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Planning for what? Challenging the assumptions of health human resources planning
Health Policy 92 (2009) 225–233
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Planning for what? Challenging the assumptions of health human resources planning Gail Tomblin Murphy a , George Kephart b , Lynn Lethbridge a,∗ , Linda O’Brien-Pallas c , Stephen Birch d,e a b c d e
School of Nursing, Dalhousie University, Canada Department of Community Health and Epidemiology, Dalhousie University, Canada Nursing Health Research Unit, University of Toronto, Canada Centre for Health Economics and Policy Analysis, McMaster University, Canada Health Economics Research at Manchester, University of Manchester, UK
a r t i c l e
i n f o
Keywords: Needs based planning Age Cohort and health
a b s t r a c t Objectives: Health human resource planning has traditionally been based on simple models of demographic changes applied to observed levels of service utilization or provider supply. No consideration has been given to the implications of changing levels of need within populations over time. Recently, needs based resource planning models have been suggested that incorporate changes in needs for care explicitly as a determinant of health care needs. Methods: In this paper, population indicators of morbidity, mortality and self-assessed health are analyzed to determine if health care needs have changed across birth cohorts in Canada from 1994 to 2005 among older age groups. Multivariate regression analysis was used to estimate the age pattern of health by birth year with interaction terms included to examine whether the association of age with health was conditional on the birth year. Results: Results indicate that while the probability of mortality, mobility problems and pain rises with age, the rate of change is greater for those born earlier. The probability of selfassessed poor health increases with age but the rate of change with age is constant across birth years. Conclusions: Even in the short time period covered, our analysis shows that health care needs by age are changing over time in Canada. © 2009 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Health human resource (HHR) planning has traditionally been a demographic exercise focusing on the size and demographic mix of provider and patient populations [1–3]. Typically, projections of future service requirements of populations, and hence the health human resource requirements are determined by current levels of health care use combined with standard population projections.
∗ Corresponding author at: School of Nursing, Dalhousie University, Halifax, NS, Canada B3H 3J5. E-mail address: [email protected] (L. Lethbridge). 0168-8510/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.healthpol.2009.04.001
These projected requirements are then compared with projections of the provider population to estimate future human resource shortfalls or surpluses [4–8]. Variants of this approach include estimating provider requirements to maintain current provider population ratios [9]. Typically these approaches recognize that the need for health services depends on the age and sex composition of the population, as well as population size. In particular, the need for health care generally increases with age, while the pattern of this increase varies by sex. Accordingly, health care utilization by age and sex groups is combined with population projections of the size and demographic structure of the population. Some approaches also recognize that practice patterns of providers vary over time and that
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the service requirements by age and sex may change (usually increase) with the development of new technologies [9–12]. Birch et al. [3] developed an extended analytical framework to define future provider requirements which moves beyond demography. It identifies a fundamental limitation of the traditional demographic approach to HHR planning: the assumption that age and sex alone adequately summarize the level of need for services. Although generally associated with more direct determinants of need such as mortality and health status, age and sex are not direct measures of the need for health care services. They are merely indirect proxies for the health problems that ultimately determine the need for health services and the association between differences in age and needs for care may change over time. As a result of changes in health determinants, the age pattern of morbidity and mortality varies considerably between populations and within populations over time [13]. Although the prevalence of health problems and risk of mortality increases with age, the progression of health problems with age is changing as the distribution of health status and risk of death by age reflects the life experiences of persons born at various points in time (cohorts). Cohorts experience different patterns of exposure to social conditions, infection, lifestyle risks (e.g. smoking) and access to effective health care services resulting in differences in longevity, morbidity, and disability experiences in adulthood and old age [14–18]. As a result, there have been steady improvements in life expectancy and changes in the distribution of age at death [19]. This has important implications for HHR planning. For example, persons in their last year of life account for 10–33% of total health care costs while the relationship between end-of-life health care costs and age is weak and may actually decrease with age [20–22]. People are, on average, healthier throughout their lifespan with the risk of dying at any age reducing over time which is increasing life expectancies. In this way, the aging population phenomena is the outcome of improved health (and hence lower levels of need for health care) within age groups. This is consistent with the compression of morbidity hypothesis [23], resulting in reductions in lifetime cumulative levels of morbidity and disability [24–26]. While it is clear that that changes in the patterns of morbidity and mortality by age will be important determinants of future service requirements, it is less clear that these changes will be important within the relatively short time horizon of most HHR forecasting models. Because of the inability to anticipate changes in the age patterns of morbidity that will alter future requirements, the margin of error of forecasts increases rapidly with time [27]. For this reason, the time horizon of forecasts is typically limited to 10 years [28] which is a much shorter time period than the changes in the age patterns generally investigated in the literature [14,15,17,18,25]. Assuming constant levels of need within age–sex groups over relatively short planning horizons can lead to surpluses or shortfalls in health human resource requirements that accumulate over time. Breyer and Felder [29] have noted that expenditures on medical technologies are likely to have a larger impact on
future health care spending than will a healthier population. However, these findings are the result of expenditures on health care being the outcome of influences of supply of and demands for care and the interactions between these two factors. The focus of this paper is to consider how the distribution of needs for care by age group have changed over a relatively short period, irrespective of what has happened to supply and demand, and the implications that has for health human resources planning. In particular we address the following research question—Is the rate of increase of health problems with age declining between age cohorts (i.e. are people ‘aging’ at a slower rate than previous population cohorts)? 2. Materials and methods A schematic overview of the study design and analysis is illustrated using a “lexis-diagram” [30], which describes a population by age and time (Fig. 1). The diagonals (from lower left to upper right) denote individuals born in the same year. Changes in measures of mortality, morbidity and health progressing up the diagonals describe the ‘aging’ of cohorts. Comparing these diagonals gives us insight as to whether younger birth cohorts are aging more slowly. This contrasts with traditional analyses employed in HHR planning examining health status that employs data corresponding to the columns in Fig. 1 (i.e. fixed in time comparisons across age and sex). We can describe differences in the health and morbidity of cohorts in terms of “level” of needs and “age progression” of levels of needs. If we imagine regression lines for each cohort summarizing changes in a needs indicator with age (i.e. y = the needs indicator and x = age), then differences in intercepts would correspond to level, and differences in slopes would correspond to differences in age progression. 2.1. Data The main data sources used in this study are the National Population Health Survey (NPHS) and the Canadian Community Health Survey (CCHS), both populationbased health surveys released by Statistics Canada. Each survey uses a multi-staged, stratified cluster design. Post-stratification adjustments were made to ensure the sum of final sampling weights represented Statistics Canada’s between-census population estimates at the province/year/sex level for each year. The CCHS replaced the NPHS in 2001 as consultations indicated a need to analyze health indicators at the individual health region level. The sampling design and cross-sectional weighting strategies are comparable for each dataset [31–33]. Since the CCHS is meant to provide reliable estimates of health indicators for the 136 health regions in Canada, the sample sizes are necessarily larger than the NPHS. However, after the initial CCHS cycle, only a subset of individuals were asked many of the health status questions to reduce costs. The NPHS surveys in 1994 and 1998 have just over 17,000 observations, while the 2001 CCHS contains about 130,000 individuals. In 2003 and 2005, the subsamples used for this study include 44,000 and 32,000 observations, respectively. As noted above, sampling weights are used to
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Fig. 1. Comparisons within diagonals measure age progression (common birth year) and comparisons within rows measure changes over surveys (common age).
