Association between daily maximum temperature and immediate-to-delayed gout flare hospitalisations: a population-level time series study in metropolitan Perth, Australia

Abstract

Background
Ambient temperature may alter the risk of gout flare. We assessed the association between daily maximum temperature on the immediate-to-delayed (lag) hospitalisations for gout flare at the whole-population-level.

Methods
Data were extracted from the Western Australian Hospital Morbidity Data Collection for this time series study for metropolitan Perth (Australia) from 1980-2014, with meteorological data obtained from the Scientific Information for Land Owners dataset. We examined the association (relative risk [RR] and 95% confidence intervals [CI]) between hospitalisation for gout flare, daily maximum temperature and lag days since exposure using quasi-Poisson regression and the distributed lag non-linear model.

Results
Average daily maximum temperature was 24°C (5^th percentile=17°C; 95^th percentile=36°C). Maximum temperatures of 17°C and 36°C were associated with 6.2 and 6.8 gout admissions per 1,000,000 population over lag day 0 to lag day 21 following exposure, respectively. Risk of hospitalisation for gout (reference=24°C) with maximum temperature was modified by age and sex. Males aged ≥75 years had higher risks of gout hospitalisation following hotter days (36°C) beginning immediately on lag day 0 (RR=1.39; 95% CI: 1.02-1.88) and from lag day 7 (RR=1.11; 95% CI: 1.01-1.21) to lag day 10 after colder days (≤15°C). Females aged ≥75 years had higher risk at 35°C from lag day 3 (RR=1.12; 95% CI: 1.02-1.23) to lag day 4. Males aged <75 years had higher risks after 35°C days from lag day 6 (RR=1.06; 95% CI: 1.01-1.12) to lag Day 8 but lower risk from lag day 7 (RR=0.96: 95% CI: 0.92-0.99) to lag day 8 following colder days (17°C).

Conclusion
Our study shows associations between extreme temperatures, both hot and cold, and immediate-to-delayed gout flare hospitalisations. Our findings will inform and guide public health measures and health system preparedness during the impending extremes of temperature with particular attention to older people.

Highlights

• Theorised biological mechanisms and small-scale observational studies suggest that temperature extremes can increase the risk of gout flares.
• Using population-level data, we show that the risk of gout flare hospitalisations was modified by sex and age, with older (≥75 years) and younger (<75 years) males and females showing different trends.
• Increased risk of gout flare hospitalisations can occur immediately or between 3 to 10 days after exposure to temperature extremes.
• Following days with maximum temperatures ≥35°C, males aged ≥75 years, females aged ≥75 years and males aged <75 years had 39%, 12% and 6% increased risk of gout flare hospitalisations relative to daily maximum temperatures of 24°C, respectively.
• Following colder days (maximum temperature ≤17°C), only males aged ≥75 years experienced an increased risk (11%) of gout flare hospitalisations relative to 24°C, and this occurred 7 to 10 days after exposure.
• Our findings will inform and guide public health measures and health system preparedness to mitigate the impacts of the anticipated increases in intensity, frequency and duration of temperature extremes associated with climate change, with particular attention to older people, such as those aged ≥75 years.

Introduction

The prevalence and incidence of gout has increased in many developed countries over the last few decades due to increasing longevity, increased use of medicines that can trigger gout, dietary trends, greater obesity and increased survival from many inflammatory-related comorbidities [1, 2]. The number of people experiencing gout flares is expected to further increase due to the impact of climate change where extremes of temperature, both hot and cold, are expected to increase in intensity, frequency and duration [3]. This is supported by theorised biological mechanisms which propose that the reduced solubility of urate crystals at cold temperatures could result in micro-crystal precipitation which would trigger an inflammatory response [4, 5]. Higher temperatures can cause volume depletion and contribute to metabolic acidosis, both of which can decrease renal urate excretion and increase serum urate. Evidence from observational studies supports the impact of temperature on gout flare. A study based on self-reports from 632 participants in the United States (US) found that higher temperatures were associated with a 40% increased risk of gout flare compared to moderate temperatures [6]. In a smaller analysis of hospital records for 82 patients in Israel, high temperatures (i.e. temperature above mean monthly temperature) in the preceding four days were associated with hospitalisation for gout flare [7]. Studies on seasonality have been inconclusive with higher risks observed during the summer months in England and Wales [8] and during the spring months in Italy [9]. Meanwhile a study of new prescriptions for urate lowering medicines to treat asymptomatic hyperuricemia or gout in Japan (where these medicines are approved for these indications) found higher prescription rates in summer and autumn compared to winter [10].

