Abstract Aims Achieving equitable healthcare access is a global challenge. Improving whole-population mental health and reducing the global burden of mental disorders is a key recommendation of the 2018 Lancet Global Mental Health Commission, which proposed monitoring national indicators, including the proportion of people with severe mental disorders who are service-users. This study aims to derive an equity indicator from national datasets integrating need, service utilisation and socioeconomic status, and demonstrate its utility in identifying gaps in mental health service use amongst those with the greatest need, thereby guiding equitable healthcare delivery. Methods We present a case study of a universal health insurance scheme (Medicare) in Australia. We developed the equity indicator using three national datasets. Geographic areas were linked to an area-based socioeconomic deprivation quintile (Census 2016). Per geographic area, we estimated the number with a mental healthcare need using scores ≥30 on the Kessler-10 (Australian National Health Surveys 2015 and 2018), and obtained the number of services used, defined as mental health-related contacts with general practitioners and mental health professionals (Medicare administrative data 2015–2019). We divided the number of services by the population with an estimated mental healthcare need and averaged these use-rates across each socioeconomic deprivation quintile. The equity indicator is the ratio of the use-rates in the least versus most deprived quintiles. Results Those estimated to have the greatest need for mental healthcare in 2019 ranged between 8.2% in the most disadvantaged area quintile (Q1) and 2.4% in the least (Q5), corresponding to a proportional increase of 27.7% in Q1 and 19.5% in Q5 since 2015. Equity-indicator-adjusted service rates of 4.2 (3.8–4.6) and 23.9 (22.4–25.4) showed that individuals with the highest need for care residing in Q1 areas received a stark 6 times fewer services compared to their Q5 counterparts, producing an equity indicator of 6. Conclusions As the global prevalence of common mental disorders may be increasing, it is crucial to calculate robust indicators evaluating the equity of mental health service use. In this Australian case study, we developed an equity indicator enabling the direct comparison of geographic areas with different need profiles. The results revealed striking inequities that persisted despite publicly-funded universal healthcare, recent service reforms and being a high-income country. This study demonstrates the importance and feasibility of generating such an indicator to inform and empower communities, healthcare providers and policymakers to pursue equitable service provision.
Objective To examine SARS-CoV-2 vaccine confidence, attitudes and intentions in Australian adults as part of the iCARE Study. Design and setting Cross-sectional online survey conducted when free COVID-19 vaccinations first became available in Australia in February 2021. Participants Total of 1166 Australians from general population aged 18–90 years (mean 52, SD of 19). Main outcome measures Primary outcome: responses to question ‘If a vaccine for COVID-19 were available today, what is the likelihood that you would get vaccinated?’. Secondary outcome: analyses of putative drivers of uptake, including vaccine confidence, socioeconomic status and sources of trust, derived from multiple survey questions. Results Seventy-eight per cent reported being likely to receive a SARS-CoV-2 vaccine. Higher SARS-CoV-2 vaccine intentions were associated with: increasing age (OR: 2.01 (95% CI 1.77 to 2.77)), being male (1.37 (95% CI 1.08 to 1.72)), residing in least disadvantaged area quintile (2.27 (95% CI 1.53 to 3.37)) and a self-perceived high risk of getting COVID-19 (1.52 (95% CI 1.08 to 2.14)). However, 72% did not believe they were at a high risk of getting COVID-19. Findings regarding vaccines in general were similar except there were no sex differences. For both the SARS-CoV-2 vaccine and vaccines in general, there were no differences in intentions to vaccinate as a function of education level, perceived income level and rurality. Knowing that the vaccine is safe and effective and that getting vaccinated will protect others, trusting the company that made it and vaccination recommended by a doctor were reported to influence a large proportion of the study cohort to uptake the SARS-CoV-2 vaccine. Seventy-eight per cent reported the intent to continue engaging in virus-protecting behaviours (mask wearing, social distancing, etc) postvaccine. Conclusions Most Australians are likely to receive a SARS-CoV-2 vaccine. Key influencing factors identified (eg, knowing vaccine is safe and effective, and doctor’s recommendation to get vaccinated) can inform public health messaging to enhance vaccination rates.
