
==== Front
Age Ageing
Age Ageing
ageing
Age and Ageing
0002-0729
1468-2834
Oxford University Press

10.1093/ageing/afae191
afae191
Research Paper
AcademicSubjects/MED00280
ageing/10
ageing/15
Visual field loss and falls requiring hospitalisation: results from the eFOVID study
Manners Siobhan School of Population and Global Health, The University of Western Australia, Clifton Street Building, Clifton Street, Nedlands 6009, Australia

Meuleners Lynn B Western Australian Centre for Road Safety, School of Psychology, The University of Western Australia, Nedlands, Australia

Ng Jonathon Q School of Population and Global Health, The University of Western Australia, Clifton Street Building, Clifton Street, Nedlands 6009, Australia

Wood Joanne M School of Optometry and Vision Science, Queensland University of Technology, Nedlands, Australia

Morgan Bill Lions Eye Institute, Nedlands, Australia

Morlet Nigel School of Population and Global Health, The University of Western Australia, Clifton Street Building, Clifton Street, Nedlands 6009, Australia

Address correspondence to: Siobhan Manners, School of Population and Global Health, The University of Western Australia, Clifton Street Building, Clifton Street, Nedlands 6009, Western Australia, Australia. Email: Siobhan.Manners@uwa.edu.au
9 2024
03 9 2024
03 9 2024
53 9 afae19129 1 2024
20 8 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Background

Visual fields are important for postural stability and ability to manoeuvre around objects.

Objective

Examine the association between visual field loss and falls requiring hospitalisation in adults aged 50 +.

Methods

Older adults aged 50+ with and without visual field loss were identified using a fields database obtained from a cross-section of ophthalmologists’ practices in Western Australia (WA). Data were linked to the Hospital Morbidity Data Collection and WA Hospital Mortality System to identify participants who experienced falls-related hospitalisations between 1990 and 2019. A generalised linear negative binomial regression model examined the association between falls requiring hospitalisation for those with and without field loss, based on the better eye mean deviation (mild: −2 to –6 dB, moderate: −6.01 dB to –12 dB, severe < −12.01 dB) in the most contemporaneous visual field test (3 years prior or if not available, 2 years after the fall), after adjusting for potential confounders.

Results

A total of 31 021 unique individuals of whom 6054 (19.5%) experienced 11 818 falls requiring hospitalisation during a median observation time of 14.1 years. Only mean deviation index of <−12.01 dB (severe) was significantly associated with an increased rate of falls requiring hospitalisations by 14% (adjusted IRR 1.14, 95% CI 1.0–1.25) compared with no field loss, after adjusting for potential confounders. Other factors included age, with those aged 80+ having an increased rate (IRR 29.16, 95% CI 21.39–39.84), other comorbid conditions (IRR 1.49, 95% CI 1.38–1.60) and diabetes (IRR 1.25, 95% CI 1.14–1.37). Previous cataract surgery was associated with a decreased rate of falls that required hospitalisations by 13% (IRR 0.87, 95% CI 0.81–0.95) compared with those who did not have cataract surgery.

Conclusion

The findings highlight the importance of continuous clinical monitoring of visual field loss and injury prevention strategies for older adults with visual field loss.

older drivers
visual field loss
linked administrative databases
falls
injury
older people
Department of Education and Training, Australian Research Council DP0987089
==== Body
pmcKey points

Of 31 021 unique individuals, 6054 (19.5%) experienced 11 818 falls requiring hospitalisation between 1990 and 2019.

Among those who fell, 2492 (41.2%) experienced two to four falls, and 364 (6%) had five or more falls requiring hospitalisation.

Almost half of older adults with visual field loss (49%) had two or more falls requiring hospitalisation during the study period.

Only severe field loss was significantly associated with an increased the rate of falls requiring hospitalisations by 14% (adjusted IRR 1.14, 95% CI 1.0–1.25) compared with no field loss, after adjusting for potential confounders which included age, gender, a diagnosis of diabetes, previous cataract surgery and other comorbid health conditions.

