Needs-based primary medical care capitation - Springer Link

Health Care Management Science 3 (2000) 89–99

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Needs-based primary medical care capitation: Development and evaluation of alternative approaches Brian Hutchison a , Jeremiah Hurley b , Stephen Birch b , Jonathan Lomas b , Stephen D. Walter c , John Eyles d and Fawne Stratford-Devai b a

Department of Family Medicine, Centre for Health Economics and Policy Analysis, Department of Clinical Epidemiology and Biostatistics, McMaster University, Health Sciences Centre Room 3H1E, 1200 Main Street West, Hamilton, Ontario, Canada L3N 3Z5 b Centre for Health Economics and Policy Analysis, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada L3N 3Z5 c Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada L3N 3Z5 d Institute of Environment and Health, McMaster University, Hamilton, Ontario, Canada L3N 3Z5

Objective. To develop and evaluate alternative methods of adjusting primary medical care capitation payments for variations in relative need for health care among enrolled practice populations. Methods. We developed alternative needs-based capitation formulae and applied them to a sample of capitation-funded primary care practices to assess each formula’s performance against a reference standard of capitation payments based on age, sex and self-assessed health status of the enrolled populations. The alternative formulae were based on: (1) age and sex; (2) age, sex and individually-measured socioeconomic characteristics; (3) age, sex and socioeconomic characteristics imputed from census data for enrollees’ neighbourhood of residence; (4) age, sex and standardized mortality ratio for enrollees’ neighbourhood of residence. Results. Age/sex-adjusted capitation payments for the six practices studied ranged from 10% higher to 18% lower than the reference standard payments. Capitation formulae based on socioeconomic and mortality data did not perform consistently better than the current age/sex-based formula. Conclusions. Primary medical care capitation payments adjusted only for age and sex do not reflect the relative health care needs of enrolled practice populations. Our alternative formulae based on socioeconomic and mortality data also failed to reflect relative needs. Methods that use other approaches to adjusting for differences in relative need among enrolled populations should be investigated.

1. Introduction Developing capitation formulae for providing health care to enrolled (rostered) populations involves challenges beyond those encountered in designing capitation formulae for geographically-defined populations. First, rostered populations tend to be smaller, requiring more precision in capturing the relationship between health care need at the individual level and the proxy for need incorporated in the formula. Second, population-based data such as standardized mortality ratios and census-derived sociodemographic profiles are more difficult to use as indicators of health care need for enrolled populations that include only a part of the population of a geographic area and may draw enrollees from several different areas. Capitation formulae that do not adequately adjust for variations in health care need may lead to inequitable, wasteful or inadequate care. Some populations (and organizations that provide their health services) will receive larger than appropriate shares of resources while others will receive smaller than appropriate shares. Depending on overall funding levels, the former case may encourage inefficiency or the provision of services that produce small health benefits. The latter case, especially when resources are constrained, may lead to underservicing and negative health effects. Because primary care practice populations tend to be small and to be drawn primarily from the ge Baltzer Science Publishers BV

ographic area immediately surrounding the practice site, marked variations in needs for health care among primary care practices are an inevitable consequence of variations in socioeconomic status among neighbourhoods and the strong relationship between socioeconomic and health status even in countries where health care is provided without direct charges to patients [6,21,22,34,39,50,58–60]. Among jurisdictions where capitation payment of primary care physicians is used [2,15,19,31,32,43–45,47], only the United Kingdom has adjusted for health care need beyond age and sex. British GPs receive a supplement for patients living in deprived areas, defined by eight census variables reported by GPs to increase their workload [27]. Scotland and Wales use a modified index that incorporates housing and standardized mortality ratios. Neither index has been validated against any reference standard measure of health care need [28]. The primary objective of the present study was to develop and evaluate alternative methods of adjusting primary medical care capitation payments for variations in relative need for health care among enrolled patient populations. We also assessed the extent of enrollee selection into capitated primary care practices. We used data from Ontario, Canada, where general practitioners have had the option of joining a capitation payment system (called Health Service Organizations [HSOs]) since the late 1970s. Under this scheme, capitation payments vary across 38 patient age/sex

