Big data study using health insurance claims to predict multidisciplinary low vision service uptake.
Journal
Optometry and vision science : official publication of the American Academy of Optometry
ISSN: 1538-9235
Titre abrégé: Optom Vis Sci
Pays: United States
ID NLM: 8904931
Informations de publication
Date de publication:
10 Jun 2024
10 Jun 2024
Historique:
medline:
10
6
2024
pubmed:
10
6
2024
entrez:
10
6
2024
Statut:
aheadofprint
Résumé
There is a lack of research from high-income countries with various health care and funding systems regarding barriers and facilitators in low vision services (LVS) access. Furthermore, very few studies on LVS provision have used claims data. This study aimed to investigate which patient characteristics predict receiving multidisciplinary LVS (MLVS) in the Netherlands, a high-income country, based on health care claims data. Data from a Dutch national health insurance claims database (2015 to 2018) of patients with eye diseases causing potentially severe visual impairment were retrieved. Patients received MLVS (n = 8766) and/or ophthalmic treatment in 2018 (reference, n = 565,496). MLVS is provided by professionals from various clinical backgrounds, including nonprofit low vision optometry. Patient characteristics (sociodemographic, clinical, contextual, general health care utilization) were assessed as potential predictors using a multivariable logistic regression model, which was internally validated with bootstrapping. Predictors for receiving MLVS included prescription of low vision aids (odds ratio [OR], 8.76; 95% confidence interval [CI], 7.99 to 9.61), having multiple ophthalmic diagnoses (OR, 3.49; 95% CI, 3.30 to 3.70), receiving occupational therapy (OR, 2.32; 95% CI, 2.15 to 2.51), mental comorbidity (OR, 1.17; 95% CI, 1.10 to 1.23), comorbid hearing disorder (OR, 1.98; 95% CI, 1.86 to 2.11), and receiving treatment in both a general hospital and a specialized ophthalmic center (OR, 1.23; 95% CI, 1.10 to 1.37), or by a general practitioner (OR, 1.23; 95% CI, 1.18 to 1.29). Characteristics associated with lower odds included older age (OR, 0.30; 95% CI, 0.28 to 0.32), having a low social economic status (OR, 0.91; 95% CI, 0.86 to 0.97), physical comorbidity (OR, 0.87; 95% CI, 0.82 to 0.92), and greater distance to an MLVS (OR, 0.95; 95% CI, 0.92 to 0.98). The area under the curve of the model was 0.75 (95% CI, 0.75 to 0.76; optimism = 0.0008). Various sociodemographic, clinical, and contextual patient characteristics, as well as factors related to patients' general health care utilization, were found to influence MLVS receipt as barriers or facilitators. Eye care practitioners should have attention for socioeconomically disadvantaged older patients when considering MLVS referral.
Identifiants
pubmed: 38856650
doi: 10.1097/OPX.0000000000002134
pii: 00006324-990000000-00204
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 American Academy of Optometry.
Déclaration de conflit d'intérêts
Conflict of Interest Disclosure: None of the authors have reported a financial conflict of interest.
Références
Langelaan M, de Boer MR, van Nispen RM, et al. Impact of visual impairment on quality of life: A comparison with quality of life in the general population and with other chronic conditions. Ophthalmic Epidemiol 2007;14:119–26.
Luu W, Kalloniatis M, Bartley E, et al. A holistic model of low vision care for improving vision-related quality of life. Clin Exp Optom 2020;103:733–41.
Lim YE, Vukicevic M, Koklanis K, et al. Low vision services in the asia-pacific region: Models of low vision service delivery and barriers to access. J Vis Impair Blind 2014;108:311–22.
van Nispen RM, Virgili G, Hoeben M, et al. Low vision rehabilitation for better quality of life in visually impaired adults. Cochrane Database Syst Rev 2020;1:CD006543.
World Health Organization (WHO). Universal eye health: A global action plan 2014–2019; 2013. Available at: www.who.int/publications/i/item/universal-eye-health-a-global-action-plan-2014-2019. Accessed October 3, 2020.
Stolwijk ML, van Nispen RM, van der Ham AJ, et al. Barriers and facilitators in the referral pathways to low vision services from the perspective of patients and professionals: A qualitative study. BMC Health Serv Res 2023;23:64.
Lam N, Leat SJ. Reprint of: Barriers to accessing low-vision care: The patient's perspective. Can J Ophthalmol 2015;50(Suppl. 1):S34–9.
Basilious A, Basilious A, Mao A, et al. Trends in low vision care provided by ophthalmologists in Ontario between 2009 and 2015. Can J Ophthalmol 2019;54:229–36.
Goldstein JE, Guo X, Boland MV, et al. Low vision care—Out of site. Out of mind. Ophthalmic Epidemiol 2020;27:252–8.
Khimani KS, Battle CR, Malaya L, et al. Barriers to low-vision rehabilitation services for visually impaired patients in a multidisciplinary ophthalmology outpatient practice. J Ophthalmol 2021;2021:1–7.
