Lolland-Falster Health Study: Study protocol for a household-based prospective cohort study

Randi Jepsen, Cecilie Lindström Egholm, John Brodersen, Erik Simonsen, Jesper Grarup, Arne Cyron, Christina Ellervik, Knud Rasmussen, Randi Jepsen, Cecilie Lindström Egholm, John Brodersen, Erik Simonsen, Jesper Grarup, Arne Cyron, Christina Ellervik, Knud Rasmussen

Abstract

Introduction: Lolland-Falster consists of two islands in the southern part of Denmark where income is lower and life expectancy is shorter than in the general Danish population. It is a mixed rural-provincial area with approximately 100,000 inhabitants. The Lolland-Falster Health Study was initiated to gain knowledge on the determinants of health in this disadvantaged area. Methods: The study is a household-based prospective cohort study including people of all ages. The entire household of randomly selected inhabitants is allocated either to an invited group or to an uninvited, non-contacted control group. The data collection encompasses questionnaires, physical examination and biological samples, i.e. blood and urine for same-day analysis and biobank storage, and saliva and faeces also for biobank storage. The civil registration number links collected data for each individual, family and household, with information in Danish registers. The data collection started in February 2016 and is estimated to end by 2019 after the enrolment of 20,000 people. Analysis: A number of in-depth sub-studies are planned. Emphasis will be given to analysis of intra- and inter-family variations in health determinants, genetics, lifestyle and health status. Ethics: Region Zealand's Ethical Committee on Health Research (SJ-421) and the Danish Data Protection Agency (REG-24-2015) approved the study. Trial registration: Clinicaltrials.gov (NCT02482896). Strength and limitations of this study: The strength of this study is that Lolland-Falster Health Study is a useful scientific resource for investigating cross-sectional difference and time trends within and between individuals, families and households. LOFUS adds diversity to the previously collected Danish population studies in urbanized areas. The limitation is that data collection is expensive. Conclusions: LOFUS will contribute to the knowledge on health in disadvantaged, rural-provincial areas.

Keywords: Epidemiology; cohort analysis; family health; genetics; households; public health.

Conflict of interest statement

Declaration of conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Map of Denmark with Lolland-Falster marked. Lolland Municipality in red and Guldborgsund Municipality in grey. Nakskov, Maribo and Nykøbing Falster are shown. Lolland is the island to the left and Falster is the island to the right. Lolland Municipality consists of the Western part of Lolland, whereas Guldborgsund Municipality consists of the Eastern part of Lolland and Falster.
Figure 2.
Figure 2.
Population, households and study sample for the Lolland-Falster Health Study. Randomly selected households are randomly allocated to either the invitation group (in grey) or the control group (in white) in a 2:1 ratio.

