Biographical disruption associated with multiple sclerosis: Using propensity scoring to assess the impact

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Social Science & Medicine 65 (2007) 524–535 www.elsevier.com/locate/socscimed

Biographical disruption associated with multiple sclerosis: Using propensity scoring to assess the impact Gill Green, Jennifer Todd, David Pevalin Department of Health and Human Sciences, University of Essex, Wivenhoe Park, Colchester C04 3SQ, UK Available online 4 May 2007

Abstract Chronic illness such as multiple sclerosis (MS) is often associated with ‘biographical disruption’, a concept that is derived from qualitative narrative analyses examining how people make sense of their illness in the context of their lives [Bury, M. (1982). Chronic illness as biographical disruption. Sociology of Health and Illness, 4, 167–182]. This paper attempts to operationalise the idea of disruption to one’s life trajectory in quantitative analysis by examining the social, economic and emotional disruption associated with MS. A number of studies have suggested that it impacts negatively on employment, income and sexual relationships; however previous research has been based upon samples of people with MS (pwMS), with a dearth of studies comparing pwMS with the general population. This study reports a systematic comparison of MS and non-MS households to enable the impact of MS to be quantified in terms of household composition and marital status; household income; economic activity; and to determine whether biographical disruptions such as relationship breakdown or unemployment are more or less prevalent among those affected by MS compared to the general population. The MS sample came from randomly selected members of the UK MS Society (n ¼ 783) and those accessing the MS Society website (n ¼ 133). Data for the general population came from the 2001/02 British General Household Survey (GHS). Cases from the MS Society sample were matched using propensity scoring with cases from the GHS. The results of the matched analysis show that both men and women with MS are significantly less likely to be employed than those in the general population and are significantly more likely to have a ‘below average’ household income, despite the fact that they are in a higher social class and have higher educational levels than people in the general population. Differences between the MS and GHS samples in terms of marital status become non-significant when socio-demographic variables are controlled for using propensity scoring. This study provides robust evidence on how MS impacts on and disrupts the life of the person with MS and their household in terms of income and employment. r 2007 Elsevier Ltd. All rights reserved. Keywords: UK; Multiple sclerosis; Biographical disruption; Employment; Income; Marital status

Introduction Biographical disruption Corresponding author. Tel.: +44 1206 874144.

E-mail addresses: [email protected] (G. Green), [email protected] (J. Todd), [email protected] (D. Pevalin). 0277-9536/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2007.03.007

Concepts such as ‘biographical disruption’, ‘biographical work’ and ‘identity reconstitution’ have been used to shed light on how illness affects a

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person’s identity or construction of a biography (see, in particular, Bury, 1982, 1991; Charmaz, 1983; Corbin & Strauss, 1987). The impact of serious and persisting symptoms on everyday life is seen to threaten one’s sense of the taken-for-granted world, which then produces a need to redesign lifestyles, reorder time and come to terms with an uncertain illness trajectory. It may affect how people see themselves and how they think others see them, often leading to social isolation and a sense of ‘difference’ from peers (e.g. Strauss et al., 1984). This has been conceptualised as ‘‘disrupted feelings of fit’’ (Mathieson & Stam, 1995, p. 293). The disruption entailed underpins a renegotiation of identity which involves biography-altering facts, altered relationships, a changed vision of the future and a changed sense of self. The notion of ‘biographical disruption’ is very pertinent to people with MS (pwMS). The prevalence of MS is between 100 and 120 per 100,000, equivalent to 52,000–62,400 people in total in England and Wales (Richards, Sampson, Beard, & Tappenden, 2002; Robertson, Deans, Fraser, & Compston, 1996). MS prevalence peaks during the middle years (Richards et al., 2002) which means that there could be a significant impact at this most economically active time and during child raising years. Robinson (1988) has shown how symptoms disrupt the normal flow of everyday life and introduce a growing sense of uncertainty into it. At this level the meaning of illness often involves an increasing awareness of its potentially disabling effects, as self-care activities and other forms of daily life, whether at work or in the home, become problematic and can prevent individuals from ‘playing their role’ (Herzlich & Pierret, 1987). Uncertainty has long been associated with chronic illness (Conrad, 1987; Wiener, 1975) and is often very pronounced in MS as illness progression is varied and unpredictable. Severity of MS may be relatively ‘benign’, with infrequent relapses resulting in no permanent disability, or it may progress rapidly, with frequent relapses resulting in progressive disability and, in many cases, dependency on others for even the most basic activities of daily living (see Robinson, Neilson, & Clifford Rose, 2000). Severity of symptoms varies among pwMS and the course tends to be episodic, fluctuating between acute phases and phases in which the symptoms are less manifest. There is uncertainty about the aetiology, treatment and prognosis, but in common with other chronic

