Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study
Abstract Background It is challenging to manage data collection as planned and creation of opportunities to adapt during the course of enrolment may be needed. This paper aims to summarize the different sampling strategies adopted in the second wave of Observation of Cardiovascular Risk Factors (ORI...
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BMC
2019-02-01
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Series: | BMC Medical Research Methodology |
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Online Access: | http://link.springer.com/article/10.1186/s12874-019-0669-0 |
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doaj-9895c6457fc4497bad8d497cadd6c373 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ala’a Alkerwi Jessica Pastore Nicolas Sauvageot Gwenaëlle Le Coroller Valéry Bocquet Marylène d’Incau Gloria Aguayo Brice Appenzeller Dritan Bejko Torsten Bohn Laurent Malisoux Sophie Couffignal Stephanie Noppe Charles Delagardelle Jean Beissel Anna Chioti Saverio Stranges Jean-Claude Schmit |
spellingShingle |
Ala’a Alkerwi Jessica Pastore Nicolas Sauvageot Gwenaëlle Le Coroller Valéry Bocquet Marylène d’Incau Gloria Aguayo Brice Appenzeller Dritan Bejko Torsten Bohn Laurent Malisoux Sophie Couffignal Stephanie Noppe Charles Delagardelle Jean Beissel Anna Chioti Saverio Stranges Jean-Claude Schmit Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study BMC Medical Research Methodology Sample representativeness Population-based study Follow-up studies, population health Epidemiology |
author_facet |
Ala’a Alkerwi Jessica Pastore Nicolas Sauvageot Gwenaëlle Le Coroller Valéry Bocquet Marylène d’Incau Gloria Aguayo Brice Appenzeller Dritan Bejko Torsten Bohn Laurent Malisoux Sophie Couffignal Stephanie Noppe Charles Delagardelle Jean Beissel Anna Chioti Saverio Stranges Jean-Claude Schmit |
author_sort |
Ala’a Alkerwi |
title |
Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study |
title_short |
Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study |
title_full |
Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study |
title_fullStr |
Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study |
title_full_unstemmed |
Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) study |
title_sort |
challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (oriscav-lux 2) study |
publisher |
BMC |
series |
BMC Medical Research Methodology |
issn |
1471-2288 |
publishDate |
2019-02-01 |
description |
Abstract Background It is challenging to manage data collection as planned and creation of opportunities to adapt during the course of enrolment may be needed. This paper aims to summarize the different sampling strategies adopted in the second wave of Observation of Cardiovascular Risk Factors (ORISCAV-LUX, 2016–17), with a focus on population coverage and sample representativeness. Methods Data from the first nationwide cross-sectional, population-based ORISCAV-LUX survey, 2007–08 and from the newly complementary sample recruited via different pathways, nine years later were analysed. First, we compare the socio-demographic characteristics and health profiles between baseline participants and non-participants to the second wave. Then, we describe the distribution of subjects across different strategy-specific samples and performed a comparison of the overall ORISCAV-LUX2 sample to the national population according to stratification criteria. Results For the baseline sample (1209 subjects), the participants (660) were younger than the non-participants (549), with a significant difference in average ages (44 vs 45.8 years; P = 0.019). There was a significant difference in terms of education level (P < 0.0001), 218 (33%) participants having university qualification vs. 95 (18%) non-participants. The participants seemed having better health perception (p < 0.0001); 455 (70.3%) self-reported good or very good health perception compared to 312 (58.2%) non-participants. The prevalence of obesity (P < 0.0001), hypertension (P < 0.0001), diabetes (P = 0.007), and mean values of related biomarkers were significantly higher among the non-participants. The overall sample (1558 participants) was mainly composed of randomly selected subjects, including 660 from the baseline sample and 455 from other health examination survey sample and 269 from civil registry sample (constituting in total 88.8%), against only 174 volunteers (11.2%), with significantly different characteristics and health status. The ORISCAV-LUX2 sample was representative of national population for geographical district, but not for sex and age; the younger (25–34 years) and older (65–79 years) being underrepresented, whereas middle-aged adults being over-represented, with significant sex-specific difference (p < 0.0001). Conclusion This study represents a careful first-stage analysis of the ORISCAV-LUX2 sample, based on available information on participants and non-participants. The ORISCAV-LUX datasets represents a relevant tool for epidemiological research and a basis for health monitoring and evidence-based prevention of cardiometabolic risk in Luxembourg. |
