Socioeconomic Factors Influencing the Failure to Measure the Blood Pressure of Children During Clinical Examinations
Jaewhan Kim, PhD, Division of Public Health, University of Utah, 375 Chipeta Way, Suite A, Salt Lake City, UT 84108
J Clin Hypertens (Greenwich). 2011;13:767–773. ©2011 Wiley Periodicals, Inc.
The authors measured the percentage of children aged 6 through 17 whose blood pressure (BP) was not measured during recent nonemergency clinical examination and assessed the relative importance of health, ability-to-pay, language, and race-ethnic factors in determining whether BP was measured. Using a pooled dataset from the Medical Expenditure Panel Survey (MEPS) for 2006 and 2007, the authors calculated the percentage of children whose BP was not measured using a sample of children aged 6 through 17 and constructed a logistic regression model to estimate the relative importance of health, economic, and social factors in the examiner’s decision to measure BP. A total of 28.9% of children did not have their BP measured. Within this unmeasured group, 31% had a family history of hypertension, 9% had a family history of diabetes, and 5% had a body mass index ≥32 kg/m2. The logistic regression model of examiners’ decisions indicates that social and economic factors strongly compete with health factors in determining which children not to measure. While examiners place many children at risk for hypertension in the measured pool, they also place many at-risk children in the unmeasured pool for economic and social reasons.
The prevalence of hypertension among children and adolescents in the United States is estimated to be between 3% and 5%.1,2 Factors related to higher blood pressure (BP) among children and adolescents are obesity/overweight,3–12 low birth weight,13–15 family history of hypertension,16,17 and poor sleep quality.18 Non-Hispanic blacks and Mexican American children are at higher risk for prehypertension and hypertension compared with non-Hispanic white children.12,19,20 In addition, lower socioeconomic status predicts hypertension.21
Current guidelines recommend that all children 3 years and older have their BP measured at each health care visit.13 It is known that this recommendation is often not followed by providers.22 Despite this prevalence, due to the complexity of the definition for hypertension among children, even when BP is measured, Falkner and colleagues2 argue that hypertension in children is underdiagnosed because it is easily missed, even by health professionals. Underdiagnosed hypertension can damage organs and cause early cardiovascular-related disease in early adulthood.2,14 However, underdiagnosis can also occur simply by failing to measure BP. Factors known to discourage routine testing include patient characteristics that predict normal BP such as younger age, female sex, low body mass index (BMI), and an absence of cardiovascular disease in the family.22 We find that factors associated with inability-to-pay, English-language handicaps, and minority status also discourage BP measurement. Here we show that in the Medical Expenditure Panel Survey (MEPS), a nationally representative sample of nonemergency clinic and hospital visits in 2006–2007, examiners did not measure the BP of 34% of children between the ages of 3 and 17.
Using MEPS data and logistic regression analysis, we investigated examiner decisions to measure or not measure BP. We found that examiners respond to indications of the probability that a child is at risk for hypertension by choosing to measure the BP of children who are older, have higher BMIs, and have family histories of hypertension or diabetes. However, examiners also respond to ability-to-pay, language, and racial-ethnic factors in ways that put into the unmeasured group children who are at risk for hypertension. Thus, the factors influencing the choice to measure a child’s BP leads to populating the unmeasured group with the aforementioned surprisingly high proportion of children with high BMIs and family histories of hypertension. Consequently, the failure to measure the BP of one third of all children during nonemergency clinical examinations is an important additional source of underdiagnosed hypertension.
This paper describes the MEPS data, how we calculated that the BP of 34% of children in clinical examinations were not measured, and the limits of this calculation. The following section describes a subsample of children between the ages of 6 and 17, which we use to model an examiner’s decision to measure a child’s BP. We also describe our logistic regression model and results followed by discussion of the overall implications and limitations of our study.
For this study, we used the MEPS, which is a set of large-scale, nationally representative surveys of families and individuals. MEPS collects data on the specific health services that families and individuals use, how frequently they use them, the cost of these services, and how they are paid. Also, patient socioeconomic and demographic information such as age, sex, family income, language spoken in the home, and self-reported recent health status are available.23 The MEPS data used in this study are patient- or a patient’s parent-reported information, which has limitations that we discuss below.
