Monitoring Changes in Health Insurance Coverage

Transcription

1 to raise new ideas and improve policy debates through quality information and analysis on issues shaping New Hampshire s future. One Eagle Square Suite 510 Concord, NH (603) Fax: (603) Health Finance Indicators #1 Board of Directors Martin L. Gross, Chair John B. Andrews Cotton M. Cleveland John D. Crosier Todd I. Selig Donna Sytek Georgie A. Thomas James E. Tibbetts Kimon S. Zachos Monitoring Changes in Health Insurance Coverage Author: Douglas E. Hall Executive Director Executive Director Douglas E. Hall (603) [email protected] June 2005

2 About this Paper This paper is the first in our planned series Health Finance Indicators. The series will present indicators that can be used to monitor access and cost of health care in New Hampshire. It joins other reports published by the NH Center for Public Policy Studies on the broad topic of healthcare finance and insuring the New Hampshire workforce. The Concord-based Endowment for Health has sponsored this work. This paper, like all of the Center s published work, is in the public domain and may be reproduced without permission. Indeed, the Center welcomes individuals and groups efforts to expand the paper s circulation. Copies are also available at no charge on the Center s web site: Contact the Center at [email protected]; or call Write to: NHCPPS, 1 Eagle Square, Suite 510, Concord NH 03301

3 Health Finance Indicators #1 Monitoring Changes in Health Insurance Coverage The number of New Hampshire residents who lack health insurance is arguably as important as the number who are unemployed. While the state s Department of Employment Security expends considerable effort tracking and publicizing employment statistics on a monthly basis, the state has no regular program or method to monitor how many residents lack health insurance. Health finance professionals, employers, and the news media have all noted that increasing health care costs are driving up the price of health insurance and that employers are shifting more of the cost of health insurance premiums onto employees directly or are dropping health insurance benefits altogether. A death spiral has been hypothesized in which relatively healthy individuals opt out of their costly insurance which, in turn, raises the rates for those remaining in the risk pool, thus driving out even more of the healthy. Is the death spiral real or just theoretical? There has been little change in recent years in the percentage of New Hampshire residents without insurance, but if that number started to change, how would we know? If health insurance coverage of the population begins to decline, New Hampshire s policy makers would need to know about it as soon as possible. Yet the state has no consistent organized method to determine what may be happening. Data already gathered by New Hampshire s hospitals can and should be used to track changes in insurance coverage. Each year, thousands of people are treated by the state s hospitals and discharged. The hospitals keep digital records on each discharge and whether the patient was covered by insurance, Medicare, Medicaid, or lacked coverage. The hospitals already report this information to the state of New Hampshire. They also report admissions on a quarterly basis to the New Hampshire Hospital Association. With minimal effort both sets of data could be used as prime indicators of changes in the extent of health insurance coverage in New Hampshire. The hospital admissions and discharge data have one important advantage over surveys of insurance coverage. They are actual counts of the total population receiving hospital care, so they are not subject to the margins of error that accompany all surveys, including those surveys that New Hampshire has relied on for estimates of insurance coverage. 1. Sample Surveys Most reports about insurance coverage in New Hampshire are based on surveys of New Hampshire residents. The annual Current Population Survey of the U. S. Census Bureau collects information each year on the health insurance status of about 1,400 New Hampshire households and uses that sample as the basis for estimates about the whole state. The NH Department of Health and Human Services (DHHS) sponsored surveys of many more New Hampshire households in 1999 and 2001, generating somewhat more precise estimates of the status of the

