Socioeconomic status (SES) has been associated with many health outcomes. Commonly used datasets such as medical records often lack data on SES but do include address information. The authors sought to determine whether an SES measure derived from housing characteristics is associated with other SES measures and outcomes known to be associated with SES. The data come from a telephone survey of parents/ guardians of children aged 1-17 years who resided in Olmsted County, Minnesota, and Jackson County, Missouri. Seven variables related to housing and six neighborhood characteristics obtained from local government assessor's offices in Olmsted County, Minnesota, were appended to survey responses. An SES index derived from housing characteristics (hereafter, HOUSES) was constructed using principal components factor analysis. For criterion validity, we assessed Pearson's correlation coefficients between HOUSES and other SES measures, including self-reported parents' educational levels, income, Hollingshead Index, and Nakao-Treas Index. For construct validity, we determined the association between HOUSES and outcomes, risks of low birth weight, overweight, and smoking exposure at home. We applied HOUSES to subjects in another community by formulating HOUSES from housing data of subjects in Jackson County, Missouri, using the same statistical algorithm as HOUSES for subjects in Olmsted County, Minnesota. We found that HOUSES had modest to good correlation with other SES measures. Overall, as hypothesized, HOUSES was inversely associated with outcome measures assessed among subjects from both counties. HOUSES may be a useful surrogate measure of individual SES in epidemiologic research, especially when SES measures for individuals are not available.

Background-Individuals with asthma have been reported to be at increased risk of invasive pneumococcal disease. These findings need to be confirmed in a different population-based study setting.

Most of the research effort regarding asthma has been devoted to its causes, therapy, and prognosis. There is also evidence that the presence of asthma can influence patients’ susceptibility to infections, yet research in this aspect of asthma has been limited. There is additional debate in this field, with current literature tending to view the increased risk of infection among atopic patients as due to opportunistic infections secondary to airway inflammation, especially in severe atopic diseases. Other evidence, however, suggests that such risk and its underlying immune dysfunction may be a phenotypic or clinical feature of atopic conditions. This review argues that 1) improved understanding of the effects of asthma or other atopic conditions on the risk of microbial infections will bring important and new perspectives to clinical practice, research, and public health concerning atopic conditions and that 2) research efforts into the causes and effects of asthma must be juxtaposed because they are likely to guide each other.

Although asthma is a helper T cell type 2-predominant condition, it may increase the risks of helper T cell type 1-polarized proinflammatory conditions, such as CHD and DM. Physicians who care for asthmatic patients need to address these unrecognized risks in asthmatic patients.

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. As a large amount of valuable clinical information is locked in clinical narratives, natural language processing techniques as an artificial intelligence approach have been leveraged to extract information from clinical narratives in electronic health records. This capability of natural language processing potentially enables automated chart review for identifying patients with distinctive clinical characteristics in clinical care and reduces methodological heterogeneity in defining phenotype, obscuring biological heterogeneity in research concerning allergy, asthma, and immunology. This brief review discusses the current literature on the secondary use of electronic health record data for clinical research concerning allergy, asthma, and immunology and highlights the potential, challenges, and implications of natural language processing techniques.

Rationale Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. Objectives To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). Methods This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. Measurements Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. Main results Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374–1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2–5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3–15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82–1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. Conclusions While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians’ burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. Trial registration ClinicalTrials.gov Identifier: NCT02865967.

Background Socioeconomic status (SES) is an important determinant of health, but SES measures are frequently unavailable in commonly used datasets. Area-level SES measures are used as proxy measures of individual SES when the individual measures are lacking. Little is known about the agreement between individual-level versus area-level SES measures in mixed urban–rural settings. Methods We identified SES agreement by comparing information from telephone self-reported SES levels and SES calculated from area-level SES measures. We assessed the impact of this agreement on reported associations between SES and rates of childhood obesity, low birth weight <2500 g and smoking within the household in a mixed urban–rural setting. Results 750 households were surveyed with a response rate of 62%: 51% male, 89% Caucasian; mean child age 9.5 years. Individual-level self-reported income was more strongly associated with all three childhood health outcomes compared to area-level SES. We found significant disagreement rates of 22–31%. The weighted Cohen’s κ indices ranged from 0.15 to 0.22, suggesting poor agreement between individual-level and area-level measures. Conclusion In a mixed urban–rural setting comprised of both rural and urbanised areas, area-level SES proxy measures significantly disagree with individual SES measures, and have different patterns of association with health outcomes from individual-level SES measures. Area-level SES may be an unsuitable proxy for SES when individual rather than community characteristics are of primary concern.

Background A significant proportion of children with asthma have delayed diagnosis of asthma by health care providers. Manual chart review according to established criteria is more accurate than directly using diagnosis codes, which tend to under-identify asthmatics, but chart reviews are more costly and less timely. Objective To evaluate the accuracy of a computational approach to asthma ascertainment, characterizing its utility and feasibility toward large-scale deployment in electronic medical records. Methods A natural language processing (NLP) system was developed for extracting predetermined criteria for asthma from unstructured text in electronic medical records and then inferring asthma status based on these criteria. Using manual chart reviews as a gold standard, asthma status (yes vs no) and identification date (first date of a “yes” asthma status) were determined by the NLP system. Results Patients were a group of children (n =112, 84% Caucasian, 49% girls) younger than 4 years (mean 2.0 years, standard deviation 1.03 years) who participated in previous studies. The NLP approach to asthma ascertainment showed sensitivity, specificity, positive predictive value, negative predictive value, and median delay in diagnosis of 84.6%, 96.5%, 88.0%, 95.4%, and 0 months, respectively; this compared favorably with diagnosis codes, at 30.8%, 93.2%, 57.1%, 82.2%, and 2.3 months, respectively. Conclusions Automated asthma ascertainment from electronic medical records using NLP is feasible and more accurate than traditional approaches such as diagnosis codes. Considering the difficulty of labor-intensive manual record review, NLP approaches for asthma ascertainment should be considered for improving clinical care and research, especially in large-scale efforts.