Driver license departments in many US states collect data on individuals' height and weight. These data can be useful to researchers in epidemiological and public health studies. As height and weight ...on driver license are self-reported, they may be prone to reporting bias. We compare height and weight obtained from driver license records and clinically measured height and weight, as well as body mass index (BMI) values calculated using the two data sources for the same individual.
We linked individual height and weight records obtained from the Driver License Division (DLD) in the Utah Department of Public Safety to clinical records from one of the largest healthcare providers in the state of Utah. We then calculated average differences between height, weight and BMI values separately for women and men in the sample, as well as discrepancies between the two sets of measures by age and BMI category. We examined how well self-reported height and weight from the driver licenses classify individuals into specific BMI categories based on clinical measures. Finally, we used two sets of BMI values to estimate individuals' relative risk of type II diabetes.
Individuals, on average, tend to overestimate their height and underestimate their weight. Consequently, the value of BMI calculated using driver license records is lower than BMI calculated using clinical measurements. The discrepancy varies by age and by BMI category. Despite the discrepancy, BMI based on self-reported height and weight allows for accurate categorization of individuals at the higher end of the BMI scale, such as the obese. When used as predictors of relative risk of type II diabetes, both sets of BMI values yield similar risk estimates.
Data on height and weight from driver license data can be a useful asset for monitoring population health in states where such information is collected, despite the degree of misreporting associated with self-report.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BACKGROUND:The effect of diabetes type on the risk of periprosthetic joint infection is not well documented. We hypothesized that patients with diabetes mellitus type 1 would be at greater risk for ...periprosthetic joint infection than those with diabetes mellitus type 2 and that a history of diabetic complications would be associated with an increased risk of periprosthetic joint infection.
METHODS:We performed a retrospective cohort study, within a statewide database, on all adult patients who underwent hip or knee arthroplasty, with follow-up of ≥2 years, from 1996 to 2013. Of the 75,478 patients included, 1,668 had type-1 diabetes and 18,186 had type-2 diabetes. Risk factors were calculated using Cox regression, adjusting for siblings and stratified by age. Logistic regression was used to analyze the effect of diabetic complications on the risk of periprosthetic joint infection, controlling for other known risks for periprosthetic joint infection.
RESULTS:There was no difference in age or sex between groups (p > 0.05). The frequency of periprosthetic joint infection in patients without diabetes was 2.6% compared with 4.3% in all patients with diabetes (relative risk, 1.47; p < 0.001). Patients with type-1 diabetes were at a 1.8 times greater risk for periprosthetic joint infection than patients with type-2 diabetes (7% compared with 4%; p < 0.001). The following diabetic complications increased the risk of periprosthetic joint infectionperipheral circulatory disorders (odds ratio OR, 2.59 95% confidence interval (CI), 1.70 to 3.94), ketoacidosis (OR, 2.52 95% CI, 1.51 to 4.19), neurological manifestations (OR, 2.33 95% CI, 1.96 to 2.78), renal manifestations (OR, 2.15 95% CI, 1.66 to 2.79), and ophthalmic manifestations (OR, 1.76 95% CI, 1.24 to 2.51). The odds of periprosthetic joint infection increased with each added complication and patients with ≥4 complications were 9 times more likely to have a periprosthetic joint infection than patients with uncomplicated diabetes (OR, 9.47 95% CI, 4.97 to 18.03). Overweight and obese patients with type-2 diabetes and underweight patients with type-1 diabetes were at greater risk for periprosthetic joint infection compared with the general population (all p < 0.05).
CONCLUSIONS:Our data showed an increased risk of periprosthetic joint infection in patients with type-1 diabetes compared with those with type-2 diabetes, along with an increasing risk associated with additional diabetic complications. These findings emphasize the need to better understand the medical history of patients with diabetes for more appropriate risk management.
LEVEL OF EVIDENCE:Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Objective
Erosive hand osteoarthritis (OA) is a severe and rapidly progressing subset of hand OA. Its etiology remains largely unknown, which has hindered development of successful treatments. This ...study was undertaken to test the hypothesis that erosive hand OA demonstrates familial clustering in a large statewide population linked to genealogical records, and to determine the association of potential risk factors with erosive hand OA.
