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COMPARATIVE PROGRAM ON HEALTH AND SOCIETY 2001/2 WORKING PAPER

WORKING PAPER

Access to Home Care Services in Ontario: The Role of Socio-economic Status

Audrey Laporte, Ph.D.*Lupina Fellow

Munk Centre for International Studies andDepartment of Health Policy, Management and Evaluation

University of Toronto

Peter C. Coyte, Ph.D.Professor of Health Economics and

CHSRF/CIHR Health Services ChairDepartment of Health Policy, Management and Evaluation


Ruth Croxford, B.Sc., M.Sc., M.Sc.Home and Community Evaluation Research Centre


October, 2002

*Dr. Laporte gratefully acknowledges the financial support of the ComparativeProgram on Health and Society at the Munk Centre for International Studies at theUniversity of Toronto, the Lupina Foundation and the Canadian Health ServicesResearch Foundation (CHSRF). The views expressed in this paper are those of theauthors and do not necessarily reflect those of the funding agencies.

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1. Introduction

The provision of health care has always involved a mix of individual or household

non-financial resources, primarily time and effort, and purchased inputs, primarily

professional care, hospital resources and pharmaceuticals, which traditionally have

had to be purchased with individual or household financial resources. This has meant

that the level of financial and non-financial resources would always impose a

constraint on the amount of care an individual could receive, and would also play a

major role in determining the type of care received. Some individuals or households

might be better able to supply non-financial resources such as the time input of a

household member, while others, might be more likely to make more intensive use of

purchased inputs for the care of a household member.

Canadian Medicare was introduced in order to ensure that no one was denied access

to necessary medical care because of a lack of financial resources. Public medical

and hospital insurance coverage were designed in the 1950s and 60s with the intention

of easing the constraint imposed by financial resources. Certain services, which had

previously been available only when purchased on the medical market, were now

available at no out-of-pocket cost to the patient. Hospital building programs in

particular also increased the total availability of inpatient services. The combination

of increased availability and reduced price unquestionably increased access to care,

especially among lower income Canadians [1-4], but it also created an incentive to

substitute inpatient for ambulatory or home-based care. This tendency to favour

inpatient care was strengthened by the fact that, during the early years of Medicare -

until the shift to Established Programmes Financing in the mid 1970s - care provided

in a doctor's office, and care provided in hospital, were eligible for federal cost-

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sharing funds while care provided in a patient's home was not. The physician's home

visit fee was a cost-sharable item but costs incurred by the family were not.

The results of Coyte and Landon [5] suggest that provincial governments were quite

sensitive to the incentives built into the cost-sharing system. From the point of view

of provincial Ministries of Finance and of Health, more care could be provided for

each provincial dollar spent on hospital care than on home care programs. The result

was a system weighted heavily in favour of inpatient care, to the point where one

study found that as many as 30% of all hospital days of care might be medically

unnecessary [6]. Those hospital days were being received by patients who could not

be treated on a strictly ambulatory basis but who did not have available to them any

alternative, intermediate level of care. They were being hospitalised because there

was nowhere else for them to receive treatment.

Recently, there has been increased interest in the potential role of home care1 as a

substitute for hospital care, and some figures have been produced suggesting that the

average cost of a day of home care is considerably less than the average total cost of a

day of in-patient care [7]. There is also evidence that, for a significant number of

patients, reduced hospital stays combined with increased home care treatment may

produce health outcomes fully comparable with those achieved by hospital care alone

[6]. Further, there is evidence that many patients would prefer to receive some of

their treatment at home [8,9]. Combined with the budgetary costs of hospital

treatment and the magnitudes of the estimates of the proportion of hospital days

1 Home care services include: nursing, physiotherapy, occupational therapy, social work, home making,personal support, meals on wheels etc. that are provided in the care recipient’s home, school, orworkplace.

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which are rated as medically unnecessary, home care has increasingly come to be seen

by some as an attractive alternative to hospitalisation.

Canadian home care expenditures have increased at an annual rate of approximately

20% since 1975 [10]. In spite of this growing trend towards home care, Canadians

over 65 years of age are four times as likely to receive institutional long term care

than they are to receive care in their homes [11]. This contrasts with Scandinavian

countries, particularly Sweden, Finland and Denmark, where those over 65 years of

age are more than three times as likely to receive home care than institutional long

term care [11].

The pervasive use of home care in Scandinavia is partially attributable to their

demographic profile. Specifically, the very elderly (those over 80 years of age) and

the elderly (those over 65 years of age) account for 4% and 15.5% of the

Scandinavian population, respectively. Equivalent Canadian figures are 3% and

12.5%. The greater use of home care in Scandinavia is also based on a clear

recognition that aging and the use of health care must be considered in a broad

systems framework that operates in concert with cultural norms and shared beliefs

about how health and social care ought to be provided to the elderly [12,13].

One problem with the current configuration of home care services in Canada is that it

places greater demands on a household’s non-financial resources than does care

associated with in-patient care [14,15,16]. And, to the extent that medication and

supplies have to be paid for by the individual, rather than being financed out of

hospital budgets, it may also place heavier demands on personal financial resources.

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This raises the possibility that greater reliance on home care may reintroduce

significant financial barriers to access to care.

As noted above, a primary objective of Canadian Medicare was to ensure that no one

be denied access to care for financial reasons. To a large extent, this objective has

been achieved [1-4, 17,18], although there is evidence that some socio-economic

gradients in utilization of various types of care remain [19-24]. If home care is to

play an increasing role in the Canadian health care system, it must be integrated in a

manner that does not disadvantage the poor. Some efforts are already in place to do

this - the single entry point programs in Ontario, Manitoba, New Brunswick and

Newfoundland are designed to assess the adequacy of family resources in general

before patients are admitted to home care programs. Despite these efforts, a socio-

economic gradient in the use of home care may exist, in which case simply extending

existing structures to the population at large would not achieve Medicare's

fundamental equity objective.

The purpose of this paper was to evaluate the degree to which an individual's financial

resources, as reflected by socio-economic status (SES), are likely to affect access to

home care in Ontario. We considered access to home care services along two

dimensions: propensity and intensity. Propensity refers to the probability that an

individual receives service, intensity to the amount of service received, conditional on

receipt. In particular, we assessed the comparative relationship between socio-

economic status and both the propensity and intensity of home care service use.

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2. Data Sources

All individuals (342,309) who received at least one home care visit in calendar year

1998 were identified from the Ontario Home Care Administration System (OHCAS)

database, obtained from the Ontario Ministry of Health and Long Term Care. OHCAS

provides information about the type and amount of publicly funded home care

services provided to care recipients in Ontario. The Registered Persons Data Base

(RPDB) was used to identify the age, sex, date of death and place of residence for

11,583,921 Ontario residents who had Ontario Health Insurance Plan (OHIP)

coverage for all of calendar year 1998. Analyses incorporated all individuals who

received at least one home care visit during calendar year 1998, along with a 10

percent subset of non-home care recipients, selected at random from the RPDB2.

Morbidity information was derived from Hospital Discharge data, provided by the

Canadian Institutes of Health Information, and Physician Claims data, provided by the

Ontario Ministry of Health and Long Term Care.

Place of residence was characterized by the Forward Sortation Area (FSA - the first

three elements of the postal code) and was linked to 1996 Statistics Canada Census

data in order to obtain ecological socio-economic descriptors. Place of residence was

also characterized by census subdivision (CSD) in order to link observations to a

‘rurality’ index, described later. All individuals, whether they received home care or

not, were assigned to one of the 43 mutually exclusive and exhaustive home care

regions (Community Care Access Centres) on the basis of their Forward Sortation

Area3. Those who:

i) resided outside of Ontario,

2 A stratified random sample of non-home care users was taken in which the elderly were over-sampled.3 See Appendix 1 for a listing of the CCACs.

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ii) died prior to the end of the year,

iii) did not have OHIP coverage for the entire year (i.e. with lapses of no more

than 30 days) or,

iv) could not be assigned a home care program by a match between their postal

code and the home care program in the postal code ‘look up’ file,

were excluded from the analysis.4 The final sample contained 1,385,265 individuals

of whom 297,497 received home care.

Episodes of Care

We distinguished between short- and long-term home care services in the analysis

because short-term service use is generally associated with acute care follow-up (i.e.

care following hospitalization) while long-term service use is generally associated

with chronic care (i.e. assistance with activities of daily living). The literature gives

no guidance as to what constitutes an episode of home care, nor to the specific length

of time or number of visits that ought to constitute short- as opposed to long-term

care. Our heuristic definition is described below. An 'intermediate' category was

included, to ensure good separation between short- and long-term.

The first home care visit of calendar year 1998 was identified for all recipients who

received at least one visit in 1998. An episode of home care was terminated by a gap

of at least 5 weeks (35 days) with no home care visits. The episode to which this first

home care visit belonged was classified as follows:

4 Of 335,918 individuals who received at least one home care visit in calendar year 1998, 31,761(9.5%) died during the year, and were excluded from the analysis. A further 4,913 did not have a validOntario postal code for at least part of the year, and 1,747 had a postal code which could not be linkedwith a CCAC. The final dataset contained 297,497 home care recipients (88.6% of the original homecare population).

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i) If the episode lasted no more than 91 days (13 weeks), it was termed ‘short-

term’.

ii) If the episode lasted 119 days (17 weeks) or more then the episode was termed

‘long-term’.

iii) Otherwise, the episode was termed ‘intermediate’.

At least 153 days of home care history were used to categorize an episode. In some

cases, this involved the use of information about visits received in calendar years

1997 and 1999. For example, if the first visit of 1998 occurred early in the year, data

from as far back as September 1, 1997 might have been used in order to characterize

the home care episode. If the first home care visit of 1998 occurred on October 31,

1998, information from as far forward as March 31, 1999 might have been used.

March 31, 1999 is the last day for which data are available (See Figure 1). Thus, only

individuals whose first home care visit in 1998 occurred before November 1, 1998

could be classified. This left 266,767 individuals, or 89.7% of the original sample.

Figure 1 Classification of Home Care Episodes

Date of First Home Care Visit in CY 1998

If first visit was early in 1998, may Search forward in time to find thehave to search back in time, examining end of the episode. If first visit washome care visits from 1997, in order to late in 1998, may have to examineclassify the episode. home care visits from 1999 in order

classify the episode.

Sept 1, 1998 Jan 1 Dec 31 Mar 31, 1999(start of CY) (end of CY) (end of data)

Based on the above definition, these home care clients were categorized as follows:

48.6% short-term, 47.6% long-term, 3.8% intermediate. The discussion which

follows focuses on the characteristics of short and long-term care episodes.

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3. Methods

We considered both the propensity and intensity of home care utilization, in

recognition of the fact that provision of service is a function of two distinct processes.

Once an individual qualifies for home care, the amount of service received may, for

example, depend on resource availability in the individual's region. Also, factors such

as health status may have a differential impact on the probability of receiving care, as

opposed to on the amount of care received.

As a consequence, the analysis was conducted in the context of a two-part model

structure. In the first part, the propensity or probability of receiving home care was

estimated using a multi-nomial logit equation. The base case for the model was

‘receives no home care’. Coefficient estimates are interpreted as log odds-ratios. The

model shows how the probability of receiving short-term home care versus none,

intermediate-term home care versus none and long-term home care versus none,

changes for different groups in the population.

In the second part of the analysis we modelled the log of service intensity and

estimated a separate ordinary least squares (OLS) equation for short-term and long-

term care as a function of the same set of explanatory variables used in the first part.

Since we were estimating the log of service intensity, for each unit increase in the

explanatory variable of interest, service intensity changes by 10β (where β is the

parameter estimate associated with the variable).

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Explanatory variables

(i) Demographic Characteristics

Age was collapsed into six categories: 0-19 years, 20-44 years, 45-64 years, 65-74

years, 75 to 84 years, and 85 years and over. An age-sex interaction term was

included in the analyses, to test whether the effect of age differed for males and

females.

(ii) Co-Morbidity

It has been shown that differences in the observed utilization of medical services

between socio-economic groups may be attributable to differences in underlying

health status [17]. We used the Adjusted Clinical Group Case-Mix System (ACG)

developed at Johns' Hopkins University [25] to characterize each individual’s level of

morbidity. The ACG classification system has been validated in the United States

[26], Manitoba, and British Columbia [27] as a means of accounting for and

predicting individual health care expenditures. The System uses ICD-9 diagnostic

codes from physician billing and hospital discharge records, to characterize

individuals using 12 ‘Collapsed Ambulatory Diagnostic Groups’ (CADGs)5 which

reflect the individual’s health status and probable level of health care expenditure,

given the degree of illness. Individuals were assigned to as many of the relevant

CADG categories as were applicable based on the diagnoses contained in their

hospital discharge records and fee for service physician claims data for calendar year

1997.6

(iii) Regional variables

In recognition of the possibility that the likelihood and amount of service provision

may be different across regions, we included dummy variables denoting the region in

which home care services were received. Because some regional differences may be

5 See Appendix 2 for a list of the CADGs.6 This approach assumes that the effects of each CADG on health status is additive.

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due to differences in the availability of medical resources or regional economic

characteristics, we incorporated a ‘rurality index’ developed by Kralj [28] for Ontario

as a means of controlling for variation in health care resources and some social

indicators across regions.