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conditions, there is often little hope of a return to full health (Robinson et al., 2000). There is a large literature based on qualitative narrative analysis which shows how the framework of biographical disruption is used by people to make sense of chronic illness within the context of their lives (see for example Bury, 1982, 1991; Williams, 1984, 1993). This has provided a great deal of understanding about the impact of chronic illness upon individuals but has had limited impact in terms of policy, as in this arena numbers tend to be more influential than words. This paper therefore attempts to utilise a limited part of the concept of biographical disruption in quantitative analysis by examining disruption in the structural dimensions of people’s lives. We focus on measurable variables: household composition, marital status, employment status and household income. It is clear that the concept of biographical disruption was not originally conceived to reflect changing structural circumstances. However, changes in these domains provide the structural context in which the process of biographical disruption takes place for both individuals and households. Impact of MS on the individual and household There is evidence that MS impacts on a number of domains (Rudick, Miller, Clough, Gragg, & Farmer, 1992). Many pwMS adjust well to their illness, retaining a positive self-concept and even adopting a ‘fighting’ attitude to minimise disruption (Pollock, 1993). However, the current evidence base for making decisions about the management of MS reflects the more generally negative findings of most studies such as those that show pwMS tend to score higher on measures of depression than the general population (Patten & Metz, 1997). PwMS score less favourably than the general population on general quality of life measures (Rotstein, Barak, Noy, & Achiron, 2000; Somerset, Campbell, Sharp, & Peters, 2001), even in studies of people categorised as having ‘mild MS’ (Canadian Burden of Illness Study Group, 1998). Lower quality of life scores are also apparent in comparisons between pwMS and other disabled people in the general population (Aronson, 1997). Social and leisure activities are curtailed following a diagnosis of MS (Aronson, 1997), and there is a perceptible shrinkage in both social and geographic worlds (Dyck, 1995). In a study in Northern Ireland, one third of the sample reported being

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unable to drive a car or use public transport, and 18% had changed their residence as a direct consequence of MS (McDonnell & Hawkins, 2001). There is a convincing body of literature that suggests that MS leads to unemployment with approximately 50% of those employed at onset being unemployed approximately 13–17 years later (de Andres & Guillem, 2000; Hakim et al., 2000; Kornblith, La Rocca, & Baum, 1986). Unemployment appears to relate to the onset of disability as in a sample of disabled people of working age, employment rates fall with disability onset, and continue to fall the longer a disability spell lasts (Jenkins & Rigg, 2004). There is evidence that people with a chronic illness who are in professional and managerial roles are better able to remain in employment than those in manual physically demanding occupations (Bartley & Owen, 1996). Work has been shown to be an important factor in terms of identity among people with chronic illness (Charmaz, 1991; Ware, 1998) and unemployment can bring with it feelings of isolation and being on the margin of ‘everyday life’ and society. The nature of MS and its prevalence means that a great deal of health care takes place in community settings and it is therefore important to look beyond the individual and document the shared, collective dimensions of social and economic disruption for a household living with MS. Chronic illness like MS can be potentially disruptive for all members of the family and household, yet the literature has largely focused upon the individual as the unit of analysis. There is a likelihood that the household will need to re-arrange their collective lifestyles to accommodate illness, for example arranging treatment and providing transport to healthcare providers. Studies have found that approximately 50% of carers find that their professional careers are adversely affected, and that over one-third of households report a decline in standard of living (Hakim et al., 2000). There is also a large body of literature about the primarily negative impact on the well-being of informal carers of people with chronic illness. Findings from the ESRC Population and Household Change Programme (http://www.brookes.ac. uk/schools/social/population-and-household-change/ 5_grundy.html) identify a clustering of chronic illness in households in that people over age 45 with a chronic illness are more likely than those without to live in households including others with chronic illness. The experience of chronic illness