calculate point estimates to represent the Canadian population each year. These survey data are supplemented with public-use Vital Statistics information available through Statistics Canada [34]. 2.2. Measures The NPHS and CCHS provide a broad range of health information with which to measure morbidity and health status but we were limited to indicators that were measured and reported equivalently across all of the surveys. Many variables could not be used because of changes in the wording of questions between survey years or questions not being asked in some years. Other potential indicators were excluded because they were directly associated with service use. For example, a diagnosis of heart disease requires consultation with a health care professional, and hence is not an independent measure of health need. This resulted in a short list of health indicators covering mobility, pain and self-reported health status along with mortality using Vital Statistic data. While not all-inclusive, these four outcomes are general indicators of health care needs. Mortality rates are commonly used as an indicator of general health conditions in populations and are an important component in models forecasting future health resource utilization [35–37]. Moreover, the end-of-life represents a period of high use of health services [20–22]. Chronic pain can originate from numerous conditions and is often multidimensional. Ohayon [38], for example, includes 42 different diseases to help ascertain the presence of pain. Self-reported-health has been shown to correlate well with physician assessments of health [39], disability [40], future mortality [41], utilization [42,43] and other measures of morbidity [44]. Functional disability, including mobility problems, can be used as an indicator of overall quality of living, particu-
larly in the elderly [45]. While reduced quality of living may not lead to increased usage of acute care services, mobility problems have been shown to be a good indicator of home care need [46–49]. Respondents were classified as having a mobility problem if they reported requiring mechanical assistance, a wheelchair, general help or were unable to walk. A respondent was considered to have a pain problem if he or she reported that pain prevents most activities. Self-assessed health was measured on a five-point scale with “poor” being the lowest category. Finally, mortality was measured by age–sex specific mortality rates corresponding to each survey year. Mortality rates were computed by dividing the number of deaths by the census population estimate. 2.3. Analysis In this study, the data come from separate crosssectional surveys with different sample designs meaning the sample and bootstrap weights are not comparable across the years, and, the data could not be combined at the individual level. Rather, data from each survey was used to obtain point estimates for each indicator (e.g. proportion with a mobility problem) by single year of age and sex and adjusted using sample weights to account for unequal probabilities of selection and non-response. As per Statistics Canada recommendations [33], standard errors were computed for each estimate by bootstrap estimation. Five hundred replicate bootstrap samples were employed, each incorporating the complex sample design of the survey. These estimates were then pooled to form an analysis dataset analogous to Fig. 1. For each cell, variables include point estimates of each need indicator, age (in single years), sex and cohort. The cohort variable represents the year of birth for each individual but is expressed as a continuous variable running in the reverse direction. That is, as the
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Table 1 Prevalence rates. Age group/survey year
Mortalitya
Mobility problem
Pain problem
Males (%)
Males (%)
Females (%)
4.5 4.2 4.6 3.7 4.8
4.8 2.5 5.0 4.1 5.3
4.5 3.5 5.3 4.2 4.6
4.1 4.1 5.2 3.9 4.7
4.5 3.0 5.3 3.3 3.8
7.4 8.1 7.4 6.9 6.3
8.4 7.5 7.4 7.1 8.8
4.8 5.4 4.9 4.0 3.5
5.3 4.2 4.7 5.1 5.3
6.1 5.1 7.3 5.6 6.2
4.0 3.5 5.4 5.4 5.4
15.2 14.7 17.2 16.3 16.2
23.1 20.6 22.7 24.4 20.5
7.0 7.7 6.2 3.5 7.1
8.7 7.3 8.5 6.8 7.4
7.5 8.3 11.3 8.8 8.3
8.5 8.4 9.6 7.9 10.5
Males (%)
Females (%)
55–64 1994 1998 2001 2003 2005
1.22 1.06 1.00 0.98 0.94
0.69 0.63 0.59 0.60 0.57
4.2 4.5 3.8 2.8 4.4
65–74 1994 1998 2001 2003 2005
3.14 2.89 2.58 2.53 2.43
1.75 1.61 1.54 1.51 1.45
75–84 1994 1998 2001 2003 2005
7.71 7.38 6.63 6.54 6.28
4.73 4.56 4.30 4.26 4.09
Females (%)
Poor self-reported health Males (%)
Females (%)
a Source: Deaths—Statistics Canada CANSIM II Table 510 002. Population—Statistics Canada CANSIM II Table 510 001. Deaths cover the 1 July–30 June time period. The denominator is the population at the beginning of the 12-month period. Mobility problem if mobility requires mechanical assistance, a wheelchair, general help, or cannot walk (top 4 categories of 6). Pain problem if pain prevents most activities (top category of 5). Self-reported health has five categories with “poor” being the lowest.
year of birth becomes larger, the cohort variable becomes smaller. Older individuals will have larger cohort values within any particular survey year. The cohort variable was computed as follows: (age − survey year + 1930). It, therefore, assumes a value of zero for any individual born in 1930. Point estimates for all need indicators by sex, survey year and age group were calculated to give a general overview of health status over the study period. Multivariate ordinary least squares (OLS) regression models were run to isolate specific effects of age and cohort. To achieve this, each health outcome was regressed on age and cohort with an interaction variable between age and cohort included and tested for statistical significance. Through this approach, we were able to analyze the relationships of age and birth cohort with health, but also examine whether these relationships are changing over time. For example, the relationship between age and health may vary depending on birth cohort—the average level of health for 71 year olds today may be higher than that for 71 year olds a decade ago. To account for non-linearity with age, we also included age-squared terms in the initial regression models. Standard errors were calculated using bootstrap weights as traditional methods of calculation will underestimate the variance due to the complex survey design. However, following accepted procedure, bootstrapping is not used to calculate point estimates as results will be biased [50]. Because a smaller standard error suggests a more precisely measured estimate, our regression models were weighted by the inverse of the standard error giving more weight to those with less variation. This avoids undue influence of unstable results and helps correct for heteroskedasticity through more efficient estimators [51]. A small number
of calculated proportions had a value of zero leading to an undefined standard error. So as not to exclude these observations, the weight of the value closest to zero for that age group was assigned. There are very low prevalence rates of the selected health outcomes for those under age 55 (i.e. a large number of ages with a prevalence of zero) which may skew results. At the same time, there are a very small number of observations for those 85 and older leading to unstable estimates. As a result, our analysis was limited to ages 55–84. Chow tests indicated there is a structural difference between males and females in our models leading to each being modeled separately. All regression models thus have 150 observations reflecting the 30 individual ages (55–84) and 5 surveys. Due to the complex nature of the relationships involved, various initial regression specifications were run for each outcome with a preferred model chosen for each sex. The selection process was based on standard adjusted R2 and F-tests. From these preferred models, the marginal effects of age and cohort were calculated and analyzed. Regression models which include interaction terms must be interpreted carefully. The marginal effects of the interacted variables are conditional on each other. Since we cannot simply use the estimated coefficients of age and birth cohort to determine the direct associations, graphs showing the age and cohort effects over a 10-year period are presented.1 The slope of the plot gives the marginal effect of age and birth cohort in the respective graphs. The steeper the slope, the stronger the association. This approach helps
