Patterns in Home Care Use in Manitoba

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

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The Relative Effects of Home Care Client Characteristics on the Resource Allocation Process: Do Personality and Demeanor Matter?

The Gerontologist ◽

10.1093/geront/42.6.740 ◽

2002 ◽

Vol 42 (6) ◽

pp. 740-750 ◽

Cited By ~ 9

Author(s):

Kirsten Corazzini-Gomez

Keyword(s):

Resource Allocation ◽

Home Care ◽

Client Characteristics ◽

Home Care Client ◽

Allocation Process ◽

Resource Allocation Process

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Temporal and Spatial Consistency

Geographic Information Systems ◽

10.4018/978-1-4666-2038-4.ch101 ◽

2013 ◽

pp. 1675-1696

Author(s):

Oliver Duke-Williams ◽

John Stillwell

Keyword(s):

Time Series ◽

Age Groups ◽

Register Data ◽

Data Sets ◽

Interaction Data ◽

Patient Register ◽

Health Authorities ◽

Spatial Consistency ◽

Using Data ◽

Over Time

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

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Use of Long-Term Care Decreased Over Time Among the Oldest-Old With and Without Dementia - A Register-Based Study

Innovation in Aging ◽

10.1093/geroni/igab046.3542 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 995-996

Author(s):

Mari Aaltonen ◽

Leena Forma ◽

Jutta Pulkki ◽

Jani Raitanen ◽

Marja Jylhä

Keyword(s):

Home Care ◽

Long Term Care ◽

Negative Binomial ◽

Oldest Old ◽

Age Groups ◽

Term Care ◽

Total Study Population ◽

Health And Social Care ◽

Over Time

Abstract Care policies for older adults emphasize aging-in-place and home care over residential long-term care (LTC). We explore how the use of residential LTC in the last five years of life among people with and without dementia changed between those who died in 2001, 2007, 2013, and 2017 in Finland. Retrospective data drawn from the national health and social care registers include all those who died aged 70+ in 2007, 2013, and 2017, plus a 40% random sample from 2001 (N=128 050). Negative binomial regression analysis was used to estimate the association of dementia with LTC use during the last five years of life (1825 days). The independent variables included dementia, age, marital status, annual income, education, and chronic conditions. In the total study population, the proportion of LTC users and the mean number of days in LTC increased until 2013, after which it decreased. Changes in LTC use differed between different age groups and by dementia status. Over time, the decrease in round-the-clock LTC use was steep in those aged 90≤ with dementia and in people aged 80≤ without dementia. The individual factors related to morbidity and sociodemographic factors did not explain these results. The changes in LTC care policy may have contributed to the decrease in LTC use among the oldest. However, according to national statistics, the availability of formal home care has not increased. This development may suggest that the oldest-old and those with dementia – a highly vulnerable group – are left without proper care.

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