Community socioeconomic deprivation and obesity trajectories in children using electronic health records

Community profiles in northeastern and central Pennsylvania characterized by distinct social, natural, food, and physical activity environments and their relation to type 2 diabetes

Background

Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities' multidimensional nature.

Methods

In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status.

Results

We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: "sparse rural," "developed rural," "inner suburb," and "deprived urban core." Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with "developed rural," T2D onset odds were higher in "deprived urban core" (1.24, CI = 1.16-1.33) and "inner suburb" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status.

Conclusions

Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.

Associations between obstructive sleep apnea and sleep characteristics with chronic kidney disease in rural Pennsylvania

STUDY OBJECTIVES

To examine the association between moderate-severe obstructive sleep apnea (msOSA) and sleep characteristics with chronic kidney disease (CKD) in a population of rural and urban adults in Pennsylvania.

METHODS

A cross-sectional study of 23,643 adults who underwent polysomnography (PSG) at a rural healthcare system in Pennsylvania between 2009 and 2019. Serum creatinine was abstracted from electronic health records to calculate estimated glomerular filtration rate (eGFR). CKD was defined as an eGFR <60 mL/min/1.73 m2. msOSA was defined as an apnea-hypoxia index (AHI) ≥15 events/hour. Poisson regression was performed to estimate the prevalence ratio (PR) of CKD for various sleep measures while adjusting for age, sex, race, smoking (never, former, current), body mass index, diabetes, and hypertension at time of PSG.

RESULTS

In this clinically-referred sample comprised of over one-third (35 %) rural individuals, the prevalence of CKD and msOSA was 9.4 % and 32.1 %, respectively. Patients with CKD had more severe OSA based on AHI and intermittent hypoxia profile and presented worse sleep quality across all studied measures. Having OSA was associated with a 13 % higher prevalence of CKD (95%CI: 1.04, 1.22). In addition, for every 5 % increment in sleep efficiency, the prevalence of CKD was 3 % lower (PR = 0.97, 95%CI: 0.96, 0.98). Significant associations that were in the expected direction were observed across most sleep characteristics in adjusted models.

CONCLUSIONS

Moderate-severe OSA, nocturnal hypoxemia, and disruptions to normal sleep duration, continuity, and architecture are associated with increased CKD prevalence in Pennsylvania adults. Management of OSA and/or sleep disturbances may be an opportunity to improve CKD outcomes. The unique health disparities among vulnerable rural populations are deserving of future study.

Associations of Longitudinal BMI-Percentile Classification Patterns in Early Childhood with Neighborhood-Level Social Determinants of Health

Background: Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable. Methods: We extracted electronic health record data from 2012 to 2019 for a children's health system that includes two hospitals and wide network of outpatient clinics spanning five East Coast states in the United States. Using data-driven and algorithmic clustering, we have identified distinct BMI-percentile classification groups in children from 0 to 7 years of age. We used two separate algorithmic clustering methods to confirm the robustness of the identified clusters. We used multinomial logistic regression to examine the associations between clusters and 27 neighborhood SDOHs and compared positive and negative SDOH characteristics separately. Results: From the cohort of 36,910 children, five BMI-percentile classification groups emerged: always having obesity (n = 429; 1.16%), overweight most of the time (n = 15,006; 40.65%), increasing BMI percentile (n = 9,060; 24.54%), decreasing BMI percentile (n = 5,058; 13.70%), and always normal weight (n = 7,357; 19.89%). Compared to children in the decreasing BMI percentile and always normal weight groups, children in the other three groups were more likely to live in neighborhoods with higher poverty, unemployment, crowded households, single-parent households, and lower preschool enrollment. Conclusions: Neighborhood-level SDOH factors have significant associations with children's BMI-percentile classification and changes in classification. This highlights the need to develop tailored obesity interventions for different groups to address the barriers faced by communities that can impact the weight and health of children living within them.

Prospective associations between neighborhood features and body mass index in Montreal adolescents

PURPOSE

To investigate the association between the neighborhood built environment and trajectories of body mass index (BMI) in youth.

METHODS

Data were collected in a prospective study of 1293 adolescents in Montreal. Built environment variables were obtained from public databases for road networks, land use, and the Canadian Census. Anthropometric data were collected when participants were ages 12.5, 15 and 17 years. We undertook hierarchical cluster analysis to identify contrasting neighborhood types based on features of the built environment (e.g., vegetation, population density, walkability). Associations between neighborhood type and trajectories of BMI z-score (BMIz) were estimated using multivariable linear mixed regression analyses, stratified by sex.

RESULTS

We identified three neighborhood types: Urban, Suburban, and Village. In contrast to the Urban type, the Suburban type was characterized by more vegetation, few services and low population density. Village and Suburban types were similar, but the former had greater land use diversity, population density with more parks and a denser food environment. Among girls, living in Urban types was associated with decreasing BMIz trajectories. Living in Village types was associated with increasing BMIz trajectories. No associations were observed among boys.

CONCLUSIONS

Neighborhoods characterized by greater opportunities for active living appear to be less obesogenic, particularly among girls.

Associations of longitudinal BMI percentile classification patterns in early childhood with neighborhood-level social determinants of health

Background

Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable.

Methods

We extracted EHR data from 2012-2019 for a children's health system that includes 2 hospitals and wide network of outpatient clinics spanning 5 East Coast states in the US. Using data-driven and algorithmic clustering, we have identified distinct BMI-percentile classification groups in children from 0 to 7 years of age. We used two separate algorithmic clustering methods to confirm the robustness of the identified clusters. We used multinomial logistic regression to examine the associations between clusters and 27 neighborhood SDOHs and compared positive and negative SDOH characteristics separately.

Results

From the cohort of 36,910 children, five BMI-percentile classification groups emerged: always having obesity (n=429; 1.16%), overweight most of the time (n=15,006; 40.65%), increasing BMI-percentile (n=9,060; 24.54%), decreasing BMI-percentile (n=5,058; 13.70%), and always normal weight (n=7,357; 19.89%). Compared to children in the decreasing BMI-percentile and always normal weight groups, children in the other three groups were more likely to live in neighborhoods with higher poverty, unemployment, crowded households, single-parent households, and lower preschool enrollment.

Conclusions

Neighborhood-level SDOH factors have significant associations with children's BMI-percentile classification and changes in classification. This highlights the need to develop tailored obesity interventions for different groups to address the barriers faced by communities that can impact the weight and health of children living within them.

Impact Statement

This study demonstrates the association between longitudinal BMI-percentile patterns and SDOH in early childhood. Five distinct clusters with different BMI-percentile trajectories are found and a strong association between these clusters and SDOH is observed. Our findings highlight the importance of targeted prevention and treatment interventions based on children's SDOH.

Childhood Obesity and Early Body Mass Index Gains Associated with COVID-19 in a Large Rural Health System

OBJECTIVE

To evaluate body mass index (BMI) change among a population of children with a high proportion residing in rural areas across two pandemic time periods.

METHODS

Electronic health records were evaluated in a rural health system.

INCLUSION CRITERIA

2-17 years at initial BMI; >2 BMIs during pre-pandemic (January 1, 2018-February 29, 2020); >1 BMI in early pandemic (June 1, 2020-December 31, 2020); and >1 BMI in later pandemic (January 1, 2021-December 31, 2021). Mixed effects linear regression models were used to estimate average monthly rate of change in BMI slope (∆BMI) from pre-pandemic to pandemic and test for effect modification of sex, race/ethnicity, age, BMI, public insurance, and rural address.

RESULTS

Among the 40,627 participants, 50.2% were female, 84.6% were non-Hispanic white, 34.9% used public insurance, and 42.5% resided in rural areas. The pre-pandemic proportion of children with overweight, obesity, and severe obesity was 15.6%, 12.8%, and 6.3%, respectively. The ∆BMI nearly doubled during the early pandemic period compared with the pre-pandemic period (0.102 vs 0.055 kg/m2), however, ∆BMI in the later pandemic was lower (0.040 vs 0.055 kg/m2). ∆BMI remained higher in the later pandemic for all race categories compared to Non-Hispanic white. Children with public insurance had higher ∆BMI compared to those with private insurance that remained higher in the later pandemic (0.051 vs 0.035 kg/m2). There was no significant difference between ∆BMI for rural and urban children during pandemic periods.