Abstract Objectives To examine trends in psychological distress in Australia between 2001 to 2017-18, including analysis by age, sex, and location. Design, setting and participants Secondary analysis of six successive national health surveys of representative samples of the working age population (18-64 years). Main outcome measures Prevalence of psychological distress at very-high symptom level (defined by a Kessler Psychological Distress Scale [K10] score of 30 or more) and combined high/very-high level (K10 score of 22 or more). Results The latest survey showed 5.1% of Australians reporting very-high level distress and 14.8% combined high/very-high level - both the largest rates recorded this century. The greatest increase from 2001 to 2017-18 was in women aged 55-64 with very-high distress significantly increasing from 3.5% (95% CI: 2.5-4.5%) to 7.2% (5.9-8.5%), and; high/very-high distress from 12.4% (10.5-14.2%) to 18.7% (16.7-20.7%). Men aged 25-34 had very-high distress increase from 2.1% (1.4-2.8) to 4.0% (2.9-5.1%); and combined high/very-high distress remained stable at 10.6% (9.1-12.1%) to 11.5% (9.7-13.3%). In 2017-18, greatest distress was in women aged 18-24 years (very-high 8.0% (5.9-10.2%); high/very-high 22.1% (18.8-25.3%)). Overall, distress was significantly more prevalent in inner regional Australia than elsewhere (very-high level 4.8% (4.4-5.1%); high/very-high 14.4% (13.8-15%)). Conclusions Australia’s annual mental health expenditure over this period has doubled, yet population level psychological distress has increased. A whole of government approach and targeted strategies focusing on groups with the poorest mental health such as older working aged women, younger people, particularly women, and those outside of major cities are indicated. Box “The known” Previous examinations of national health surveys had suggested that population mental health was stable as measured by psychological distress. “The new” Examining six consecutive national surveys we provide evidence that mental health has significantly deteriorated between 2001 and 2018. The latest survey showed 5.1% of Australians reporting very-high distress and 14.8% combined high/very-high distress, which are the largest rates reported this century. “The implications” Whole of government approach and targeted strategies focusing on groups with the poorest mental health such as older working aged women, younger people particularly women, and those outside of major cities are indicated.
Purpose To examine Australian psychological distress trends from 2001 to 2017/18, including analysis by age, sex, location, and household income. Methods Secondary analysis of the working age population (18–64 years) in six successive representative national health surveys. Measures were prevalence of psychological distress at very-high symptom level (defined by a Kessler Psychological Distress Scale (K10) score of 30 or more) and combined high/very-high level (K10 score of 22 or more). Very-high K10 scores are associated with mental health problems meeting diagnostic thresholds in past year. Results From 2001 to 2017/18 Australian rates of K10 very-high distress rose significantly from 3.8 to 5.1% and combined high/very-high from 13.2 to 14.8%. In women aged 55–64, very-high distress rose significantly and substantially from 3.5 to 7.2% and high/very-high distress from 12.4 to 18.7%. In men aged 25–34, very-high distress increased from 2.1 to 4.0% and high/very-high from 10.6 to 11.5%. Income was strongly and inversely associated with distress (lowest vs. highest quintile adjusted OR 11.4). An apparent association of increased distress with regional location disappeared with adjustment for income. Conclusion Australia’s population level of psychological distress increased significantly from 2001–2017/18, with levels highest in women and with rates inversely associated with income. This is likely to be indicative of increased community rates of mental disorders. Given that this has occurred whilst mental healthcare expenditure has increased, there is an urgent need to reconsider how best to respond to mental illness, including targeting the most vulnerable based on social determinants such as age, gender, and lower incomes.
ABSTRACT Introduction The Monash early pregnancy prediction model calculates risks of developing GDM and is internationally externally validated and implemented in practice, however some gaps remain. Objective To validate and update Monash GDM model, revising ethnicity categorisation, updating to recent diagnostic criteria, to improve performance and generalisability. Methods Routine health data for singleton pregnancies from 2016 to 2018 in Australia included updated GDM diagnostic criteria. The Original Model predictors were included (age, body mass index, ethnicity, diabetes family history, past-history of GDM, past-history of poor obstetric outcomes, ethnicity), with ethnicity revised. Updating model methods were: recalibration-in-the-large (Model A); re-estimation of intercept and slope (Model B), and; coefficients revision using logistic regression (Mode1 C1 with original eight ethnicity categories, and Mode1 C2 with updated 6 ethnicity categories). Analysis included ten-fold cross-validation, performance measures (c-statistic, calibration-in-the-large value, calibration slope and expected-observed (E:O) ratio) and closed testing examining log-likelihood scores and AIC compared models. Results In 26,474 singleton pregnancies (4,756, 18% with GDM), we showed that temporal validation of the original model was reasonable ( c -statistic 0.698) but with suboptimal calibration (E:O of 0.485). Model C2 was preferred, because of the high c-statistic (0.732), and it performed significantly better in closed testing compared to other models. Conclusions Updating of the original model sustains predictive performance in a contemporary population, including ethnicity data, recent diagnostic criteria, and universal screening context. This supports the value of risk prediction models to guide risk-stratified care to women at risk of GDM. Trial registration details This study was registered as part of the PeRSonal GDM study on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results.