We combined the most recently available data (from 2007) with 2006 data to create a pooled sample of 11,798 unique patients who had clinic or/and hospital visits (except emergency department visits). We considered only patients within the age range of 3 through 17 years to reflect current guidelines for BP readings. MEPS does not provide whether patients have had or received at this examination a hypertension diagnosis. However, MEPS provides whether the examiner measured BP, and this decision is the focus of our study. The report of a BP measurement comes from the patient or the patient’s parent or guardian. The MEPS question was: “Doctor checked blood pressure.” Respondents had the option of answering “yes,”“no,” or “don’t know,” and among those answering yes or no, 34% said no. If this answer had come from medical professionals who did the examination themselves and were relying on both their memory and medical records, there would be little concern regarding the accuracy of their responses. Because the MEPS responses are from patients or parents relying on recall, the survey responses risk inaccuracy associated with three factors. First, the child, parent, or guardian may not know what a BP measurement is or involves. Second, the parent or other reporting adult may not have been present when the BP was taken. Third, the most recent clinical visit may have been sufficiently in the distant past to raise recall issues.
These potential sources of inaccuracy are mitigated by the survey option of answering “don’t know.” In constructing our sample of 11,798 respondents, we discarded 737 respondents (5.9% of total) who stated “don’t know,” thus reducing the number of potentially inaccurate responses due to the aforementioned factors. This narrows the notional confidence interval banding our estimate of 34% of children were unmeasured. However, an unknown number of respondents remain in our sample who answered “yes” or “no” when in fact they were wrong. These false-positive and false-negative responses, in themselves, generate uncertainty around our 34% estimate, but the presence of inaccurate responses, per se, does not bias our estimate unless there is a predominance of false-negative over false-positive responses or vice versa.
Using the MEPS, family histories of hypertension and diabetes variables were created by linking parents’ records to their children. Poverty level was classified using “family income as percent of poverty line” (poor, near poor, low income, middle income, and high income), and health insurance status was based on “health insurance coverage indicator” (private, public, and no insurance) in the dataset. The health status variable was generated using self-reported health status (ie, “less healthy than other children”). Because less-healthy children might be more likely to have the BP measured or clinic/hospital visits than healthy children, we included this variable as a proxy variable. We used BMI as a continuous variable because classifying BMI by degree of obesity (healthy weight: <85th percentile for age and sex, overweight: BMI of 85th–94th percentile, obese: BMI of ≥95th percentile) was not possible due to a lack of height and weight values for children.
Below we construct a logistic regression to model the behavior of examiners in deciding whether to measure a child’s BP. In the MEPS, BMI, an important indicator of the risk of hypertension,3–12 is reported only for children 6 years and older. Consequently, our regression examines a 6- through 17-year-old subsample of our overall sample spanning ages 3 through 17. Our smaller subsample consists of 7242 children and adolescents. Within this subsample of older children, 71.1% had their BP measured (as opposed to 66% for the whole sample) (Table I provides descriptive statistics for the regression subsample). This reflects examiners’ tendency to measure BP in older children. Within the regression subsample, the average age of children whose BP was measured was 12.37 years while the average age of children with unmeasured BP was a statistically significantly lower 11.05 years (P<.001). Similarly, the BMI of the measured group was statistically significantly higher than the unmeasured group (20.98 vs 19.72, P<.001). However, the difference in BMI between two groups may not be clinically significant. While there was no statistically significant difference between males and females in the percentage measured, both family histories of hypertension and diabetes were associated with BP measurement: 32.9% of those with measured BP had family histories of hypertension compared with 26.5% of those with unmeasured BP had family histories of hypertension (P<.001). Similarly, 9.9% of those with measured BP had family histories of diabetes compared with 7.4% in those with unmeasured BP (P<.026). Finally, among those with measured BP, 7.3% reported a recent history of illness, while 4.9% of those with unmeasured BP reported a recent history of illness (P<.001). Thus, it appears that examiners are selecting to measure the BP of patients at greater risk of hypertension, but these examiners also omit measuring BP in children in whom there is a surprising proportion with personal and family risk factors.
