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At least 19 recordsLinked to original sources
PubMed1990

Biased selection under an experimental enrollment and marketing Medicare HMO broker.

All studies conducted to date suggest that nearly all Medicare HMOs have experienced favorable risk selection in their Medicare HMO enrollments. While there is little definitive empiric knowledge about the extent to which Medicare HMOs can and do encourage favorable selection through marketing and enrollment activities, it has been speculated that centralizing all such functions through an independent broker could reduce enrollment selection bias. In 1985, the Health Care Financing Administration initiated a three-year demonstration of a HMO broker model in Portland, Oregon, known as Health Choice, Incorporated (HCI). This study reports empiric findings that provide no evidence to support claims of the efficacy of an enrollment brokerage function in reducing Medicare HMO enrollment selection bias.

Contract Services↗
PubMed1992

Consumer information and biased selection in the demand for coverage supplementing Medicare.

This study examines how the relationship between health insurance knowledge and the health status of health insurance consumers influences their decisions to purchase insurance coverage. Data from the federal Medicare health insurance program for the elderly in the United States are used. The basic Medicare program provides a limited amount of coverage for health care services obtained from any provider in the private fee-for-service (FFS) market. Beneficiaries of this program may choose to supplement the basic coverage which they receive by two mechanisms: either they may purchase private insurance designed to fill some of the gaps left by the federal program ('Medigap' policies), thereby remaining in the FFS market and preserving their choice of provider, or they may enroll in health maintenance organizations (HMOs), thereby leaving the FFS market and agreeing to use only those providers affiliated with the HMO, and in return receiving broader coverage at little additional out-of-pocket cost. The study was made possible by a unique data set which combines measures of beneficiary knowledge of Medicare coverage with measures of perceived health status, socio-economic characteristics, and insurance coverage choices for a sample of Medicare beneficiaries who participated in an educational workshop about their insurance coverage options. These data were used to estimate a multinomial logistic model of the determinants of insurance choices, where the options included the two listed above and a basic Medicare option. The study explicitly recognizes the interaction between insurance information and health status in health plan choice. These results show that knowledge of coverage does have a differential impact on the decision to purchase health insurance depending on health status. With a high level of knowledge, sicker beneficiaries are less likely to have basic Medicare alone, compared with HMOs or Medigap policies, while healthier beneficiaries are less likely to be enrolled in HMOs, compared with Medigap policies. This finding has important implications for the use of health status measures to adjust capitated payment formulas when knowledgable consumers have the option to enroll in HMOs or remain in the FFS environment. In the absence of health status adjusters for the HMO capitation payments, high levels of coverage knowledge may exacerbate inherent selection bias among these coverage options by healthier and sicker consumers of health insurance.

Aged↗
PubMed1990

Biased selection under the senior health plan prior use capitation formula.

A widely acknowledged shortcoming of the current AAPCC capitation formula for Medicare HMOs is its inability to adjust capitation levels for differences in health status among enrolled groups. Prior use models have been proposed as one alternative to the current AAPCC risk classes. From 1984 through 1987, Senior Health Plan (SHP) participated in a HCFA-sponsored demonstration project in which capitation payments were determined by a prior use formula incorporating information on inpatient hospital days and Part B deductibles. This paper contains findings about selection bias in SHP enrollment from analyses of preenrollment reimbursements and postenrollment mortality rates of SHP enrollees compared with Medicare FFS beneficiaries and enrollees of other Medicare HMOs in the Minneapolis-St. Paul area.

Capitation Fee↗
PubMed1989

Symptoms and selection bias: the influence of selection towards specialist care on the relationship between symptoms and diagnoses.

Observations with respect to the relationship between symptoms and diseases can seriously be biased by selection phenomena. This selection may occur from the general population, via consultation behavior, diagnostic and therapeutic activities of the general practitioner, and by referral. Relationships may be suggested and reproduced even if they do not exist in unselected populations, as a product of diagnostic routines. Correction for selection bias can only be achieved by choosing proper comparison groups. While this can be done in a general practice setting, this is almost impossible after referral, as is demonstrated in this paper. Surprisingly, the most unbiased estimation of the relationship between symptoms and diseases after referral can be made from patient groups that are referred for reason unrelated to the disease under study. Definitive answers for the general practitioner can only be provided by prospective studies from a primary care setting. In the meantime, however, biased relationships can be maintained by teaching knowledge derived from a specialist experience.