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cells and cover a full range of acute, chronic and preventive services provided by general practitioners, excluding intrapartum obstetrical care. As of January 1997, there were 77 HSOs in Ontario, averaging 3.6 full-time equivalent physicians per HSO, with a total enrolled patient population of 425,000 (3.8% of the Ontario population). Citizens have free choice of general practitioner. Capitated physicians are not permitted to refuse or terminate enrollment of patients because of health status or need for health services, although this contractual requirement is not closely monitored. We first used health survey data and provincial expenditure data to develop a reference standard capitation formula based on the age, sex and self-assessed health status of the enrolled populations. Second, we developed alternative capitation formulae based on socioeconomic characteristics and small-area mortality using health survey data, provincial-level health care expenditure data and population mortality data. Third, we applied these formulae to a sample of capitation-funded group family practices. Our aim was to identify a needs-based capitation formula that was both valid and administratively feasible. Age and sex are important variables to include because of their strong relationship to morbidity risk [3,52,55,56,61] and to needs for preventive and reproductive health care. Further adjustment is required, however, for variations in need for health care that remain after accounting for the age-sex structure of a practice population. For our reference standard we chose to adjust beyond age and sex, using selfassessed health status because of its established construct and predictive validity and because of the availability of provincial-level data on self-assessed health linked to socioeconomic characteristics and health care utilization from a recent population survey (Ontario Health Survey [OHS] 1990). The self-assessed health measure was based on the following question: In general, compared to other persons your age, would you say your health is . . . 1. Excellent 2. Very good 3. Good 4. Fair

after adjustment for other measures of health status and (when measured) determinants of health such as life satisfaction [41], health behaviours [23–25,29], and social networks [23,29,40,62]. These studies were conducted in six countries (Canada, the United States, Britain, Israel, Australia and Hong Kong). Socioeconomic characteristics and small area mortality data have been used extensively in capitation formulae for health care resource allocation to geographic areas [4,7,9,16,17,20,26,46,48,51] and have also been used in calculating capitation payments to GPs in the UK [15]. Among geographically-defined populations in Quebec, variations in socioeconomic indicator scores explained 37% of the variation in a dichotomous measure of self-assessed health, while a mortality-based proxy explained 33% of the observed variation [1]. We assessed two sets of adjusters that might be administratively feasible to apply: (1) age, sex and socioeconomic characteristics estimated from census data for patients’ area of residence and (2) age, sex and standardized mortality ratios (SMRs) for patients’ area of residence. In both cases enrollees’ age, sex and postal code are required for their application. The methods and results are presented in two parts: (1) development of capitation formulae; and (2) validation of the formulae in sample practices. 2. Development of capitation formulae The objective was to develop a formula for allocating a fixed budget among primary care practice populations on the basis of the populations’ relative need for health care. The formulae therefore indicate budget shares for individuals in different need categories; these shares are then aggregated across enrollees to arrive at the practice’s share. The budget share in a need category (e.g., an age/sex/health status cell) is equal to the ratio of the expected resource needs for individuals in the category relative to the mean per capita resource use. For example, a resource share of 2.0 reflects per capita resource needs twice the average for the entire jurisdiction (e.g., province, state, country). The dollar amount corresponding to this share then depends on the overall budget. A step-by-step summary of the methods for developing the formulae is presented in the appendix.

5. Poor Variants of this measure have been extensively validated through their relationship to physician assessments [14,33, 35–37], functional ability [5,36,42,53], number and/or type of self-reported health problems [5,13,36,53], number of medications [13,36], acute symptoms [5], and various composite measures of health status [29,41]. Nine longitudinal studies have shown that self-assessed health predicts subsequent mortality [23–25,29,30,40,41,57,62] even after adjustment for sociodemographic characteristics. In seven studies [23–25,29,30,40,41,62], the relationship persisted