The need for a comprehensive approach encompassing socioeconomic, clinical and demographic factors to mitigate falls risk in older adults is warranted.

From a clinical perspective, the results emphasise the importance of visual field loss testing and educating people about the risks associated with declining visual fields.

Background

Prevention of injury among the older population is a priority in Australia [1], and internationally [2]. An injury, regardless of its type, can represent a pivotal event for older adults, resulting in loss of confidence, social isolation, decreased quality of life, declining physical health, institutionalisation and mortality. [3] It is widely acknowledged that vision impairment increases the risk of injury involvement for older adults and is an important predictor of falls, motor vehicle crashes and other injuries [4–7]. Vision impairment can involve central vision and/or the visual fields, where sections of the normal field of vision (visual field) may be missing or less sensitive (described as visual field loss).

Current evidence suggests that relying on visual acuity or contrast sensitivity may not be adequate for predicting falls among older adults and that visual fields may play a crucial role [8–10]. The strongest evidence for the association between visual field loss and falls comes from two prospective studies from the United States of America (USA). One study followed 4071 community-dwelling white women aged 70+ for a year and found that severe binocular visual field loss increased the risk of experiencing two or more falls by 50% (OR 1.50; 95% CI 1.11–2.02) [11]. However, the study focused solely on older white women, therefore limiting the generalisability of the. The other study followed 2375 older adults for up to 20 months, demonstrating an 8% higher risk of experiencing at least one fall for each 10% loss in the binocular visual field [12]. However, visual acuity, contrast sensitivity and stereoacuity measures were not associated with self-reported falls in this study [12]. A Malaysian case–control study also found that visual field loss significantly increased the risk of falls and hip fractures [13]. A Japanese cross-sectional study found that greater field posed a significant risk for injurious falls in participants with primary open-angle glaucoma [14].However, this study may have been limited by the low number of self-reported falls and the long recall period for falls (previous 10 years) [14].

Conversely, a prospective Australian study following 76 older adults with age-related macular degeneration that primarily affects central fields, found no significant association between visual field loss within the central 24 degrees and self-reported falls or other injuries recorded in falls diaries over a one year follow-up [6]. Freeman et al. (2007) specifically examined the location of visual field loss and found that when both central (≤20° radius) and peripheral visual fields were considered, only peripheral visual field loss was associated with an increased falls risk. These findings imply the significance of peripheral visual field loss in falls, although further research is required for confirmation. Lastly, a prospective Australian study involving 71 older adults with primary open-angle glaucoma that affects peripheral fields found that greater inferior (lower) field loss was associated with a higher rate of falls (RR 1.57; 95% CI 1.06–2.32) and injurious falls (RR, 1.80; 95% CI 1.12–2.98) over a 12 month period. However, superior (upper) field loss, contrast sensitivity and visual acuity were not associated with the rate of falls [15].

To date, no large-scale investigations have been conducted on the association between visual field loss and falls for older adults. To address this gap a large clinical ophthalmic database was linked to objective population-based hospitalisation and death data to examine falls that required hospitalisation, the most severe type of fall, for those with and without visual field loss from 1990 to 2019. The findings of this innovative study will help identify older adults with elevated rates of falls that required hospitalisation, enabling targeted interventions.

Methods

Study design

A population-based retrospective cohort study involving older adults aged 50+ with and without visual field loss was undertaken. The participants were identified through a customised database of visual field tests obtained from a broad cross-section of ophthalmologists’ practices in WA, spanning from Geraldton in the north to Bunbury in the south–west of WA. The study population had attended an ophthalmology service between 1990 and 2019 and had a Humphrey Visual Field test undertaken at Royal Perth Hospital, Sir Charles Gairdner Hospital, Fremantle Hospital, Lions Eye Institute or other private practices in WA. This database was linked to the Hospital Morbidity Data Collection (HMDC) and the WA Hospital Mortality System to identify those who had been hospitalised or died due to a fall by the WA Data Linkage Services (WADLS) at the Department of Health WA. This study is part of a larger cohort study known as the Epidemiology of Field of Vision Disorders (eFOVID), which leveraged linked administrative health data, including crash data, to examine different health and injury outcomes associated with visual field loss [16].