To the best of our knowledge, no study has examined the association between ambient temperature and gout flare hospitalisations at the population level. In this study, we used population-level data to assess the association between daily maximum temperature on the immediate-to-delayed risk of gout flare hospitalisations in a metropolitan city. We also determined if this association was modified by sex and/or age because males and females of different ages are known to have different risks for gout flare [11–13]. Findings from this study will inform and guide public health measures and health system preparedness during the impending extremes of temperature associated with climate change.

Methods

Data sources

We used the Western Australian Rheumatic Disease Epidemiological Registry (WARDER) to access unit-record linked data from the Western Australian Hospital Morbidity Data Collection (HMDC). This data collection is maintained by the Western Australian Department of Health and linked through the Western Australian Data Linkage System (WADLS). WARDER contains all public and private hospital admissions between 1980 and 2014 for patients with principal and secondary discharge diagnosis of systemic autoimmune rheumatic disease, gout, or osteoarthrosis [14].

Study setting

Metropolitan Perth in Western Australia (WA) is bounded by the Indian Ocean on the west and the Darling Range on the east. It is situated on relatively flat land spanning (at the time of writing) approximately 150km from north to south and up to approximately 45km, from east to west. This region experiences a hot-summer Mediterranean climate and cool to mild wet winters [15].

Hospitalisation data

For this time series study, we used the HMDC dataset to identify daily hospitalisations to public and private hospitals where the principal diagnosis was gout (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 274 and the International Classification of Diseases Tenth Revision, Australian Modification [ICD-10-AM] code M10) among residents of the Perth metropolitan area (identified from residential postcode) between 1 January 1980 and 31 December 2014 (observation period). We excluded patient transfers where the interval between discharge and subsequent admission for the patient was within one day consistent with our previous work [16, 17]. During this period, the estimated resident population was 1.02 million in 1980 and increased to 2.01 million in 2014.

Meteorological data

We obtained daily maximum temperature data for the Perth Regional Office station (latitude=31.96°S; longitude=115.87°E; elevation=19.0 m above mean sea level) from the Scientific Information for Land Owners (SILO) dataset in Australia, hosted by the Science and Technology Division of Queensland Government’s Department of Environment and Science [18]. This was the only station in the metropolitan area with temperature data from 1980 to 2014. Data available from this weather station included daily maximum/minimum temperatures, rainfall, vapour pressure, vapour pressure deficit, evaporation, solar radiation, relative humidity at maximum temperature, relative humidity at minimum temperature, mean sea level pressure.

Other data

Data on WA public holidays were obtained from Nager.Date [19] and used to control for public holidays in the regression model. Estimated resident population data for population denominators were obtained from the Australian Bureau of Statistics [20].

Statistical analyses

We calculated descriptive statistics for daily maximum temperature (mean, standard deviation [SD], median, 1^st, 5^th 95^th, 99^th percentiles) and number of daily gout flare hospitalisations for the observation period. Colder days are defined as those ≤5^th percentile of daily maximum temperatures, while hotter days are those ≥95^th percentile of daily maximum temperatures.

In addition, for each day during our observation period with a particular temperature value (e.g. 16°C) we counted the number of gout flare hospitalisations per 1,000,000 population over lag days 0-21. We then averaged these counts/1,000,000 population for each temperature value.

Risk of immediate-to-delayed gout flare hospitalisations by daily maximum temperature

We used a distributed lag non-linear model (DLNM) [21] to investigate the relative risk of gout flare hospitalisations with maximum daily temperature, allowing for potential non-linear associations as well as lagged effects. This model was selected to account for the potential lag period between temperature exposure and gout flare hospitalisations [7]. Specifically, a cross-basis function, derived as a special tensor product of natural cubic splines, was used to jointly model maximum daily temperature (°C) and the lag of the number of days since that exposure. Spline parameters were consistent with another Australian study [22]. Spline 1 of the cross-basis function was specified using three internal knots placed at equally spaced values of temperature, and spline 2 using an intercept plus three internal knots placed at equally spaced values of the logged lag days with a maximum lag of 21 days [22]. We used the mean of the daily maximum temperatures across all years 1980-2014 as the reference temperature (24°C) for calculating relative risks (RRs). Furthermore, a spline term was included to control for changes with time during the study period by modelling day number as an integer, from 1 (1 January 1980) to ‘12784’ (31 December 2014) using a natural spline with 6 degrees of freedom (df) per year [22]. Three additional model terms were included: daily humidity (%), modelled using a natural spline with 4 df; weekend day (yes or no); public holiday (yes or no). This was fitted as a quasi-Poisson Generalized Linear Model (GLM) with a log link function. We adjusted for weekends and public holidays to account for differences in health service utilisation and patient behaviours on these days [23].