Table I. Descriptive Statistics for Logistic Regression Sample
|BP measured=1, other=0||71.1%||71.1%||28.9%|| |
|Age, y||12.011 (0.0501)||12.373 (0.058)||11.054 (0.092)||<.001|
|BMI,||20.638 (0.076)||20.982 (0.090)||19.723 (0.135)||<.001|
|Family history of hypertension=1, other=0||31.12||32.87||26.46||<.001|
|Family history of diabetes=1, other=0||9.2||9.87||7.4||.026|
|Race/ethnicity|| || || ||<.001|
| Caucasian (reference)||60.7||62.82||58.38|| |
| Black=1, other=0||15.34||16.55||15.86|| |
| Hispanic=1, other=0||17.74||15.15||20.88|| |
| Asian=1, other=0||2.42||1.94||0.37|| |
| Others=1, other=0||3.8||3.54||4.51|| |
|Poverty level|| || || ||<.001|
| Poor=1, other=0||14.78||14.39||15.84|| |
| Near poor =1, other=0||4.64||4.27||5.62|| |
| Low income=1, other=0||15.16||14.31||17.38|| |
| Middle income=1, other=0||34.79||34.61||35.27|| |
| High income (reference)||30.63||32.42||25.89|| |
|Health insurance status|| || || ||<.001|
| Private insurance (reference)||65.5||67.04||61.43|| |
| Public insurance=1, other=0||27.06||26.15||29.44|| |
| No insurance=1, other=0||7.45||6.81||9.14|| |
|Language other than English at home=1, English=0||11.94||10.47||15.86||<.001|
Adverse economic factors discourage the measurement of BP. The unmeasured group was poorer and less frequently insured. Grouping patients into 5 income-and-wealth categories, 32.4% of the measured group was high-income compared with 25.9% of the unmeasured group. In contrast, 14.4% of the measured group were poor and 4.3% were near-poor compared with the unmeasured group, where 15.8% were poor and 5.6% were-near poor. Within the measured group, 14.3% were low-income, while in the unmeasured group, 17.4% were low-income. A chi-square test showed that the distribution of the measured group across 5 income/wealth categories statistically significantly differed from the distribution of the unmeasured group (P<.001). The unmeasured group was also less well insured. Within the measured group, 67.0% had private health insurance, while 61.4% of the unmeasured group had private health insurance; 26.2% of the measured group had public health insurance, while 29.4% of the unmeasured group relied on public health insurance; and 6.8% of the measured group had no insurance, while 9.1% of the unmeasured group was uninsured. A chi-square test showed that the distribution of the measured group across 3 insurance categories statistically significantly differed from the distribution of the unmeasured group (P<.001). Thus, for ability-to-pay reasons, examiners appear to have placed some children in the unmeasured group and, thus, the unmeasured group appears not to have been populated based solely on the basis that the child was at lower risk for hypertension.
Social and language factors also differ between the measured and unmeasured groups. Within the measured group, 10.5% did not speak English in the home compared with 15.9% in the unmeasured group (P<.001). Similarly, 15.2% of the measured group were Hispanic while 20.9% of the unmeasured group were Hispanic. The measured group was disproportionately white (62.8% vs 58.4%), disproportionately Asian (1.9% vs 0.4%), disproportionately black (16.6% vs 15.9%), and less other (3.5% vs 4.5%). A chi-square test showed that the distribution of the measured group across 5 racial/ethnic categories statistically significantly differed from the distribution of the unmeasured group (P<.001). While non-black Hispanics were at greater risk for hypertension,12,19,20 examiners nonetheless disproportionately placed them in the unmeasured group. This may reflect a combined effect associated with ability-to-pay and language as well as ethnicity.
To sort out the independent effects of health, economic, and social factors in determining whether a child’s BP was taken, we construct a logistic regression estimating the odds that BP was measured. We hypothesized that examiners were more likely to measure BP if the patient manifested risks of hypertension, but the examiner was less likely to measure BP if the patient manifested inability-to-pay. We also hypothesize that patient English-language handicaps make it less likely that BP will be measured. We do not believe there was bias regarding racial/ethnic characteristics. The literature indicates that minority status is a risk factor for hypertension.12,19,20 Thus, examiners may be more likely to measure minority BP. However, controlling for ability-to-pay, language handicaps, and health factors, minority status may be associated with a poorer quality of local health services or limited family information regarding the health risks associated with hypertension. Such adverse supply or demand factors might lead to a failure to measure BP. All analyses including summary statistics were conducted with Stata 11 software (Stata Version 11; College Station, TX) to reflect the study design, sampling, and complexity of the MEPS.
Table II showed that all the personal and family health characteristics in the model had expected signs and were statistically significant at the 1% level except having a family history of diabetes, which was statistically significant at the 10% level. Each additional year of age makes it 1.1 times more likely that a patient will have their BP measured. Each additional 1-point increase in BMI makes it 1.03 times more likely that BP is measured. Family histories of hypertension make it 1.25 times more likely that BP will be measured, while a family history of diabetes makes it 1.22 times more likely. A recent history of ill health makes it 1.58 times more likely that a BP measure will be taken. Thus, examiners are making the decision to take a BP reading in part based on the medical characteristics of the patient.