Databases

The databases were linked using probabilistic matching based on name, phonetic compression algorithms and other identifiers [17].

Ophthalmic database : Visual field loss was assessed with a Humphrey Fields Analyser machine which is the most commonly used device worldwide. The ophthalmic database comprised tests performed for routine clinical care rather than a clinical registry and represented the full range of visual field defects encountered in clinical practice.

Further detail on the construction of this database is published elsewhere [16]. WA Death Registry contains detailed information on death certificates issued by the Registrar General. By law all deaths in WA are recorded in the WA Death Registry. Hospital Morbidity Data Collection (HMDC) contains all inpatient admissions from all private or public WA hospitals since 1970.

Definition of visual field loss in the ophthalmic datase

Abnormal visual fields were defined based on the Total Deviation maps as ≥2 adjacent points of ≥5 dB each, or ≥ 1 adjacent points of ≥10 dB each, or a difference of ≥5 dB across the nasal horizontal meridian at ≥2 adjacent points, excluding the physiological blind spot and points 3 degrees above and below the horizontal meridian. The comparison group were aged 50+, with normal visual fields [18]. They had been tested as they were either glaucoma suspects or had visual field testing for screening purposes. The mean deviation (MD) of the better eye was used in the analysis, which quantified the average deviation of the participant’s point sensitivity score at each tested point, compared to a normal reference field. The decision to only use the MD of the better eye was based on previous research which found that better eye MD rarely differs from integrated visual field MD and predicts disability no differently than integrated visual field MD. [19] Visual fields were categorised according to the severity of visual field loss based on the mean deviation index, where negative values represent greater field loss (mild: −2 to −6 dB, moderate: −6.01 dB to −12 dB, severe < −12.01 dB) [20].

Outcome of interest and operational definitions

The outcome of interest was each participant’s total number of falls requiring hospitalisation. The Australian Modification of the International Classification of Diseases, 10th Revision (ICD-10-AM) codes were used to extract hospital morbidity and death records (Appendix 1).

This study was approved by the ethics committee at The University of Western Australia (#2020/ET000250) and the WA Department of Health Human Research Committee (RGS000000433).

Statistical analysis

Descriptive statistics were used to summarise the socio-demographic characteristics of the cohort. A generalised linear model (GLM) using a negative binomial distribution was undertaken to examine the rate of falls requiring hospitalisation for those with and without visual field loss after adjusting for potential confounders, including age, gender, Aboriginal status, previous cataract surgery, diabetes diagnosis, presence/absence of non-visual related comorbid conditions, area (metropolitan, rural, remote), Australian Socioeconomic Indexes for Areas (SEIFA) and marital status. The GLM negative binomial model is appropriate as it contains an extra parameter to model the over-dispersion in the number of falls events among participants (mean = 0.38, variance = 1.03). [21]

The presence or absence and severity of visual field loss were based on the most recent visual field test that was conducted in the three years preceding the fall. If no such test result was available, the visual field test conducted within the two years following the fall was used. This decision ensured that the visual test data was as current as possible, acknowledging the gradual progression of visual field changes [22] and based on real-world clinical practices where follow-up tests might not occur for up to two years depending on the initial test results. Observation time varied according to when the fall event occurred in the overall linked data set (Fig. 1). Residential location was categorised at the index admission using the Accessibility Remoteness Index of Australia based on Statistical Area-2 (SA2), resulting in three groups ‘metropolitan’ (representing major cities), ‘rural’ (inner regional areas) and ‘remote’ (representing all other areas) [23] Marital status was classified as having a partner (married or de facto) or not (single, separated, divorced, or widowed). International Classification of Disease and Health-Related Problems (ICD) codes were used to classify comorbid health conditions (described by Holman et al., 1999) recorded during hospital admissions up to 5 years before, including the index record for each participant. [24] Each patient was assigned an unweighted comorbidity score representing the total number of identified comorbid conditions. Previous research determined that 5 years is the most appropriate lookback period, given that the effects of different comorbid conditions can vary depending on the conditions and duration. [25] The presence or absence of comorbid conditions was used in the GLM negative binomial regression analysis. The potential confounders of cataract surgery and diabetes were also included in the model, as both conditions can result in rapid changes to the visual field [26–28]. However, these conditions (including visual field loss) were not included in the comorbidity score. The SEIFA classification system was used to categorise socioeconomic disadvantage [23]. Deciles were combined into quintiles, with the highest quintile representing individuals residing in areas with Australia’s top 20% of SEIFA scores. Data analysis was conducted using STATA version 16 [29].

