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What is now often referred to as ‘welfare’ is the most contentious, but often least understood, part of social policy. At its broadest, ‘welfare’ could mean the whole welfare state – including the twothirds of public spending that goes on healthcare, education, housing and personal social services, as well as cash social security benefits, including pensions. At its narrowest – following US terminology, and often accompanied by similar stigmatisation – it could mean cash payments to working-age people who are not in work (about a twentieth of public spending). In between, it could refer to what are, for clarity, described here as ‘cash transfers’ – social security benefits (including state pensions) and tax credits.
A popular perception is that the 1997-2010 Labour government greatly increased spending on benefits and tax credits, particularly for those out of work, creating much of the deficit by the time it left office, and in some versions causing the financial and economic crisis itself. The coalition government coming to office in May 2010 set reducing the deficit as its highest priority, and argued that the ‘welfare budget’ should make a major contribution – albeit with state pensions largely protected. Some of the resultant cuts became among its most controversial policies.
This chapter examines what actually happened to cash transfers in the period since the crisis started, looking at policies in the final years of the Labour government from 2007/08 and under the coalition, levels of public spending, benefit levels and the distributional effects of policy change (including direct taxes) since 2010. These form part, alongside other developments, such as in the labour market (see Chapter Six), of what drove the changes in poverty and inequality discussed later in this book, in Chapter Eleven.
The situation on the eve of the crisis
Labour's aims for poverty and inequality were selective. Child and pensioner poverty were key priorities, alongside wider objectives for life chances and social inclusion. Equality was discussed in terms of ‘equality of opportunity’, not of outcomes, with little emphasis on inequalities at the top.
Correspondingly, Labour's spending increases concentrated on families with children and pensioners. Its emphasis for the working-age population was on education, training, ‘making work pay’ (including the first National Minimum Wage), and support into work.
Aims – In the last years, in Italy as well as in many other developed countries, there has been a growing interest for health economics by researchers. As for as the psychiatric care is concerned, more recently, many research's groups have pointed their attention on new possible funding systems for mental health services and on their effects on services' functioning. The aim of this study is to define a new list of services' costs based on services actually delivered by a Community Mental Health Service (CMHS). Methods. – All psychiatric contacts recorded by the South-Verona Psychiatric Case Register during a 7-year period (1992-1998) have been included in the study (125,623 contacts made by 2,819 patients). Contacts were grouped into 19 type of services. The cost function methodology was used to describe, also reporting elasticity values, costs' behaviour in the South-Verona CMHS. The cost of each service includes expenses for professionals involved (directly or indirectly) in the contacts with the patients and capital costs. Results. – For each service were reported a) the cost of the service as it is actually supplied in our CMHS, b) the cost per minute, c) an estimate of the cost of service delivered with standard modalities (duration equal to the mode value registered; staff composition take into account either the actual functioning of the CMHS either indication about a good clinical practice) and, finally, d) cost of the eight services included into the reimbursement system currently in use in Italy. Conclusions. – Our results showed that services' definition used in this study allow to describe different types of psychiatric care supplied from the South-Verona CMHS. The national list currently adopted for the reimbursement in Italy should allowed to describe only 28% of the registered psychiatric contacts (35,230 vs. 125,632). The urgent need for a new list of psychiatric services, accepted at a national level, was confirmed. Cost values obtained clearly show that the funding system currently used underestimates the true costs of care delivered by the CMHS. The cost function makes available a tool to test a prospective per-capita funding system as provided in the Act No. 229 of the Italian Government.
Aims – To obtain a new, well-balanced mental health funding system, through the creation of i) a list of psychiatric interventions provided by Italian Community-based Psychiatric Services (CPS), and associated costs; ii) a new prospective funding system for patients with a high use of resources, based on packages of care. Methods – Five Italian Community-based Psychiatric Services collected data from 1250 patients during October 2002. Socio-demographical and clinical characteristics and GAF scores were collected at baseline. All psychiatric contacts during the following six months were registered and categorised into 24 service contact types. Using elasticity equation and contact characteristics, we estimate the costs of care. Cluster analysis techniques identified packages of care. Logistic regression defined predictive variables of high use patients. Multinomial Logistic Model assigned each patient to a package of care. Results – The sample's socio-demographic characteristics are similar, but variations exist between the different CPS. Patients were then divided into two groups, and the group with the highest use of resources was divided into three smaller groups, based on number and type of services provided. Conclusions – Our findings show how is possible to develop a cost predictive model to assign patients with a high use of resources to a group that can provide the right level of care. For these patients it might be possible to apply a prospective per-capita funding system based on packages of care.
Aim – To develop predictive models to allocate patients into frequent and low service users groups within the Italian Community-based Mental Health Services (CMHSs). To allocate frequent users to different packages of care, identifing the costs of these packages. Methods – Socio-demographic and clinical data and GAF scores at baseline were collected for 1250 users attending five CMHSs. All psychiatric contacts made by these patients during six months were recorded. A logistic regression identified frequent service users predictive variables. Multinomial logistic regression identified variables able to predict the most appropriate package of care. A cost function was utilised to estimate costs. Results – Frequent service users were 49%, using nearly 90% of all contacts. The model classified correctly 80% of users in the frequent and low users groups. Three packages of care were identified: Basic Community Treatment (4,133 Euro per six months); Intensive Community Treatment (6,180 Euro) and Rehabilitative Community Treatment (11,984 Euro) for 83%, 6% and 11% of frequent service users respectively. The model was found to be accurate for 85% of users. Conclusion – It is possible to develop predictive models to identify frequent service users and to assign them to pre-defined packages of care, and to use these models to inform the funding of psychiatric care.
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