We initially analysed all cases and then stratified by biological sex (hereafter sex) and age (<75 years, ≥75 years). We chose this age cut-off as it is commonly used in age-related analyses [8, 24, 25]. Furthermore, we explored using 54 and 64 years as age cut-offs, however there was only a small number of total hospitalisations in females aged 54-64 years (<90 hospitalisations) to produce a stable model fit and estimates. However, there were sufficient numbers of total hospitalisations for both males and females in the <75-year and ≥75-year age groups for the statistical modelling and convergence. Due to the small number of days with hot and cold extremes (<1% each), temperatures <15°C were recoded to 15°C while those >40°C were recoded to 40°C.

We used Stata for data handling and preparation and R for DLNM and GLM. DLNM was fitted using the dlnm package in R [26].

Sensitivity analyses

In sensitivity analyses to determine the robustness of our results, we increased the number of internal knots for Splines 1 and 2 from three to four. We also increased the df for ‘changes with time’ from 6 to 7 df per year, and for ‘daily humidity’ from 4 to 5 df.

Ethics

Approval to conduct this study was obtained from the Human Research Ethics Committee of WA Department of Health (approval no. 2016/24) where a waiver of consent was granted as the research met the criteria outlined in the National Statement on Ethical Conduct in Human Research. Analyses were conducted according to relevant local and national guidelines and regulations.

Results

Descriptive statistics

During the study observation period of 12,784 days, daily maximum temperatures ranged from 12°C to 46°C, with mean and median of 24°C (SD=5.9) and 23°C, respectively (Figure 1). First, 5^th, 95^th and 99^th percentile daily maximum temperatures were 15°C, 17°C, 36°C and 40°C respectively. Hence in this study, colder and hotter days are when daily maximum temperatures are ≤17°C and ≥36°C, respectively.

Figure 1: Distribution of daily maximum temperatures (bar) and gout flare hospitalisations per 1,000,000 population (line) over lag days 0-21 from 1980 to 2014.

There were 5,731 gout flare hospitalisations, and in general. the number of admissions increased with higher temperatures (Figure 1). For example, there were, on average, 5.8, 6.2, 6.8 and 6.5 hospitalisations per 1,000,000 population over lag days 0-21 (i.e. from lag day 0 to lag day 21) following exposure to daily maximum temperatures of 15°C, 17°C, 36°C and 40°C respectively. Similarly, the number of gout flare hospitalisations increased with higher temperatures amongst older (≥75years) and younger (<75years) males and females (Supplementary Table 1). However, these are crude counts and do not consider the complex exposure-time-outcome relationship. Findings hereafter are based on DLNM which provide a comprehensive picture of the time-course of the exposure-outcome relationship [27] and considers possible effect modification due to sex and age.

Risk of immediate-to-delayed gout flare hospitalisations by daily maximum temperature

Significant exposure-time-outcome associations were observed for the risk of gout flare hospitalisations with daily maximum temperature (i.e., relative to the reference temperature of 24°C) in all data, and separately by sex and age group (<75 years, ≥75 years) (Figure 2). At lag day 7, (i.e. 7 days following temperature exposure), hotter days (36°C) were associated with increased risk of gout flare hospitalisations in all data (RR=1.04; 95% CI: 1.01-1.08), with a similar RR estimated for males only (RR=1.08; 95% CI: 1.01-1.15) but no significant association estimated for females. People aged ≥75 years had higher estimated RRs following both colder (≤15°C: RR=1.08; 95% CI: 1.01-1.16) and hotter days (36°C: RR=1.07; 95% CI: 1.02-1.12), relative to the reference temperature (Figure 2). However, those younger than 75 years had higher but non-significant estimated RRs on lag day 7 on hotter days (36°C: RR=1.03; 95% CI: 0.99-1.07), relative to the reference temperature. Given these findings, results presented hereafter are stratified by sex and age (i.e. males aged ≥75 years [1,465 hospitalisations], females aged ≥75 years [869 hospitalisations], males aged <75 years [2,936 hospitalisations] and females aged <75 years [461 hospitalisations]).