Anemia↗
PubMed1992

Reporting and selection bias in case-control studies of congenital malformations.

Retrospective studies of congenital malformations frequently rely on exposures reported by study subjects. Differential error in exposure reporting by cases and controls, which has alternatively been referred to as "recall bias" and "reporting bias," may result in a biased effect measure. Some authors have attempted to avoid reporting bias by comparing exposures between two malformed groups, rather than between cases and nonmalformed controls. This approach, however, may introduce its own bias, which we call selection bias. Both reporting bias and selection bias are shown to be algebraically equivalent to bias arising from exposure misclassification. The magnitudes of these biases are compared for a range of plausible parametric values. The case-control design is sensitive to both differential reporting and selection bias, and the choice of study design involves balancing these two sources of bias.

Case-Control Studies↗
PubMed1987

The role of health practices, health status, and prior health care claims in HMO selection bias.

To examine selection bias in terms of demographics, self-reported health practices, professionally evaluated health status, and prior health care utilization by employees and dependents, we examined health insurance choice between traditional insurance and a health maintenance organization (HMO) by central office employees of a large southwestern utility company. The HMO attracted a relatively younger and female population compared with the traditional plan. We found no difference in health practices and health status measures between the two groups. Consistent with recent studies, the HMO group experienced lower health claims cost the year prior to enrollment compared with persons who remained in the traditional plan. This difference was largely due to dependents' utilization-a factor that should be examined in future studies and considered by those structuring insurance premiums for HMOs and traditional plans.

Adult↗
PubMed1991

Evaluation of selection bias in a cross-sectional survey.

Selection bias is inherent in all occupational cohorts. Selection bias at entry has long been known and is commonly referred to as a "healthy worker effect." Less well appreciated is selection during the life of a cohort resulting from life-style factors (e.g., cigarette smoking); aging with accompanying chronic diseases, economic and demographic circumstances; and diseases that might result from exposures suffered by the cohort being studied, that influence whether individuals remain in a trade. These factors weigh differently at different times. Thus, at any point in time, "surviving" members of a cohort reflect an amalgam of selection factors. When such groups are studied in cross-sectional surveys there can be uncertainty whether clinical, radiological and physiological findings are necessarily representative for the trade or occupation as a whole. We analyzed the results of a large clinical field survey of long-term asbestos insulation workers to investigate whether the non-participants differed substantially from those who were examined. Five thousand three hundred and fifty-five (5,355) men, of an initial cohort of 17,800 established January 1, 1967, had reached 30 or more years from onset of their work by July 1, 1981. All were invited to come for examination. Two thousand and seventy-seven (2,077) came, and 3,278 did not. We questioned a sample of 1,393 non-responders to see why they failed to appear. The answers did not give evidence of significant health-related selection influence. Sickness only infrequently kept them away. We then followed both groups--those examined and those not examined--to the end of 1987 for their mortality experience. There was no great difference. The non-responders had somewhat fewer deaths overall and proportionately fewer of asbestos-associated cancers, such as mesothelioma and lung cancer. The results indicated that, in this cohort, there did not seem to be health-related selection bias that determined whether or not cohort members responded to invitations for examinations.

Asbestos↗
PubMedDate not supplied

Affirmative actions: can the discriminant accuracy of a test be determined in the face of selection bias?

Clinical estimates of test efficacy can be distorted by the differential referral of positive and negative test responders for outcome verification. Accordingly, a series of computer simulations was performed to quantify the effects of various degrees of this selection bias on the observed true-positive rate, false-positive rate, and discriminant accuracy of a hypothetical test. The error in observed true- and false-positive rates was positive with respect to diagnosis, and negative with respect to prognosis. The magnitude of error was highly correlated with the magnitude of bias associated with the test response (primary selection bias), but not with the magnitude of bias associated with additional independent factors (secondary selection bias). Mathematical correction for preferential referral based on the test response using a previously published algorithm completely removed the correlation with primary selection bias for both diagnosis and prognosis. Although a significant correlation with secondary selection bias persisted at intermediate base rates, its magnitude was small. Discriminant accuracy was assessed in terms of area under a receiver operating characteristic (ROC) curve. Biased values of true- and false-positive rates were distributed along the curve defined by the actual true- and false-positive rates of the test for both diagnosis and prognosis. As a result, the areas under ROC curves calculated from biased true- and false-positive rates were within 2% of the areas calculated from the actual rates. Only when the primary and secondary observations were independent with respect to one outcome and dependent with respect to the other outcome did a systematic error appear in ROC area.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Simulation↗
PubMed1991

Selection bias in TEFRA at-risk HMOs.