CONCLUSIONS

Despite the decreased ∆BMI among children in the later pandemic, prevalence of obesity and severe obesity remain high. Efforts must continue to be made to limit excess weight gain during childhood and to assess the impact of forces like structural and social factors in both etiology and prevention.

Change in neighborhood socioeconomic status and childhood weight status and body composition from birth to adolescence

BACKGROUND

We aim to assess the associations between the change in neighborhood socioeconomic score (SES) between birth and 6 years and childhood weight status and body composition from 6 to 13 years.

METHODS

Data for 3909 children from the Generation R Study, a prospective population-based cohort in the Netherlands were analyzed. The change in neighborhood SES between birth and 6 years was defined as static-high, static-middle, static-low, upward, and downward mobility. Child body mass index (BMI), overweight and obesity (OWOB), fat mass index (FMI) and lean mass index (LMI) were measured at age 6, 10, and 13 years. The associations were explored using generalized estimating equations. The effect modification by child sex was examined.

RESULTS

In total, 19.5% and 18.1% of children were allocated to the upward mobility and downward mobility neighborhood SES group. The associations between the change in neighborhood SES and child weight status and body composition were moderated by child sex (p < 0.05). Compared to girls in the static-high group, girls in the static-low group had relatively higher BMI-SDS (β, 95% confidence interval (CI): 0.24, 0.09-0.40) and higher risk of OWOB (RR, 95% CI: 1.98, 1.35-2.91), together with higher FMI-SDS (β, 95% CI: 0.27, 0.14-0.41) and LMI-SDS (β, 95% CI: 0.18, 0.03-0.33). The associations in boys were not significant.

CONCLUSIONS

An increased BMI and fat mass, and higher risk of OWOB from 6 to 13 years were evident in girls living in a low-SES neighborhood or moving downward from a high- to a low-SES neighborhood. Support for children and families from low-SES neighborhoods is warranted.

Where a child lives matters: neighborhood deprivation and pediatric obesity

PURPOSE OF REVIEW

This article outlines what is currently known regarding the relationship between neighborhood deprivation and pediatric obesity. It discusses the intersectionality between neighborhood deprivation, race, ethnicity, and pediatric obesity. We conclude by proposing several potential solutions to disparities in pediatric obesity related to neighborhood deprivation.

RECENT FINDINGS

Neighborhood deprivation, independent of individual socioeconomic status, is a risk factor for pediatric obesity. The obesogenic characteristics of high deprivation neighborhoods (e.g., lack of safe spaces to be active, easy access to fast food) and the psychological aspects of residing within high deprivation neighborhoods may also contribute to this risk. Intervention strategies and policies designed to address neighborhood related risk for pediatric obesity are needed.

SUMMARY

Pediatric obesity is a growing problem of complex etiology. Neighborhood risk factors should be considered when assessing risk burden and when designing intervention strategies.

The Effectiveness of a Healthy Lifestyle in Obese Pediatric Patients: A Systematic Review and Meta-Analysis

Child and adolescent obesity represents a significant and escalating health concern in the United States. Notably, Hispanic adolescents face a higher prevalence of obesity and an increased risk of cardiovascular disease compared to their peers from different racial and ethnic backgrounds. This was obtained through systematic investigations in which different approaches were used. Therefore, obesity interventions of long duration, at least one year, and with a beginning phase intensive enough to produce significant early weight loss may be needed for adolescents with obesity. Surprisingly, despite this elevated risk, there is a glaring underrepresentation of Hispanics in obesity intervention studies aimed at youth. It is therefore imperative to develop interventions tailored specifically to overweight adolescents, with a particular focus on the Hispanic population. While researchers have addressed numerous interventions targeting adolescent obesity, many of these initiatives have demonstrated limited treatment efficacy, failed to achieve all desired treatment objectives, experienced high attrition rates, and encountered waning participant engagement. To evaluate the impact of adopting a healthy lifestyle among pediatric patients struggling with obesity, we undertook a comprehensive systematic review of the literature, and with the information obtained from the articles chosen, we will undergo a meta-analysis. Our review encompassed a 10-year span of published literature, drawing upon online databases including the Cochrane Library, PubMed, Web of Science, PubMed Central, and Google Scholar. Our review exclusively considered randomized controlled trials that focused on the effectiveness of various lifestyle modifications for pediatric patients grappling with obesity. We synthesized the pooled incidence, risk ratio, and associated 95% confidence intervals to gauge the efficacy of these interventions, employing the fixed-effect model to account for potential between-study variations rather than the random-effect model. After the calculation of each one of the studies selected, we could conclude that it gave good outcomes after the modification of lifestyle in these patients, giving a statistical significance and p-value in our three representative figures of <0.001.

Longitudinal exposure to neighborhood poverty and obesity risk in emerging adulthood

This study uses data from the longitudinal Panel Study of Income Dynamics data and its Transition to Adulthood (TA) Study (2005-2017), in conjunction with decades of neighborhood-level data from the U.S. decennial census and American Community Survey, to examine the relationship between individuals' neighborhood poverty exposure trajectories in childhood and the likelihood of obesity in emerging adulthood. Latent growth mixture models reveal that exposure to neighborhood poverty differs considerably for white and nonwhite individuals over their childhood life course. Durable exposure to neighborhood poverty confers greater subsequent obesity risks in emerging adulthood than transitory experiences of neighborhood poverty. Racial differences in the changing and persistent trajectories of neighborhood poverty help explain part of the racial differences in obesity risks. Among nonwhites, and compared to consistent nonpoor neighborhood conditions, both durable and transitory neighborhood poverty exposures are significantly associated with higher obesity risks. This study suggests that a theoretical framework that integrates key elements of the life-course perspective is helpful to uncover the individual and structural pathways through which neighborhood histories in poverty shape population health in general.

Longitudinal association between density of retail food stores and body mass index in Mexican school children and adolescents

BACKGROUND

Obesity is rapidly increasing in Mexican children and adolescents, while food environments are rapidly changing. We evaluated the association between changes in retail food stores and change in body mass index (BMI) in Mexican children and adolescents.

METHODS

Data on 7507 participants aged 5-19 years old came from the Mexican Family Life Survey 2002-2012. Density of food stores at the municipal-level (number of food stores/area in km²) came from the Economic Censuses of 1999, 2004 and 2009. We categorized food stores as small food retail (small neighborhood stores, tiendas de abarrotes in Mexico), specialty foods, fruit/vegetables, convenience foods, and supermarkets. Associations between change in food stores and change in BMI were estimated using five longitudinal linear fixed-effects regression models (one per type of food store) adjusted for age, parental education, municipal-level socioeconomic deprivation and population density. Density of each food store type was operationalized as quartiles. Analyses were stratified by urbanization.

RESULTS

There was an inverse dose-response association between increases in fruit/vegetable store density and BMI (β = -0.455 kg/m², β = -0.733 kg/m², and β = -0.838 kg/m² in the second, third, and fourth quartile). In non-urban areas, children living in municipalities with the highest density of small food retail stores experienced a reduction in BMI (β = -0.840 kg/m²). In urban areas, there was an inverse association between specialty food stores with BMI (β = -0.789 kg/m² in third quartile, and β = -1.204 kg/m² in fourth quartile). We observed dynamic associations with age; results suggested stronger associations in adolescents.

CONCLUSIONS

The availability of fruit/vegetable stores may influence a reduction in children and adolescents BMI. These results indicate that policy approaches could be tailored by type of food store - with some consideration for level of urbanization and children's age.

Area Deprivation Index is Associated with Variation in Quality of Life and Psychosocial Well-being Following Breast Cancer Surgery

BACKGROUND

Neighborhood-level factors have been shown to influence surgical outcomes through material deprivation, psychosocial mechanisms, health behaviors, and access to resources. To date, no study has examined the relationship between area-level deprivation (ADI) and post-mastectomy outcomes.

