Prevalence of Financial Fraud and Scams Among Older Adults in the United States: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
BACKGROUND: The financial exploitation of older adults was recently recognized by the Centers for Disease Control and Prevention as a serious public health problem. Knowledge of the prevalence of elder financial exploitation is mostly limited to the category of financial abuse, which occurs in relationships involving an expectation of trust. Little is known about the other major category of elder financial exploitation-elder financial fraud and scams, which is perpetrated by strangers. A valid estimate of elder financial fraud-scam prevalence is necessary as a foundation for research and prevention efforts. OBJECTIVES: To estimate the prevalence of elder financial fraud-scam victimization in the United States based on a systematic review and meta-analysis. SEARCH METHODS: Multiple investigators independently screened titles and abstracts and reviewed relevant full-text records from PubMed, Medline, PsycINFO, Criminal Justice Abstracts, Social Work Abstracts, and AgeLine databases. SELECTION CRITERIA: To maximize the validity and generalizability of prevalence estimation, we restricted eligibility to general population-based studies (English speaking, 1990 onward) using state- or national-level probability sampling and collecting data directly from older adults. DATA COLLECTION AND ANALYSIS: Information on elder financial fraud-scam prevalence and study-level characteristics was extracted independently by 2 investigators. Meta-analysis of elder financial fraud-scam prevalence used generalized mixed models with individual studies as levels of a random classification factor. MAIN RESULTS: We included 12 studies involving a total of 41 711 individuals in the meta-analysis. Overall pooled elder financial fraud-scam prevalence (up to 5-year period) across studies was 5.6% (95% confidence interval [CI] = 4.0%, 7.8%), with a 1-year period prevalence of 5.4% (95% CI = 3.2%, 7.6%). Studies using a series of questions describing specific fraud-scam events to measure victimization found a significantly higher prevalence (7.1%; 95% CI = 4.8%, 9.4%) than studies using a single, general-question self-report assessment approach (3.6%; 95% CI = 1.8%, 5.4%). AUTHOR'S CONCLUSIONS: Elder financial fraud and scams is a common problem, affecting approximately 1 of every 18 cognitively intact, community-dwelling older adults each year; it requires further attention from researchers, clinicians, and policymakers. Elder financial fraud-scam prevalence findings in this study likely underestimate the true population prevalence. We provide methodological recommendations to limit older adult participation and reporting bias in future population-based research. Public Health Implications. Elder financial exploitation victimization is associated with mortality, hospitalization, and poor physical and mental health. Health care professionals working with older adults likely routinely encounter patients who are fraud-scam victims. Validation of instruments to screen for elder financial fraud and scams in clinical settings is an important area of future research. Without effective primary prevention strategies, the absolute scope of this problem will escalate with the growing population of older adults.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,015 | 0,031 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».