2.1. Methods 2.1.1. Reference standard formula We developed a reference standard capitation formula based on age, sex, self-assessed health status and health status-specific utilization of primary medical (general/family practitioner) services for the province of Ontario. The self-assessed health status measure was available for all OHS respondents 12 years of age or older. We computed needs-adjusted shares of general/family practitioner resources for age/sex/health status cells based


on province-wide fee-for-service expenditures by the Ministry of Health on general/family physicians services by age and sex in 1991–92, and individual-level OHS data on general/family physician contacts, health status, age and sex. We used current age-, sex-, and health-status-specific aggregate utilization of primary medical care as the basis for defining the relationship between age, sex, health status and the requirement for primary medical services. This approach assumes that, at the provincial level of aggregation, the population in each age/sex/health status cell is currently receiving an appropriate share of primary medical services relative to all other cells (i.e., current provinciallevel under- or over-utilization of services is proportionately the same in all cells). Computation of resource shares for primary medical services between age/sex strata was based on provincial expenditures by the Ministry of Health (appendix, #1). Computation of resource shares by health status within age/sex strata was based on the proportionate distribution of primary medical services among the five levels of self-assessed health over a 12 month period (appendix, #2). For example, if within an age/sex stratum, the 10% of persons with “fair” health accounted for 20% of self-reported general/family practitioner contacts, the appropriate needs-adjusted resource share for persons in fair health would be 0.2 ÷ 0.1 = 2.0. Needs-adjusted shares were multiplied by the average provincial per capita expenditure to generate needs-adjusted capitation rates (in dollars). 2.1.2. Socioeconomic capitation formula To develop the socioeconomic-based formula, we first considered the relationship between individuals’ socioeconomic characteristics and their health status. Given this relationship, we then used the reference standard needsadjusted shares calculated for age/sex/health status cells to estimate the resource shares using socioeconomic variables. We developed an ordered logistic probability model using individual-level data from the OHS. The model estimates the probability that an individual with given age, sex and socioeconomic characteristics (educational level, household income level, marital status, employment status and home ownership) is in each of the five possible health status categories. Those probabilities are then multiplied by the corresponding reference standard needs-adjusted shares, to give a needs-adjusted share for each individual. The socioeconomic variables correspond to variables included in the census. 2.1.3. Mortality-based formula We computed all cause, all age SMRs for census enumeration areas (125–375 households) and census tracts (5,000– 8,000 population) based on mortality data for 1985–94. To compute SMR-adjusted capitation rates, average provincial expenditures for family/general practitioners in each age/sex cell were multiplied by the SMR for the enumer-

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ation area (EA) or census tract (CT) in which the enrollee lived. 2.2. Results 2.2.1. Reference standard formula Age/sex/health status-specific shares of general/family practitioner resources based on this formula are shown in table 1. There is an 18-fold difference between the cells with the lowest and highest needs adjusted shares (12–14 year old females in excellent health [0.39] versus 80–84 year old males in poor health [7.01]). This compares with a 6-fold difference in the current age/sex based capitation payment system (12–14 year old females versus females 85 years of age or older). 2.2.2. Socioeconomic capitation formula The model relating socioeconomic characteristics and health status is shown in table 2. A likelihood-ratio test indicated that the overall explanatory power of the model is statistically significant. All of the socioeconomic variables except sex were statistically significant and the coefficients follow the expected pattern in most cases [8].

3. Application and validation of alternative capitation formulae To validate our socioeconomic- and mortality-based formulae and to evaluate the performance of the current age/sex-based formula, we applied formulae based on each of the following sets of adjusters to enrollee samples from capitation-funded practices: 1. Age, sex and self-assessed health status (reference standard). 2. Age and sex (current capitation formula). 3. Age, sex and individually-measured socioeconomic characteristics. 4. Age, sex and socioeconomic characteristics of enrollees’ neighbourhood of residence. 5. Age, sex and SMRs of enrollees’ neighbourhood of residence. We then compared payments based on the alternative formulae to those based on the reference standard formula. We also compared payments based on individually measured socioeconomic characteristics to those estimated from neighbourhood-level census data. The logic of our comparisons was as follows: (a) The reference standard payment based on age, sex and self-assessed health status is the best “gold standard” possible with currently available data. (b) Deviation of the current age and sex adjusted payment from the reference standard would indicate over- or

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B. Hutchison et al. / Needs-based primary medical care capitation Table 1 Needs-adjusted shares of general/family practitioner resources. Age Excellent males 00–04 05–11 12–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85+ females 00–04 05–11 12–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85+

0.414 0.433 0.434 0.424 0.404 0.430 0.438 0.465 0.423 0.431 0.555 0.705 1.032 1.102 1.403 1.919

0.392 0.646 0.966 1.052 0.975 0.763 0.635 0.632 0.573 0.666 0.467 0.644 0.683 0.998 1.268 2.716