The index is calculated for Census subdivisions (CSD)7 and is an attempt to go

beyond the dichotomous urban/rural designation which is based solely on population

size and density. This is particularly relevant in Ontario, as many small cities, such as

Guelph and Windsor, have been classified as under-served in terms of the availability

of certain types of medical services. A CSD is allotted points if, relative to the

Ontario average, it:

i) lacks any of the measured services,

ii) experiences extreme weather conditions,

iii) residents must travel greater distances to services,

iv) lacks educational institutions or an airport.

v) has a high unemployment rate.

The higher the overall point-score, the more rural the CSD. Scores were transformed

so that they range between a low of zero and a high of 100. Appendix 3 reports the

formula used to generate the Rurality Index (RIO) and the potential scores and

weights associated with each of its components.

(iv) Socio-economic Status

The population health literature has adopted a broader definition of SES than that

used in economics, which tends to focus on income-based measures, such as median

7 The index was also calculated for unorganized areas, aboriginal reserves and settlements and excludesCSDs with a population of less than 500. As a result, there were some missing values for the ruralityindex (1.3% of sample observations). All subsequent analysis was conducted on those observations forwhich there were no missing data.

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income. A more recent strand of the population health literature has identified the

distribution of income – the earnings gap between the rich and poor - as measured, for

example, by the Gini coefficient, as a potentially important determinant of health and

as a consequence, of health care service utilization [29,30]. Some studies have

focused on the impact of non-monetary dimensions of SES. Grossman [31] argued

that a more educated person (independent of his/her income level) is better able to

make use of health information and can therefore make more efficient use of health

care resources, compared to an otherwise identical individual. The Whitehall studies

[32,33] purported to show that an individual’s rank in the occupational hierarchy had

an effect on his/her health status independent of observed differences in health

behaviours (i.e. smoking, drinking) and income; those working in non-professional

jobs faced a significantly greater risk of premature mortality.

SES was measured as an ecologic variable; individuals were assigned the values of

the SES indicators associated with the neighbourhood (Forward Sortation Area) in

which they lived8. The assumption underlying this approach is that the

neighbourhoods are fairly homogeneous and, as a result, an individual is probably

well characterized by the neighbourhood in which he/she lives. While there is a

degree of measurement error associated with the accuracy of individuals’ FSA, as

reported in the RPDB, this approach has been validated elsewhere [34].

In an attempt to measure the different aspects of SES identified in the literature, the

following FSA-level variables were explored:

8 Ontario is composed of 503 FSAs each corresponding to approximately 7000 dwellings.

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i) proportion of the population living in low-income households i.e. below the

low income cut-off9. (high values = low SES);

ii) median household income (high value = high SES);

iii) income distribution as measured by the Gini coefficient (high value = low

SES);

iv) proportion of population aged 15 years or older with at least a

college/university degree (high values = high SES);

v) proportion of population working in occupations which were not classified as

white collar (professional) (high values = low SES).

vi) Modified deprivation score [35]

The deprivation score was calculated using three measures: proportion of people

living in low-income households, male unemployment rate and proportion of males

employed in non-professional (blue-collar) occupations. Each of the three measures

was standardized (i.e., ((value-average value)/standard deviation) and the average of

the three standardized values was calculated.

We also considered the proportion of the population who are recent (within 5 years)

immigrants to Canada since Ontario receives a large influx of immigrants each year.

These newcomers may have greater difficulty accessing services due to linguistic or

cultural differences or lack of information about the structure of the health care

system. There is also evidence to suggest that recent immigrants, because they are

screened for illnesses before entering the country, may have higher than average

9 The low income cut-off is a measure of poverty developed by Statistics Canada, based on theproportion of household income spent on food, clothing and shelter and is adjusted for household sizeand urban/rural characteristics.

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(compared to long-term immigrants or native born Canadians) health status [36] and

may therefore use fewer health care resources.

The discussion to follow will focus on the results which were based on low-income,

median household income and the deprivation index since these measures of SES

were significant in the regression analysis10. For each investigation (of propensity to

receive home care, and of short- and long-term intensity of use), three models were

estimated. Each model included age, sex, CCAC, Health Status, the Rurality Index,

recent immigrant and one of the SES measures.

4. Results

A. Probability of Receiving Home Care

Age, sex, CCAC, and health status (H) were significant (p<0.0001) predictors of the

probability of home care, no matter what other variables were included in the model.

Individuals with co-morbid conditions were more likely to receive home care, a

finding which is consistent with that reported in Hall and Coyte [37], based on the

National Population Health Survey (NPHS). Females aged 85 years and older were

more likely to receive home care than any other group. The probability of receiving

home care increases with age (Figure 2). Except for children, males were less likely

to receive home care than were females in the same age group, and when males did

receive home care, they were more likely than females in the same age group to

receive short-term care. Among adult recipients of home care, the chances that the

home care episode was short-term (rather than long-term) decreased with increasing

age (Figure 3). Until age 65 for women and age 75 for men, an individual who

received home care was more likely to receive short-term care, rather than long-term

10 The significance threshold was set at the 5% level. The Gini coefficient was dropped from theanalysis because it was highly correlated (r= 0.9) with median income.

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care (i.e. the odds-ratio was > 1). After that, the odds-ratio was less than 1:

individuals receiving home care were more likely to get long-term care. The detailed

results for the CCAC, age, sex and health status variables are reported in Appendix 4

(Tables A to C).

Figure 2 Probability of Receiving Home Care by AgeGroup

0

0.05

0.1

0.15

0.2

0.25

0-19 20-44 45-64 65-74 75-84 85+

age group

Pro

bab

ility

of

Ho

me

Car

e

female short-term female long-termmale short-term male long-term

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Figure 3 Probability of Short-Term vs. Long-TermHome Care

0

2

4

6

8

10

12

0-19 20-44 45-64 65-74 75-84 85+

Age Group

Od

ds

Rat

io

females males

The coefficient estimates for the rurality index (RIO), recent immigrant and each of

the SES variables are reported in Table 1, below. For each variable, we report an

overall p-value, which tests the hypothesis that the variable in question was correlated

with the probability of receiving home care. More specifically, it tests the null

hypothesis that the parameter estimates associated with each of the alternatives (short-

term vs. none, long-term vs. none, intermediate vs. none), were all equal to zero. We

also report a p-value for the three individual odds-ratios, testing the hypotheses that

the variable was a significant predictor of the particular probability being calculated.

No matter how SES is measured, the probability of receiving home care increased

with lower SES, once other factors, including age, sex, health status, region and recent

immigrant were held constant. Furthermore, SES had a larger effect on the

probability of long-term home care than it did on the probability of short-term home

care (intermediate home care looked similar to short-term home care).

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The effect of proportion of recent immigrants in the neighborhood was stable across

the three types of home care (short-term, intermediate-term, long-term). For each

type of home care, the probability of receiving home care decreased with increased

immigrant population, and it decreased fairly evenly for all three types of home care.

Table 1 Probability of Receiving Home CareOdds ratios (95% confidence interval)p-valueShort-term homecare vs. none

Long-term homecare vs. none

Intermediate-termhome care vs. none

Deprivation modelRurality Index(RIO), per 10 pointincrease in the indexp = 0.0104

0.98 (0.97, 0.99)p = 0.0025

0.99 (0.98, 1.01)p = 0.2863

0.97 (0.93, 1.02)p = 0.2139

Recent immigrant,per 1% increasep < 0.0001

0.98 (0.97, 0.99)p < 0.0001

0.98 (0.97, 0.98)p < 0.0001

0.97 (0.95, 0.99)p= 0.0104

Deprivation Index,per 1 unit increasep < 0.0001

1.12 (1.081.15)p < 0.0001

1.22 (1.19, 1.26)p < 0.0001

1.16 (1.05, 1.29)p = 0.0051

Low income modelRurality Index, per10 point increase inthe indexp = 0.0486

1.00 (0.98, 1.01)p = 0.5257

1.02 (1.00, 1.03)p = 0.0082

0.98 (0.94, 1.04)p = 0.6920

Recent Immigrant,per 1% increasep < 0.0001

0.98 (0.97, 0.98)p < 0.0001

0.97 (0.97, 0.98)p < 0.0001

0.97 (0.95, 0.99)p = 0.0037

Low income,per 1% increasep < 0.0001

1.01 (1.01, 1.02)p < 0.0001

1.02 (1.02, 1.03)p < 0.0001

1.02 (1.001, 1.027)p = 0.0017

Median incomemodelRurality Index(RIO), p = 0.0926

- - -

Recent Immigrant,per 1% increasep < 0.0001

0.98 (0.98, 0.99)p < 0.0001

0.98 (0.98, 0.99)p < 0.0001

0.98 (0.96, 1.00)p = 0.0145

Median income,per $10,000 increasep < 0.0001

0.917 (0.899,0.935))p < 0.0001

0.860 (0.842, 0.878)p < 0.0001

0.892 (0.833, 0.954)p = 0.0010

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The rurality index (RIO) was significant when SES was measured using the

deprivation index (p-value for RIO = 0.0104), and when SES was measured using the

proportion of low-income households in the neighbourhood (p-value for the RIO =

0.0486), but not when SES was measured using median income (p-value for the RIO

= 0.0926). However, when SES was measured using the deprivation score, the

probability of short-term home care was lower in more rural areas, but when SES was

measured using low income, the rurality index was significant only in predicting long-

term home care, and the probability of home care was higher in more rural areas.

In order to understand why the significance of the rurality index depended on which

measure of SES was used, we divided Ontario by the first letter of the postal code,

and obtained median values for the various measures of SES (See Table 2).

Table 2 Median Values of SES and Rurality by RegionRegion Rurality

Index(RIO)

DeprivationIndex

%lowincome

Medianincome($000’s)

%bluecollar

%unemploy-ment

%RecentImmigrant

L(‘905’area)

8.55 -0.39 13% 55 60% 3.9% 2.4%

N(south-westernOntario)

12.07 -0.14 13% 43 70% 4.2% 1.4%

K(easternOntario)

17.57 0.07 15% 41 58% 6.4% 0.8%

M(Toronto)

7.06 0.37 28% 40 52% 7.7% 13.4%

P(northernOntario)

38.04 0.44 15% 41 71% 8.0% 0.3%

The so-called ‘905’ area surrounding Ontario (named after the area code for phone

numbers in the region) had the lowest deprivation, while Northern Ontario had the

most deprivation. Toronto, too, had high deprivation, but it also stood out as having a

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high percentage of households classified as low income. In terms of median income,

the ‘905’ area was clearly well off, but Toronto was not significantly different than

other areas (living in Toronto, one needed more income to avoid being classified as

low income).

The deprivation index is composed of three measures: the same low income variable

used on its own in the low income model plus the proportion of men in occupations

classified as blue collar, and the unemployment rate. While low income and

unemployment reflect monetary aspects of SES, ‘blue collar’ relates to lower social

status. Northern Ontario has high deprivation because it has high unemployment, a

large proportion of the men employed in blue-collar occupations, and low income.

Toronto has high deprivation due to high unemployment and a large number of low-

income people. Southwestern Ontario is low deprivation, despite the high proportion

of men with blue-collar occupations, because it has low unemployment and high

incomes.

The rurality index, or the lack of health care services, is high in Northern Ontario.

Eastern and Southern Ontario are more rural than Toronto and the 905 area but much

less rural than Northern Ontario; and Toronto and the 905 area are non-rural.

The contradictory results for the rurality index in the deprivation model appear to be a

function of the degree of correlation between the rurality index (RIO) and the

components of the deprivation index. The lack of significance of the rurality index in

the median income model seems to indicate that medical rurality is positively

correlated with the absolute standard of living in the CCAC. The fact that the

probability of receiving long-term home care increased with the rurality index in the

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low income model may suggest that home care services are used as a substitute for

institutional long-term care in medically rural areas, if, the lack of institutional beds is

correlated with a lack of other resources (i.e. hospitals) as measured by the index.

The Role of SES in the Prediction of Short- Versus Long-term Home Care

To determine whether the effect of a particular variable on the probability of short-

term home care (versus no home care) was the same as its effect on the probability of

long-term care (versus no home care), contrasts were calculated to compare the two

odds-ratios. For example, the odds-ratio for receipt of short-term home care was 1.12

for each one-unit increase in the deprivation index (Appendix 4, Table A). The

corresponding odds-ratio for receipt of long-term care was 1.22. The p-value of

<0.0001, reported in the last column of the first row in Table 3, indicates that the two

odds ratios are significantly different from one another. We observe from Table 3

that no matter how SES was measured, the odds ratio for short-term home care

differed significantly from the odds ratio for long-term home care. Examination of

the parameter estimates indicates that SES had a larger effect on the probability of

receiving long-term home care than on the probability of receiving short-term care.

Table 3. Contrasts of Short- and Long-termModel Rurality Index (RIO) Immigration SESDeprivation Model p=0.1884 p=0.5800 p<0.0001Low income Model p=0.0137 p=0.3510 p<0.0001Median Income Model p=0.0778 p=0.9024 p<0.0001

Interpretation of the Model Results

In order to help interpret the odds-ratios, we report, in Table 4, the range of values for

rurality, immigration, and SES observed for the population of Ontario. Using the

21

deprivation model as an illustration, the predicted effect on the probability of short-

term home care, of moving from the neighbourhood with the lowest deprivation to the

neighbourhood with the highest deprivation, is 2.911. That is, an individual living in a

neighbourhood with the highest deprivation score is approximately three times more

likely to receive short-term home care than someone living in a neighbourhood with

the lowest deprivation score, all else being constant. Likewise the predicted effect on

the probability of short-term home care of moving from the neighbourhood with the

25th percentile deprivation to the neighbourhood with the 75th percentile deprivation is

1.120.95, or 1.11.