for the affected individual and the rest of the family, is, quite literally, an on-going lived experience (Anderson & Bury, 1988; Williams, 1984). A Canadian study estimated the direct and indirect lifetime costs of MS to be $CDN1,608,000 (approximately £800,000) (Canadian Burden of Illness Study Group, 1998). A recent survey at three specialist centres in England estimated a mean total cost per patient of approximately £17,000 per year (a figure that includes direct and indirect costs) (Kobelt et al., 2000a). A cost of approximately £3400 per patient per year falls on the NHS, and the remainder is borne by pwMS and their families and carers. In general, cost evaluations of MS show that direct costs (associated with treatment) are much lower than indirect costs (associated with loss of income and informal care) which account for about 75% of the total cost (Battaglia, Zagami, & Uccelli, 2000). To date, findings are contradictory with respect to the impact of MS on marital status. Some studies suggest that marital status is not much affected by diagnosis (de Andres & Guillem, 2000; Hakim et al., 2000) and a study in California found that MS may result in a deepening of close relationships (Mohr et al., 1999). Others report that the stress of caregiving can be considerable for a partner, particularly in chronic progressive disease (Jackson & Kelsey, 1999). When combined with other problems such as sexual dysfunction, cognitive impairment or financial strain, the relationship might not survive. Higher rates of marital separation and divorce have been reported among the MS population (Burgess, 2002; Hammond, McLeod, Macaskill, & English, 1996) yet other studies have found an unchanged rate (e.g. Hakim et al., 2000). The inconsistent findings are perhaps not surprising and they may reflect differences in intimate relationships whereby some partnerships are endangered by MS whereas others are strengthened. The lack of consensus about the impact of MS on marital status also reflects the limitations of the literature on the social and economic disruption of MS which tends to look at specific aspects or specific groups and studies are often based on very small samples of pwMS (e.g. Dyck, 1995; Miller, 1997; Monks, 1995). Furthermore there is a dearth of studies comparing pwMS with matched populations. More robust evidence of social and economic disruption is required to support the economic evaluations and decision-making processes that are

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carried out in relation to treatment with disease modifying drugs. There has been much debate about whether costly MS treatments such as beta interferon and glatiramer acetate are an efficient use of resources (Kobelt, 2006) and a lack of systematic evidence to inform this debate has been noted (Phillips, 2004). A Cost Effectiveness of Multiple Sclerosis Therapies Study Group also noted that ‘‘None of the existing estimates of cost effectiveness can be viewed as robust’’ (Chilcott et al., 2003). Cost-benefit analysis increasingly builds in costs that occur outside the healthcare system (Kobelt, Jonsson, Henriksson, Fredrikson, & Jonsson, 2000b) in order to inform budget allocation decision-makers about the full impact of MS. There is recognition that disability associated with MS affects a range of biographical domains and this needs to be taken into account in order to accurately calculate quality-adjusted life years upon which most economic evaluations are based. This study makes use of a biographical disruption framework—a concept more commonly seen in qualitative sociological analyses, and uses it as a shorthand for social and economic disruption in the lives of pwMS and their household. Using innovative propensity scoring methodology, the study draws on a large national sample of people at varying stages of MS and makes a systematic comparison of MS and non-MS households. The study benefits from drawing comparisons with the general population, using objectively measurable variables from a large panel survey data set (The General Household Survey), methodology which is rarely seen in studies which seek to investigate the impact of MS on household composition and income, together with marital and employment status. Knowing in detail about the potential (non-clinical) disruption that MS can cause is important because it can help a variety of agencies take into account the true financial, physical and emotional costs of MS.

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survey of people living in private households that collects data at both individual and household level and, thus, constitutes a useful comparative data set of the general population. The GHS is conducted annually by the Social Survey Division of the government’s Office for National Statistics. The GHS consists of a household questionnaire and an individual questionnaire for each adult aged over 16 residing in the household. Data were taken from the 2001/02 GHS, the most up-to-date data available at the time of initial analysis. Data on household composition, household tenure, income, gender, ethnicity, age, social class by way of the National Statistics Socio-economic Classification (see Rose & Pevalin, 2003), educational qualifications, economic status, and marital status were extracted from the GHS for comparison with the MS sample. The MS sample consisted of members of the MS Society living in Great Britain, contacted via national membership lists, stratified by geographical location and gender to ensure both men and women were appropriately represented and that there was geographical spread. Working closely with the MS Society, a randomly selected list of MS Society members who met study criteria (all age groups but only pwMS) was drawn across the regions. This resulted in 4683 names from which 1200 were randomly selected in order to ensure a sufficiently large sample to make robust comparisons with the general population and detect differences in relevant sub-samples, for example the proportion of those in employment. One clear limitation in sampling solely from members of the MS Society was that the younger age groups and those newly diagnosed would be under-represented. Therefore, the postal questionnaire was supplemented by recruiting from the MS Society website with invitations to ‘opt into’ the study placed on the MS research pages and chat rooms for young and newly diagnosed pwMS. Data collection

