowledge into clinical care to reduce the burden of hypertensive-related outcomes and improve health equity.

scholarly journals Analyzing 24-Hour Blood Pressure Measurements with a Novel Cuffless Pulse Transit Time Device in Clinical Practice—Does the Software for Heartbeat Detection Matter?

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 361
Author(s):  
Leo Kilian ◽  
Philipp Krisai ◽  
Thenral Socrates ◽  
Christian Arranto ◽  
Otmar Pfister ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Transit Time ◽  
Pulse Transit Time ◽  
Population Level ◽  
Raw Data ◽  
Invasive Blood Pressure ◽  
Individual Level ◽  
Non Invasive ◽  
The Individual ◽  
The Impact

Background: The Somnotouch-Non-Invasive-Blood-Pressure (NIBP) device delivers raw data consisting of electrocardiography and photoplethysmography for estimating blood pressure (BP) over 24 h using pulse-transit-time. The study’s aim was to analyze the impact on 24-hour BP results when processing raw data by two different software solutions delivered with the device. Methods: We used data from 234 participants. The Somnotouch-NIBP measurements were analyzed using the Domino-light and Schiller software and compared. BP values differing >5 mmHg were regarded as relevant and explored for their impact on BP classification (normotension vs. hypertension). Results: Mean (±standard deviation) absolute systolic/diastolic differences for 24-hour mean BP were 1.5 (±1.7)/1.1 (±1.3) mm Hg. Besides awake systolic BP (p = 0.022), there were no statistically significant differences in systolic/diastolic 24-hour mean, awake, and asleep BP. Twenty four-hour mean BP agreement (number (%)) between the software solutions within 5, 10, and 15 mmHg were 222 (94.8%), 231 (98.7%), 234 (100%) for systolic and 228 (97.4%), 232 (99.1%), 233 (99.5%) for diastolic measurements, respectively. A BP difference of >5 mmHg was present in 24 (10.3%) participants leading to discordant classification in 4–17%. Conclusion: By comparing the two software solutions, differences in BP are negligible at the population level. However, at the individual level there are, in a minority of cases, differences that lead to different BP classifications, which can influence the therapeutic decision.

Improving Cancer Care Through the Patient Experience: How to Use Patient-Reported Outcomes in Clinical Practice

2017 ◽  
pp. 695-704 ◽  
Author(s):  
Kathi Mooney ◽  
Donna L. Berry ◽  
Meagan Whisenant ◽  
Daniel Sjoberg
Keyword(s):  
Patient Experience ◽  
Cancer Care ◽  
Patient Reported Outcomes ◽  
Clinical Care ◽  
Population Data ◽  
Population Level ◽  
Clinical Work ◽  
Aggregated Data ◽  
Individual Level ◽  
Patient Reported

Poorly controlled symptoms are common and debilitating during cancer treatment and can affect functional status and quality of life, health care resource utilization, treatment adherence, and cancer survivorship. Historically, the patient experience, including symptoms during treatment, has not been tracked or documented in the patient health record. Measurement of patient-reported outcomes (PROs), including symptoms, is an essential component to cancer care focused on the illness impact to the patient and family. PROs can be useful at the individual level for monitoring and promoting symptom care both in the clinic and remotely and at the population level for aggregating population data for use in research and quality improvement initiatives. Implementation of PROs in cancer clinical care requires a carefully thought out process to overcome challenges related to integrating PROs into existing electronic health records and clinical work flow. Issues with implementing PRO collection may include making decisions about measurement tools, modes of delivery, frequency of measurement, and interpretation that are guided by a clarification of the purpose for collecting PROs. We focus on three aspects of PRO use: (1) improving care for individual patients, (2) analyzing aggregated data to improve care and outcomes overall, and (3) considerations in implementing PRO collection.

Embodying Inequality: A Review of Concepts, Measures, and Methods for Studying Health Consequences of Discrimination

1999 ◽  
Vol 29 (2) ◽  
pp. 295-352 ◽  
Author(s):  
Nancy Krieger
Keyword(s):  
Population Level ◽  
The United States ◽  
Public Health Research ◽  
Economic Consequences ◽  
Cumulative Exposure ◽  
Health Consequences ◽  
Individual Level ◽  
Economic Level ◽  
Personal Experiences ◽  
The Individual

Investigating effects of discrimination upon health requires clear concepts, methods, and measures. At issue are both economic consequences of discrimination and accumulated insults arising from everyday and at times violent experiences of being treated as a second-class citizen, at each and every economic level. Guidelines for epidemiologic investigations and other public health research on ways people embody racism, sexism, and other forms of social inequality, however, are not well defined, as research in this area is in its infancy. Employing an ecosocial framework, this article accordingly reviews definitions and patterns of discrimination within the United States; evaluates analytic strategies and instruments researchers have developed to study health effects of different kinds of discrimination; and delineates diverse pathways by which discrimination can harm health, both outright and by distorting production of epidemiologic knowledge about determinants of population health. Three methods of studying health consequences of discrimination are examined (indirect; direct, at the individual level, in relation to personal experiences of discrimination; at the population level, such as via segregation), and recommendations are provided for developing research instruments to measure acute and cumulative exposure to different aspects of discrimination.