Figure 2: Relative risk of gout flare hospitalisations plotted against maximum temperature. Plots are for lag day 7 and presented for all cases and stratified by sex and age. Reference temperature=24°C. For ease of display, relative risks and 95% confidence intervals (grey shading) have been capped at 0.80 and 1.20.

Males aged ≥75 years had higher risk of gout flare hospitalisations (Figure 3, Supplementary Figure 1, Supplementary Table 2) following hotter days (36°C) beginning immediately on lag day 0 (RR=1.39; 95% CI: 1.02-1.88) and again from lag day 4 (RR=1.10; 95% CI: 1.02-1.18) to lag day 8 (RR=1.06; 95% CI: 1.01-1.12). They also experienced higher risk of gout flare hospitalisations from lag day 7 (RR=1.11; 95% CI: 1.01-1.21) to lag day 10 (RR=1.09; 95% CI: 1.01-1.18) after colder days (≤15°C). Females aged ≥75 years did not experience increased risk of gout flare hospitalisations following hotter (≥36°C) days but had higher risk at 35°C from lag day 3 (RR=1.12; 95% CI: 1.02-1.23) to lag day 4 (RR=1.11; 95% CI: 1.01-1.22).

Figure 3: Relative risk of gout flare hospitalisations plotted against maximum temperature for lag days 0, 7, 14 and 21, for people aged ≥75 years. Plots are stratified by sex and age. Reference temperature=24°C. For ease of display, relative risks and 95% confidence intervals (grey shading) have been capped at 0.80 and 1.50.

Males aged <75 years (Figure 4, Supplementary Figure 2, Supplementary Table 2) did not experience increased risk after hotter days (≥36°C) but had higher risks due to 35°C days from lag day 6 (RR=1.06; 95% CI: 1.01-1.12) to lag Day 8 (RR=1.05; 95% CI: 1.01-1.09). They had higher but non-significant risks following colder days (17°C) from lag day 1 (RR=1.04: 95% CI: 0.96-1.13) to lag day 3 (RR=1.01: 95% CI: 0.95-1.06), but significant lower risk from lag day 7 (RR=0.96: 95% CI: 0.92-0.99) to lag day 8 (RR=0.96: 95% CI: 0.93-0.99). Females aged <75 years did not experience higher risk of gout flare hospitalisations after hotter or colder days over lag day 0 to lag day 21, relative to the reference temperature.

Figure 4: Relative risk of gout flare hospitalisations plotted against maximum temperature for lag days 0, 7, 14 and 21, for people aged <75 years. Plots are stratified by sex and age. Reference temperature=24°C. For ease of display, relative risks and 95% confidence intervals (grey shading) have been capped at 0.80 and 1.50.

Sensitivity analyses

In sensitivity analyses to determine robustness of our results, we observed similar shapes of exposure-time-outcome associations when we modified the number of knots for Splines 1 and 2 and df for ‘changes with time’ and ‘daily humidity’ (Supplementary Figures 3-6).

Discussion

In this first of its kind population-level study of gout flare hospitalisations following exposure to temperature extremes, our findings align with theorised biological mechanisms and earlier small-scale observational studies indicating hot [6, 7] and cold [4, 5] temperatures are associated with increased risk of gout flares. We found that the risk of gout flare hospitalisations was modified by the sex and age of admitted patients, with older (≥75 years) and younger (<75 years) males and females showing different trends. The risk of gout flare hospitalisations was 39%, 12% and 6% higher following days with maximum temperatures ≥35°C, relative to reference temperature days (daily maximum=24°C) for males aged ≥75 years, females aged ≥75 years and males aged <75 years, respectively. Only males aged ≥75 years experienced increased risk (11%) of gout flare hospitalisations after exposure to cold temperatures and this occurred 7 to 10 days after exposure. We also found that increased risk of gout flare hospitalisations can occur immediately or between 3 to 10 days after exposure to temperature extremes. Our study adds to the number of conditions that have increased risks of morbidity during temperature extremes including cardiovascular disease, kidney disease, diabetes and mental health conditions [22, 28–30].

