Objective. Population-based surveillance data from California and Georgia for years 2004 through 2008 were linked to state death record files to determine the all-cause death rate among 12,143 patients identified with sickle cell disease (SCD).Methods. All-cause death rates, by age, among these SCD patients were compared with all-cause death rates among both African Americans and the total population in the two states. All-cause death rates were also compared with death rates for SCD derived from publicly available death records: the compressed mortality files and multiple cause of death files.Results. Of 12,143 patients identified with SCD, 615 patients died. The all-cause mortality rate for the SCD population was lower than the all-cause mortality rate among African Americans and similar to the total population all-cause mortality rates from birth through age 4 years, but the rate was higher among those with SCD than both the African American and total population rates from ages 5 through 74 years. The count of deceased patients identified by using population-based surveillance data (n5615) was more than twice as high as the count identified in compressed mortality files using SCD as the underlying cause of death alone (n5297).Conclusion. Accurate assessment of all-cause mortality and age at death requires long-term surveillance via population-based registries of patients with accurately diagnosed SCD.

Purpose The lack of an ongoing surveillance system for hemoglobinopathies in the United States impedes the ability of public health organizations to identify individuals with these conditions, monitor their health-care utilization and clinical outcomes, and understand the effect these conditions have on the health-care system. This article describes the results of a pilot program that supported the development of the infrastructure and data collection methods for a state-based surveillance system for selected hemoglobinopathies. Methods The system was designed to identify and gather information on all people living with a hemoglobinopathy diagnosis (sickle cell diseases or thalassemias) in the participating states during 2004–2008. Novel, three-level case definitions were developed, and multiple data sets were used to collect information. Results In total, 31,144 individuals who had a hemoglobinopathy diagnosis during the study period were identified in California; 39,633 in Florida; 20,815 in Georgia; 12,680 in Michigan; 34,853 in New York, and 8,696 in North Carolina. Conclusion This approach provides a possible model for the development of state-based hemoglobinopathy surveillance systems.

Objective: Several states are building infrastructure and data collection methods for longitudinal, population-based surveillance systems for selected hemoglobinopathies. The objective of our study was to improve an administrative case definition for sickle cell disease (SCD) to aid in longitudinal surveillance. Methods: We collected data from 3 administrative data sets (2004-2008) on 1998 patients aged 0-21 in Georgia who had ≥1 encounter in which an SCD International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code was recorded, and we compared these data with data from a laboratory and medical record review. We assessed performance (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of case definitions that differed by number and type of SCD-coded encounters; addition of SCD-associated treatments, procedures, and complications; and length of surveillance (1 vs 5 years). We identified correct diagnoses for patients who were incorrectly coded as having SCD. Results: The SCD case definition of ≥3 SCD-coded encounters in 5 years simplified and substantially improved the sensitivity (96.0% vs 85.8%) and NPV (68.2% vs 38.2%) of the original administrative case definition developed for 5-year, state-based surveillance (≥2 encounters in 5 years and ≥1 encounter for an SCD-related treatment, procedure, or complication), while maintaining a similar PPV (97.4% vs 97.4%) and specificity (76.5% vs 79.0%). Conclusions: This study supports an administrative case definition that specifies ≥3 ICD-9-CM–coded encounters to identify SCD with a high degree of accuracy in pediatric patients. This case definition can be used to help establish longitudinal SCD surveillance systems.

Sickle cell disease affects more than 100,000 individuals in the United States, among whom disease severity varies considerably. One factor that influences disease severity is the sickle cell disease genotype. For this reason, clinical prevention and treatment guidelines tend to differentiate between genotypes. However, previous research suggests caution when using a claimsbased determination of sickle cell disease genotype in healthcare quality studies.The objective of this study was to describe the extent of miscoding for the major sickle cell disease genotypes in hospital discharge data. Individuals with sickle cell disease were identified through newborn screening results or hemoglobinopathy specialty care centers, along with their sickle cell disease genotypes. These genotypes were compared to the diagnosis codes listed in hospital discharge data to assess the accuracy of the hospital codes in determining sickle cell disease genotype. Eighty-three percent (sickle cell anemia), 23% (Hemoglobin SC), and 31% (Hemoglobin Sβ+ thalassemia) of hospitalizations contained a diagnosis code that correctly reflected the individual's true sickle cell disease genotype. The accuracy of the sickle cell disease genotype coding was indeterminate in 11% (sickle cell anemia), 12% (Hemoglobin SC), and 7% (Hemoglobin Sβ+ thalassemia) and incorrect in 3% (sickle cell anemia), 61% (Hemoglobin SC), and 52% (Hemoglobin Sβ+ thalassemia) of the hospitalizations. The use of ICD-9-CM codes from hospital discharge data for determining specific sickle cell disease genotypes is problematic. Research based solely on these or other types of administrative data could lead to incorrect understanding of the disease.

Understanding patient experiences, quality of life, and treatment needs in individuals with sickle cell disease (SCD) is essential in promoting health and well‐being. We used measures from the Adult Sickle Cell Quality of Life Measurement Information System (ASCQ‐Me), Patient Reported Outcomes Measurement Information System (PROMIS), and Quality of Life in Neurological Disorders (NeuroQol) to evaluate pain impact, sleep impact, social functioning, depressive symptoms, tiredness, and cognitive function (collectively, patient reported outcomes [PROs]) and to identify associated demographic and clinical characteristics. Participants (n = 2201) between 18 and 45 years were recruited through the eight Sickle Cell Disease Implementation Consortium (SCDIC) sites. In multivariate models, PROs were significantly associated with one another. Pain impact was associated with age, education, employment, time since last pain attack, hydroxyurea use, opioid use, sleep impact, social functioning, and cognitive function (F = 88.74, P < .0001). Sleep impact was associated with household income, opioid use, pain impact, social functioning, depressive symptoms, and tiredness (F = 101.40, P < .0001). Social functioning was associated with employment, pain attacks in the past year, autoimmune/inflammatory comorbidities, pain impact, sleep impact, depressive symptoms, tiredness, and cognitive function (F = 121.73, P < .0001). Depressive symptoms were associated with sex, sleep impact, social functioning, tiredness, and cognitive function (F = 239.51, P < .0001). Tiredness was associated with sex, education, sleep impact, social functioning, depressive symptoms, and cognitive function (F = 129.13, P < .0001). These findings reflect the baseline PRO assessments among SCDIC registry participants. Further research is needed to better understand these outcomes and new targets for interventions to improve quality of life and function in people with SCD.