Abstract P117: Comparison Between Routine Clinical And Research Blood Pressure Measurements

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Mark Sanders ◽  
Paul Muntner ◽  
Rong Wei ◽  
Daichi Shimbo ◽  
Joseph E Schwartz ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Clinical Care ◽  
Pearson Correlation ◽  
Mean Difference ◽  
Limits Of Agreement ◽  
Individual Level ◽  
Oscillometric Device ◽  
Research Grade ◽  
The Mean ◽  
The Individual

Background: Prior studies have found a large difference between blood pressure (BP) when measured routinely in the clinic compared with research studies. We aimed to compare routine clinic BP to research-grade BP in a large, integrated health care system that has initiatives to standardize clinic BP measurements. Methods: We identified Kaiser Permanente Southern California members ≥ 65 years old diagnosed with hypertension and taking antihypertensive medication from the Ambulatory Blood Pressure in Older Adults (AMBROSIA) study. Research-grade BPs were obtained under standardized conditions by certified research staff using a semi-automatic oscillometric device, pre-programmed to take 3 measurements at 1-minute intervals. The average of the 3 BPs was used. The most recent (prior to study enrollment) routine clinic BP from an outpatient, non-urgent clinical care encounter, measured using a semi-automatic oscillometric device, was obtained via electronic health records. If there were multiple BP readings on the same day, the first reading was used. The mean difference between clinic BP and research-grade BP was tested using paired t-tests, while the Pearson correlation and a Bland-Altman analysis were used to assess level of agreement. Results: We included 309 participants (mean age 75 ± 6 years; 54% female; 49% non-Hispanic white, 17% non-Hispanic Black, 17% Hispanic, 15% Asian/Pacific Islander). When measured in routine clinic practice and in the research study, the mean (SD) systolic BP (SBP) was 135 (16) mm Hg and 132 (15) mm Hg, respectively, (mean difference = - 2.7 mm Hg; 95% CI -4.6 to -0.9; limits of agreement = -36 to 30 mm Hg) and the mean diastolic BP (DBP) was 70 (10) mm Hg and 69 (10) mm Hg, respectively (mean difference = - 0.9 mm Hg; 95% CI -2.1 to 0.3; limits of agreement = -22 to 20 mm Hg). Pearson correlation analysis showed modest correlations between the two types of BP measurements (SBP r=0.40, p<0.01; DBP r=0.45, p<0.01). Conclusion: The difference between clinic and research-grade BP was, on average, small, but differences at the individual level were often substantial.

scholarly journals Birthweight: EN-BIRTH multi-country validation study

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Stefanie Kong ◽  
◽  
Louise T. Day ◽  
Sojib Bin Zaman ◽  
Kimberly Peven ◽  
...  
Keyword(s):  
Low Birthweight ◽  
Clinical Care ◽  
Qualitative Interviews ◽  
Population Level ◽  
Exit Interview ◽  
Actual Weight ◽  
Individual Level ◽  
Survey Report ◽  
Interview Survey ◽  
The Individual