4 Monitoring Changes in Health Insurance Coverage 2 whole population. 1 In 2003, the Endowment for Health and the Healthy New Hampshire Foundation sponsored a survey that covered about 750 households. Surveys that reach only a small part of a population are subject to what statisticians call sampling error, the probability that the sample surveyed is not exactly representative of the whole population. That s why statisticians typically report their results as a point within a range of error: for example, 12 percent, plus-or-minus 4 percentage points means that the statistician is reasonably sure that the actual answer in the total population is somewhere between 8 and 16 percent. 2 Figure 1: Surveys cannot track changes in uninsurance rate with sufficient precision Estimated Percent of NH Population Uninsured by Year 13% 12% 11% 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% 95% confidence intervals shown 1999 & 2001 surveys funded by NH Department of Health & Human Services Year 2003 survey funded by Endowment for Health and HNHfoundation Estimates from annual Current Population Survey of the Census Bureau indicated by filled central markers One of the surveys conducted in New Hampshire reported that the percentage of the population that is uninsured is 9.0 percent, with a 95 percent confidence interval of +/- 2.0 percent. That means that the actual uninsurance rate 3 in the total population is probably in the 7.0 percent to 1 DHHS also annually conducts the Behavioral Risk Factor Surveillance System survey which includes a question about respondent insurance status. 2 The larger the sample is in relation to the whole population, the smaller the sampling error is likely to be and the more confident the analyst can be that the actual rate is within the specified range. Thus the range of values is called a confidence interval and it, too, is subject to the laws of probability enabling statisticians to define the precision of their confidence interval. A 95 percent confidence interval, means that 95 percent of the time, the actual value will be within the specified range, and only 5 percent of the time the actual value will be outside of that range. 3 Uninsurance rate is a term that may be jarring when first encountered, but has become common in the literature about health policy. It is defined as the percentage of the population that does not have health insurance coverage, whether private or public. In some ways it is analogous to the more familiar unemployment rate.

5 Monitoring Changes in Health Insurance Coverage percent range, although there is even a 5 percent chance that it is outside this range. If the population in a subsequent survey shows 9.8 percent uninsured with a +/-2.0 percent confidence interval, the actual rate in the full population is probably somewhere in the 7.8 percent to 11.8 percent range. Accordingly, the apparent increase of 0.8 percent in the rate of uninsurance among those in the two survey samples may not reflect any trend in the population, but only the difference in the samples. Surveys do not give a sufficiently refined measure to determine if the uninsurance rate in New Hampshire is rising or falling. A review of the surveys conducted in New Hampshire by the U.S. Census Bureau, the NH Department of Health and Human Services, and the Endowment for Health shows that the changes they have reported in the uninsurance rate have been too small to distinguish from the background noise of sampling error. Figure 1 illustrates this dilemma; the error bars illustrate the confidence interval surrounding each point estimate. 2. The Hospital Discharge Database Health care consumers utilize physician services, hospitals, specialty clinics, laboratories, and outpatient surgery centers among other provider types. They also purchase prescription drugs and medical equipment. However, only hospital services provide a single common database so that all access and utilization can be monitored. Nothing similar exists for physician services, drug purchases, or other health care service types. 4 The state s Uniform Hospital Discharge Database (UHDD) is the responsibility of the Bureau of Health Care Research (BHCR) of the Department of Health and Human Services. The database can be mined to determine whether changes are occurring in the number of uninsured who are accessing hospital care. Databases exist for inpatient discharges and for outpatient discharges (emergency room visits, observation stays, and ambulatory surgery). Hospital staff enter a code for each discharge identifying the principal payer. One of those codes is self-pay. Self-pay is essentially synonymous with uninsured. 5 Some self-pay patients will pay the full bill. Others will make only partial payment and the hospital will write off the rest as bad debt. For yet others, the hospital will have agreed to provide charity care from the beginning. Thus, to get a useful measure of changes in the underlying uninsurance rate, one needs only to track changes in the percentage of hospital discharges labeled self-pay. 3. Not an Absolute Measure The suggested proxy measure is not a measure of the absolute rate of uninsurance in the New Hampshire population for a number of reasons: hospital discharges include some residents of other states and do not include the discharges of New Hampshire residents from out-of-state hospitals 4 The state is in the beginning stages of designing a uniform claims database, the Comprehensive Healthcare Information System. When complete in two or three years, that database will not include information on services provided to people who lack health insurance, so it will not be able to address this problem directly. 5 Discharges of patients with very high deductible insurance policies are not normally coded as self-pay because the hospitals will send the charge information to the insurance companies for credit against the total deductibles.