Methods Procedure and participants Self-reported demographic and life event data were collected in 2003 for the MS sample via a specially designed cross-sectional national survey. This data set was used for comparisons with preexisting data gathered in the British General Household Survey (GHS). The GHS is a national

A 15-page self-completion survey instrument was developed and constructed in consultation with pwMS. This instrument was used to collect demographic and household information. Data on education, employment, economic activity, health and disability, and social and leisure activities were collected. Questions were generally of a closed ‘yes/ no’ format but provided opportunities for respondents to elaborate their responses. Questions were

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based, as far as possible, upon questions used in the GHS in order to make valid comparisons. The study was independently reviewed by the North and Mid-Essex NHS Local Research Ethics Committee to ensure that the conduct of the study conformed to the protocols of research governance. All data were registered with the University of Essex’s Data Protection Officer and held in accordance with the Data Protection Act (1988). The survey was piloted locally and then sent out to 1200 members of the MS Society and produced a 67% response rate (n ¼ 800) with no follow up. Of those who responded, 12 were excluded due to lack of data or because they declined to take part and one respondent lived outside the study’s geographical boundaries. Four lived in institutions so their responses were not comparable with the GHS data (people living in private households). The final eligible sample recruited from the postal survey (n ¼ 783) was supplemented by the responses from the online survey (n ¼ 133) to create a final data set of 916 cases. This ensured that the sample included sufficient numbers of young and newly diagnosed pwMS who are known to be under-represented in the MS Society. Analytic strategy Data from both the GHS and MS samples were used to produce descriptive statistics and unadjusted comparisons on the outcomes of interest. The final stage of analysis utilised propensity score matching where cases from the MS sample were ‘matched’ to cases in the GHS sample. Propensity score matching was pioneered by Rubin and colleagues (Rosenbaum & Rubin, 1983; Rubin, 1973; Rubin & Thomas, 2000). Theoretically, propensity score matching is an attempt to get as close as possible to experimental conditions from survey or observational data and has the potential to reduce selection bias. In simple terms, the propensity score is the likelihood or probability that an individual belongs in a naturally occurring group, in this case the MS sample, based on the individual’s background characteristics (covariates). The propensity score summarises the information on background characteristics into a single summary score or probability. Once the propensity scores have been calculated, the MS and GHS (control) groups are stratified into similarly matched comparison groups based upon their propensity scores. For each stratum, it is then

possible to examine two groups of respondents (one from the MS sample and one from the GHS sample) that have similar propensity scores. For more information and detail on propensity score matching see Rubin and colleagues cited above along with Dehejia and Wahba (1999, 2002) In these analyses, the propensity score is derived from a logit, or logistic, regression equation where a dichotomous-dependent variable (GHS/MS) is regressed onto a series of covariates or matching variables. The covariates or matching variables are chosen by plausible selection factors and other factors known to confound MS status with outcome variables to reduce bias in the comparison results. The matching was done using one MS case to ‘k’ number of GHS cases that have the same propensity score. This way of matching usually results in the control group having more cases. The numbers in each group for each set of matching variables are reported in the tables in the results section. Tests for differences between the groups are done by way of bootstrapping the differences and inspecting the 95% confidence interval. The GHS data were restricted to the same age range as the MS sample (22–84 years) to leave a sample of 15,007 to be used in the matching analysis. All of the analyses were conducted separately for men and women to investigate whether or not the differences in the outcome variables were similar across gender. Data were cleaned and analysed using SPSS for Windows version 10 (SPSS, 1989–1999) and the matched data propensity score analysis was performed using Stata 7.0 (StataCorp, 1984–2002). Results Table 1 shows the characteristics of the MS sample and that most of the sample have either relapsing-remitting (34%) or secondary progressive (28%) MS. There is also a high degree of disability and dependency reported with over 75% of the sample having difficulty with at least one of the daily tasks listed. The majority of the sample report their level of dependency as either moderate (Level 3: 46%) or severe (Levels 4 and 5: 18%). Table 1 also shows the impact of MS on the household across a range of domains. Almost half (46%) of the sample report an impact on their sexual relationships, over three-quarters report an impact on their employment (76%) and social life (80%), and over half (56%) an impact on the