METHODS

A cross-sectional survey of adult female breast cancer patients who underwent lumpectomy or mastectomy between January 2018 to June 2019 was carried out. Patient-specific characteristics and ADI information were abstracted and correlated with postoperative global- (SF-12) and condition-specific (BREAST-Q) quality-of-life performance via multivariable regression. Patients were classified into three ADI terciles: 0-39 (low deprivation), 40-59 (moderate deprivation), and 60-100 (high deprivation).

RESULTS

A total of 564 consecutive patients were identified, being mostly white (75%) with mean age of 60.2 ± 12.4 years, median body mass index of 27.8 [interquartile range (IQR) 24.3-32.2) kg/m², median Charlson Comorbidity Index of 3 (IQR 2-5), and mean ADI of 42.3 ± 25.7. African American and Hispanic patients and those with high BMI were more likely to reside in highly deprived neighborhoods (p = 0.003 and p < 0.001). In adjusted models, patients in highly deprived neighborhoods had significantly lower mean SF-12 physical (44.9 [95% CI, 43.8-46.0] versus 44.9 [95% CI, 43.7-46.1] versus 46.3 [95% CI, 45.3-47.3], p = 0.03) and BREAST-Q psychosocial well-being scores (63.5 [95% CI, 59.32-67.8] versus 69.3 [95% CI, 65.1-73.6] versus 69.7 [95% CI, 66.4-73.1], p = 0.01) relative to moderate- and low-deprivation groups.

CONCLUSIONS

Patients residing in the most deprived neighborhoods were identified to have worse psychological well-being and quality-of-life. The ADI should be incorporated into the shared decision-making process and perioperative counseling to engender value-based and personalized care, especially for vulnerable populations.

Comparing enhancements to well-child visits in the prevention of obesity: ENCIRCLE cluster-randomized controlled trial

BACKGROUND

Obesity disproportionally impacts rural, lower-income children in the United States. Primary care providers are well-positioned to engage parents in early obesity prevention, yet there is a lack of evidence regarding the most effective care delivery models. The ENCIRCLE study, a pragmatic cluster-randomized controlled trial, will respond to this gap by testing the comparative effectiveness of standard care well-child visits (WCV) versus two enhancements: adding a patient-reported outcome (PRO) measure (PRO WCV) and PRO WCV plus Food Care (telehealth coaching and a grocery store tour).

METHODS

A total of 2,025 parents and their preschool-aged children (20-60 months of age) will be recruited from 24 Geisinger primary care clinics, where providers are randomized to the standard WCV, PRO WCV, or PRO WCV plus Food Care intervention arms. The PRO WCV includes the standard WCV plus collection of the PRO-the Family Nutrition and Physical Activity (FNPA) risk assessment-from parents. Parents complete the PRO in the patient-portal or in the clinic (own device, tablet, or kiosk), receive real-time feedback, and select priority topics to discuss with the provider. These results are integrated into the child's electronic health record to inform personalized preventive counseling by providers. PRO WCV plus Food Care includes referrals to community health professionals who deliver evidence-based obesity prevention and food resource management interventions via telehealth following the WCV. The primary study outcome is change in child body mass index z-score (BMIz), based on the World Health Organization growth standards, 12 months post-baseline WCV. Additional outcomes include percent of children with overweight and obesity, raw BMI, BMI50, BMIz extended, parent involvement in counseling, health behaviors, food resource management, and implementation process measures.

DISCUSSION

Study findings will inform health care systems' choices about effective care delivery models to prevent childhood obesity among a high-risk population. Additionally, dissemination will be informed by an evaluation of mediating, moderating, and implementation factors.

TRIAL REGISTRATION

ClinicalTrials.gov identifier (NCT04406441); Registered May 28, 2020.

Neighborhood Food Environment and Children's BMI: A New Framework with Structural Equation Modeling

The relationship between neighborhood food environment and childhood obesity is complex and not yet well defined by current research in China, especially when considering the integrated effects with other relative factors. The main purpose of this article is to introduce a framework of children's weight status, based on their neighborhood food environment, and to identify the impact of food environment on the children's BMI and potential pathways. The participants of this cross-sectional study were students aged 8-16.5 years old and their parents. Two conceptual frameworks were tested using the structural equation modeling method, and two models were extracted. Model B added the neighborhood food environment based on model A. By comparing the two models, the neighborhood environment was potentially correlated with the children's BMI directly and may have a positive impact on unhealthy-food eating behaviors, which were positively associated with the children's BMI. The results suggest that the focus should be placed on the integrated effects of the potential risk factors of childhood obesity, based on considering the neighborhood food environment, which may relate to children's unhealthy-food eating behaviors and weight status.

Evaluation of General Health Status of Persons Living in Socio-Economically Disadvantaged Neighborhoods in a Large European Metropolitan City

Background: Living in socio-economically disadvantaged neighborhoods can predispose persons to numerous health conditions. The purpose of this study was to report the general health conditions of persons living in disadvantaged neighborhoods in Rome, Italy, a large European metropolitan city. Participants were reached through the mobile facilities of the primary care services of the Dicastery for the Charity Services, Vatican City. Methods: People living in disadvantaged neighborhoods were reached with mobile medical units by doctors, nurses, and paramedics. Demographic characteristics, degree of social integration, housing conditions, and history of smoking and/or alcohol use were investigated. Unstructured interviews and general health assessments were performed to investigate common acute and/or chronic diseases, and history of positivity to COVID-19. Basic health parameters were measured; data were collected and analyzed. Results: Over a 10-month period, 436 individuals aged 18–95 years were enrolled in the study. Most lived in dormitories, whereas a few lived in unsheltered settings. Most participants (76%) were unemployed. Smoking and drinking habits were comparable to the general population. The most common pathological conditions were cardiovascular diseases in 103 subjects (23.39%), diabetes in 65 (14.9%), followed by musculoskeletal system disorders (11.7%), eye diseases (10.5%), psychiatric conditions such as anxiety and depression (9.2%), and chronic respiratory conditions (8.7%). Conclusions: Subjects in our sample showed several pathologic conditions that may be related to their living conditions, thus encouraging the development of more efficient and effective strategies for a population-tailored diagnosis and treatment.

Maternal Morbidity Predicted by an Intersectional Social Determinants of Health Phenotype: A Secondary Analysis of the NuMoM2b Dataset

Maternal race, ethnicity and socio-economic position are known to be associated with increased risk for a range of poor pregnancy outcomes, including maternal morbidity and mortality. Previously, researchers seeking to identify the contributing factors focused on maternal behaviors and pregnancy complications. Less understood is the contribution of the social determinants of health (SDoH) in observed differences by race/ethnicity in these key outcomes. In this secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset, latent mixture modeling was used to construct groups of healthy, nulliparous participants with a non-anomalous fetus in a cephalic presentation having a trial of labor (N = 5763) based on SDoH variables. The primary outcome was a composite score of postpartum maternal morbidity. A postpartum maternal morbidity event was experienced by 350 individuals (6.1%). Latent class analysis using SDoH variables revealed six groups of participants, with postpartum maternal morbidity rates ranging from 8.7% to 4.5% across groups (p < 0.001). Two SDoH groups had the highest odds for maternal morbidity. These higher-risk groups were comprised of participants with the lowest income and highest stress and those who had lived in the USA for the shortest periods of time. SDoH phenotype predicted MM outcomes and identified two important, yet distinct groups of pregnant people who were the most likely have a maternal morbidity event.

Investigating socioeconomic inequalities in BMI growth rates during childhood and adolescence

BACKGROUND

Many countries report socioeconomic inequalities in childhood obesity, but when they develop is not well-characterised. Studies rarely isolate BMI growth rates from overall BMI, perhaps overlooking an important precursor to the observed inequalities in obesity. The objective of this study was to determine the age at which inequalities in BMI growth rates develop in children and whether they are similar across the BMI spectrum.

METHODS

Using the Longitudinal Study of Australian Children (n = 9024), a cohort study, we measured socioeconomic inequalities in annual BMI growth from age 2 to 17 years by age, sex and weight status. We fit a linear model using generalised estimating equations (GEE) to estimate simultaneously the effects of age and weight status on inequalities in BMI growth rate.