6 Monitoring Changes in Health Insurance Coverage 4 the age distribution of hospital patients is not the age distribution of the general population and insurance status is related to age inpatient discharges, by definition, represent the most acutely ill persons in the population. In other words, the hospital discharge data will not measure precisely how many people are uninsured in New Hampshire, but changes in the percentage of self-pay discharges each quarter will give a reasonably accurate picture of the degree to which the uninsurance rate is rising or falling. The tool is analogous to a water mark on a pier or dock: an observer can tell if the tide is coming in or going out, even if he can t tell exactly how deep the water is. 4. The Rates for 2001, 2002, and 2003 Each calendar quarter more than 1,400 inpatient discharges and more than 20,000 outpatient discharges are for self-pay patients. Table 1 displays the counts of all discharges and self-pay discharges for each calendar quarter of 2001, 2002, and 2003, as now available from the Bureau of Health Care Research. Table 1: Hospital discharges by calendar quarter Inpatient Discharges Outpatient Discharges Quarter Self-Pay Total Percent Self-Pay Total Percent 2001-Q1 1,416 29, % 21, , % 2001-Q2 1,372 29, % 22, , % 2001-Q3 1,504 29, % 24, , % 2001-Q4 1,442 29, % 21, , % 2002-Q1 1,456 29, % 22, , % 2002-Q2 1,435 29, % 22, , % 2002-Q3 1,628 29, % 26, , % 2002-Q4 1,567 29, % 23, , % 2003-Q1 1,551 29, % 2003-Q2 1,596 30, % 2003-Q3 1,690 30, % 2003-Q4 1,663 30, % 2004-Q1 Not yet available 2004-Q Q3 Not yet available 2004-Q Q1 As shown in Figure 2, from the first quarter of 2001 to the fourth quarter of 2003, these data show a slight increase in the percentage of inpatient discharges that are to self-pay patients, from 4.7 percent in the January-March quarter of 2001 to 5.5 percent in the October-December quarter of 2003.

7 Monitoring Changes in Health Insurance Coverage 5 Figure 2: More than 5 percent of discharges in 2003 were "self-pay" 6% Percent of Inpatient Hospital Disharges that are Self-Pay 5% 4% Percent 3% 2% 1% 0% Q Q Q Q Q Q Q Q Q Q Q Q4 Calendar Quarter One disadvantage of the UHDD is that the data typically have been made available only after a full year of discharge records are submitted and cleaned of any errors. The state s hospitals submit their UHDD discharge data quarterly to the Maine Health Information Center. The Center releases six months of compiled data to the Bureau about nine months after the end of the two quarters involved. Typically, this process takes nearly one year to complete from the close of the last quarter of the data year. For example, near the end of 2004, the most recent measure available was for the fourth quarter of Monthly Hospital Admission Data Hospitals submit certain data monthly to the New Hampshire Hospital Association. Among the elements submitted are a monthly count of inpatient admissions to self-pay patients and the total number of inpatient admissions during that month. The hospital association then prepares quarterly Trending Reports that include 89 different measures of hospital operations. None of the measures on these reports currently relates to the number or percentage of patients who are primarily self-pay. Instead of waiting for the UHDD to become available, the hospital association could calculate the percentage of admissions that are to self-pay patients each quarter and share that with the Endowment for Health and the NH Center for Public Policy Studies. The three organizations could then make regular quarterly public announcements about the new numbers and any implications for health care in New Hampshire.

