Abstract MP68: Populations with Multiple Adverse Behavioral Factors

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Thomas Eckstein ◽  
Laura Houghtaling ◽  
Mariah Quick
Keyword(s):  
Risk Factors ◽  
Strong Association ◽  
Asian Population ◽  
Behavioral Risk ◽  
Excessive Alcohol Consumption ◽  
Alaskan Native ◽  
Individual Level ◽  
Behavioral Risk Factor Surveillance ◽  
Highly Correlated ◽  
The Individual

Traditionally, population behavioral characteristics are reported at the individual level. However, just like diseases, the combination of multiple adverse risk factors, even though they are often highly correlated, presents a different challenge and impact on community health than examining these same behaviors in isolation. We analyzed 5 risk factors in the 2014 Behavioral Risk Factor Surveillance Survey; smoking (currently a smoker), inactivity (no physical activity outside of work), excessive alcohol consumption (for women, 4 or more drinks in one sitting or an average of greater than one drink per day; for men, 5 or more drinks in one sitting or an average of greater than two drinks per day), obesity (BMI >=30), and insufficient sleep (< 7 hours per night). These five measures were selected because of their strong association with heart and other disease. We studied the frequency of multiple factors by state, sex, race, urbanicity, educational attainment and income. The attached map illustrates the variation among states in the prevalence of 3 or more risk factors in the population. In Utah, Colorado, and California, less than 9% of the population has 3 or more; in Arkansas, Louisiana, Kentucky and Mississippi, 16% or more of the population has 3 or more of these risk factors. Among demographic subpopulations, differences also exist. For non-Hispanic Asian population 4.5% (CI:3.5%-5.4%) have 3 or more risk factors compared to 16% or more exhibiting 3 or more risk factors in the non-Hispanic black, non-Hispanic American Indian and Alaskan Native, and non-Hispanic multi-racial populations. The variation among these subpopulations within states was also explored. Understanding the distribution of multiple adverse risk factors within a state’s population can help guide the efforts of public health officials, policy makers, advocacy groups and others to focus on the most affected populations and develop interventions that address multiple related conditions.

Can an automated sleep detection algorithm for waist-worn accelerometry replace sleep logs?

2018 ◽  
Vol 43 (10) ◽  
pp. 1027-1032
Author(s):  
Tiago V. Barreira ◽  
Jessica G. Redmond ◽  
Tom D. Brutsaert ◽  
John M. Schuna ◽  
Emily F. Mire ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Good Evidence ◽  
Mean Difference ◽  
Absolute Difference ◽  
Individual Level ◽  
Wake Time ◽  
Highly Correlated ◽  
Mean Differences ◽  
Dependent Sample ◽  
The Individual

The purpose of this study was to test whether estimates of bedtime, wake time, and sleep period time (SPT) were comparable between an automated algorithm (ALG) applied to waist-worn accelerometry data and a sleep log (LOG) in an adult sample. A total of 104 participants were asked to log evening bedtime and morning wake time and wear an ActiGraph wGT3X-BT accelerometer at their waist for 24 h/day for 7 consecutive days. Mean difference and mean absolute difference (MAD) were computed. Pearson correlations and dependent-sample t tests were used to compare LOG-based and ALG-based sleep variables. Effect sizes were calculated for variables with significant mean differences. A total of 84 participants provided 2+ days of valid accelerometer and LOG data for a total of 368 days. There was no mean difference (p = 0.47) between LOG 472 ± 59 min and ALG SPT 475 ± 66 min (MAD = 31 ± 23 min, r = 0.81). There was no significant mean difference between bedtime (2348 h and 2353 h for LOG and ALG, respectively; p = 0.14, MAD = 25 ± 21 min, r = 0.92). However, there was a significant mean difference between LOG (0741 h) and ALG (0749 h) wake times (p = 0.01, d = 0.11, MAD = 24 ± 21 min, r = 0.92). The LOG and ALG data were highly correlated and relatively small differences were present. The significant mean difference in wake time might not be practically meaningful (Cohen’s d = 0.11), making the ALG useful for sample estimates. MAD, which gives a better estimate of the expected differences at the individual level, also demonstrated good evidence supporting ALG individual estimates.

Pre-rheumatoid arthritis

2020 ◽  
pp. 121-132
Author(s):  
Karim Raza ◽  
Catherine McGrath ◽  
Laurette van Boheemen ◽  
Dirkjan van Schaardenburg
Keyword(s):  
Rheumatoid Arthritis ◽  
Risk Factors ◽  
At Risk ◽  
Genetic Risk ◽  
Inflammatory Arthritis ◽  
Primary Site ◽  
Individual Level ◽  
Mucosal Surfaces ◽  
Risk Patients ◽  
The Individual