Table II. Logistic Regression Results
|Family history of hypertension||1.248||0.095||.003||1.076–1.449|
|Family history of diabetes||1.220||0.136||.075||0.980–1.519|
| Near poor||0.674||0.105||.011||0.497–0.914|
| Low income||0.698||0.082||.002||0.554–0.879|
| Middle income||0.797||0.078||.020||0.658–0.964|
|Health insurance status|
| Public insurance||0.983||0.097||.857||0.810–1.191|
| No insurance||0.739||0.093||.016||0.578–0.945|
|Other language at home (no English)||0.733||0.084||.007||0.586–0.917|
Relative to the privately insured, the uninsured are only 74% as likely to have their BP measured as shown in Table II. There is no statistically significant difference in the probability of a BP measurement of a publicly insured child compared with a privately insured child. Controlling for this insurance effect, relative to the children of high-income families, children of the poor are 82% as likely to have their BP measured, but this result is not statistically significant (P<.122). Children from both near-poor and low-income families are <70% as likely to have their BP taken. Middle-income families’ children are only 80% as likely to have their BP measured. Thus, patient economic resources play a role in determining whether BP is measured.
Language and Social/Ethnic Characteristics
Patients whose families do not speak English at home are 73% as likely to have their BP read (see Table II). This may involve communication challenges during the examination, may reflect family awareness regarding hypertension, or may capture unmeasured economic differences or other factors. BP readings are also influenced by the race-ethnicity of the patient. Relative to whites and controlling for language, Hispanics are 84% as likely to have their BP taken (P<.092), blacks are 79% as likely (P<.010), Asians 55% as likely (P<.004), and others 66% as likely (P<.022). Because the literature indicates that minority status is a risk factor for hypertension, we cannot read these results as examiners selecting to measure BP based on health-related racial-ethnic characteristics. It may be that our economic variables do not adequately control for differences in ability-to-pay and that these ethnic/racial variables are capturing additional economic differences. However, assuming that our measures of poverty and insurance status have adequately captured differences in family savings, income, and insurance, these racial/ethnic variables may be capturing differences in the quality and delivery of health services or differences in patient/family demand for services that include BP measurement. Further research is needed to clarify these results. Regardless of possible interpretations, these results cannot be understood as examiners triaging BP measurement based strictly on patient risks for hypertension. The pool of unmeasured BP in children is being populated, in part, for economic and social reasons that are either random to the risk of hypertension or more probably adverse to that risk. To assess the magnitude of these results, we used the model odds ratio estimates to predict whether a child’s BP would be measured based on variations in their health, economic, and social characteristics.
Probability of BP Measurement
Table III provides predicted probabilities that a patient’s BP will be measured based on the health, economic, language, and ethnic-racial characteristics of the individual. Column 2 presents the case of a white boy with the sample mean age of 12 years and the sample mean BMI of 20. This hypothetical person is healthy with no family history of either hypertension or diabetes. This boy has private health insurance, comes from a high-income family, and speaks English in the home. The model predicts that this healthy boy stands a 78% chance of having his BP measured.
Table III. Probability of Measuring Patient’s Blood Pressure (BP)
|Body mass index||20.64||32||32||32||32||32||20.64||32||20.64|
|Family history of hypertension||No||Yes||Yes||Yes||Yes||Yes||No||Yes||No|
|Family history of diabetes||No||Yes||Yes||Yes||Yes||Yes||No||Yes||No|
|Private health insurance||Yes||Yes||No||No||No||No||No||No||Yes|
|Public health insurance||No||No||No||No||No||No||No||No||No|
|No health insurance||No||No||Yes||Yes||Yes||Yes||Yes||Yes||No|
|English spoken at home||Yes||Yes||Yes||Yes||Yes||No||Yes||Yes||No|
To gauge the effect of the ability-to-pay on the probability of measuring this child’s BP during the examination, column 8 presents the same boy with only two hypothetical changes: he is a healthy white boy from a near-poor family and he has no health insurance. The probability of this healthy but poor child having his BP measured falls from 78% to 64% due to adverse ability-to-pay factors. To gauge the effect of language and ethnicity on the probability of measuring this child’s BP during the examination, column 9 presents the healthy rich boy of column 2 with only two language-ethnicity changes: he is a healthy, rich, insured boy who is Hispanic and who does not speak English at home. The probability of this child having his BP measured falls from 78% to 69%. This indicates that among healthy children, the effect of ability-to-pay is somewhat more important than language-ethnicity in deciding to place the child in the unmeasured group. What about an unhealthy child?