Table 4 Descriptive Statistics for SES and RuralityRange Median 25th percentile 75th percentile

Low income 0 to 53.4% 15.6% 11.2% 22.9%Deprivation index -4.06 to

5.34-0.02 -0.52 +0.43

Median income(000’s)

0 to 119 44 37.5 55

Immigration 0 to 29.6% 2.4% 0.7% 8.2%

As a way of quantifying the predictive ability of each variable, a series of logistic

regressions were performed. The R2 values are reported in Table 5. Health status was

the best single predictor of whether or not someone would receive short-term home

care (middle column), with age being the next best predictor. Age was the best

predictor of whether or not someone would receive long-term home care, with health

status also being a good predictor. Age was also the best predictor of whether the first

home care episode would be short- or long-term. Once age was accounted for, adding

sex produced very little improvement in any of the models. We observed that rurality,

11 From Table 4 we first calculate the difference in deprivation, which is 9.4. The odds ratio for a one-unit change in deprivation is 1.12. Thus, the odds ratio for a change of 9.4 in deprivation is 1.129.4, or2.90.

22

SES, and immigrant status played relatively minor roles in terms of predicting an

individual’s likelihood of receiving home care12.

Table 5 Univariate Analyses from Logistic Regression

VariableAdj-R2

Short- vs. Long-term

Adj-R2

Short-term vs.none

Adj-R2

Long-term vs.none

Age 7.5% 8.4% 23.9%Age + Sex 8.0% 8.4% 24.2%Age/Sex interaction 8.2% 8.6% 24.5%Health Status(CADGs)

4.3% 10.5% 20.2%

CCAC 1.0% 1.0% 1.1%Rurality Index (RIO) 0.0% 0.2% 0.2%Recent Immigrant 0.0% 0.5% 0.2%SES: deprivation

low incomemedian income

0.1%0.3%0.3%

0.0%0.0%0.4%

0.2%0.1%0.9%

B. Service Intensity

(i) Determinants of Short-term Service Intensity

Service intensity for short-term episodes was defined as the number of visits during

the episode.13 During the index episode, 1,600,426 visits were made to 129,755 short-

term care clients. 11.0% of the clients received only a single day of home care, and

27.1% had episodes of a week or less. The average length of a short-term episode

was 28 days (standard deviation 24.5 days) and the median was 21 days. The 25th

percentile was 7 days, the 75th percentile was 44 days. The 90th percentile was 67

days. The distribution of short-term clients and visits by type of service is presented

in Table 6.

12 Likelihood ratio tests were conducted and indicated that all the models fit well.13 The ‘intermediate’ category contained 10,021 clients, who received 360,360 visits during their indexepisodes, and a total of 446,441 visits during the whole of the calendar year. The 25th percentileepisode length was 98 days, the median was 104 days, and the 75th percentile was 111 days.

23

Table 6 Distribution of Short-Term Care Clients and VisitsVisits Care Recipients

Nursing 71.06% 66.32%Personal Support and/orHomemaking

12.52% 15.00%

Physiotherapy and/orOccupational Therapy

13.31% 34.18%

Other Services (e.g. SocialWork, Speech LanguagePathology etc.)

3.12% 8.91%

In terms of service mix, Table 7 shows the proportion of people who received at least

one visit of each type. Almost half the short-term episodes involved only nursing, and

one-fifth involved only physio- /occupational therapy.

Table 7 Service Mix for Short-term ClientsNursing Personal

Supportand/or

Homemaking

Physiotherapyand/or

OccupationalTherapy

OtherServices

% (of 129,755people)

X 4.82%X 21.58%X X 0.84%

X 3.28%X X 0.08%X X 2.92%X X X 0.15%

X 50.55%X X 1.94%X X 4.98%X X X 0.50%X X 4.94%X X X 0.40%X X X 2.83%X X X X 0.38%

In terms of service intensity, the median number of short-term visits was 6; the 25th

and 75th percentiles were 3 and 15 visits, respectively. The 10th percentile was 1 visit

and the 90th percentile was 29 visits. Figure 4 depicts the distribution of short-term

clients by number of visits.

24

Figure 4 Distribution Of Short-Term Clients byNumber of Visits

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

1 2 3 4 5 6 7 8 9 10

11-1

5'

16-2

0

21-2

5

26-3

0

31-4

0

41-5

0

51+

Number of Visits

Per

cen

tag

eo

fC

lien

ts(%

)

Empirical Model Results

Age, sex, CCAC and health status (H) were significant (p< 0.0001) predictors of

short-term service intensity in all three SES models14. Their coefficient estimates are

reported in Appendix 5. Among short-term home care recipients, children received

the fewest services, and boys received fewer services than girls. The most elderly

(aged 85 years and older) received the next fewest number of services, followed by

adults aged 75 to 84 years. Generally, males received more services than females of

the same age. Health status as measured by the vector of co-morbidity groups

(CADGs) was significant.

The results from adding rurality, immigration, and each of the three SES measures to

the base model (age, sex, CCAC, Health status) are reported in Table 8. The first row

indicates that recent immigration was not a significant predictor of short-term service

intensity in any of the models. In contrast, the rurality index was significant in all

three models. Short term service intensity decreased with increasing rurality. In the

14 In order to remove extreme outliers from the data, short-term service intensity was capped at 100visits, which retained 99.55% of the observations and long-term care service intensity was capped at730 visits (an annualized rate of 2 visits per day), which retained 98.87% of the observations. Bothshort- and long-term service intensity were skewed, but their logs were reasonably close to a normaldistribution.

25

model with SES measured using the deprivation index, short-term service intensity

increased by 2% for each unit increase in deprivation (p = 0.0001) and decreased by

0.7% for each 10-point increase in the rurality index. In the low-income model, SES

was not significant. In the median income model, short-term service intensity

decreased by 0.9% for each additional $10,000 in median income (p = 0.0065) and

decreased by 0.6% for each 10-point increase in the rurality index. Thus, in both

models, service intensity increased with lower SES. The R2 for the three models

ranged from 9.45% to 9.97%, which is not unexpected given the cross-section

analysis and the large sample size.

Table 8 Results of Intensity Equation for Short-term Home CareDeprivation model Low income model Median income

model ($000’s)Rurality Index(RIO)

p = 0.0012β = -0.00029(0.00009)

p = 0.0084Β=-0.0002(0.00009)

p = 0.0029Β=-0.00027(0.00009)

Recentimmigration

p = 0.7252β= -0.00015(0.0004)

p = 0.3765β=0.0004(0.0005)

p = 0.4610β=0.0003(0.0004)

SES p = 0.0001β = 0.0087(0.0023)

p = 0.3913β=0.00020(0.00023)

p = 0.0065β=-0.00038(0.00014)

R2 9.97% 9.45% 9.97%

SES * H p = 0.0009 p < 0.0001 p < 0.0001

R2 10.00% 10.00% 10.00%

In model containing only the main effects of immigration, rurality, and SES, it

appeared that immigration did not predict short-term service intensity, but rurality did.

The effect of rurality was negative - people living in areas which are more medically

rural received less service, on average. Low SES, in so far as it is measured by the

deprivation index or median income, was associated with more service, on average.

The SES-Health Status Connection

To investigate whether SES affected service intensity differentially depending on

health, we introduced an interaction between each SES measure and the co-morbidity

categories (CADGs) into the regression models. The significance of the SES-health

26

interaction in all three models indicated that the role of SES in predicting service

intensity depended on health status15.

If SES was measured using the proportion of people living in low-income households,

there was a significant interaction between SES and chronic medical stable

(p=0.0016), psychosocial (p=0.0004), prevention/administration (p=0.0097), and

pregnancy (p=0.0270). In the first three cases, the direction of the interaction was

such that someone living in a lower SES neighbourhood received fewer services than

would be predicted by illness or SES alone. For pregnancy, the effect was in the

opposite direction.

When SES was measured using median household income, there were, again,

significant interactions between SES and illness for psychosocial illness (p=0.0009),

prevention/administration (p=0.0027), and pregnancy (p=0.0005). For a given SES,

the pregnancy CADG carried the largest “penalty”, in terms of number of short-term

home care visits, of the three CADGs. But visits paid to pregnant women increased

most steeply as SES increased.

The interaction parameter between health (H) and SES, when using deprivation as the

measure, was only significant for psychosocial illness and prevention/administration,

and was associated with decreased service use on the part of the person with the

condition, relative to someone living in a less deprived area. The direction of the

interaction effects between SES and illness, regardless of the SES measure used, were

such as to reduce the amount of service for people living in low SES neighbourhoods.

In comparison with a neighbour without illness, a person in a lower SES

neighbourhood fared worse than a person in a higher SES neighbourhood. While the

person in the lower SES neighbourhood might still receive more visits than their

neighbour without illness, the incremental gain due to illness was less that that

obtained by their counterpart in the high SES neighbourhood.

It is important to note, however, that the main predictors in the short-term intensity

model were age, sex, age/sex interaction (R2 = 7.1%). Adding health status to the

model increased the model R2 to 7.7%. Rurality, SES, the SES-Health interactions

15 The coefficients for the SES*Health interactions are reported in Appendix 6 (Tables A to C).

27

and immigrant status added little in the way of predictive ability. In fact the model R2

increased to, at most, 10.2% (for a model containing CCAC, immigration, SES, and

interactions between health status and both SES and CCAC).

(ii) Long-term Home Care

Service intensity for long-term care episodes was defined as the annualized number of

visits i.e. the number of visits the client would have received had the visits continued

with the same intensity for a complete year. If the client received services for at least

half of 1998, then only the 1998 visits were used to calculate service intensity.

Otherwise, services from 1997 or 1999, if any, were included in order to obtain a

robust estimate of service intensity16.

The mean length of a home care episode used to calculate long-term service intensity

was 278 days, and the median was 296 days. The 25th percentile was 199 days and

the 75th percentile was 358 days. Table 9 presents the distribution of long-term care

visits and clients across types of service, and Table 10 shows the service mix. Of

those who received long-term service, the vast majority of clients received some

homemaking services and over half received some nursing care. Most long-term care

visits were either to provide home making services or nursing care.

Table 9 Distribution of Long-term Care Clients and VisitsType of service Visits ClientsNursing 35.95% 57.54%Personal Support and/orHomemaking

57.41% 75.43%

Physiotherapy and/orOccupational Therapy

4.35% 40.35%

Other Services 2.00% 18.74%

16 In order to avoid ‘edge’ effects for long-term care clients who were receiving visits once per weekor less, if the annualized service intensity was 56 or less (indicating a visit once per week for 18weeks), the first day of home care was moved back to the nearest Monday and the last day of homecare was moved forward to the next Sunday, and the service intensity was recalculated.

28

Table 10 Service Mix for Long-term Care ClientsNursing Personal

Supportand/or

Homemaking

Physiotherapyand/or

OccupationalTherapy

OtherServices

% (of 126,991Clients)

X 2.97%X 4.55%X X 1.74%

X 21.55%X X 0.90%X X 9.28%X X X 1.47%

X 9.71%X X 1.81%X X 2.74%X X X 1.06%X X 19.21%X X X 3.51%X X X 14.22%X X X X 5.29%

Less than 10% of long-term care clients received nursing services only, almost 20%

received both nursing and home making services and another 19.5% received nursing,

home making and either occupational or physiotherapy.

The average service intensity was 138 with a standard deviation 163. The range was

from 12.4 (one visit per month) to 5,010 (the 99th percentile was 749 visits or just

over 2 per day). The median was 90 and the 25th and 75th percentiles were 52 (one

visit per week and 159 (one visit every other day).

Empirical Model Results

We modeled the log of service intensity in long-term homecare, where service

intensity was the annualized number of visits, as a function of age, sex, the age-sex

interaction, health status, rurality, CCAC, immigration, SES and the SES-health

interaction. Age, sex, CCAC and health status were significant (p<0.0001). Females,

other than children, received fewer services than males in the same age group.

29

Relative to males aged 85 years and over, males aged 20-74 received more, males

aged 0-19 years received less and males aged 75-84 years received the same amount

of long-term services. The coefficient estimates for age, sex, health status and CCAC

are reported in Appendix 6 (Tables A to C). Table 11 reports the estimates and p-

values for the remaining variables.

Table 11 Results of Intensity Equation for Long-Term Home CareDeprivation model Low income model Median income model

($000’s)Rurality Index(RIO)

p < 0.0001β = -0.0005(0.00007)

p < 0.0001β=-0.00037(0.00007)

p < 0.0001β=-0.00043(0.00007)

Recentimmigration

p < 0.0001β= -0.0015(0.0003)

p < 0.0001β=-0.0014(0.0003)

p = 0.0257β=-0.00063(0.00028)

SES p < 0.0001β = 0.0081(0.0016)

p = 0.0006β=0.00054(0.00016)

p = 0.4918β=0.00007(0.0001)

R2 16.6% 16.6% 16.6%

SES * H p = 0.3555 p = 0.0240 p = 0.0001

R2 16.6% 16.6% 16.6%

Unlike short-term intensity, immigrant status was a significant predictor of long-term

service intensity. People living in neighbourhoods with a high proportion of

immigrants received less service, on average. Rurality was also a significant

predictor, and SES was significant when it was measured using low-income or the

deprivation index, but not when it was measured as median income. As with short-

term service intensity, service was lower in more rural areas and people living in

lower SES areas received on average, more service.

