The Implications of Regional Variations in Medicare Spending. Part 1: The Content, Quality, and Accessibility of CareFREE

One approach to determining whether the increased spending in some U.S. regions leads to better care and better outcomes would be to conduct a randomized trial. This would ensure that assignment to the treatment and control groups (those receiving more and less spending) was independent of patient characteristics. Logistic barriers to such a trial, however, would be substantial. We therefore conducted a cohort study in four parallel cohorts using a natural randomization approach (17), in which one or more exposure variables allowed assignment of patients into treatment groups (different levels of average spending), as would a randomized trial. An overview of the design is provided in Figure 1.

Because some of the regional differences in Medicare spending are due to differences in illness levels (enrollees in Louisiana are sicker than those in Colorado) and price (Medicare pays more for the same service in New York than in Iowa), we could not use Medicare spending itself as the exposure. We therefore assigned U.S. hospital referral regions (HRRs), and thus the cohort members residing within them, to different exposure levels. We did this by using the End-of-Life Expenditure Index (EOL-EI), a measure reflecting the component of regional variation in Medicare spending that is due to physician practice rather than regional differences in illness or price. Because regional differences in end-of-life spending are unrelated to underlying illness levels, it is reasonable to consider residence in HRRs with different end-of-life spending as a random event. The index was calculated as standardized spending on hospital and physician services provided to a reference cohort distinct from the study cohorts: Medicare enrollees in their last 6 months of life.

We confirmed that the exposure used to assign the HRRs achieved the goals of natural randomization: 1) Study samples assigned to different levels of the exposure [the EOL-EI] were similar in baseline health status, and 2) the actual quantity of services delivered to the individuals within the study samples nevertheless differed substantially across exposure levels and was highly correlated with average per capita Medicare spending in the HRRs. We then followed the cohort members for up to 5 years after study enrollment and compared the processes of care (Part 1) and health outcomes (Part 2) across HRRs assigned to different exposure levels.

We sought study samples that would be similarly ill across regions based on the occurrence of an incident illness (acute myocardial infarction [MI], hip fracture, colorectal cancer) or in which we had excellent data for case-mix adjustment (acute MI, Medicare Current Beneficiary Survey [MCBS] sample). We restricted the eligible population to Medicare enrollees between the ages of 65 and 99 years who, at the time of study enrollment, were eligible for both Medicare Parts A and B and were not enrolled in a Medicare health maintenance organization (HMO).

The acute MI cohort was drawn from patients included in the Cooperative Cardiovascular Project, which identified from billing records a national sample of Medicare beneficiaries who were discharged after acute MI between February 1994 and November 1995 (18). We excluded patients with an unconfirmed acute MI (with the same criteria as in previous studies [19]) and included only the first episode of acute MI for a given patient. The hip fracture and colorectal cancer cohorts were selected based on a first admission between 1993 and 1995 for a primary diagnosis of hip fracture or colorectal cancer with resection, using the same International Classification of Diseases, Ninth Revision, Clinical Modification codes as in earlier work (20). Hospitalization rates for acute MI, hip fracture, and colorectal cancer vary little across regions (21), and patients with incident cases of these conditions are likely to be similarly ill in different communities (20). We excluded patients with a previous hospitalization for the same diagnosis in the year before their index stay. The general population cohort was drawn from the access-to- care component of the MCBS, a continuous panel survey that is representative of the Medicare population (22). Our inclusion criteria are detailed in the Appendix.

Trained abstractors working in the Cooperative Cardiovascular Project obtained characteristics of patients in the acute MI cohort from the medical record (18). Quality of the chart review process was monitored by random reabstractions, and percentage agreement was generally very high (93.3% to 94.8%) (23). Missing data for clinical variables were handled by including a specific categorical variable for patients with each missing variable (for example, admission blood pressure missing). Income was defined based on ZIP code of residence by using 1990 U.S. Census data.

For the hip fracture and colorectal cancer cohorts, we coded the presence of specific comorbid conditions based on diagnoses recorded on the discharge abstract, as was done in previous work (24, 25). Cancer stage was classified as distant versus local or regional because this classification has been found to correspond most closely to reported stage, according to analyses of linked MedicareSurveillance, Epidemiology, and End Results data (26). Data from the 1990 U.S. Census, measured at the level of the ZIP code, were used to provide measures of income, education, disability status, urban or rural residence, employment, marital status, and Hispanic origin. For all three chronic disease cohorts, we used American Hospital Association data to characterize the hospital teaching status (27) and the Medicare claims files of patients' index hospitals to determine the volume of cases of hip fracture, colorectal cancer, and acute MI treated per year.