Self-assessed health status Very good Good Fair

0.530 0.540 0.535 0.546 0.609 0.611 0.618 0.648 0.670 0.663 0.716 0.884 1.091 1.338 1.899 2.273

0.514 0.787 0.905 1.172 1.121 0.946 0.810 0.961 0.808 0.816 0.820 0.993 1.049 1.132 1.970 2.326

under-payment to a capitation-funded practice in relation to the relative health care needs of the enrolled population. (c) Consistent over-payment across capitation-funded practices would indicate possible patient selection based on health status, either directly through selection of lowerneed patients or indirectly through location of practice in an area with a lower-need population. (d) Payments based on individually measured socioeconomic characteristics that were consistently lower than those based on imputed characteristics would indicate that patient selection on the basis of socioeconomic characteristics may be occurring. (e) Systematically larger deviations from the reference standard of payments computed on the basis of census tract data compared to those computed on the basis of enumeration area data would indicate that using

0.569 0.648 0.566 0.679 0.773 0.728 0.825 0.885 0.906 1.001 1.091 1.321 1.425 1.835 2.275 3.090

0.537 0.931 1.265 1.326 1.473 1.335 1.296 1.411 1.529 1.248 1.310 1.355 1.731 1.911 2.352 2.850

0.677 0.653 0.907 1.095 1.480 1.819 1.476 1.642 1.824 1.760 1.675 2.067 2.626 2.611 2.794 4.184

0.935 1.387 1.794 2.514 2.411 2.292 2.645 2.645 2.230 2.418 2.100 2.298 2.544 2.757 2.653 4.379

Poor

Mean per capita NAS

1.682 1.803 1.146 1.454 1.814 3.871 2.884 3.431 3.155 3.065 4.180 2.725 3.191 3.551 7.012 5.362

1.187 0.623 0.501 0.538 0.539 0.586 0.665 0.721 0.726 0.829 0.885 0.978 1.120 1.309 1.576 1.854 2.273 2.943

4.014 2.075 3.729 2.834 4.792 5.778 4.899 4.067 4.472 3.811 4.307 3.181 3.295 3.068 4.343 3.369

1.120 0.608 0.516 0.850 1.107 1.254 1.273 1.179 1.116 1.216 1.237 1.219 1.236 1.368 1.594 1.843 2.262 3.015

smaller population units may be worthwhile as a means of minimizing the ecologic fallacy [18]. 3.1. Methods 3.1.1. Enrollee survey in sample practices We recruited one randomly selected capitation-funded practice from each of four sociodemographically varied geographic areas within Hamilton-Wentworth, the Regional Municipality that contains the highest concentration of capitated practices (38 of 77) in Ontario. In a pilot survey, we examined the comparative cost and effectiveness of conducting the survey by mail or telephone. Random samples of patients were drawn from one suburban practice and one inner city practice. The response rates were 80.4% and 63.3%, respectively. Response rates were similar by mail and telephone, but costs per subject were substantially higher by mail.


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Table 2 Ordered logistic probability model for health status and socioeconomic characteristics. Variable

Coefficient

Constant 5.273 Age −0.020 Sex (reference category male) Female 0.020 Education (reference category college or university graduate) Less than secondary −0.691 Some secondary school −0.523 Secondary school graduate −0.258 Some post secondary school −0.255 Household income (reference category > $50,000) Less than $20,000 −0.370 $20,000–$50,000 −0.114 Marital status (reference category single) Married −0.103 Widowed 0.128 Separated −0.105 Employment (reference category employed) Unemployed −0.247 Not in labour force −0.223 Home ownership (reference category renting) 0.244

t-ratio 110.051 −32.717 1.172 −23.941 −21.019 −10.252 −8.276 −13.469 −6.158 −4.049 2.715 −2.325 −3.231 −11.282 11.845

Dependent variable: self-assessed health status (0 = poor . . . 4 = excellent). Number of observations = 48,721. Chi-squared (14 df) 4216.921. Significance level 0.0000000.