The issue of selection bias was investigated using data from 22 HMOs who are enrolling Medicare beneficiaries under Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) at-risk contracts. The study differs from previously published analyses of this issue in that it deals with the current Medicare risk program (TEFRA) rather than with earlier Demonstration Programs; as an indicator of selection bias, it utilizes beneficiary functional health status at enrollment; and it examines selection not only at the mean of the health status distribution, but at the two tails (very disabled, very able) as well. For each of the participating HMOs, the functional health status of recent Medicare enrollees was compared with that of a control group of randomly chosen fee-for-service beneficiaries. None of the HMOs experienced adverse selection, whether measured in terms of overall (mean) health status of enrollees or in terms of the proportion of the very disabled population that chose to join. Nine of the 22 HMOs were considered to have experienced favorable selection on the basis of the mean health status of new enrollees. In addition, ten more HMOs were found to have experienced favorable selection in one or both tails of the health status distribution. Although a specific cause for the observed enrollment patterns is not identified, speculation is made on factors that may or may not contribute. Evidence suggests that beneficiary self-selection is probably a more important explanation of these patterns than purposeful actions of HMOs to discourage enrollment by sicker beneficiaries (i.e., "skimming").

Aged↗
PubMed1992

HMO marketing and selection bias: are TEFRA HMOs skimming?

The research evidence indicates that health maintenance organizations (HMOs) participating in the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) At-Risk Program tend to experience favorable selection. Although favorable selection might result from patient decisions, a common conjecture is that it can be induced by HMOs through their marketing activities. The purpose of this study is to examine the relationship between HMO marketing strategies and selection bias in TEFRA At-Risk HMOs. A purposive sample of 22 HMOs that were actively marketing their TEFRA programs was selected and data on organizational characteristics, market area characteristics, and HMO marketing decisions were collected. To measure selection bias in these HMOs, the functional health status of approximately 300 enrollees in each HMO was compared to that of 300 non-enrolling beneficiaries in the same area. Three dependent variables, reflecting selection bias at the mean, the low health tail, and the high health tail of the health status distribution were created. Weighted least squares regressions were then used to identify relationships between marketing elements and selection bias. Subject to the statistical limitations of the study, our conclusion is that it is doubtful that HMO marketing decisions are responsible for the prevalence of favorable selection in HMO enrollment. It also appears unlikely that HMOs were differentially targeting healthy and unhealthy segments of the Medicare market.

Advertising↗
PubMed1989

Human teratogens, prenatal mortality, and selection bias.

Etiologic inferences on human teratogens are usually derived from case-control studies conducted either at birth or in spontaneous abortion series. Because both teratogens and defects may be associated with an increased risk of prenatal mortality, the possibility exists that selection bias may affect etiologic inferences. The authors derive relations between the true odds ratio (OR) relating a teratogen and a defect at the time of the occurrence of the defect and the apparent odds ratios observed in spontaneous abortion series and at birth, as functions of prenatal mortality. Depending on the pattern of interaction between the teratogen and the defect in affecting the rate of prenatal mortality, selection bias may lead to overestimation or underestimation of the true odds ratio. With increasing multiplicative effects on prenatal mortality, changes in selection bias tend to increase the observed odds ratio in spontaneous abortion series but to decrease the observed odds ratio at birth. For a range of rates of prenatal mortality, weak associations between exposures and defects (OR = 0.3-3) may well be due to selection bias; conversely, weak teratogens (OR less than 3) may be missed in case-control studies of defects conducted at birth. Selection bias due to prenatal mortality must be considered in the interpretation of etiologic studies of birth defects.

Abortion, Spontaneous↗
PubMed2026

A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

Selection Bias↗
PubMed1985

Biased selection in the Federal Employees Health Benefits Program.

The existence of biased selection in health insurance markets has long been assumed by economic theorists as well as seen between classes of health plans. In this paper, we use a model of the premium rate that takes into consideration the effects of moral hazard to make empirical estimates of the extent of selection in the Federal Employees Health Benefits Program. We find that biased selection has raised the premium of the Blue Cross Plan high-option coverage by 21% and lowered the premium of the low-option coverage by 29%, both relative to premiums that would have been charged in the absence of selection.