Methods
Patients diagnosed as having erosive hand OA were identified by searching 4,741,840 unique medical records from a comprehensive statewide database, the Utah Population Database (UPDB). Affected individuals were mapped to pedigrees to identify high‐risk families with excess clustering of erosive hand OA as defined by a familial standardized incidence ratio (FSIR) of ≥2.0. The magnitude of familial risk of erosive hand OA in related individuals was calculated using Cox regression models. Association of potential erosive hand OA risk factors was analyzed using multivariate conditional logistic regression and logistic regression models.
Results
We identified 703 affected individuals linked to 240 unrelated high‐risk pedigrees with excess clustering of erosive hand OA (FSIR ≥2.0, P < 0.05). The relative risk of developing erosive hand OA was significantly elevated in first‐degree relatives (P < 0.001). There were significant associations between a diagnosis of erosive hand OA and age, sex, diabetes, and obesity (all P < 0.05).
Conclusion
Familial clustering of erosive hand OA observed in a statewide database indicates a potential genetic contribution to the etiology of the disease. Age, sex, diabetes, and obesity are risk factors for erosive hand OA. Identification of causal gene variants in these high‐risk families may provide insight into the genes and pathways that contribute to erosive hand OA onset and progression.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Socially disadvantaged groups generally are more likely to reside in areas with less desirable conditions. We examined longitudinal relationships between neighborhood resident characteristics and ...amenities from 1990 to 2010 in an urban area of Utah, U.S. Four temporal patterns of social inequities are described using mixed-effects models: historical inequities; differential selection into amenity-rich tracts; differential investment in amenities; and simultaneous twenty-year change. Results indicate historical differences by neighborhood socioeconomic status, with lower status tracts having fewer green/natural amenities and higher air pollution in 1990 but also greater walkability and more food stores. Differences in amenities by neighborhood socioeconomic status widened over time as aggregate socioeconomic status disproportionately increased in tracts with more green/natural amenities, less air pollution, and lower walkability in 1990, consistent with differential selection. Tract percentage non-Hispanic White did not predict historical differences, but tracts that were less walkable and had fewer healthy food stores in 1990 experienced larger subsequent increases in racial/ethnic diversity. Tracts with higher relative to lower percentage non-Hispanic White in 1990 had larger decreases in air pollution but declining green/natural amenities. This study shows how social inequities in neighborhood amenities change over time, providing evidence of historical socioeconomic differences increasing from differential resident selection.
•Urban and nature-based amenities for four urban counties were assessed over 20 years.•Socioeconomically advantaged areas had more nature-based but fewer urban amenities.•Socioeconomic differences widened as higher status residents moved into amenity-rich areas.•Racial composition was not associated with neighborhood amenities in 1990.•Racial diversity increased in areas that initially had fewer urban amenities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Gene panel testing for breast cancer susceptibility has become relatively cheap and accessible. However, the breast cancer risks associated with mutations in many genes included in these panels are ...unknown.
We performed custom-designed targeted sequencing covering the coding exons of 17 known and putative breast cancer susceptibility genes in 660 non-BRCA1/2 women with familial breast cancer. Putative deleterious mutations were genotyped in relevant family members to assess co-segregation of each variant with disease. We used maximum likelihood models to estimate the breast cancer risks associated with mutations in each of the genes.
We found 31 putative deleterious mutations in 7 known breast cancer susceptibility genes (TP53, PALB2, ATM, CHEK2, CDH1, PTEN and STK11) in 45 cases, and 22 potential deleterious mutations in 31 cases in 8 other genes (BARD1, BRIP1, MRE11, NBN, RAD50, RAD51C, RAD51D and CDK4). The relevant variants were then genotyped in 558 family members. Assuming a constant relative risk of breast cancer across age groups, only variants in CDH1, CHEK2, PALB2 and TP53 showed evidence of a significantly increased risk of breast cancer, with some supportive evidence that mutations in ATM confer moderate risk.
Panel testing for these breast cancer families provided additional relevant clinical information for <2% of families. We demonstrated that segregation analysis has some potential to help estimate the breast cancer risks associated with mutations in breast cancer susceptibility genes, but very large case-control sequencing studies and/or larger family-based studies will be needed to define the risks more accurately.