The typical evolution of rheumatoid arthritis (RA) is that a person, with genetic risk factors, develops autoantibodies and subclinical inflammation under relevant environmental influences. There are indications that the primary site of the pathology is at mucosal surfaces (e.g. in the gums, lungs, and/or the gut), after which the disease translocates to the joints. Preclinical RA can be defined at the phase during which no clinically apparent features are present (i.e. no symptoms of inflammatory arthritis or clinically apparent joint swelling) but during which RA related biologic derangements such as the presence of autoantibodies are present. This chapter presents an overview of the risk factors, stages, and events occurring during the pre-RA phase. A better understanding of the factors involved will enable more accurate prediction of RA at the individual level and selection of high-risk individuals for inclusion in preventive studies. Several pharmacologic and non-pharmacologic studies aiming to prevent or delay the onset of RA in at-risk individuals are currently underway. It is hoped that such interventions in the pre-RA and indeed in the preclinical-RA phases will allow us to reduce the risk of RA and prevent RA developing in at least a proportion of at-risk patients.

scholarly journals Validation of a digitally displayed photographic food portion-size estimation aid among women in urban and rural Malawi

2019 ◽  
Vol 22 (17) ◽  
pp. 3140-3150 ◽  
Author(s):  
Valerie L Flax ◽  
Chrissie Thakwalakwa ◽  
Courtney H Schnefke ◽  
Heather Stobaugh ◽  
John C Phuka ◽  
...  
Keyword(s):  
Rural Communities ◽  
Low Income ◽  
Portion Size ◽  
Size Estimation ◽  
Individual Level ◽  
Low Income Country ◽  
Urban Rural ◽  
Highly Correlated ◽  
The Individual ◽  
Portion Size Estimation

AbstractObjective:To validate digitally displayed photographic portion-size estimation aids (PSEA) against a weighed meal record and compare findings with an atlas of printed photographic PSEA and actual prepared-food PSEA in a low-income country.Design:Participants served themselves water and five prepared foods, which were weighed separately before the meal and again after the meal to measure any leftovers. Participants returned the following day and completed a meal recall. They estimated the quantities of foods consumed three times using the different PSEA in a randomized order.Setting:Two urban and two rural communities in southern Malawi.Participants:Women (n 300) aged 18–45 years, equally divided by urban/rural residence and years of education (≤4 years and ≥5 years).Results:Responses for digital and printed PSEA were highly correlated (>91 % agreement for all foods, Cohen’s κw = 0·78–0·93). Overall, at the individual level, digital and actual-food PSEA had a similar level of agreement with the weighed meal record. At the group level, the proportion of participants who estimated within 20 % of the weighed grams of food consumed ranged by type of food from 30 to 45 % for digital PSEA and 40–56 % for actual-food PSEA. Digital PSEA consistently underestimated grams and nutrients across foods, whereas actual-food PSEA provided a mix of under- and overestimates that balanced each other to produce accurate mean energy and nutrient intake estimates. Results did not differ by urban and rural location or participant education level.Conclusions:Digital PSEA require further testing in low-income settings to improve accuracy of estimations.

Evaluating Chronic Disease Surveillance Systems: Lessons Learned From Applying a Framework to Assess the Alaska Behavioral Risk Factor Surveillance System

2020 ◽  
Vol 41 (3) ◽  
pp. 420-431
Author(s):  
Katie Cueva ◽  
Andrea Fenaughty ◽  
Jessica Aulasa Liendo ◽  
Samantha Hyde-Rolland
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Lessons Learned ◽  
Behavioral Risk ◽  
Surveillance Systems ◽  
Behavioral Risk Factor ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance

Chronic diseases with behavioral risk factors are now the leading causes of death in the United States. A national Behavioral Risk Factor Surveillance System (BRFSS) monitors those risk factors; however, there is a need for national and state evaluations of chronic disease surveillance systems. The Department of Health and Human Services/Centers for Disease Control and Prevention (CDC) has developed a framework on evaluating noncommunicable disease–related surveillance systems; however, no implementation of this framework has yet been published. This article describes the process of, and offers lessons learned from, implementing the evaluation framework to assess the Alaska BRFSS. This implementation evaluation may inform assessments of other state and regional chronic disease surveillance systems and offers insight on the positive potential to consult key stakeholders to guide evaluation priorities.

Macrosystem and Women’s Fear of Crime in Intimate Relations: An Integrated Model

2019 ◽  
Vol 57 (1-2) ◽  
pp. 172-197
Author(s):  
Camila Iglesias ◽  
Carla Cardoso ◽  
Pedro Sousa
Keyword(s):  
Intimate Partner Violence ◽  
Partner Violence ◽  
Fear Of Crime ◽  
Empirical Studies ◽  
Strong Association ◽  
Integrated Approach ◽  
Intimate Partner ◽  
Outcome Variable ◽  
Individual Level ◽  
The Individual

AbstractFear of crime has been the focus of fruitful criminological investigation for the last 50 years. Simultaneously, the literature related to intimate partner violence has also been developing. Empirical studies have neglected the association between fear of crime and violence committed in private spaces, so this research seeks to provide an integrated approach to these concepts. Using the data from the first Brazilian National Victimization Survey, this research aims to explore how fear of violence in intimate relationships is associated with both individual and macro-contextual variables, in an ecological framework as proposed by Heise. Statistical analyses were conducted by aggregating data, and the analytical model integrated both individual- and macro-level variables and took the fear of intimate partner violence as the outcome variable. The results demonstrate a strong association between the fear of intimate partner violence and the individual-level predictors tested, especially previous victimization as well as social inequality (Gini coefficient). This way, results indicate that fear of crime manifests its predictors far beyond what the dyad “victim–aggressor” may suggest.

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