Figure 1 Flow chart illustrating how fall events and visual field test identification were performed, and how end points were defined.

Table 1 Demographic and medical characteristics of the cohort (n = 31,021) by falls requiring hospitalisation status

		Falls requiring hospitalisation	No falls requiring hospitalisation	
		N (%)*	N (%)*	
		N = 6054 (19.5%)	N = 24,967 (80.5%)	
Sex+	Male	2090 (34.5%)	11,427 (45.8%)	
	Female	3964 (65.5%)	11,934 (47.8%)	
Age group+	50–59	98 (1.6%)	4679 (18.7%)	
	60–69	724 (12.0%)	10,955 (43.9%)	
	70–79	1740 (28.7%)	7235 (29.0%)	
	80+	3492 (57.7%)	2098 (8.4%)	
Number of falls	0	0 (0.0%)	24,967 (100.0%)	
1	3198 (52.8%)	0 (0.0%)	
2–4	2492 (41.2%)	0 (0.0%)	
≥5	364 (6.0%)	0 (0.0%)	
Comorbid conditions	Yes	2914 (48.2%)	5778 (23.1%)	
No	3140 (51.8%)	19,189 (76.9%)	
Diabetes	Yes	1174 (19.4%)	2196 (8.8%)	
	No	4880 (80.6%)	22,771(91.2%)	
Cataract surgery	Yes	1094 (18.1%)	3379 (13.7%)	
	No	4960 (81.9%)	21,588 (86.3%)	
Visual field loss	Yes	4051 (66.9%)	13,450 (53.8%)	
No	2003 (33.1%)	11,517 (46.1%)	
Area+	Metro	5219 (90.7%)	14,387 (88.8%)	
	Remote	69 (1.2%)	312 (1.9%)	
	Rural	465 (8.1%)	1496 (9.2%)	
SEIFA+	Lowest	1109 (18.4%)	3511 (16.3%)	
	Low	1095 (18.1%)	3677 (17.0%)	
	Moderate	962 (15.9%)	3512 (16.3%)	
	High	1157 (19.2%)	4193 (19.4%)	
	Highest	1714 (28.4%)	6703 (31.0%)	
Marital status+	Never married	356 (5.9%)	1084 (6.2%)	
Widow	2059 (34.0%)	1798 (10.2%)	
Divorced	448 (7.4%)	1102 (6.3%)	
Separated	113 (1.9%)	276 (1.6%)	
Married/de facto	3006 (49.7%)	12,731 (72.4%)	
Unknown	71 (1.2%)	601 (3.4%)	
+Missing information; * P < .001 for all.

Results

Table 1 summarises the descriptive characteristics of the cohort. The study involved 31 021 unique participants, with a median follow-up time of 14.1 years. Among these participants, 9123 (29.2%) individuals died during the study period, and 6054 (19.5%) individuals experienced 11,818 falls requiring hospitalisation (range: 1–17, mean = 1.95, SD = 1.49). Among those who fell, 2492 (41.2%) experienced two to four falls, and 364 (6%) had five or more falls requiring hospitalisation. Almost half of older adults with visual field loss (49%) had two or more falls requiring hospitalisation during the study period. Approximately 56% (n = 17 501) of the whole cohort had visual field loss.

