Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies-2

<p><b>Copyright information:</b></p><p>Taken from "Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies"</p><p>Environmental Health Perspectives 2004;112(9):1016-1025.</p><p>Published online 15 Apr 2004</p><p>PMCID:PMC1247195.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p

Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies-0

<p><b>Copyright information:</b></p><p>Taken from "Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies"</p><p>Environmental Health Perspectives 2004;112(9):1016-1025.</p><p>Published online 15 Apr 2004</p><p>PMCID:PMC1247195.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p

Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies-1

<p><b>Copyright information:</b></p><p>Taken from "Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies"</p><p>Environmental Health Perspectives 2004;112(9):1016-1025.</p><p>Published online 15 Apr 2004</p><p>PMCID:PMC1247195.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p

The challenge of opt-outs from NHS data: a small-area perspective.

[First paragraph] One of the founding principles of the NHS is that it offers comprehensive, universal and free public health services at the point of delivery. As a result, NHS data provide a huge and invaluable resource of routinely collected primary (e.g. visits to GP practices) and secondary (e.g. hospital admissions, outpatient appointments, A&E attendances) healthcare data covering near-100% of the population of England. NHS Digital has the responsibility for collecting and publishing data and information from across the health and social care system in England and controls the dissemination of these data. Detailed analysis of NHS data by public health and research institutions has the potential to considerably improve health and social care in England

Characteristics of participants from two major recruitment campaigns to the UK COSMOS study (Phases 2 and 7).

<p>Legend: Blue bars represent Phase 2, red bars represent Phase 7. Fig 2 Footnotes: Phase 2 used a mobile phone subscriber sampling frame, letter invitation, web-based consent, registration, and questionnaire and a prize draw incentive, and recruited N = 67,793. Phase 7 used an electoral register sampling frame, letter invitation, web-based consent, registration, and questionnaire and a gift voucher incentive, and recruited N = 36,316. Together Phases 2 and 7 recruited N = 104,109. The profile of participants presented here is based on N = 67627 from Phase 2 and N = 36218 from Phase 7, i.e. excluding 264 withdrawals. With the exception of socio-economic classification, the percentages calculated exclude Missing from the denominator. N for missing are as follows: Phase 2: Sex N = 290, Age group N = 306, Ethnicity N = 9205, Highest Educational Qualification N = 9124, Smoking N = 8404; Phase 7: Sex N = 2, Age group N = 9, Ethnicity N = 5135, Highest Educational Qualification N = 5083, Smoking N = 4760. For socio-economic classification Missing are included in the Not classified category, which also contains people who never worked or were long-term unemployed and therefore could not be assigned a classification based on occupation.</p

Summary of Study Methods and Participation By Recruitment Phase of the UK COSMOS Study, 2009–2012.

<p>Footnotes:</p><p>^a Total includes N = 53 additional volunteers who were recruited between Phase 3 and 5 (and chronologically collectively classed as Phase 4), in response to various recruitment strategies, including 2 participants recruited via a Facebook advert trial; these strategies were run concurrently and response rates cannot be calculated for comparisons, therefore are not shown in detail here.</p><p>^b Gift voucher offer in Phase 6 ceased at Day 25 (17/06/2012).</p><p>^c Gift voucher offer in Phase 7 ceased at Day 20 (05/09/2012), as recruitment target of 100,000 reached.</p><p>^d Number of invitations actually received, opened and read may be lower, e.g. if invitation is returned to Sender</p><p>Summary of Study Methods and Participation By Recruitment Phase of the UK COSMOS Study, 2009–2012.</p

Cumulative response rates to UK COSMOS study invitations, by recruitment phase, 2009–2012.

<p>Fig 1 Footnotes: Phase 1 used a mobile phone subscriber sampling frame, letter invitation, paper consent and registration, questionnaire via paper or web and no incentive. Phase 2 used a mobile phone subscriber sampling frame, letter invitation, web-based consent, registration, and questionnaire and a prize draw incentive. Phase 3 used a direct marketing list sampling frame, SMS invitation, web-based consent, registration, and questionnaire and no incentive. Phase 5 used an electoral register sampling frame, letter invitation, web-based consent, registration, and questionnaire and a prize draw incentive. Phase 6 used an electoral register sampling frame, letter invitation, web-based consent, registration, and questionnaire and a gift voucher incentive. Phase 7 used an electoral register sampling frame, letter invitation, web-based consent, registration, and questionnaire and a gift voucher incentive. ‘Invitation only’ represents recruitment of invitee named on letter, and ‘Spin-off recruitment’ represents recruitment of additional friends and family.</p

Automation of cleaning and reconstructing residential address histories to assign environmental exposures in longitudinal studies.

BACKGROUND:We have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposures to cohort participants. The first application of this algorithm was to construct prenatal and early life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC) in the South West of England, using previously estimated particulate matter ≤10  µm (PM10) concentrations. METHODS:ALSPAC recruited 14 541 pregnant women between 1991 and 1992. We assigned trimester-specific estimated PM10 exposures for 12 752 pregnancies, and first year of life exposures for 12 525 births, based on maternal residence and residential mobility. RESULTS:Average PM10 exposure was 32.6  µg/m3 [standard deviation (S.D.) 3.0  µg/m3] during pregnancy and 31.4 µg/m3 (S.D. 2.6  µg/m3) during the first year of life; 6.7% of women changed address during pregnancy, and 18.0% moved during first year of life of their infant. Exposure differences ranged from -5.3  µg/m3 to 12.4  µg/m3 (up to 26% difference) during pregnancy and -7.22  µg/m3 to 7.64  µg/m3 (up to 27% difference) in the first year of life, when comparing estimated exposure using the address at birth and that assessed using the complete cleaned address history. For the majority of individuals exposure changed by <5%, but some relatively large changes were seen both in pregnancy and in infancy. CONCLUSIONS:ALGAE provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life stage-specific exposure estimates with the potential to reduce exposure misclassification

Small-area methods for investigation of environment and health.

Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases

Evaluation of sampling bias using remote recruitment.

*<p>Welsh Index of Multiple Deprivation.</p