Our observations from this population dataset can be explained by physiological, lifestyle and social factors. Older people have a greater number of risk factors for gout flares during temperature extremes including higher levels of serum uric acid, poorer thermoregulation and more likely to consume alcohol daily (e.g. 12.6% of those aged ≥70 years consumed alcohol daily compared to 7.3% aged 50-59 years) than younger people [12, 31, 32]. We observed that males aged ≥75 years experienced a 39% increased risk of gout flare hospitalisations following hotter days (daily maximum temperature ≥36°C) and with a shorter lag time (on lag day 0 vs lag day 3, respectively) following exposure than females in the same age group, relative to the reference temperature (24°C). Additionally, elderly males but not elderly females had increased risk of gout flare hospitalisations following colder days (daily maximum temperature ≤17°C) relative to the reference temperature. It is possible that during our study period, elderly males were more likely to be involved in outdoor activities (e.g. gardening, fishing) [33–35] during the hot and cold temperatures than elderly females, thus increasing exposure which may predispose them to a higher risk of gout flare. In contrast, younger (<75 years) males, but not females, experienced increased risk of gout flare hospitalisations only following hotter days, relative to the reference temperature. Males <75 years are perhaps more likely to be involved in outdoor occupations (e.g. farming, construction work) [36, 37] and consume alcohol daily (e.g. 8.8% of males aged 50-59 years consumed alcohol daily compared to 5.8% of females aged 50-59 years) [32] than females in this age group. We also observed a small (4%) but statistically significant decrease in risk of gout flare hospitalisations on lag day 7 to lag day 8 following colder days for males aged <75 years relative to the reference temperature. This suggests possible ‘harvesting’, a phenomenon where outcomes are only brought forward by a brief period of time by the effect of exposure. Here, the non-significant 1-4% increases in risk of hospitalisations on lag day 1 to lag day 3 resulted in later decreases in risk from lag day 7 to lag day 8.

Whilst our findings appear consistent with earlier papers, there are some notable differences apart from different populations and climate zones. A study in the US using a time-stratified case-crossover study design found that hot but not cold temperatures increased this risk of gout flares [6]. Unlike our population-level study, their participants were respondents to a search engine identifying the search term “gout” and were directed to the study website to provide self-reported information on their gout condition. It is likely that their study excluded many elderly patients who were less likely to regularly use the internet [38]. Furthermore, that study did not stratify by sex and age, as was performed in the current study. Without stratification, we found that cold temperatures were not associated with the risk of hospitalisation for gout flare suggesting that failure to stratify by sex and age will mask the effect modification.

The strength of this study was its use of de-identified individual-level hospital data for an entire metropolitan population, which allowed systematic ascertainment of all public and private hospital admissions for gout flare ensuring complete identification of cases. This reduces issues with selection bias as well as reporting or recall bias in terms of experiencing a gout flare, as potentially experienced with other research [39]. There are several limitations to this study. Firstly, we only used hospitalisation data and did not have primary healthcare data which would have captured less severe gout flares that did not require hospitalisation. Although we did not use mortality data, we do not expect this to influence our findings as deaths related to gout represented 0.3% of all deaths in Australia [40]. Secondly, we used temperature data from the Perth Regional Office station, which is higher than coastal areas and lower than more inland regions. However, a previous time-series modelling study showed that using temperature data from a single site produced similar temperature-health estimates compared to models that used averaged temperature or spatio-temporal data [41]. Thirdly, our findings are from a single Australian region with distinct topography and maximum temperatures (range 12-46°C) and may not be generalisable to other jurisdictions. Each jurisdiction will need to evaluate the risk separately and our study using population-level data in conjunction with DLNM offers a viable approach. Fourthly, due to small case numbers, we were unable to stratify by smaller age groups. Fifthly, whilst we did not adjust for air quality, earlier studies have shown minimal confounding by air pollution [42, 43]. Moreover, adjustment for air quality in temperature studies is usually not warranted unless the causal inference assumption is firstly established [44]. Finally, our data are up to the end of 2014 and findings may be different with more recent data where more extremes in temperature may have occurred in more recent years. Although there are now new treatments for gout [45] and modern buildings are designed to accommodate temperature fluctuations, the number of hospital admissions for gout flare in Australia has increased modestly from 25 per 100,000 population in 2011/12 to 27 per 100,000 population in 2021/22 [40].

