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AbstractObjectivesTo identify data availability, gaps, and patterns for population level prevalence of loneliness globally, to summarise prevalence estimates within World Health Organization regions when feasible through meta-analysis, and to examine temporal trends of loneliness in countries where data exist.DesignSystematic review and meta-analysis.Data sourcesEmbase, Medline, PsycINFO, and Scopus for peer reviewed literature, and Google Scholar and Open Grey for grey literature, supplemented by backward reference searching (to 1 September 2021)Eligibility criteria for selecting studiesObservational studies based on nationally representative samples (n≥292), validated instruments, and prevalence data for 2000-19. Two researchers independently extracted data and assessed the risk of bias using the Joanna Briggs Institute checklist. Random effects meta-analysis was conducted in the subset of studies with relatively homogeneous research methods by measurement instrument, age group, and WHO region.ResultsPrevalence data were available for 113 countries or territories, according to official WHO nomenclature for regions, from 57 studies. Data were available for adolescents (12-17 years) in 77 countries or territories, young adults (18-29 years) in 30 countries, middle aged adults (30-59 years) in 32 countries, and older adults (≥60 years) in 40 countries. Data for all age groups except adolescents were lacking outside of Europe. Overall, 212 estimates for 106 countries from 24 studies were included in meta-analyses. The pooled prevalence of loneliness for adolescents ranged from 9.2% (95% confidence interval 6.8% to 12.4%) in South-East Asia to 14.4% (12.2% to 17.1%) in the Eastern Mediterranean region. For adults, meta-analysis was conducted for the European region only, and a consistent geographical pattern was shown for all adult age groups. The lowest prevalence of loneliness was consistently observed in northern European countries (2.9%, 1.8% to 4.5% for young adults; 2.7%, 2.4% to 3.0% for middle aged adults; and 5.2%, 4.2% to 6.5% for older adults) and the highest in eastern European countries (7.5%, 5.9% to 9.4% for young adults; 9.6%, 7.7% to 12.0% for middle aged adults; and 21.3%, 18.7% to 24.2% for older adults).ConclusionProblematic levels of loneliness are experienced by a substantial proportion of the population in many countries. The substantial difference in data coverage between high income countries (particularly Europe) and low and middle income countries raised an important equity issue. Evidence on the temporal trends of loneliness is insufficient. The findings of this meta-analysis are limited by data scarcity and methodological heterogeneity. Loneliness should be incorporated into general health surveillance with broader geographical and age coverage, using standardised and validated measurement tools.Systematic review registrationPROSPERO CRD42019131448.

Background A significant proportion of the global respiratory syncytial virus (RSV) associated morbidity is accounted for by infants aged 0 to 6 months, who are particularly vulnerable to severe disease. In 2015, 44% of global hospitalisations in infants in this age group were secondary to RSV. The objective of this systematic review is to appraise and synthesise the local evidence of RSV infection morbidity among Australian infants aged 0 to 6 months and to assess the implications for future immunisation strategies. Methods Electronic databases (Medline, Embase, Pubmed and Global Health) were searched for full-text articles published between 2000 and 2023 in English language. Studies that examined markers of RSV disease morbidity in infants aged 0 to 6 months in Australia who had laboratory confirmed RSV infection were eligible for inclusion. The outcomes of interest were incidence, prevalence, testing rate, positivity rate, mortality, emergency department visits, community health visits, hospitalisation, intensive care unit admission, supplementary oxygen use, mechanical ventilation, risk factors for disease severity and monoclonal antibody use. Results The database search identified 469 studies. After removal of duplicates and full-text review, 17 articles were eligible for inclusion. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Synthesis without meta-analysis guidelines. Conclusions Qualitative analysis of the included studies showed that Australian infants aged 0 to 6 months have higher rates of RSV testing, positivity and incidence; and more likely to develop severe disease that requires hospitalisation, intensive care unit admission or respiratory support, compared to children and adults of all ages. Aboriginal and Torres Strait Islander infants aged 0 to 6 months demonstrated higher rates of RSV infection and hospitalisation, compared to non-Indigenous infants. Age-related trends persisted in geographic areas with varying seasonal transmission of RSV, and during the SARS-CoV-2 pandemic. Passive immunisation strategies targeting infants in their first 6 months of life, either via vaccination of pregnant women or administration of long-acting monoclonal antibody during infancy, could effectively reduce RSV disease burden in Australia.

Wildfires are increasing and cause health effects. The immediate and ongoing health impacts of prolonged wildfire smoke exposure in severe asthma are unknown. This longitudinal study examined the experiences and health impacts of prolonged wildfire (bushfire) smoke exposure in adults with severe asthma during the 2019/2020 Australian bushfire period. Participants from Eastern/Southern Australia who had previously enrolled in an asthma registry completed a questionnaire survey regarding symptoms, asthma attacks, quality of life and smoke exposure mitigation during the bushfires and in the months following exposure. Daily individualized exposure to bushfire particulate matter (PM2.5) was estimated by geolocation and validated modelling. Respondents (n = 240) had a median age of 63 years, 60% were female and 92% had severe asthma. They experienced prolonged intense PM2.5 exposure (mean PM2.5 32.5 μg/m3 on 55 bushfire days). Most (83%) of the participants experienced symptoms during the bushfire period, including: breathlessness (57%); wheeze/whistling chest (53%); and cough (50%). A total of 44% required oral corticosteroid treatment for an asthma attack and 65% reported reduced capacity to participate in usual activities. About half of the participants received information/advice regarding asthma management (45%) and smoke exposure minimization strategies (52%). Most of the participants stayed indoors (88%) and kept the windows/doors shut when inside (93%), but this did not clearly mitigate the symptoms. Following the bushfire period, 65% of the participants reported persistent asthma symptoms. Monoclonal antibody use for asthma was associated with a reduced risk of persistent symptoms. Intense and prolonged PM2.5 exposure during the 2019/2020 bushfires was associated with acute and persistent symptoms among people with severe asthma. There are opportunities to improve the exposure mitigation strategies and communicate these to people with severe asthma.

