Age, Ethnicity, and Socioeconomic Factors Impacting Infant and Toddler Fall-Related Trauma

Factors associated with healthcare utilization for infant falls in South Korea: a cross-sectional online survey

PURPOSE

Falls are a common cause of unintentional injuries in infants. This study was conducted to examine the patterns of healthcare utilization following infant falls in South Korea.

METHODS

This cross-sectional descriptive study utilized an online survey designed to gather information regarding the general characteristics of parents and infants, fall-related variables, and healthcare use.

RESULTS

The most serious falls identified by parents occurred at an average infant age of 6.97 months. Most fall incidents took place indoors (95.7%), and many occurred under the supervision of caregivers (68.0%). Following the fall, 36.4% of the participants used healthcare services. Logistic regression analysis revealed that healthcare use following an infant fall was significantly associated with being a firstborn child (odds ratio [OR]=5.32, 95% confidence interval [CI], 2.19-15.28) and falling from a caregiver's arms (OR=4.22; 95% CI, 1.45-13.68).

CONCLUSION

To prevent and decrease the frequency of infant falls, improvements are needed in both the domestic environment and parenting approaches.

Risk factors for falls among children aged 0-18 years: a systematic review

BACKGROUND

Accidental falls are the most common cause of injury in children. These falls not only result in pain and injury to children but also can pose a significant financial burden to their families and society. The aim of this study is to identify risk factors for falls in children.

METHODS

We conducted a systematic review of the literature describing falls in children aged 0-18 years. Studies of falls from a height of 1 m or more were excluded from the analysis. We analyzed the included studies to identify risk factors for falls.

RESULTS

A total of 1496 articles were initially retrieved, leading to an included set of nine articles, which were published from 1995 to 2021. Risk factors related to fall injury in children aged 0-18 years included age, sex, extroversion, rural areas, history of falls, family factors, caregiver factors, medication use, intravenous therapy, tests requiring movement, disease factors and long hospital stay.

CONCLUSION

We identified 12 risk factors affecting falls in children, including individual characteristics and family and social factors.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Background

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

Methods

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

Results

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

Conclusions

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

The Relationship between Self-Rated Economic Status and Falls among the Elderly in Shandong Province, China

(1) Background: Older people are more vulnerable and likely to have falls and the consequences of these falls place a heavy burden on individuals, families and society. Many factors directly or indirectly affect the prevalence of falls. The aims of this study were to understand the prevalence and risk factors of falls among the elderly in Shandong, China; the relationship between economic level and falls was also preliminary explored. (2) Methods: Using a multi-stage stratified sampling method, 7070 elderly people aged 60 and over were selected in Shandong Province, China. General characteristics and a self-rated economic status were collected through face to face interviews. Chi-square tests, rank sum tests and two logistic regression models were performed as the main statistical methods. (3) Results: 8.59% of participants reported that they had experienced at least one fall in the past half year. There was a significant difference in experienced falls regarding gender, residence, marital status, educational level, smoking, drinking, hypertension, diabetes, coronary disease, and self-reported hearing. The worse the self-rated economic status, the higher the risk of falling, (poor and worried about livelihood, OR = 3.60, 95%; CI = 1.76–7.35). (4) Conclusions: Women, hypertension, diabetes and self-reported hearing loss were identified as the risk factors of falls in the elderly. The difference of economic level affects the falls of the elderly in rural and urban areas. More fall prevention measures should be provided for the elderly in poverty.