For the main survey we conducted telephone interviews with a stratified random sample of patients about their socioeconomic characteristics and perceived health status. The size of the sample was chosen to estimate the average per capita resource share for each practice within 5% of the its true value, with a minimum of 20 subjects in each age/sex stratum per practice. 3.1.2. Application of capitation formulae The reference standard formula was applied by summing the appropriate age/sex-specific needs-adjusted shares for each individual in each practice, and then multiplying by the average capitation rate for the province under the capitation funding program. To apply the formula based on age, sex and individuallymeasured socioeconomic characteristics we used the procedure outlined in the appendix (#7a) to compute needsadjusted shares for each enrollee, summed these weighted needs-adjusted shares across enrollees, and multiplied by the mean capitation rate for the province. Application of the formula based on age, sex and socioeconomic characteristics of enrollees’ area of residence (EA or CT) was identical to that for the individual-level socioeconomic characteristics, except that socioeconomic characteristics were imputed to individual enrollees from census data, based on their area of residence. We applied the formula based on age, sex and SMRs of enrollees’ area of residence (EA or CT) by multiplying the enrollees’ age and sex-adjusted capitation rates by the SMRs for enrollees’ area of residence and summing across enrollees. We assessed the performance of the alternative formulae by comparing their implied payments to each practice to the

reference standard payment. We also assessed the nature and extent of patient selection in two ways. First, we compared reference standard payments to payments based on the current age/sex-based formula. Second, we compared payments based on individually measured socioeconomic characteristics to those based on socioeconomic characteristics imputed from neighbourhood-level census data. We included the pilot study data in the first of these “patient selection” comparisons, yielding a total of six practices for that analysis. 3.2. Results 3.2.1. Enrollee survey Response rates are presented in table 3. Overall, 15% of the sample were ineligible because they had left the practice or died. Twenty-four percent of interviews could not be completed, mainly because of incorrect or new unlisted phone numbers or inability of the interviewer to make contact in five attempts. Five percent refused to be interviewed. The overall response rate among eligible enrollees was 66%. A higher proportion of females than males was interviewed. Males represented 41.0% of those interviewed and 46.1% of those who refused or could not be interviewed for other reasons (X 2 = 10.38, p = 0.0013). The mean age of enrollees interviewed was 47.2 years (SD 21.8 years) compared to 48.8 years (SD 24.0 years) for enrollees who refused or could not be interviewed. This difference, although small, was statistically significant (p = 0.03). 3.2.2. Application of capitation formulae Table 4 shows a comparison of average daily capitation payments under the reference standard formula with pay-

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B. Hutchison et al. / Needs-based primary medical care capitation Table 3 Enrollee survey response rates.

Interviews attempted Ineligible (left practice, moved, dead) Unable to interview* Refused Completed interviews Response rate†

Practice A

Practice B

Practice C

Practice D

Total

1231 54 (4.4%) 265 (21.5%) 76 (6.2%) 836 (67.9%) 836/1177 (71.0%)

1241 322 (25.9%) 313 (25.2%) 44 (3.5%) 562 (45.3%) 562/919 (61.2%)

1228 205 (16.7%) 259 (21.1%) 63 (5.1%) 701 (57.1%) 701/1023 (68.6%)

1258 172 (13.7%) 358 (28.5%) 61 (4.8%) 667 (53.0%) 667/1086 (61.4%)

4958 753 (15.2%) 1195 (24.1%) 244 (4.9%) 2766 (55.8%) 2766/4205 (65.8%)

* Incorrect

phone number/new unlisted number, no contact after 5 calls, no English, out of country/province, ill/in hospital, hearing/cognitive impairment. † Completed interviews/(interviews attempted – ineligible). Table 4 Reference standard vs current formula: Average daily capitation ($; age 12+).

Practice A (n = 788) Practice B (n = 543) Practice C (n = 672) Practice D (n = 633) Practice E (n = 98) Practice F (n = 73)

Reference standard: age, sex, health status (95% CI)

Current formula: age, sex (95% CI)

Relative difference of current formula from reference standard* (95% CI)

0.426 (0.422, 0.430) 0.496 (0.490, 0.502) 0.473 (0.467, 0.478) 0.540 (0.533, 0.547) 0.408 (0.395, 0.414) 0.548 (0.533, 0.562)

0.470 (0.455, 0.484) 0.503 (0.501, 0.506) 0.449 (0.434, 0.465) 0.522 (0.517, 0.527) 0.426 (0.417, 0.436) 0.450 (0.430, 0.470)