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Table 1 MS sample: self-reported type of MS, level of disability, and impact of MS on self and others in the household Type of MS (n ¼ 912) Benign Relapsing-remitting Primary-progressive Secondary-progressive Unknown/other

82 306 163 255 106

(9.0%) (33.6%) (17.9%) (28.0%) (11.6%)

Difficulty with daily tasks (n ¼ 906) Managing the housework Climbing stairs Dressing yourself Walking for at least 10 min

668 576 298 680

(73.7%) (63.6%) (32.9%) (75.1%)

Level of disabilitya (n ¼ 900) Level 1: It really does not affect what I do in my daily life Level 2: I can’t do some things but I can still look after myself (without help) Level 3: Having MS stops me doing a lot of things that I would like to do, and I sometimes need help Level 4: I need help from someone else if I want to do anything Level 5: I need someone looking after me all the time

120 209 411 82 78

(13.3%) (23.2%) (45.7%) (9.1%) (8.7%)

Impact of MS on self and others in the household MS has had an impact on, etc. Composition of household (n ¼ 909) Sex and relationships (n ¼ 897) Social life (n ¼ 910) Employment (n ¼ 885) Employment of others in household (n ¼ 908) Standard of living (n ¼ 908)

104 413 727 674 251 508

(11.4%) (46.0%) (79.9%) (76.2%) (27.6%) (55.9%)

a

Adapted from Oxford Handicap Scale (Bamford, Sandercock, Warlow, & Slattery, 1989).

household’s standard of living. The subsequent analyses focus on those variables available in both samples. Descriptive statistics from the two samples are shown in Table 2. This shows a predominance of females in the MS sample (3:1) compared to the GHS sample. The MS sample is also older with less ethnic diversity, more likely to come from the higher social classes and be homeowners. Despite generally lower levels of income in the MS households, there are a greater proportion of GHS households in the very lowest band of household income (under £10,000). The MS sample has a higher proportion of people receiving state benefits. Chi-squared tests were used to explore associations between biographical disruption outcomes of interest and MS status in a series of 2  2 tables. The results from these unadjusted tests, by sex, are shown in Table 3. The outcome variables are household composition (single person household), housing tenure (owner-occupier), marital status (divorced), annual household income (under £10,000 and under £30,000) and employment status (in paid employment).

Table 3 shows that a significantly higher proportion of both men and women with MS are owneroccupiers and a significantly lower proportion live in single person households. There is no significant difference in the proportion divorced for men but women with MS are more likely to be divorced than those in the general population. For both men and women, those with MS are less likely to be in households with an annual income of under £10,000 but more likely to be in a household where the annual income is under £30,000. A far lower proportion of both men and women with MS are in paid employment compared to the general population. Tables 4 and 5 show the results of the propensity score matching analysis for men (Table 4) and women (Table 5). Both tables are laid out in the same manner with the biographical disruption outcome variables in the rows and the sets of matching variables and sub-samples in the columns. The sample sizes for each of the columns are presented at the top of the table. The first column of results shows the proportion in each sample on the relevant outcome and if that difference is significant

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Table 2 Descriptive data from MS and GHS samples MS (n ¼ 916)

GHS (n ¼ 15007)

Gender Male Female

214 (23.4%) 702 (76.6%)

7147 (47.6%) 7860 (52.4%)

Ethnic group White Non-White

899 (98.4%) 15 (1.6%)

13957 (93.0%) 1050 (7.0%)

agea Mean (SD) Male Female

52.8 (11.6) 50.4 (11.5)

48.4 (16.0) 49.1 (16.6)

667 (72.8%)

7896 (52.6%)

Social classb Managerial/ intermediate

Housing tenure Homeowner 789 (86.1%) Economic statusc Paid employment 271 (29.9%) Gross annual household incomec Under £10,000 137 (16.5%) £10,000–£29,999 455 (55.0%) £30,000 and higher 236 (28.5%) Receiving state benefits Education Degree level or higher