Consider the healthy rich boy in column 2 with a 78% chance of having his BP measured. Column 3 assumes this rich healthy boy in column 2 is now unhealthy and has 4 health warning signs (BMI of 32, family histories of hypertension and diabetes, plus a recent personal history of illness). This rich but unhealthy boy’s probability of having his BP measured rises from 78% to 92%. We will now use this unhealthy, wealthy, insured, white boy as a baseline.
Consider how the predicted probability of BP measurement for the unhealthy rich white child in column 3 changes as we alter this hypothetical individual’s economic, ethnic, and language characteristics. In columns 4 through 7, we track how the probability of this boy having his BP measured falls as the child’s hypothetical language and racial-ethnic characteristics change. In column 4, this child loses his insurance (but still comes from a high-income family); the probability of being measured falls from 92% to 90%. In column 5, this unhealthy, uninsured white child is hypothetically placed in a near-poor family, and the probability of having his BP measured falls from 90% to 85%. Together, the adverse ability-to-pay factors drop the probability of measurement by 7 points, with not having insurance being less important than dropping from a high-income status to a near-poor status.
In column 6, this unhealthy, uninsured, and near-poor child is Hispanic but speaks English at home, and the probability of having BP measured falls from 85% in column 5 to 83%, and when this hypothetical unhealthy, uninsured, near-poor, Hispanic child no longer speaks English in the home, the probability of having his BP measured falls to 78%. Of this total 7-point drop in BP measurement probability associated with ethnicity and language, the language factor dominates, accounting for 5 of the 7-point loss. In sum, the ability-to-pay factors for this unhealthy child reduce the probability of being measured by 7 points from 92% to 85%, while the ethnic-language factors drop the probability an additional 7 points to 78%. Again, this indicates that language and social reasons for not measuring the BP of children at risk for hypertension are as important as ability-to-pay reasons. Our results further indicate that income status is more important than insured status and that English-language skills are more important than being Hispanic, per se, in influencing the decision to measure BP.
We investigate patient- and patient-parent–reported data based on recall, which risks inaccurate responses based on possible limited understanding of what a BP measurement is, possible absence of the parent when the BP measurement occurred, and possible inaccuracies associated with distant recall. These factors are mitigated to some extent by the survey option of responding “don’t know.” When respondents were asked what type of physician they visited, however, the overwhelming majority of responses were “don’t know” or “inapplicable.” The small remaining sample size prevents obtaining statistically significant conclusions regarding the relationship between physician type and BP measurement. Also, the survey does not report whether BP was taken by the physician, nurse, or other medical personnel. Finally, data limitations prevent knowing whether the patient had BP measured at a previous recent examination and whether such a measure increased or decreased the likelihood of BP being measured at the current examination.
Controlling for family ability-to-pay economic factors we find that race/ethnicity and language adversely affect the probability that a child’s BP will be measured. While we show that examiners’ use of race/ethnicity factors in deciding to measure BP are inverse to known race/ethnicity risk factors for hypertension, we do not provide an explanation for why examiners use language and minority status to place children in the unmeasured group. We suggest that these factors may capture differences in the quality and availability of medical services, cultural differences in the demand for thorough health examinations, or unmeasured differences in family economic status. Regardless, these are issues for further research.
We estimate that 29% of children between the ages of 6 and 17 did not have their BP measured during nonemergency clinical examinations in 2006–2007 MEPS. Within this unmeasured group, the percentage of children presenting with risk factors for hypertension, while statistically significantly lower than in the measured group, is nonetheless from a health standpoint significantly large. Examiners pay substantial attention to health-related risk factors associated with hypertension in deciding whose BP to measure. For instance, a healthy, insured, white boy’s probability of having his BP measured rises from 78% to 92% when he presents with a family history of hypertension and diabetes, has recently been ill, and has a BMI of 32. However, examiners consider socioeconomic factors in deciding whether to measure BP. For instance, the rich, insured, unhealthy child’s probability of being measured falls back to 78% when that child becomes poor, uninsured, Hispanic, and speaks only Spanish at home. The failure to measure the BP of children during routine clinical examinations would not be an important additional source of the underdiagnosis of prehypertension and hypertension among children and adolescents if examiners failed to measure only those with a very low risk of hypertension. However, examiners are failing to measure BP in children for economic and social reasons that are either random or adverse to the risk of hypertension.
This study was supported by the University of Utah Department of Family & Preventive Medicine.