RESULTS

The slope (SII) and relative (RII) indexes of inequality for annual BMI growth were greatest in middle childhood (age 4-11 years) (SII 0.25, RII 1.83 (boys) 1.78 (girls)) and were moderate during adolescence (age 10-17 years) (SII 0.11, RII 1.16 [boys] 1.15 [girls]). In early childhood, there was little evidence of inequality in annual BMI growth except in children with obesity. In middle childhood and adolescence, inequalities were greater at higher weight status. The GEE indicated that both weight status (P < 0.001) and age period (P < 0.001) affected inequalities in BMI growth rates.

CONCLUSIONS

Inequalities in annual BMI growth were strongest in middle childhood, and widest in children at the upper end of the BMI spectrum. This could signify a key age bracket to intervene clinically and at a public health level and improve inequalities in childhood obesity.

Disparities by household income and race/ethnicity: the utility of BMI for surveilling excess adiposity in children

Objectives: Low-income children (6-19 years) are at higher risk for BMI-determined overweight and obesity, but this relationship varies by children's race/ethnicity. BMI, however, is a poor marker of excess adiposity in minority children. The objective of this study was to determine if the relationships of income and/or race/ethnicity with weight status was consistent between BMI-determined overweight or obesity and adiposity measured via dual energy X-ray absorptiometry (DXA).Design: This study included a nationally representative sample of U.S. children (N = 9857, 14.0 years, 52.8% male, 31.8% low-income, 52.1% middle-income). Disparities in household income-to-poverty ratio (low-income = 0.00-1.00, middle-income = 1.01-4.00, high-income > 4.00) was the exposure with prevalence of BMI-determined overweight or obesity (i.e. age/sex specific CDC cutoffs) and DXA-determined excess adiposity (i.e. body fat%≥75th percentile) as the outcome.Results: For DXA, children from high-income households were 0.47 (95CI = 0.35, 0.65) and 0.55 (95CI = 0.44, 0.70) times as likely to have excess adiposity compared to children in middle and low-income households, respectively. Similar findings were observed with BMI-determined overweight and obesity. Stratified analyses by individual racial/ethnic groups showed children from high-income households were less likely to have excess adiposity compared to their low-income peers for White, Black, and Hispanic children. However, these relationships did not hold for BMI-determined overweight and obesity in Black and Hispanic children.Conclusions: This study revealed that the relationships between income and DXA-determined adiposity differed from the relationships between income and BMI-determined overweight and obesity for children who are Black and Hispanic. This suggests that BMI may be an inappropriate surveillance tool when exploring relationships between race/ethnicity, income, and adiposity.

Birth and death notification via mobile devices: a mixed methods systematic review

BACKGROUND

Ministries of health, donors, and other decision-makers are exploring how they can use mobile technologies to acquire accurate and timely statistics on births and deaths. These stakeholders have called for evidence-based guidance on this topic. This review was carried out to support World Health Organization (WHO) recommendations on digital interventions for health system strengthening.

OBJECTIVES

Primary objective: To assess the effects of birth notification and death notification via a mobile device, compared to standard practice. Secondary objectives: To describe the range of strategies used to implement birth and death notification via mobile devices and identify factors influencing the implementation of birth and death notification via mobile devices.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, the Global Health Library, and POPLINE (August 2, 2019). We searched two trial registries (August 2, 2019). We also searched Epistemonikos for related systematic reviews and potentially eligible primary studies (August 27, 2019). We conducted a grey literature search using mHealthevidence.org (August 15, 2017) and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies in Web of Science and Google Scholar (May 15, 2020). We searched for studies published after 2000 in any language.  SELECTION CRITERIA: For the primary objective, we included individual and cluster-randomised trials; cross-over and stepped-wedge study designs; controlled before-after studies, provided they have at least two intervention sites and two control sites; and interrupted time series studies. For the secondary objectives, we included any study design, either quantitative, qualitative, or descriptive, that aimed to describe current strategies for birth and death notification via mobile devices; or to explore factors that influence the implementation of these strategies, including studies of acceptability or feasibility. For the primary objective, we included studies that compared birth and death notification via mobile devices with standard practice. For the secondary objectives, we included studies of birth and death notification via mobile device as long as we could extract data relevant to our secondary objectives. We included studies of all cadres of healthcare providers, including lay health workers; administrative, managerial, and supervisory staff; focal individuals at the village or community level; children whose births were being notified and their parents/caregivers; and individuals whose deaths were being notified and their relatives/caregivers.

DATA COLLECTION AND ANALYSIS

For the primary objective, two authors independently screened all records, extracted data from the included studies and assessed risk of bias. For the analyses of the primary objective, we reported means and proportions, where appropriate. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess the certainty of the evidence and we prepared a 'Summary of Findings' table. For the secondary objectives, two authors screened all records, one author extracted data from the included studies and assessed methodological limitations using the WEIRD tool and a second author checked the data and assessments. We carried out a framework analysis using the Supporting the Use of Research Evidence (SURE) framework to identify themes in the data. We used the GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative research) approach to assess our confidence in the evidence and we prepared a 'Summary of Qualitative Findings' table.

MAIN RESULTS

For the primary objective, we included one study, which used a controlled before-after study design. The study was conducted in Lao People's Democratic Republic and assessed the effect of using mobile devices for birth notification on outcomes related to coverage and timeliness of Hepatitis B vaccination. However, we are uncertain of the effect of this approach on these outcomes because the certainty of this evidence was assessed as very low. The included study did not assess resource use or unintended consequences. For the primary objective, we did not identify any studies using mobile devices for death notification. For the secondary objective, we included 21 studies. All studies were conducted in low- or middle-income settings. They focussed on identification of births and deaths in rural, remote, or marginalised populations who are typically under-represented in civil registration processes or traditionally seen as having poor access to health services. The review identified several factors that could influence the implementation of birth-death notification via mobile device. These factors were tied to the health system, the person responsible for notifying, the community and families; and include: - Geographic barriers that could prevent people's access to birth-death notification and post-notification services - Access to health workers and other notifiers with enough training, supervision, support, and incentives - Monitoring systems that ensure the quality and timeliness of the birth and death data - Legal frameworks that allow births and deaths to be notified by mobile device and by different types of notifiers - Community awareness of the need to register births and deaths - Socio-cultural norms around birth and death - Government commitment - Cost to the system, to health workers and to families - Access to electricity and network connectivity, and compatibility with existing systems - Systems that protect data confidentiality We have low to moderate confidence in these findings. This was mainly because of concerns about methodological limitations and data adequacy.

AUTHORS' CONCLUSIONS

We need more, well-designed studies of the effect of birth and death notification via mobile devices and on factors that may influence its implementation.

Proximity to freshwater blue space and type 2 diabetes onset: the importance of historical and economic context

Salutogenic effects of living near aquatic areas (blue space) remain underexplored, particularly in non-coastal and non-urban areas. We evaluated associations of residential proximity to inland freshwater blue space with new onset type 2 diabetes (T2D) in central and northeast Pennsylvania, USA, using medical records to conduct a nested case-control study. T2D cases (n=15,888) were identified from diabetes diagnoses, medication orders, and laboratory test results and frequency-matched on age, sex, and encounter year to diabetes-free controls (n=79,435). We calculated distance from individual residences to the nearest lake, river, tributary, or large stream, and residence within the 100-year floodplain. Logistic regression models adjusted for community socioeconomic deprivation and other confounding variables and stratified by community type (townships [rural/suburban], boroughs [small towns], city census tracts). Compared to individuals living ≥1.25 miles from blue space, those within 0.25 miles had 8% and 17% higher odds of T2D onset in townships and boroughs, respectively. Among city residents, T2D odds were 38-39% higher for those living 0.25 to <0.75 miles from blue space. Residing within the floodplain was associated with 16% and 14% higher T2D odds in townships and boroughs. A post-hoc analysis demonstrated patterns of lower residential property values with nearer distance to the region's predominant waterbody, suggesting unmeasured confounding by socioeconomic disadvantage. This may explain our unexpected findings of higher T2D odds with closer proximity to blue space. Our findings highlight the importance of historic and economic context and interrelated factors such as flood risk and lack of waterfront development in blue space research.

Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

BACKGROUND

Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions.

OBJECTIVE

Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions.

METHODS

A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed. To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO).

RESULTS

A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable. Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low.

CONCLUSIONS

In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions. Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.

Operationalizing and Testing the Concept of a Physical Activity Desert

BACKGROUND

The prevalence of childhood obesity is higher in economically and socially deprived areas. Higher levels of physical activity reduce the risk of excessive weight gain in youth, and research has focused on environmental factors associated with children's physical activity, though the term "physical activity desert" has not come into wide use.

METHODS

This exploratory study operationalized the term "physical activity desert" and tested the hypothesis that children living in physical activity deserts would be less physically active than children who do not. A cross-sectional study design was applied with 992 fifth-grade students who had provided objectively measured physical activity data. Five of 12 possible elements of the built environment were selected as descriptors of physical activity deserts, including no commercial facilities, no parks, low play spaces, no cohesion, and the presence of incivilities.

RESULTS

Univariate and multivariate analyses showed that only the absence of parks was associated with less physical activity in children.

CONCLUSION

Children living in a "no park" zone were less active than their counterparts who lived near a park. This study contributes preliminary conceptual and operational definitions of "physical activity desert." Future studies of physical activity deserts should be undertaken in larger and more diverse samples.

Unconventional Natural Gas Development and Hospitalization for Heart Failure in Pennsylvania

BACKGROUND

Growing literature linking unconventional natural gas development (UNGD) to adverse health has implicated air pollution and stress pathways. Persons with heart failure (HF) are susceptible to these stressors.

OBJECTIVES

This study sought to evaluate associations between UNGD activity and hospitalization among HF patients, stratified by both ejection fraction (EF) status (reduced [HFrEF], preserved [HFpEF], not classifiable) and HF severity.

METHODS

We evaluated the odds of hospitalization among patients with HF seen at Geisinger from 2008 to 2015 using electronic health records. We assigned metrics of UNGD activity by phase (pad preparation, drilling, stimulation, and production) 30 days before hospitalization or a frequency-matched control selection date. We assigned phenotype status using a validated algorithm.

RESULTS

We identified 9,054 patients with HF with 5,839 hospitalizations (mean age 71.1 ± 12.7 years; 47.7% female). Comparing 4th to 1st quartiles, adjusted odds ratios (95% confidence interval) for hospitalization were 1.70 (1.35 to 2.13), 0.97 (0.75 to 1.27), 1.80 (1.35 to 2.40), and 1.62 (1.07 to 2.45) for pad preparation, drilling, stimulation, and production metrics, respectively. We did not find effect modification by HFrEF or HFpEF status. Associations of most UNGD metrics with hospitalization were stronger among those with more severe HF at baseline.

CONCLUSIONS

Three of 4 phases of UNGD activity were associated with hospitalization for HF in a large sample of patients with HF in an area of active UNGD, with similar findings by HFrEF versus HFpEF status. Older patients with HF seem particularly vulnerable to adverse health impacts from UNGD activity.

Neighborhoods matter; but for whom? Heterogeneity of neighborhood disadvantage on child obesity by sex

Although evidence suggests that neighborhood context, particularly socioeconomic context, influences child obesity, little is known about how these neighborhood factors may be heterogeneous rather than monolithic. Using a novel dataset comprised of the electronic medical records for over 250,000 children aged 2-17 nested within 992 neighborhoods in the greater Houston area, we assessed whether neighborhoods influenced the obesity of children differently based on sex. Results indicated that neighborhood disadvantage, assessed using a comprehensive, multidimensional, latent profile analysis-generated measure, had a strong, positive association with the odds of obesity for both boys and girls. Interactions revealed that the relationship between disadvantage and obesity was stronger for girls, relative to boys. Our findings demonstrated the complex dynamics underlying the influence of residential neighborhood context on obesity for specific subgroups of children.

Association of community types and features in a case-control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania

OBJECTIVES

To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.

DESIGN

Nested case-control study within the open dynamic cohort of health system patients.

SETTING

Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.

PARTICIPANTS AND ANALYSIS

We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.

RESULTS

Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.

CONCLUSIONS

Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.

Association of Greenness with Blood Pressure among Individuals with Type 2 Diabetes across Rural to Urban Community Types in Pennsylvania, USA

Greenness may impact blood pressure (BP), though evidence is limited among individuals with type 2 diabetes (T2D), for whom BP management is critical. We evaluated associations of residential greenness with BP among individuals with T2D in geographically diverse communities in Pennsylvania. To address variation in greenness type, we evaluated modification of associations by percent forest. We obtained systolic (SBP) and diastolic (DBP) BP measurements from medical records of 9593 individuals following diabetes diagnosis. Proximate greenness was estimated within 1250-m buffers surrounding individuals’ residences using the normalized difference vegetation index (NDVI) prior to blood pressure measurement. Percent forest was calculated using the U.S. National Land Cover Database. Linear mixed models with robust standard errors accounted for spatial clustering; models were stratified by community type (townships/boroughs/cities). In townships, the greenest communities, an interquartile range increase in NDVI was associated with reductions in SBP of 0.87 mmHg (95% CI: −1.43, −0.30) and in DBP of 0.41 mmHg (95% CI: −0.78, −0.05). No significant associations were observed in boroughs or cities. Evidence for modification by percent forest was weak. Findings suggest a threshold effect whereby high greenness may be necessary to influence BP in this population and support a slight beneficial impact of greenness on cardiovascular disease risk.

Factors Related to Non-participation in the Basque Country Colorectal Cancer Screening Programme

Background: Despite the high participation rates in the Basque Country, colorectal cancer screening programme (Spain), there is still a part of the population that has never participated. Since it is essential to ensure equal access to health services, it is necessary to identify the determinants of health and socio-economic factors related to non-participation in the screening programme. Methods: Cross sectional descriptive study including all invited population in a complete round between 2015 and the first trimester of 2017. Health risk factors available in medical records and their control have been analyzed using univariate and multivariate analyses. Results: 515,388 people were invited at the programme with a 71.9% of fecal immunochemical test participation rate. Factors that increase the risk of non-participation are: being men (OR = 1.10, 95% CI 1.09-1.12); younger than 60 (OR = 1.18, 95% CI 1.17-1.20); smoker (OR = 1.20, 95% CI 1.18-1.22); hypertensive (OR = 1.14, 95% CI 1.12-1.15) and diabetic (OR = 1.40, 95% CI 1.36-1.43); having severe comorbidity (OR = 2.09, 95% CI 2.00-2.19) and very high deprivation (OR = 1.15, 95% CI 1.12-1.17), as well as making <6 appointments to Primary Care in 3 years (OR = 2.39, 95% CI 2.33-2.45). Still, the area under the curve (AUC) indicates that there are more factors related to non-participation. Conclusions: The participation in the Basque Country colorectal cancer-screening Programme is related to some risk factors controlled by Primary Care among others. Therefore, the involvement of these professionals could improve, not only the adherence to the CRC screening, but also other health styles and preventive interventions.

Household socioeconomic status modifies the association between neighborhood SES and obesity in a nationally representative sample of first grade children in the United States

•

We investigate household SES, neighborhood SES and childhood obesity.

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Sample was nationally representative of US first graders in 2011.

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Both SES variables–household and neighborhood SES–were associated with obesity.

•

No evidence of greater prevalence of obesity due to low SES on both dimensions.

•

In low SES neighborhoods, high household SES did not mitigate obesity prevalence.

•

In low SES households, high neighborhood SES did not mitigate obesity prevalence.