Data collection and preparation procedures for the MCBS are described elsewhere (22). Because not all respondents completed all survey items, analyses of utilization, access to care, satisfaction, and survival are based on slightly different numbers of respondents (Appendix, Section C). Among the patient attributes, responses were most likely to be missing for income (5%); patients for whom income data were missing were recoded to the lowest-income group after analyses showed them to be similar in other attributes and survival to other patients in that group. The MCBS includes responses from proxies, which represented a maximum of 8.0% of our initial cohort. There were no differences in the proportion of proxy responses across the quintiles of spending, and excluding proxy responses did not alter our findings.

We used information from Medicare enrollment files to determine the percentage of all Medicare enrollees in each HRR who were enrolled in HMOs, to assign patients to one of nine major regions of the country, to determine whether patients had moved within an HRR or across HRRs within 1 or 2 years before their index admission, and to determine when patients should be censored based on loss of Medicare fee-for-service coverage (for utilization analyses) or based on relocation from their original HRR.

We used two approaches to determine cohort members' exposure to different levels of Medicare spending in their regions of residence. Previous research has shown that the dramatic differences in end-of-life treatment across U.S. regions are highly predictive of differences in total spending (13, 28) but are not due to differences in case mix or patient preferences (29). Our primary measure of exposure was the EOL-EI, which was calculated as age-sex-raceadjusted spending (measured with standardized national prices) on hospital and physician services provided to Medicare enrollees in their last 6 months of life in each of the 306 U.S. HRRs from mid-1994 to 1997, excluding any members of the study cohorts (Appendix, Sections D and E).

Although Medicare enrollees identified 6 months before death are identical in terms of their risk for death (which is 100%), they may differ in other ways across HRRs (for example, in illness levels unrelated to risk for death). We therefore repeated the major analyses with an alternative exposure measurean Acute Care Expenditure Index (AC-EI)which was based on differences across HRRs in risk-adjusted spending during an acute care episode, calculated as follows. For each of the four study cohorts, we determined age-, sex-, race, and illness-adjusted spending on physician and hospital services (measured with standardized national prices) provided during the first 6 months after index hospitalization across the 306 HRRs. The AC-EI subsequently used to assign each cohort to different exposure levels was the average of the age-sex-race-illnessadjusted spending during the acute episode of care in the other cohorts (Appendix, Section F).

The EOL-EI measures regional differences in practice at the end of life, while the AC-EI measures regional differences in practice during acute illness (or exacerbation of a chronic illness). Both measures were highly predictive of average age-sex-raceadjusted Medicare spending at the HRR level (r = 0.81 for the EOL-EI and 0.79 for the AC-EI in the acute MI cohort) and of regional differences in utilization. Both exposure measures produced similar results, so we present our findings on utilization based on the EOL-EI. (A sensitivity analysis of our mortality analyses using the AC-EI is presented in Part 2.) For many analyses, we grouped HRRs into quintiles of increasing exposure to Medicare spending based on the EOL-EI.

We used 100% Part A and B Medicare claims for all four cohorts to determine rates of specific physician and hospital services within the first year of follow-up and a 5% sample to examine aggregate physician utilization over the full 5 years of follow-up. The major categories of physician services provided to the Medicare population (MCBS) were defined by using Medicare's BerensonEggers Type of Service classification (cms.hhs.gov/data/betos/default.asp). The acute MI chart review allowed us to describe the proportion of patients in defined clinical subgroups who received specific medications or interventions during the initial hospitalization. Analyses of the quality of care were restricted to patients who were ideal candidates for each therapy (that is, patients with any absolute or potential contraindication to the treatment were excluded), as in the original description of the Cardiovascular Cooperative Project (18). The in-person interviews for the MCBS included specific questions about the types of visits received during the past year, waiting times at these visits, receipt of specific preventive services, and access to care.

