Miriam L. Haaksma
Leiden University Medical Center
Miriam L. Haaksma
Miriam Haaksma was born on 31 July 1990 in Groningen (the Netherlands). In 2008 she finished pre-university education at CSG Wessel Gansfort in Groningen and started her studies Nutrition & Health at Wageningen University. She conducted her graduation project at the Laboratory of Clinical Chemistry and Haematology at Rijnstate Hospital in Arnhem, under supervision of Prof. Huub Savelkoul and clinical chemist Dr. Janneke Ruinemans-Koerts. After graduating in 2014, she enrolled in the PhD programme “Understanding and predicting personalized growth rate curves to aid individualized care in late-onset dementia” (UPPGRAID) at the department of Geriatric Medicine of Radboud university medical center in Nijmegen, under supervision of Prof. Marcel Olde Rikkert (geriatrician) and Dr. René Melis (epidemiologist). During a two-month work visit at Johns Hopkins University in Baltimore (US), she immersed herself in advanced epidemiological techniques to analyse disease trajectories, under supervision of biostatistician Dr. Jeannie-Marie Leoutsakos. In 2017 she worked as a visiting researcher at the Aging Research Center of Karolinska Institutet in Stockholm (Sweden) for 10 months, under supervision of Prof. Laura Fratiglioni, to make use of the local cohort studies and national registers, as part of her PhD programme. Through her work and her passion for conducting research, Miriam obtained several grants, received a Junior Investigator Award from International College of Geriatric Psychoneuropharmacology, and was awarded the Christine Mohrmann Stipend for excellent female researchers. In the course of her PhD track she supervised several interns, was trained as an epidemiologist, and obtained the University Teaching Qualification. In 2019 she obtained her PhD degree with honors and worked as a postdoc at the Netherlands Cancer Institute Since 2022 she works on projects to improve quality of life and quality of care in nursing homes at the University Network for the Care Sector South-Holland (UNC-ZH) and Leiden University Medical Center (the Netherlands).
FURTHER INFORMATION
Countries | Netherlands; |
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Topics | COVID-19 and other infectious diseases and LTC; Data catalogues; Dementia care and support; End-of-life care and LTC; Epidemiology and ageing trajectories; Outcome measurement in LTC; Prevention and rehabilitation and LTC; Technology and LTC; |
Methods | Quantitative data analysis; |
Role | Research; |
Interest Groups | Data Science; |
ORC.ID | 0000-0002-3518-9152 |
https://twitter.com/MiriamHaaksma | |
https://www.linkedin.com/in/mhaaksma/ | |
Other 1 | https://unc-zh.nl; |
Research interests | Nursing home care, dementia, cognition, and epidemiology. |
Key publications | Haaksma ML, O’Driscoll C, Joling KJ, Achterberg WP, et al. Evaluating the feasibility, experiences, facilitators of and barriers to carers and volunteers delivering Namaste Care to people with dementia in their own home. BMJ Open. 2022;12(11):e063422. DOI: 10.1136/bmjopen-2022-063422 Grund S, Caljouw MAA, Haaksma ML et al. Pan-European study on functional and medical recovery and geriatric rehabilitation services of post-COVID-19 patients: protocol of the EU-COGER study. J Nutr Health Aging. 2020. DOI: 10.1007/s12603-021-1607-5 Haaksma ML, Eriksdotter M, Rizzuto D, Leoutsakos JMS et al. Survival time tool to guide care planning in people with dementia. Neurology. 2019 Feb 4;94(5):e538-e548. DOI: 10.1212/WNL.0000000000008745 Haaksma ML, Rizzuto D, Leoutsakos JMS, et al. Predicting cognitive and functional trajectories in people with late-onset dementia: two population-based studies. J Am Med Dir Assoc. 2019 Nov;20(11):1444-1450. DOI: 10.1016/j.jamda.2019.03.025 Haaksma ML, Rizzuto D, Ramakers IHGB, et al. The impact of frailty and comorbidity on institutionalization and mortality in persons with dementia: a prospective cohort study. J Am Med Dir Assoc. 2019 Feb;20(2):165-170. DOI: 10.1016/j.jamda.2018.06.020 Haaksma ML, Calderón-Larrañaga A, Olde Rikkert MGM, et al. Cognitive and functional progression in Alzheimer’s Disease: a prediction model of latent classes. Int J of Geriatr Psychiatry. 2018 Aug;33(8):1057-1064. DOI: 10.1002/gps.4893 |