Of older adults who had a fall(s) requiring hospitalisation, 57.7% were aged 80+ (n = 3492), 65.5% were female (n = 3964), 48.2% had at least one comorbid health condition (n = 2914), 19.4% had diabetes (n = 1174) and 18.1% had undergone cataract surgery prior to their fall (n = 1094). Most of this subgroup lived in metropolitan areas (90.7%, n = 5219), nearly half were married or in a de facto relationship (49.7%, n = 3006) and two-thirds had visual field loss (67%, n = 4051).

Comparatively, individuals who did not have a fall requiring hospitalisation (n = 24 967), 43.9% were aged 60–69 (n = 10 955), 45.8% were female (n = 11 427), 23.1% had at least one comorbid health condition (5778), 8.8% had diabetes (n = 2196) and 13.7% had cataract surgery (n = 3379). Most of this subgroup lived in metropolitan areas (88.8%, n = 14 387), over two-thirds were married or in a de facto relationship (72.4%, n = 12 731) and over half had visual field loss (54%, n = 13 450).

The overall MD for the better eye in the entire cohort was −4.82 dB (range: −33.43 to 24.28, SD = 6.70). When stratified by falls status, the MD for the better eye was −6.20 (SD = 7.29) for those who had one or more falls requiring hospitalisation and − 4.48 (SD = 6.50) for those who did not experience a hospital-related fall. Table 2 provides a detailed breakdown of the MD values for the better eye for individuals who experienced falls requiring hospitalisation. Notably, a worsening trend is evident, ranging from −3.73 (SD = 1.12) for those with mild visual field loss to −19.75 (SD = 5.59) for those with severe visual field loss, regardless of aetiology.

Table 3 shows the outcomes of the adjusted GLM negative binomial regression analysis. Only participants with severe visual field loss exhibited a significant increase in falls requiring hospitalisation, being 14% higher (adjusted IRR 1.14, 95% CI 1.0–1.25). Females had a 75% higher rate of falls requiring hospitalisation (adjusted IRR 1.75, 95% CI 1.64–1.86). The rate of falls requiring hospitalisation also significantly increased with age, particularly for those aged 80+ with 29 times the rate of falls that required hospitalisation compared to those aged 50 to 59. The presence of comorbid conditions (IRR 1.49, 95% CI 1.38–1.60) and a diagnosis of diabetes (IRR 1.25, 95% CI 1.14–1.37) significantly increased the rate of falls requiring hospitalisation. Individuals who had undergone previous cataract surgery decreased the rate of falls requiring hospitalisation by 13% (IRR 0.87, 95% CI 0.81–0.95) compared with those who had not.

Table 2 Mean deviation (MD) for the better eye for individuals who experienced falls requiring hospitalisation (n = 4051)

Visual field loss	N	Falls requiring hospitalisation
MD (SD)	
Mild	1978	−3.73 (SD = 1.12)	
Moderate	1021	−8.51 (SD = 1.70)	
Severe	1051	−19.75 (SD = 5.59)	

Table 3 Severity of visual field loss and risk of falls requiring hospitalisation from GLM negative binomial regression model

	Adjusted IRR+
(95% CI)	P-value	
Visual field loss	No impairment (ref)	1.0		
Mild	1.01 (0.93–1.08)	.98	
Moderate	1.02 (0.92–1.11)	.75	
Severe	1.14 (1.04–1.25)	.006 +	
Age group	50–59 (ref)	1.0		
60–69	2.81 (2.04–3.88)	<.0001 *	
70–79	10.08 (7.36–13.80)	<.0001 *	
80+	29.16 (21.39–39.84)	<.0001 *	
Gender	Male (ref)	1.0		
Female	1.75 (1.64–1.86)	<.0001 *	
Co-morbidities	No (ref)	1.0		
Yes	1.49 (1.38–1.60)	<.0001 *	
Cataract Surgery	No (ref)	1.0		
Yes	0.87 (0.81–0.95)	.001	
Diabetes	No (ref)	1.0		
Yes	1.25 (1.1–1.37)	<.0001 *	
+only significant variables included in the model

