Characterizing asset-based studies in public health: development of a framework.

Asset-based approaches are becoming more common within public health interventions; however, due to variations in terminology, it can be difficult to identify asset-based approaches. The study aimed to develop and test a framework that could distinguish between asset-based and deficit-based community studies, whilst acknowledging there is a continuum of approaches. Literature about asset-based and deficit-based approaches were reviewed and a framework was developed based on the Theory of Change model. A scoring system was developed for each of the five elements in the framework based on this model. Measurement of community engagement was built in, and a way of capturing how much the study involved an asset approach. The framework was tested on 13 studies examining community-based interventions to investigate whether it could characterize asset-based versus deficit-based studies. The framework demonstrated how much the principles underpinning asset-based approaches were present and distinguished between studies where the approach was deficit-based to those that had some elements of an asset-based approach. This framework is useful for researchers and policymakers when determining how much of an intervention is asset-based and identifying which elements of asset-based approaches lead to an intervention working.

Deficit-based approaches are a common approach to addressing public health issues within a community and involve identifying a health problem or need and finding a way to solve these. However, asset-based approaches, those that involve the community using its assets, or strengths, to enable community members to have more control over their health and wellbeing, are increasingly common. The terminology used to describe these methods varies greatly so it can be difficult to identify whether an approach is more deficit-based or asset-based. To address this a framework was developed to identify and score elements of asset-based studies. We did this by reviewing academic information describing asset-based approaches and built into this a scoring system. This framework was used to assess and measure the degree to which 13 community-based studies took an asset-based approach. The framework was able to identify studies which were more asset-based in their approach compared to those which were more deficit-focused, acknowledging that some studies may have elements of each approach. This framework will be useful for people working in health policy and research who want a resource to help identify asset-based approaches in practice and which aspects of the approach were important for its success in the community.

Using epidemiological evidence to forecast population need for early treatment programmes in mental health: a generalisable Bayesian prediction methodology applied to and validated for first-episode psychosis in England.

Background: Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable. Aims: To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections. Method: We used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16-64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, initial assessment by EIP services and referral to EIP services for 'suspected psychosis'. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels. Results: A model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 (predicted 8112, 95% CI 7623-8597; observed 8038, difference of 74 [0.92%]). By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10383-11740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively. Conclusions: Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis.

The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population.

BACKGROUND: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. RESULTS: The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. CONCLUSIONS: Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries' settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

How will COVID-19 impact Australia's future population? A scenario approach.

The impact of COVID-19 has been massive and unprecedented, affecting almost every aspect of our daily lives. This paper attempts to quantify the impact of COVID-19 on the future size, composition and distribution of Australia's population by projecting a range of scenarios. Drawing on the academic literature, historical data and informed by expert judgement, four scenarios representing possible future courses of economic and demographic recovery are formulated. Results suggest that Australia's population could be 6 per cent lower by 2040 in a Longer scenario than in the No Pandemic scenario, primarily due to a huge reduction in international migration. Impacts on population ageing will be less severe, leading to a one percentage point increase in the proportion of the population aged 65 and over by 2040. Differential impacts will be felt across Australian States and Territories, with the biggest absolute and relative reductions in growth occurring in the most populous states, Victoria and New South Wales. Given the ongoing nature of the crisis at the time of writing, there remains significant uncertainty surrounding the plausibility of the proposed scenarios. Ongoing monitoring of the demographic impacts of COVID-19 are important to ensure appropriate planning and recovery in the years ahead.

Is late-life dependency increasing or not? A comparison of the Cognitive Function and Ageing Studies (CFAS).