1 Alternatively, basic calculus can be utilized to obtain partial effects for selected birth years and ages.
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Table 2 Ordinary least squares—ages 55–84 (p-values in parentheses). Mortality
Age Cohort Age squared Age*cohort Intercept Adj R2 n
Mobility problems
Pain
Males
Females
Males
Females
Males
Females
Males
Females
−0.0104 (0.0001) −0.0036 (0.0001) 0.000093 (0.0001) 0.000063 (0.0001) 0.3017 (0.0001)
−0.0099 (0.0001) −0.0017 (0.0005) 0.000084 (0.0001) 0.000030 (0.0001) 0.2992 (0.0001)
0.0064 (0.0001) −0.0189 (0.0001) –
0.0095 (0.0001) −0.0286 (0.0001) –
0.0015 (0.0087) −0.0067 (0.0008) –
0.0022 (0.0001) −0.0071 (0.0001) –
0.0033 (0.0001) −0.0014 (0.0400) –
0.0044 (0.0001) −0.0022 (0.0005) –
0.000280 (0.0001) −0.3758 (0.0001)
0.000418 (0.0001) −0.5736 (0.0001)
0.000093 (0.0012) −0.0697 (0.0840)
0.000098 (0.0002) −0.1049 (0.0058)
–
–
−0.1732 (0.0010)
−0.2618 (0.0001)
0.988 150
0.980 150
0.714 150
0.813 150
0.141 150
0.301 150
0.211 150
0.370 150
to disentangle the relationships with age from relationships with birth year. The mid-range ages of 65 and 75 and birth years of 1935 and 1924 were chosen for illustrative purposes. The 10-year span aligns with the length of our study period as well as all ages occurring in the retirement years. SAS Version 9.1 was used for all analyses.
3. Results To help give an overall sense of trends, Table 1 shows the prevalence rates across the four needs indicators by sex and age group over the five survey cycles used in this analysis. The clearest picture for both males and females is in the mortality data where death rates rise with age but fall when comparing across survey years. Recall, these estimates are from Vital Statistics records and not subject to sampling variability. Reported mobility problems, pain and poor selfreported health also increase with older ages but a trend is not as apparent across surveys. A comparison of males with females indicates lower mortality rates for females for each age group. Finally, females are more likely to experience mobility problems in the 75–84 year age group. The OLS regression results are presented in Table 2. Findings show both age and birth year individually have significant effects on all outcomes measured. Nested Ftests indicated the interaction term between age and birth year should be included for mortality, mobility and pain. This implies the magnitude of the relationship between age and health is conditional on birth year and, conversely, the relationship between birth year and health is conditional on age. That is, aging’s association with the probability of dying, having mobility problems or being in pain is not constant across birth years. The final model predicting the probability of reporting poor health does not include an interaction term so the coefficients, therefore, can be interpreted in the usual manner. Older individuals are more likely to describe their health as poor than those born more recently. Only for mortality was an age squared term included in the preferred specification. While some previous studies have shown significance with the inclusion of an age squared term (see, for example [21,22,29]), these studies model expenditures rather than health needs indicators directly.
Poor self-assessed health
Fig. 2 shows the age patterns, or age progression, for males and females born in 1924 and 1935.2 The probability of health problems increases with age for all outcomes, as illustrated by the positive slopes. For mortality, mobility problems and pain, the slope is steeper for those born in 1924 compared with those born 11 years later. Health declines with age but the progression is slower for those born more recently. This result is consistent for both males and females. Since there is no interaction term included in the poor self-reported health model, the relationship with age is constant regardless of birth year.3 We also ran our regressions including education as a control for socio-economic factors.4 Consistent with previous research [52,53], higher education is associated with better health. Noteworthy, however, regardless of education level, individuals born more recently are healthier. The age/cohort interaction is significant for those with high or low education. Fig. 3 compares the marginal effect of birth year for 65 year olds with that of 75 year olds.5 Moving along any single line represents a comparison of individuals who are the same age at different points in time. For example, a 65year-old born in 1929 would be observed in 1994 while a 65-year-old born in 1936 would be observed in 2001. The solid lines indicate the trend for 65 year olds born in 1930–1938 while the dashed lines show 75 year olds born 1920–1928. To keep plots aligned, “survey”6 year rather than birth year is plotted on the X-axis. Unlike with age progression, the slopes do not all move in the same direction. Mortality declines across birth years for both ages while poor self-reported health rises. The slopes are mixed for
2 The curve in the plots for mortality is due to the inclusion of an agesquared term in the model meaning the relationship between age and the probability of dying is non-linear. 3 When fair and poor health categories are combined, results confirm that there is no interaction age/cohort effect on self-assessed health. 4 Education is highly correlated with other factors such as income meaning it captures much of the SES effects. In fact, education may be the preferred measure as it attained prior to our outcome measures and is, therefore, exogenous. As well, it will be generally stable throughout our study period and not affected by health for our age range [53,67]. 5 Since the constructed cohort variable runs in reverse to the year of birth, probabilities for Fig. 3 are multiplied by −1. 6 We put survey in quotations as the points plotted were not necessarily actual survey years in our sample but rather points used in predicting.
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Fig. 2. Marginal effect of age.
mobility and pain with 65 year olds observed more recently being more likely to experience problems while 75 year olds are less likely over time. In Table 3 we summarize the marginal effects of age and birth year from Figs. 2 and 3. Except for poor self-reported health, it is clear that those born in 1924 have a greater rate of deterioration in health than those born in 1935. For example, the likelihood of mobility problems increases
0.81% for each year of age for males born in 1924 but 0.50% for those born in 1935. Comparing females born in 1924 to those born in 1935 shows a probability of 1.2% versus 0.74%, respectively. Notice in all cases, a change in age has a stronger association with ill-health than a change in birth year. Predicted overall probabilities are illustrative in making comparisons using various age and birth year scenarios. Our
Fig. 3. Marginal effect of birth year.