In contrast to the model for short-term intensity, the role of SES did not appear to

depend on illness when SES was measured by low-income or the deprivation index.

The SES-health interaction was significant in the median income model in relation to

30

unstable chronic medical and psychosocial conditions but the differences between low

and high SES neighborhoods were very small.

Age, sex, and their interaction were the best predictor of long-term service intensity

(R2=8.4%), followed by CCAC (R2=7.3%). While rurality was negatively correlated

with long-term service intensity, it is not as important a predictor (R2=0.1%) as the

region identifier (CCAC). Lower SES was associated with greater service intensity

and recent immigrant with less service intensity but neither variable was an important

predictor, with R2s of less than 0.01% for both. The full model had an R2 of 16.8%.

5. Discussion

In this paper we have explored the determinants of access to publicly-funded home

care services. The two-part approach applied in the analysis was justified, since some

variables had a differential effect on propensity to receive care and intensity of care.

The distinction between short- and long-term care was also appropriate, given that the

importance and strength of some variables were found to differ across the types of

care.

Whether we considered propensity and intensity of care for long-term episodes, we

found that living in a neighbourhood with a high proportion of recent immigrants was

associated with reduced access. We did not find a relationship between recent

immigrant and short-term home care service. Long-term home care service is

composed largely of home making services whereas short-term home care involves

nursing care for the vast majority (66.52%) of clients. Perhaps this is an indication

that recent immigrants are more likely to rely on and receive family/ informal

caregiver support for long-term care due to cultural norms or linguistic/cultural

31

barriers to service. As mentioned earlier, there is evidence to suggest that recent

immigrants are generally healthier than long-term immigrants and native-born

residents of Canada [36]. However, in all the regression equations estimated, health

status, age and sex were significant which indicates that recent immigrants are less

likely to receive service and use less service than individuals of comparable age, sex

and morbidity profile. In other words, the negative association between recent

immigrant and access is independent of need and may therefore be indicative of

barriers to access for that segment of the Ontario population. While our data set does

not permit us to isolate the specific causes of the negative relationship between recent

immigrant and access to home care services, barriers to access appear to exist, and

merit further investigation in future research.

Medical service availability, as measured by the rurality index (RIO), produced mixed

results in terms of determining likelihood of service receipt but was clearly negatively

related to service intensity. Lack of medical resources appeared to be more important

in the determination of the amount of service received as opposed to determining

whether or not one received service in the first place. There were differences in terms

of the determinants of access across the regions that were not explained by the RIO, as

evidenced by the consistent significance of the regional variable (CCAC). This may

be linked to differences in referral patterns across regions [38] or simply reflect the

fact that there are aspects of community-based care which do not fall into the domain

of medical resources – such as the availability of homemaking services or that things

like distance to tertiary care facilities may not be meaningful in the context of home

care, where the personnel are deployed locally.

In terms of socio-economic status (SES) we found that the relationship to access both

in terms of propensity and intensity is consistent with the stated intent of Medicare.

32

That is, people living in more socio-economically disadvantaged neighbourhoods

were more likely to receive all types (short, long, and intermediate) of home care

service and received more of those services than those from higher SES

neighbourhoods, all else being equal.

6. Conclusion

Our results, particularly the significance of the age, sex and health status variables,

indicate that the allocation of home care services is primarily based on need rather

than financial clout. We did identify barriers for those from areas with a high

proportion of recent immigrants, which may be cultural or linguistic in nature or may

simply indicate that there is a learning curve associated with accessing health care

resources. While it appears that people from poorer neighbourhoods are qualifying

for and receiving more publicly-funded service, we cannot determine from our data

whether the amount of service received is adequate i.e. should even more care be

provided? Wealthier individuals may supplement publicly-funded service with

privately-funded services more readily than can the poor. Alternatively, high SES

people may acquire other (non-home care) services as substitutes for home care. That

is, they may use more physician and hospital services and less home care compared to

those of lower SES [22, 23].

We started this paper by pointing out that financial and non-financial resources affect

accessibility to home care services, and we have shown that there appears to be an

income gradient in access to home care services in Ontario which favours the poor.

Future research is needed to assess whether the publicly-funded resources directed to

the financially disadvantaged are enough to offset the added costs associated with

33

home-based care, in terms of informal care and medication/materials, that impose a

disproportionate burden on those of lower socio-economic status.

7. Acknowledgements

The authors wish to thank Eric Nauenberg (MOHLTC), Brian Ferguson, Denise

Guerriere, Ruth Hall, Elizabeth Kaegi, Susan Donaldson (OACCAC), Carrie

Hayward (MOHLTC) and Frank Thiel (MOHLTC) for helpful comments and

suggestions. Thanks also to participants of the Munk Centre seminar series, the

Karolinska Health Care and Place Workshop, the Canadian Home Care Association

Conference and the Canadian Health Economics Research Association (CHERA)

Conference for useful feed-back and discussion.

34

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37

Appendix 1

Community Care Access Centre (CCAC) Locations

Central East RegionDurham Region (1)Haliburton, Northumberland and Victoria Counties (4)Peterborough County (5)Simcoe County (6)York Region (7)

Central South RegionBrant County (8)Haldimand-Norfolk Region (10)Hamilton-Wentworth Region (11)Niagara Region (12)

Central West RegionHalton Region (13)Peel Region (14)Waterloo Region (16)Wellington-Dufferin Counties (19)

East RegionEastern Counties (20)Hastings and Prince Edward Counties (21)Kingston, and Frontenac, Lennox and Addington Counties (24)Lanark, Leeds and Grenville Counties (25)Ottawa-Carleton Region (26)Renfrew County (27)

North RegionAlgoma District (28)Cochrane District (29)Kenora and Rainy River Districts (30)Manitoulin, and Sudbury County and Region (32)Muskoka, and (East) Parry Sound Counties (33)Nipissing County (35)Thunder Bay District (36)Timiskaming District (37)(West) Parry Sound County (38)

South West RegionChatham, and Kent County (39)Elgin County (40)Grey-Bruce Counties (41)Huron County (42)London, and Middlesex County (44)Oxford County (48)Perth County (49)Sarnia, and Lambton County (50)

38

Windsor, and Essex County (70)

Toronto Region (78)East YorkEtobicokeNorth YorkScarboroughTorontoYork (former City of)

39

Appendix 2

The Collapsed ADG Clusters (CADGs) and the ADGs that they CompriseCollapsedADG(CADG)

ADGs included in the CADG Common ICD-9 CodesIncluded in the CADG

1. AcuteMinor

• Time Limited: Minor• Time Limited: Minor –

Primary Infection• Inuries/Adverse Events: Minor• Signs/Symptoms: Minor

558.9

691.0

079.9464.4847.0959.1784.0729.5

NoninfectiousGastroenteritisDiaper or Napkin RashUnspecified ViralInfectionCroupNeck SprainInjury to TrunkHeadachePain in Limb

2. AcuteMajor

• Time Limited: Major• Time Limited: Major –

Primary Infection• Inuries/Adverse Events: Major• Signs/Symptoms: Major

451.2

560.3573.3711.0854.0

972.1

429.3780.2

Phlebitis of LowerExtremitiesImpaction of IntestineHepatitis, UnspecifiedPyogenic ArthritisIntercranial InjuryPoisoning byCardiotonicGlycosides and SimilarDrugsCardiomegalySyncope and Collapse

3. Likely toRecur

• Allergies• Likely to Recur: Discrete• Likely to Recur: Discrete-

Infections• Dermatologic• Discretionary

477.9

708.9274.9724.5474.0599.0078.1448.1550.9706.2

Allergic Rhinitis,Cause UnspecifiedUnspecified UrticariaGout, UnspecifiedBackache, UnspecifiedChronic TonsillitisUrinary Tract InfectionViral WartsNevus, Non-NeoplasticInguinal Hernia (NOS)Sebaceous Cyst

4. Asthma • Asthma 493.0493.1

Extrinsic AsthmaIntrinsic Asthma

40

5. ChronicMedical:Unstable

• Likely to Recur: Progressive• Chronic Medical: Unstable• Malignancy

250.10

434.0282.6277.0174.9

201.9

Adult Onset Type IIDiabetes w/KetoacidosisCerebral ThrombosisSickle-Cell AnemiaCystic FibrosisMalignant Neoplasmof Breast (NOS)Hodgkin's Disease,Unspecified Type

6. ChronicMedical:Stable

• Chronic Medical: Stable• See and Reassure

250.00

401.9611.1278.1

Adult-Onset Type IDiabetesEssential HypertensionHypertrophy of BreastLocalized Adiposity

7. ChronicSpecialty:Stable

• Chronic Specialty: Stable-Orthopedic

• Chronic Specialty: Stable: Ear,Nose, Throat

721.0

718.8

389.14385.3

Cervical SpondylosisWithout MyelopathyOther JointDerangementCentral Hearing LossCholesteatoma

8. Eye/Dental

• Chronic Specialty: Stable-Eye• Dental

367.1372.9

521.0523.1

MyopiaUnspecified Disorderof ConjunctivaDental CariesChronic Gingivitis

9. ChronicSpecialty:Unstable

• Chronic Specialty: Unstable-Orthopedic

• Chronic Specialty: Unstable-Ear, Nose, Throat

• Chronic Specialty: Unstable-Eye

724.02

732.7

383.1386.0078.1379.0

Spinal Stenosis ofLumbar RegionOsteochrondritisDissecansChronic MastoiditisMeniere’s DiseaseUnspecified GlaucomaScleritis/Episcleritis

10. Psycho-social

• Psycho-social: Time Limited,Minor

• Psycho-social: Recurrent orPersistent, Stable

• Psycho-social: Recurrent orPersistent, Unstable

305.20

309.0

300.01307.51295.2

291.0

Cannabis Abuse,UnspecifiedBrief DepressiveReactionPanic DisorderBulimiaCatatonicSchizophreniaAlcohol WithdrawalDelirium Tremens

11. Preven-tion/Adminis-trative

• Prevention/Administrative V20.2

V72.3

Routine Infant or ChildHealth CheckGynecologicalExamination

41

12. Pregnan-cy

• Pregnancy V22.2650

Pregnant StateDelivery in aCompletely NormalCase

42

Appendix 3

Rurality Index for Ontario (RIO) = TIMEb + TIMEa + POPm + GPR + GP + HOSP +

AMB + SOC + WTHR + MSS

Where,TIMEb = Measure of travel time to nearest basic referral centre.TIMEa = Measure of travel time to nearest advanced referral centre.POPm = Measure of community population.GPR = Measure of population to GP ratio.GP = Measure of the number of active GP/FPs in community.HOSP = Measure of the presence of a hospital.AMB = Measure of the availability of ambulance service.SOC = Measure of social indicators.

= [Education + Airport + Unemployment]/3WTHR = Measure of weather conditions.MSS = Measure of selected services (i.e. GP, obstetrics and anesthesia).

RIO Components: Potential Scores and WeightsComponent

VariablePotential Max.

ScorePotential Min. Score Weight in RIO (%)

Total/RIO 185 -20 100TIMEb 40 -10 24.3TIMEa 15 -10 12.2POPm 35 0 17.1GPR 10 0 4.9GP 20 0 9.8HOSP 20 0 9.8AMB 5 0 2.4SOC 10 0 4.9WTHR 15 0 7.3MSS 15 0 7.3Source: Kralj, Boris (2000) Measuring “rurality” for purposes of health–careplanning: an empirical measure for Ontario, Ontario Medical Review, pg. 37.