All analyses used the patient as the unit of analysis and measured other attributes at progressively higher levels of aggregation where appropriate (ZIP code of residence, hospital of index admission, and HRR of residence). To test whether patients' baseline characteristics differed across HRRs with differing EOL-EI levels, we used logistic regression at the individual level with the attribute (for example, age 65 to 74 years) as a dichotomous dependent variable and the HRR-level EOL-EI as the independent variable. To assess the aggregate impact of any differences in individual attributes on average baseline risk for death across regions of increasing EOL-EI, we used logistic regression to determine each individual's predicted 1-year risk for death as a function of his or her baseline characteristics. The models had modest to excellent predictive ability (c-statistics were 0.61 for the colorectal cancer cohort, 0.68 for the hip fracture cohort, 0.77 for the acute MI cohort, and 0.82 for the MCBS cohort). We used these models to determine the average predicted risk for death across quintiles of EOL-EI. We then determined average predicted 1-year risk for death in each HRR and tested for an association across HRRs using logistic regression.

For the analyses of aggregate utilization, cohort members were censored (no longer followed) if they lost Medicare insurance (either Part A or B eligibility), enrolled in an HMO, or moved out of the original HRR of residence. The average amount of hospital and physician services provided to each cohort member across quintiles of the EOL-EI was calculated by using standardized national prices (Appendix, Section D) and was calculated separately for the acute episode (index admission to 6 months for the three chronic disease cohorts) and for 6 months to 5 years (chronic disease cohorts) or for 5 years (MCBS cohort). To control for baseline differences, we used linear regression, weighting by follow-up time, with the log-transformed utilization of hospital and physician services per person-year as the dependent variable for the individual and the quintile of end-of-life spending as the primary independent variable. Coefficients and confidence intervals were back-transformed to provide estimates of the adjusted relative rates of utilization in the highest compared with the lowest quintile for each cohort.

We analyzed only the first year of follow-up (for which we had 100% physician claims) to compare rates of specific physician services across quintiles. We used Poisson regression to calculate adjusted relative rates of specific services (dependent variable) in quintile 5 compared with quintile 1, controlling for baseline differences (independent variables) (30). We then computed pooled relative rates across the cohorts, using a weighted average of the individual regression coefficients for each cohort and weighting by the inverse of the variance (31).

The specific independent and dependent variables included in each regression are described in Appendix Table 5. We used the REG routine of Stata 6.0 (Stata Corp., College Station, Texas) to perform all regression analyses. For the analyses of the MCBS cohort, we used SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina) to account for sampling weights and the two-stage design.

The 306 U.S. HRRs were assigned to quintiles of Medicare spending based on the primary exposure measure, the EOL-EI, which averaged $14 644 in quintile 5 (the highest-spending quintile) and $9074 in quintile 1 (the lowest-spending quintile) (Figure 2). Average age-sex-raceadjusted per capita Medicare spending was highly correlated with EOL-EI (r = 0.81); per capita Medicare spending was $6304 in quintile 5 and $3922 in quintile 1. Residents of the highest quintile received 61% more Medicare resources than those in the lowest quintile whether measured by the EOL-EI or by average Medicare spending. Quintiles with a higher expenditure index had more hospital beds and physicians, a relative predominance of large hospitals and teaching hospitals, and a higher proportion of urban residents. The percentage of Medicare beneficiaries enrolled in HMOs was higher in both the lowest and highest quintiles than in the middle quintiles.

Tables 1, 2, 3, and 4 present selected characteristics of each study cohort in each quintile and a test for trend across HRRs that had a higher EOL-EI. Because of the large sample sizes, many differences in the chronic disease cohorts were statistically significant. Notable differences were found in racial composition (more black patients in higher quintiles) and income (higher quintiles had more enrollees in the highest and lowest income categories). Smaller differences across quintiles were apparent in age, sex, comorbid conditions, and cancer stage. For the acute MI cohort, patients in the highest quintile had a higher prevalence of nonQ-wave infarctions and congestive heart failure but were less likely to have creatine kinase levels over 1000 IU/L. For the MCBS cohort, residents of HRRs in the quintiles with a higher EOL-EI were more likely to report being in fair or poor health but were less likely to live in a facility. Few differences were found, however, in activities of daily living or instrumental activities of daily living, smoking, or reported chronic conditions.