BACKGROUND: Little is known about how the proportions of dependency states have changed between generational cohorts of older people. We aimed to estimate years lived in different dependency states at age 65 years in 1991 and 2011, and new projections of future demand for care. METHODS: In this population-based study, we compared two Cognitive Function and Ageing Studies (CFAS I and CFAS II) of older people (aged ≥65 years) who were permanently registered with a general practice in three defined geographical areas (Cambridgeshire, Newcastle, and Nottingham; UK). These studies were done two decades apart (1991 and 2011). General practices provided lists of individuals to be contacted and were asked to exclude those who had died or might die over the next month. Baseline interviews were done in the community and care homes. Participants were stratified by age, and interviews occurred only after written informed consent was obtained. Information collected included basic sociodemographics, cognitive status, urinary incontinence, and self-reported ability to do activities of daily living. CFAS I was assigned as the 1991 cohort and CFAS II as the 2011 cohort, and both studies provided prevalence estimates of dependency in four states: high dependency (24-h care), medium dependency (daily care), low dependency (less than daily), and independent. Years in each dependency state were calculated by Sullivan's method. To project future demands for social care, the proportions in each dependency state (by age group and sex) were applied to the 2014 UK [corrected] population projections. FINDINGS: Between 1991 and 2011, there were significant increases in years lived from age 65 years with low dependency (1·7 years [95% CI 1·0-2·4] for men and 2·4 years [1·8-3·1] for women) and increases with high dependency (0·9 years [0·2-1·7] for men and 1·3 years [0·5-2·1] for women). The majority of men's extra years of life were spent independent (36·3%) or with low dependency (36·3%) whereas for women the majority were spent with low dependency (58·0%), and only 4·8% were independent. There were substantial reductions in the proportions with medium and high dependency who lived in care homes, although, if these dependency and care home proportions remain constant in the future, further population ageing will require an extra 71 215 care home places by 2025. INTERPRETATION: On average older men now spend 2·4 years and women 3·0 years with substantial care needs, and most will live in the community. These findings have considerable implications for families of older people who provide the majority of unpaid care, but the findings also provide valuable new information for governments and care providers planning the resources and funding required for the care of their future ageing populations. FUNDING: Medical Research Council (G9901400) and (G06010220), with support from the National Institute for Health Research Comprehensive Local research networks in West Anglia and Trent, UK, and Neurodegenerative Disease Research Network in Newcastle, UK.

A comparison of health expectancies over two decades in England: results of the Cognitive Function and Ageing Study I and II.

BACKGROUND: Whether rises in life expectancy are increases in good-quality years is of profound importance worldwide, with population ageing. We investigate how various health expectancies have changed in England between 1991 and 2011, with identical study design and methods in each decade. METHODS: Baseline data from the Cognitive Function and Ageing Studies in populations aged 65 years or older in three geographically defined centres in England (Cambridgeshire, Newcastle, and Nottingham) provided prevalence estimates for three health measures: self-perceived health (defined as excellent-good, fair, or poor); cognitive impairment (defined as moderate-severe, mild, or none, as assessed by Mini-Mental State Examination score); and disability in activities of daily living (defined as none, mild, or moderate-severe). Health expectancies for the three regions combined were calculated by the Sullivan method, which applies the age-specific and sex-specific prevalence of the health measure to a standard life table for the same period. FINDINGS: Between 1991 and 2011, gains in life expectancy at age 65 years (4·5 years for men and 3·6 years for women) were accompanied by equivalent gains in years free of any cognitive impairment (4·2 years [95% CI 4·2-4·3] for men and 4·4 years [4·3-4·5] for women) and decreased years with mild or moderate-severe cognitive impairment. Gains were also identified in years in excellent or good self-perceived health (3·8 years [95% CI 3·5-4·1] for men and 3·1 years [2·7-3·4] for women). Gains in disability-free years were much smaller than those in excellent-good self-perceived health or those free from cognitive impairment, especially for women (0·5 years [0·2-0·9] compared with 2·6 years [2·3-2·9] for men), mostly because of increased mild disability. INTERPRETATION: During the past two decades in England, we report an absolute compression (ie, reduction) of cognitive impairment, a relative compression of self-perceived health (ie, proportion of life spent healthy is increasing), and dynamic equilibrium of disability (ie, less severe disability is increasing but more severe disability is not). Reasons for these patterns are unknown but might include increasing obesity during previous decades. Our findings have wide-ranging implications for health services and for extension of working life. FUNDING: UK Medical Research Council.

Comparison of socio-economic indicators explaining inequalities in Healthy Life Years at age 50 in Europe: 2005 and 2010.

BACKGROUND: The first estimates of Healthy Life Years at age 50 (HLY50) across the EU25 countries in 2005 showed substantial variation in healthy ageing. We investigate whether factors contributing to HLY50 inequalities have changed between 2005 and 2010. METHODS: HLY50 for each country and year were calculated using Sullivan's method, applying the age-specific prevalence of activity limitation from the European Union Statistics on Income and Living Conditions (EU-SILC) survey to life tables. Inequalities in life expectancy at age 50 (LE50) and HLY50 between countries were defined as the difference between the maximum and minimum LE50 or HLY50. Relationships between HLY50 and macro-level socio-economic indicators were investigated using meta-regression. Men and women were analysed separately. RESULTS: Between 2005 and 2010 HLY50 inequalities for both men and women in Europe increased. In 2005 and 2010 HLY50 inequalities exceeded LE50 inequalities, particularly in the established EU15 countries in 2010 where HLY50 inequalities (men: 10.7 years; women: 12.5 years) were four times greater for men and three times for women than LE50 inequalities (men: 2.4 years; women: 4.1 years). Only material deprivation significantly explained variation in EU25 HLY50 in both years with, additionally, long-term unemployment in 2010. CONCLUSIONS: Our results suggest that inequalities in HLY50 across Europe are large, increasing and partly explained by levels of material deprivation. Moreover long-term unemployment has become more influential in explaining variation in HLY50 between 2005 and 2010.

