Type-2 diabetes is a leading cause of death and disability. Emerging evidence suggests that ultraviolet radiation or sun exposure may limit its development. We used freely available online datasets to evaluate the associations between solar radiation and type-2 diabetes prevalence across Australia. We extracted prevalence data for 1822 postcodes from the Australian Diabetes Map on 25 January 2020. Daily solar radiation data averaged over 30-years (1990–2019) were collated from online databases (Australian Bureau of Meteorology). Population-weighted linear regression models were adjusted for covariates at the postcode level including socioeconomic status (IRSAD), remoteness, mean age, gender, Aboriginal and Torres Strait Islander status, as well as mean annual ambient temperature (1961–1990) and rainfall (1981–2010). A consistent inverse correlation was observed between type-2 diabetes prevalence and solar radiation, after adjusting for these covariates (ß (coefficient of regression) = −0.045; 95% CI: −0.086, −0.0051; p = 0.027). However, the relative contribution of solar radiation towards type-2 diabetes prevalence was small (2.1%) in this model. Other significant correlations between type-2 diabetes prevalence and covariates included: socioeconomic status (ß = −0.017; 95% CI: −0.017, −0.016; p < 0.001), mean age (ß = 0.041; 95% CI: 0.028, 0.054; p < 0.015), remoteness (ß = −0.05; 95% CI: −0.088, −0.011; p < 0.001) and rainfall (ß = −0.0008; 95% CI: −0.00097, −0.00067; p < 0.001). In conclusion, in Australian postcodes, higher levels of solar radiation and rainfall was associated with reduced type-2 diabetes prevalence. Further studies are needed that consider lifestyle covariates such as physical activity.

Logistic regression analysis of the association between diabetes and generation membership of Generation X (aged 25–44 years 2007/08 NHS data) and Baby Boomers (aged 25–44 years 1989/90 NHS data) using data from the Australian Bureau of Statistics.

Australia Ophthalmic Device Market size was valued at USD 1.88 Billion in 2024 and is projected to reach USD 2.8 Billion by 2031, growing at a CAGR of 5.5% from 2024 to 2031.

Australia Ophthalmic Device Market Drivers

Increasing Prevalence of Age-Related Eye Disorders: The aging population in Australia is a key driver of the ophthalmic device market. According to the Australian Bureau of Statistics, the population aged 65 and up is expected to grow from 15.9% in 2020 to 22.2% in 2030. According to the National Health and Medical Research Council, roughly 170,000 Australians suffer from age-related macular degeneration (AMD), which is anticipated to increase to 220,000 by 2025. Furthermore, the Australian Institute of Health and Welfare reported that glaucoma affects 300,000 Australians, with half of cases going untreated, increasing the demand for better ophthalmic diagnostic and treatment technology. Technological Advances and Government Healthcare Innovation: The Australian government's commitment to medical technology innovation is considerably boosting the ophthalmic device sector. The Department of Health and Aged Care has been aggressively supporting medical device innovation through its Medical Research Future Fund, which will provide AUD 480 million for medical technology research in 2022. The Therapeutic Goods Administration (TGA) has simplified the regulatory procedure for novel medical devices, resulting in a 35% increase in ophthalmic device approvals over the last three years. Rising Prevalence of Diabetes and Associated Eye Complications: The increasing incidence of diabetes in Australia is driving significant growth in the ophthalmic device market. The Australian Institute of Health and Welfare reports that 1.3 million Australians have diabetes, with diabetic retinopathy affecting approximately 30% of diabetes patients. The National Diabetes Services Scheme indicates that diabetic eye complications are a leading cause of preventable blindness, with over 75,000 new cases of diabetic retinopathy diagnosed annually.

NA or ‘level not determined’ categories not included.*p

Abstract

The Nauru-STEPS was a nation-wide representative survey of 15 to 64 year olds with the following objectives: 1. To document the national prevalence and patterns of tobacco use, alcohol consumption, dietary behaviours, physical activity, body mass index, elevated blood pressure, and biochemical markers such as blood glucose and blood lipids in Nauru. 2. To provide reliable and up-to-date information on NCD risk factors for planning and evaluating public health initiatives, and for identifying future demands for health services in managing and treating NCDs.

The planning and implementation of the survey was a collaborative initiative between the Nauru Ministry of Health (MOH), the World Health Organization (WHO) and the Centre for Physical Activity and Health at the Universities of New South Wales and Sydney in Australia. The study was supported by the Australian Agency for International Development (AusAID).