Both low family socioeconomic status (SES) and low neighborhood SES have been associated with higher levels of childhood obesity. However, little is known about how these two factors operate together. The purpose of this study was to determine if the association between neighborhood SES and obesity varies across household SES. We used the first-grade round of the Early Childhood Longitudinal Study, Kindergarten Class of 2011 (ECLS-K:2011). Household SES was defined based on income, education, and occupation. Neighborhood SES was defined by the percent of households living in poverty in the child’s school district. Log-binomial regression models estimated the association between neighborhood SES and obesity and tested whether this association varied by household SES. We found the association between neighborhood SES and obesity varied significantly by household SES (p-interaction = 0.002). For children in the lowest tertile of neighborhood SES, prevalence of obesity was not statistically significantly different comparing children with low, middle or high household SES (Predicted probability (PP)_lowest 0.20 (95% CI: 0.17, 0.23), PP_middle 0.21 (95%CI: 0.18, 0.24), PP_highest 0.16 (95%CI: 0.12, 0.20)). Conversely, within the highest and the middle tertiles of neighborhood SES, children with high household SES have significantly lower prevalence of obesity compared to children with the lowest household SES (PP: 0.09 (95%CI: 0.07, 0.11) vs 0.19 (0.16, 0.21) and (PP: 0.07 (95%CI: 0.05, 0.09) vs 0.17 (0.13, 0.21) for highest vs lowest household SES in middle and high neighborhood SES, respectively). Hence, low-SES in either variable is enough to be associated with increased prevalence of obesity.

The Diabetes Location, Environmental Attributes, and Disparities Network: Protocol for Nested Case Control and Cohort Studies, Rationale, and Baseline Characteristics

BACKGROUND

Diabetes prevalence and incidence vary by neighborhood socioeconomic environment (NSEE) and geographic region in the United States. Identifying modifiable community factors driving type 2 diabetes disparities is essential to inform policy interventions that reduce the risk of type 2 diabetes.

OBJECTIVE

This paper aims to describe the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a group funded by the Centers for Disease Control and Prevention to apply harmonized epidemiologic approaches across unique and geographically expansive data to identify community factors that contribute to type 2 diabetes risk.

METHODS

The Diabetes LEAD Network is a collaboration of 3 study sites and a data coordinating center (Drexel University). The Geisinger and Johns Hopkins University study population includes 578,485 individuals receiving primary care at Geisinger, a health system serving a population representative of 37 counties in Pennsylvania. The New York University School of Medicine study population is a baseline cohort of 6,082,146 veterans who do not have diabetes and are receiving primary care through Veterans Affairs from every US county. The University of Alabama at Birmingham study population includes 11,199 participants who did not have diabetes at baseline from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a cohort study with oversampling of participants from the Stroke Belt region.

RESULTS

The Network has established a shared set of aims: evaluate mediation of the association of the NSEE with type 2 diabetes onset, evaluate effect modification of the association of NSEE with type 2 diabetes onset, assess the differential item functioning of community measures by geographic region and community type, and evaluate the impact of the spatial scale used to measure community factors. The Network has developed standardized approaches for measurement.

CONCLUSIONS

The Network will provide insight into the community factors driving geographical disparities in type 2 diabetes risk and disseminate findings to stakeholders, providing guidance on policies to ameliorate geographic disparities in type 2 diabetes in the United States.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/21377.

One-year surveillance of body mass index and cardiorespiratory fitness in UK primary school children in North West England and the impact of school deprivation level

OBJECTIVES

Cardiorespiratory fitness (CRF) is independently associated with health and academic attainment in childhood and adolescence. Yet overweight/obesity remains the focus in public health policy. Surveillance of body mass index (BMI) and CRF considering school deprivation levels is limited. Therefore, we examined this in English primary schools.

METHODS

Participants (n=409) were students (9-10 years) from 13 schools. BMI and CRF (20 m shuttle run) were measured at three time points across the academic year and a fourth after summer recess.

RESULTS

BMI z-scores significantly decreased (p=0.015) from autumn (z=0.336 (95% CI 0.212 to 0.460)) to spring (z=0.252 (95% CI 0.132 to 0.371)), and then significantly increased (p=0.010) to summer (z=0.327 (95% CI 0.207 to 0.447)). CRF significantly increased (p<0.001) from autumn (z=0.091 (95% CI -0.014 to 0.196)) to spring (z=0.492 (95% CI 0.367 to 0.616)), no change (p=0.308) into summer (z=0.411 (95% CI 0.294 to 0.528)) and a significant decrease (p<0.001) into the following autumn term (z=0.125 (95% CI 0.021 to 0.230)). BMI was unaffected by deprivation; however, pupils from the most deprived areas saw significantly greater reductions in CRF compared with pupils from affluent areas. No time, or deprivation level, by sex interactions were found.

CONCLUSION

Significant reductions in children's CRF occurred over the summer recess and were greater among children from schools in the most deprived areas. This may help inform future research into interventions targeting physical activity of schoolchildren, particularly over the summer recess.

Neighborhood Socioeconomic Deprivation Associated with Fat Mass and Weight Status in Youth

(1) Background: Few studies have examined the relationship between neighborhood socioeconomic deprivation (SED) and weight-related outcomes in youth, controlling for weight-related behaviors. Hence, the purpose of this study was to examine the association between neighborhood SED, weight status, and fat mass in a diverse sample of youth, before and after controlling for physical activity and diet. (2) Methods: The sample included 828 youth from the Transitions and Activity Changes in Kids study. Neighborhood SED was expressed as an index score at the census tract of residence. Height, weight, and body composition were measured and used to calculate fat mass index (FMI) and weight status. Moderate-to-vigorous physical activity (MVPA) and sedentary behavior (min/h) were measured via accelerometry. Diet quality was assessed via the Block Food Screener for Kids. Multilevel regression models were employed to examine these relationships. (3) Results: Neighborhood SED was significantly associated with FMI and weight status before and after controlling for MVPA, sedentary behavior, and diet. Notably, youth residing in the most deprived neighborhoods had significantly higher FMI and were 30% more likely to be overweight/obese (OR = 1.30; 95% CI = 1.03-1.65). (4) Conclusions: Greater neighborhood SED was consistently and significantly associated with higher fat mass index and increased likelihood of overweight/obesity among youth.

Distinguishing factors that influence attendance and behaviour change in family-based treatment of childhood obesity: A qualitative study

OBJECTIVES

For the effective treatment of childhood obesity, intervention attendance and behaviour change at home are both important. The purpose of this study was to qualitatively explore influences on attendance and behaviour change during a family-based intervention to treat childhood obesity in the North West of England (Getting Our Active Lifestyles Started (GOALS)).

DESIGN

Focus groups with children and parents/carers as part of a broader mixed-methods evaluation.

METHODS

Eighteen focus groups were conducted with children (n = 39, 19 boys) and parents/carers (n = 34, 5 male) to explore their experiences of GOALS after 6 weeks of attendance (/18 weeks). Data were analysed thematically to identify influences on attendance and behaviour change.

RESULTS

Initial attendance came about through targeted referral (from health care professionals and letters in school) and was influenced by motivations for a brighter future. Once at GOALS, it was the fun, non-judgemental healthy lifestyle approach that encouraged continued attendance. Factors that facilitated behaviour change included participatory learning as a family, being accountable and gradual realistic goal setting, whilst challenges focussed on fears about the intervention ending and a lack of support from non-attending significant others.

CONCLUSIONS

Factors that influence attendance and behaviour change are distinct and may be important at different stages of the family's change process. Practitioners are encouraged to tailor strategies to support both attendance and behaviour change, with a focus on whole family participation within and outside the intervention.

Neighbourhood greenspace influences on childhood obesity in Sheffield, UK

BACKGROUND

One cause of childhood obesity is a reduction in the amount of unstructured time spent outdoors, resulting in less physical activity. Greenspaces have the potential to increase children's physical activity levels, so it is desirable to understand how to create spaces that promote visitation and activity.

OBJECTIVES

We investigate the relationship between rates of obesity at ages 4 to 5 and 10 to 11 in small-area census geographies, and indicators of the neighbourhood greenspace environment, in the northern English city of Sheffield.

METHODS

To capture the environment at scales relevant to children, we test the importance of overall green cover; garden size; tree density around residential addresses; and accessibility within 300 m of any greenspace, greenspaces that meet quality criteria, and greenspaces with play facilities. We use a multimodel inference approach to improve robustness.

RESULTS

The density of trees around addresses is significant at both ages, indicating the importance of the greenspace environment in the immediate vicinity of houses. For 10 to 11 year olds, accessibility of greenspaces meeting quality criteria is also significant, highlighting that the wider environment becomes important with age and independence.

CONCLUSIONS

More attention should be given to children's requirements of greenspace when considering interventions to increase physical activity or planning new residential areas.

