Evaluating the Relative Vaccine Effectiveness of Adjuvanted Trivalent Influenza Vaccine Compared to High-Dose Trivalent and Other Egg-Based Influenza Vaccines among Older Adults in the US during the 2017-2018 Influenza Season

Purpose Limited studies have shown positive and negative associations of serum amylase A1 (SAA1) gene in childhood obesity, previously showed the relation with obesity in different ethnicity. The current study therefore investigated the impact of single nucleotide polymorphisms present in the SAA1 gene on subjects of Saudi obesity. Participants and methods In this case-control study, we selected 140 subjects of Saudi population and categorized them into 83 cases of obesity and 57 healthy controls. Genotyping was performed with quantitative/real time-polymerase chain reaction in the SAA1 gene for rs11603089A/G, rs4638289A/T and rs7131332A/G polymorphisms. Results In rs11603089 polymorphism, co-dominant model (AG vs AA+GG; OR-2.23 [95% CI:1.02-4.86]; p=0.04) and rs4638289 polymorphism, a disparity in significance was observed between the homozygous variant (TT vs AA; OR-16.8 [95% CI: 2.06-136.8]; p=0.0009), dominant model (AT+TT vs AA; OR-2.57 [95% CI: 1.28-5.19]; p=0.007), recessive model (TT vs AA+AT; OR-11.36 [95% CI: 1.45-89.06]; p=0.004) and allelic frequency for (T vs A: OR-2.35 [95% CI: 1.39-3.98]; p=0.001) between the obesity cases and control subjects. However, statistical correlations did not reveal the rs7131332A/G polymorphism either (p>0.05). Conclusion In conclusion, rs4638289 polymorphism was associated with risk allele and dominant model with obesity subjects. Further additional studies were warranted.

Relationship Between Serum Amyloid A1 (SAA1) Gene Polymorphisms Studies with Obesity in the Saudi Population.

Triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that disproportionately affects Black women and contributes to racial disparities in breast cancer mortality. Prior research has suggested that neighborhood effects may contribute to this disparity beyond individual risk factors.The sample included a cohort of 3316 breast cancer cases diagnosed between 2012 and 2020 in New Castle County, Delaware, a geographic region of the US with elevated rates of TNBC. Multilevel methods and geospatial mapping evaluated whether the race, income, and race/income versions of the neighborhood Index of Concentration at the Extremes (ICE) metric could efficiently identify census tracts (CT) with higher odds of TNBC relative to other forms of invasive breast cancer. Odds ratios (OR) and 95% confidence intervals (CI) were reported; p-values < 0.05 were significant. Additional analyses examined area-level differences in exposure to metabolic risk factors, including unhealthy alcohol use and obesity.The ICE-Race, -Income-, and Race/Income metrics were each associated with greater census tract odds of TNBC on a bivariate basis. However, only ICE-Race was significantly associated with higher odds of TNBC after adjustment for patient-level age and race (most disadvantaged CT: OR = 2.09; 95% CI 1.40-3.13), providing support for neighborhood effects. Higher counts of alcohol and fast-food retailers, and correspondingly higher rates of unhealthy alcohol use and obesity, were observed in CTs that were classified into the most disadvantaged ICE-Race quintile and had the highest odds of TNBC.The use of ICE can facilitate the monitoring of cancer inequities and advance the study of racial disparities in breast cancer. 

/pdf/racial-disparities-in-triple-negative-breast-cancer-toward-a-9me6iy6m.pdf

Racial disparities in triple negative breast cancer: toward a causal architecture approach

Background: Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure
Objective: The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption
Methods: Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models
Results: Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (112% to 175% less) and mobile health for self-regulation (08% to 97% less) Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=01; 101% higher in 2019, P=01) Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 198, 95% CI 126-311)
Conclusions: Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities

/pdf/patterns-and-influencing-factors-of-ehealth-tools-adoption-45on7x7ibz.pdf

Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study.