Inequalities in healthy life expectancy between ethnic groups in England and Wales in 2001.

OBJECTIVES: We aim to develop robust estimates of disability-free life expectancy (DFLE) and healthy life expectancy (HLE) for ethnic groups in England and Wales in 2001 and to examine observed variations across ethnic groups. DESIGN: DFLE and HLE by age and gender for five-year age groups were computed for 16 ethnic groups by combining the 2001 Census data on ethnicity, self-reported limiting long-term illness and self-rated health using mortality by ethnic group estimated by two methods: the Standardised Illness Ratio (SIR) method and the Geographically Weighted Method (GWM). RESULTS: The SIR and GWM methods differed somewhat in their estimates of life expectancy (LE) at birth but produced very similar estimates of DFLE and HLE by ethnic group. For the more conservative method (GWM), the range in DFLE at birth was 10.5 years for men and 11.9 years for women, double that in LE. DFLE at birth was highest for Chinese men (64.7 years, 95% CI 64.0-65.3) and women (67.0 years, 95% CI 66.4-67.6). Over half of the ethnic minority groups (men: 10; women: 9) had significantly lower DFLE at birth than White British men (61.7 years, 95% CI 61.7-61.7) or women (64.1 years, 95% CI 64.1-64.2), mostly the Black, Asian and mixed ethnic groups. The lowest DFLE observed was for Bangladeshi men (54.3 years, 95% CI 53.7-54.8) and Pakistani women (55.1 years, 95% CI 54.8-55.4). Notable were Indian women whose LE was similar to White British women but who had 4.3 years less disability-free (95% CI 4.0-4.6). CONCLUSIONS: Inequalities in DFLE between ethnic groups are large and exceed those in LE. Moreover, certain ethnic groups have a larger burden of disability that does not seem to be associated with shorter LE. With the increasing population of the non-White British community, it is essential to be able to identify the ethnic groups at higher risk of disability, in order to target appropriate interventions.

The use of a new indirect method to estimate ethnic-group fertility rates for subnational projections for England.

To project the ethnic-group populations of local authorities in England to 2051, estimates of ethnic-specific fertility rates were needed. In the absence of ethnic information on birth records, we developed indirect estimation methods that use a combination of vital statistics, the census (both microdata and aggregate tables), and survey data (Labour Force Survey). We estimated age-specific and total fertility rates successively for five broad ethnic groups encompassed by all data-sets, and for eight ethnic groups encompassed by the 1991 and 2001 Censuses for England. We then used census data to disaggregate the estimates to the 16 ethnic groups required for the subnational projections and the Hadwiger function to estimate single-year-of-age estimates. We estimated the uncertainty around the fertility estimates and used a logistic model to project rates to 2021, after which we assumed rates would remain constant.

Breathlessness and sexual activity in older adults: the Australian Longitudinal Study of Ageing.

Sexual activity is important to older adults (65 + ). Breathlessness affects about 25% of older adults but impact on sexual activity is unknown. We evaluated the relationships between breathlessness and sexual inactivity and self-reported health among older community-dwelling adults in the Australian Longitudinal Study of Ageing. Associations between self-reported breathlessness (hurrying on level ground or walking up a slight hill) at baseline, self-reported sexual activity, overall health and health compared to people of the same age were explored using logistic regression at baseline and 2 years, adjusted for potential confounders (age, sex, marital status, smoking status and co-morbidities). Of 798 participants (mean age 76.4 years [SD, 5.8] 65 to 103; 53% men, 73% married), 688 (86.2%) had 2-year follow-up data. People with breathlessness had higher prevalence and duration of sexual inactivity (77.7% vs. 65.6%; p < 0.001; 12 [IQR, 5-17] vs. 9.5 [IQR, 5-16] years; p = 0.043). Breathlessness was associated with more sexual inactivity, (adjusted OR 1.75; [95% CI] 1.24-2.45), worse health (adjusted OR 2.02; 1.53-2.67) and worse health compared to peers (adjusted OR 1.72; 1.25-2.38). Baseline breathlessness did not predict more sexual inactivity at 2 years. In conclusion, breathlessness contributes to sexual inactivity and worse perceived health in older adults, which calls for improved assessment and management.