Prevalence of steatosis and fibrosis in young adults in the UK: a population-based study

BACKGROUND

The estimated worldwide prevalence of non-alcoholic fatty liver disease (NAFLD) in adults is 25%; however, prevalence in young adults remains unclear. We aimed to identify the prevalence of steatosis and fibrosis in young adults in a sample of participants recruited through the Avon Longitudinal Study of Parents and Children (ALSPAC), based on transient elastography and controlled attenuation parameter (CAP) score.

METHODS

In this population-based study, we invited active participants of the ALSPAC cohort to our Focus@24+ clinic at the University of Bristol (Bristol, UK) between June 5, 2015, and Oct 31, 2017, for assessment by transient elastography with FibroScan, to determine the prevalence of steatosis and fibrosis. FibroScan data were collected on histologically equivalent fibrosis stage (F0-F4) and steatosis grade (S0-S3); results with an IQR to median ratio of 30% or greater were excluded for median fibrosis results greater than 7·1 kPa, and CAP scores for steatosis were excluded if less than ten valid readings could be obtained. Results were collated with data on serology (including alanine aminotransferase, aspartate aminotransferase, and γ-glutamyl transferase) and exposures of interest: alcohol consumption (via the Alcohol Use Disorder Identification Test for Consumption [AUDIT-C] and the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for alcohol use disorder), body-mass index (BMI), waist-to-height ratio, socioeconomic status (based on predefined ALSPAC markers), and sex. We used logistic regression models to calculate odds ratios (ORs) for the effect of exposures of interest on risk of steatosis and fibrosis, after dichotomising the prevalences of fibrosis and steatosis and adjusting for covariates (excessive alcohol intake [hazardous drinking, AUDIT-C score ≥5; or harmful drinking, evidence of alcohol use disorder], social class, smoking, and BMI).

FINDINGS

10 018 active ALSPAC participants were invited to our Focus@24+ clinic, and 4021 attended (1507 men and 2514 women), with a mean age of 24·0 years (IQR 23·0-25·0). 3768 CAP scores were eligible for analysis. 780 (20·7% [95% CI 19·4-22·0]) participants had suspected steatosis (S1-S3; ≥248 dB/m), with 377 (10·0%) presenting with S3 (severe) steatosis (≥280 dB/m). A BMI in the overweight or obese range was positively associated with steatosis when adjusted for excessive alcohol consumption, social class, and smoking (overweight BMI: OR 5·17 [95% CI 4·11-6·50], p<0·0001; obese BMI: 27·27 [20·54-36·19], p<0·0001). 3600 participants had valid transient elastography results for fibrosis analysis. 96 participants (2·7% [95% CI 2·2-3·2]) had transient elastography values equivalent to suspected fibrosis (F2-F4; ≥7·9 kPa), nine of whom had values equivalent to F4 fibrosis (≥11·7 kPa). Individuals with alcohol use disorder and steatosis had an increased risk of fibrosis when adjusted for smoking and social class (4·02 [1·24-13·02]; p=0·02).

INTERPRETATION

One in five young people had steatosis and one in 40 had fibrosis around the age of 24 years. The risk of fibrosis appears to be greatest in young adults who have harmful drinking patterns and steatosis. A holistic approach to the UK obesity epidemic and excessive drinking patterns is required to prevent an increasing health-care burden of adults with advanced liver disease in later life.

FUNDING

Medical Research Council UK, Alcohol Change UK, David Telling Charitable Trust.

Family Income Gradients in Adolescent Obesity, Overweight and Adiposity Persist in Extremely Deprived and Extremely Affluent Neighbourhoods but Not in Middle-Class Neighbourhoods: Evidence from the UK Millennium Cohort Study

We investigated whether family income gradients in obesity, overweight, and adiposity persist at geographic-level deprivation quintiles using a nationally representative cohort of UK adolescents. Data from 11,714 eligible adolescents from the sixth sweep of the Millennium Cohort Study (14 years old) were analysed in this study. The International Obesity Task Force age- and sex-specific thresholds were used to define obesity and overweight. Self-reported family income was standardized using the Organisation for Economic Co-operation and Development (OECD)'s equivalised income scale. Geographic-level deprivation was defined by the index of multiple deprivation 2004. Results showed that the prevalence of obesity and overweight was 8.0% and 27.2%, respectively. Mean percentage body fat was 16.9% (standard error, SE = 0.2%) in male and 27.3% (SE = 0.1%) in female adolescents. Risk of obesity, overweight, and adiposity increased with decreasing family income quintiles (p for trend <0.001). After stratifying by geographic-level deprivation quintiles, a U-shaped association emerged, whereby family income gradients in the risk of adolescent obesity and adiposity persisted in extremely affluent and extremely deprived neighbourhoods but attenuated to non-significance in middle-class neighbourhoods. These results focus on the findings from England. Recognition of the persistence of inequalities in the risk of obesity in the most deprived and affluent neighbourhoods may be necessary in planning public health resources and interventions.

Peridomestic and community-wide landscape risk factors for Lyme disease across a range of community contexts in Pennsylvania

Land use and forest fragmentation are thought to be major drivers of Lyme disease incidence and its geographic distribution. We examined the association between landscape composition and configuration and Lyme disease in a population-based case control study in the Geisinger health system in Pennsylvania. Lyme disease cases (n = 9657) were identified using a combination of diagnosis codes, laboratory codes, and antibiotic orders from electronic health records (EHRs). Controls (5:1) were randomly selected and frequency matched on year, age, and sex. We measured six landscape variables based on prior literature, derived from the National Land Cover Database and MODIS satellite imagery: greenness (normalized difference vegetation index), percent forest, percent herbaceous, forest edge density, percent forest-herbaceous edge, and mean forest patch size. We assigned landscape variables within two spatial contexts (community and ½-mile [805 m] Euclidian residential buffer). In models stratified by community type, landscape variables were modeled as tertiles and flexible splines and associations were adjusted for demographic and clinical covariates. In general, we observed positive associations between landscape metrics and Lyme disease, except for percent herbaceous, where associations differed by community type. For example, compared to the lowest tertile, individuals with highest tertile of greenness in residential buffers had higher odds of Lyme disease (odds ratio: 95% confidence interval [CI]) in townships (1.73: 1.55, 1.93), boroughs (1.70: 1.40, 2.07), and cities (3.71: 1.74, 7.92). Similarly, corresponding odds ratios (95% CI) for forest edge density were 1.34 (1.22, 1.47), 1.56 (1.33, 1.82), and 1.90 (1.13, 3.18). Associations were generally higher in residential buffers, compared to community, and in cities, compared to boroughs or townships. Our results reinforce the importance of peridomestic landscape in Lyme disease risk, particularly measures that reflect human interaction with tick habitat. Linkage of EHR data to public data on residential and community context may lead to new health system-based approaches for improving Lyme disease diagnosis, treatment, and prevention.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

The Association Between Neighborhood Socioeconomic Deprivation, Cardiorespiratory Fitness, and Physical Activity in US Youth

BACKGROUND

Growing evidence suggests that the broader neighborhood socioeconomic environment is independently associated with cardiometabolic health. However, few studies have examined this relationship among younger populations.

PURPOSE

The purpose of the study was to (1) investigate the association between neighborhood socioeconomic deprivation (SED) and cardiorespiratory fitness and (2) determine the extent to which physical activity mediates this relationship.

METHODS

Data from 312 youth (aged 12-15 y) were obtained from the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey. Cardiorespiratory fitness was measured using a standard submaximal treadmill test, and maximal oxygen consumption was estimated. Physical activity was self-reported time spent in moderate to vigorous activity. Neighborhood SED was measured by a composite index score at the census tract of residence. Logistic regression analyses examined relationships between neighborhood SED, physical activity, and cardiorespiratory fitness, adjusting for individual-level characteristics and the complex sampling design.

RESULTS

Neighborhood SED was not significantly associated with cardiorespiratory fitness or physical activity among youth in the study sample.

CONCLUSIONS

While not significant, cardiorespiratory fitness levels were observed to decrease as neighborhood SED increased. Future research is needed to better understand this relationship and to identify underlying mechanisms beyond fitness or physical activity that may drive the relationship between neighborhood SED and health.

