Impact of intercensal population projections and error of closure on breast cancer surveillance: examples from 10 California counties

Phipps, Amanda I.; Clarke, Christina A.; Ereman, Rochelle

doi:10.1186/bcr1266

Cited by 11 publications

(14 citation statements)

References 6 publications

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“…Second, coal counties were relatively small in terms of population, so even if the migration inflows had a large effect in those counties, they may not have had any meaningful impact elsewhere. Finally, the low costs of migration in this setting may have caused many of those returning to the coal counties during the boom decade to have originated form a set of locations that was much more spatially diffuse than in our cotton (Surveillance, Epidemiology, and End Results Program) population data published by the National Cancer Institute and used in studies like Phipps et al (2005) may be slightly more reliable than published U.S. Census estimates. However, these are only available beginning in 1969, and so are unsuitable for our analysis.…”

Section: Migrationmentioning

confidence: 71%

“…Indeed, the 1990-2000 census period in the U.S. has been widely acknowledged as having an unusually large error of closure, and this large error in turn has been attributed to precisely those issues we highlight as concerns in this analysis: the mis-measurement of highly mobile populations-in the case of the U.S. in the 1990s, the growing Hispanic population (Robinson & West, 2005). 66 Recent studies in the medical and biostatistics literatures have also raised the issue of denominator measurement error, highlighting in particular the issue of measurement error in small demographic strata, such as those by age and race (see Phipps et al (2005) for a discussion of intercensal interpolation-driven bias in estimates of breast cancer incidence rates, and Hund (2012, Ch. 4-5) for a broader methodological discussion of related denominator issues).…”

Section: Resultsmentioning

confidence: 99%

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Estimating the Recession-Mortality Relationship when Migration Matters

Arthi¹,

Beach²,

Hanlon³

2017

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A large literature following Ruhm (2000) suggests that mortality falls during recessions and rises during booms. The panel-data approach used to generate these results assumes that either there is no substantial migration response to temporary changes in local economic conditions, or that any such response is accurately captured by intercensal population estimates. To assess the importance of these assumptions, we examine two natural experiments: the recession in cotton textile-producing districts of Britain during the U.S. Civil War, and the coal boom in Appalachian counties of the U.S. that followed the OPEC oil embargo in the 1970s. In both settings, we find evidence of a substantial migratory response. Moreover, we show that estimates of the relationship between business cycles and mortality are highly sensitive to assumptions related to migration. After adjusting for migration, we find that mortality increased during the cotton recession, but was largely unaffected by the coal boom. Overall, our results suggest that migration can meaningfully bias estimates of the impact of business-cycle fluctuations on mortality.

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Section: Migrationmentioning

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Estimating the Recession-Mortality Relationship when Migration Matters

Arthi¹,

Beach²,

Hanlon³

2017

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“…For instance, 24% of hospital admissions at a hospital in Almeria (southern Spain) were patients who were living in the city but were not registered residents, leading to an overestimation of hospital admissions (Salas et al, 2003). Also, some studies warn of major differences in calculating cancer incidence depending on the source of information used (Roos, 1993;Phipps et al, 2005).…”

Section: Excessive Confidence In Information Sourcesmentioning

confidence: 99%

Common errors in disease mapping

Ocaña-Riola

2010

Geospat Health

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Abstract. Many morbid-mortality atlases and small-area studies have been carried out over the last decade. However, the methods used to draw up such research, the interpretation of results and the conclusions published are often inaccurate. Often, the proliferation of this practice has led to inefficient decision-making, implementation of inappropriate health policies and negative impact on the advancement of scientific knowledge. This paper reviews the most frequent errors in the design, analysis and interpretation of small-area epidemiological studies and proposes a diagnostic evaluation test that should enable the scientific quality of published papers to be ascertained. Nine common mistakes in disease mapping methods are discussed. From this framework, and following the theory of diagnostic evaluation, a standardised test to evaluate the scientific quality of a small-area epidemiology study has been developed. Optimal quality is achieved with the maximum score (16 points), average with a score between 8 and 15 points, and low with a score of 7 or below. A systematic evaluation of scientific papers, together with an enhanced quality in future research, will contribute towards increased efficacy in epidemiological surveillance and in health planning based on the spatio-temporal analysis of ecological information.

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“…Así, el 24% de los ingresos que se produjeron durante ocho meses en un hospital del sur de España correspondía a pacientes que residían en la ciudad pero no estaban empadronados en ella, por lo que la tasa de ingresos hospitalarios por 100.000 habitantes estaba sobreestimada. 32 De igual modo, algunos estudios advierten de la existencia de importantes diferencias en el cálculo de la incidencia de cáncer dependiendo del denominador utilizado 33,34 . Así, investigaciones realizadas en USA muestran que las tasas obtenidas a partir de estimaciones de población difieren alrededor de un 60% de las obtenidas utilizando el censo, lo que puede implicar una diferencia en el cálculo de la tasa de incidencia de cáncer de mama de hasta un 22% 34 .…”

Section: Municipios Con Exceso De Mortalidad Significativounclassified

Atlas interactivo de mortalidad en Andalucía (AIMA)

Ocaña-Riola¹,

Mayoral-Cortés²,

Sánchez-Cantalejo³

et al. 2008

Rev. Esp. Salud Publica

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Until now, mortality atlases have been static. Most of them describe the geographical distribution of mortality using count data aggregated over time and standardized mortality rates. However, this methodology has several limitations. Count data aggregated over time produce a bias in the estimation of death rates. Moreover, this practice difficult the study of temporal changes in geographical distribution of mortality. On the other hand, using standardized mortality hamper to check differences in mortality among groups. The Interactive Mortality Atlas in Andalusia (AIMA) is an alternative to conventional static atlases. It is a dynamic Geographical Information System that allows visualizing in web-site more than 12.000 maps and 338.00 graphics related to the spatio-temporal distribution of the main death causes in Andalusia by age and sex groups from 1981. The objective of this paper is to describe the methods used for AIMA development, to show technical specifications and to present their interactivity. The system is available from the link products in www.demap.es. AIMA is the first interactive GIS that have been developed in Spain with these characteristics. Spatio-temporal Hierarchical Bayesian Models were used for statistical data analysis. The results were integrated into web-site using a PHP environment and a dynamic cartography in Flash. Thematic maps in AIMA demonstrate that the geographical distribution of mortality is dynamic, with differences among year, age and sex groups. The information nowadays provided by AIMA and the future updating will contribute to reflect on the past, the present and the future of population health in Andalusia.

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Impact of intercensal population projections and error of closure on breast cancer surveillance: examples from 10 California counties

Cited by 11 publications

References 6 publications

Estimating the Recession-Mortality Relationship when Migration Matters

Estimating the Recession-Mortality Relationship when Migration Matters

Common errors in disease mapping

Atlas interactivo de mortalidad en Andalucía (AIMA)

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