To determine if there is evidence of heritable risk for nonunion using a large, state-wide population database.
Database.
Level 1 Trauma Center.
All Utah residents from 1996 - 2021 that sustained a ...long bone fracture and their family members.
The primary outcome was nonunion and the prevalence of nonunion among the patients' first-, second-, and third-degree relatives. The secondary objective was to identify demographic, injury, and socioeconomic risk factors associated with nonunion.
In total, 150,263 fractures and 6,577 (4.4%) nonunions were identified. This was highly refined to a 1:3 matched cohort of 4,667 nonunions of 13,981 fractures for familial clustering analysis. Cox proportional hazards did not demonstrate excessive risk of nonunion amongst first- (p = 0.863), second- (p = 0.509), and third-degree relatives (p = 0.252). Further analysis of the entire cohort demonstrated male sex (RR = 1.15; p < 0.001), Medicaid enrollment (RR = 2.64; p < 0.001), open fracture (RR = 2.53; p < 0.001), age group 41-60 (RR = 1.43; p < 0.001), a history of obesity (RR = 1.20; p < 0.001) were independent risk factors for nonunion.
Our results demonstrate no evidence of heritable risk for nonunion. Independent risk factors for nonunion were male sex, Medicaid enrollment, open fracture, middle age, and a history of obesity. While it is important to identify modifiable and non-modifiable risk factors, these results continue to support that the risk of nonunion is multifactorial, relating to injury characteristics, operative techniques, and patient specific risk factors.
Level III. See Instructions for Authors for a complete description of levels of evidence.
Periprosthetic joint infection (PJI) is a devastating complication after total joint arthroplasty, carrying significant economic and personal burden. The goal of this study is to use an established ...database to analyze socioeconomic variables and assess their relationship to PJI. Additionally, we sought to evaluate whether socioeconomic factors, along with other known risk factors of PJI, when controlled for in a statistical model affected the familial risk of PJI.
With approval from our Institutional Review Board we performed a population-based retrospective cohort study on all primary total joint arthroplasty cases of the hip or knee (n = 85,332), within a statewide database, between January 1996 and December 2013. We excluded 9854 patients due to age <18 years, missing data, history of PJI prior to index procedure, and no evidence of 2-year follow-up (excluding those with PJI). Cases that developed PJI following the index procedure (n = 2282) were compared to those that did not (n = 73,196).
After adjusting for covariates, patients with Medicaid as a primary payer were at greater risk for experiencing PJI (relative risk 1.40, 95% confidence interval CI 1.08-1.82, P = .01). There was no difference in risk between the groups associated with education level or median household income (all, P > .05). First-degree relatives of patients who develop PJI (hazard ratio 1.66, 95% CI 1.23-2.24, P = .001) and first-degree and second-degree relatives combined (hazard ratio 1.39, 95% CI 1.09-1.77, P = .007) were at greater risk despite controlling for the above socioeconomic factors.
Our study provides further support that genetic factors may underlie PJI as we did observe significant familial risk even after accounting for socioeconomic factors and payer status. We did not find a correlation between education level or household income and PJI; however, Medicaid payees were at increased risk. Continued study is needed to define a possible heritable disposition to PJI in an effort to optimize treatment and possibly prevent this complication.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Breast cancer risks conferred by many germline missense variants in the
and
genes, often referred to as variants of uncertain significance (VUS), have not been established. In this study, ...associations between 19 BRCA1 and 33 BRCA2 missense substitution variants and breast cancer risk were investigated through a breast cancer case-control study using genotyping data from 38 studies of predominantly European ancestry (41,890 cases and 41,607 controls) and nine studies of Asian ancestry (6,269 cases and 6,624 controls). The BRCA2 c.9104A>C, p.Tyr3035Ser (OR = 2.52;
= 0.04), and BRCA1 c.5096G>A, p.Arg1699Gln (OR = 4.29;
= 0.009) variant were associated with moderately increased risks of breast cancer among Europeans, whereas BRCA2 c.7522G>A, p.Gly2508Ser (OR = 2.68;
= 0.004), and c.8187G>T, p.Lys2729Asn (OR = 1.4;
= 0.004) were associated with moderate and low risks of breast cancer among Asians. Functional characterization of the BRCA2 variants using four quantitative assays showed reduced BRCA2 activity for p.Tyr3035Ser compared with wild-type. Overall, our results show how BRCA2 missense variants that influence protein function can confer clinically relevant, moderately increased risks of breast cancer, with potential implications for risk management guidelines in women with these specific variants.