Background COVID-19 outbreaks have disproportionately affected Residential Aged Care Facilities (RACFs) around the world, with devastating impacts for residents and their families. Many factors such as community prevalence, facility layout, and infection control practices have been linked to resident outcomes. At present, there are no scoring systems designed to quantify these factors and assess their level of association with resident attack rates and mortality rates. Methods We constructed a novel Infection Prevention and Control (IPC) scoring system to quantify facility layout, ability to cohort residents, and IPC practices in RACFs. We conducted a retrospective observational cohort study of COVID-19 outbreaks, applying our IPC scoring system to all COVID-19 outbreaks occurring in RACFs in Sydney Local Health District during the Delta and Omicron waves of the COVID-19 pandemic in New South Wales, Australia. Results Twenty-six COVID-19 outbreaks in 23 facilities in the Delta wave, and 84 outbreaks in 53 facilities in the Omicron wave were included in the study. A linear Generalised Estimating Equation model was fitted to the Omicron data. Higher IPC scores were associated with higher attack rates and mortality rates. Facilities with IPC scores greater than 75.0% had attack rates 19.6% higher [95% CI: 6.4%-32.8%] and mortality rates 1.7% higher [95% CI: 0.6%-2.7%] than facilities with an IPC score of less than 60.0%. Conclusions The results of this study suggest the utility of the IPC scoring system for identifying facilities at greater risk of adverse outcomes from COVID-19 outbreaks. While further validation and replication of accuracy is required, the IPC scoring system could be used and adapted to improve planning, policy, and resource allocation for future outbreaks.

Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m3. We predicted daily PM2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM2.5 attributable to extreme pollution events in different locations across the country.

IntroductionLong-term exposure to fine particulate matter (≤2.5 µm (PM2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM2.5- attributable TB notifications during 2007–2017 in Ningxia Hui Autonomous Region (NHAR), China.MethodsPM2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM2.5-associated TB-notifications were estimated for total and anthropogenic PM2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM2.5 (30 µg/m3) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m3) and 25th percentiles (38 µg/m3), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM2.5 concentrations, population growth and baseline TB-notification rates on PM2.5 attributable TB notifications.ResultsOver 2007–2017, annual PM2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m3 as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM2.5. Relative to 2007, overall changes in PM2.5 attributable TB notifications were due to reduced TB-notification rates (−23.8%), followed by decreasing PM2.5 (−6.2%), and population growth (+4.9%).ConclusionWe have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.

Background and objective: Acute rheumatic fever (ARF) is a complication of infection with group A streptococcus. ARF is treated with a long-term regimen of antibiotic secondary prophylaxis. Recent data have shown that only 36% of clients receive >80% of their regimen. The aim of this study was to determine clinic-level factors independently associated with the performance of primary healthcare clinics in delivering secondary prophylaxis to patients with ARF. Methods: Cross-sectional de-identified data from clinics agreeing to data retention through the Audit and Best Practice for Chronic Disease National Research Partnership were accessed to calculate secondary prophylaxis performance scores and clinic-level factors associated with secondary prophylaxis performance using regression analysis. Results: Thirty-six clinics and 496 client records met eligibility criteria for analysis. Clinic secondary prophylaxis performance was significantly associated with 'systematic processes of follow-up'. Every one unit increase in 'systematic approach to follow-up' increased the median level of secondary prophylaxis performance by 30% (95% confidence interval: 2, 66). Clinic accreditation status, location or workforce were not associated with secondary prophylaxis performance. Discussion: General practitioners as clinical leaders are well placed to support managers to critically review follow-up and electronic reminder systems for secondary prophylaxis delivery at clinic level.

While periodontal disease is associated with many risk factors, socioeconomically disadvantaged communities experience the highest disease burden. The aim of this study was to evaluate the effectiveness of a personalized oral health education program, in combination with routine dental treatment, in participants from a low socioeconomic community. We used a randomized, controlled, examiner-blinded clinical trial. A total of 579 participants (aged 18–60 years) were randomly grouped: the intervention group (n = 292) received a personalized oral health education program in combination with routine dental care and the control group (n = 287) received routine dental care. All participants were assessed for improvement in oral health care behaviors, dental plaque, and periodontal status at baseline, 12 months, and 24 months. We found a significant drop (p < 0.001) in the plaque indices, Periodontal Probing Depths (PPD) and Bleeding on Probing (BOP) between baseline and the 12-month follow-up for both groups. For BOP, the number of sites positive was significantly different between baseline and the 24-month follow-up (p = 0.037). No differences were found between the two groups for any evaluated clinical outcome. The personalized oral health education program used in the current study did not appear to add significant improvement to clinical outcomes of periodontal health compared with routine restorative dental care per se.

Robust high spatiotemporal resolution daily PM[sub.2.5] exposure estimates are limited in Australia. Estimates of daily PM[sub.2.5] and the PM[sub.2.5] component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM[sub.2.5] at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM[sub.2.5] component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m[sup.3] . We predicted daily PM[sub.2.5] across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM[sub.2.5] attributable to extreme pollution events in different locations across the country.