Associations of multidimensional socioeconomic and built environment factors with body mass index trajectories among youth in geographically heterogeneous communities

Understanding contextual influences on obesity requires comparison of heterogeneous communities and concurrent assessment of multiple contextual domains. We used a theoretically-based measurement model to assess multidimensional socioeconomic and built environment factors theorized to influence childhood obesity across a diverse geography ranging from rural to urban. Confirmatory factor analysis specified four factors-community socioeconomic deprivation (CSED), food outlet abundance (FOOD), fitness and recreational assets (FIT), and utilitarian physical activity favorability (UTIL)-which were assigned to communities (townships, boroughs, city census tracts) in 37 Pennsylvania counties. Using electronic health records from 2001 to 2012 from 163,820 youth aged 3-18 years from 1288 communities, we conducted multilevel linear regression analyses with factor quartiles and their cross products with age, age2, and age3 to test whether community factors impacted body mass index (BMI) growth trajectories. Models controlled for sex, age, race/ethnicity, and Medical Assistance. Factor scores were lowest in townships, indicating less deprivation, fewer food and physical activity outlets, and lower utilitarian physical activity favorability. BMI at average age was lower in townships versus boroughs (beta [SE]) (0.217 [0.027], P < 0.001) and cities (0.378 [0.036], P < 0.001), as was BMI growth over time. Factor distributions across community types lacked overlap, requiring stratified analyses to avoid extrapolation. In townships, FOOD, UTIL, and FIT were inversely associated with BMI trajectories. Across community types, youth in the lowest (versus higher) CSED quartiles had lower BMI at average age and slower BMI growth, signifying the importance of community deprivation to the obesogenicity of environments.

Socioeconomic Differences and the Potential Role of Tribes in Young People's Food and Drink Purchasing Outside School at Lunchtime

Socioeconomic deprivation has been linked to food consumption practices, but studies investigating the food environment around schools provide mixed findings. Peer influence and marketing cues are considered important influencers of young people's behaviors. This study used a tribal theory lens to investigate the factors affecting pupils' purchasing and consumption of food/drinks outside schools at lunchtime. A survey was conducted with 243 pupils from seven UK secondary schools of differing socioeconomic status (SES). A purchasing recall questionnaire (PRQ) was developed and administered online at the participating schools to capture food and drink purchasing, intake, and expenditure. No significant differences were found in terms of energy and nutrients consumed or food/drink expenditure between pupils from schools of lower and higher SES. Enjoyment of food shopping with friends was linked with higher food energy intake and spend. Higher susceptibility to peer influence was associated with greater influence from food advertising and endorsements. Without ignoring the impact that SES can have on young people's food choices, we suggest that tribal theory can be additionally used to understand pupils' eating behaviors and we present implications for social marketers and policy makers.

Feasibility of enhancing well-child visits with family nutrition and physical activity risk assessment on body mass index

OBJECTIVE

Integration of behavioural risk assessment into well-child visits is recommended by clinical guidelines, but its feasibility and impact is unknown.

METHODS

A quasi-experimental study evaluated the feasibility and effectiveness of risk assessment on body mass index (BMI) at 1-year follow-up. Children with assessments (intervention) were compared with those who did not complete assessments (non-respondent) and those who received standard care (non-exposed).

RESULTS

Analyses included 10,647 children aged 2-9 years (2,724 intervention, 3,324 non-respondent and 4,599 non-exposed). Forty-five per cent of parents completed the assessments. Intervention and non-respondent groups differed in change in BMI z-score at 1 year by -0.05 (confidence interval [CI]: -0.08, -0.02; P = 0.0013); no difference was observed with non-exposed children. The intervention group had a smaller increase in BMI z-score (0.07 ± 0.63) than non-respondent group (0.13 ± 0.63). For children with normal weight at baseline, intervention versus non-respondent groups differed in BMI z-score change by -0.06 (CI: -0.10, -0.02; P = 0.0025). However, children with overweight at baseline in the intervention versus the non-exposed group differed in BMI z-score change (0.07 [CI: 0.02, 0.14]; P = 0.016). When analysed by age, results were similar for 2- to 5-year-olds, but no differences were found for 6- to 9-year-olds.

CONCLUSION

Automating risk assessment in paediatric care is feasible and effective in promoting healthy weight among preschool but not older children.

Effect of Social Media on Child Obesity: Application of Structural Equation Modeling with the Taguchi Method

Through public health studies, specifically on child obesity modeling, research scholars have been attempting to identify the factors affecting obesity using suitable statistical techniques. In recent years, regression, structural equation modeling (SEM) and partial least squares (PLS) regression have been the most widely employed statistical modeling techniques in public health studies. The main objective of this study to apply the Taguchi method to introduce a new pattern rather than a model for analyzing the body mass index (BMI) of children as a representative of childhood obesity levels mainly related to social media use. The data analysis includes two main parts. The first part entails selecting significant indicators for the proposed framework by applying SEM for primary and high school students separately. The second part introduces the Taguchi method as a realistic and reliable approach to exploring which combination of significant variables leads to high obesity levels in children. AMOS software (IBM, Armonk, NY, USA) was applied in the first part of data analysis and MINITAB software (Minitab Inc., State College, PA, USA) was utilized for the Taguchi experimental analysis (second data analysis part). This study will help research scholars view the data and a pattern rather than a model, as a combination of different factor levels for target factor optimization.

Understanding health policy to improve primary care management of obesity

NPs are ideal candidates for implementing positive health changes for obese patients. Providers have medical expertise and can promote obesity reduction strategies to their patients. Increased awareness of the influence of health policy and clinical implications for obesity management are needed.

Association of Educational Level and Marital Status With Obesity: A Study of Chinese Twins

The prevalence of overweight and obesity is growing rapidly in many countries. Socioeconomic inequalities might be important for this increase. The aim of this study was to determine associations of body mass index (BMI), overweight and obesity with educational level and marital status in Chinese twins. Participants were adult twins recruited through the Chinese National Twin Registry (CNTR), aged 18 to 79 years, and the sample comprised 10,448 same-sex twin pairs. Current height, weight, educational attainment, and marital status were self-reported. Regression analyses and structural equation models were conducted to evaluate BMI, overweight, and obesity associated with educational level and marital status in both sexes. At an individual level, both educational level and marital status were associated with higher BMI and higher risk of being overweight and obesity in men, while in women the effects of educational level on BMI were in the opposite direction. In within-Monozygotic (MZ) twin-pair analyses, the effects of educational level on BMI disappeared in females. Bivariate structural equation models showed that genetic factors and shared environmental confounded the relationship between education and BMI in females, whereas marital status was associated with BMI on account of significant positive unique environmental correlation apart in both sexes. The present data suggested that marital status and BMI were associated, independent of familiar factors, for both sexes of this study population, while common genetic and shared environmental factors contributed to education-associated disparities in BMI in females.

Using electronic health record data for environmental and place based population health research: a systematic review

PURPOSE

We conducted a systematic review of literature published on January 2000-May 2017 that spatially linked electronic health record (EHR) data with environmental information for population health research.

METHODS

We abstracted information on the environmental and health outcome variables and the methods and data sources used.

RESULTS

The automated search yielded 669 articles; 128 articles are included in the full review. The number of articles increased by publication year; the majority (80%) were from the United States, and the mean sample size was approximately 160,000. Most articles used cross-sectional (44%) or longitudinal (40%) designs. Common outcomes were health care utilization (32%), cardiometabolic conditions/obesity (23%), and asthma/respiratory conditions (10%). Common environmental variables were sociodemographic measures (42%), proximity to medical facilities (15%), and built environment and land use (13%). The most common spatial identifiers were administrative units (59%), such as census tracts. Residential addresses were also commonly used to assign point locations, or to calculate distances or buffer areas.

CONCLUSIONS

Future research should include more detailed descriptions of methods used to geocode addresses, focus on a broader array of health outcomes, and describe linkage methods. Studies should also explore using longitudinal residential address histories to evaluate associations between time-varying environmental variables and health outcomes.