Tables 1, 2, 3, and 4 also present the average predicted risk for death in each quintile and a formal test of trend assessing whether HRRs with a higher EOL-EI had higher predicted mortality at baseline. A higher expenditure index was associated with an increased predicted risk for death for the AMI cohort, a lower predicted risk for death for the hip fracture cohort, and no significant difference in predicted mortality across spending levels for the colorectal cancer or MCBS cohorts. These data indicate that the burden of illness in these cohorts is similar across HRRs that differ by more than 60% in both EOL-EI and, as grouped according to the EOL-EI, average per capita Medicare spending.

Figure 3 displays the aggregate utilization of hospital and physician services by the study cohorts across quintiles. The data presented exclude the initial acute episode of care for the acute MI, hip fracture, and colorectal cancer cohorts because utilization rates were, as expected, similar during this period. Differences across the cohorts are apparent: Within each quintile, the MCBS cohort received the least care while the acute MI cohort received the most care. Within each cohort, however, patients who resided in regions with a higher EOL-EI received more medical care. After adjustment for baseline differences in health status, overall use of hospital and physician services was between 52% higher (MCBS cohort) and 77% higher (acute MI cohort) in the highest compared with the lowest quintile. For each of the cohorts, approximately half of the spending within a quintile was for acute inpatient hospital care and half was for physician services (data not shown).

Figure 4 characterizes the content of physician services provided to the Medicare population (MCBS) across quintiles. Only a small proportion of total physician services in any quintile was for major surgical procedures, and the overall rate of major surgery was relatively constant across quintiles. Evaluation and management services (visits), tests, radiology services, and minor procedures made up the vast majority of physician activity and explained the differences in physician practice found across the quintiles.

Figure 5 provides detailed information on differences in the specific services provided to the chronic disease cohorts across HRRs with differing spending levels. We present the pooled relative rates and 95% CIs for all three chronic disease cohorts combined in quintile 5 (highest expenditure index) compared with quintile 1 (lowest expenditure index) because the relative use rates across quintiles were similar for each cohort. For example, although pulmonary function tests were performed twice as frequently overall in patients with acute MI than in those with hip fracture, they were performed nearly three times more often in the highest quintile than in the lowest quintile in all three cohorts (Appendix Table 11).

Rates of major procedures differed little across quintiles and were sometimes lower and sometimes higher in quintile 5. Rates of several minor, relatively nondiscretionary procedures (skin laceration repair, breast biopsy) were also similar across quintiles.

As was found in the general population sample, differences in utilization across quintiles were largely due to increased use of evaluation and management services and associated tests, imaging and minor procedures, and use of the hospital as a site of care. Rates of outpatient physician office visits averaged 1.27 (95% CI, 1.26 to 1.28) times higher in quintile 5 than in quintile 1. Inpatient visits, however, were 2.13 (CI, 2.12 to 2.14) times higher, and inpatient specialist consultations were 2.36 (CI, 2.33 to 2.39) times higher. The proportion of patients seeing more than 10 different physicians during the first year after their index admission was 2.97 (CI, 2.84 to 3.11) times higher in quintile 5. More frequent physician contact was associated with more frequent use of diagnostic tests and minor procedures. Patients in quintile 5 spent more time in the hospital (rate ratio, 1.52 [CI, 1.50 to 1.54]) and in the intensive care unit (rate ratio, 1.55 [CI, 1.50 to 1.60]).

Some of the most dramatic differences were found in rates of services provided to severely ill patients. For example, among patients in their last 6 months of life, intensive care unit days were 2.28 (CI, 2.18 to 2.38) times higher in quintile 5 than in quintile 1 and the use of vena cava filters, feeding tubes, and emergency intubation were all more than 2.3 times as frequent in quintile 5 as in quintile 1.

Regions with higher expenditure indices did not provide better quality of care on most measures (Table 5). Among patients in whom the specific treatment was recommended, patients with acute MI in the highest quintile were no more likely to receive acute reperfusion, were less likely to receive aspirin at admission or discharge and angiotensin-converting enzyme inhibitors in the setting of a low ejection fraction, and were more likely to receive -blockers. Because some preventive services (for example, influenza vaccination) may be provided in nonreimbursed settings, the results for preventive services are based on patient reports from the MCBS. Although mammography was performed as frequently in high as in low quintiles, influenza and pneumococcal immunization and Papanicolaou smears were provided less frequently in HRRs with higher expenditure indices. Lack of better-quality care in HRRs in the highest quintile was not related to the greater predominance of teaching or large hospitals (Figure 6). Major teaching hospitals had somewhat higher quality on several of the measures, but the differences in quality across quintiles were small and inconsistent.