In conclusion, our study shows associations between extreme ambient temperatures, both hot and cold, and gout flare hospitalisations in metropolitan Perth. Our findings will inform and guide public health measures and health system preparedness during the impending extremes of temperature associated with climate change with particular attention to the older population, such as males and females aged ≥75 years.

Acknowledgements

The authors wish to thank the staff at WA Data Linkage Services, the WA Department of Health, and Hospital Morbidity Data Collection.

Author contributions

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Derrick Lopez had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Lopez

Analysis and interpretation of data. Lopez, Marriott

Acquisition of data. Nossent, Preen, Keen, Inderjeeth

Data sharing statement

The datasets generated and/or analysed during the current study are not publicly available due to the terms of the ethics approval granted by the Western Australian Department of Health Human Research Ethics Committee (WADOH HREC) and data disclosure policies of the Data Providers. The datasets may be available from the corresponding author upon request and subject to approval from the WADOH HREC and relevant custodians.

Conflicts of interest

DL and DBP are on the Editorial Board of this journal and had no role in the editorial process of this manuscript. All other authors have no conflicts of interest to report.

References

1. Kuo CF, Grainge MJ, Zhang W, Doherty M. Global epidemiology of gout: prevalence, incidence and risk factors. Nat Rev Rheumatol. 2015;11(11):649-62. 10.1038/nrrheum.2015.91

   10.1038/nrrheum.2015.91

2. Bieber JD, Terkeltaub RA. On the brink of novel therapeutic options for an ancient disease. Arthritis Rheum. 2004;50(8):2400-14. 10.1002/art.20438

   10.1002/art.20438

3. Bush T. Potential adverse health consequences of climate change related to rheumatic diseases. J Clim Change Health. 2021;3:100029. 10.1016/j.joclim.2021.100029

   10.1016/j.joclim.2021.100029

4. Loeb JN. The influence of temperature on the solubility of monosodium urate. Arthritis Rheumatol. 1972;15(2):189-92. 10.1002/art.1780150209

   10.1002/art.1780150209

5. Latman NS. Influence of atmospheric factors on the rheumatic diseases. Experientia. 1987;43(1):32-8. 10.1007/Bf01940350

   10.1007/Bf01940350

6. Neogi T, Chen C, Niu JB, Chaisson C, Hunter DJ, Choi H, et al. Relation of temperature and humidity to the risk of recurrent gout attacks. Am J Epidemiol. 2014;180(4):372-7. 10.1093/aje/kwu147

   10.1093/aje/kwu147

7. Arber N, Vaturi M, Schapiro JM, Jelin N, Weinberger A. Effect of weather conditions on acute gouty-arthritis. Scand J Rheumatol. 1994;23(1):22-4. 10.3109/03009749409102130

   10.3109/03009749409102130

8. Elliot AJ, Cross KW, Fleming DM. Seasonality and trends in the incidence and prevalence of gout in England and Wales 1994-2007. Ann Rheum Dis. 2009;68(11):1728-33. 10.1136/ard.2008.096693

   10.1136/ard.2008.096693

9. Gallerani M, Govoni M, Mucinelli M, Bigoni M, Trotta F, Manfredini R. Seasonal variation in the onset of acute microcrystalline arthritis. Rheumatol. 1999;38(10):1003-6. 10.1093/rheumatology/38.10.1003

   10.1093/rheumatology/38.10.1003

10. Kurajoh M, Akari S, Nakamura T, Ihara Y, Imai T, Morioka T, et al. Seasonal variations for newly prescribed urate-lowering drugs for asymptomatic hyperuricemia and gout in Japan. Front Pharmacol. 2024;15:1230562. 10.3389/fphar.2024.1230562

    10.3389/fphar.2024.1230562

11. Kumar S, Gupta R, Suppiah R. Gout in women: differences in risk factors in young and older women. NZ Med J. 2012;125(1363):39-45.