References

    1. Statistics Denmark. The whole country [in Danish], → Befolkning og valg → Folketal → FOLK1A → 2017K4, Hele landet: 2018 (accessed 11 January 2018).
    1. Brønnum-Hansen H, Baadsgaard M. Widening social inequality in life expectancy in Denmark. A register-based study on social composition and mortality trends for the Danish population. BMC Public Health 2012;12:994.
    1. Statistics Denmark. Lolland and Guldborgsund municipalities [in Danish], → Befolkning og valg → Folketal → FOLK1A → 2017K4, Lolland og Guldborgsund: 2018 (accessed 11 January 2018).
    1. Sabiers SE, Larsen HB. Residence of the social classes in 2012 [in Danish]. Copenhagen: Arbejderbevægelsens Erhvervsråd, 2014.
    1. Statistics Denmark. Income in the whole country, Lolland, and Guldborgsund [in Danish], → Arbejde, indkomst og formue → Person- og familieindkomster → INDKP101 → 2016, Hele landet, Lolland og Guldborgsund Kommune: 2018. (accessed 11 January 2018).
    1. Bjørsted E, Mølgaard A. More than one third are on social benefits in some municipalities [in Danish]. Copenhagen: : Arbejderbevægelsens Erhvervsråd, 2013.
    1. Juul JS, Blicher SP. Exit of successful inhabitants from and entry of vulnerable newcomers to Lolland [in Danish]. Copenhagen: Arbejderbevægelsens Erhvervsråd, 2016.
    1. Statistics Denmark. Life expectancy in Denmark [in Danish], → Befolkning og valg → Dødsfald og Middellevetid → HISB77 → 2015:2016, Hele landet: 2018. (accessed 11 January 2018).
    1. Bjelland I, Krokstad S, Mykletun A, et al. Does a higher educational level protect against anxiety and depression? The HUNT study. Soc Sci Med 2008;66:1334–45.
    1. Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc Sci Med 2013;90:24–31.
    1. Molarius A, Berglund K, Eriksson C, et al. Mental health symptoms in relation to socio-economic conditions and lifestyle factors: A population-based study in Sweden. BMC Public Health 2009;9:302.
    1. Gershon AS, Dolmage TE, Stephenson A, et al. Chronic obstructive pulmonary disease and socioeconomic status: A systematic review. COPD 2012;9:216–26.
    1. Stringhini S, Carmeli C, Jokela M, et al. Socioeconomic status and the 25 x 25 risk factors as determinants of premature mortality: A multicohort study and meta-analysis of 1.7 million men and women. Lancet 2017;389:1229–37.
    1. Iguacel I, Fernandez-Alvira JM, Bammann K, et al. Social vulnerability as a predictor of physical activity and screen time in European children. Int J Public Health 2018;63:283–95.
    1. Blaakilde AL, Hansen BH, Olesen LS, et al. Health Profile 2017 for Region Zealand and municipalities - “How are you?”[in Danish]. Sorø, Danmark: Region Zealand, 2018.
    1. Tjønneland A, Olsen A, Boll K, et al. Study design, exposure variables, and socioeconomic determinants of participation in Diet, Cancer and Health: a population-based prospective cohort study of 57,053 men and women in Denmark. Scand J Public Health 2007;35:432–41.
    1. Aguib Y, Al Suwaidi J. The Copenhagen City Heart Study (Østerbroundersøgelsen). Glob Cardiol Sci Pract 2015;2015:33.
    1. Bergholdt HK, Bathum L, Kvetny J, et al. Study design, participation and characteristics of the Danish General Suburban Population Study. Dan Med J 2013;60:A4693.
    1. Naslund-Koch C, Nordestgaard BG, Bojesen SE. Common breast cancer risk alleles and risk assessment: A study on 35,441 individuals from the Danish general population. Ann Oncol 2017;28:175–81.
    1. Olsen J, Melbye M, Olsen SF, et al. The Danish National Birth Cohort: Its background, structure and aim. Scand J Public Health 2001;29:300–7.
    1. Statistics Denmark. Lolland Municipality [in Danish], → Befolkning og valg → Folketal → FOLK1A → 2017K4, Lolland Kommune: 2018. (accessed 11 January 2018).
    1. Statistics Denmark. Guldborgsund Municipality [in Danish], → Befolkning og valg → Folketal → FOLK1A → 2017K4, Guldborgsund Kommune: 2018. (accessed 11 January 2018).
    1. Pedersen CB. The Danish Civil Registration System. Scand J Public Health 2011;39:22–5.
    1. Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register. Scand J Public Health 2011;39:30–3.
    1. Gjerstorff ML. The Danish Cancer Registry. Scand J Public Health 2011;39:42–5.
    1. Kildemoes HW, Sørensen HT, Hallas J. The Danish National Prescription Registry. Scand J Public Health 2011;39:38–41.
    1. Nielsen MG, ørnbøl E, Bech P, et al. The criterion validity of the web-based Major Depression Inventory when used on clinical suspicion of depression in primary care. Clin Epidemiol 2017;9:355–65.
    1. Gerlach J. (ed). The anxiety book. The symptoms, causes and treatment of anxiety [in Danish]. Copenhagen: PsykiatriFondens Forlag, 2008, p.304.
    1. . eHealth in Denmark, (accessed 12 February 2018).
    1. Statistics Denmark. Statistical legislation, (accessed 22 January 2018).
    1. Pedersen HK, Gudmundsdottir V, Nielsen HB, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 2016;535:376–81.
    1. Allin KH, Nielsen T, Pedersen O. Mechanisms in endocrinology: Gut microbiota in patients with type 2 diabetes mellitus. Eur J Endocrinol 2015;172:R167–77.
    1. Langhammer A, Krokstad S, Romundstad P, et al. The HUNT study: participation is associated with survival and depends on socioeconomic status, diseases and symptoms. BMC Med Res Methodol 2012;12:143.
    1. Knudsen AK, Hotopf M, Skogen JC, et al. The health status of nonparticipants in a population-based health study: the Hordaland Health Study. Am J Epidemiol 2010;172:1306–14.
    1. May HT, Anderson JL, Muhlestein JB, et al. Improvement in the predictive ability of the Intermountain Mortality Risk Score by adding routinely collected laboratory tests such as albumin, bilirubin, and white cell differential count. Clin Chem Lab Med 2016;54:1619–28.
    1. Bello GA, Dumancas GG, Gennings C. Development and validation of a clinical risk-assessment tool predictive of all-cause mortality. Bioinform Biol Insights 2015;9:1–10.

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