Evaluation of Bias and Logistics in a Survey of Adults at Increased Risk for Oral Health Decrements

Gilbert, Gregg H.; Duncan, R. Paul; Kulley, Andrew M.; Coward, Raymond T.; Heft, Marc W.

doi:10.1111/j.1752-7325.1997.tb02472.x

Cited by 89 publications

(161 citation statements)

References 33 publications

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“…This sample had a dental care recency at baseline that was very similar to that shown in 1989 NHIS data, and conclusions drawn from the FDCS and 1989 NHIS regarding sociodemographic determinants of dental care recency were the same. 20 Additionally, the percentage of the sample that reported one or more dental visits in the first two years of the FDCS, 77%, was very similar to the figure among the comparable group of 1989 NHIS respondents, 75% over a two-year period. 20,21 Possible bias in the sample due to attrition was assessed by comparing characteristics of those who participated in an interview at 48 months with those who did not for any reason.…”

Section: Sampling Methods and Attritionsupporting

confidence: 49%

“…20 Additionally, the percentage of the sample that reported one or more dental visits in the first two years of the FDCS, 77%, was very similar to the figure among the comparable group of 1989 NHIS respondents, 75% over a two-year period. 20,21 Possible bias in the sample due to attrition was assessed by comparing characteristics of those who participated in an interview at 48 months with those who did not for any reason. We have detailed these analyses previously.…”

Section: Sampling Methods and Attritionsupporting

confidence: 49%

“…("Rural" meant residence in one of three nonmetropolitan counties.) This resulted in a sample with minimal bias with respect to the population of interest, 20 defined as individuals 45 years old or older who had a telephone, did not reside in an institutional setting, resided in one of four counties in north Florida, could engage in a coherent telephone conversation, and had at least one tooth (one study objective was to investigate tooth loss). This sample had a dental care recency at baseline that was very similar to that shown in 1989 NHIS data, and conclusions drawn from the FDCS and 1989 NHIS regarding sociodemographic determinants of dental care recency were the same.…”

Section: Sampling Methods and Attritionmentioning

confidence: 99%

“…Sampling details have been provided elsewhere. 20 Briefly, however, subjects were residents of four counties in north Florida: three nonmetropolitan counties (Hamilton, Jefferson, Madison) and one metropolitan county (Duval, which contains the city of Jacksonville). Telephone screening using Donnelley listings and random digit dialing identified 5,254 individuals meeting age (Ն45 years) and residence eligibility.…”

Section: Sampling Methods and Attritionmentioning

confidence: 99%

“…"Poor" meant annual household income below the U.S. Bureau of the Census definition of poverty based on household size. 20 The 873 subjects who participated at baseline were recruited from a random sample of the 3,998 eligible individuals, stratified by rural/urban residence (50% rural and 50% urban). ("Rural" meant residence in one of three nonmetropolitan counties.)…”

Section: Sampling Methods and Attritionmentioning

confidence: 99%

See 4 more Smart Citations

A Prospective Study of the Validity of Self-Reported Use of Specific Types of Dental Services

Gilbert

Rose²,

Shelton³

2003

Public Health Reports

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Section: Sampling Methods and Attritionsupporting

confidence: 49%

Section: Sampling Methods and Attritionsupporting

confidence: 49%

Section: Sampling Methods and Attritionmentioning

confidence: 99%

Section: Sampling Methods and Attritionmentioning

confidence: 99%

Section: Sampling Methods and Attritionmentioning

confidence: 99%

See 3 more Smart Citations

A Prospective Study of the Validity of Self-Reported Use of Specific Types of Dental Services

Gilbert

Rose²,

Shelton³

2003

Public Health Reports

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Racial and Socioeconomic Disparities in Health from Population‐Based Research to Practice‐Based Research: The Example of Oral Health

Gilbert

2005

Journal of Dental Education

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Oral health serves as an excellent model for understanding social disparities in health. Associations among race, socioeconomic status, oral health, and dental care are strong. Multiple points along the dental care process allow investigation of disparities and mechanisms; and there are multiple types of treatment services (ranging from those that all patients and clinicians would likely agree on, to multiple treatment options, to discretionary treatments). Florida Dental Care Study (FDCS) data are used to provide examples of these concepts. Without regard to whether the dental care system was ultimately entered, the FDCS found substantive social differences in 1) incidence of need; 2) responsiveness to this need; and 3) propensity to seek preventive services. Once the dental care system had been accessed, substantive social differences were still evident with regard to 1) clinical condition; 2) awareness of treatment options; and 3) treatment discussions and recommendations. Once differences in clinical condition were taken into account, and once analysis was limited to persons who had entered the dental care system, social differences in receipt of care were still evident. Findings suggested that although dental care was effective at treating and preventing certain oral conditions, social differences in treatment effectiveness were evident.

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Comparing longitudinal binary outcomes in an observational oral health study

Shelton

Gilbert

et al. 2003

Statistics in Medicine

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Observational studies continue to be recognized as viable alternatives to randomized trials when making treatment group comparisons, in spite of drawbacks due mainly to selection bias. Sample selection models have been proposed in the economics literature, and more recently in the medical literature, as a method to adjust for selection bias due to observed and unobserved confounders in observational studies. Application of these models has been limited to cross-sectional observational data and to outcomes that are continuous in nature. In this paper we extend application of these models to include longitudinal studies and binary outcomes. We apply a two-stage probit model using GEE to account for correlated longitudinal binary chewing difficulty outcomes. Chewing difficulty was measured every six months during a 24-month period between two groups of subjects: those either receiving or not receiving dental care. Dental care use was measured at six-month intervals as well. Results from our proposed model are compared to results using a standard GEE model that ignores the potential selection bias introduced by unobserved confounders. In this application, accounting for selection bias made a major difference in the substantive conclusions about the outcomes of interest. This is due in part to an adverse selection phenomenon in which those most in need of treatment (and consequently most likely to benefit from it) are actually the ones least likely to seek treatment. Our application of sample selection models to binary longitudinal observational outcome data should serve as impetus for increased utilization of this promising set of models to other health outcomes studies.

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Evaluation of Bias and Logistics in a Survey of Adults at Increased Risk for Oral Health Decrements

Abstract: This method of sampling persons at high risk for oral health decrements resulted in only modest bias with respect to the population of interest.

Cited by 89 publications

References 33 publications

A Prospective Study of the Validity of Self-Reported Use of Specific Types of Dental Services

A Prospective Study of the Validity of Self-Reported Use of Specific Types of Dental Services

Racial and Socioeconomic Disparities in Health from Population‐Based Research to Practice‐Based Research: The Example of Oral Health

Comparing longitudinal binary outcomes in an observational oral health study

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