8 Monitoring Changes in Health Insurance Coverage 6 Table 2 displays the data from the NH Hospital Association and compares the quarterly admissions data to the quarterly discharge data. There will always be small differences because the number of admissions and discharges during any month or quarter will not be equal and whether a patient is recorded as self-pay may change during the person s hospital stay. Table 2: Quarterly admissions and discharge data Inpatient Discharges from UHDD Inpatient Admissions from NHHA Quarter Self-Pay Total Percent Self-Pay Total Percent 1999-Q1 1,366 27, % 1999-Q2 1,372 26, % 1999-Q3 1,381 26, % 1999-Q4 1,291 25, % Have not obtained this data 2000-Q1 1,363 27, % 2000-Q2 1,305 27, % 2000-Q3 1,376 27, % 2000-Q4 1,318 28, % 2001-Q1 1,416 29, % 1,469 28, % 2001-Q2 1,372 29, % 1,444 28, % 2001-Q3 1,504 29, % 1,560 28, % 2001-Q4 1,442 29, % 1,460 28, % 2002-Q1 1,456 29, % 1,480 28, % 2002-Q2 1,435 29, % 1,448 28, % 2002-Q3 1,628 29, % 1,637 28, % 2002-Q4 1,567 29, % 1,366 27, % 2003-Q1 1,551 29, % 1,553 28, % 2003-Q2 1,596 30, % 1,489 29, % 2003-Q3 1,690 30, % 1,632 29, % 2003-Q4 1,663 30, % 1,650 29, % 2004-Q1 1,505 29, % 2004-Q2 1,582 29, % Not yet available 2004-Q3 1,597 29, % 2004-Q4 1,561 29, % It appears that the differences between the two data sets are minor. The more rapidly available admissions data from NHHA should be used to monitor any changes in the uninsured population. When the bureau releases its full year of discharge data many months later, more detailed analysis could be performed on the characteristics of the uninsured, such as their age and sex, medical diagnoses, etc. The percentages in Table 2 could be misinterpreted to be the actual percentage of the entire population that is uninsured. As pointed out above, this is not the case. Only the change in the percentage over time indicates a change in the insurance status of the population. In order to avoid this potential misinterpretation, the data can be normalized and presented as an index. The average percentage among hospital admissions in 2001 was 5.21 percent. Benchmarking the indicator to this percentage allows the measure in other time periods to be stated as a multiplier

9 Monitoring Changes in Health Insurance Coverage 7 of this rate. We have set the benchmark period to in Table 3. 6 Figure 3 displays those results. Figure 4 presents the index from the NHHA admissions data with a superimposed four quarter moving average. The moving average smoothes out some of the seasonal variation and random statistical fluctuations. Table 3: Index data Index of Uninsurance Raw Quarterly Data Trailing 4 Quarter Average Quarter 1999-Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q1 6 This is similar to the way in which the Consumer Price Index (CPI) is benchmarked at 100 to the average prices in the years A CPI of 150 means that prices are 50 percent higher than the average but does not indicate what the prices actually are.

10 Monitoring Changes in Health Insurance Coverage 8 Figure 3: Raw admissions and discharge data show slight upward trend in uninsured Q Q Q Q4 Index of Population Lacking Health Insurance (Average for 2001 = 100) 2000-Q Q2 From UHDDS discharges provided annually by DHHS From monthly reports of admissions submitted to NHHA 2000-Q Q Q Q Q Q Q Q Q3 Calendar Quarter 2002-Q Q Q Q Q Q Q Q Q4 Figure 4: Smoothed moving average of admissions data shows slight upward trend in uninsured. 140 Index of Population Lacking Health Insurance (Average for 2001 = 100) Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q4 Calendar Quarter

11 Monitoring Changes in Health Insurance Coverage 9 6. Additional Analysis This report has noted that discharge data will not provide an absolute measure of the rate of uninsurance in New Hampshire. It might be possible with more detailed analysis, however, to develop a formula that age-adjusts the raw numbers and benchmarks them so that the result is a very good approximation of the absolute measure of the percentage of the population without health insurance. With experience, other measures might also be collected and used to supplement this proposed measure. For example, the self-pay portion of hospital outpatients, of patient-days, of outpatient discharges, or of emergency room visits might be used to confirm trends or fine-tune the estimate of the percentage of the uninsured in the population as a whole. The UHDD data set is sufficiently large to allow analysis of any changes in apparent insurance coverage to include subsets of persons by age group, by geographical location, and other factors. This should be done on an annual basis. Using the suggested proxy of hospital admissions should provide a way to monitor changes in the insurance status of New Hampshire residents that will be more accurate and more timely than surveys of the population and at essentially no additional cost. Surveys will still be necessary to determine attitudes toward health care and health insurance, reasons for lack of insurance, and other issues. But these could, perhaps, be done less often if the suggested proxy is used to track short term changes in the basic rate of uninsurance.

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