Drivers of inequality in disability-free expectancy at birth and age 85 across space and time in Great Britain.

BACKGROUND: Although mortality and health inequalities at birth have increased both geographically and in socioeconomic terms, little is known about inequalities at age 85, the fastest growing sector of the population in Great Britain (GB). AIM: To determine whether trends and drivers of inequalities in life expectancy (LE) and disability-free life expectancy (DFLE) at age 85 between 1991 and 2001 are the same as those at birth. METHODS: DFLE at birth and age 85 for 1991 and 2001 by gender were calculated for each local authority in GB using the Sullivan method. Regression modelling was used to identify area characteristics (rurality, deprivation, social class composition, ethnicity, unemployment, retirement migration) that could explain inequalities in LE and DFLE. RESULTS: Similar to values at birth, LE and DFLE at age 85 both increased between 1991 and 2001 (though DFLE increased less than LE) and gaps across local areas widened (and more for DFLE than LE). The significantly greater increases in LE and DFLE at birth for less-deprived compared with more-deprived areas were still partly present at age 85. Considering all factors, inequalities in DFLE at birth were largely driven by social class composition and unemployment rate, but these associations appear to be less influential at age 85. CONCLUSIONS: Inequalities between areas in LE and DFLE at birth and age 85 have increased over time though factors explaining inequalities at birth (mainly social class and unemployment rates) appear less important for inequalities at age 85.

The estimation of mortality for ethnic groups at local scale within the United Kingdom.

As an input to projections of sub-national populations by ethnicity, this paper develops the first estimates of the mortality risks experienced by the UK ethnic groups. Two estimates were developed using alternative methods. In the first, UK 2001 Census data on limiting long-term illness to predict mortality levels and regression equations between local Standardized Illness and Mortality Ratios for all ethnicities are assumed to apply to individual ethnic groups. In the second, the geographical distribution of ethnic groups by local areas is combined with local mortality for all ethnicities to estimate national mortality rates by ethnicity, which are then employed to estimate local ethnic mortality. A comparison of the two estimates indicates that the method based on illness rates produces more plausible outcomes. The local SMRs produced for each ethnic group were used to generate ethnic group life tables for 432 UK local authority areas in 2001, which included estimates of survivorship probabilities by single year of age, gender and ethnic group for each local area for use in a projection model.

Bioenergetics of the alkaliphilic sulfate-reducing bacterium Desulfonatronovibrio hydrogenovorans.

Energy metabolism of the alkaliphilic sulfate-reducing bacterium Desulfonatronovibrio hydrogenovorans strain Z-7935 was investigated in continuous culture and in physiological experiments on washed cells. When grown in chemostats with H2 as electron donor, the cells had extrapolated growth yields [Y(max), g dry cell mass (mol electron acceptor)(-1)] of 5.5 with sulfate and 12.8 with thiosulfate. The maintenance energy coefficients were 1.9 and 1.3 mmol (g dry mass)(-1) x h(-1), and the minimum doubling times were 27 and 20 h with sulfate and thiosulfate, respectively. Cell suspensions reduced sulfate, thiosulfate, sulfite, elemental sulfur and molecular oxygen in the presence of H2. In the absence of H2, sulfite, thiosulfate and sulfur were dismutated to sulfide and sulfate. Sulfate and sulfite were only reduced in the presence of sodium ions, whereas sulfur was reduced also in the absence of Na+. Plasmolysis experiments showed that sulfate entered the cells via an electroneutral symport with Na+ ions. The presence of an electrogenic Na+-H+ antiporter was demonstrated in experiments applying monensin (an artificial electroneutral Na+-H+ antiporter) and propylbenzylylcholine mustard.HCl (a specific inhibitor of Na+-H+ antiporters). Sulfate reduction was sensitive to uncouplers (protonophores), whereas sulfite reduction was not affected. Changes in pH upon lysis of washed cells with butanol indicated that the intracellular pH was lower than the optimum pH for growth (pH 9.5). Pulses of NaCl (0.52 M) to cells incubated in the absence of Na+ did not result in ATP formation, whereas HCl pulses (shifting the pH from 9.2 to 7.0) did. Small oxygen pulses, which were reduced within a few seconds, caused a transient alkalinization. The results of preliminary experiments with chemiosmotic inhibitors provided further evidence that the alkalinization was caused by sodium--proton antiport following a primary electron-transport-driven sodium ion translocation. It is concluded that energy conservation in D. hydrogenovorans depends on a proton-translocating ATPase, whereas electron transport appears to be coupled to sodium ion translocation.