10.2% (10.1%, 10.4%) 1.5% (1.3%, 1.8%) −4.9% (−5.1%, −4.7%) −3.3% (−3.5%, −3.1%) 4.5% (4.2%, 4.8%) −17.8% (−18.0%, −17.5%)

* Current formula − Reference standard Reference standard

× 100.

ments under the current age/sex-based formula. For all six practices, the difference in payments is statistically significant. In one case the current payment is more than 10% higher than the reference standard payment and in another it is 18% lower. Three of the practices receive more and three receive less per capita under the current formula than they would under the reference standard formula. Average daily capitation payments for the enrollee samples based on each of the alternative formulae are shown in table 5. Figure 1 shows the difference between payments under the reference standard and those under each of the alternative formulae. The two pilot study practices are excluded because of their small sample sizes. None of the formulae based on socioeconomic or mortality data performed consistently better than the current age/sex-based formula. Average payments are consistently lower when the formulae based on socioeconomic characteristics are imputed from census data than when individual data from the enrollee survey are used. The mortality-based formulae produce capitation payments that are consistently, and often substantially, higher

than the reference standard. For three of the four practices the magnitude of overpayment is higher when based on EA SMRs than on CT SMRs. 4. Discussion Our results indicate that capitation payments to family/general practices adjusted only for age and sex (the current formula) do not reflect the relative health care needs of practice populations. Among the six practices we studied, three appear to be receiving higher and three lower than appropriate payments under the current capitation formula, arguing against the existence of systematic patient selection based on health status. A plausible explanation for this finding is that the health status of a primary care practice population largely reflects practice location. Most of a primary care practice population is likely to live near the practice site, so practices in socioeconomically or environmentally disadvantaged areas would be expected to have populations that were less healthy than average, whereas those located in affluent areas would be likely to have pop-


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Table 5 Reference standard vs alternative capitation formulas: Average daily capitation ($; age 12+).

Practice A (n = 663)§ Practice B (n = 517)§ Practice C (n = 649)§ Practice D (n = 627)§

Reference standard (RS): age, sex, health status (95% CI)

Current formula: age, sex (difference from RS)

Age, sex measured SE* (difference from RS)

Age, sex, imputed SE [CT]† (difference from RS)

Age, sex, imputed SE [EA]‡ (difference from RS)

Age, sex, SMR [CT] (difference from RS)

Age, sex, SMR [EA] (difference from RS)

0.420 (0.415, 0.425) 0.497 (0.491, 0.503) 0.476 (0.470, 0.482) 0.542 (0.535, 0,549)

0.468 (+11.4%) 0.507 (+2.0%) 0.450 (−5.5%) 0.523 (−3.4%)

0.463 (+10.2%) 0.500 (+0.6%) 0.453 (−4.8%) 0.522 (−3.7%)

0.451 (+7.5%) 0.497 (−0.06%) 0.450 (−5.5%) 0.508 (−6.2%)

0.458 (+9.1%) 0.489 (−1.5%) 0.445 (−6.5%) 0.505 (−6.8%)

0.444 (+5.7%) 0.618 (+24.3%) 0.493 (+3.7%) 0.574 (+6.0%)

0.581 (+38.3%) 0.597 (+20.1%) 0.500 (+5.1%) 0.609 (+12.4%)

* SE

= socioeconomic characteristics. = census tract level (5,000–8,000 population). ‡ EA = enumeration area level (125–375 households). § The number of enrollees included in these comparisons is smaller than in table 4 because of incomplete socioeconomic data from the enrollee survey and failure to link some enrollees to EAs and CTs because of incorrect postal codes. † CT

Figure 1. Percent difference of average daily capitation ($) for alternative capitation formulae relative to reference standard (0%). ∗ SE = socioeconomic characteristics; † CT = census tract level; ‡ EA = enumeration area level.