11226 (74.8%) 8946 (59.6%) 3053 (22.8%) 5555 (41.5%) 4764 (35.6%)

690 (76.0%)

6396 (42.6%)

90 (16.2%)

850 (15.1%)

a

Age range of GHS was matched to MS sample (i.e. 22–84 years). b Soc2000 code converted to NSSEC code. c Sample slightly reduced as some data missing.

at the 5% level. Columns A–D are the results of the analyses for different sets of matching variables. Columns D-I and D-D are sub-samples of those with MS who are categorised as ‘‘independent’’ (Level of disability 1 and 2 in Table 1) and ‘‘dependent’’ (Level of disability 3–5 in Table 1), respectively. Table 4 shows that, after matching on age, region of country and ethnic group, those with MS are significantly more likely to be in a household with an annual income under £30,000, less likely to be in paid employment, more likely to be in the managerial or professional classes, and more likely to have any educational qualifications. Column B adds education into the matching variables but significant differences are still apparent in income, employment status and social class. When the samples are restricted to those of working age (o65) in Column C, the three significant differences still remain. Column D adds social class to the set of matching variables but differences in income and employment status still remain. When the MS sample is divided by level of disability into ‘‘independent’’ and ‘‘dependent’’ there are marked differences in the results. Column D-I shows only one significant difference in the proportion in paid employment but this difference is less than 20%. In Column D-D, those with MS in the ‘‘dependent’’ category are significantly less likely to be in a single person household, more likely to be in a household with an annual income under £30,000 and are about 60% less likely to be in paid employment.

Table 3 Biographical disruption by sex and sample Males MS Owner-occupier Single person household Divorced Annual household income p£10,000a Annual household income p£30,000a In paid employmentb

184 28 15 27 136 61

Females GHS

(86.0%) (13.1%) (7.0%) (13.9%) (70.1%) (33.7%)

5441 1332 324 1241 3899 4676

(76.1%) (18.6%) (4.5%) (19.6%) (61.5%) (79.7%)

w2

MS

11.1 4.2 2.9 3.9 5.5 230.0

605 89 71 110 456 204

Pearson Chi-Square  po0.05;  po0.01;  po0.001. a Sample slightly reduced as some data missing. b Data for respondents of working age only (o65 for males and o60 for females).

w2

GHS (86.2%) (12.7%) (10.1%) (17.4%) (71.9%) (32.8%)

5785 1633 581 1812 4709 4135

(73.6%) (20.8%) (7.4%) (25.8%) (66.9%) (65.9%)

53.9 26.3 6.9 21.9 6.6 267.5

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Table 4 Matched cases analysis for males Outcomes

Matching variables A

B

C

D

D-I

D-D

No of MS cases No. of matched GHS cases

185 925

185 925

159 795

159 795

56 260

111 555

Owner-occupier MS GHS

0.859 0.808

0.859 0.816

0.861 0.833

0.862 0.834

0.808 0.873

0.892 0.856

Single person household MS GHS

0.135 0.174

0.135 0.166

0.126 0.153

0.126 0.151

0.212 0.146

0.081 0.144

Divorced MS GHS

0.070 0.059

0.070 0.066

0.069 0.067

0.069 0.055

0.090 0.015

0.054 0.063

Annual household income p£10,000 MS 0.140 GHS 0.147

0.141 0.154

0.150 0.130

0.151 0.137

0.077 0.092

0.180 0.142

Annual household income p£30,000 MS 0.708 GHS 0.525

0.708 0.538

0.679 0.498

0.679 0.465

0.461 0.300

0.801 0.513

In paid employment MS GHS

0.324 0.718

0.324 0.751

0.371 0.826

0.371 0.830

0.692 0.881

0.198 0.793

Higher social class (managerial/intermediate) MS 0.724 GHS 0.560

0.724 0.579

0.723 0.595

—

—

—

0.783 0.652

—

—

—

—

—

0.194 0.160

—

—

—

—

—

Has qualifications MS GHS Educated to degree level MS GHS

Levels of analysis: A ¼ age, region and ethnic group; B ¼ A+education; C ¼ B+restricted to working age (o65); D ¼ C+social class; D-I ¼ D for disability level ‘‘independent’’; D-D ¼ D for disability level ‘‘dependent’’.  Sig different po.05.