.
PURPOSEOur goals were to identify individuals who required surgery for thumb carpometacarpal (CMC) joint osteoarthritis (OA), determine if CMC joint OA clusters in families, define the magnitude of ...familial risk of CMC joint OA, identify risk factors associated with CMC joint OA, and identify rare genetic variants that segregate with familial CMC joint OA. METHODSWe searched the Utah Population Database to identify a cohort of CMC joint OA patients who required surgery. Affected individuals were mapped to pedigrees to identify high-risk families with excess clustering of CMC joint OA. Cox regression models were used to calculate familial risk of CMC joint OA in related individuals. Risk factors were evaluated using logistic regression models. Whole exome sequencing was used to identify rare coding variants associated with familial CMC joint OA. RESULTSWe identified 550 pedigrees with excess clustering of severe CMC joint OA. The relative risk of CMC joint OA requiring surgical treatment was elevated significantly in first- and third-degree relatives of affected individuals, and significant associations with advanced age, female sex, obesity, and tobacco use were observed. We discovered candidate genes that dominantly segregate with severe CMC joint OA in 4 independent families, including a rare variant in Chondroitin Sulfate Synthase 3 (CHSY3). CONCLUSIONSFamilial clustering of severe CMC joint OA was observed in a statewide population. Our data indicate that genetic and environmental factors contribute to the disease process, further highlighting the multifactorial nature of the disease. Genomic analyses suggest distinct biological processes are involved in CMC joint OA pathogenesis. CLINICAL RELEVANCEAwareness of associated comorbidities may guide the diagnosis of CMC joint OA in at-risk populations and help identify individuals who may not do well with nonoperative treatment. Further pursuit of the genes associated with severe CMC joint OA may lead to assays for detection of early stages of disease and have therapeutic potential.
Introduction
Acute extremity compartment syndrome (“CS”) is an under-researched, highly morbid condition affecting trauma populations. The purpose of this study was to analyze incidence rates and ...risk factors for extremity compartment syndrome using a high-quality population database. Additionally, we evaluated heritable risk for CS using available genealogic data. We hypothesized that diagnosis of extremity compartment syndrome would demonstrate heritability.
Materials and methods
Adult patients with fractures of the tibia, femur, and upper extremity were retrospectively identified by ICD-9, ICD-10, and CPT codes from 1996 to 2020 in a statewide hospital database. Exposed and unexposed cohorts were created based on a diagnosis of CS. Available demographic data were analyzed to determine risk factors for compartment syndrome using logistic regression. Mortality risk at the final follow-up was evaluated using Cox proportional hazard modeling. Patients with a diagnosis of CS were matched with those without a diagnosis for heritability analysis.
Results
Of 158,624 fractures, 931 patients were diagnosed with CS. Incidence of CS was 0.59% (tibia 0.83%, femur 0.31%, upper extremity 0.27%). Male sex (78.1% vs. 46.4%;
p
< 0.001; RR = 3.24), younger age at fracture (38.8 vs. 48.0 years;
p
< 0.001; RR = 0.74), Medicaid enrollment (13.2% vs. 9.3%;
p
< 0.001; RR = 1.58), and smoking (41.1% vs. 31.1%;
p
< 0.001; RR 1.67) were significant risk factors for CS. CS was associated with mortality (RR 1.61,
p
< 0.001) at mean follow-up 8.9 years in the CS cohort. No significant heritable risk was found for diagnosis of CS.
Conclusions
Without isolating high-risk fractures, rates of CS are lower than previously reported in the literature. Male sex, younger age, smoking, and Medicaid enrollment were independent risk factors for CS. CS increased mortality risk at long-term follow-up. No heritable risk was found for CS.
Level of evidence
III.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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