Correction of misclassification bias induced by the residential mobility in studies examining the link between socioeconomic environment and cancer incidence

Bryère, Joséphine; Pornet, Carole; Dejardin, Olivier; Launay, Ludivine; Guittet, Lydia; Launoy, Guy

doi:10.1016/j.canep.2014.12.008

Cited by 10 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study presents novel information regarding the utility of EMR address histories for determining longitudinal environmental and neighborhood exposure, and fits with other studies that employed different methods to recover longitudinal environmental exposure information [2,42]. Within a large, mature, integrated EMR, we explored the effects of collection bias for location-based measures of environmental exposure using a survey designed to detect differences in participant’s EMR-derived versus true (self-report) recent address history.…”

Section: Discussionsupporting

confidence: 72%

“…Healthcare system EMR data collection procedures may lead to misclassification or other biases [2]. For example, if address collection is driven by patient attendance, then the healthcare system simultaneously fails to collect full address histories for patients in good health and excels at collecting full address histories for patients in poor health.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The utility of EMR address histories for assessing neighborhood exposures

Hughes

Pruitt

2017

Annals of Epidemiology

View full text Add to dashboard Cite

Purpose Electronic medical records (EMR) include residential address histories, which may alleviate exposure misclassification caused by exclusion of patient spatiotemporal location. EMR data are increasingly available but rarely leveraged as a measure of cumulative environmental exposure, in part due to limited understanding of the validity of EMR-derived address histories. Methods We compared EMR address histories to self-reported histories among 100 patients of a safety-net healthcare system completing a telephone survey. We assessed agreement and compared 7 neighborhood-level environmental exposures as assessed using both data sources. Results While 17.1% of respondents did not live at the most recent EMR-derived address during the survey, nearly all (98%) lived there at some point. For respondents with >1 EMR-derived address (N=64), 87.5% had once lived at the previous EMR address. Of these, 30.4% lived at ≥1 additional residences between the two most recent EMR address. For all measures, neighborhood-level environmental exposures did not differ when using EMR-derived vs. self-report addresses. Conclusions More recent EMR-derived addresses are more accurate and differences compared to self-reported addresses in neighborhood-level exposures are negligible. EMR-derived address histories are incomplete and likely suffer from collection bias; future research should further assess their validity and reliability.

show abstract

Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

The utility of EMR address histories for assessing neighborhood exposures

Hughes

Pruitt

2017

Annals of Epidemiology

View full text Add to dashboard Cite

show abstract

“…Therefore, it is unlikely that our findings are driven by residential mobility patterns. Furthermore some evidence suggests that residential mobility is most likely to cause an underestimation of spatial inequalities and socioeconomic effects (Bryere et al, 2015). Another limitation of the study, is related to the possibility that the location of nursing homes, which might have lower survival among their community residents could have influenced the analysis (Shah et al, 2013).…”

Section: Discussionmentioning

confidence: 93%

The influence of socioeconomic, biogeophysical and built environment on old-age survival in a Southern European city

et al. 2016

View full text Add to dashboard Cite

Old-age survival is a good indicator of population health and regional development. We evaluated the spatial distribution of old-age survival across Porto neighbourhoods and its relation with physical (biogeophysical and built) and socioeconomic factors (deprivation). Smoothed survival rates and odds ratio (OR) were estimated using Bayesian spatial models. There were important geographical differentials in the chances of survival after 75 years of age. Socioeconomic deprivation strongly impacted old-age survival (Men: least deprived areas OR=1.31(1.05-1.63); Women OR=1.53(1.24-1.89)), explaining over 40% of the spatial variance. Walkability and biogeophysical environment were unrelated to old-age survival and also unrelated to socioeconomic deprivation, being fairly evenly distributed through the city.

show abstract

“…The majority measured deprivation at an aggregate level because other data are often not available in cancer registries. While neighborhood deprivation itself may be an important risk factor for poor health that needs to be investigated, bias can be introduced when such aggregated data are used to ascertain individual socio‐economic position (SEP) …”

mentioning

confidence: 99%

“…While neighborhood deprivation itself may be an important risk factor for poor health 11 that needs to be investigated, bias can be introduced when such aggregated data are used to ascertain individual socio-economic position (SEP). 12,13 If individual data on SEP are available in the registries or via record linkage, they are sometimes only rough. For example, the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute (NCI) only records data on age, sex, ethnicity, marital status, place of birth and residence, whereas education, occupation, income and employment status are not documented.…”

mentioning

confidence: 99%

Impact of socio‐economic position on cancer stage at presentation: Findings from a large hospital‐based study in Germany

Schnittger

Roick²,

Briest

et al. 2016

Intl Journal of Cancer

View full text Add to dashboard Cite

We explored the relationship between socio‐economic characteristics and cancer stage at presentation. Patients admitted to a university hospital for diagnosis and treatment of cancer provided data on their education, vocational training, income, employment, job, health insurance and postcode. Tumor stage was classified according to the Union International Contre le Cancer (UICC). To analyze disparities in the likelihood of late‐stage (UICC III/IV vs. I/II) diagnoses, logistic regression models adjusting for age and gender were used. Out of 1,012 patients, 572 (59%) had late‐stage cancer. Separately tested, increased odds of advanced disease were associated with post‐compulsory education compared to college degrees, with apprenticeship and no vocational training, with unemployment, disability pension, jobs with a low hierarchy level, blue collar jobs and with low income. Health insurance and community size were not related with late‐stage cancer. Jointly modelled, there was evidence for an independent effect of unemployment (odds ratio (OR) 1.7, CI 1.0–2.8), disability pension (OR 1.8, CI 1.0–3.2) and very low income (OR 2.6, CI 1.1–6.1) on the likelihood of advanced disease stage. It is of great concern that these socio‐economic gradients occur even in systems with equal access to health care.

show abstract

Correction of misclassification bias induced by the residential mobility in studies examining the link between socioeconomic environment and cancer incidence

Cited by 10 publications

References 20 publications

The utility of EMR address histories for assessing neighborhood exposures

The utility of EMR address histories for assessing neighborhood exposures

The influence of socioeconomic, biogeophysical and built environment on old-age survival in a Southern European city

Impact of socio‐economic position on cancer stage at presentation: Findings from a large hospital‐based study in Germany

Contact Info

Product

Resources

About