Table 5 shows the results of the propensity score analysis for women. In Column A where groups are matched on age, region of the country and ethnic group, women with MS are more likely to be owneroccupiers, less likely to be in a single person household, more likely to be in a household with an annual income under £30,000, less likely to be in paid employment, more likely to be in the higher social classes, and more likely to have any educational qualifications. These differences remain when education is used as a matching variable. When the analysis is restricted to working age women (o60) the differences in household tenure and composition

are not significant anymore but large differences remain in income, employment status, and social class. When social class is added to the set of matching variables significant differences remain in income and employment status. When the MS sample is divided into ‘‘independent’’ and ‘‘dependent’’ sub-samples by level of disability, those women with MS in the ‘‘independent’’ category are significantly more likely to be owner-occupiers and less likely to be in paid employment although this difference is around 17%. In the ‘‘dependent’’ category, women with MS are significantly less likely to be in a single

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Table 5 Matched cases analysis for females Outcomes

Matching variables A

B

C

D

D-I

D-D

No. of MS cases No. of matched GHS cases

625 3125

625 3125

508 2540

508 2540

234 1170

329 1645

Owner-occupier MS GHS

0.862 0.822

0.862 0.829

0.850 0.829

0.850 0.853

0.897 0.823

0.830 0.845

Single person household MS GHS

0.134 0.174

0.134 0.166

0.116 0.145

0.116 0.139

0.158 0.153

0.097 0.165

Divorced MS GHS

0.108 0.108

0.108 0.115

0.112 0.120

0.112 0.107

0.103 0.093

0.116 0.116

Annual household income p£10,000 MS 0.173 GHS 0.179

0.173 0.177

0.156 0.146

0.156 0.132

0.111 0.145

0.210 0.154

Annual household income p£30,000 MS 0.720 GHS 0.587

0.720 0.552

0.669 0.519

0.669 0.485

0.538 0.488

0.821 0.521

In paid employment MS GHS

0.312 0.642

0.312 0.636

0.378 0.723

0.378 0.763

0.572 0.740

0.170 0.683

Higher social class (managerial/intermediate) MS 0.749 GHS 0.569

0.749 0.626

0.756 0.640

—

—

—

Has qualifications MS GHS

0.808 0.663

—

—

—

—

—

Educated to degree level MS GHS

0.170 0.114

—

—

—

—

—

Levels of analysis: A ¼ age, region and ethnic group; B ¼ A+education; C ¼ B+restricted to working age (o60); D ¼ C+social class; D-I ¼ D for disability level ‘‘independent’’; D-D ¼ D for disability level ‘‘dependent’’.  Significant difference po0.05.

person household (as with men), more likely to be in a household with an annual income both under £10,000 and £30,000, and far less likely to be in paid employment by a difference of around 50%. Discussion Evidence of biographical disruption Findings from the current study suggest that MS has a significant effect upon employment and the economic position of the household. Both prelimin-

ary (unadjusted) analysis and secondary analysis (that matched on socio-demographic variables) show that pwMS are significantly less likely to be employed than people in the general population. Compared to the general population, pwMS are about half as likely to be in paid employment as people in the general population, after taking age, region, ethnic group and education into account. The impact on employment is also noted by pwMS in the survey. Over three quarters (76.2%) of the sample report that MS has impacted on their employment, usually in a negative way. Some report

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adverse impact on prospects and promotion and a large proportion have retired early. Over a quarter (27.6%) report that MS has an impact on the employment of others in their household. This confirms previous findings (Catanzaro & Weinert, 1992; Hakim et al., 2000) that a significant number of pwMS lose their jobs or retire prematurely because of the disability associated with the disease. Loss of employment has been attributed to physical difficulties of getting to the place of work (Cervera-Deval et al., 1994), excessive fatigue (Jongbloed, 1996), visual impairment (Rudick et al., 1992), and cognitive impairment (Hakim et al., 2000). The impact of MS on employment is highly correlated to level of disability in that there is a much larger difference in rates of employment between pwMS and the general population among those pwMS who are dependent on others than those who are independent. It should, however, be noted that pwMS whose disability is less severe and who are able to live without assistance are still significantly less likely to be in employment compared to the general population. The impact on employment clearly impacts on household income. Both men and women with MS are significantly more likely than people in the general population to have a ‘below average’ household income of £30,000 or less (CACI Ltd, 2003), despite the fact that pwMS are in a higher social class and have a higher educational level than people in the general population sample. Over onehalf (56%) of the MS sample report an impact (generally negative) on their standard of living. This supports findings from previous studies based on MS samples alone, which have shown that pwMS who are employed at the onset of the disease retire prematurely because of disability and experience financial difficulties which are directly attributable to loss of earnings and the additional cost of medical care (Catanzaro & Weinert, 1992, Jongbloed, 1996). The impact of MS on income is clearly related to level of disability as in the matched analysis on both males and females there is no significant difference in income between pwMS who are able to live independently and the general population whereas the difference between pwMS who are dependent and the general population is pronounced. The literature would suggest that higher social class offers protection from the disruption to employment and income related to MS. However,

