Testing the Feasibility of a Passive and Active Case Ascertainment System for Multiple Rare Conditions Simultaneously: The Experience in Three US States

Residential proximity to unconventional oil and gas development and birth defects in Ohio

BACKGROUND

Chemicals used or emitted by unconventional oil and gas development (UOGD) include reproductive/developmental toxicants. Associations between UOGD and certain birth defects were reported in a few studies, with none conducted in Ohio, which experienced a thirty-fold increase in natural gas production between 2010 and 2020.

METHODS

We conducted a registry-based cohort study of 965,236 live births in Ohio from 2010 to 2017. Birth defects were identified in 4653 individuals using state birth records and a state surveillance system. We assigned UOGD exposure based on maternal residential proximity at birth to active UOG wells and a metric specific to the drinking-water exposure pathway that identified UOG wells hydrologically connected to a residence ("upgradient UOG wells"). We estimated odds ratios (ORs) and 95% confidence intervals (CIs) for all structural birth defects combined and specific birth defect types using binary exposure metrics (presence/absence of any UOG well and presence/absence of an upgradient UOG well within 10 km), adjusting for confounders. Additionally, we conducted analyses stratified by urbanicity, infant sex, and social vulnerability.

RESULTS

The odds of any structural defect were 1.13 times higher in children born to mothers living within 10 km of UOGD than those born to unexposed mothers (95%CI: 0.98-1.30). Odds were elevated for neural tube defects (OR: 1.57, 95%CI: 1.12-2.19), limb reduction defects (OR: 1.99, 95%CI: 1.18-3.35), and spina bifida (OR 1.93; 95%CI 1.25-2.98). Hypospadias (males only) was inversely related to UOGD exposure (OR: 0.62, 95%CI: 0.43-0.91). Odds of any structural defect were greater in magnitude but less precise in analyses using the hydrological-specific metric (OR: 1.30; 95%CI: 0.85-1.90), in areas with high social vulnerability (OR: 1.27, 95%CI: 0.99-1.60), and among female offspring (OR: 1.28, 95%CI: 1.06-1.53).

CONCLUSIONS

Our results suggest a positive association between UOGD and certain birth defects, and findings for neural tube defects corroborate results from prior studies.

Validation of case definition algorithms for the ascertainment of congenital anomalies

BACKGROUND

Congenital anomalies (CA) are one of the leading causes of infant mortality and long-term disability. Many jurisdictions rely on health administrative data to monitor these conditions. Case definition algorithms can be used to monitor CA; however, validation of these algorithms is needed to understand the strengths and limitations of the data. This study aimed to validate case definition algorithms used in a CA surveillance system in British Columbia (BC), Canada.

METHODS

A cohort of births between March 2000 and April 2002 in BC was linked to the Health Status Registry (HSR) and the BC Congenital Anomalies Surveillance System (BCCASS) to identify cases and non-cases of specific anomalies within each surveillance system. Measures of algorithm performance were calculated for each CA using the HSR as the reference standard. Agreement between both databases was calculated using kappa coefficient. The modified Standards for Reporting Diagnostic Accuracy guidelines were used to enhance the quality of the study.

RESULTS

Measures of algorithm performance varied by condition. Positive predictive value (PPV) ranged between approximately 73%-100%. Sensitivity was lower than PPV for most conditions. Internal congenital anomalies or conditions not easily identifiable at birth had the lowest sensitivity. Specificity and negative predictive value exceeded 99% for all algorithms.

CONCLUSION

Case definition algorithms may be used to monitor CA at the population level. Accuracy of algorithms is higher for conditions that are easily identified at birth. Jurisdictions with similar administrative data may benefit from using validated case definitions for CA surveillance as this facilitates cross-jurisdictional comparison.