Geographic coverage

National coverage

Analysis unit

-individual -households

Universe

The survey population included non-institutionalised individuals in the 15-64 year-old age category living in Nauru during the survey period. Indigenous Nauruans, I-Kiribati and Tuvaluan residents comprised approximately 90% of the total population (Bureau of Statistics, 2004). The remaining population consisted of Asians (Chinese, Filipinos and other South East Asians), other Pacific islanders and expatriate residents (i.e. Australians, Europeans, New Zealanders).

This latter group was excluded from the sampling frame as they were considered to be highly transient and relatively low users of health services in Nauru. Individuals with mental illness, physical or developmental disabilities were also excluded from the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample selection - approximately 2,500 randomly selected participants aged 15-64 years Selection process - using a simple random sampling method Stratification - stratified by age and sex Stages of sample selection - initial sample size calculations were performed assuming a prevalence of approximately 10% for major variables of interest (e.g.diabetes), an ability to ascertain an estimated prevalence within approximately 1% of the true prevalence with a 95% confidence level. These calculations suggested that a total sample size of approximately 2,584 in the target population of 15 to 64 year olds would be sufficient for the purposes of this study Strategy for absent respondent/not found/refusals (replacement or not) - a reserve list of an additional 250 participants was generated for each age/sex group to replace any of the original participants in that age/sex group who were ineligible to participate in the study (i.e. those not being in the country during the survey or those individuals with physical or mental disabilities or already deceased). Overall, usable data for STEPS 1-3 were obtained from 2,272 participants, with a total response rate of 89.7%. Of the 2,272 respondents, 1086 were men and 1186 were women.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires or instrument for the Nauru Steps Survey 2004 were the core questions in the STEPS 1-3 instrument remained unchanged (Bonita et al., 2001). The Step 1 questionnaire was administered in each household, which collected various information on household members including sex, age and time spent in schools. The Step 1 questionnaire includes behaviours measures for tobacco use, alcohol consumption, diet, physical activity, history of high blood pressure, history of diabetes and general well being.

In addition to a Step 1 questionnaire, questionnaires were administered in each selected household for peoples age 15-64.

The Step 2 questionnaire is mainly for Physical measurements and it includes measurements of heights and weights, blood pressure and heart rate.

The Step 3 questionnaire is for women respondents for Biochemical measurements and it includes blood glucose, blood lipids and albuminuria.

The questionnaires were developed in English from the Steps 1-3 instruments model Questionnaires. After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey.

The survey team agreed for additional social and or environmental items relating to NCD control and prevention to be included in the STEPS 1 questionnaire. Examples of some optional items include self-reported health status, perceived susceptability to diabetes, perceived barriers or factors that would enhance adoption of a healthy lifestyle.

To investigate the prevalence of kidney disease in Nauru, items measuring the albuminuria level were added to STEP 3 measures, but the results for this are not presented in the report. Survey participants were requested to bring their urine sample in a collection jar provided by the staff when they presented for STEP 3. For those who forgot to bring in their sample, their urine was collected on the day of the visit.

All questionnaires and modules are provided as external resources.

Cleaning operations

Two staff manually double-entered all survey data into EpiInfo 6.04d database. The double data entry process was preceded by a series of data cleaning activities by STEP 1 staff. These activities included identifying and investigating various issues related to ineligible handwriting, duplicate records, data values outside of preset ranges, and inconsistencies between answers to different but related questions. Any inconsistencies noted by the data entry staff were resolved with the STEPS personnel or Team Supervisors before data entry was completed. Data entry was conducted concurrently with data collection.

Response rate

Usable data for STEPS 1-3 were obtained from 2,272 participants, with a total response rate of 89.7%. Of the 2,272 respondents, 1086 were men and 1186 were women.

This dataset, released February 2021, contains statistics relating to avoidable mortalities during the year 2014-2018 from the following causes: cancer, diabetes, circulatory systems diseases, respiratory system diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2014 to 2018 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population is the ABS Estimated Resident Population (ERP), 30 June 2014 to 30 June 2018. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released December 2016, contains statistics for deaths of people aged 0-74 years during the years 2010-2014 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical …Show full descriptionThis dataset, released December 2016, contains statistics for deaths of people aged 0-74 years during the years 2010-2014 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SDR" - Indirectly age-standardised death ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)