Quality of Care

Narrative

Quality health care can be defined as the degree to which health services increase the likelihood of desired health outcomes for individuals and populations and which are consistent with current professional knowledge.1 Quality is reflected in numerous program aspects, including access to services, clinical effectiveness, comprehensiveness, and integration of services. High quality care can play a significant role in improving health care outcomes and decreasing overall health care costs, while impacting consumer information and choice.2 Quality of care can be measured in a number of ways, including receipt of appropriate treatment for infections and weight counseling.3

An upper respiratory infection (URI), also referred to as the common cold, is a type of acute respiratory tract infection (ARTI)—an infection of the lining in the nose and throat that can cause symptoms including coughing, congestion, and fever that last for 1–2 weeks. Though most URIs are viral in nature, antibiotics are often inappropriately prescribed for children despite no evidence that they are helpful against viral infections.4

In 2012, children had 12.7 million outpatient visits for URIs, 4.7 million of which involved children under the age of 2 years, and 8.1 million for children aged 2–17 years. None of the children presenting at these visits were expected to have a bacterial pathogen; however, 3.1 million children were prescribed a “potentially preventable” antibiotic. Of these prescriptions, 871,000 were for children under the age of 2, and more than 2.2 million were for children aged 2–17 years (Figure 1). This is equivalent to 11.4 million potentially avoidable antibiotic prescriptions for all ARTIs in children. This number has not decreased notably in the past decade.5

annual visits with antibiotic prescriptions by age

Figure 1 Source

While the number of infection-related hospitalizations from antibiotic resistance have increased in all age groups, the greatest increase has been in children under 18 years of age (395 percent between 1997 and 2006). Antibiotic-resistant infections often lead to prolonged treatments or hospital stays and greater rates of disability and death. The use of antibiotics is the most important element leading to antibiotic resistance.6 Greater awareness of the dangers of antibiotic-resistant bacteria and better communication between patients and providers could improve treatment and reduce unnecessary antibiotic use.7

Since the 1980s, childhood obesity has increased more than twofold in children and threefold in adolescents (see pages on childhood and adolescent overweight and obesity). Obesity has negative effects on children’s health including high blood pressure and cholesterol, insulin resistance, type 2 diabetes, and breathing problems, although early intervention and nutrition counseling can improve children’s overall health and lifestyle. For the second consecutive year, clinical assessments of childhood weight status have improved.

In 2012, 51.6 percent of children aged 3–17 years in commercial health maintenance organizations (HMOs), 31.2 percent of children in commercial preferred provider organizations (PPOs), and 51.8 percent of children in Medicaid HMOs had an outpatient visit with a primary care practitioner during the year when BMI percentile was documented (Figure 2). This translates to increases from 2011 of 15.4 percent in commercial HMOs, 26.8 percent in commercial PPOs, and 12.6 percent in Medicaid HMOs.

body mass index assessment

Figure 2 Source

The percentage of providers counseling children on proper nutrition and/or physical activity also showed improvement. From 2011 to 2012, rates for counseling children on nutrition increased in commercial HMOs from 46.4 to 54.3 percent, in commercial PPOs from 28.4 to 35.4 percent, and in Medicaid HMOs from 50.1 to 55.0 percent. For the same period, rates for counseling children on physical activity increased in commercial HMOs from 43.0 to 50.4 percent, in commercial PPOs from 25.7 to 32.6 percent, and in Medicaid HMOs from 40.6 to 44.2 percent.8

Data Sources

Figure 1. Kronman MP, Zhou C, Mangione-Smith R. Bacterial prevalence and antimicrobial prescribing trends for acute respiratory tract infections. Pediatrics. September 15, 2014;134(4):e956–e965.

Figure 2. National Committee for Quality Assurance. Improving Quality and Patient Experience: The State of Health Care Quality 2013 (PDF). Accessed September 15, 2014.

Endnotes

1 U.S. Department of Health and Human Services. Public Health Quality. Accessed September 15, 2014.

2 U.S. Department of Health and Human Services. Public Health Quality. Accessed September 15, 2014.

3 U.S. Department of Health and Human Services, Centers for Medicare & Medicaid Services. Quality Measures. Accessed September 22, 2014.

4 National Committee for Quality Assurance. Improving Quality and Patient Experience: The State of Health Care Quality 2013 (PDF). Accessed September 15, 2014.

5 Kronman MP, Zhou C, Mangione-Smith R. Bacterial prevalence and antimicrobial prescribing trends for acute respiratory tract infections. Pediatrics. September 15, 2014;134(4):e956–e965. Accessed September 16, 2014.

6 U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 2013 (PDF). Accessed September 15, 2014.

7 National Committee for Quality Assurance. Improving Quality and Patient Experience: The State of Health Care Quality 2013 (PDF). Accessed September 15, 2014.

8 National Committee for Quality Assurance. Improving Quality and Patient Experience: The State of Health Care Quality 2013 (PDF). Accessed September 15, 2014.

Data

Statistical Significance Test

Calculate the difference between two estimates:

Calculated Z-Test Result 0.9567433 Not statistically significant

We follow statistical conventions in defining a significant difference by a p-value less than 0.05 where there is a less than 5% probability of observing a difference of that magnitude or greater by chance alone if there were really no difference between estimates. The 95% confidence interval includes a plausible range of values for the observed difference; 95% of random samples would include the true difference with fewer than 5% of random samples failing to capture the true difference.

This website allows comparisons between two estimates using the independent z-test for differences in rates or proportions. This test is appropriate for comparing independent populations across years (e.g., 2011 versus 2012) or subgroups (e.g., Male versus Female) on corresponding measures. To the extent possible, the functionality of this application has limited estimate comparisons based on appropriate use of the independent z-test. However, some tables present subgroup categories within broader categories that will allow comparisons between non-independent populations (e.g., low birth weight and very low birth weight). Users should exercise caution when interpreting these test results, which will frequently overstate statistical significance.

For some tables, the website does not allow for comparisons between two estimates, even though the data represent independent populations. Generally, this is because the standard errors were not publicly available at the time this website was created.

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