Estimating the potential impacts of intervention from observational data
Methods for estimating causal attributable risk in a cross-sectional analysis of depressive symptoms in Latin America: Table 1
Datos Bibliográficos
| ID | 11181727 |
|---|---|
| Autores | Nancy L Fleischer (0000-0002-4371-9133, University of Michigan, autor de correspondencia), Lia C H Fernald (0000-0003-1555-4607, Berkeley Public Health Division), Anne Hubbard (0000-0002-3769-0127, Berkeley Public Health Division), A E Hubbard |
| Año | 2010 |
| Volumen | 64 |
| Número | 01 |
| Páginas | 16-21 |
| Fecha de publicación | 2010-01-01 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Journal of Epidemiology and Community Health (JOURNAL) |
| Identificadores de la revista | ISSN: 0143-005X • E-ISSN: 1470-2738 |
| Editorial | BMJ (PUBLISHER • GB) |
| DOI | 10.1136/jech.2008.085985 |
| PMID | 19643766 |
| OpenAlex | W2031980847 |
| Idioma | EN |
| Citas recibidas | 6 |
| Referencias citadas | 38 |
BACKGROUND: The field of epidemiology struggles both with enhancing causal inference in observational studies and providing useful information for policy makers and public health workers focusing on interventions. Population intervention models, analogous to population attributable fractions, estimate the causal impact of interventions in a population, and are one option for understanding the relative importance of various risk factors. With population intervention parameters, risk factors are effectively standardised, allowing one to compare their values directly and determine which potential intervention may have the greatest impact on the outcome. METHODS: The difference between total effects and population intervention parameters was examined using naïve, G-computation and inverse probability of treatment weighting approaches. The differences between these parameters and the intuitions they provide were explored using data from a 2003 cross-sectional study in rural Mexico. RESULTS: The assumptions, specific analytic steps, limitations and interpretations of the total effects and population intervention parameters are discussed, and code is provided in Stata. CONCLUSION: Population intervention parameters are a valuable and straightforward approach in epidemiological studies for making causal inference from the data while also supplying information that is relevant for researchers, public health practitioners and policy makers
Attributable risk · Causal inference · Environmental health · Inference · Intervention (counseling) · Observational study · Population · Psychiatry · Psychological intervention · Public health · Weighting · Advanced Causal Inference Techniques · Computer Science · Epidemiology · Medicine · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
Estimating predicted probabilities from logistic regression
Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children
Unemployment and Crime in US Cities During the Coronavirus Pandemic
Causal Impact
Predicting the Population Health Impacts of Community Interventions
Projected Cognitive and Brain Aging Benefits of Eliminating Cardiometabolic Risks in Non-Hispanic White and Black Males – Habs-HD
An Introduction to the Bootstrap
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An Introduction to the Bootstrap
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Global burden of blood-pressure-related disease, 2001
The Effects of Stressful Life Events on Depression
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
Mindfulness-based stress reduction and health benefits
Measuring the Functional Components of Social Support
Impact of the Mexican Program for Education, Health, and Nutrition (Progresa) on Rates of Growth and Anemia in Infants and Young Children
Relationship of subjective and objective social status with psychological and physiological functioning
The CES-D Scale
Prenatal Depression in Latinas in the U.S. and Mexico
A definition of causal effect for epidemiological research
Estimating causal effects from epidemiological data
The (mis)estimation of neighborhood effects
A Global Measure of Perceived Stress
The Structure of Coping
| Obras citantes distintas | 6 |
|---|---|
| Citas por año | 0,5 |
| Intervalo de citas | 2014 - 2026 (13) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 6 |