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Foundations of the Epidemiology of Child Maltreatment

Defining Child Maltreatment and Core Epidemiologic Concepts

Data Sources and Surveillance Systems

NCANDS

Measuring Burden, Patterns, and Disparities

Estimating Prevalence, Incidence, and Trends

Disparities, Determinants, and Protective Factors

Methodological Rigor and Interpretation

Bias, Confounding, and Causality in Child Maltreatment Research

Outcomes and Burden of Harm

Application to Practice and Policy

Translating Epidemiologic Evidence into Prevention and Policy

Knowledge Checks

Quiz: Concepts and Definitions

Quiz: Data Sources and Surveillance

Quiz: Trends and Disparities

Quiz: Bias and Causal Inference

Quiz: Outcomes and Prevention Translation

Course Feedback

Learner Feedback Survey

Epidemiology of Child Maltreatment: Trends, Disparities, and Determinants is a graduate‑level course designed for professionals who use data to understand, prevent, and respond to child abuse and neglect. Grounded in core epidemiologic principles, the course guides learners through operational definitions, surveillance systems, and analytic methods that reveal the true burden of maltreatment across populations. You will learn how to estimate prevalence and incidence, interpret trends amid policy and definition changes, and examine geographic and seasonal patterns using spatial and time-series approaches. A strong emphasis is placed on identifying disparities by age, sex, race and ethnicity, disability status, immigration status, and socioeconomic position—while centering equity and avoiding stigmatization and ecological fallacies. The curriculum covers bias, confounding, and causal inference, translating evidence into prevention strategies and policy evaluation. Practical modules highlight data sources such as NCANDS, CPS administrative records, health systems data, and population-based surveys, with candid discussion of underreporting, misclassification, and data quality. By the end of the course, you will be able to link rigorously analyzed evidence to actionable prevention, monitoring, and resource allocation that support safe, stable, nurturing relationships for all children.

Epidemiology of Child Maltreatment: Trends, Disparities, and Determinants

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            <p>This course examines the epidemiology of child maltreatment through a rigorous, data-informed lens, focusing on trends, disparities, and determinants across populations. Learners will begin with clear operational definitions of maltreatment—physical, sexual, and emotional abuse; neglect; and exposure to violence—situated within mandated reporting contexts that shape how cases are identified and counted. Foundational epidemiologic concepts such as prevalence, incidence, rates, ratios, and population attributable fraction are introduced alongside data quality principles, including case definitions, surveillance sensitivity and specificity, underreporting, and misclassification. Ethical considerations—confidentiality, equity, and minimizing harm—are integrated throughout to ensure respectful, responsible use of sensitive data.</p>
<p>Building on these foundations, the course surveys the major data sources and surveillance systems used to quantify child maltreatment. Administrative records (CPS, law enforcement, health care EHR/ICD codes, hospital discharges, mortality reviews), population-based surveys (including NCANDS, YRBSS modules, the National Survey of Children’s Exposure to Violence, and international DHS), and sentinel/cohort designs are compared for their strengths and limitations, with attention to data linkage, deduplication, and proper denominators. Learners will practice estimating prevalence and incidence, analyzing trends with age–period–cohort perspectives, and applying spatial methods to detect geographic patterns, seasonality, and neighborhood effects while appropriately quantifying uncertainty using confidence intervals, small-area estimation, and Bayesian approaches.</p>
<p>A central emphasis is on disparities, determinants, and protective factors. The course explores socio-demographic differences by age, sex, race and ethnicity, disability status, immigration status, and socioeconomic position, and examines contextual drivers such as caregiver mental health and substance use, intimate partner violence, housing instability, poverty, and structural racism. Using the ecological model, learners will distinguish individual, relationship, community, and societal influences; evaluate protective factors like social support and safe, stable, nurturing relationships; and interpret risk ratios with attention to confounding, mediation, and effect modification. Throughout, an equity-centered lens helps avoid stigmatization and ecological fallacies, keeping focus on structural contributors to risk and resilience.</p>
<p>Methodological rigor is reinforced through modules on bias, confounding, and causal inference, with practical strategies including stratification, multivariable adjustment, propensity scores, and directed acyclic graphs. Learners will engage with quasi-experimental approaches—natural experiments, interrupted time series, and difference-in-differences—to assess policy impacts. The course then connects exposure to outcomes across the life course, addressing near- and long-term consequences such as injury, mental and behavioral health, education, justice involvement, chronic disease, and economic costs, as well as the burden of harm through metrics like years lived with disability and excess healthcare utilization. Comorbidity with other adversities and intergenerational dynamics are considered in a lifecourse context.</p>
<p>Finally, the curriculum translates evidence into action. Learners will review prevention frameworks (primary, secondary, tertiary; universal, selective, indicated), appraise evidence-informed strategies (parenting programs, home visiting, economic supports, safe housing, and behavioral health services), and practice monitoring and evaluation using logic models, indicators, implementation fidelity, and equity impact assessment. Practical skills include building and interpreting data dashboards and conducting community needs assessments to prioritize resources and reduce disparities. Knowledge checks throughout—spanning concepts and definitions, data sources, trends and disparities, bias and causality, outcomes, and prevention—consolidate learning and prepare participants to apply epidemiologic evidence to policy and practice in diverse settings.</p>
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