Abstract Background Accurate birthweight is critical to inform clinical care at the individual level and tracking progress towards national/global targets at the population level. Low birthweight (LBW) < 2500 g affects over 20.5 million newborns annually. However, data are lacking and may be affected by heaping. This paper evaluates birthweight measurement within the Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study. Methods The EN-BIRTH study took place in five hospitals in Bangladesh, Nepal and Tanzania (2017–2018). Clinical observers collected time-stamped data (gold standard) for weighing at birth. We compared accuracy for two data sources: routine hospital registers and women’s report at exit interview survey. We calculated absolute differences and individual-level validation metrics. We analysed birthweight coverage and quality gaps including timing and heaping. Qualitative data explored barriers and enablers for routine register data recording. Results Among 23,471 observed births, 98.8% were weighed. Exit interview survey-reported weighing coverage was 94.3% (90.2–97.3%), sensitivity 95.0% (91.3–97.8%). Register-reported coverage was 96.6% (93.2–98.9%), sensitivity 97.1% (94.3–99%). Routine registers were complete (> 98% for four hospitals) and legible > 99.9%. Weighing of stillbirths varied by hospital, ranging from 12.5–89.0%. Observed LBW rate was 15.6%; survey-reported rate 14.3% (8.9–20.9%), sensitivity 82.9% (75.1–89.4%), specificity 96.1% (93.5–98.5%); register-recorded rate 14.9%, sensitivity 90.8% (85.9–94.8%), specificity 98.5% (98–99.0%). In surveys, “don’t know” responses for birthweight measured were 4.7%, and 2.9% for knowing the actual weight. 95.9% of observed babies were weighed within 1 h of birth, only 14.7% with a digital scale. Weight heaping indices were around two-fold lower using digital scales compared to analogue. Observed heaping was almost 5% higher for births during the night than day. Survey-report further increased observed birthweight heaping, especially for LBW babies. Enablers to register birthweight measurement in qualitative interviews included digital scale availability and adequate staffing. Conclusions Hospital registers captured birthweight and LBW prevalence more accurately than women’s survey report. Even in large hospitals, digital scales were not always available and stillborn babies not always weighed. Birthweight data are being captured in hospitals and investment is required to further improve data quality, researching of data flow in routine systems and use of data at every level.

scholarly journals Religion and Death in the United States: A Meta-Regression Comparative Assessment of Between-County Mortality Heterogeneity in the United States

2022 ◽  
Vol 19 (2) ◽  
pp. 757
Author(s):  
Frances Sissamis ◽  
Karina Villalba ◽  
Jordan Garcia ◽  
Vickie Melus ◽  
Emily J. Markentell ◽  
...  
Keyword(s):  
United States ◽  
Population Health ◽  
Clinical Care ◽  
Critical Role ◽  
Population Level ◽  
The United States ◽  
Added Value ◽  
Individual Level ◽  
Health Factors ◽  
Meta Regression

Religion can have a favorable impact on individual-level health. The influence of religion on population health, however, remains less clear. This study investigated the association between religion and mortality at the population-level. Using county data, a meta-regression was performed to examine between-county mortality heterogeneity. The percent heterogeneity associated with religion variables were compared to demographics (i.e., place, race, language, age, and gender) and health factors (i.e., individual behaviors, clinical care, social and economic, and physical environment) as predictors of mortality. Religion was measured in terms of adherence (i.e., prevalence attending/belonging to a congregation), congregation density, and the diversity of adherents and congregation by denominations. Results showed counties with lower mortality were associated with higher proportions of religion adherents and a greater diversity of adherents and congregations. Counties with higher mortality were associated with higher religion congregation density. Religion, as a parsimonious multivariate model with all demographic and health factor predictors, had less added value when controlled for individual variables or constructs. The direction of association between religion and mortality was consistent, even when controlling for demographics and health factors, and thus merits further consideration as a population health determinant, as it may play a critical role in understanding other population health outcomes.

Abstract 037: The Accuracy of Self-reported Blood Pressure in the Medication Adherence Improvement Support App for Engagement - Blood Pressure (MedISAFE-BP) Trial

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Kyle Morawski ◽  
Roya Ghazinouri ◽  
Alexis Krumme ◽  
Julie Lauffenburger ◽  
Jessica Lee ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Clinical Care ◽  
Population Level ◽  
Smartphone Application ◽  
Self Report ◽  
Study Cohort ◽  
Use Of Self ◽  
Hypertension Knowledge

Introduction: The use of self-reported biometric values, such as blood pressure (BP), has been proposed as an efficient strategy for monitoring clinical care, evaluating health system performance, and conducting pragmatic randomized trials. Unfortunately, there is limited evidence about whether self-reported biometric readings are accurate and, if so, whether their accuracy is predicted by readily identifiable patient characteristics. Enrollment data from the ongoing Med ication adherence I mprovement S upport A pp F or E ngagement - B lood P ressure (MedISAFE-BP) trial provide a unique opportunity to address these questions. Methods: MedISAFE-BP is a randomized clinical trial evaluating the effect of the Medisafe smartphone application on BP among subjects with poorly controlled hypertension, defined as ≥140mmHg systolic per JNC8 guidelines. Subjects were recruited through online patient communities, social media, and targeted advertisements. Subjects who indicated that their BP was poorly controlled while on medication underwent further screening. After informed consent, subjects provided baseline information including demographics, comorbidities, the number of BP medications they were currently taking, hypertension knowledge, patient activation measured by the Consumer Health Activation Index, and self-reported adherence. Subjects were then mailed a home BP cuff to verify their self-reported blood pressure. We evaluated the positive predictive value of self-reported poorly controlled hypertension using the measured BP readings. We then used multivariable logistic regression to identify predictors of having a measured BP value that was actually poorly controlled. Results: Our study cohort consisted of 1,142 individuals who self-reported as having poorly controlled BP. The positive predictive value of poorly-controlled BP by self-report was only 37%. In fact, 284 (24%) subjects had systolic BPs that were normal (systolic BP < 120 mmHg). Factors that were independently associated with accurate self-report included older age (odds ratio [OR] 1.3 per decade, 95% confidence interval [CI] 1.2-1.5), a history of prior stroke (OR 2.5, CI 1.2-5.2), diabetes mellitus (OR 1.5, CI 1.1-2.2), and a low level of activation (OR 1.63, CI 1.2-2.2). Hypertension knowledge, education, and self-reported adherence were not associated with accurately self-reporting BP. Discussion: In this cohort of individuals who reported that their BP was poorly controlled, only one-third actually had elevated BP when measured with a home BP cuff. While this discrepancy may have many underlying causes, it suggests that the use of self-reported BPs is not an accurate method of monitoring hypertension control at the population-level. Reassuringly, several factors are independently associated with accurate self-reported BPs, and thus there may be some subgroups for whom self-report can be relied upon.