* Adjusted cataract surgery in past two years, presence/absence of diabetes, marital status, gender, age group, co-morbidities, SEIFA score, location (metropolitan, rural, remote), Indigenous status;

Discussion

This study, which is the first to link a large-scale ophthalmic database to population-based hospitalisation and death data, found that severe visual field loss in the better eye significantly increased the rate of falls requiring hospitalisation by 14% after accounting for relevant confounders, irrespective of the aetiology of the visual field lossand is consistent with previous research [11, 12]. While mild or moderate visual field loss was not significantly associated with the rate of falls that required hospitalisation, the incidence rate ratios tended towards an increase in the rate of falls. The study also found that 49% of the cohort with visual field loss had two or more falls requiring hospitalisation during the study period.

These findings highlight the importance of consistent monitoring for visual field changes, as deteriorations in vision can be associated with decreased quality of life and depression [30–33].

The results of our study also found several non-visual factors associated with an increase in the rate of these falls, including being female, older adults, a diagnosis of diabetes and the presence of at least one comorbid health condition, which is consistent with previous research [15, 32, 34, 35]. Approximately 29% of the cohort who had diabetes and visual field loss had an increased rate of falls requiring hospitalisation. It is well known that diabetes, treatments and complications such as diabetic neuropathy, can interfere with good balance and a steady gait, increasing the risk of falling. Diabetes patients may also have an increased risk fo other vision proglems, such as cataracts abd glaucoma, which are also linked to an increased falls risk. [32] Depending on the underlying cause, some of the identified risks in our study are potentially modifiable. For instance, individuals who had previous cataract surgery—a procedure known to improve contrast sensitivity and visual acuity in Australians —demonstrated a significantly lower falls risk [36, 37]. However, we found no association between socioeconomic disadvantage based on the SEIFA index and the rate of falls that required hospitalisation. This finding is noteworthy given that previous research highlighted the impact of an individual’s social and economic status on falls risk [34], stemming from various factors such as housing, income, education, social isolation, and disability [35]. Healthcare professionals are well-positioned to engage in behaviour change conversations to improve the social and behavioural determinants of falls among older adults.

The strength of this retrospective study was the ophthalmic database, which was linked to administrative health data across a defined and geographically isolated population. The specialised ophthalmic database contained an extensive number of records with detailed visual field information including point sensitivities for each eye and in many cases, multiple tests over time. Additionally, the data linkage methodology mitigates selection bias, minimises loss to follow-up, and enables a comprehensive examination at the population level. Furthermore, the study benefits from high-quality, objective data relating to falls, which is more accurate than self-reported information [24]. Using a comparison group without visual field loss enabled a meaningful comparison of falls and visual field severity to those with visual field loss. Importantly, the data from the WADLS represented the most severe cases of falls that required hospitalisation, which have the greatest human and financial cost at the population level. Future research including older adults with less severe falls who commonly present to emergency departments, would provide a more accurate falls risk profile.

Available data did not capture other specific measures of visual function, such as contrast sensitivity and visual acuity, hearing and cognitive status, or whether the patients lived independently or were in residential care. Moreover, it is plausible that older individuals take medications, use mobility appliances or experience balance and gait problems, which we were not able to determine using the WADLS [38]. The interaction between cataracts, visual field loss and falls is complex with prescence of cataract, subsequent removal and refractive status all playing a role in vision. Unfortunately this relationship cannot be completely accounted for using the current linked data.

Ultimately, the mechanism for falls risk for older people with visual field loss is multifactorial and a large prospective cohort study is warranted to better clarify the determinants of risk not possible using the linked databases.

