Dataset Information

Use of the Bacterial Lysate OM-85 in the Paediatric Population in Italy: A Retrospective Cohort Study.

ABSTRACT:

Background

In Italy, the bacterial lysate OM-85 (Broncho-Vaxom^®, Broncho-Munal^®, Ommunal^®, Paxoral^®, Vaxoral^®) is registered for the prophylaxis of recurrent respiratory tract infections (RTIs) in adults and children above one year of age, but there are limited data on its use in the paediatric population. We aim to estimate the impact of OM-85 treatment on RTIs and antibiotic prescriptions in children.

Methods

This study included children aged 1 to 14 years enrolled in Pedianet, a paediatric general practice research database, from January 2007 to June 2017, having at least one prescription of OM-85. Children with less than 12 months of follow-up before (PRE period) and after (POST period) the OM-85 prescription were excluded. The frequency of antibiotic prescriptions and the frequency of RTI episodes in the PRE and POST periods were compared through the post-hoc test. Subgroup analysis was performed in children with recurrent RTIs.

Results

1091 children received 1382 OM-85 prescriptions for a total follow-up of 619,525.5 person-years. Overall, antibiotic prescriptions decreased from a mean of 2.8 (SD (standard deviation) 2.7) prescriptions in the PRE period to a mean of 2.2 (SD 2.6) prescriptions in the POST period (p < 0.0001). RTIs decreased from a mean of 3.4 (SD 2.9) episodes in the PRE period to a mean of 2.5 (SD 2.6) episodes in the POST period (p < 0.0001). No change in antibiotic class was noted, and co-amoxiclav remained the preferred therapy in 28% of cases, followed by amoxicillin. These results were confirmed among children with recurrent RTIs.

Conclusions

OM-85 is effective in preventing both antibiotic prescriptions and RTIs in children.

SUBMITTER: Cantarutti A

PROVIDER: S-EPMC8297025 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVES:Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN:Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA:Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527?458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES:Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS:Of the 527?458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49?486 children (9.4%) of the population were classified in the 'At Higher Risk' group using a threshold of predicted risk >2.5%. The observed risk of hospitalisation (5%) for the 'At Higher Risk' group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS:It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.