43

Appendix 4Table A: Propensity to Use Service (SES = deprivation)Parameter Function Number Estimate Standard Error Chi-Square Pr > ChiSqIntercept Short-term -3.5052 0.0503 4853.01 <.0001

Long-term -2.8338 0.0433 4282.54 <.0001Intermediate -6.3155 0.1720 1348.05 <.0001

CCAC 1 Short-term 0.1247 0.0656 3.61 0.05741 Long-term -0.00757 0.0744 0.01 0.91901 Intermediate -0.1354 0.2627 0.27 0.60644 Short-term -0.0473 0.0907 0.27 0.60244 Long-term 0.0232 0.0935 0.06 0.80404 Intermediate 0.1122 0.3036 0.14 0.71175 Short-term -0.1175 0.0442 16.11 <.00015 Long-term -0.0778 0.0458 2.88 0.08965 Intermediate -0.2262 0.1635 1.91 0.16656 Short-term 0.2430 0.0562 18.69 <.00016 Long-term 0.1869 0.0627 8.88 0.00296 Intermediate 0.3207 0.1972 2.65 0.10387 Short-term 0.1978 0.0596 11.01 0.00097 Long-term 0.2046 0.0619 10.92 0.00097 Intermediate 0.3428 0.2023 2.87 0.09028 Short-term -0.0107 0.0764 0.02 0.88848 Long-term 0.0735 0.0782 0.88 0.34738 Intermediate 0.0447 0.2690 0.03 0.868010 Short-term -0.1037 0.0514 4.07 0.043610 Long-term -0.2888 0.0621 21.62 <.000110 Intermediate -0.2394 0.1974 1.47 0.225211 Short-term 0.0714 0.0625 1.30 0.253711 Long-term -0.0266 0.0684 0.15 0.697811 Intermediate -0.3815 0.2800 1.86 0.173012 Short-term 0.0369 0.0980 0.14 0.706712 Long-term 0.2745 0.0915 9.00 0.002712 Intermediate 0.2250 0.3232 0.48 0.486313 Short-term 0.2623 0.0679 14.95 0.000113 Long-term 0.2271 0.0735 9.54 0.002013 Intermediate 0.2965 0.2374 1.56 0.211814 Short-term 0.1316 0.0622 4.48 0.034314 Long-term -0.1217 0.0736 2.73 0.098314 Intermediate -0.2442 0.2637 0.86 0.354516 Short-term 0.1147 0.0621 3.42 0.064516 Long-term 0.0692 0.0665 1.08 0.298516 Intermediate -0.1204 0.2531 0.23 0.634419 Short-term -0.0514 0.0439 1.37 0.241319 Long-term -0.0242 0.0474 0.26 0.609019 Intermediate -0.1040 0.1618 0.41 0.520420 Short-term -0.0976 0.1472 0.44 0.507220 Long-term 0.3170 0.1257 6.36 0.011720 Intermediate -0.0552 0.5240 0.01 0.916021 Short-term -0.0870 0.0398 4.78 0.028821 Long-term -0.3500 0.0462 57.29 <.000121 Intermediate -0.1868 0.1477 1.60 0.206124 Short-term -0.3214 0.0460 48.84 <.0001

CCAC 24 Long-term -0.2571 0.0509 25.57 <.0001

44

24 Intermediate -0.1589 0.1565 1.03 0.309925 Short-term -0.4275 0.0418 104.58 <.000125 Long-term 0.0820 0.0391 4.41 0.035825 Intermediate -0.1687 0.1394 1.46 0.226226 Short-term 0.0070 0.0796 0.01 0.930026 Long-term 0.1281 0.0807 2.52 0.112426 Intermediate 0.0267 0.2863 0.01 0.925627 Short-term -0.4216 0.0464 82.57 <.000127 Long-term -0.3281 0.0521 39.60 <.000127 Intermediate -0.1756 0.1593 1.22 0.270328 Short-term 0.1756 0.0882 3.97 0.046428 Long-term 0.0741 0.0975 0.58 0.447228 Intermediate 0.1567 0.3207 0.24 0.625129 Short-term 0.3088 0.0562 28.61 <.000129 Long-term -0.2286 0.0721 10.05 0.001529 Intermediate 0.0293 0.2246 0.02 0.896230 Short-term 0.0819 0.0567 2.09 0.148530 Long-term 0.5842 0.0502 135.60 <.000130 Intermediate 0.2653 0.1890 1.97 0.160432 Short-term 0.3099 0.0716 18.74 <.000132 Long-term 0.5994 0.0677 78.36 <.000132 Intermediate 0.5343 0.2343 5.20 0.022633 Short-term -0.2165 0.0472 21.05 <.000133 Long-term -0.3135 0.0522 36.03 <.000133 Intermediate -0.1963 0.1687 1.35 0.244435 Short-term 0.1410 0.0551 6.55 0.010535 Long-term -0.0989 0.0623 2.52 0.112535 Intermediate 0.1616 0.1957 0.68 0.408836 Short-term 0.0731 0.0449 2.65 0.103836 Long-term -0.0336 0.0526 0.41 0.523136 Intermediate 0.0204 0.1689 0.01 0.903737 Short-term 0.1262 0.0549 5.28 0.021537 Long-term -0.0857 0.0671 1.63 0.201537 Intermediate 0.1154 0.2025 0.32 0.568938 Short-term -0.1604 0.0429 13.99 0.000238 Long-term -0.1548 0.0462 11.24 0.000838 Intermediate -0.2148 0.1588 1.83 0.176239 Short-term -0.4124 0.0500 68.07 <.000139 Long-term -0.3425 0.0559 37.50 <.000139 Intermediate -0.2780 0.1750 2.52 0.112340 Short-term -0.0392 0.0724 0.29 0.558540 Long-term -0.0691 0.0788 0.77 0.380940 Intermediate -0.0109 0.2601 0.00 0.966541 Short-term 0.00297 0.0840 0.00 0.971841 Long-term 0.0542 0.0897 0.037 0.545341 Intermediate -0.2587 0.3504 0.54 0.460442 Short-term 0.0437 0.1106 0.16 0.693142 Long-term 0.1105 0.1126 0.96 0.3264

CCAC 42 Intermediate -0.0403 0.4279 0.01 0.919944 Short-term 0.1964 0.0737 7.09 0.007744 Long-term -0.1225 0.0889 1.90 0.168344 Intermediate -0.1348 0.3065 0.19 0.660248 Short-term -0.3437 0.0658 27.31 <.0001

45

48 Long-term -0.0073 0.0613 0.01 0.905048 Intermediate -0.1969 0.220 0.79 0.375249 Short-term 0.3172 0.0559 32.18 <.000149 Long-term 0.0329 0.0689 0.23 0.633149 Intermediate 0.0526 0.2265 0.05 0.816250 Short-term 0.1601 0.1242 1.66 0.197650 Long-term -0.1019 0.1408 0.52 0.469250 Intermediate 0.3620 0.4130 0.77 0.380770 Short-term 0.2164 0.0959 5.09 0.024070 Long-term 0.2037 0.0995 4.19 0.040670 Intermediate 0.5751 0.2908 3.91 0.0480

male Short-term -0.1715 0.0723 5.63 0.0177Long-term -0.4622 0.0526 77.20 <.0001Intermediate -0.1641 0.2280 0.52 0.4719

0-19 Short-term -2.1416 0.0547 1532.16 <.0001Long-term -3.4870 0.0561 3866.16 <.0001Intermediate -1.8492 0.1797 105.95 <.0001




75-84 Short-term -0.5099 0.0461 122.19 <.0001Long-term -0.8211 0.0325 639.84 <.0001Intermediate -0.3344 0.1434 5.44 0.0197

m_0-19 Short-term 0.7722 0.0842 84.01 <.0001Long-term 1.1295 0.0774 213.11 <.0001Intermediate 0.9688 0.2666 13.21 0.0003

m_20-44 Short-term 0.2236 0.0827 7.32 0.0068Long-term 0.2396 0.0863 7.70 0.0055Intermediate 0.0728 0.2923 0.06 0.8033

m_45-64 Short-term 0.2406 0.0806 8.92 0.0028Long-term 0.0824 0.0702 1.38 0.2406Intermediate -0.00975 0.2673 0.00 0.9709

m_65-74 Short-term 0.1169 0.0819 2.04 0.1535Long-term -0.1197 0.0666 3.23 0.0722Intermediate -0.1355 0.2648 0.26 0.6087

m_75-84 Short-term -0.0570 0.0829 0.47 0.4919Long-term -0.3021 0.0631 22.94 <.0001Intermediate -0.3690 0.2641 1.95 0.1624

cadg1 Short-term 0.1417 0.0190 55.41 <.0001Long-term 0.1482 0.0204 52.75 <.0001Intermediate 0.2080 0.0693 9.01 0.0027

cadg2 Short-term 0.5189 0.0193 725.26 <.0001Long-term 0.5556 0.0211 695.35 <.0001Intermediate 0.5131 0.0694 54.59 <.0001


46





cadg8 Short-term 0.0435 0.0244 3.19 0.0742Long-term -0.0351 0.0231 2.32 0.1274Intermediate -0.0096 0.0823 0.01 0.9070



cadg11 Short-term 0.0890 0.0190 21.86 <.0001Long-term 0.0723 0.0199 13.24 0.0003Intermediate 0.1621 0.0652 6.18 0.0130

cadg12 Short-term 0.8358 0.0517 261.03 <.0001Long-term -0.1827 0.1120 2.66 0.1027Intermediate -0.0842 0.2972 0.08 0.7770

rural_index Short-term -0.00196 0.000648 9.16 0.0025Long-term -0.00075 0.000704 1.14 0.2863Intermediate -0.00299 0.00240 1.54 0.2139

rec_immigrant Short-term -0.0196 0.00309 40.40 <.0001Long-term -0.0220 0.00322 46.91 <.0001Intermediate -0.0268 0.0105 6.57 0.0104

deprivation Short-term 0.1101 0.0155 50.44 <.0001Long-term 0.2016 0.0159 161.24 <.0001Intermediate 0.1484 0.0530 7.85 0.0051

47

Table B: Propensity to Use Service (SES=low income)Parameter Number Estimate Error Square Pr > ChiSqIntercept Short-term -3.7420 0.0568 4334.04 <.0001


CCAC 1Short-term 0.1392 0.0656 4.50 0.03391Long-term 0.0193 0.0744 0.07 0.79511Intermediate -0.1162 0.2627 0.20 0.65824Short-term -0.0236 0.0907 0.07 0.79454Long-term 0.0661 0.0935 0.50 0.47944Intermediate 0.1440 0.3037 0.22 0.63535Short-term -0.1475 0.0445 11.00 0.00095Long-term -0.0311 0.0461 0.46 0.49915Intermediate -0.1889 0.1643 1.32 0.25046Short-term 0.2190 0.0563 15.13 0.00016Long-term 0.1429 0.0629 5.17 0.02306Intermediate 0.2890 0.1976 2.14 0.14367Short-term 0.1649 0.0598 7.61 0.00587Long-term 0.1502 0.0620 5.86 0.01557Intermediate 0.3099 0.2029 2.20 0.13818Short-term 0.0163 0.0764 0.05 0.83118Long-term 0.1201 0.0782 2.36 0.12488Intermediate 0.0799 0.2693 0.09 0.7668

10Short-term -0.1042 0.0508 4.21 0.040110Long-term -0.3045 0.0616 24.46 <.000110Intermediate -0.2463 0.1956 1.59 0.207911Short-term 0.0656 0.0625 1.10 0.294311Long-term -0.0312 0.0684 0.21 0.647811Intermediate -0.3871 0.2800 1.91 0.166812Short-term 0.0199 0.0980 0.04 0.839212Long-term 0.2433 0.0915 7.07 0.007812Intermediate 0.2025 0.3232 0.39 0.531113Short-term 0.2825 0.0678 17.36 <.000113Long-term 0.2651 0.0735 13.02 0.000313Intermediate 0.3241 0.2373 1.87 0.172014Short-term 0.1670 0.0622 7.21 0.007214Long-term -0.0589 0.0736 0.64 0.423114Intermediate -0.1970 0.2638 0.56 0.455216Short-term 0.111 0.0621 3.20 0.073516Long-term 0.0656 0.0665 0.97 0.324216Intermediate -0.1241 0.3532 0.24 0.624019Short-term -0.0489 0.0439 1.24 0.264819Long-term -0.0234 0.0474 0.24 0.621919Intermediate -0.1019 0.1619 0.40 0.529020Short-term -0.1067 0.1471 0.53 0.468320Long-term 0.3132 0.1256 6.22 0.012620Intermediate -0.0619 0.5239 0.01 0.906021Short-term -0.0762 0.0398 3.67 0.055621Long-term -0.3298 0.0462 50.92 <.000121Intermediate -0.1720 0.1477 1.36 0.244024Short-term -0.2985 0.0463 41.61 <.0001

CCAC 24Long-term -0.02220 0.0511 18.87 <.000124Intermediate -0.1314 0.1575 0.70 0.4043

48

25Short-term -0.4914 0.0406 146.16 <.000125Long-term -0.0455 0.0380 1.44 0.230425Intermediate -0.2573 0.1355 3.61 0.057526Short-term 0.0297 0.0797 0.14 0.709826Long-term 0.1671 0.0808 4.28 0.038626Intermediate 0.0558 0.2866 0.04 0.845727Short-term -0.3891 0.0470 68.54 <.000127Long-term -0.2872 0.0527 29.74 <.000127Intermediate -0.1414 0.1612 0.77 0.380328Short-term 0.1770 0.0881 4.03 0.044628Long-term 0.0725 0.0974 0.55 0.456928Intermediate 0.1571 0.3206 0.24 0.624129Short-term 0.2945 0.0562 27.43 <.000129Long-term -0.2329 0.0721 10.44 0.001229Intermediate 0.0236 0.2246 0.01 0.916330Short-term 0.0715 0.0568 1.59 0.207730Long-term 0.5712 0.0520 129.85 <.000130Intermediate 0.2542 0.1892 1.81 0.179032Short-term 0.2995 0.0716 17.49 <.000132Long-term 0.5854 0.0677 74.77 <.000132Intermediate 0.5225 0.2344 4.97 0.025833Short-term -0.2130 0.0472 20.38 <.000133Long-term -0.3063 0.0522 34.41 <.000133Intermediate -0.1915 0.1687 1.29 0.256335Short-term 0.1419 0.0550 6.65 0.009935Long-term -0.0921 0.0622 2.19 0.138935Intermediate 0.1652 0.1955 0.71 0.398036Short-term 0.0907 0.0451 4.04 0.044336Long-term -0.0094 0.0527 0.03 0.858436Intermediate 0.0410 0.1694 0.06 0.808737Short-term 0.1267 0.0547 5.36 0.020637Long-term -0.0911 0.0669 1.86 0.173037Intermediate 0.1132 0.2020 0.31 0.575038Short-term -0.1890 0.0432 19.13 <.000138Long-term -0.2058 0.0466 19.52 <.000138Intermediate -0.2523 0.1600 2.49 0.114939Short-term -0.4052 0.0494 67.18 <.000139Long-term -0.3465 0.0554 39.07 <.000139Intermediate -0.2766 0.1734 2.54 0.110740Short-term -0.0592 0.0725 0.67 0.414440Long-term -0.0923 0.0789 1.37 0.241740Intermediate -0.0324 0.2605 0.02 0.900941Short-term 0.00365 0.0839 0.00 0.965341Long-term 0.0620 0.0895 0.48 0.488741Intermediate -0.2548 0.3501 0.53 0.466842Short-term 0.0601 0.1106 0.30 0.586742Long-term 0.1387 0.1127 1.52 0.2181