In conclusion, the findings underscore the need for a comprehensive approach encompassing socioeconomic, clinical and demographic factors to mitigate falls risk in older adults. From a clinical perspective, the results emphasise the importance of visual field loss testing and educating people about the risks associated with declining visual fields. Public health resources and prevention strategies should be directed towards improved balance and mobility programs and orientation programs tailored to older adults with visual field loss, thereby alleviating the burden on healthcare systems.

Supplementary Material

aa-24-0209-File002_afae191

Declaration of Conflicts of Interest:

None.

Declaration of Sources of Funding

Department of Education and Training, Australian Research Council, DP0987089.
==== Refs
References

1. Australian Institute of Health and Welfare . Injury in Australia. Canberra: Australian Institute of Health and welfare, 2023.
2. World Health Organisation . Injuries and Violence. Geneva: WHO. https://www.who.int/teams/social-determinants-of-health/injuries-and-violence (Date Accessed 2021 Accessed, date last accessed).
3. Cutugno CL . The ‘Graying’ of trauma care: addressing traumatic injury in older adults Am J Nurs. 2011;111 :40–50.
4. Khow KSFMF , VisvanathanRMFP. Falls in the aging population Clin Geriatr Med. 2017;33 :357–68.28689568
5. Kristalovich L , MortensonWB. Visual field impairment and driver fitness: a 1-year review of crashes and traffic violations Am J Occup Ther. 2019;73 :7305345010p1–6.
6. Wood JM , LacherezP, BlackAAet al. Risk of falls, injurious falls, and other injuries resulting from visual impairment among older adults with age-related macular degeneration Invest Ophthalmol Vis Sci. 2011;52 :5088–92.21474773
7. Muir C , CharltonJL, OdellMet al. Medical review licensing outcomes in drivers with visual field loss in Victoria, Australia Clin Exp Optom. 2016;99 :462–8.27530283
8. Mihailovic A , deLunaRM, WestSKet al. Gait and balance as predictors and/or mediators of falls in glaucoma Invest Ophthalmol Vis Sci. 2020;61 :30.
9. White UE , BlackAA, DelbaereKet al. Determinants of concern about falling in adults with age-related macular degeneration Ophthalmic Physiol Opt. 2021;41 :245–54.33368495
10. Van Landingham SW , MassofRW, ChanEet al. Fear of falling in age-related macular degeneration BMC Ophthalmol. 2014;14 :10.24472499
11. Coleman AL , CummingsSR, YuFet al. Binocular visual-field loss increases the risk of future falls in older White women J Am Geriatr Soc (JAGS). 2007;55 :357–64.
12. Freeman EE , MunozB, RubinGet al. Visual field loss increases the risk of falls in older adults: the Salisbury eye evaluation Invest Ophthalmol Vis Sci. 2007;48 :4445–50.17898264
13. Chew FLM , YongC-K, AyuSMet al. The association between various visual function tests and low fragility hip fractures among the elderly: a Malaysian experience Age Ageing. 2010;39 :239–45.20065356
14. Tanabe S , YukiK, OzekiN, ShibaD, TsubotaK; The association between primary open-angle glaucoma and fall: an observational study. Clin Ophthalmol (Auckland, NZ) 2012;6 :327–31.
15. Black AA , WoodJM, Lovie-KitchinJE. Inferior field loss increases rate of falls in older adults with glaucoma Optom Vis Sci. 2011;88 :1275–82.21873923
16. Manners S , MorganW, MorletNet al. The epidemiology of field of VIsion disorders (eFOViD) study, Western Australia, 1988-2022. Report 1: data collection and aggregation protocol Clin Exp Ophthalmol. 2024;6 . 10.1111/ceo.14422.
17. Eitelhuber T , ThackrayJ, HodgesSet al. Fit for purpose - developing a software platform to support the modern challenges of data linkage in Western Australia Int J Popul Data Sci. 2018;3 :435–5.32935016