ulations that were healthier than average. This hypothesis is supported by the finding that our inner city practice (practice F) receives a 17.8% lower payment under the current age/sex-based formula than under the reference standard formula. On the other hand, practice A, which receives 10.2% more under the current formula than under the reference standard formula, is in a relatively affluent suburban area. Majeed has recently observed that “general practices serve small populations that differ greatly from each other in their demographic, social and clinical characteristics”, thus hampering efforts to develop capitation formulae for GP fundholding and prescribing in the UK [38]. Capitation payments based on individually measured socioeconomic characteristics are higher than those based on imputed characteristics. This suggests that patient selec-

tion on the basis of socioeconomic status is not occurring. If capitated practices selectively attracted or retained socioeconomically advantaged patients, payments based on individually measured characteristics should be lower than payments based on imputed characteristics. Given the small number of practices included in our study, our findings regarding the possibility of patient selection can only be viewed as suggestive. None of the alternative formulae generated payments that were consistently closer to the reference standard payments than those based on age and sex alone. This reflects the imperfect correspondence between socioeconomic characteristics and small area SMRs on the one hand and individual health status on the other, as well as the fact that family/general practice enrollees are not a representative sample of the surrounding population. Contrary to expecta-

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tions, formulae using socioeconomic or mortality data from EAs tended to perform less well than those using CT-level data. For the mortality-based formulae, the estimates of SMRs for some EAs are unstable because of small numbers of deaths, despite our use of 10 years of mortality data. The number of deaths in the 279 EAs ranged from 0 (1 EA) to 113, with a median of 19, a mean of 27 and a standard deviation of 23. Although our results raise questions about the use of socioeconomic characteristics or mortality data as proxies for relative health care needs in a primary care capitation formula for enrolled populations, there is scope for further research. For example, other sets of socioeconomic characteristics and different functional forms of the relationship between health status, mortality rates and socioeconomic characteristics might be explored. We used all-age SMRs in our mortality-based formulae. Conceivably, SMRs for ages 0–74, which has been proposed as an indicator of premature mortality, might perform differently [10–12,54]. Combinations of mortality and socioeconomic based proxies for health status among geographically-defined populations have previously been considered and might provide a fruitful area of research for practice-based populations [1]. Sheldon and colleagues [49] attempted to develop a capitation formula for general practitioner fundholding budgets based on correlates of utilization of fundholding procedures at the small area level (age, sex, SMR and census-derived socioeconomic variables and self-reported activity-limiting longstanding illness) after adjusting for variations in supply of health care (GPs, hospital beds and chronic care beds). They found that “No sensible simple model including determinants of use other than age and sex could be derived” and concluded that “A capitation formula based on information derived from individual cohort data may be the only means of promoting equity and efficiency and of avoiding discriminating against patients with known high cost health problems.”

For the present we are faced with a situation in which age/sex-adjusted capitation payments inadequately capture differences between general practices in health care needs, while alternative approaches to computing capitation payments using readily available data perform no better than using age and sex alone – a policy conundrum awaiting further investigation. Contributors Brian Hutchison initiated the research, brought together the research team, participated in the discussion of core ideas, drafted the research protocol, supervised research personnel in the collection and analysis of data, and drafted the paper. Jeremiah Hurley, Stephen Birch, Jonathan Lomas and John Eyles participated in all phases of the research from conceptualization to the writing of the paper. Jeremiah Hurley played a major role in developing the analytic strategy. Stephen D. Walter took major responsibility for developing the sampling strategy for the enrollee survey, addressed problems in data analysis, and developed formulae for computing confidence intervals on estimates of payments to the study practices under different capitation formulae. Fawne Stratford-Devai participated in the discussion of study design and implementation and collected and analyzed data. Acknowledgement Funded by the Ontario Ministry of Health (Research Grant # 04473A). Dr. Walter holds a National Health Scientist award from Health Canada. Dr. Eyles is supported by the Eco-Research Program in Environmental Health. We also acknowledge support from the Ontario Ministry of Health to the Centre for Health Economics and Policy Analysis.

Appendix: Process of developing and applying alternative needs-based capitation formulae Stage 1 – Developing alternative needs-based capitation formulae Adjustment variables 1. Age, sex.

Data required and source • health care expenditures by age and sex (Ontario Ministry of Health)

Process • calculate province-wide expenditures for relevant programs within each of 38 age-sex cells • calculate the age-sex adjusted share as follows:  Proportion of provincial  expenditure accounted  for by cell i, j ˆ i,j =  AS   Proportion of provincial  population accounted for by cell i, j where: i = sex, j = age

      

B. Hutchison et al. / Needs-based primary medical care capitation 2. Age, sex and self-assessed health status (reference standard formula).