CCAC 42Intermediate -0.0202 0.4278 0.00 0.962444Short-term 0.1771 0.0739 5.75 0.016544Long-term -0.1458 0.0890 2.69 0.101244Intermediate -0.1562 0.3069 0.26 0.610748Short-term -0.3458 0.0657 27.70 <.000148Long-term -0.0061 0.0613 0.01 0.9205

49

48Intermediate -0.1971 0.2218 0.79 0.374049Short-term 0.3655 0.0557 43.10 <.000149Long-term 0.1222 0.0687 3.16 0.075449Intermediate 0.1180 0.2258 0.27 0.601250Short-term 0.0928 0.1249 0.55 0.457850Long-term -0.2098 0.1415 2.20 0.138150Intermediate 0.2783 0.4155 0.45 0.502970Short-term 0.2299 0.0957 5.77 0.016370Long-term 0.2362 0.0992 5.67 0.017370Intermediate 0.5965 0.2901 4.23 0.0398



20-44 Short-term -2.3005 0.0486 2236.92 <.0001Long-term 04.0928 0.0512 6383.87 <.0001Intermediate -2.6740 0.1744 235.17 <.0001












cadg4 Short-term 0.1609 0.0331 23.68 <.0001

50

Long-term 0.2538 0.0337 56.72 <.0001Intermediate 0.1583 0.1099 2.07 0.1499

cadg5 Short-term 0.9033 0.0199 2052.33 <.0001Long-term 1.2857 0.0207 3840.05 <.0001Intermediate 0.1423` 0.0715 255.16 <.0001







cadg12 Short-term 0.8365 0.0517 261.51 <.0001Long-term -0.1807 0.1120 2.61 0.1065Intermediate 0.0829 0.2972 0.08 0.7802

rural_index Short-term -0.00043 0.00067 0.40 0.5257Long-term 0.00192 0.000727 6.99 0.0082Intermediate -0.00098 0.00247 0.16 0.6920


low income Short-term 0.0131 0.00158 69.19 <.0001Long-term 0.0223 0.00160 193.88 <.0001Intermediate 0.0170 0.00541 9.83 0.0017

51

Table C: Propensity to use Service (SES=Median income, in $10,000s)Parameter Function Number Estimate Standard Error Chi-Square Pr > ChiSqIntercept Short-term -3.1514 0.0653 2326.90 <.0001


CCAC 1Short-term 0.1130 0.0656 2.97 0.08501Long-term -0.0246 0.0744 0.11 0.74071Intermediate -0.1504 0.2628 0.33 0.56724Short-term -0.0449 0.0907 0.24 0.62084Long-term 0.0335 0.0934 0.13 0.71964Intermediate 0.1169 0.3035 0.15 0.70015Short-term -0.1378 0.0446 9.55 0.00205Long-term -0.0173 0.0462 0.14 0.70805Intermediate -0.1760 0.1646 1.14 0.28506Short-term 0.2199 0.0563 15.26 <.00016Long-term 0.1460 0.0628 5.41 0.02016Intermediate 0.2901 0.1975 2.16 0.14187Short-term 0.1376 0.0600 5.26 0.02197Long-term 0.1032 0.0623 2.74 0.09787Intermediate 0.2642 0.2039 1.68 0.19498Short-term -0.0238 0.0764 0.10 0.75528Long-term 0.0537 0.0781 0.47 0.49188Intermediate 0.0281 0.2691 0.01 0.9169

10Short-term -0.0284 0.0531 0.29 0.592510Long-term -0.1794 0.0637 7.93 0.004910Intermediate -0.1461 0.2029 0.52 0.471611Short-term 0.0466 0.0627 0.55 0.457611Long-term -0.0635 0.0685 0.86 0.354211Intermediate -0.4127 0.2804 2.17 0.141112Short-term -0.0149 0.0981 0.02 0.879012Long-term 0.1858 0.0916 4.12 0.042412Intermediate 0.1571 0.3235 0.24 0.627213Short-term 0.2567 0.0678 14.31 0.000213Long-term 0.2232 0.0735 9.24 0.002413Intermediate 0.2904 0.2374 1.50 0.221314Short-term 0.1566 0.0621 6.35 0.011714Long-term -0.0776 0.0735 1.11 0.291314Intermediate -0.2106 0.2636 0.64 0.424316Short-term 0.0861 0.0622 1.92 0.165916Long-term 0.0235 0.0666 0.12 0.723716Intermediate -0.1570 0.2534 0.38 0.535619Short-term -0.0549 0.0438 1.57 0.210219Long-term -0.0366 0.0474 0.60 0.439519Intermediate -0.1099 0.1616 0.46 0.496320Short-term -0.1411 0.1474 0.92 0.338420Long-term 0.2516 0.1259 3.99 0.045720Intermediate -0.1089 0.5249 0.04 0.835621Short-term -0.0949 0.0398 5.68 0.017221Long-term -0.3596 0.0462 60.55 <.000121Intermediate -0.1964 0.1477 1.77 0.183724Short-term -0.2472 0.0474 27.17 <.000124Long-term -0.1385 0.0522 7.03 0.008024Intermediate -0.0637 0.1615 0.16 0.6935

52

25Short-term -0.4007 0.0420 90.84 <.000125Long-term 0.1142 0.0394 8.41 0.003725Intermediate -0.1369 0.1404 0.95 0.329626Short-term -0.0049 0.0796 0.00 0.950626Long-term 0.1094 0.0806 1.84 0.174726Intermediate 0.0111 0.2863 0.00 0.969027Short-term -0.3363 0.0490 47.09 <.000127Long-term -0.1947 0.0550 12.51 0.000427Intermediate -0.0678 0.1690 0.16 0.688428Short-term 0.1166 0.0880 1.75 0.185328Long-term -0.0303 0.0973 0.10 0.755328Intermediate 0.7080 0.3202 0.06 0.807629Short-term 0.2799 0.0564 24.65 <.000129Long-term -0.2601 0.0722 12.97 0.000329Intermediate 0.0032 0.2250 0.00 0.988830Short-term 0.0672 0.0568 1.40 0.236630Long-term 0.5637 0.0502 125.89 <.000130Intermediate 0.2474 0.1893 1.71 0.191132Short-term 0.2598 0.0720 13.03 0.000332Long-term 0.5169 0.0681 57.57 <.000132Intermediate 0.4697 0.2357 3.97 0.046333Short-term -0.2235 0.0472 22.43 <.000133Long-term -0.3248 0.0522 38.68 <.000133Intermediate -0.2053 0.1687 1.48 0.223735Short-term 0.1119 0.0553 4.09 0.043135Long-term -0.1445 0.0625 5.34 0.020935Intermediate 0.1250 0.1965 0.40 0.524836Short-term 0.0793 0.0449 3.12 0.077236Long-term -0.0333 0.0526 0.40 0.526736Intermediate 0.0263 0.1687 0.02 0.876137Short-term 0.1215 0.0546 4.96 0.026037Long-term -0.0999 0.0668 2.24 0.134937Intermediate 0.1071 0.2016 0.28 0.595338Short-term -0.1332 0.0429 9.64 0.001938Long-term -0.1103 0.0462 5.70 0.016938Intermediate -0.1794 0.1588 1.28 0.258739Short-term -0.3189 0.0529 36.38 <.000139Long-term -0.1932 0.0592 10.64 0.001139Intermediate -0.1596 0.1857 0.74 0.390140Short-term -0.0350 0.0721 0.24 0.627240Long-term -0.0516 0.0785 0.43 0.510540Intermediate -0.0027 0.2592 0.00 0.991841Short-term 0.0348 0.0837 0.17 0.677841Long-term 0.1136 0.0893 1.62 0.203441Intermediate -0.2149 0.3495 0.38 0.538742Short-term 0.0552 0.1105 0.25 0.617542Long-term 0.1329 0.1126 1.39 0.2378

CCAC 42Intermediate -0.0263 0.427 0.00 0.951044Short-term 0.1782 0.0738 5.83 0.015744Long-term -0.1460 0.0889 2.70 0.100644Intermediate -0.1564 0.3067 0.26 0.610148Short-term -0.3251 0.0654 24.69 <.000148Long-term 0.0305 0.0609 0.25 0.6168

53

48Intermediate -0.1710 0.2207 0.60 0.438649Short-term 0.3602 0.0557 41.80 <.000149Long-term 0.1140 0.0688 2.75 0.097249Intermediate 0.1102 0.2259 0.24 0.625850Short-term 0.1140 0.1245 0.84 0.359750Long-term -0.1754 0.1411 1.55 0.213850Intermediate 0.3038 0.4141 0.54 0.463270Short-term 0.1731 0.0962 3.24 0.071970Long-term 0.1370 0.0997 1.89 0.169570Intermediate 0.5210 0.2920 3.18 0.0744















cadg4 Short-term 0.1616 0.0331 23.86 <.0001

54

Long-term 0.2547 0.0337 57.12 <.0001Intermediate 0.1592 0.1099 2.10 0.1476








cadg12 Short-term 0.8369 0.0517 261.77 <.0001Long-term -0.1809 0.1120 2.61 0.1063Intermediate -0.0827 0.2972 0.08 0.7807

rural_index Short-term -0.00151 0.000649 5.38 0.0203Long-term 0.000123 0.000705 0.03 0.8616Intermediate -0.00238 0.00241 0.98 0.3222


median_income Short-term -0.0869 0.00984 78.12 <.0001Long-term -0.1514 0.0106 202.42 <.0001Intermediate -0.1148 0.0347 10.92 0.0010

55

Appendix 5

Table A: Short-term Service Intensity (SES=deprivation)

Parameter EstimateStandard

Error t Value Pr > |t|Intercept 0.856101 0.011055 77.44 <.0001SEX F -0.029503 0.010369 -2.85 0.0044SEX M 0 . . .0-19 -0.219469 0.009904 -22.16 <.000120-44 0.132667 0.010049 13.20 <.000145-64 0.127497 0.009665 13.19 <.000165-74 0.112259 0.009771 11.49 <.000175-84 0.076261 0.009957 7.66 <.000185+ 0 . . .SEX*0-19 F 1 0.050710 0.012326 4.11 <.0001SEX*20-44 F 2 -0.050988 0.012075 -4.22 <.0001SEX*45-64 F 3 0.008243 0.011705 0.70 0.4813SEX*65-74 F 4 0.040259 0.011879 3.39 0.0007SEX*75-84 F 5 0.019707 0.011962 1.65 0.0995SEX*85+ F 6 0 . . .SEX*0-19 M 1 0 . . .SEX*20-44 M 2 0 . . .SEX*45-64 M 3 0 . . .SEX*65-74p M 4 0 . . .SEX*75-84 M 5 0 . . .SEX*85+ M 6 0 . . .CCAC 1 -0.076297 0.010880 -7.01 <.0001CCAC 4 -0.057278 0.014645 -3.91 0.0006CCAC 5 0.037733 0.008007 4.71 <.0001CCAC 6 -0.096409 0.009815 -9.82 <.0001CCAC 7 -0.154325 0.010655 -14.48 <.0001CCAC 8 -0.056514 0.012904 -4.38 <.0001CCAC 10 -0.102705 0.008299 -12.37 <.0001CCAC 11 -0.163742 0.010806 -15.15 <.0001CCAC 12 -0.155232 0.015939 -9.74 <.0001CCAC 13 -0.171242 0.011383 -15.04 <.0001CCAC 14 -0.094842 0.010842 -8.75 <.0001CCAC 16 -0.168776 0.010919 -15.46 <.0001CCAC 19 -0.104375 0.007682 -13.59 <.0001CCAC 20 -0.086570 0.035874 -2.41 0.0158CCAC 21 -0.146961 0.007652 -19.21 <.0001CCAC 24 -0.050722 0.008120 -6.25 <.0001CCAC 25 -0.158166 0.006943 -22.78 <.0001CCAC 26 -0.170363 0.013349 -12.76 <.0001CCAC 27 -0.031447 0.006429 -4.89 <.0001CCAC 28 -0.025891 0.014344 -1.80 0.0711CCAC 29 -0.154828 0.009977 -15.52 <.0001CCAC 30 -0.138530 0.010235 -13.54 <.0001CCAC 32 -0.193191 0.012113 -15.95 <.0001CCAC 33 -0.093697 0.008786 -10.95 <.0001CCAC 35 -0.175694 0.010069 -17.45 <.0001

56

CCAC 36 -0.151906 0.007788 -19.50 <.0001CCAC 37 -0.152016 0.009362 -16.24 <.0001CCAC 38 -0.128936 0.007649 -16.86 <.0001CCAC 39 -0.055706 0.007355 -7.57 <.0001CCAC 40 -0.161413 0.012863 -12.55 <.0001