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Table 3 Marginal effects of age and birth year. Mortalitya Males (%) Age effect Born 1935 Born 1924
Females (%)
Mobility problems
Pain
Males (%)
Males (%)
Females (%)
Poor self-assessed health Females (%)
Males (%)
Females (%)
0.21 0.28
0.15 0.19
0.50 0.81
0.74 1.20
0.11 0.21
0.17 0.27
0.33 0.33
0.44 0.44
Birth year effectb Age 65 −0.05 Age 75 −0.12
−0.02 −0.05
0.06 −0.22
0.14 −0.28
0.07 −0.03
0.07 −0.02
0.14 0.14
0.22 0.22
a Since the age/predicted probability plot for mortality is curved, the effect is not constant across ages. The marginal effect of age shown is the trend from age 65 to 73. b As with Fig. 3, the signs are reversed to reflect a larger (i.e. more recent) birth year as a positive change.
mortality model suggests a 71-year-old male born in 1924 has a 3.6% chance of dying but a male of the same age born in 1935, a 2.6% probability. Comparing a 71-year-old male born in 1924 to one born in 1936 shows mobility problems are predicted for 8.6% and 7.5%, respectively. The probability of pain is about equal at 3.8% for this comparison with older cohorts showing higher probabilities for those over 71 years of age. Results for females show a similar pattern. 4. Discussion Our results show that although the probability of mortality, mobility problems, pain and poor self-assessed health increases with age, the relationship between age and health has changed over time. For three of our four measures, those born more recently are ‘aging’ more slowly. Controlling for age, both males and females born more recently are less likely to die than those born earlier suggesting Canadians are living longer over time. Our analysis reveals mortality rates are shifting over time meaning the period of high health care usage prior to death [54,55] is changing as well. Planning based on past patterns of utilization or current age distributions of needs could therefore lead to overprovision of the capacity required to meet the needs of what are no longer ‘close to death’ age groups. Mobility and functioning problems can lead to considerable resources being devoted to daily care, often through a team of health care professionals [46]. As well “. . .lower extremity function is predictive of severe disability, death and institutionalization” [56] (p. 1006). Even small changes in the prevalence of mobility problems can affect planning for a broad range of health care providers. Nurses in particular play a crucial role in mobility assistance and rehabilitation [46,57,58]. The effects of pain can be felt across a wide spectrum of the health care system both through the investigation of the source of the problem and also its possible consequences. The literature suggests that self-reported pain is associated with high utilization of health care services as it can lead to other impairments [28,59–61] and is a key predictor of patient-initiated visits [62]. If the prevalence of mobility problems and pain in the population is falling within age groups, fewer resources would be required to serve needs arising from these conditions per 1000 population. Although the numbers within older age groups may be increasing in an aging population
this does not mean that the needs for health care resources to serve these age groups are increasing at the same rate. The consequences of overlooking changes in age specific needs for care for health care costs can be considerable. For example, Birch and Maynard [63] argued that British plans to expand dental school places in order to maintain service levels in the 1980s based on constant levels of oral health need would lead to an excess supply of dentists given the rapid improvements in oral health status occurring at the time. Excess supply did not occur however because of unplanned increases in the average level services per patient need as dentists sought to maintain workloads. Birch et al. [3] found that applying observed trends in levels of need in the Atlantic provinces of Canada was associated with a 5% reduction in the expansion of training places required to meet population needs compared to basing training requirements on the assumption of observed levels of needs continuing indefinitely. The cumulative effects on health care costs of the annual flow of new medical graduates being 5% greater than required are likely to be substantial as service levels expand to absorb this supply. Reductions in birth rates as well as improvements in child health have led pediatricians to search for ways of expanding service levels as opposed to aligning supply with needs [8]. Poor self-assessed health does not show the same agespecific reduction in prevalence between cohorts. This may be because of the subjective nature of the measure (as compared with the other health measures used). The prevalence of the perception of poor health within age groups is the same regardless of year of birth. This outcome may be capturing a sense of well-being beyond more objective measurements of health associated with the increase in service provision within needs groups by providers seeking to maintain workloads. Although the NPHS and CCHS are large-scale nationally representative surveys, sample sizes are prohibitively small to calculate proportions by individual age for those 85 years and older. This is an important group to analyze particularly as life expectancies increase and baby boomers become elderly [64]. For these same reasons, however, future surveys will likely include larger samples of elderly allowing for a closer examination of the very old for HHR planning. Previous literature indicates that patterns of change in aging vary across ages even within older age groups [14,23,25,65]. The shift in the aging pattern for 75–85 year
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olds may be different than that for 55–65 year olds. To address these potential differences, we analyzed 10-year age groupings separately for each outcome. We found some evidence of a concentration of the aging effects within the 30-year age span, however, results were not consistent across outcomes or gender. We note that the power of these models was greatly reduced with only 50 observations each. As Aiken et al. [66] argue, larger sample sizes may be required for models where interactive terms are included, particularly if the effects are small. 5. Conclusion Even in the short time period covered in this study, the analysis shows that health care needs by age are changing over time in Canada. Planning based on constant levels of age-specific needs would therefore generate health care workforces that could not be sustained by simply maintaining the same approaches to care for those with health care needs. Given that providers have an important role in determining the demand for services we might therefore expect an expansion of the services provided to individuals with need, not necessarily as a result of planned public policy on the delivery care but as the outcome of provider’s concerns with maintaining workloads. Effective and efficient health care depends on clear distinction being made the needs for health care arising from the expected future levels and distribution of sickness and health risks in the population and the levels and mix of health care providers required to deliver that care. Attention should therefore be given to accommodating the complex dynamic relationships between aging and health in health human resources planning. Acknowledgments We wish to thank the Canadian Health Services Research Foundation, Nova Scotia Health Research Foundation, Ontario Ministry of Health and Long-Term Care, Health Canada (Communication Branch and Office of Nursing Policy), Saskatchewan Innovation and Science Fund, and the Capital District Health Authority for the generous financial support that made this research program possible. We would also like to thank Arden Bell and Heather Hobson at the Atlantic Research Data Centre for their assistance in vetting output. References [1] Birch S. Health human resource planning for the new millennium: inputs in the production of health, illness, and recovery in populations. Canadian Journal of Nursing Research 2002;33:109–14. [2] Birch S, O’Brien-Pallas L, Alksnis C, Tomblin Murphy G, Thomson D. Beyond demographic change in human resources planning: an extended framework and application to nursing. Journal of Health Service Research Policy 2003;8:225–9. [3] Birch S, Kephart G, Tomblin-Murphy G, O’Brien-Pallas L. Human resources planning and the production of health: a needs-based analytical framework. Canadian Public Policy 2007;33:S1. [4] Denton F, Gafni A, Spencer B. The SHARP way to plan health care services: a description of the system and some illustrative applications in nursing human resource planning. Socio-Economic Planning Sciences 1995;29:125–37.