• as in 1 above, plus self-assessed health status and self-reported health care utilization (Ontario Health Survey [OHS])

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• take age-sex-adjusted shares and adjust for need using self-assessed health status • needs-adjusted share calculated as follows:   Proportion of physician contacts within cell i, j    for individuals within i, j      with health state h  ˆ i,j,h = AS ˆ i,j ×  NAS  Proportion of population     within cell i, j made up by    individuals with health state h where: i = sex [male or female], j = age, h = health status [excellent, very good, good, fair, poor]

3. Age, sex and socioeconomic characteristics.

• as in 2 above, plus individual-level socioeconomic characteristics (OHS)

(a) use ordered logit to estimate the relationship between self-assessed health status and socioeconomic characteristics (b) given an individual’s age, sex and socioeconomic characteristics, use the parameters estimated in 3(a) to predict the probability that an individual is in each of the five possible health status categories b i,j,h ) for a (c) estimate the socioeconomic needs-adjusted resource share (SENAS person, given their age and sex, as follows: b i,j,h = SENAS

4 X

ˆ i,j,h pˆi,j,h × NAS

h=0

4. Adjust using age, sex and mortality risk.

• as in 2 above, plus provincial level mortality data by age and sex (Vital Statistics), plus small-area mortality data (census tract or enumeration area) (Registrar General), plus census population data

(a) calculate standardized mortality by enumeration area (EA) or census tract (CT) in the province as follows: P P mi Pi di SMR = P i = Pi i Mi Pi i Di where mi = regional age-specific death rate for age group i Mi = standard age-specific death rate for age group i Pi = regional population in age group i di = actual numbers of deaths in age group i Di = expected number of deaths in region in age group i if standard age-specific death rates prevailed (b) calculate the needs-adjusted resource share by multiplying the age-sex-adjusted share (from 2 above) by the SMR for the individual’s EA or CT

Stage 2 – Applying the alternative formulae to patient rosters of capitation-funded practices 5. Sample the enrolled patient populations of capitation-funded practices. Survey to obtain information on age, sex, self-assessed health status and socioeconomic characteristics. ˆ i,j,h (calculated in 2 above) based on the individual’s age, sex and self-assessed health status. 6. Assign each sample individual the relevant NAS 7. Estimate the needs-based share for each sample individual based on three alternative measures of their socioeconomic characteristics: (a) Individual-level socioeconomic characteristics (obtained in survey in step 5 above): (i) use the parameters estimated in 3(a) and an individual’s socioeconomic characteristics to estimate the probability that an individual is in each health status level, given their age, sex and socioeconomic characteristics (ii) calculate the socioeconomic needs-adjusted share for each individual using the formula described in 3(c) above and the probabilities from 7(a)(i) (b) Imputed census socioeconomic data (enumeration area level): (i) use the parameters estimated in 3(a) and the individual’s imputed socioeconomic characteristics, based on the socioeconomic characteristics of the enumeration area in which the individual lives, to estimate the probability that an individual is in each health status level, given their age, sex and the socioeconomic characteristics of their enumeration area (ii) calculate the imputed socioeconomic needs-adjusted share for each individual using the formula described in 3(c) above and the probabilities from 7(b)(i)

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B. Hutchison et al. / Needs-based primary medical care capitation (c) Imputed census socioeconomic data (census tract level): (i) use the parameters estimated in 3(a) and the individual’s imputed socioeconomic characteristics, based on the socioeconomic characteristics of the census tract in which the individual lives, to estimate the probability that an individual is in each health status level, given their age, sex and the socioeconomic characteristics of their census tract (ii) calculate the imputed socioeconomic needs-adjusted share for each individual using the formula described in 3(c) above and the probabilities from 7(c)(i)

ˆ i,j ) by the standardized mortality 8. Estimate the needs-based share for each sample individual by multiplying the relevant age-sex adjusted share (AS ratio associated with the enumeration area in which the individual resides. ˆ i,j ) by the standardized mortality 9. Estimate the needs-based share for each sample individual by multiplying the relevant age-sex adjusted share (AS ratio associated with the census tract in which the individual resides. 10. To obtain the share of resources each practice would receive under each formula, sum the needs-adjusted shares estimated under each formula across all of its members. 11. To obtain the funding allocation to each practice under each formula, multiply the sum of the needs-adjusted shares by the average capitation rate for the province under the capitation funding program.

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