Introduction: Obesity is associated with greater utilization of medical resources, but it is unclear if a similar relationship exists for dental care. Objectives: This retrospective cohort study compared periodontal disease treatment among obese, overweight, and normal-weight patients attending an urban US dental school clinic. Methods: Periodontal, demographic, and medical history data for 3,443 adult patients examined between July 1, 2010, and July 31, 2019, were extracted from electronic health records. Body mass index (BMI) was computed from self-reported height and weight and categorized as obese (≥30 kg/m2), overweight (25–29.9 kg/m2), or normal (18–24.9 kg/m2). Periodontal disease was categorized using clinical probing measures. Procedure codes defined treatment type (surgical, nonsurgical, local chemotherapeutics, or none). Logistic regression models controlling for initial periodontal disease severity, age, gender, tobacco use, history of diabetes, dental insurance type, and follow-up (log of days) estimated odds ratios (ORs) and 95% confidence intervals (CIs) of any treatment among obese and overweight relative to normal-weight patients. The association between BMI and a periodontal treatment intensity score, based on treatment type, number of teeth treated, and number of visits, was evaluated with multivariable negative binomial regression. Results: Mean age at baseline was 44 ± 15 y, and severe periodontal disease was present in 32% of obese, 31% of overweight, and 21% of normal-weight patients. Average follow-up was 3.9 ± 1.6 y. Obese and overweight patients were more likely to have nonsurgical scaling and root planing or surgical procedures than normal-weight patients. Adjusted odds of any treatment were higher among obese (OR = 1.34; 95% CI, 1.14–1.72) and overweight (OR = 1.18; 95% CI, 0.97–1.42) relative to normal weight. Obese and overweight patients had 40% and 24% higher treatment intensity scores, respectively, than normal-weight patients. Conclusion: These results indicate obese and overweight individuals require more intensive periodontal treatment compared to normal-weight individuals, independent of initial disease severity. Knowledge of Transfer Statement: The results of this study can be used by dental providers and policymakers to better understand patient characteristics that influence the variability in frequency and length of periodontal treatment. Knowledge of a patient’s body mass index may be useful in identifying patients who possibly will have a poorer periodontal prognosis.

Effect of Overweight and Obesity on Periodontal Treatment Intensity

The association of obesity with health insurance coverage and demographic characteristics: a statewide cross-sectional study.

Influenza is a contagious respiratory illness and can lead to hospitalization and even death. Understanding how comorbidities affect the severity of influenza can help clinical management. The aim of this study is to offer more information about comorbidities that might be associated with the severity of influenza in children. We used a statewide network in Rhode Island, USA, to extract data for laboratory-confirmed influenza cases among children 19 years old or younger. We identified 1169 lab-confirmed influenza cases. The most common comorbidities were asthma (17.1%), neurodevelopmental disorders (10.3%), gastrointestinal disorders (7.6%), atopic dermatitis (7%), and endocrine and metabolic diseases (6.8%). Interestingly, 80.8% (63 out of 78) of children who had an influenza-related hospital admission had at least one comorbidity, and among hospitalized children with influenza, the most common comorbidities were neurological diseases (28.2%, 22/78), gastrointestinal disorders (25.6%, 20/78), endocrine and metabolic diseases (24.4%, 19/78), and neurodevelopmental disorders (23.1%, 18/78). Children with endocrine or metabolic diseases were 8.23 times more likely to be admitted to the hospital, and children with neurological disorders were 6.35 times more likely to be admitted (OR: 8.23, 95% CI: 4.42–15.32 and OR: 6.35, 95% CI: 3.60–11.24, respectively). In summary, we identified specific comorbidities associated with influenza hospitalization and length of hospital stay, and these groups should be prioritized for public health interventions.

/pdf/how-comorbidities-affect-hospitalization-from-influenza-in-1327e7hz.pdf

Sophia C Mylonakis

Papers

The association of obesity with health insurance coverage and demographic characteristics: a statewide cross-sectional study.

How Comorbidities Affect Hospitalization from Influenza in the Pediatric Population