12. Zitt E, Fischer A, Lhotta K, Concin H, Nagel G. Sex- and age-specific variations, temporal trends and metabolic determinants of serum uric acid concentrations in a large population-based Austrian cohort. Sci Rep. 2020;10(1):7578. 10.1038/s41598-020-64587-z

    10.1038/s41598-020-64587-z

13. Harrold LR, Etzel CJ, Gibofsky A, Kremer JM, Pillinger MH, Saag KG, et al. Sex differences in gout characteristics: tailoring care for women and men. BMC Musculoskel Disord. 2017;18:108. 10.1186/s12891-017-1465-9

    10.1186/s12891-017-1465-9

14. Lopez D, Dwivedi G, Nossent J, Preen DB, Murray K, Raymond W, et al. Risk of major adverse cardiovascular event following incident hospitalization for acute gout: a Western Australian population-level linked data study. ACR Open Rheumatol. 2023;5(6):298-304. 10.1002/acr2.11540

    10.1002/acr2.11540

15. Bureau of Meteorology. Perth: Climate and water [webpage]. 2025 Available from: https://www.bom.gov.au/water/nwa/2024/perth/climateandwater/climateandwater.shtml.

16. Lopez D, Lu J, Sanfilippo FM, Katzenellenbogen JM, Briffa T, Nedkoff L. Comparative algorithms for identifying and counting hospitalisation episodes of care for coronary heart disease using administrative data. Clin Epidemiol. 2024;16:921-8. 10.2147/CLEP.S497760

    10.2147/CLEP.S497760

17. Lopez D, Katzenellenbogen JM, Sanfilippo FM, Woods JA, Hobbs MS, Knuiman MW, et al. Transfers to metropolitan hospitals and coronary angiography for rural Aboriginal and non-Aboriginal patients with acute ischaemic heart disease in Western Australia. BMC Cardiovasc Disord. 2014;14:58. 10.1186/1471-2261-14-58

    10.1186/1471-2261-14-58

18. Queensland Government. Australian climate data from 1889 to yesterday [webpage]. 2024 [cited 9 Aug 2024]. Available from: https://www.longpaddock.qld.gov.au/silo/.

19. Nager.Date. Public holidays in Australia [webpage]. 2024 [cited 2 Oct 2024]. Available from: https://date.nager.at/PublicHoliday/Australia.

20. Australian Bureau of Statistics. National, state and territory population [webpage]. Canberra: ABS; 2022 [cited 12 Jul 2022]. Available from: https://www.abs.gov.au/statistics/people/population/national-state-and-territory-population/latest-release#data-download.

21. Gasparrini A, Armstrong B, Kenward MG. Distributed lag non-linear models. Stat Med. 2010;29(21):2224-34. 10.1002/sim.3940

    10.1002/sim.3940

22. Talukder MR, Islam MT, Mathew S, Perry C, Phung D, Rutherford S, et al. The effect of ambient temperatures on hospital admissions for kidney diseases in Central Australia. Environ Res. 2024;259:119502. 10.1016/j.envres.2024.119502

    10.1016/j.envres.2024.119502

23. Buckingham-Jeffery E, Morbey R, House T, Elliot AJ, Harcourt S, Smith GE. Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats. Bmc Public Health. 2017;17. 10.1186/s12889-017-4372-y

    10.1186/s12889-017-4372-y

24. Lopez D, Murray K, Preen DB, Sanfilippo FM, Trevenen M, Hankey GJ, et al. The Hospital Frailty Risk Score identifies fewer cases of frailty in a community-based cohort of older men than the FRAIL Scale and Frailty Index. J Am Med Dir Assoc. 2022;23(8):1348-53. 10.1016/j.jamda.2021.09.033

    10.1016/j.jamda.2021.09.033

25. Lopez D, Nedkoff L, Briffa T, Preen DB, Etherton-Beer C, Flicker L, et al. Effect of frailty on initiation of statins following incident acute coronary syndromes in patients aged ≥75 years. Maturitas. 2021;153:13-8. 10.1016/j.maturitas.2021.07.006

    10.1016/j.maturitas.2021.07.006

26. Gasparrini A. Distributed lag linear and non-linear models: the R the package dlnm. 2021. https://cran.r-project.org/web/packages/dlnm/vignettes/dlnmOverview.pdf

27. Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw. 2011;43(8):1-20. 10.18637/jss.v043.i08

    10.18637/jss.v043.i08

28. Tian LW, Qiu H, Sun SZ, Lin HL. Emergency cardiovascular hospitalization risk attributable to cold temperatures in Hong Kong. Circ-Cardiovasc Qual. 2016;9(2):135-42. 10.1161/Circoutcomes.115.002410

