Newborn Screening for Cystic Fibrosis: A Qualitative Study of Successes and Challenges from Universal Screening in the United States

Cystic fibrosis (CF) newborn screening (NBS) was universally adopted in 2009 in the United States. Variations in NBS practices between states may impact the timing of diagnosis and intervention. Quantitative metrics can provide insight into NBS programs (NBSP), but the nuances cannot be elucidated without additional feedback from programs. This study was designed to determine facilitators and barriers to timely diagnosis and intervention following NBS for CF. The median age at the first CF event for infants with CF within each state was used to define early and late states (n = 15 per group); multiple CF centers were invited in states with more than two CF centers. Thirty states were eligible, and 61 NBSP and CF centers were invited to participate in structured interviews to determine facilitators and barriers. Once saturation of themes was reached, no other interviews were conducted. Forty-five interviews were conducted (n = 16 early CF center, n = 12 late CF center, n = 11 early NBSP, and n = 6 late NBSP). Most interviewees reported good communication between CF centers and NBSP. Communication between primary care providers (PCPs) and families was identified as a challenge, leading to delays in referral and subsequent diagnosis. The misperception of low clinical risk in infants from racial and ethnic minority groups was a barrier to early diagnostic evaluation for all groups. NBSP and CF centers have strong relationships. Early diagnosis may be facilitated through more engagement with PCPs. Quality improvement initiatives should focus on continuing strong partnerships between CF centers and NBS programs, improving education, communication strategies, and partnerships with PCPs, and improving CF NBS timeliness and accuracy

Quantifying Geographic Variation in Health Care Outcomes in the United States before and after Risk-Adjustment

Background: Despite numerous studies of geographic variation in healthcare cost and utilization at the local, regional, and state levels across the U.S., a comprehensive characterization of geographic variation in outcomes has not been published. Our objective was to quantify variation in US health outcomes in an all-payer population before and after risk-adjustment. Methods and Findings: We used information from 16 independent data sources, including 22 million all-payer inpatient admissions from the Healthcare Cost and Utilization Project (which covers regions where 50% of the U.S. population lives) to analyze 24 inpatient mortality, inpatient safety, and prevention outcomes. We compared outcome variation at state, hospital referral region, hospital service area, county, and hospital levels. Risk-adjusted outcomes were calculated after adjusting for population factors, co-morbidities, and health system factors. Even after risk-adjustment, there exists large geographical variation in outcomes. The variation in healthcare outcomes exceeds the well publicized variation in US healthcare costs. On average, we observed a 2.1-fold difference in risk-adjusted mortality outcomes between top- and bottom-decile hospitals. For example, we observed a 2.3-fold difference for risk-adjusted acute myocardial infarction inpatient mortality. On average a 10.2-fold difference in risk-adjusted patient safety outcomes exists between top and bottom-decile hospitals, including an 18.3-fold difference for risk-adjusted Central Venous Catheter Bloodstream Infection rates. A 3.0-fold difference in prevention outcomes exists between top- and bottom-decile counties on average; including a 2.2-fold difference for risk-adjusted congestive heart failure admission rates. The population, co-morbidity, and health system factors accounted for a range of R2 between 18–64% of variability in mortality outcomes, 3–39% of variability in patient safety outcomes, and 22–70% of variability in prevention outcomes. Conclusion: The amount of variability in health outcomes in the U.S. is large even after accounting for differences in population, co-morbidities, and health system factors. These findings suggest that: 1) additional examination of regional and local variation in risk-adjusted outcomes should be a priority; 2) assumptions of uniform hospital quality that underpin rationale for policy choices (such as narrow insurance networks or antitrust enforcement) should be challenged; and 3) there exists substantial opportunity for outcomes improvement in the US healthcare system

Table quantifying variation in US outcomes.

<p>(A) Significant geographic variation exists across all outcomes measures both before and after risk-adjustment. The values in the table quantify the extent of outcomes variation between the top decile and bottom decile geographies. For example, for IQI 15 AMI inpatient mortality, we observe a 4.0-fold difference in outcomes between the top 10% and bottom 10% of hospitals. For IQIs and PSIs, "observed" refers to values that were adjusted for low-volume noise using Bayesian shrinkage method but did not risk adjust for any other factors. For PQIs, the unit of analysis was at the county level, and therefore PQIs did not need to be shrunk. Risk-adjustments are performed incrementally. "+ pop. factors adjusted" values are shrunk and adjusted for populations factors. "+ co-morb. adjusted" values are shrunk, adjusted for population factors, and adjusted for co-morbidities. Lastly, "+ system adjusted" values are shrunk, population adjusted, co-morbidities adjusted, and system factors adjusted. For IQI 15 AMI inpatient mortality, we observe a 2.3-fold difference in outcomes between the top 10% and bottom 10% of hospitals after risk-adjustment for demographic, co-morbidities, and health system factors. Dash (-) indicates numbers that were not calculated. Counties were not mapped to HSA or HRR, and therefore PQI ratios were not determined. Star (*) indicates a D1 (top decile) value of 0, such that it was not possible to calculate a ratio. (B) R-squared values with 95% confidence intervals are shown. For the confidence intervals [X, Y], X refers to the lower bound of a given R-squared value; Y refers to the upper bound of a given R-squared value. For example, for IQI 15 AMI inpatient mortality, we are able to account for 64% of the variability in outcomes after risk-adjusting for demographics, co-morbidities, and health system factors.</p

Fully risk-adjusted geographic distributions of select outcomes (IQI 15, PSI 07, PQI 08).

<p>Large variation in outcomes is present both between and within US states. Substantially different performances highlight the variation in outcomes across the US. This variation is observed across all outcomes plotted. (A) IQI 15 Acute Myocardial Infarction (AMI) Mortality Rate and PSI 07 Central Venous Catheter-Related Blood Stream Infection Rate are adjusted for low-volume noise using a Bayesian shrinkage methodology and are adjusted for population, co-morbidities, and health system factors. After risk-adjustment, there is 2.1-fold variation in IQI 15 between the top and bottom decile HSAs. After risk-adjustment, there is 12.6-fold variation in PSI 07 between the top and bottom decile HSAs. (B) PQI 08 Heart Failure Admission Rate data has been adjusted for population, co-morbidities, and health system factors. After risk-adjustment, there is 2.2-fold variation in PQI 08 between the top and bottom decile counties. Areas shown in white are due to HCUP not making geographically identifiable data on hospital or county performance available.</p

Correlations among outcomes after adjustment for population factors and co-morbidities and system factors.

<p>Inpatient mortality measures are weakly correlated with each other. Inpatient safety measures show little to no correlation with each other. Prevention quality measures show little to no correlation with each other. Correlation categorization after Dancey and Reidy (2004), analysis following low denominator number outlier removal and risk-adjustment based on the identified population factors, co-morbidities and system factors.</p

Volume-outcome relationship for inpatient mortality (IQI).

<p>We assessed the relationship between outcomes and hospital case volume by modeling the mortality (M) as M = -α*ln(V)+β, where V is hospital case volume, and α and β are constants for each of the inpatient mortality outcomes. As case volume increases in a hospital, mortality decreases across all IQI measures.</p

Overview of 24 AHRQ-outcomes investigated.

<p>We examined 24 AHRQ-defined outcomes to quantify the degree of geographic variation in outcomes across the US. The outcomes selected are collectively the set of measures which form the combined inpatient mortality (IQI 91), inpatient safety (PSI 90), and prevention (PQI 90) indices respectively, that have currently maintained endorsement by the National Quality Forum through 2015 either individually or as a part of an index.</p