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in this study adding social class to the matching variables has a negligible impact upon the propensity scores related to employment and income. Whilst preliminary analysis suggests that there are differences in marital status for women between the MS and GHS samples, this difference becomes non-significant when socio-demographic variables are controlled for in the matched analysis. As the analysis controls for more factors, differences in divorce rates disappear and pwMS, whether dependent on others or able to live independently, are no more likely to be divorced than people in the general population. Any apparent increases in divorce rates for MS populations as reported in some of the literature (Burgess, 2002; Hammond et al., 1996) would appear to be attributable to confounding factors such as age. This, however, does not mean that MS has no impact on marital relationships. Almost half the MS sample (46.0%) report an impact much of it relating to the physical restrictions MS places on a sexual relationship. The stress of care giving can be considerable, particularly in chronic progressive disease (Jackson & Kelsey, 1999), and when combined with other problems such as sexual dysfunction or financial strain, the marital relationship may require substantial readjustment. Limitations of the study The study has several limitations. It was retrospective and could therefore introduce recall bias with a reliance on self-report. No objective measures were used to ascertain MS diagnosis. However, Somerset et al. (2001) found that respondents’ selfreported MS type, in most cases, seemed to be a reasonable indicator of their health status. There was no validated multi-item instrument to measure disruption which led to the construction of a unique instrument for use by the study. The study recruited a wide range and number of pwMS at various stages of the disease drawn from all parts of Great Britain. Participants were mainly selected at random from a list of MS Society members but they were not necessarily representative of the general population of pwMS. The matching by propensity scoring is designed to reduce selection bias that may be present in unmatched samples but it is based on the assumption that the variables used to match cases are all those that impact on the outcome variable. Inadequate matching may lead to biased results but these

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data contain most, if not all, of the usual determinants found in other studies. In addition to these technical limitations, the study has a number of conceptual limitations. It attempts to operationalise ‘biographical disruption’ in a quantitative way by focusing upon structural disruptions. Such structural disruptions are not normally what are meant by the term ‘biographical disruption’, a term which was coined to express the overriding loss of self and changes to identity that the onset of chronic illness may engender. This study has not included measurement of concepts such as quality of life, attitudes or identity and in this respect it adds little direct knowledge about how chronic illness affects identity. However, it has begun to address the question of whether, and in what ways, ‘biographical disruption’ can be measured and in doing so it provides the contextual structural framework in which the process of recrafting of biography and self-hood by households and individuals takes place. It is disruptions to this structural framework that underpin the process of renegotiation of identity that inevitably follows. Conclusion In summary, this study has quantified and illustrated how MS disadvantages people and their households in terms of employment and income when compared to the general population. It is important that pwMS maintain some control of their situation (Robinson, Hunter, & Neilson, 1996) and a way of facilitating this is by recognising that disruption in a number of domains is occurring, can be quantified and therefore targeted for improvement. It would also help if care agencies are aware of such disruption in order to provide care for pwMS in the context of the impact it may have on the household rather than focusing solely on clinical aspects that the individual presents with. The study has provided robust evidence of the biographical disruption of MS to the household. Such evidence is a pre-requisite if the true cost of MS is ever to be accurately assessed and understood. It also provides a platform from which biographical work must be played. Acknowledgements We gratefully acknowledge the help and support of the Multiple Sclerosis (MS) Society who funded

this study (Grant 711/02). We would like to thank all the people with MS who took part in the study and generously gave their time and energies to this research. We acknowledge use of the original General Household Survey (GHS) Data creators, depositors and copyright holders, funders of the Data Collections and the UK Data Archive who bear no responsibility for this analysis of their data or its interpretation.

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