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Contents lists available at ScienceDirect
Health Policy journal homepage: www.elsevier.com/locate/healthpol
Planning for what? Challenging the assumptions of health human resources planning Gail Tomblin Murphy a , George Kephart b , Lynn Lethbridge a,∗ , Linda O’Brien-Pallas c , Stephen Birch d,e a b c d e
School of Nursing, Dalhousie University, Canada Department of Community Health and Epidemiology, Dalhousie University, Canada Nursing Health Research Unit, University of Toronto, Canada Centre for Health Economics and Policy Analysis, McMaster University, Canada Health Economics Research at Manchester, University of Manchester, UK
a r t i c l e
i n f o
Keywords: Needs based planning Age Cohort and health
a b s t r a c t Objectives: Health human resource planning has traditionally been based on simple models of demographic changes applied to observed levels of service utilization or provider supply. No consideration has been given to the implications of changing levels of need within populations over time. Recently, needs based resource planning models have been suggested that incorporate changes in needs for care explicitly as a determinant of health care needs. Methods: In this paper, population indicators of morbidity, mortality and self-assessed health are analyzed to determine if health care needs have changed across birth cohorts in Canada from 1994 to 2005 among older age groups. Multivariate regression analysis was used to estimate the age pattern of health by birth year with interaction terms included to examine whether the association of age with health was conditional on the birth year. Results: Results indicate that while the probability of mortality, mobility problems and pain rises with age, the rate of change is greater for those born earlier. The probability of selfassessed poor health increases with age but the rate of change with age is constant across birth years. Conclusions: Even in the short time period covered, our analysis shows that health care needs by age are changing over time in Canada. © 2009 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Health human resource (HHR) planning has traditionally been a demographic exercise focusing on the size and demographic mix of provider and patient populations [1–3]. Typically, projections of future service requirements of populations, and hence the health human resource requirements are determined by current levels of health care use combined with standard population projections.
∗ Corresponding author at: School of Nursing, Dalhousie University, Halifax, NS, Canada B3H 3J5. E-mail address: [email protected] (L. Lethbridge). 0168-8510/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.healthpol.2009.04.001
These projected requirements are then compared with projections of the provider population to estimate future human resource shortfalls or surpluses [4–8]. Variants of this approach include estimating provider requirements to maintain current provider population ratios [9]. Typically these approaches recognize that the need for health services depends on the age and sex composition of the population, as well as population size. In particular, the need for health care generally increases with age, while the pattern of this increase varies by sex. Accordingly, health care utilization by age and sex groups is combined with population projections of the size and demographic structure of the population. Some approaches also recognize that practice patterns of providers vary over time and that
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the service requirements by age and sex may change (usually increase) with the development of new technologies [9–12]. Birch et al. [3] developed an extended analytical framework to define future provider requirements which moves beyond demography. It identifies a fundamental limitation of the traditional demographic approach to HHR planning: the assumption that age and sex alone adequately summarize the level of need for services. Although generally associated with more direct determinants of need such as mortality and health status, age and sex are not direct measures of the need for health care services. They are merely indirect proxies for the health problems that ultimately determine the need for health services and the association between differences in age and needs for care may change over time. As a result of changes in health determinants, the age pattern of morbidity and mortality varies considerably between populations and within populations over time [13]. Although the prevalence of health problems and risk of mortality increases with age, the progression of health problems with age is changing as the distribution of health status and risk of death by age reflects the life experiences of persons born at various points in time (cohorts). Cohorts experience different patterns of exposure to social conditions, infection, lifestyle risks (e.g. smoking) and access to effective health care services resulting in differences in longevity, morbidity, and disability experiences in adulthood and old age [14–18]. As a result, there have been steady improvements in life expectancy and changes in the distribution of age at death [19]. This has important implications for HHR planning. For example, persons in their last year of life account for 10–33% of total health care costs while the relationship between end-of-life health care costs and age is weak and may actually decrease with age [20–22]. People are, on average, healthier throughout their lifespan with the risk of dying at any age reducing over time which is increasing life expectancies. In this way, the aging population phenomena is the outcome of improved health (and hence lower levels of need for health care) within age groups. This is consistent with the compression of morbidity hypothesis [23], resulting in reductions in lifetime cumulative levels of morbidity and disability [24–26]. While it is clear that that changes in the patterns of morbidity and mortality by age will be important determinants of future service requirements, it is less clear that these changes will be important within the relatively short time horizon of most HHR forecasting models. Because of the inability to anticipate changes in the age patterns of morbidity that will alter future requirements, the margin of error of forecasts increases rapidly with time [27]. For this reason, the time horizon of forecasts is typically limited to 10 years [28] which is a much shorter time period than the changes in the age patterns generally investigated in the literature [14,15,17,18,25]. Assuming constant levels of need within age–sex groups over relatively short planning horizons can lead to surpluses or shortfalls in health human resource requirements that accumulate over time. Breyer and Felder [29] have noted that expenditures on medical technologies are likely to have a larger impact on
future health care spending than will a healthier population. However, these findings are the result of expenditures on health care being the outcome of influences of supply of and demands for care and the interactions between these two factors. The focus of this paper is to consider how the distribution of needs for care by age group have changed over a relatively short period, irrespective of what has happened to supply and demand, and the implications that has for health human resources planning. In particular we address the following research question—Is the rate of increase of health problems with age declining between age cohorts (i.e. are people ‘aging’ at a slower rate than previous population cohorts)? 2. Materials and methods A schematic overview of the study design and analysis is illustrated using a “lexis-diagram” [30], which describes a population by age and time (Fig. 1). The diagonals (from lower left to upper right) denote individuals born in the same year. Changes in measures of mortality, morbidity and health progressing up the diagonals describe the ‘aging’ of cohorts. Comparing these diagonals gives us insight as to whether younger birth cohorts are aging more slowly. This contrasts with traditional analyses employed in HHR planning examining health status that employs data corresponding to the columns in Fig. 1 (i.e. fixed in time comparisons across age and sex). We can describe differences in the health and morbidity of cohorts in terms of “level” of needs and “age progression” of levels of needs. If we imagine regression lines for each cohort summarizing changes in a needs indicator with age (i.e. y = the needs indicator and x = age), then differences in intercepts would correspond to level, and differences in slopes would correspond to differences in age progression. 2.1. Data The main data sources used in this study are the National Population Health Survey (NPHS) and the Canadian Community Health Survey (CCHS), both populationbased health surveys released by Statistics Canada. Each survey uses a multi-staged, stratified cluster design. Post-stratification adjustments were made to ensure the sum of final sampling weights represented Statistics Canada’s between-census population estimates at the province/year/sex level for each year. The CCHS replaced the NPHS in 2001 as consultations indicated a need to analyze health indicators at the individual health region level. The sampling design and cross-sectional weighting strategies are comparable for each dataset [31–33]. Since the CCHS is meant to provide reliable estimates of health indicators for the 136 health regions in Canada, the sample sizes are necessarily larger than the NPHS. However, after the initial CCHS cycle, only a subset of individuals were asked many of the health status questions to reduce costs. The NPHS surveys in 1994 and 1998 have just over 17,000 observations, while the 2001 CCHS contains about 130,000 individuals. In 2003 and 2005, the subsamples used for this study include 44,000 and 32,000 observations, respectively. As noted above, sampling weights are used to
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Fig. 1. Comparisons within diagonals measure age progression (common birth year) and comparisons within rows measure changes over surveys (common age).