Blue Cross Blue Shield Insurance Plans↗
PubMed1990

Analysis of selection bias in a case-control study of renal adenocarcinoma.

Crude estimation of the selection probability ratio (SPR), described previously, was extended to stratified and multivariate estimation and used to assess selection bias in a case-control study of renal adenocarcinoma. It was shown that the directly pooled estimate of the SPR, using the same weights as the directly pooled estimate of the exposure odds ratio (OR) from the case-control study (assuming the OR and SPR are common to all strata and data are abundant), can be multiplied with the OR to yield an adjusted OR that is free from selection bias. Medical records of 548 interviewed cases were compared with 640 noninterviewed cases, and interviews of 640 controls were compared with mailed questionnaires from 272 (60%) of the noninterviewed controls. Age-sex-adjusted point estimates of SPRs ranged from 0.65 to 1.4. Multivariate estimates from binomial regression ranged from 0.34 to 2.0. Higher socioeconomic status and history of renal stones were predictors of participation by both cases and controls. Obesity in women, hypertension, and nonsmoking were predictors in cases only. Heart disease was associated with control participation and case nonparticipation. This study cast doubt on the OR for obesity in women and hypertension in the case-control risk analysis.

Adenocarcinoma↗
PubMed1988

Subject selection biases in alcoholic samples: effects on cognitive performance.

The effects of subject selection bias in research on cognitive deficits in sober alcoholics were studied in a sample of 523 subjects (98 controls, 276 ineligible alcoholics, 40 eligible alcoholics who declined to participate, and 144 eligible and participatory alcoholics). All subjects received the Shipley Institute of Living Scale (Vocabulary and Abstracting subscales) and measures of anxiety, depressive symptoms, childhood hyperkinesis and attentional deficit disorders were obtained. Results indicate that current guidelines for alcoholic subject selection are biasing analyses toward support of the null hypothesis. Declined alcoholics performed more poorly on the Shipley Abstracting than did the "Used" group, yet did not differ significantly from the Used alcoholic groups on depression or anxiety. Declined alcoholics did, however, report significantly fewer Hk/MBD symptoms than did other alcoholic groups. ANCOVAs that used the affective and childhood disorders as covariates did not alter the differences in cognitive performance described above.

Adult↗
PubMed1992

The role of selection bias in comparing cesarean birth rates between physician and midwifery management.

OBJECTIVE: The midwifery service at our hospital has been observed to have a 2% cesarean birth rate consistently over a 10-year period. There are substantial differences in labor management style between the midwives and physicians. We sought to test the hypothesis that the low cesarean birth rate on the midwifery service was the result of patient selection bias. METHODS: A randomized blinded clinical trial was conducted in which 492 low-risk patients were assigned to either physician or midwifery management. The provider responsible for labor management was unable to determine group assignment. Patients in the midwifery group were managed by previously established protocols, and outcome was attributed to the midwives even if the patients subsequently required transfer to physician management. Route of delivery was the primary outcome measurement. Continuous variables were analyzed using Student t test and discrete variables using chi 2. RESULTS: There were no demographic differences between the groups, and the admission pelvic examinations were the same. The patients assigned to the midwifery group had a 2.1% cesarean birth rate, whereas those assigned to physician management had a 0.4% rate. The higher rate of operative vaginal deliveries in the physician group was statistically significant. There were no differences in neonatal outcomes. The physician-managed group had significantly more episiotomies and third- and fourth-degree extensions. CONCLUSIONS: The 2% cesarean birth rate observed on the midwifery service appeared to be the result of patient selection bias. A low cesarean birth rate can be achieved by either physician or midwifery management in a selected low-risk population.

Adult↗
PubMed1989

Selection bias in case-control studies using relatives as the controls.

Investigators have suggested using relatives of cases as the control group when studying complex diseases thought to have a major genetic component. However, there is a concern about possible bias and we developed a model to examine the possibility of bias in the selection of relatives as the control group. Assuming the exposure-specific risks of disease remain constant over time, the results indicate that even when there is a correlation in the exposure status among relatives, selection of controls from relatives of cases does not, of itself, introduce bias in the estimate of effect.

Bias↗