Natural history and associated early life factors of childhood asthma: a population registry-based cohort study in Denmark

Short-term and long-term effects of cesarean section on asthma and wheezing: A cohort study and meta-analysis

OBJECTIVE

To describe the short-term and long-term effects of cesarean section on childhood asthma and wheezing.

METHOD

Firstly, in the cohort study, 6640 infants were included in the cohort baseline from January 2018 to December 2019, in which 6501 children completed the follow-up study for respiratory diseases at age 2 years. The effect of cesarean section on asthma and wheezing was estimated by the logistic regression model. Secondly, we conducted a meta-analysis of studies with outcomes of childhood asthma and wheezing under 2 years of age and over 2 years of age, respectively, to investigate the short-term and long-term effects of cesarean section on asthma and wheezing.

RESULTS

In our cohort study, the cumulative incidence of asthma and wheezing was 1.3% (84/6501). 45.5% of children (2961/6501) were born by cesarean section. The adjusted odds ratio for the effect of cesarean section on asthma and wheezing in children under 2 years of age was 1.14 (95%CI 0.73-1.78). Combining previous studies (outcomes of asthma and wheezing under 2 years of age) with our results for a meta-analysis, the odds ratio was 1.15 (95%CI 1.05-1.25, I² = 46.82%). Meanwhile, cesarean section had a long-term effect on asthma and wheezing in the child population over 2 years of age (OR = 1.17, 95%CI 1.11-1.24, P < 0.001, I² = 79.38%).

CONCLUSION

Cesarean section had a short-term effect on asthma and wheezing before the age of 2, in addition, the long-term effect of cesarean section on asthma and wheezing persisted in the child population (under 18).

Loss of control in preschoolers with asthma is a risk factor for disease persistency

AIM

To describe the relationship between loss of control events in preschoolers with asthma and persistence of disease.

METHODS

We reviewed medical records of children <6 years diagnosed with asthma in 2018 to assess loss of control events during three years of follow-up. Asthma persistency was defined by redeem of short-acting β2-agonist or asthma controllers within one year after the end of follow-up. Logistic regression models were applied to analyse the association between loss of control events and persistence of asthma.

RESULTS

We included 172 patients (median age 1.8 years), whereof 126 (73.3%) experienced a loss of control event and 87 (50.6%) had asthma one year after the end of follow-up. Any loss of control event was associated with persistence of asthma adjusted for controller treatment at inclusion, prior exacerbations, atopic comorbidity and caesarean section: aOR, 10.9 (95% CI, 3.9-34.6), p < 0.001. This was also significant restricted to events in the first year of follow-up: 3.52 (1.50-8.67), p < 0.01 and among children only experiencing one event: 6.4 (1.7-27.3), p = 0.01.

CONCLUSION

Loss of control events during a 3-year period among preschoolers with asthma are closely related to disease persistency, which may aid clinicians to assess risk of persistent asthma in young children.

Reducing asthma exacerbations in vulnerable children through a medical-legal partnership

BACKGROUND

Asthma health disparities are widely recognized, with worse outcomes in children from low income families. In a Medical-Legal Partnership (MLP), an attorney is embedded in a healthcare setting to address social determinants of health. We studied whether an MLP could impact asthma exacerbation rates in a vulnerable urban population at an academic children's hospital.

METHODS

The study population comprised children with asthma who were referred to the MLP between 2013 and 2017. We compared healthcare utilization for asthma exacerbations managed in primary care, emergency department and inpatient settings in the year before and year after MLP intervention.

RESULTS

98 children with asthma were included in the study. The mean total encounters per person per year decreased from 1.16 to 0.66 (relative reduction 44.2%, p < 0.01). The largest effect was on hospitalizations, with a reduction from 0.33 to 0.10 hospitalizations per patient per year (relative reduction 69.7%, p < 0.01). Encounters for asthma exacerbations in the primary care office and emergency department also decreased but these changes did not meet statistical significance.

CONCLUSION

In a pediatric population with asthma, an MLP intervention was associated with a significant reduction in asthma exacerbation encounters and hospitalizations comparing the year before and after MLP intervention. Further studies are needed to better understand which interventions are most effective, and for which patient groups MLP referral would be particularly useful. MLPs may be an important way to reduce health disparities in patients with asthma and other chronic illnesses.

Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition

Count data occur widely in many bio-surveillance and healthcare applications, e.g. the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusual infectious rates so that we can respond appropriately. In this paper, we develop a method called Poisson assisted Smooth Sparse Tensor Decomposition (PoSSTenD), which not only detect when hot-spots occur but also localize where hot-spots occur. The main idea of our proposed PoSSTenD method is articulated as follows. First, we represent the observed count data as a three-dimensional tensor including (1) a spatial dimension for location patterns, e.g. different cities/countries/states; (2) a temporal domain for time patterns, e.g. daily/weekly/monthly; (3) a categorical dimension for different types of data sources, e.g. different types of diseases. Second, we fit this tensor into a Poisson regression model, and then we further decompose the infectious rate into two components: smooth global trend and local hot-spots. Third, we detect when hot-spots occur by building a cumulative sum (CUSUM) control chart and localize where hot-spots occur by their LASSO-type sparse estimation. The usefulness of our proposed methodology is validated through numerical simulation studies and a real-world dataset, which records the annual number of 10 different infectious diseases from 1993 to 2018 for 49 mainland states in the United States.