Diseños evaluativos en salud pública: Aspectos metodológicos

López, Mª José; Olmo, Marc Marí-Dell’; Pérez-Giménez, Anna; Nebot, Manel

doi:10.1016/S0213-9111(11)70003-6

Gaceta Sanitaria

ISSN: 0213-9111

Gaceta Sanitaria (¿¿¿¿) is the official scientific journal of the Sociedad Española de Salud Publica y Administración Sanitaria (Spanish Society of Public Health and Health Administration) (SESPAS).

Gaceta Sanitaria accepts and publishes articles in Spanish and English. It is an open access journal. All of its articles are freely available without charge and distributed under the Creative Commons Attribution-NonCommercial- NoDerivatives 4.0 International licence.

Call for papers: Special Issue on Health Economics

Convocatoria abierta y permanente para incorporar Editores/as asociados/as al Comité Editorial de Gaceta Sanitaria

Call for papers: Special Issue on Health Economics

Open and permanent call to incorporate Associate Editors to the Editorial Committee of Gaceta Sanitaria

Indexed in:

Web of Science (Science Citation Index, SCI, y Social Sciences Citation Index, SSCI), Medline/PubMed, Index Medicus, Scopus, Scielo, IBECS, Índice Médico Español, Toxline, Cancerlit, Aidsline, Cab Health, Bibliomed, Cuiden, Eventline y Healthstar

La evaluación de las intervenciones de salud pública, en la cual rara vez es posible la aleatorización de individuos y habitualmente intervienen múltiples factores, implica numerosos retos metodológicos. Para afrontarlos hay que tener en cuenta determinados aspectos, como la elección de un diseño evaluativo apropiado y la realización de un análisis estadístico que considere los posibles confusores. El objetivo de este artículo es describir los diseños más frecuentes en la evaluación de intervenciones (políticas, programas o campañas) de salud pública, enumerando sus características, analizando sus principales ventajas y limitaciones, y haciendo una breve descripción del análisis estadístico más utilizado en cada uno de ellos.

Palabras clave:

Evaluación

Salud pública

Diseños evaluativos

Abstract

Evaluation of public health interventions poses numerous methodological challenges. Randomization of individuals is not always feasible and interventions are usually composed of multiple factors. To face these challenges, certain elements, such as the selection of the most appropriate design and the use of a statistical analysis that includes potential confounders, are essential. The objective of this article was to describe the most frequently used designs in the evaluation of public health interventions (policies, programs or campaigns). The characteristics, strengths and weaknesses of each of these evaluative designs are described. Additionally, a brief explanation of the most commonly used statistical analysis in each of these designs is provided.

Keywords:

Evaluation

Public health

Evaluative designs

Full text is only aviable in PDF

Bibliografía

[1.]

P.H. Rossi, M.W. Lipsey, H.E. Freeman.

Evaluation: a systematic approach.

7th, Sage Publi, (2004),

[2.]

E. Posavac, R. Carey.

Program evaluation: methods and case studies.

7th, Pearson Education, (1992),

[3.]

M. Nebot, M.J. López, C. Ariza, et al.

Impact of the Spanish smoking law on exposure to secondhand smoke in offices and hospitality venues: before-and-after study.

Environ Health Perspect, 117 (2009), pp. 344-347

http://dx.doi.org/10.1289/ehp.11845 | Medline

[4.]

J. Arnau Gras.

Diseños temporales: técnicas de análisis.

Edicions de la Universitat de Barcelona, (2001),

[5.]

L.I. Langbein, C.L. Felbinger.

Public program evaluation: a statisticall guide.

M.E. Sharpe, (2006),

[6.]

A.M. Novoa, K. Pérez, E. Santamarina-Rubio, et al.

Road safety in the political agenda: the impact on road traffic injuries.

J Epidemiol Community Health, (2010),

[7.]

A. Tobías, M. Sáez, I. Galán.

[Graphic tools for the descriptive analysis of temporary series in medical research].

Med Clin (Barc), 122 (2004), pp. 701-706

[8.]

D.K. Simonton.

Cross-sectional time-series experiments: some suggested statistical analyses.

Psychol Bull, 84 (1977), pp. 489-502

[9.]

A.M. Stolwijk, H. Straatman, G.A. Zielhuis.

Studying seasonality by using sine and cosine functions in regression analysis.

J Epidemiol Community Health, 53 (1999), pp. 235-238

Medline

[10.]

