Inequalities in health and community-oriented social work: lessons from Cuba?

Social justice is, as the World Health Organization Commission on Social Determinants of Health (WHO CSDH, 2008) reminds us, ‘a matter of life and death’. While the stark differences in mortality rates and life expectancy between rich and poor countries might be the most obvious example of this, it is also true that ‘Within countries, the differences in life chances are dramatic and are seen in all countries – even the richest’ (WHO CSDH, 2008: 26). As the Commission demonstrates, the roots of these inequities lie in social conditions, suggesting an important role for social work in this area. Unfortunately, the Commission says very little about the type of social work that might be appropriate: nevertheless, the report does provide fresh impetus to the debate about what social workers might contribute to tackling health inequalities. In this article, we suggest that a community-oriented approach to social work is required. In making a case for this, we review the progress of the government’s drive to reduce inequalities in England,1 arguing that this has, thus far, been largely unsuccessful because it has primarily been pursued through health-care services, while addressing the wider (social) determinants of health has been a secondary consideration. In contrast, we offer the example of Cuban community-oriented social work (COSW) which has helped maintain population health at a level that stands comparison with much wealthier nations, despite the hardships and inequalities which followed economic collapse in the 1990s. In many ways the Cuban situation is unusual, perhaps unique, so we are not arguing that Cuban social work methods can be readily transferred. Rather, we suggest that, in the neglected field of tackling health inequalities, social workers can learn from the general approach taken in Cuba. To establish the context of this discussion, we begin by defining key concepts: COSW itself, health inequalities and inequity, the health gap and the health gradient

Health Equity Indicators for the English NHS: a longitudinal whole-population study at the small-area level

Background: Inequalities in health-care access and outcomes raise concerns about quality of care and justice, and the NHS has a statutory duty to consider reducing them. Objectives: The objectives were to (1) develop indicators of socioeconomic inequality in health-care access and outcomes at different stages of the patient pathway; (2) develop methods for monitoring local NHS equity performance in tackling socioeconomic health-care inequalities; (3) track the evolution of socioeconomic health-care inequalities in the 2000s; and (4) develop ‘equity dashboards’ for communicating equity findings to decision-makers in a clear and concise format. Design: Longitudinal whole-population study at the small-area level. Setting: England from 2001/2 to 2011/12. Participants: A total of 32,482 small-area neighbourhoods (lower-layer super output areas) of approximately 1500 people. Main outcome measures: Slope index of inequality gaps between the most and least deprived neighbourhoods in England, adjusted for need or risk, for (1) patients per family doctor, (2) primary care quality, (3) inpatient hospital waiting time, (4) emergency hospitalisation for chronic ambulatory care-sensitive conditions, (5) repeat emergency hospitalisation in the same year, (6) dying in hospital, (7) mortality amenable to health care and (8) overall mortality. Data sources: Practice-level workforce data from the general practice census (indicator 1), practice-level Quality and Outcomes Framework data (indicator 2), inpatient hospital data from Hospital Episode Statistics (indicators 3–6) and mortality data from the Office for National Statistics (indicators 6–8). Results: Between 2004/5 and 2011/12, more deprived neighbourhoods gained larger absolute improvements on all indicators except waiting time, repeat hospitalisation and dying in hospital. In 2011/12, there was little measurable inequality in primary care supply and quality, but inequality was associated with 171,119 preventable hospitalisations and 41,123 deaths amenable to health care. In 2011/12, > 20% of Clinical Commissioning Groups performed statistically significantly better or worse than the England equity benchmark. Limitations: General practitioner supply is a limited measure of primary care access, need in deprived neighbourhoods may be underestimated because of a lack of data on multimorbidity, and the quality and outcomes indicators capture only one aspect of primary care quality. Health-care outcomes are adjusted for age and sex but not for other risk factors that contribute to unequal health-care outcomes and may be outside the control of the NHS, so they overestimate the extent of inequality for which the NHS can reasonably be held responsible. Conclusions: NHS actions can have a measurable impact on socioeconomic inequality in both health-care access and outcomes. Reducing inequality in health-care outcomes is more challenging than reducing inequality of access to health care. Local health-care equity monitoring against a national benchmark can be performed using any administrative geography comprising ≥ 100,000 people