BACKGROUND The experience of infertility and its treatment engenders considerable stress and is often described as an emotional rollercoaster. A mobile health (mHealth) app may be a novel solution to address the psychoeducational and psychosocial support needs of fertility patients because of its potential to reduce stress and increase patient empowerment. There are a few fertility-related apps that provide information and support to both men and women undergoing fertility treatment; however, none have documented their development and evaluation process. OBJECTIVE This study aims to describe the development and evaluation process of a bilingual mHealth app, <i>Infotility</i>, designed to meet the psychoeducational and psychosocial support needs of men and women undergoing fertility treatment. METHODS To develop the <i>Infotility</i> app, we adhered to the Medical Research Council guidelines for the development and evaluation of complex interventions. First, we conducted literature reviews and needs assessment surveys of fertility patients and health care providers who informed the content and design of the app. Second, we tested the intervention with a small group of end users who provided feedback on the design and appropriateness of the app’s content. Third, we evaluated the uptake and usability of the app using a pre-post study design. Finally, we updated the app’s content based on participants’ feedback and searched for partners to disseminate the app to the broader public. RESULTS This study is the first to describe the development and evaluation process of an mHealth app for men and women undergoing fertility treatment. The app met its goal in providing fertility patients with a clinician-approved, portable resource for reliable information about medical and psychosocial aspects of infertility and its treatments and a confidential peer support forum monitored by trained peer supporters. Participants rated the engagement, functionality, information, and esthetics of the app positively, with an overall app quality mean score of 3.75 (SD 0.53) and a star rating of 3.43 (SD 0.75), with a total possible score and star rating of 5.00. CONCLUSIONS By documenting the systematic development and evaluation of the mHealth app for men and women undergoing fertility treatment, this paper can facilitate the replication of the study intervention and the development of similar mHealth apps.
Background The experience of infertility and its treatment engenders considerable stress and is often described as an emotional rollercoaster. A mobile health (mHealth) app may be a novel solution to address the psychoeducational and psychosocial support needs of fertility patients because of its potential to reduce stress and increase patient empowerment. There are a few fertility-related apps that provide information and support to both men and women undergoing fertility treatment; however, none have documented their development and evaluation process. Objective This study aims to describe the development and evaluation process of a bilingual mHealth app, Infotility, designed to meet the psychoeducational and psychosocial support needs of men and women undergoing fertility treatment. Methods To develop the Infotility app, we adhered to the Medical Research Council guidelines for the development and evaluation of complex interventions. First, we conducted literature reviews and needs assessment surveys of fertility patients and health care providers who informed the content and design of the app. Second, we tested the intervention with a small group of end users who provided feedback on the design and appropriateness of the app’s content. Third, we evaluated the uptake and usability of the app using a pre-post study design. Finally, we updated the app’s content based on participants’ feedback and searched for partners to disseminate the app to the broader public. Results This study is the first to describe the development and evaluation process of an mHealth app for men and women undergoing fertility treatment. The app met its goal in providing fertility patients with a clinician-approved, portable resource for reliable information about medical and psychosocial aspects of infertility and its treatments and a confidential peer support forum monitored by trained peer supporters. Participants rated the engagement, functionality, information, and esthetics of the app positively, with an overall app quality mean score of 3.75 (SD 0.53) and a star rating of 3.43 (SD 0.75), with a total possible score and star rating of 5.00. Conclusions By documenting the systematic development and evaluation of the mHealth app for men and women undergoing fertility treatment, this paper can facilitate the replication of the study intervention and the development of similar mHealth apps.
Background: Achieving equitable access to healthcare is a global challenge. This national case study occurs within an aspirational universally accessible health service (Medicare). Our objective was to create a national mental healthcare equity indicator that integrates need, service utilization, and socioeconomic status to guide equitable mental healthcare delivery.Methods: We developed the indicator by analyzing three national Australian datasets (need: National Health Surveys 2015 & 2018; services: Medicare 2015–2019; socioeconomic status: Census 2016). Mental healthcare need was represented by rates of very-high psychological distress; service utilizationby contacts with general practitioners and mental health professionals; and socioeconomic status via area-based quintiles and rate ratios.Findings: The estimated need for mental healthcare in 2019 ranged between 8·2% in the most disadvantaged area quintile (Q1) and 2·4% in the least disadvantaged (Q5). These correspond to a proportional increased need for care since 2015 of 27·7% in Q1 and 19·5% in Q5. Equity-indicator-adjusted service rates of 4·2 (3·8 – 4·6) and 23·9 (22·4 – 25·4) showed that individuals with the highest need for care residing in Q1 areas received a stark 5·7 fewer services compared to their Q5 counterparts.Interpretation: We have developed a mental health equity indicator that enables direct comparison of areas with different need profiles. The indicator revealed striking inequities in publicly-funded universal mental health services in Australia. This study demonstrates the feasibility of generating such an indicator, which can inform and empower communities, healthcare providers, and policymakers to pursue equitable accessible healthcare services.Funding: This study was funded by a grant from Equity Trustees and Monash Partners.Declaration of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Ethical Approval: Ethics approval was not required to analyse these anonymised datasets. The Medicare dataset is publicly available. National Health Survey (NHS) data collection occurred under the auspices of the Census and Statistics Act 1905, and per the ABS and Universities Australia Agreement (35), researchers affiliated with participating universities can access the basic, anonymised, NHS microdata, as we did in this study.Ethical Approval:
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)