    10.1161/Circoutcomes.115.002410

29. Xu Z, Tong S, Cheng J, Crooks JL, Xiang H, Li X, et al. Heatwaves and diabetes in Brisbane, Australia: a population-based retrospective cohort study. Int J Epidemiol. 2019;48(4):1091-100. 10.1093/ije/dyz048

    10.1093/ije/dyz048

30. Hansen A, Bi P, Nitschke M, Ryan P, Pisaniello D, Tucker G. The effect of heat waves on mental health in a temperate Australian city. Environ Health Persp. 2008;116(10):1369-75. 10.1289/ehp.11339

    10.1289/ehp.11339

31. Székely M, Garai J. Thermoregulation and age. Handb Clin Neurol. 2018;156:377-95. 10.1016/B978-0-444-63912-7.00023-0

    10.1016/B978-0-444-63912-7.00023-0

32. Australian Institute of Health and Welfare. National Drug Strategy Household Survey 2019. Drug Statistics series no. 32. PHE 270 [report]. Canberra: AIHW; 2020. https://www.aihw.gov.au/getmedia/77dbea6e-f071-495c-b71e-3a632237269d/aihw-phe-270.pdf?v=20230605184325&inline=true

33. Bennett KM. Gender and longitudinal changes in physical activities in later life. Age Ageing. 1998;27 Suppl 3:24-8. 10.1093/ageing/27.suppl_3.24

    10.1093/ageing/27.suppl_3.24

34. Henry GW, Lyle JM. The National Recreational and Indigenous Fishing survey [report]. 2003 Jul 2003. https://www.frdc.com.au/sites/default/files/products/1999-158-DLD.pdf

35. McManus A, Hunt W, McManus J, Creegan R. Investigating the health and well-being benefits of recreational fishing in Western Australia [report]. 2014. https://recfishwest.org.au/wp-content/uploads/2015/10/Recfish-Report-Final-September-2014.pdf

36. Australian Government Workplace Gender Equality Agency. Gender segregation in Australia’s workforce [webpage]. 2019 [cited 7 Jul 2025]. Available from: https://www.wgea.gov.au/publications/gender-segregation-in-australias-workforce#gender-seg-industry

37. Biswas A, Harbin S, Irvin E, Johnston H, Begum M, Tiong M, et al. Sex and gender differences in occupational hazard exposures: a scoping review of the recent literature. Curr Environ Health Rep. 2021;8(4):267-80. 10.1007/s40572-021-00330-8

    10.1007/s40572-021-00330-8

38. Australian Bureau of Statistics. Use of information technology by people with disability, older people and primary carers [webpage]. Canberra: ABS; 2020 [cited 7 Jul 2025]. Available from: https://www.abs.gov.au/articles/use-information-technology-people-disability-older-people-and-primary-carers

39. Chang ET, Smedby KE, Hjalgrim H, Glimelius B, Adami HO. Reliability of self-reported family history of cancer in a large case–control study of lymphoma. J Natl Cancer Inst. 2006;98(1):61-8. 10.1093/jnci/djj005

    10.1093/jnci/djj005

40. Australian Institute of Health and Welfare. Gout [webpage]. 2024 [cited 25 Nov 2024]. Available from: https://www.aihw.gov.au/reports/chronic-musculoskeletal-conditions/gout

41. Guo Y, Barnett AG, Tong S. Spatiotemporal model or time series model for assessing city-wide temperature effects on mortality? Environ Res. 2013;120:55-62. 10.1016/j.envres.2012.09.001

    10.1016/j.envres.2012.09.001

42. Basu R, Pearson D, Malig B, Broadwin R, Green R. The effect of high ambient temperature on emergency room visits. Epidemiology. 2012;23(6):813-20. 10.1097/EDE.0b013e31826b7f97

    10.1097/EDE.0b013e31826b7f97

43. Green RS, Basu R, Malig B, Broadwin R, Kim JJ, Ostro B. The effect of temperature on hospital admissions in nine California counties. Int J Public Health. 2010;55(2):113-21. 10.1007/s00038-009-0076-0

    10.1007/s00038-009-0076-0

44. Buckley JP, Samet JM, Richardson DB. Commentary: Does air pollution confound studies of temperature? Epidemiology. 2014;25(2):242-5. 10.1097/EDE.0000000000000051

    10.1097/EDE.0000000000000051

45. Robinson PC, Stamp LK. The management of gout: Much has changed. Aust Fam Physician. 2016;45(5):299-302.