scholarly journals Recomposing persons: Scavenging and storytelling in a birth cohort archive

2021 ◽  
pp. 095269512199539
Author(s):  
Penny Tinkler ◽  
Resto Cruz ◽  
Laura Fenton
Keyword(s):  
Life Course ◽  
Cohort Studies ◽  
Birth Cohort ◽  
Quantitative Data ◽  
Population Level ◽  
Relational Embeddedness ◽  
The Life Course ◽  
Over Time

Birth cohort studies can be used not only to generate population-level quantitative data, but also to recompose persons. The crux is how we understand data and persons. Recomposition entails scavenging for various (including unrecognised) data. It foregrounds the perspective and subjectivity of survey participants, but without forgetting the partiality and incompleteness of the accounts that it may generate. Although interested in the singularity of individuals, it attends to the historical and relational embeddedness of personhood. It examines the multiple and complex temporalities that suffuse people’s lives, hence departing from linear notions of the life course. It implies involvement, as well as reflexivity, on the part of researchers. It embraces the heterogeneity and transformations over time of scientific archives and the interpretive possibilities, as well as incompleteness, of birth cohort studies data. Interested in the unfolding of lives over time, it also shines light on meaningful biographical moments.

scholarly journals Family history assessment significantly enhances delivery of precision medicine in the genomics era

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yasmin Bylstra ◽  
Weng Khong Lim ◽  
Sylvia Kam ◽  
Koei Wan Tham ◽  
R. Ryanne Wu ◽  
...  
Keyword(s):  
Family History ◽  
Cancer Risk ◽  
Clinical Care ◽  
Genomic Analysis ◽  
Population Level ◽  
Cancer Genes ◽  
Family History Information ◽  
Screening Programs ◽  
Family History Assessment ◽  
Risk Of Cancer

Abstract Background Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection. Methods To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes. Results Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant. Conclusions These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs. Trial registration ClinicalTrials.gov NCT02791152. Retrospectively registered on May 31, 2016.

scholarly journals Examining the Effects of the Sacramento Dockless E-Bike Share on Bicycling and Driving

2021 ◽  
Vol 13 (1) ◽  
pp. 368
Author(s):  
Dillon T. Fitch ◽  
Hossain Mohiuddin ◽  
Susan L. Handy
Keyword(s):  
Travel Behavior ◽  
Regression Models ◽  
Population Level ◽  
Panel Survey ◽  
Multilevel Regression ◽  
Combine Data ◽  
Cross Sectional ◽  
Individual Level ◽  
Before And After ◽  
Bike Share

One way cities are looking to promote bicycling is by providing publicly or privately operated bike-share services, which enable individuals to rent bicycles for one-way trips. Although many studies have examined the use of bike-share services, little is known about how these services influence individual-level travel behavior more generally. In this study, we examine the behavior of users and non-users of a dockless, electric-assisted bike-share service in the Sacramento region of California. This service, operated by Jump until suspended due to the coronavirus pandemic, was one of the largest of its kind in the U.S., and spanned three California cities: Sacramento, West Sacramento, and Davis. We combine data from a repeat cross-sectional before-and-after survey of residents and a longitudinal panel survey of bike-share users with the goal of examining how the service influenced individual-level bicycling and driving. Results from multilevel regression models suggest that the effect of bike-share on average bicycling and driving at the population level is likely small. However, our results indicate that people who have used-bike share are likely to have increased their bicycling because of bike-share.

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