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Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures
BACKGROUND: To demonstrate the potential of Population Impact Measures in helping to prioritise alternative interventions for psychiatry, this paper estimates the number of relapses and hospital readmissions prevented for depression and schizophrenia by adopting best practice recommendations. The re...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1475571/ https://www.ncbi.nlm.nih.gov/pubmed/16553956 http://dx.doi.org/10.1186/1745-0179-2-3 |
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author | Heller, Richard F Gemmell, Islay Patterson, Lesley |
author_facet | Heller, Richard F Gemmell, Islay Patterson, Lesley |
author_sort | Heller, Richard F |
collection | PubMed |
description | BACKGROUND: To demonstrate the potential of Population Impact Measures in helping to prioritise alternative interventions for psychiatry, this paper estimates the number of relapses and hospital readmissions prevented for depression and schizophrenia by adopting best practice recommendations. The results are designed to relate to particular local populations. METHODS: Literature-based estimates of disease prevalence, relapse and re-admission rates, current and best practice treatment rates, levels of adherence with interventions and relative risk reduction associated with different interventions were obtained and calculations made of the Number of Events Prevented in your Population (NEPP). RESULTS: In a notional population of 100,000 adults, going from current to 'best' practice for different interventions, the number of relapses prevented in the next year for schizophrenia were 6 (increasing adherence to medication), 23 (family intervention), 43 (relapse prevention), and 44 (early intervention); and for depression the number of relapses prevented in the next year were 100 (increasing care management), 227 (continuing treatment with antidepressants), 279 (increasing rate of diagnosis), and 325 (Cognitive Behaviour Therapy). Hospital re-admissions prevented in the next year for schizophrenia were 6 (increasing adherence to medication), 36 (relapse prevention) and 40 (early intervention). CONCLUSION: Population Impact measures provide the possibility for a policy-maker to see the impact of a new intervention on the population as a whole, and to compare alternative interventions to best improve psychiatric disease outcomes. The methods are much simpler than others, and have the advantage of being transparent. |
format | Text |
id | pubmed-1475571 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-14755712006-06-08 Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures Heller, Richard F Gemmell, Islay Patterson, Lesley Clin Pract Epidemiol Ment Health Research BACKGROUND: To demonstrate the potential of Population Impact Measures in helping to prioritise alternative interventions for psychiatry, this paper estimates the number of relapses and hospital readmissions prevented for depression and schizophrenia by adopting best practice recommendations. The results are designed to relate to particular local populations. METHODS: Literature-based estimates of disease prevalence, relapse and re-admission rates, current and best practice treatment rates, levels of adherence with interventions and relative risk reduction associated with different interventions were obtained and calculations made of the Number of Events Prevented in your Population (NEPP). RESULTS: In a notional population of 100,000 adults, going from current to 'best' practice for different interventions, the number of relapses prevented in the next year for schizophrenia were 6 (increasing adherence to medication), 23 (family intervention), 43 (relapse prevention), and 44 (early intervention); and for depression the number of relapses prevented in the next year were 100 (increasing care management), 227 (continuing treatment with antidepressants), 279 (increasing rate of diagnosis), and 325 (Cognitive Behaviour Therapy). Hospital re-admissions prevented in the next year for schizophrenia were 6 (increasing adherence to medication), 36 (relapse prevention) and 40 (early intervention). CONCLUSION: Population Impact measures provide the possibility for a policy-maker to see the impact of a new intervention on the population as a whole, and to compare alternative interventions to best improve psychiatric disease outcomes. The methods are much simpler than others, and have the advantage of being transparent. BioMed Central 2006-03-22 /pmc/articles/PMC1475571/ /pubmed/16553956 http://dx.doi.org/10.1186/1745-0179-2-3 Text en Copyright ©2006 Heller et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Heller, Richard F Gemmell, Islay Patterson, Lesley Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures |
title | Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures |
title_full | Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures |
title_fullStr | Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures |
title_full_unstemmed | Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures |
title_short | Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures |
title_sort | helping to prioritise interventions for depression and schizophrenia: use of population impact measures |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1475571/ https://www.ncbi.nlm.nih.gov/pubmed/16553956 http://dx.doi.org/10.1186/1745-0179-2-3 |
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