calculate point estimates to represent the Canadian population each year. These survey data are supplemented with public-use Vital Statistics information available through Statistics Canada [34]. 2.2. Measures The NPHS and CCHS provide a broad range of health information with which to measure morbidity and health status but we were limited to indicators that were measured and reported equivalently across all of the surveys. Many variables could not be used because of changes in the wording of questions between survey years or questions not being asked in some years. Other potential indicators were excluded because they were directly associated with service use. For example, a diagnosis of heart disease requires consultation with a health care professional, and hence is not an independent measure of health need. This resulted in a short list of health indicators covering mobility, pain and self-reported health status along with mortality using Vital Statistic data. While not all-inclusive, these four outcomes are general indicators of health care needs. Mortality rates are commonly used as an indicator of general health conditions in populations and are an important component in models forecasting future health resource utilization [35–37]. Moreover, the end-of-life represents a period of high use of health services [20–22]. Chronic pain can originate from numerous conditions and is often multidimensional. Ohayon [38], for example, includes 42 different diseases to help ascertain the presence of pain. Self-reported-health has been shown to correlate well with physician assessments of health [39], disability [40], future mortality [41], utilization [42,43] and other measures of morbidity [44]. Functional disability, including mobility problems, can be used as an indicator of overall quality of living, particu-
larly in the elderly [45]. While reduced quality of living may not lead to increased usage of acute care services, mobility problems have been shown to be a good indicator of home care need [46–49]. Respondents were classified as having a mobility problem if they reported requiring mechanical assistance, a wheelchair, general help or were unable to walk. A respondent was considered to have a pain problem if he or she reported that pain prevents most activities. Self-assessed health was measured on a five-point scale with “poor” being the lowest category. Finally, mortality was measured by age–sex specific mortality rates corresponding to each survey year. Mortality rates were computed by dividing the number of deaths by the census population estimate. 2.3. Analysis In this study, the data come from separate crosssectional surveys with different sample designs meaning the sample and bootstrap weights are not comparable across the years, and, the data could not be combined at the individual level. Rather, data from each survey was used to obtain point estimates for each indicator (e.g. proportion with a mobility problem) by single year of age and sex and adjusted using sample weights to account for unequal probabilities of selection and non-response. As per Statistics Canada recommendations [33], standard errors were computed for each estimate by bootstrap estimation. Five hundred replicate bootstrap samples were employed, each incorporating the complex sample design of the survey. These estimates were then pooled to form an analysis dataset analogous to Fig. 1. For each cell, variables include point estimates of each need indicator, age (in single years), sex and cohort. The cohort variable represents the year of birth for each individual but is expressed as a continuous variable running in the reverse direction. That is, as the
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Table 1 Prevalence rates. Age group/survey year
Mortalitya
Mobility problem
Pain problem
Males (%)
Males (%)
Females (%)
4.5 4.2 4.6 3.7 4.8
4.8 2.5 5.0 4.1 5.3
4.5 3.5 5.3 4.2 4.6
4.1 4.1 5.2 3.9 4.7
4.5 3.0 5.3 3.3 3.8
7.4 8.1 7.4 6.9 6.3
8.4 7.5 7.4 7.1 8.8
4.8 5.4 4.9 4.0 3.5
5.3 4.2 4.7 5.1 5.3
6.1 5.1 7.3 5.6 6.2
4.0 3.5 5.4 5.4 5.4
15.2 14.7 17.2 16.3 16.2
23.1 20.6 22.7 24.4 20.5
7.0 7.7 6.2 3.5 7.1
8.7 7.3 8.5 6.8 7.4
7.5 8.3 11.3 8.8 8.3
8.5 8.4 9.6 7.9 10.5
Males (%)
Females (%)
55–64 1994 1998 2001 2003 2005
1.22 1.06 1.00 0.98 0.94
0.69 0.63 0.59 0.60 0.57
4.2 4.5 3.8 2.8 4.4
65–74 1994 1998 2001 2003 2005
3.14 2.89 2.58 2.53 2.43
1.75 1.61 1.54 1.51 1.45
75–84 1994 1998 2001 2003 2005
7.71 7.38 6.63 6.54 6.28
4.73 4.56 4.30 4.26 4.09
Females (%)
Poor self-reported health Males (%)
Females (%)
a Source: Deaths—Statistics Canada CANSIM II Table 510 002. Population—Statistics Canada CANSIM II Table 510 001. Deaths cover the 1 July–30 June time period. The denominator is the population at the beginning of the 12-month period. Mobility problem if mobility requires mechanical assistance, a wheelchair, general help, or cannot walk (top 4 categories of 6). Pain problem if pain prevents most activities (top category of 5). Self-reported health has five categories with “poor” being the lowest.
year of birth becomes larger, the cohort variable becomes smaller. Older individuals will have larger cohort values within any particular survey year. The cohort variable was computed as follows: (age − survey year + 1930). It, therefore, assumes a value of zero for any individual born in 1930. Point estimates for all need indicators by sex, survey year and age group were calculated to give a general overview of health status over the study period. Multivariate ordinary least squares (OLS) regression models were run to isolate specific effects of age and cohort. To achieve this, each health outcome was regressed on age and cohort with an interaction variable between age and cohort included and tested for statistical significance. Through this approach, we were able to analyze the relationships of age and birth cohort with health, but also examine whether these relationships are changing over time. For example, the relationship between age and health may vary depending on birth cohort—the average level of health for 71 year olds today may be higher than that for 71 year olds a decade ago. To account for non-linearity with age, we also included age-squared terms in the initial regression models. Standard errors were calculated using bootstrap weights as traditional methods of calculation will underestimate the variance due to the complex survey design. However, following accepted procedure, bootstrapping is not used to calculate point estimates as results will be biased [50]. Because a smaller standard error suggests a more precisely measured estimate, our regression models were weighted by the inverse of the standard error giving more weight to those with less variation. This avoids undue influence of unstable results and helps correct for heteroskedasticity through more efficient estimators [51]. A small number
of calculated proportions had a value of zero leading to an undefined standard error. So as not to exclude these observations, the weight of the value closest to zero for that age group was assigned. There are very low prevalence rates of the selected health outcomes for those under age 55 (i.e. a large number of ages with a prevalence of zero) which may skew results. At the same time, there are a very small number of observations for those 85 and older leading to unstable estimates. As a result, our analysis was limited to ages 55–84. Chow tests indicated there is a structural difference between males and females in our models leading to each being modeled separately. All regression models thus have 150 observations reflecting the 30 individual ages (55–84) and 5 surveys. Due to the complex nature of the relationships involved, various initial regression specifications were run for each outcome with a preferred model chosen for each sex. The selection process was based on standard adjusted R2 and F-tests. From these preferred models, the marginal effects of age and cohort were calculated and analyzed. Regression models which include interaction terms must be interpreted carefully. The marginal effects of the interacted variables are conditional on each other. Since we cannot simply use the estimated coefficients of age and birth cohort to determine the direct associations, graphs showing the age and cohort effects over a 10-year period are presented.1 The slope of the plot gives the marginal effect of age and birth cohort in the respective graphs. The steeper the slope, the stronger the association. This approach helps