18. Thomas R , GeorgeR. Interpreting automated perimetry Indian J Ophthalmol. 2001;49 :125–40.15884520
19. Kulkarni KM , MayerJR, LorenzanaLLet al. Visual field staging systems in glaucoma and the activities of daily living Am J Ophthalmol. 2012;154 :445–451.e3.22633358
20. Carl Zeiss Meditec . In: Carl Zeiss Meditecs (ed.), Humphrey® Field Analyzer 3 (HFA3) Instructions for Use – Models 830, 840, 850, 860. United States: Carl Zeiss, 2018.
21. Nichols D. From Manova to GLM: Basics of Parameterization. University of California, Los Angeles. https://stats.oarc.ucla.edu/spss/library/spss-librarymanova-and-glm/ (Date Accessed 2020 Accessed, date last accessed).
22. De Moraes CG , LiebmannJM, LevinLA. Detection and measurement of clinically meaningful visual field progression in clinical trials for glaucoma Prog Retin Eye Res. 2017;56 :107–47.27773767
23. Australian Bureau of Statistics . Socio-Economic Indexes for Areas (SEIFA), Australia. Canberra: Australian Bureau of Statistics, 2023.
24. Holman CDAJ , BassAJ, RouseILet al. Population-based linkage of health records in Western Australia: development of a health services research linked database Aust N Z J Public Health. 1999;23 :453–9.10575763
25. Preen DB , HolmanCDAJ, SpilsburyKet al. Length of comorbidity lookback period affected regression model performance of administrative health data J Clin Epidemiol. 2006;59 :940–6.16895817
26. Investigators A . The advanced glaucoma intervention study, 6: effect of cataract on visual field and visual acuity Arch Ophthalmol. 2000;118 :1639–52.11115258
27. Bao YK , YanY, GordonMet al. Visual field loss in patients with diabetes in the absence of clinically-detectable vascular retinopathy in a nationally representative survey Invest Ophthalmol Vis Sci. 2019;60 :4711–6.31725170
28. Gelcho GN , GariFS. Time to diabetic retinopathy and its risk factors among diabetes mellitus patients in Jimma University medical center, Jimma, Southwest Ethiopia Ethiop J Health Sci. 2022;32 :937–46.36262700
29. StataCorp . Stata Statistical Software. StataCorp, Texas, USA. (Date Accessed 2023 Accessed, date last accessed).
30. Medeiros FAMDP , GracitelliCPBMD, BoerERPet al. Longitudinal changes in quality of life and rates of progressive visual field loss in glaucoma patients Ophthalmology (Rochester, Minn). 2015;122 :293–301.
31. Abe RY , GracitelliCPB, Diniz-FilhoAet al. Frequency doubling technology perimetry and changes in quality of life of glaucoma patients: a longitudinal study Am J Ophthalmol. 2015;160 :114–122.e1.25868760
32. Meuleners LB , HendrieD, FraserMLet al. The impact of first eye cataract surgery on mental health contacts for depression and/or anxiety: a population-based study using linked data Acta Ophthalmol (Oxford, England). 2013;91 :e445–9.
33. Fraser ML , MeulenersLB, LeeAHet al. Vision, quality of life and depressive symptoms after first eye cataract surgery Psychogeriatrics. 2013;13 :237–43.24118634
34. Mehta J , CzannerG, HardingSet al. Visual risk factors for falls in older adults: a case-control study BMC Geriatr. 2022;22 :134–4.35177024
35. Marmot M . Fair Society, Healthy Lives: The Marmot Review: Strategic Review of Health Inequalities in England Post-2010. Institute of Health Equity, United Kingdon, 2010.
36. Feng YR , MeulenersLB, FraserMLet al. The impact of first and second eye cataract surgeries on falls: a prospective cohort study Clin Interv Aging. 2018;13 :1457–64.30197507
37. Palagyi A , MorletN, McCluskeyPet al. Visual and refractive associations with falls after first-eye cataract surgery J Cataract Refract Surg. 2017;43 :1313–21.29056303
38. Skelton DA . Effects of physical activity on postural stability Age Ageing. 2001;30 :33–9.