CCAC 41 -0.301652 0.014337 -21.04 <.0001CCAC 42 -0.151431 0.018410 -8.23 <.0001CCAC 44 -0.141107 0.012444 -11.34 <.0001CCAC 48 -0.007718 0.011614 -0.66 0.5063CCAC 49 -0.139297 0.010001 -13.92 <.0001CCAC 50 -0.129430 0.020467 -6.32 <.0001

CCAC 70 -0.132684 0.014268 -9.30 <.0001CCAC 78 0 . . .cadg1 0.014329 0.0028984 4.97 <.0001cadg2 0.027659 0.002948 9.38 <.0001cadg3 0.012655 0.002691 4.70 <.0001cadg4 0.018204 0.004995 3.64 0.0003

cadg5 0.009789 0.002919 3.35 0.0008cadg6 0.015290 0.002915 5.25 <.0001cadg7 -0.000353 0.005422 -0.07 0.9481cadg8 -0.009379 0.003616 -2.59 0.0095cadg9 0.005649 0.004102 1.38 0.1685cadg10 -0.471915 0.002755 -17.20 <.0001

cadg11 0.011893 0.002895 4.11 <.0001cadg12 -0.099144 0.008122 -12.21 <.0001rec_imm -0.000111 0.000418 -0.27 0.7902rural_index -0.000290 0.000090 -3.21 0.0013deprivation 0.020098 0.004367 4.60 <.0001cadg1*deprivation 0.000809 0.004111 0.20 0.8440

cadg2*deprivation 0.000661 0.004191 0.16 0.8746cadg3*deprivation -0.005543 0.003833 -1.45 0.1481cadg4*deprivation -0.006689 0.006818 -0.98 0.3266cadg5*deprivation 0.006592 0.003926 1.68 0.0932cadg6*deprivation -0.004572 0.003887 -1.18 0.2395cadg7*deprivation -0.001587 0.007649 -0.21 0.8356

cadg8*deprivation -0.007194 0.004857 -1.48 0.1386cadg9*deprivation -0.001738 0.005760 -0.30 0.7628cadg10*deprivation -0.014566 0.003817 -3.82 0.0001cadg11*deprivation -0.008853 0.004074 -2.17 0.0298cadg12*deprivation -0.002532 0.009908 -0.26 0.7983

57

Table B: Short-term Service Intensity (SES=low income)Parameter Estimate Error t Value Pr > |t|Intercept 0.820367 0.012479 65.74 <.0001SEX F -0.029201 0.010369 -2.82 0.0049SEX M 0 . . .

0-19 -0.218332 0.009905 -22.04 <.000120-44 0.133871 0.010046 13.33 <.000145-64 0.128415 0.009664 13.39 <.000165-74 0.112933 0.009769 11.56 <.000175-84 0.076685 0.009957 7.70 <.000185+ 0 . . .

SEX*0-19 F 1 0.050292 0.012325 4.08 <.0001SEX*20-44 F 2 -0.051648 0.012075 -4.28 <.0001SEX*45-64 F 3 0.007621 0.011705 0.65 0.5150SEX*65-74 F 4 0.040022 0.011879 3.37 0.0008SEX*75-84 F 5 0.019437 0.011962 1.62 0.1042SEX*85+ F 6 0 . . .

SEX*0-19 M 1 0 . . .SEX*20-44 M 2 0 . . .SEX*45-64 M 3 0 . . .SEX*65-74p M 4 0 . . .SEX*75-84 M 5 0 . . .SEX*85+ M 6 0 . . .

CCAC 1 -0.071640 0.010799 -6.63 <.0001CCAC 4 -0.052910 0.014587 -3.63 0.0003CCAC 5 0.041285 0.007921 5.21 <.0001CCAC 6 -0.092462 0.009789 -9.45 <.0001CCAC 7 -0.150992 0.010652 -14.17 <.0001CCAC 8 -0.052779 0.012846 -4.11 <.0001

CCAC 10 -0.104657 0.008328 -12.57 <.0001CCAC 11 -0.158061 0.010731 -14.73 <.0001CCAC 12 -0.153160 0.015927 -9.62 <.0001CCAC 13 -0.166210 0.011283 -14.73 <.0001CCAC 14 -0.089475 0.010730 -8.34 <.0001CCAC 16 -0.164640 0.010877 -15.14 <.0001

CCAC 19 -0.101042 0.007619 -13.26 <.0001CCAC 20 -0.080616 0.035834 -2.25 0.0245CCAC 21 -0.142144 0.007534 -18.87 <.0001CCAC 24 -0.048961 0.008103 -6.04 <.0001CCAC 25 -0.157204 0.006985 -22.51 <.0001CCAC 26 -0.167080 0.013309 -12.55 <.0001


CCAC 35 -0.170597 0.009984 -17.09 <.0001CCAC 36 -0.149605 0.007758 -19.28 <.0001CCAC 37 -0.151669 0.009361 -16.20 <.0001CCAC 38 -0.124215 0.007610 -16.32 <.0001

58


CCAC 48 0.000583 0.011488 0.05 0.9595CCAC 49 -0.131649 0.009801 -13.43 <.0001CCAC 50 0.123581 0.020521 -6.02 <.0001CCAC 70 -0.125717 0.014172 -8.87 <.0001CCAC 78 0 . . .cadg1 0.014913 0.006452 2.31 0.0208

cadg2 0.032257 0.006602 4.89 <.0001cadg3 0.014748 0.006038 2.44 0.0146cadg4 0.021287 0.011048 1.93 0.0540cadg5 0.010232 0.006300 1.62 0.1043cadg6 0.032626 0.006234 5.23 <.0001cadg7 0.002362 0.012355 0.19 0.8484

cadg8 -0.006943 0.008142 -0.85 0.3938cadg9 0.019034 0.009352 2.04 0.0418cadg10 -0.028474 0.006120 -4.65 <.0001cadg11 0.026186 0.006465 4.05 <.0001cadg12 -0.131828 0.016778 -7.86 <.0001rec_imm 0.000461 0.000456 1.01 0.3120

rural_index -0.000226 0.000092 -2.45 0.0143low income 0.001689 0.000375 4.50 <.0001cadg1*low income -0.000030 0.000332 -0.09 0.9269cadg2*low income -0.000270 0.0003390 -0.80 0.4253cadg3*low income -0.000134 0.000309 -0.43 0.6652cadg4*low income -0.000179 0.000547 -0.33 0.7431

cadg5*low income -0.000001 0.000316 -0.00 0.9981cadg6*low income 0.000988 0.000313 -3.16 0.0016cadg7*low income -0.000160 0.000632 -0.25 0.8805cadg8*low income -0.000142 0.000396 -0.36 0.7201cadg9*low income -0.000741 0.000469 -1.58 0.1141cadg10*low income -0.0010935 0.000307 -3.56 0.0004

cadg11*low income -0.000846 0.000327 -2.59 0.0097cadg12*low income 0.001751 0.000792 2.21 0.0270

59

Table C: Short-term Service Intensity (SES=median income, in $000’s)Parameter Estimate Error t Value Pr > |t|Intercept 0.887051 0.016605 53.42 <.0001SEX F -0.029696 0.010369 -2.86 0.0042SEX M 0 . . .

0-19 -0.218731 0.009906 -22.08 <.000120-44 0.133298 0.010046 13.27 <.000145-64 0.127952 0.009665 13.24 <.000165-74 0.112447 0.009769 11.51 <.000175-84 0.076193 0.009957 7.65 <.000185+ 0 . . .

SEX*0-19 F 1 0.051158 0.012325 4.15 <.0001SEX*20-44 F 2 -0.051308 0.012075 -4.25 <.0001SEX*45-64 F 3 0.008157 0.011705 0.70 0.4859SEX*65-74 F 4 0.040327 0.011879 3.39 0.0007SEX*75-84 F 5 0.020024 0.011961 1.67 0.0941SEX*85+ F 6 0 . . .

SEX*0-19 M 1 0 . . .SEX*20-44 M 2 0 . . .SEX*45-64 M 3 0 . . .SEX*65-74 M 4 0 . . .SEX*75-84 M 5 0 . . .SEX*85+ M 6 0 . . .

CCAC 1 0.075087 0.010886 -6.90 <.0001CCAC 4 -0.055616 0.014630 -3.80 0.0001CCAC 5 0.040783 0.007937 5.14 <.0001CCAC 6 -0.095030 0.009849 -9.65 <.0001CCAC 7 -0.155042 0.010767 -14.40 <.0001CCAC 8 -0.055560 0.012908 -4.30 <.0001

CCAC 10 -0.101514 0.008378 -12.12 <.0001CCAC 11 -0.162579 0.010825 -15.02 <.0001CCAC 12 -0.156475 0.015986 -9.79 <.0001CCAC 13 -0.169289 0.011363 -14.90 <.0001CCAC 14 -0.091884 0.010773 -8.53 <.0001CCAC 16 -0.168431 0.010948 -15.38 <.0001



CCAC 35 -0.174436 0.010093 -17.28 <.0001CCAC 36 -0.152001 0.007788 -19.52 <.0001CCAC 37 -0.152867 0.009366 -16.32 <.0001CCAC 38 -0.125641 0.007542 -16.66 <.0001

60


CCAC 48 -0.003262 0.011496 -0.28 0.7766CCAC 49 -0.134492 0.009854 -13.65 <.0001CCAC 50 -0.127877 0.020492 -6.24 <.0001CCAC 70 -0.130939 0.014299 -9.16 <.0001CCAC 78 0 . . .cadg1 0.008503 0.011582 0.73 0.4628

cadg2 0.043016 0.011763 3.66 0.0003cadg3 -0.0081307 0.010786 -0.75 0.4510cadg4 0.022661 0.0194782 1.16 0.2448cadg5 0.023139 0.011132 2.08 0.0377cadg6 0.026894 0.010998 2.45 0.0145cadg7 0.022721 0.022029 1.03 0.3023

cadg8 -0.015412 0.014161 -1.09 0.2765cadg9 0.010569 0.016405 0.64 0.5194cadg10 -0.082932 0.010873 -7.63 <.0001cadg11 -0.022180 0.011544 -1.92 0.0547cadg12 -0.195953 0.029107 -6.73 <.0001rec_imm 0.000304 0.000388 0.78 0.4346

rural_index -0.000269 0.000090 -2.99 0.0027median_income -0.000752 0.000267 -2.81 0.0049cadg1*median_income 0.000135 0.000251 0.54 0.5920cadg2*median_income -0.000342 0.000254 -1.34 0.1794cadg3*median_income 0.000457 0.000233 1.96 0.0501cadg4*median_income -0.000111 0.000417 -0.26 0.7910

cadg5*median_income -0.000293 0.000241 -1.22 0.2236cadg6*median_income -0.000264 0.000237 -1.11 0.2650cadg7*median_income -0.000517 0.000475 -1.09 0.2766cadg8*median_income 0.000122 0.000307 0.40 0.6905cadg9*median_income -0.000114 0.000357 -0.32 0.7486cadg10*median_income 0.000778 0.000235 3.31 0.0009

cadg11*median_income 0.000749 0.000250 3.00 0.0027cadg12*median_income 0.002180 0.000629 3.46 0.0005

61

Appendix 6

Table A: Long-term Service Intensity (SES=deprivation)Parameter Estimate Standard Error t Value Pr > |t|Intercept 1.948635 0.006340 307.37 <.0001SEX F -0.015302 0.004833 -3.17 0.0015SEX M 0 . . .0-19 -0.327648 0.005821 -56.29 <.000120-44 0.028048 0.007310 3.84 0.000145-64 0.046120 0.005859 7.87 <.000165-74 0.024920 0.005524 4.51 <.000175-84 0.000576 0.005199 0.11 0.911785+ 0 . . .SEX*0-19 F 1 0.067702 0.007935 8.53 <.0001SEX*20-44 F 2 -0.018494 0.008899 -2.03 0.0377SEX*45-64 F 3 -0.031725 0.006984 -4.54 <.0001SEX*65-74 F 4 -0.028312 0.006491 -4.36 <.0001SEX*75-84 F 5 -0.023550 0.005972 -3.94 <.0001SEX*85+ F 6 0 . . .SEX*0-19 M 1 0 . . .SEX*20-44 M 2 0 . . .SEX*45-64 M 3 0 . . .SEX*65-74p M 4 0 . . .SEX*75-84 M 5 0 . . .SEX*85+ M 6 0 . . .CCAC 1 0.052324 0.008191 6.39 <.0001CCAC 4 0.064952 0.010169 6.39 <.0001CCAC 5 0.174055 0.005552 31.35 <.0001CCAC 6 0.018783 0.007320 2.54 0.0111CCAC 7 0.048677 0.007466 6.52 <.0001CCAC 8 0.125098 0.009107 13.74 <.0001CCAC 10 0.062766 0.006739 9.31 <.0001CCAC 11 0.081832 0.007833 10.45 <.0001CCAC 12 -0.052051 0.10070 -5.17 <.0001CCAC 13 0.047789 0.008167 5.85 <.0001CCAC 14 0.066456 0.008626 7.70 <.0001CCAC 16 0.036208 0.007886 4.59 <.0001CCAC 19 0.090063 0.005554 16.22 <.0001CCAC 20 0.001815 0.016111 0.11 0.9103CCAC 21 -0.036500 0.005837 -6.25 <.0001CCAC 24 0.120315 0.006018 19.99 <.0001CCAC 25 -0.046993 0.004242 -11.08 <.0001CCAC 26 -0.259891 0.009023 -28.80 <.0001CCAC 27 0.092619 0.004877 18.99 <.0001CCAC 28 0.011403 0.010614 1.07 0.2827CCAC 29 -0.087726 0.008482 -10.34 <.0001CCAC 30 -0.005171 0.006370 =0.81 0.4170CCAC 32 -0.000974 0.007879 -0.12 0.9016CCAC 33 0.073457 0.006523 11.26 <.0001CCAC 35 -0.264437 0.007648 -34.58 <.0001CCAC 36 0.113231 0.006009 18.84 <.0001CCAC 37 0.066189 0.007595 8.71 <.0001CCAC 38 -0.218039 0.005444 -40.05 <.0001