1 Alternatively, basic calculus can be utilized to obtain partial effects for selected birth years and ages.
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Table 2 Ordinary least squares—ages 55–84 (p-values in parentheses). Mortality
Age Cohort Age squared Age*cohort Intercept Adj R2 n
Mobility problems
Pain
Males
Females
Males
Females
Males
Females
Males
Females
−0.0104 (0.0001) −0.0036 (0.0001) 0.000093 (0.0001) 0.000063 (0.0001) 0.3017 (0.0001)
−0.0099 (0.0001) −0.0017 (0.0005) 0.000084 (0.0001) 0.000030 (0.0001) 0.2992 (0.0001)
0.0064 (0.0001) −0.0189 (0.0001) –
0.0095 (0.0001) −0.0286 (0.0001) –
0.0015 (0.0087) −0.0067 (0.0008) –
0.0022 (0.0001) −0.0071 (0.0001) –
0.0033 (0.0001) −0.0014 (0.0400) –
0.0044 (0.0001) −0.0022 (0.0005) –
0.000280 (0.0001) −0.3758 (0.0001)
0.000418 (0.0001) −0.5736 (0.0001)
0.000093 (0.0012) −0.0697 (0.0840)
0.000098 (0.0002) −0.1049 (0.0058)
–
–
−0.1732 (0.0010)
−0.2618 (0.0001)
0.988 150
0.980 150
0.714 150
0.813 150
0.141 150
0.301 150
0.211 150
0.370 150
to disentangle the relationships with age from relationships with birth year. The mid-range ages of 65 and 75 and birth years of 1935 and 1924 were chosen for illustrative purposes. The 10-year span aligns with the length of our study period as well as all ages occurring in the retirement years. SAS Version 9.1 was used for all analyses.
3. Results To help give an overall sense of trends, Table 1 shows the prevalence rates across the four needs indicators by sex and age group over the five survey cycles used in this analysis. The clearest picture for both males and females is in the mortality data where death rates rise with age but fall when comparing across survey years. Recall, these estimates are from Vital Statistics records and not subject to sampling variability. Reported mobility problems, pain and poor selfreported health also increase with older ages but a trend is not as apparent across surveys. A comparison of males with females indicates lower mortality rates for females for each age group. Finally, females are more likely to experience mobility problems in the 75–84 year age group. The OLS regression results are presented in Table 2. Findings show both age and birth year individually have significant effects on all outcomes measured. Nested Ftests indicated the interaction term between age and birth year should be included for mortality, mobility and pain. This implies the magnitude of the relationship between age and health is conditional on birth year and, conversely, the relationship between birth year and health is conditional on age. That is, aging’s association with the probability of dying, having mobility problems or being in pain is not constant across birth years. The final model predicting the probability of reporting poor health does not include an interaction term so the coefficients, therefore, can be interpreted in the usual manner. Older individuals are more likely to describe their health as poor than those born more recently. Only for mortality was an age squared term included in the preferred specification. While some previous studies have shown significance with the inclusion of an age squared term (see, for example [21,22,29]), these studies model expenditures rather than health needs indicators directly.
Poor self-assessed health
Fig. 2 shows the age patterns, or age progression, for males and females born in 1924 and 1935.2 The probability of health problems increases with age for all outcomes, as illustrated by the positive slopes. For mortality, mobility problems and pain, the slope is steeper for those born in 1924 compared with those born 11 years later. Health declines with age but the progression is slower for those born more recently. This result is consistent for both males and females. Since there is no interaction term included in the poor self-reported health model, the relationship with age is constant regardless of birth year.3 We also ran our regressions including education as a control for socio-economic factors.4 Consistent with previous research [52,53], higher education is associated with better health. Noteworthy, however, regardless of education level, individuals born more recently are healthier. The age/cohort interaction is significant for those with high or low education. Fig. 3 compares the marginal effect of birth year for 65 year olds with that of 75 year olds.5 Moving along any single line represents a comparison of individuals who are the same age at different points in time. For example, a 65year-old born in 1929 would be observed in 1994 while a 65-year-old born in 1936 would be observed in 2001. The solid lines indicate the trend for 65 year olds born in 1930–1938 while the dashed lines show 75 year olds born 1920–1928. To keep plots aligned, “survey”6 year rather than birth year is plotted on the X-axis. Unlike with age progression, the slopes do not all move in the same direction. Mortality declines across birth years for both ages while poor self-reported health rises. The slopes are mixed for
2 The curve in the plots for mortality is due to the inclusion of an agesquared term in the model meaning the relationship between age and the probability of dying is non-linear. 3 When fair and poor health categories are combined, results confirm that there is no interaction age/cohort effect on self-assessed health. 4 Education is highly correlated with other factors such as income meaning it captures much of the SES effects. In fact, education may be the preferred measure as it attained prior to our outcome measures and is, therefore, exogenous. As well, it will be generally stable throughout our study period and not affected by health for our age range [53,67]. 5 Since the constructed cohort variable runs in reverse to the year of birth, probabilities for Fig. 3 are multiplied by −1. 6 We put survey in quotations as the points plotted were not necessarily actual survey years in our sample but rather points used in predicting.
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Fig. 2. Marginal effect of age.
mobility and pain with 65 year olds observed more recently being more likely to experience problems while 75 year olds are less likely over time. In Table 3 we summarize the marginal effects of age and birth year from Figs. 2 and 3. Except for poor self-reported health, it is clear that those born in 1924 have a greater rate of deterioration in health than those born in 1935. For example, the likelihood of mobility problems increases
0.81% for each year of age for males born in 1924 but 0.50% for those born in 1935. Comparing females born in 1924 to those born in 1935 shows a probability of 1.2% versus 0.74%, respectively. Notice in all cases, a change in age has a stronger association with ill-health than a change in birth year. Predicted overall probabilities are illustrative in making comparisons using various age and birth year scenarios. Our
Fig. 3. Marginal effect of birth year.
