Understanding the circumstances of paediatric fall injuries: a machine learning analysis of NEISS narratives

OBJECTIVES

Falls are the leading cause of non-fatal injury among young children. The aim of this study was to identify and quantify the circumstances contributing to medically attended paediatric fall injuries among 0-4 years old.

METHODS

Cross-sectional data for falls among kids under 5 years recorded between 2012 and 2016 in the National Electronic Injury Surveillance System was obtained. A sample of 4546 narratives was manually coded for: (1) where the child fell from; (2) what the child fell onto; (3) the activities preceding the fall and (4) how the fall occurred. A natural language processing model was developed and subsequently applied to the remaining uncoded data to yield a set of 91 325 cases coded for what the child fell from, fell onto, the activities preceding the fall, and how the fall occurred. Data were descriptively tabulated by age and disposition.

RESULTS

Children most often fell from the bed accounting for one-third (33%) of fall injuries in infants, 13% in toddlers and 12% in preschoolers. Children were more likely to be hospitalised if they fell from another person (7.4% vs 2.6% for all other sources; p<0.01). After adjusting for age, the odds of a child being hospitalised following a fall from another person were 2.1 times higher than falling from other surfaces (95% CI 1.6 to 2.7).

CONCLUSIONS

The prevalence of injuries due to falling off the bed, and the elevated risk of serious injury from falling from another person highlights the need for more robust and effective communication to caregivers on fall injury prevention.

Number Needed to Screen for Tuberculosis Disease Among Children: A Systematic Review

CONTEXT

Improving detection of pediatric tuberculosis (TB) is critical to reducing morbidity and mortality among children.

OBJECTIVE

We conducted a systematic review to estimate the number of children needed to screen (NNS) to detect a single case of active TB using different active case finding (ACF) screening approaches and across different settings.

DATA SOURCES

We searched 4 databases (PubMed, Embase, Scopus, and the Cochrane Library) for articles published from November 2010 to February 2020.

STUDY SELECTION

We included studies of TB ACF in children using symptom-based screening, clinical indicators, chest x-ray, and Xpert.

DATA EXTRACTION

We indirectly estimated the weighted mean NNS for a given modality, location, and population using the inverse of the weighted prevalence. We assessed risk of bias using a modified AXIS tool.

RESULTS

We screened 27 221 titles and abstracts, of which we included 31 studies of ACF in children < 15 years old. Symptom-based screening was the most common screening modality (weighted mean NNS: 257 [range, 5-undefined], 19 studies). The weighted mean NNS was lower in both inpatient (216 [18-241]) and outpatient (67 [5-undefined]) settings (107 [5-undefined]) compared with community (1117 [28-5146]) and school settings (464 [118-665]). Risk of bias was low.

LIMITATIONS

Heterogeneity in the screening modalities and populations make it difficult to draw conclusions.

CONCLUSIONS

We identified a potential opportunity to increase TB detection by screening children presenting in health care settings. Pediatric TB case finding interventions should incorporate evidence-based interventions and local contextual information in an effort to detect as many children with TB as possible.

Number needed to screen for TB in clinical, structural or occupational risk groups

BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies substantially across populations. We systematically reviewed studies reporting on ACF to calculate the number needed to screen (NNS) for groups at high risk for TB.METHODS: We conducted a literature search for studies reporting ACF for adults published between November 2010 and February 2020. We determined active TB prevalence detected through various screening strategies and calculated crude NNS for - TB confirmed using culture or Xpert^® MTB/RIF, and weighted mean NNS stratified by screening strategy, risk group, and country-level TB incidence.RESULTS: We screened 27,223 abstracts; 90 studies were included (41 in low/moderate and 49 in medium/high TB incidence settings). High-risk groups included inpatients, outpatients, people living with diabetes (PLWD), migrants, prison inmates, persons experiencing homelessness (PEH), healthcare workers, and miners. Screening strategies included symptom-based screening, chest X-ray and Xpert testing. NNS varied widely across and within incidence settings based on risk groups and screening methods. Screening tools with higher sensitivity (e.g., Xpert, CXR) were associated with lower NNS estimates.CONCLUSIONS: NNS for ACF strategies varies substantially between adult risk groups. Specific interventions should be tailored based on local epidemiology and costs.

A systematic review of the number needed to screen for active TB among people living with HIV

BACKGROUND: Systematic screening for active TB is recommended for all people living with HIV (PLWH); however, case detection remains poor globally. We investigated the yield of active case finding (ACF) by calculating the number needed to screen (NNS) to detect a case of active TB among PLWH.METHODS: We identified studies reporting ACF for TB among PLWH published from November 2010 to February 2020. We calculated crude NNS for Xpert- or culture-confirmed TB and weighted mean NNS stratified by screening approach, population/risk group, and country TB burden.RESULTS: Of the 27,221 abstracts screened, we identified 58 studies eligible for inclusion, including 5 in low/moderate TB incidence settings and 53 in medium/high incidence settings. Populations screened for TB included inpatients, outpatients not receiving antiretroviral therapy (ART), outpatients receiving ART, those with CD4 < 200 cells/µL, children aged ≤15 years, pregnant PLWH, and PLWH in prisons. Screening tools included symptom-based screening, chest X-ray, C-reactive protein levels, and Xpert. The weighted mean NNS varied across groups but was consistently low, ranging from 4 among inpatients in moderate/high TB burden settings to 137 among pregnant PLWH in moderate/high TB burden settings.CONCLUSIONS: ACF is a high yield intervention among PLWH. Approaches to screening should be tailored to local epidemiological and health-system contexts, and sensitive screening tools such as Xpert should be implemented where feasible.