Stelzer D, Graf E, Köster I, et al. Assessing the effect of a regional integrated care model over ten years using quality indicators based on claims data—The basic statistical methodology of the INTEGRAL project. BMC Health Serv Res 2022;22:247.
Tyree PT, Lind BK, Lafferty WE. Challenges of using medical insurance claims data for utilization analysis. Am J Med Qual 2006;21:269–75.
Stolwijk ML, van Nispen RM, Verburg IWM, et al. Trends in low vision service utilisation: A retrospective study based on general population healthcare claims. Ophthalmic Physiol Opt 2022;42:828–38.
Kroneman M, Boerma W, van den Berg M, et al. Netherlands: Health system review. Health Syst Transit 2016;18:1–240.
Dutch Healthcare Authority. What are our tasks? How does the Dutch healthcare system work? Available at: https://english.zorginstituutnederland.nl/. Accessed March 15, 2023.
Dutch Healthcare Authority. Monitor zorgverzekeringen 2018 [health insurance monitor 2018]. Available at: https://puc.overheid.nl/nza/doc/PUC_254666_22/1/. Accessed: March 15, 2023.
Dutch Tax and Customs Administration. [Income-related contribution to the healthcare insurance act]. Available at: https://www.belastingdienst.nl/bibliotheek/handboeken/html/boeken/FISIN2018/fiscale_informatie_2018-inkomensafhankelijke_bijdrage_zorgverzekeringswet.html. Accessed April 7, 2023.
van Rens GH, Vreeken HL, van Nispen RM. [Guideline vision disorders: Rehabilitation and referral]. Available at: http://www.vivis.nl/wp-content/uploads/2019/10/Richtlijn-visusstoornissen-revalidatie-en-verwijzing.pdf. Accessed March 22, 2024.
Rubin DB. Inference and missing data. Biometrika 1976;63:581–92.
Lanning D, Berry D. An alternative to PROC MI for large samples; 2003. Available at: https://support.sas.com/resources/papers/proceedings/proceedings/sugi28/271-28.pdf. Accessed March 22, 2024.
Pregibon D. Logistic regression diagnostics. Ann Statist 1981;9:705–24.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995;57:289–300.
Austin PC, Steyerberg EW. Interpreting the concordance statistic of a logistic regression model: Relation to the variance and odds ratio of a continuous explanatory variable. BMC Med Res Methodol 2012;12:82.
Steyerberg EW, Harrell FE Jr., Borsboom GJ, et al. Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001;54:774–81.
Hosmer DW Jr., Lemeshow S, Sturdivant RX. Applied Logistic Regression. New York: Wiley; 2013.
Griffith JF, Goldberg JL. Prevalence of comorbid retinal disease in patients with glaucoma at an academic medical center. Clin Ophthalmol 2015;9:1275–84.
Coker MA, Huisingh CE, McGwin G Jr., et al. Rehabilitation referral for patients with irreversible vision impairment seen in a public safety-net eye clinic. JAMA Ophthalmol 2018;136:400–8.
Kaldenberg J. Low vision rehabilitation services: Perceived barriers and facilitators to access for older adults with visual impairment. Br J Occup Ther 2019;82:466–74.
Matti AI, Pesudovs K, Daly A, et al. Access to low-vision rehabilitation services: Barriers and enablers. Clin Exp Optom 2011;94:181–6.
Vreeken HL, van Rens GH, Knol DL, et al. Dual sensory loss: A major age-related increase of comorbid hearing loss and hearing aid ownership in visually impaired adults. Geriatr Gerontol Int 2014;14:570–6.
Minhas R, Jaiswal A, Chan S, et al. Prevalence of individuals with deafblindness and age-related dual-sensory loss. J Vis Impair Blind 2022;116:36–47.
Overbury O, Wittich W. Barriers to low vision rehabilitation: The Montreal Barriers Study. Invest Ophthalmol Vis Sci 2011;52:8933–8.
O'Connor PM, Mu LC, Keeffe JE. Access and utilization of a new low-vision rehabilitation service. Clin Exp Optom 2008;36:547–52.
Armstrong RA. Is there a large sample size problem? Ophthalmic Physiol Opt 2019;39:129–30.
Working Group for the Collection and Use of Secondary Data (AGENS) of the German Society for Social Medicine and Prevention (DGSMP) and the German Society for Epidemiology (DGEpi). Good practice in secondary data analysis (GPS); 2008. Available at: https://www.dgepi.de/de/berichte-und-publikationen/leitlinien-und-empfehlungen/. Accessed January 20, 2023.
Eindhoven DC, van Staveren LN, van Erkelens JA, et al. Nationwide claims data validated for quality assessments in acute myocardial infarction in the Netherlands. Neth Heart J 2018;26:13–20.
van Oosten MJ, Brohet RM, Logtenberg SJ, et al. The validity of Dutch health claims data for identifying patients with chronic kidney disease: A hospital-based study in the Netherlands. Clin Kidney J 2020;14:1586–93.