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Over the past 20 years, policy attention has been focused upon the implications of below-replacement fertility for the aging of populations. This article argues that another potential consequence, a decline in the absolute size of the labor force, may prove to be an equally compelling issue because of its impact on rates of economic growth. Because the United States will experience both increasing labor productivity and an increase in its labor supply, the growth orientation of the global economy is likely to persist. In this circumstance, given relatively comparable changes in the productivity of labor across countries, countries that face major declines in their labor supply will fare less well than countries that are able to maintain their labor supply at least constant. The article examines the labor supply prospects of 16 developed countries for the period 2000-2050, drawing attention to the ways in which countries may be able to influence the future levels of their labor supply. Copyright 2001 by The Population Council, Inc..

Background and objective Characterising the general practice response to the COVID-19 pandemic is important for ongoing policy planning. The objective of this study was to explore challenges, responses and effects of COVID-19 in Australian general practice in the early stages of the pandemic, and to consider variance by geographic location. Methods A national cross-sectional online survey of Australian general practitioners was conducted in April and May 2020, with 572 respondents. Results The COVID-19 pandemic in Australia has resulted in major changes to general practice business models. Most practices have experienced increased workload and reduced income. Discussion Australian general practices have undertaken major innovation and realignment to respond to staff safety and patient care challenges during the COVID-19 pandemic. Increased administration, reduced billable time, managing staffing and pivoting to telehealth service provision have negatively affected practice viability. Major sources of information for general practice are primary care-specific, but many practices turn to colleagues for support and resources. AT THE TIME OF WRITING this article in May 2020, the government, health service and community response to the COVID-19 pandemic in Australia had resulted in an extraordinarily low level of infection and death from SARS-CoV-2. At the end of May 2020, there had been just over 7000 diagnosed infections (282 per million population) and just over 100 deaths in Australia (four per million population), 1 with approximately 1-3 new deaths per week. At the time, this compared favourably with pandemic death rates in some other developed countries, which were more than 100 times higher and rising. Unfortunately, the situation in Australia, and particularly in Victoria, changed markedly during the time this paper was being reviewed and revised in June-August 2020. The successful population health outcomes correlate with enormous economic and social impacts in Australia. Major health service realignment has been required of general practices at the frontline of prevention and early intervention of the COVID-19 response. However, the nature of the general practice response, and any geographical variations, remains poorly described. Using a national online survey of general practitioners (GPs), this exploratory study investigates challenges, responses and effects of the COVID-19 pandemic in Australian general practice. RESEARCH

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BackgroundCommunity misconception of what causes cancer is an important consideration when devising communication strategies around cancer prevention, while those initiating social marketing campaigns must decide whether to target the general population or to tailor messages for different audiences. This paper investigates the relationships between demographic characteristics, identification of selected cancer risk factors, and associated protective behaviours, to inform audience segmentation for cancer prevention social marketing.MethodsData for this cross-sectional study (n = 3301) are derived from Cancer Council New South Wales’ 2013 Cancer Prevention Survey. Descriptive statistics and logistic regression models were used to investigate the relationship between respondent demographic characteristics and identification of each of seven cancer risk factors; demographic characteristics and practice of the seven ‘protective’ behaviours associated with the seven cancer risk factors; and identification of cancer risk factors and practising the associated protective behaviours, controlling for demographic characteristics.ResultsMore than 90% of respondents across demographic groups identified sun exposure and smoking cigarettes as moderate or large cancer risk factors. Around 80% identified passive smoking as a moderate/large risk factor, and 40–60% identified being overweight or obese, drinking alcohol, not eating enough vegetables and not eating enough fruit. Women and older respondents were more likely to identify most cancer risk factors as moderate/large, and to practise associated protective behaviours. Education was correlated with identification of smoking as a moderate/large cancer risk factor, and with four of the seven protective behaviours. Location (metropolitan/regional) and country of birth (Australia/other) were weak predictors of identification and of protective behaviours. Identification of a cancer risk factor as moderate/large was a significant predictor for five out of seven associated cancer-protective behaviours, controlling for demographic characteristics.ConclusionsThese findings suggest a role for both audience segmentation and whole-of-population approaches in cancer-prevention social marketing campaigns. Targeted campaigns can address beliefs of younger people and men about cancer risk factors. Traditional population campaigns can enhance awareness of being overweight, alcohol consumption, and poor vegetable and fruit intake as cancer risk factors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-017-3540-x) contains supplementary material, which is available to authorized users.

Survival models are widely used in demography to analyse the timing of events such as death or leaving school. However, for events such as marriage or childbirth that are not experienced by everyone, standard survival analysis conflates the speed of progressing to an event with the proportion that never experience the event. The problem can be overcome by applying a 'split population' or 'cure' survival time model which splits the population into those who eventually experience the event and those who do not, and determines the speed of progression for the former. This paper demonstrates the use of split-population models in examining variables which affect the propensity and timing of additional births. The data analysed are from a sample of women from the 2001 Household, Income and Labour Dynamics in Australia (HILDA) Survey (N = 4,611). We model the propensity and time to have another child given sex composition of existing children, number of siblings and age at first birth for three cohorts of women. The study finds evidence of a preference for a mixed-sex composition, and an increased propensity for women with two boys to try for a third child. Women in later cohorts tend to have more children if they themselves come from larger families. Age at first birth is strongly associated with the propensity to have another child and with the speed of progression.

Background More than 60% of the world’s rural population live in the Asia-Pacific region. Of these, more than 90% reside in low- and middle-income countries (LMICs). Asia-Pacific LMICs rural populations are more impoverished and have poorer access to medical care, placing them at greater risk of poor health outcomes. Understanding factors associated with doctors working in rural areas is imperative in identifying effective strategies to improve rural medical workforce supply in Asia-Pacific LMICs. Method We performed a scoping review of peer-reviewed and grey literature from Asia-Pacific LMICs (1999 to 2019), searching major online databases and web-based resources. The literature was synthesized based on the World Health Organization Global Policy Recommendation categories for increasing access to rural health workers. Result Seventy-one articles from 12 LMICs were included. Most were about educational factors (82%), followed by personal and professional support (57%), financial incentives (45%), regulatory (20%), and health systems (13%). Rural background showed strong association with both rural preference and actual work in most studies. There was a paucity in literature on the effect of rural pathway in medical education such as rural-oriented curricula, rural clerkships and internship; however, when combined with other educational and regulatory interventions, they were effective. An additional area, atop of the WHO categories was identified, relating to health system factors, such as governance, health service organization and financing. Studies generally were of low quality—frequently overlooking potential confounding variables, such as respondents’ demographic characteristics and career stage—and 39% did not clearly define ‘rural’. Conclusion This review is consistent with, and extends, most of the existing evidence on effective strategies to recruit and retain rural doctors while specifically informing the range of evidence within the Asia-Pacific LMIC context. Evidence, though confined to 12 countries, is drawn from 20 years’ research about a wide range of factors that can be targeted to strengthen strategies to increase rural medical workforce supply in Asia-Pacific LMICs. Multi-faceted approaches were evident, including selecting more students into medical school with a rural background, increasing public-funded universities, in combination with rural-focused education and rural scholarships, workplace and rural living support and ensuring an appropriately financed rural health system. The review identifies the need for more studies in a broader range of Asia-Pacific countries, which expand on all strategy areas, define rural clearly, use multivariate analyses, and test how various strategies relate to doctor’s career stages.