3.5 Quantitative analysis

Pre-existing data

The quantitative analysis in Chapter 4 addresses two of the research questions: (i) what is the demographic context in which social care services are being delivered in LA, and; (ii) what are the factors that are impacting (positively or negatively) on LA’s ability to deliver enablement services. Analysis of the quantitative data which was drawn from pre-existing datasets that are in the public domain and from sources within LA, was conducted in May 2013. Specifically, the data on the population distribution was drawn from central sources (Office of National Statistics Office, Scottish Government, General Registers of Scotland) thereby ensuring there is a greater degree of consistency and validity. The analysis in Chapter 4 focuses on the national age demographic through a historical lens in the first instance thus providing a context to the changing profile of Scotland’s population distribution with particular attention being paid to the population that is over 65 years old; the same consideration was given to the data as it presented for LA. The researcher then used the raw data to establish a framework to allow more detailed, analytical work to be carried out. Specifically, data has been gathered from the national datasets on the following: (i) UK population trends 2002 – 2035; (ii) Scotland population trends 2002 – 2035; (iii) Scotland age profile population 2002- 2035; (iii) local authority age profile population 2002 – 2035; (iv) Scotland Home Care activity (general) 2002 – 2012. This information was then input to an Excel spreadsheet thus allowing the researcher the opportunity to produce a variety of graphs and tables to demonstrate national and local trends. For analytical purposes population projections have been used extensively to demonstrate the national and local variations as well as being used for comparison purposes. Population projections are trend related and are, therefore, based on a number of assumptions [Assumptions are made that all factors influencing population will remain a constant. Consequently, projections can only provide indicative predications about future trends (Population Reference Bureau, 2013)].  Consequently, the reliability of the data can be challenged as the assumptions about future migration, fertility and mortality is often limited by the inertia in population change (Scottish Government, 2013). As the process of change is cumulative, the reliability of projections decreases over time. However, the projections provide a common basis for use in planning and policy development of public services in geographic areas (Office for National Statistics, 2013).

There is significant data held around demography and many references are made to the over 75 and over 85 age group within datasets. However, the data required for the Home Care annual census [The census presents the latest national figures for home care services provided or purchased by local authorities in Scotland. All local authorities in Scotland provide Home Care services which give people the support, practical help and personal care that they need to live as independently as possible in the community. All figures relate to the last week in March] refers to that age group which is over 65 years. Consequently, this paper refers throughout to this age group in relation to the ageing population. To ensure consistency, and for comparison purposes, all figures drawn from the various sources have been aggregated by the researcher to provide the whole number of people over 65 years. Similarly, the ageing population increases are predicted to stabilise in the 2030s. Again, depending on the source, the intended year of stabilisation varies from publication to publication, but for the purposes of this paper the researcher suggests that the period for consideration extends from the present and concludes in 2035. Data was added to the Excel spreadsheet in relation to care at home service provision from two sources. From the national datasets, the following data was drawn: (i) Scotland Home Care activity (age profile) 2002 – 2012; (ii) Scotland Home Care activity (intensive) 2002 – 2012; (iii) LA Home Care activity (age profile) 2002 – 2012; (iv) LA Home Care activity (intensive) 2002 – 2012, and; (v) Long Term Conditions data. There are no national datasets produced on enablement services. Consequently, additional data was provided from the local authority’s internal data sources which provided the background information for the organisation in terms of its size, the activity, the nature of the work carried out and the measurable outcomes.

The Local Authority Home Care Service Enablement Team (LAHCSET) commenced December 2009 and data has been collected since mid-2010 through to present day. All of the local data has been collated onto separate, password-protected Excel spreadsheets that are held within a common drive of the LA’s IT network. Information is either directly input to the spreadsheet by Social Care Organisers (SCOs), or is transferred in by administrative staff from: (i) a paper-based recording system completed by the SCOs, and/or; (ii) directly from service user databases. Presently, LAHCS has no centralised, electronic means of gathering this information [The local authority is about to invest in a new scheduling tool that will have the facility to gather data and produce reports for performance management purposes]. It is with caution therefore that the local data is analysed as there have been a number of issues identified in the recording of and interpretation of the data requirements which calls its validity into question. In some cases monthly recording sheets have been altered to include additional data for some teams but not others. This 13 The local authority is about to invest in a new scheduling tool that will have the facility to gather data and produce reports for performance management purposes has required information to be transposed and merged to deliver standardised results. Despite these issues, however, the data collected represents what is going on at present within LAHCS. Specifically, data relating to 2010/11; 2011/12, and 2012/2013 has been gathered on the number of people progressing through the enablement teams in a year running from 1st April to 31st March.

By adopting an inductive logic approach [Inductive logic is a form of reasoning that uses data or statements to draw a likely conclusion (Social Research Methods, 2013)], the researcher has accumulated and aggregated the data to produce generalisations about the patterns or connections between the variables and events. Data has been gathered on: (i) the number of hours committed; (ii) the mean average per individual service user; (iii) the outcomes for each individual (independent, hospital admission, long term care, deceased); (iv) the aggregated number of increased or decreased hours noted per individual exiting the enablement service but requiring on-going support, and; (v) those individuals whose service has remained unaltered. This data has afforded the researcher the opportunity to consider the LAHCET’s current obligations and measureable outcomes.

New data

To determine the impact that the increased demand for service may have had, the researcher developed a tool that measured the capacity that exists within LAHCS. The measurement of productivity is important as it provides an indicator of the actual level of service delivery that can be delivered week-on-week. Often literature refers to staff in terms of whole time equivalent (wte) or as whole numbers employed. What these figures fail to acknowledge is that only a percentage of time is actually available for hands-on service delivery. Consequently, the development of the productivity tool seeks to address this issue by providing this analysis with a more specific account of staff availability. To measure the productivity levels of the teams carrying out the front-line service provision, two weeks were chosen to provide a comparative analysis: (i) week beginning the 12th November 2012, and; (ii) week beginning 18th February 2013. Each Social Care Organiser (SCO) was then asked to input the data for the respective weeks into an Excel spreadsheet against the headings described in Appendix 1.

From this information the researcher was able to determine the actual productivity of the respective teams in the allotted weeks against the contractual commitments. To determine the figure the productivity figure was arrived at by totalling the figures in each field, aggregating the figures and then subtracting that from the monthly contractual commitments. To substantiate the figures, a third week was analysed in May 2013 and cross-referenced against the original data.

It is acknowledged that demands within the service can vary from week to week and to provide a more comprehensive analytical breakdown then figures detailed should be gathered over a longer timeframe (12-24 months) to more robustly validate the measurement tool.  However, owing to time constraints, this has not been possible, but offers the opportunity for the researcher to develop this tool further in a separate research project.