R-E-S-P-E-C-T: Patient reports of disrespect in the health care setting and its impact on care

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Original Research

R-E-S-P-E-C-T: Patient reports of disrespect in the health care setting and its impact on care

J Fam Pract. 2004 September;53(9):721-730

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References

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associations between demographic characteristics, utilization variables, and negative perceptions of the health care encounter using chisquared tests and multivariate logistic regression. In these analyses, we dichotomized education into high school graduate or less, and some technical school/college and more. We dichotomized the primary language spoken at home into non-English and English, and we used federal poverty level groupings (<100%, 100%–200% and >200%) to categorize household income. Almost 19% percent of respondents did not report incomes, so we created a dummy variable to account for those with unreported incomes.

We classified insurance status as none or any (either public or private); race/ethnicity as white, black, Hispanic, Asian, and other (Native American, mixed race, or other). We examined the effect of these variables alone and in concert. For example, we calculated predicted percentages to evaluate the combined effects of race and gender, as well as race and education, in relationship to our outcome variables.

Finally, we used multivariate logistic regression to test the relationship between negative perceptions of the patient-provider relationship and our utilization variables. In these analyses, perceptions were the covariate of interest; we controlled for patient characteristics that could also influence utilization, including education, income, insurance status, presence of a primary physician and existence of a comorbid condition (in this case, hypertension, diabetes, heart disease, asthma, and cancer.) This last variable was, by necessity, excluded from the analysis involving optimal chronic disease testing.

Results

Table 1 describes demographic characteristics and utilization measures for our sample. Consistent with prior literature, blacks and Hispanics had lower incomes and higher rates of non-insurance than both whites and Asians. Hispanics responded most frequently that English was not their primary language.

Hispanics and Asians were less likely than whites to have received optimal chronic disease care, while blacks and Hispanics were more likely than whites to have received optimal cancer screening. There were no differences between racial/ethnic groups in not following the doctor’s advice or in putting off care.

TABLE 1
Demographics/characteristics and health care utilization of study participants

Overall sample	Whites (%) n=3488 (69)	Blacks (%) n=1037 (11)	Hispanics (%) n=1153 (10.3)	Asians (%) n=669 (4.2)
Gender
Male	45.1	41.9	45.9	49.7
Age (years)
18–64	79.9*	86.1*	91.1*	91.2*
65+	18.9*	12.5*	8.6*	6.9*
Education
High school grad or less	44.0	56.0	68.3	25.8
Some college/technical school or more	56.0	44.0*	31.7*	74.2*
Income as percentage of poverty level
<100%	7.7	15.7*	23.0*	10.7
100%–200%	17.2	25.4*	23.2*	16.5
>200%	57.4	40.0*	31.4*	53.9
Unknown	17.7	18.9*	22.4*	18.8
Insurance status
None	10.6*	20.6*	32.8*	13.6
Medicaid	2.4*	8.6*	5.8*	3.3
All other	87.0*	70.8*	61.4*	83.1
Presence of chronic illness^†	35.9*	44.4*	30.2*	24.5*
English as primary language at home	99.9*	99.6*	59.4*	91.7*
No primary physician	19.1*	28.6*	41.1*	32.1*
Physical exam within prior year	47.1*	56.8*	48.5	41.0
Put off care in prior year	19.5*	19.4*	19.2	16.3
Sub-sample	n=3205	n=947	n=969	n=561
Not followed doctor’s advice	24.9*	21.9*	21.7	22.1
Sub-sample	n=974	n=367	n=258	n=111
Optimal chronic illness screening	76.9*	73.7	54.8*	61.5*
Sub-sample	n=2612	n=811	n=770	n=401
Optimal cancer screening	50.2*	61.9*	60.1*	53.3
*Statistically significant difference detected between whites and blacks, Hispanics or Asians with chi-squared test for P<.05.
†Hypertension, heart disease, diabetes, asthma.

Negative perceptions of the patient-provider relationship

Race. Over 14% of blacks, 19% of Hispanics, and 20% of Asians reported they had been treated with disrespect by their doctor. Members of these groups were also more likely than whites to report that they were treated unfairly because of their race or their language, and that they would have received better care had they belonged to a different race (Table 2).

Language. Persons for whom English was not the primary language were also more likely to say they had been treated with disrespect, and to report they would have received better care had they been of a different race. For each racial/ethnic group, bivariate relationships persisted after controlling for other respondent characteristics, including education and income (Table 2).

Sex. Men were significantly more likely than women to perceive being treated with disrespect by the doctor (15.9% vs 11.6%), and the percentage varied by race/ethnicity. Using our model to predict the combined effects of race and gender, we found that Asian and Hispanic men (24% and 23%, respectively) were more likely than black men (17%) or white men (11%) to perceive being treated with disrespect.

Education. Education was similarly associated with perceptions of disrespect. Almost 18% of persons without a college education believed they had been treated with disrespect, versus only 10% of those with a college education. Minorities with lower education were more likely to have this perception. Twenty-nine percent of Asians, 22% of Hispanics, and 19% of blacks without a college education reported being treated with disrespect or being looked down upon, versus 13% of whites.

TABLE 2
Relationship of demographic variables to measures of negative perceptions

	Looked down on or treated with disrespect (%)	Treated unfairly because of race (%)	Treated unfairly because of language (%)	Would have received better care if different race (%)
Overall sample	n=6663	n=6008	n=6008	n=6722
Gender
Male	11.6*	4.0*	2.5*	7.0*
Female	15.8*	4.3*	2.7*	7.2*
Primary language
English	13.0*	3.7*	2.0*	6.0*
Non-English	15.9*	9.8* 1	0.1*	19.5^‡
Income as percentage of poverty level
<100%	19.6^†	8.4^§	4.6*	12.5*
100%–200%	17.3^‡	7.3^‡	3.9*	9.5*
>200%	10.1*	9.9*	1.7*	5.1*
Insurance status
Insured	11.4*	2.9*	1.9*	5.3*
Not insured	23.0*	11.4*	6.4‡	16.4*
Race
White	9.4*	1.2*	0.5*	1.4*
Black	14.1^§	7.9*	3.5*	15.2*
Hispanic	19.4*	7.9*	7.2*	3.3*
Asian	20.2*	6.1*	4.5*	12.2*
Education
High school grad or less	17.9*	5.0*	3.7*	7.8*
Some college/technical school or more	10.3*	3.6*	1.9*	6.6^‡
Adjusted percentages using multivariate regression analysis.
This table reports predicted percentages derived from our multivariate regression analysis. The dependent variables of interest: “looked down on/treated with disrespect,” “treated unfairly because of race,” “treated unfairly because of language,” and “would have received better care if different race.” Independent variables: gender, language, income, insurance, race, and education.
* P.001 † P.01 ‡ P.05 § P.10