Data Organization And Management For Longitudinal DERVAN Cohort Study In KONKAN Region Of India (DERVAN-3)
DOI:
https://doi.org/10.37506/3eypyh33Keywords:
cohort, Epidemiology, Data Management, Rural, MS-AccessAbstract
Background: A skilful data management is the heart of any cohort study as results of such studies have far reaching impact on development national policies which will improve public health.
Methods: DERVAN cohort is a longitudinal study set up in KONKAN region of India. The study is expected to last at least 20 years. It plans to investigate impact of adolescent growth, diet and cognition on the risk of development of non-communicable diseases in the adulthood with diabetes as the main focus. The study will also investigate parents and investigate their contribution to the risk. We plan to recruit 1520 adolescent girls. MS Access was used to design data management system. Each family was given a unique identity number. At base line we have created 21 tables, 10 queries for adolescent data and 5 tables, 2 queries for parental data. The data is analysed using statistical software SPSS.
Conclusion: Though our system may not be designed using high end data base systems, it still caters to our needs in the initial stage of the project but its skilful design is expected to make a smooth adaptation to new database environment in the subsequent stages.
References
Last JM. A dictionary of epidemiology 4th ed. New York:Oxford University Press;2001.
Barker DJ. The fetal and infant origins of adult disease. BMJ. 1990;301(6761):1111.
GluckmanPD,Buklijas T, Hanson MA. The Developmental Originsof Health and Disease (DOHaD) concept: past, present, and future.Cheryl R, ed. Boston, MA: Academic Press; 2016.
Abhinav RP, Williams J, Livingston P, Anjana RM, Mohan V. Burden of diabetes and oral cancer in India. J DiabCompl. 2020;34(11):107670.
Mohan V, Anjana RM, Unnikrishnan R, Venkatesan U, Uma SG, Rahulashankiruthiyayan T, Samhita SK, Coimbatore SS. Incidence of hypertension among Asian Indians: 10 year follow up of the Chennai Urban Rural Epidemiology Study (CURES-153). J DiabCompl. 2020;34:107652.
Eriksson J. Developmental pathways and programming of diabetes: epidemiological aspects. J Endocrinol. 2019;0680.
Plant DT, Pawlby S, Sharp D, Zunszain PA, Pariante CM. Prenatal maternal depression is associated with offspring inflammation at 25 years: a prospective longitudinal cohort study. Transl Psychiatry. 2016;6(11):155
Huang RC, Prescott SL, Godfrey KM, Davis EA. Assessment of cardiometabolic risk in children in population studies: underpinning developmental origins of health and disease mother-offspring cohort studies. J Nutr Sci.2015; 4:69.
Yajnik CS, Deshmukh US. Maternal nutrition, intrauterine programming and consequential risks in the offspring. Rev EndocrMetabDisord. 2008;9(3):203-11.
Krishnaveni GV, Yajnik CS. Developmental origins of diabetes-an Indian perspective. Eur J ClinNutr. 2017;71(7):865-69.
Sinha S, Aggarwal AR, Osmond C, D Fall CH, Bhargava SK, Sachdev HS. Intergenerational Change in Anthropometric Indices and Their Predictors Among Children in New Delhi Birth Cohort. Indian Pediatr. 2017;54(3):185-92.
Sachdev HS, Fall CH, Osmond C, Ramakrishnan L, Biswas SK, Leary SD L, Reddy KS, Barker DJ, Bhargava SK. Anthropometric indicators of body composition in young adults: relation to size at birth and serial measurements of body mass index in childhood in the New Delhi birth cohort. Am J ClinNutr. 2005;82(2):456-66.
Antonisamy B, Raghupathy P, Christopher S, Richard J, Rao PS, David JP Barker, Fall CH. Cohort Profile: the 1969-73 Vellore birth cohort study in South India. Int J Epidemiol. 2009;38(3):663-9.
Raghupathy P, Antonisamy B, Geethanjali FS, Saperia J, Leary SD, Priya G, Richard J, Barker DJ, Fall CH. Glucose tolerance, insulin resistance and insulin secretion in young south Indian adults: Relationships to parental size, neonatal size and childhood body mass index. Diabetes Res ClinPract. 2010;87(2):283-92.
Patil S, Patil N, Joglekar C, Yadav A, Nilwar A, Banawali U, Bhat R, Domable V, Warape B, Mohite R, Joshi K.aDolescent and prEconception health peRspectiVe of Adult Non-communicable diseases (DERVAN): protocol for rural prospective adolescent girls cohort study in Ratnagiri district of Konkan region of India (DERVAN-1). BMJ Open. 2020;10(9):e035926.
Meyer J, Ostrzinski S, Fredrich D, Havemann C, Krafczyk J, Hoffmann W. Efficient data management in a large-scale epidemiology research project. Comput Methods Programs Biomed. 2012;107:425-35.
Youngblut JM, Loveland-Cherry CJ, Horan M.Data management issues in longitudinal research. Nurs Res. 1990; 39;188-9.
GoldingJ. Data organisation and preparation for statistical analysis in a longitudinal birth cohort. PaediatrPerinatEpidemiol. 2009;23:219-25
Schneider JK, Schneider JF, Lorenz RA. Creating user-friendly databases with Microsoft Access. Nurse Res. 2005;13:57-75.
Lee H,Chapiro J,Schernthaner R. How I do it: A practical database management system to assist clinical research teams with data collecting, organization, and reporting.AcadRadiol. 2015; 22:527-33.
Dennert K, Friedrich L, Kumar R. Creating an affordable, user-friendly electronic inventory system for lab samples. SLAS Technol. 2020; 11.
Carmichael D. Information technology for longitudinal birth cohorts. PaediatrPerinatEpidemiol. 2009;23:213-8.
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Copyright (c) 2024 Suvarna Patil, Netaji Patil, Omkar Dervankar, Dnyaneshwar Jadhav, Charudatta Joglekar
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