topic |
Sample representativeness Population-based study Follow-up studies, population health Epidemiology |
url |
http://link.springer.com/article/10.1186/s12874-019-0669-0 |
work_keys_str_mv |
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doaj-9895c6457fc4497bad8d497cadd6c3732020-11-25T02:51:52ZengBMCBMC Medical Research Methodology1471-22882019-02-0119111010.1186/s12874-019-0669-0Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk factors (ORISCAV-LUX 2) studyAla’a Alkerwi0Jessica Pastore1Nicolas Sauvageot2Gwenaëlle Le Coroller3Valéry Bocquet4Marylène d’Incau5Gloria Aguayo6Brice Appenzeller7Dritan Bejko8Torsten Bohn9Laurent Malisoux10Sophie Couffignal11Stephanie Noppe12Charles Delagardelle13Jean Beissel14Anna Chioti15Saverio Stranges16Jean-Claude Schmit17Luxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthLuxembourg Institute of Health (LIH), Department of Population HealthCentre Hospitalier du Luxembourg (CHL)Centre Hospitalier du Luxembourg (CHL)Centre Hospitalier du Luxembourg (CHL)Ministry of Health, Directorate of HealthLuxembourg Institute of Health (LIH), Department of Population HealthMinistry of Health, Directorate of HealthAbstract Background It is challenging to manage data collection as planned and creation of opportunities to adapt during the course of enrolment may be needed. This paper aims to summarize the different sampling strategies adopted in the second wave of Observation of Cardiovascular Risk Factors (ORISCAV-LUX, 2016–17), with a focus on population coverage and sample representativeness. Methods Data from the first nationwide cross-sectional, population-based ORISCAV-LUX survey, 2007–08 and from the newly complementary sample recruited via different pathways, nine years later were analysed. First, we compare the socio-demographic characteristics and health profiles between baseline participants and non-participants to the second wave. Then, we describe the distribution of subjects across different strategy-specific samples and performed a comparison of the overall ORISCAV-LUX2 sample to the national population according to stratification criteria. Results For the baseline sample (1209 subjects), the participants (660) were younger than the non-participants (549), with a significant difference in average ages (44 vs 45.8 years; P = 0.019). There was a significant difference in terms of education level (P < 0.0001), 218 (33%) participants having university qualification vs. 95 (18%) non-participants. The participants seemed having better health perception (p < 0.0001); 455 (70.3%) self-reported good or very good health perception compared to 312 (58.2%) non-participants. The prevalence of obesity (P < 0.0001), hypertension (P < 0.0001), diabetes (P = 0.007), and mean values of related biomarkers were significantly higher among the non-participants. The overall sample (1558 participants) was mainly composed of randomly selected subjects, including 660 from the baseline sample and 455 from other health examination survey sample and 269 from civil registry sample (constituting in total 88.8%), against only 174 volunteers (11.2%), with significantly different characteristics and health status. The ORISCAV-LUX2 sample was representative of national population for geographical district, but not for sex and age; the younger (25–34 years) and older (65–79 years) being underrepresented, whereas middle-aged adults being over-represented, with significant sex-specific difference (p < 0.0001). Conclusion This study represents a careful first-stage analysis of the ORISCAV-LUX2 sample, based on available information on participants and non-participants. The ORISCAV-LUX datasets represents a relevant tool for epidemiological research and a basis for health monitoring and evidence-based prevention of cardiometabolic risk in Luxembourg.http://link.springer.com/article/10.1186/s12874-019-0669-0Sample representativenessPopulation-based studyFollow-up studies, population healthEpidemiology |