Although the absolute differences in access to care were small, the findings suggest a general pattern of slightly lower access to care in HRRs with higher expenditure indices (Table 6). Patients with acute MI who lived in regions with higher expenditure indices were significantly less likely to receive exercise testing and angiography, and a slightly smaller percentage saw a physician within 30 days of discharge. Larger differences emerged in the type of specialists seen during the first 30 days: Those in HRRs with lower expenditure indices were more likely to see family practitioners, and those in HRRs with higher expenditure indices were more likely to see medical subspecialists.

In HRRs with higher expenditure indices, a slightly smaller proportion of patients in the general population (MCBS) reported having a usual source of care. Some differences in the site of physician visits were seen, with more frequent outpatient and office visits in regions with high expenditure indices. Waiting times for emergency department, outpatient facility, and office visits were significantly longer in higher-spending regions. Finally, on the basis of one of three traditional measures of access (having a problem but not seeing a physician), HRRs with a higher expenditure index provided significantly worse access to care.

The Appendix was developed to provide interested readers with additional detail on the methods of the study as well as supplementary findings referred to in the body of the papers that could not be included there because of space constraints. Section B provides an expanded discussion of the rationale for our study design and its relationship to instrumental variables analysis. Section C describes in greater detail our study populations, exclusions applied, and data quality. Section D describes in detail the rationale behind the approach and the methods used to calculate spending and utilization rates using measures free of bias that could be introduced because of differences in wages, prices, or policy payments to physicians or hospitals. Section E describes in greater detail the End-of-Life Expenditure Index (EOL-EI), the primary exposure used in the analysis, including the study population within which it was calculated and how members of each study cohort were excluded from the sample used to calculate the index used as the exposure for that cohort. Section F describes the motivation, methodology, and results of our sensitivity analysis using the Acute Care Expenditure Index.

In addition, the Appendix also includes supplementary tables that present additional detail on individual patient attributes (Appendix Tables 1, 2, 3, and 4), a table that lists specifically which variables are included in each of the major models used in the analyses (Appendix Table 5), the main models examining survival (Appendix Tables 6, 7, 8, and 9) and change in functional status (Appendix Table 10), a table presenting specific procedure rates for each chronic disease cohort and for all three cohorts combined (Appendix Table 11), and tables summarizing overall health care utilization rates across quintiles for each chronic disease cohort (Appendix Tables 12, 13, and 14).

As is discussed in the overview of the study design in Parts 1 and 2, the ideal approach to addressing the study questionwhether the increased spending observed in some regions of the United States leads to better care or outcomeswould be to carry out a randomized trial. However, such a trial would be difficult and would probably end up answering a slightly different question (depending on the intervention under study).

The field of economic research has addressed this problem through approaches that attempt to create a natural randomization through what is termed instrumental variables analysis. The key notion is that an exposure is identified that allows the study sample to be assigned to different treatment groups in a way that assures that those in different treatment groups are similar in terms of attributes that might affect the outcome (that is, that case mix is similar in the groups). They are nonetheless treated differently.

A good example of this type of natural randomization comes from a study of how serving in the Vietnam War affected the probability of suicides and vehicular deaths (39). Clearly, comparing suicide rates for Vietnam veterans and nonveterans would be statistically suspect, since the underlying characteristics of the two groups would be expected to differ by so much. Draft lottery numbers, chosen randomly on the basis of ones birthday, were used as a natural randomization to place men into the treatment group, those most likely to be sent to Vietnam, and the control group, those least likely to be sent. This method qualified as an instrument because it fulfilled the two [intuitive] requirements of an instrumental variable: 1) It was highly correlated with the exposure variable, which was serving in Vietnam, and 2) it was plausibly uncorrelated with the underlying mental health of the population (or, more formally, with any unmeasured differences in the populations). In other words, any differences in suicide and accident rates between the two groups were very likely to have been the result of serving (or not serving) in Vietnam, and not individual risks for suicide or poor driving. The article by Hearst and colleagues, like our articles, took a reduced-form approach to the problem. In other words, they compared what they called draft eligible (the treatment group) with draft ineligible (the control group).