Y. Robitaille, S. Laforest, M. Fournier, et al.

Moving forward in fall prevention: an intervention to improve balance among older adults in real-world settings.

Am J Public Health, 95 (2005), pp. 2049-2056

http://dx.doi.org/10.2105/AJPH.2004.057612 | Medline

[11.]

J.K. Luellen, W.R. Shadish, M.H. Clark.

Propensity scores: an introduction and experimental test.

Eval Rev, 29 (2005), pp. 530-558

http://dx.doi.org/10.1177/0193841X05275596 | Medline

[12.]

P.R. Rosenbaum, D.B. Rubin.

The central role of the propensity score in observational studies for causal effects.

Biometrika, 70 (1983), pp. 41-55

[13.]

M. Expósito Ruiz, M. Ruiz Bailén, S. Pérez Vicente, et al.

Uso de la metodología propensity score en la investigación sanitaria.

Rev Clin Esp, 208 (2008), pp. 358-360

Medline

[14.]

J.L. Lauby, P.J. Smith, M. Stark, et al.

A community-level HIV prevention intervention for inner-city women: results of the women and infants demonstration projects.

Am J Public Health, 90 (2000), pp. 216-222

Medline

[15.]

T.M. Braun.

A mixed model-based variance estimator for marginal model analyses of cluster randomized trials.

Biom J, 49 (2007), pp. 394-405

http://dx.doi.org/10.1002/bimj.200510280 | Medline

[16.]

W.R. Shadish, T.D. Cook.

The renaissance of field experimentation in evaluating interventions.

Annu Rev Psychol, 60 (2009), pp. 607-629

http://dx.doi.org/10.1146/annurev.psych.60.110707.163544 | Medline

[17.]

F. Faggiano, F. Vigna-Taglianti, G. Burkhart, et al.

The effectiveness of a school-based substance abuse prevention program: 18-month follow-up of the EU-Dap cluster randomized controlled trial.

Drug Alcohol Depend, 108 (2010), pp. 56-64

http://dx.doi.org/10.1016/j.drugalcdep.2009.11.018 | Medline

[18.]

B.A. Jacob, L. Lefgren.

Remedial education and student achievement: a regressiondiscontinuity analysis.

Review of Economics and Statistics, 86 (2004), pp. 226-244

[19.]

A. Linden, J.L. Adams, N. Roberts.

Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design.

J Eval Clin Pract, 12 (2006), pp. 124-131

http://dx.doi.org/10.1111/j.1365-2753.2005.00573.x | Medline

[20.]

W.R. Shadish, T.D. Cook.

Experimental and quasi-experimental designs for generalized causal inference.

Houghton Mifflin Company, (2002),

[21.]

S.R. Khandker, G.B. Koolwal, H. Samad.

Handbook on impact evaluation: quantitative methods and practices.

World Bank Publications, (2009),

[22.]

F.G. Benavides, F. Rodrigo, A.M. García, et al.

Evaluation of the effectiveness of preventive activities (Strategic Action Plans) on the incidence of non-fatal traumatic occupational injuries leading to disabilities in Spain (1994–2004).

Rev Esp Salud Publica, 81 (2007), pp. 615-624

Medline

[23.]

R.E. Hilliard.

A post-hoc analysis of music therapy services for residents in nursing homes receiving hospice care.

J Music Ther, 41 (2004), pp. 266-281

Medline

[24.]

D.M. Dimitrov, P.D. Rumrill.

Pretest-posttest designs and measurements of change.

Work, 20 (2003), pp. 159-165

Medline

[25.]

G.H. Marin, C. Homar, G. Niedfeld, et al.

Evaluation of the state intervention project to improve quality of life and reduce the complications associated with aging: “Add health to your years”.

Gac Sanit, 23 (2009), pp. 272-277

http://dx.doi.org/10.1016/j.gaceta.2009.02.010 | Medline

[26.]

K. Pérez, M. Mari-Dell’olmo, A. Tobías, et al.

Reducing road traffic injuries: effectiveness of speed cameras in an urban setting.

Am J Public Health, 97 (2007), pp. 1632-1637

http://dx.doi.org/10.2105/AJPH.2006.093195 | Medline

[27.]

H.R. Fletcher, W.S. Fletcher, H.E. Wagner.

Epidemiología clínica: aspectos fundamentales, Masson–Williams & Wilkins, (1989),