62

CCAC 39 0.096065 0.005702 16.85 <.0001CCAC 40 -0.049416 0.009415 -5.25 <.0001CCAC 41 0.004309 0.010255 0.42 0.6743CCAC 42 -0.071630 0.012540 -5.71 <.0001CCAC 44 0.017691 0.010209 1.73 0.0831CCAC 48 0.006649 0.007458 0.89 0.3726CCAC 49 0.105740 0.008134 13.00 <.0001CCAC 50 0.006517 0.015469 0.42 0.6735CCAC 70 -0.000511 0.010193 -0.05 0.9600CCAC 78 0 . . .cadg1 0.003787 0.002150 1.76 0.0781cadg2 0.027595 0.002267 12.17 <.0001cadg3 0.008276 0.001970 4.20 <.0001cadg4 0.003007 0.003475 0.87 0.3868cadg5 0.0425842 0.002104 20.24 <.0001cadg6 0.012126 0.002079 5.83 <.0001cadg7 -0.0067430 0.003631 -1.86 0.0633cadg8 -0.024587 0.002291 -10.73 <.0001cadg9 -0.009949 0.002547 -3.91 <.0001cadg10 -0.005915 0.001911 -3.09 0.0020cadg11 0.016549 0.002078 7.96 <.0001cadg12 0.046649 0.012075 3.86 <.0001rec_imm -0.001529 0.000306 -5.00 <.0001rural_index -0.000452 0.000067 -6.77 <.0001deprivation 0.006597 0.003330 1.98 0.0476cadg1*deprivation 0.000159 0.002937 0.050 0.9569cadg2*deprivation 0.001807 0.003114 0.58 0.5618cadg3*deprivation 0.002241 0.002710 0.83 0.4082cadg4*deprivation 0.009549 0.004587 2.08 0.0374cadg5*deprivation -0.004057 0.002733 -1.48 0.1376cadg6*deprivation -0.001380 0.002757 -0.50 0.6167cadg7*deprivation 0.000549 0.004876 0.11 0.9103cadg8*deprivation -0.000137 0.003055 -0.04 0.9641cadg9*deprivation -0.002457 0.003449 -0.71 0.4763cadg10*deprivation 0.003752 0.002557 1.47 0.1423cadg11*deprivation 0.000464 0.002844 0.16 0.8703cadg12*deprivation 0.019030 0.015042 1.27 0.2048

63

Table B: Long-term Service Intensity (SES=low income)Parameter Estimate Standard Error t Value Pr > |t|Intercept 1.930816 0.007735 249.62 <.0001SEX F -0.015189 0.004833 -3.14 0.0017SEX M 0 . . .0-19 -0.327091 0.005827 -56.14 <.000120-44 0.028563 0.007312 3.91 <.000145-64 0.046556 0.005858 7.95 <.000165-74 0.025277 0.005523 4.58 <.000175-84 0.000852 0.005199 0.16 0.869885+ 0 . . .SEX*0-19 F 1 0.067587 0.007935 8.52 <.0001SEX*20-44 F 2 -0.018821 0.008901 -2.11 0.0345SEX*45-64 F 3 -0.031792 0.006985 -4.55 <.0001SEX*65-74 F 4 -0.028312 0.006492 -4.36 <.0001SEX*75-84 F 5 -0.023745 0.005972 -3.98 <.0001SEX*85+ F 6 0 . . .SEX*0-19 M 1 0 . . .SEX*20-44 M 2 0 . . .SEX*45-64 M 3 0 . . .SEX*65-74p M 4 0 . . .SEX*75-84 M 5 0 . . .SEX*85+ M 6 0 . . .CCAC 1 0.056770 0.008122 6.99 <.0001CCAC 4 0.069493 0.010111 6.87 <.0001CCAC 5 0.178592 0.005459 32.71 <.0001CCAC 6 0.021486 0.007290 2.95 0.0032CCAC 7 0.050892 0.007456 6.83 <.0001CCAC 8 0.129750 0.009052 14.33 <.0001CCAC 10 0.063353 0.006750 9.39 <.0001CCAC 11 0.085712 0.007767 11.04 <.0001CCAC 12 -0.049547 0.010054 -4.93 <.0001CCAC 13 0.052735 0.008081 6.53 <.0001CCAC 14 0.072149 0.008531 8.46 <.0001CCAC 16 0.039561 0.007840 5.05 <.0001CCAC 19 0.093528 0.005493 17.03 <.0001CCAC 20 0.006704 0.016052 0.42 0.6762CCAC 21 -0.032190 0.005739 -5.61 <.0001CCAC 24 0.123873 0.005987 20.69 <.0001CCAC 25 -0.047555 0.004293 -11.08 <.0001CCAC 26 -0.255785 0.008974 -28.50 <.0001CCAC 27 0.094425 0.004964 19.02 <.0001CCAC 28 0.013809 0.010601 1.30 0.1927CCAC 29 -0.083308 0.008403 -9.91 <.0001CCAC 30 -0.001007 0.006282 -0.16 0.8726CCAC 32 0.002503 0.007827 0.32 0.7491CCAC 33 0.076977 0.006463 11.91 <.0001CCAC 35 -0.260072 0.007564 -34.38 <.0001CCAC 36 0.116827 0.005972 19.56 <.0001CCAC 37 0.068400 0.007586 9.02 <.0001CCAC 38 -0.215104 0.005409 -39.77 <.0001CCAC 39 0.096485 0.005736 16.82 <.0001CCAC 40 -0.044783 0.009333 -4.80 <.0001

64

CCAC 41 0.009190 0.010175 0.90 0.3664CCAC 42 -0.067269 0.012483 -5.39 <.0001CCAC 44 0.022074 0.010146 2.18 0.0296CCAC 48 0.011780 0.007336 1.61 0.1083CCAC 49 0.112134 0.007981 14.05 <.0001CCAC 50 0.008506 0.015499 0.55 0.5832CCAC 70 0.004362 0.010104 0.43 0.6660CCAC 78 0 . . .cadg1 0.006660 0.004771 1.40 0.1628cadg2 0.030835 0.005015 6.15 <.0001cadg3 0.003657 0.004390 0.83 0.4047cadg4 -0.018074 0.007679 -2.35 0.0186cadg5 0.043807 0.004535 9.66 <.0001cadg6 0.010432 0.004524 2.31 0.0211cadg7 -0.005921 0.008231 -0.72 0.4719cadg8 -0.014103 0.005181 -2.72 0.0065cadg9 -0.003822 0.005736 -0.67 0.5052cadg10 -0.006605 0.004237 -1.56 0.1190cadg11 0.019814 0.004648 4.26 <.0001cadg12 0.021737 0.025840 0.84 0.4002rec_imm -0.001393 0.000332 -4.20 <.0001rural_index -0.000370 0.000068 -5.41 <.0001low income 0.000717 0.000275 2.61 0.0091cadg1*low income -0.000151 0.000230 -0.65 0.5127cadg2*low income -0.000163 0.000243 -0.67 0.5021cadg3*low income 0.000262 0.000212 1.24 0.2163cadg4*low income 0.001170 0.000357 3.27 0.0011cadg5*low income -0.000092 0.000213 -0.43 0.6673cadg6*low income 0.000087 0.000216 0.40 0.6877cadg7*low income -0.000047 0.000393 -0.12 0.9047cadg8*low income -0.000549 0.000242 -2.27 0.0234cadg9*low income -0.000342 0.000273 -1.25 0.2103cadg10*low income 0.000056 0.000201 0.28 0.7823cadg11*low income -0.000176 0.000223 -0.79 0.4290cadg12*low income 0.001381 0.001176 1.17 0.2402

65

Table C: Long-term Service Intensity (SES=median income, in $000’s)Parameter Estimate Standard Error t Value Pr > |t|Intercept 1.936213 0.011675 165.85 <.0001SEX F -0.015174 0.004833 -3.14 0.0017SEX M 0 . . .0-19 -0.327323 0.005833 -56.12 <.000120-44 0.028687 0.007307 3.93 <.000145-64 0.046634 0.005858 7.96 <.000165-74 0.025367 0.005523 4.59 <.000175-84 0.000742 0.005199 0.14 0.886585+ 0 . . .SEX*0-19 F 1 0.067289 0.007936 8.48 <.0001SEX*20-44 F 2 -0.018654 0.008900 -2.10 0.0361SEX*45-64 F 3 -0.032121 0.006985 -4.60 <.0001SEX*65-74 F 4 -0.028538 0.006491 -4.40 <.0001SEX*75-84 F 5 -0.023727 0.005972 -3.97 <.0001SEX*85+ F 6 0 . . .SEX*0-19 M 1 0 . . .SEX*20-44 M 2 0 . . .SEX*45-64 M 3 0 . . .SEX*65-74p M 4 0 . . .SEX*75-84 M 5 0 . . .SEX*85+ M 6 0 . . .CCAC 1 0.059562 0.008191 7.27 <.0001CCAC 4 0.071593 0.010152 7.05 <.0001CCAC 5 0.179773 0.005482 32.80 <.0001CCAC 6 0.025813 0.007347 3.51 0.0004CCAC 7 0.056288 0.007549 7.46 <.0001CCAC 8 0.131661 0.009108 14.46 <.0001CCAC 10 0.061612 0.006772 9.10 <.0001CCAC 11 0.091228 0.007852 11.62 <.0001CCAC 12 -0.046726 0.010116 -4.62 <.0001CCAC 13 0.055616 0.008148 6.83 <.0001CCAC 14 0.074124 0.008574 8.65 <.0001CCAC 16 0.043073 0.007909 5.45 <.0001CCAC 19 0.095520 0.005558 17.19 <.0001CCAC 20 0.013902 0.016128 0.86 0.3887CCAC 21 -0.029196 0.005835 -5.00 <.0001CCAC 24 0.123114 0.005989 20.56 <.0001CCAC 25 -0.045344 0.004235 -10.71 <.0001CCAC 26 -0.253956 0.009026 -28.14 <.0001CCAC 27 0.090307 0.004972 18.16 <.0001CCAC 28 0.015294 0.010664 1.43 0.1515CCAC 29 -0.077949 0.008484 -9.19 <.0001CCAC 30 0.004669 0.006354 0.73 0.4624CCAC 32 0.007965 0.007953 1.00 0.3166CCAC 33 0.079732 0.006515 12.24 <.0001CCAC 35 -0.255299 0.007668 -33.29 <.0001CCAC 36 0.117129 0.006013 19.49 <.0001CCAC 37 0.068534 0.007600 9.02 <.0001CCAC 38 -0.209965 0.005357 -39.20 <.0001CCAC 39 0.092700 0.005804 15.97 <.0001CCAC 40 -0.037223 0.009333 -3.99 <.0001

66

CCAC 41 0.014400 0.010162 1.42 0.1565CCAC 42 -0.063232 0.012508 -5.06 <.0001CCAC 44 0.029535 0.010200 2.90 0.0038CCAC 48 0.018338 0.007353 2.49 0.0126CCAC 49 0.114746 0.008019 14.31 <.0001CCAC 50 0.018213 0.015490 1.18 0.2397CCAC 70 0.010456 0.010230 1.02 0.3067CCAC 78 0 . . .cadg1 0.011072 0.008596 1.29 0.1977cadg2 0.027396 0.008596 1.29 0.1977cadg3 0.022155 0.007894 2.81 0.0050cadg4 0.013266 0.013520 0.98 0.3265cadg5 0.018477 0.008029 2.30 0.0214cadg6 0.002907 0.008071 0.36 0.7187cadg7 0.008486 0.014625 0.58 0.5617cadg8 -0.028271 0.009155 -3.09 0.0020cadg9 -0.018947 0.010213 1.86 0.0636cadg10 0.023043 0.007548 3.05 0.0023cadg11 0.005273 0.008314 0.63 0.5259cadg12 0.078671 0.045147 1.74 0.0814rec_imm -0.000639 0.000283 -2.26 0.0239rural_index -0.000430 0.000067 -6.45 <.0001median income 0.000098 0.000218 0.45 0.6527cadg1*median income -0.000169 0.000193 -0.88 0.3794cadg2*median income 0.000006 0.000203 0.03 0.9775cadg3*median income -0.000314 0.000176 -1.78 0.0750cadg4*median income -0.000199 0.000299 -0.66 0.5065cadg5*median income 0.000543 0.000180 3.03 0.0025cadg6*median income 0.000211 0.000180 1.17 0.2413cadg7*median income -0.000346 0.000322 -1.08 0.2822cadg8*median income 0.000083 0.000205 0.40 0.6867cadg9*median income 0.000200 0.000229 0.88 0.3814cadg10*median income -0.000658 0.000168 -3.92 <.0001cadg11*median income 0.000256 0.000185 1.38 0.1673cadg12*median income -0.000669 0.000967 -0.69 0.4889

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