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Table 3 Marginal effects of age and birth year. Mortalitya Males (%) Age effect Born 1935 Born 1924
Females (%)
Mobility problems
Pain
Males (%)
Males (%)
Females (%)
Poor self-assessed health Females (%)
Males (%)
Females (%)
0.21 0.28
0.15 0.19
0.50 0.81
0.74 1.20
0.11 0.21
0.17 0.27
0.33 0.33
0.44 0.44
Birth year effectb Age 65 −0.05 Age 75 −0.12
−0.02 −0.05
0.06 −0.22
0.14 −0.28
0.07 −0.03
0.07 −0.02
0.14 0.14
0.22 0.22
a Since the age/predicted probability plot for mortality is curved, the effect is not constant across ages. The marginal effect of age shown is the trend from age 65 to 73. b As with Fig. 3, the signs are reversed to reflect a larger (i.e. more recent) birth year as a positive change.
mortality model suggests a 71-year-old male born in 1924 has a 3.6% chance of dying but a male of the same age born in 1935, a 2.6% probability. Comparing a 71-year-old male born in 1924 to one born in 1936 shows mobility problems are predicted for 8.6% and 7.5%, respectively. The probability of pain is about equal at 3.8% for this comparison with older cohorts showing higher probabilities for those over 71 years of age. Results for females show a similar pattern. 4. Discussion Our results show that although the probability of mortality, mobility problems, pain and poor self-assessed health increases with age, the relationship between age and health has changed over time. For three of our four measures, those born more recently are ‘aging’ more slowly. Controlling for age, both males and females born more recently are less likely to die than those born earlier suggesting Canadians are living longer over time. Our analysis reveals mortality rates are shifting over time meaning the period of high health care usage prior to death [54,55] is changing as well. Planning based on past patterns of utilization or current age distributions of needs could therefore lead to overprovision of the capacity required to meet the needs of what are no longer ‘close to death’ age groups. Mobility and functioning problems can lead to considerable resources being devoted to daily care, often through a team of health care professionals [46]. As well “. . .lower extremity function is predictive of severe disability, death and institutionalization” [56] (p. 1006). Even small changes in the prevalence of mobility problems can affect planning for a broad range of health care providers. Nurses in particular play a crucial role in mobility assistance and rehabilitation [46,57,58]. The effects of pain can be felt across a wide spectrum of the health care system both through the investigation of the source of the problem and also its possible consequences. The literature suggests that self-reported pain is associated with high utilization of health care services as it can lead to other impairments [28,59–61] and is a key predictor of patient-initiated visits [62]. If the prevalence of mobility problems and pain in the population is falling within age groups, fewer resources would be required to serve needs arising from these conditions per 1000 population. Although the numbers within older age groups may be increasing in an aging population
this does not mean that the needs for health care resources to serve these age groups are increasing at the same rate. The consequences of overlooking changes in age specific needs for care for health care costs can be considerable. For example, Birch and Maynard [63] argued that British plans to expand dental school places in order to maintain service levels in the 1980s based on constant levels of oral health need would lead to an excess supply of dentists given the rapid improvements in oral health status occurring at the time. Excess supply did not occur however because of unplanned increases in the average level services per patient need as dentists sought to maintain workloads. Birch et al. [3] found that applying observed trends in levels of need in the Atlantic provinces of Canada was associated with a 5% reduction in the expansion of training places required to meet population needs compared to basing training requirements on the assumption of observed levels of needs continuing indefinitely. The cumulative effects on health care costs of the annual flow of new medical graduates being 5% greater than required are likely to be substantial as service levels expand to absorb this supply. Reductions in birth rates as well as improvements in child health have led pediatricians to search for ways of expanding service levels as opposed to aligning supply with needs [8]. Poor self-assessed health does not show the same agespecific reduction in prevalence between cohorts. This may be because of the subjective nature of the measure (as compared with the other health measures used). The prevalence of the perception of poor health within age groups is the same regardless of year of birth. This outcome may be capturing a sense of well-being beyond more objective measurements of health associated with the increase in service provision within needs groups by providers seeking to maintain workloads. Although the NPHS and CCHS are large-scale nationally representative surveys, sample sizes are prohibitively small to calculate proportions by individual age for those 85 years and older. This is an important group to analyze particularly as life expectancies increase and baby boomers become elderly [64]. For these same reasons, however, future surveys will likely include larger samples of elderly allowing for a closer examination of the very old for HHR planning. Previous literature indicates that patterns of change in aging vary across ages even within older age groups [14,23,25,65]. The shift in the aging pattern for 75–85 year
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olds may be different than that for 55–65 year olds. To address these potential differences, we analyzed 10-year age groupings separately for each outcome. We found some evidence of a concentration of the aging effects within the 30-year age span, however, results were not consistent across outcomes or gender. We note that the power of these models was greatly reduced with only 50 observations each. As Aiken et al. [66] argue, larger sample sizes may be required for models where interactive terms are included, particularly if the effects are small. 5. Conclusion Even in the short time period covered in this study, the analysis shows that health care needs by age are changing over time in Canada. Planning based on constant levels of age-specific needs would therefore generate health care workforces that could not be sustained by simply maintaining the same approaches to care for those with health care needs. Given that providers have an important role in determining the demand for services we might therefore expect an expansion of the services provided to individuals with need, not necessarily as a result of planned public policy on the delivery care but as the outcome of provider’s concerns with maintaining workloads. Effective and efficient health care depends on clear distinction being made the needs for health care arising from the expected future levels and distribution of sickness and health risks in the population and the levels and mix of health care providers required to deliver that care. Attention should therefore be given to accommodating the complex dynamic relationships between aging and health in health human resources planning. Acknowledgments We wish to thank the Canadian Health Services Research Foundation, Nova Scotia Health Research Foundation, Ontario Ministry of Health and Long-Term Care, Health Canada (Communication Branch and Office of Nursing Policy), Saskatchewan Innovation and Science Fund, and the Capital District Health Authority for the generous financial support that made this research program possible. We would also like to thank Arden Bell and Heather Hobson at the Atlantic Research Data Centre for their assistance in vetting output. References [1] Birch S. Health human resource planning for the new millennium: inputs in the production of health, illness, and recovery in populations. Canadian Journal of Nursing Research 2002;33:109–14. [2] Birch S, O’Brien-Pallas L, Alksnis C, Tomblin Murphy G, Thomson D. Beyond demographic change in human resources planning: an extended framework and application to nursing. Journal of Health Service Research Policy 2003;8:225–9. [3] Birch S, Kephart G, Tomblin-Murphy G, O’Brien-Pallas L. Human resources planning and the production of health: a needs-based analytical framework. Canadian Public Policy 2007;33:S1. [4] Denton F, Gafni A, Spencer B. The SHARP way to plan health care services: a description of the system and some illustrative applications in nursing human resource planning. Socio-Economic Planning Sciences 1995;29:125–37.
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