The use of income information of census enumeration area as a proxy for the household income in a household survey

<p>Abstract</p> <p>Background</p> <p>Some of the Census Enumeration Areas' (CEA) information may help planning the sample of population studies but it can also be used for some analyses that require information that is more difficult to obtain at the individual or household level, such as income. This paper verifies if the income information of CEA can be used as a proxy for household income in a household survey.</p> <p>Methods</p> <p>A population-based survey conducted from January to December 2003 obtained data from a probabilistic sample of 1,734 households of Niterói, Rio de Janeiro, Brazil. Uniform semi-association models were adjusted in order to obtain information about the agreement/disagreement structure of data. The distribution of nutritional status categories of the population of Niterói according to income quintiles was performed using both CEA- and household-level income measures and then compared using Wald statistics for homogeneity. Body mass index was calculated using body mass and stature data measured in the households and then used to define nutritional status categories according to the World Health Organization. All estimates and statistics were calculated accounting for the structural information of the sample design and a significance level lower than 5% was adopted.</p> <p>Results</p> <p>The classification of households in the quintiles of household income was associated with the classification of these households in the quintiles of CEA income. The distribution of the nutritional status categories in all income quintiles did not differ significantly according to the source of income information (household or CEA) used in the definition of quintiles.</p> <p>Conclusion</p> <p>The structure of agreement/disagreement between quintiles of the household's monthly per capita income and quintiles of the head-of-household's mean nominal monthly income of the CEA, as well as the results produced by these measures when they were associated with the nutritional status of the population, showed that the CEA's income information can be used when income information at the individual or household levels is not available.</p

Trials and tribulations : the 'use' (and 'misuse') of evidence in public policy

Randomized controlled trials (RCTs) are increasingly playing a central role in shaping policy for development. By comparison, social experimentation has not driven the great transformation of welfare within the developed world. This introduces a range of issues for those interested in the nature of research evidence for making policy. In this article we will seek a greater understanding of why the RCT is increasingly seen as the ‘gold standard’ for policy experiments in low- and middle-income countries (LMICs), but not in the more advanced liberal democracies, and we will explore the implications of this. One objection to the use of RCTs, however can be cost, but implementing policies and programmes without good evidence or a good understanding of their effectiveness is unlikely to be a good use of resources either. Other issues arise. Trials are often complex to run and ethical concerns often arise in social ‘experiments’ with human subjects. However, rolling out untested policies may also be morally objectionable. This article sheds new light on the relationship between evidence and evaluation in public policy in both the global north and developing south. It also tackles emerging issues concerning the ‘use’ and ‘misuse’ of evidence and evaluation within public policy

Levels and determinants of psychological distress in eight countries of the former Soviet Union

Although it is well recognised that the collapse of the Soviet Union and the subsequent widespread social and economic changes impacted on the levels and distribution of physical health, there is very limited evidence on the social patterning of mental health in the countries that emerged. The aim of this paper is to assess levels of psychological distress and describe its demographic, social and economic correlates in eight former Soviet countries.Cross-sectional surveys using multi-stage random sampling were conducted in Armenia, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia and Ukraine. A standardised questionnaire was used for all countries, including the main outcome for this study of psychological distress, which consisted of 12 items on symptoms of psychological distress. Respondents who repor ted 10-12 of the symptoms were considered to have a high psychological distress score. Multivariate logistic regression analysis was then used to investigate how demographic, social and economic factors were associated with a high psychological distress score.High psychological distress in seven of the eight countries ranges from 3.8% in Kazakhstan to 10% in Ukraine but was substantially higher (21.7%) in Armenia. Factors associated with psychological distress in the multivariate analysis included: being female; increasing age; incomplete secondary education; being disabled; experiencing two or more stressful events in the past year; lack of trust in people; lack of personal suppor t in crisis; being unemployed; and poor household economic situation.The study contributes evidence on the association of impoverishment and social isolation on psychological distress in countries of the former Soviet Union and highlights the impor tance of exploring ways of improving mental health by addressing its social determinants