By the same token, in our papers, we compared outcomes of people living in areas where the health system displays a more aggressive approach to end-of-life care with those of people living in areas where the health system displays a less aggressive approach. We have no a priori reason for believing that these populations in these regions should differ in their underlying health status, but they are treated differently.

Why didnt we use the formal instrumental variables approach, in which we would predict how much an additional $1000 in Medicare spending affects survival? There are three main reasons. First, we are interested primarily in the direction and general magnitude of effect, rather than in the cost of achieving that effect. We recognize that if increased expenditures across regions result in improved health outcomes, knowing the magnitude of the effect of an additional 10% increase in regional spending on survival and functional status for Medicare patients would be important for policy research. If we find no association or that higher spending is associated with lower survival, however, the precise estimate of the coefficient (in terms of dollars) is relatively unimportant. Second, instrumental variables analysis is able to provide unbiased estimates only in certain settings, one of which is a linear model. Our need to use Cox proportional-hazards regression for our mortality analyses precluded a formal instrumental variables analysis using currently developed statistical tools. Finally, it is important to recognize that the fundamental limitation of instrumental variables analysis would remain. One cannot prove that one has a perfect instrument.

We therefore presented our analysis as an observational study. We recognize that unmeasured confounding remains a possibility, but we nevertheless believe that our findings represent a major advance over previous research and that our conclusions that residence in higher-spending regions does not cause improved quality, access to care, or survival (and may cause worse survival) are sound.

For all three study cohorts, we restricted the eligible population to Medicare enrollees between the ages of 65 and 99 years who, at the time of their index admission, were eligible for both Medicare Parts A and B and were not enrolled in a health maintenance organization (HMO).

All of our utilization analyses in which dollar amounts are reported were based on measures of expenditures that have been purged of regional differences in prices or policy payments because the use of actual payments would introduce a bias. Actual reimbursements for hospital and physician services vary substantially according to geographic region due to wage, price, and policy differences (such as subsidies for the costs of medical education). To develop a measure of Medicare spending that was free of regional differences in price and policy payments, we followed the general approach developed by the Medicare Prospective Payment Commission in an earlier report (56) to calculate spending as follows. For inpatient hospital services, we based our measure on the diagnosis-related group (DRG) weight. All DRGs are assigned a relative weight proportional to the average national cost for Medicare patients within that DRG compared to the average cost for all Medicare patients. We converted DRG weights to dollars by multiplying the weight times the national average DRG price for 1996 ($3799). The measure reflects average national resource use for this condition. Hospital spending was defined as the sum of all DRG weights for an individual during a specified period times the DRG price. For physician services, we used the Resource-Based Relative Value Scale that forms the basis of the current Medicare physician fee schedule (57). Relative value units (RVUs) are assigned to each physician service to reflect physician work and the associated practice expense. For services included in the physician fee schedule, we assigned the total RVU value for the specific service from the Medicare fee schedule. For services not included in the fee schedule (primarily laboratory services), we calculated an RVU equivalent by dividing either the standard national price (laboratory services) or the median national allowed charge (for physician services without an RVU in the fee schedule) by the average 1996 factor ($36.14) used to convert RVUs to dollars. When DRG weights and RVUs are used, the measure of spending treats the value of a given service equally regardless of where the service is performed in the country. The measure removes the effect of any geographic differences in prices, wages, and policy payments.

Physician spending was defined as the sum of all RVUs for a given beneficiary during a specified period times the conversion factor. Aggregate spending for an individual is calculated in dollars and equals the sum of hospital spending and physician spending.

Because of concern that our primary exposure (the EOL-EI) may not have fully accounted for differences in population characteristics in different regions, we developed an alternative measure and repeated the analyses using this measure. Although the ideal measure would be risk-adjusted differences in total Medicare spending, we know of no way to calculate such a measure using currently available data. An alternative was to define study populations in which we were reasonably confident in our case-mix measures. Given the probable similarity of the cohorts at baseline across regions, and the high quality of the risk-adjustment data for short-term mortality (for example, 6 months), we decided to use as our alternative exposure measure the regional differences in risk-adjusted 6-month utilization in the complementary cohorts as our measure of the exposure. We describe this approach, and our findings, in the sections that follow.