Incidence of amyotrophic lateral sclerosis in the United States, 2014-2016

Objective: To estimate the incidence of amyotrophic lateral sclerosis (ALS) in the United States for calendar years 2014-2016 using data from the National ALS Registry (Registry). The Registry collects data on ALS patients in the United States to better describe the epidemiology of ALS, examine risk factors such as environmental and occupational exposures, and characterize the demographics of those living with the disease. Methods: To identify adult incident cases of ALS, the Registry compiles data from three national administrative databases (maintained by the Centers for Medicare and Medicaid Services, the Veterans Health Administration, and the Veterans Benefits Administration). For cases that are not included in these databases, the Registry includes data collected from patients who voluntarily enroll via a secure web portal. Results: The Registry identified 5695 ALS cases in 2014; 6045 cases in 2015; and 4861 cases in 2016 for age-adjusted incidence rates of 1.7 (2014), 1.5 (2015), and 1.5 (2016) per 100,000 U.S. population, respectively. ALS was more common among whites, males, and persons aged 60-79 years. Conclusions: This is the first time administrative and self-reported databases have been used to describe the incidence of ALS for the United States resulting in a better estimate of disease demographics.

30-day all-cause readmission rates among a cohort of individuals with rare conditions

BACKGROUND

There is a need to examine health care utilization of individuals with the rare conditions muscular dystrophies, spina bifida, and fragile X syndrome. These individuals have a greater need for health care services, particularly inpatient admissions. Prior studies have not yet assessed 30-day all-cause readmission rates.

OBJECTIVE

To estimate 30-day hospital readmission rates among individuals with three rare conditions.

HYPOTHESIS

Rare conditions patients will have a higher 30-day all-cause readmission rate than those without.

METHODS

Data from three sources (2007-2014) were combined for this case-control analysis. A cohort of individuals with one of the three conditions was matched (by age in 5 year age groups, gender, and race) to a comparison group without a rare condition. Inpatient utilization and 30-day all-cause readmission rates were compared between the two groups. Logistic regression analyses compared the odds of a 30-day all-cause readmission across the two groups, controlling for key covariates.

RESULTS

A larger proportion in the rare condition group had at least one inpatient visit (46.1%) vs. the comparison group (23.6%), and a higher 30-day all-cause readmission rate (Spina Bifida-46.7%, Muscular Dystrophy-39.7%, and Fragile X Syndrome-35.8%) than the comparison group (13.4%). Logistic regression results indicated that condition status contributed significantly to differences in readmission rates.

CONCLUSIONS

Higher rates of inpatient utilization and 30-day all-cause readmission among individuals with rare conditions vs. those without are not surprising, given the medical complexity of these individuals, and indicates an area where unfavorable outcomes may be improved with proper care coordination and post discharge care.

Muscular Dystrophy Surveillance, Tracking, and Research Network pilot: Population-based surveillance of major muscular dystrophies at four U.S. sites, 2007-2011

BACKGROUND

For 10 years, the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) conducted surveillance for Duchenne and Becker muscular dystrophy (DBMD). We piloted expanding surveillance to other MDs that vary in severity, onset, and sources of care.

METHODS

Our retrospective surveillance included individuals diagnosed with one of nine eligible MDs before or during the study period (January 2007-December 2011), one or more health encounters, and residence in one of four U.S. sites (Arizona, Colorado, Iowa, or western New York) at any time within the study period. We developed case definitions, surveillance protocols, and software applications for medical record abstraction, clinical review, and data pooling. Potential cases were identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 359.0, 359.1, and 359.21 and International Classification of Diseases, Tenth Revision (ICD-10) codes G71.0 and G71.1. Descriptive statistics were compared by MD type. Percentage of MD cases identified by each ICD-9-CM code was calculated.

RESULTS

Of 2,862 cases, 32.9% were myotonic, dystrophy 25.8% DBMD, 9.7% facioscapulohumeral MD, and 9.1% limb-girdle MD. Most cases were male (63.6%), non-Hispanic (59.8%), and White (80.2%). About, half of cases were genetically diagnosed in self (39.1%) or family (6.2%). About, half had a family history of MD (48.9%). The hereditary progressive MD code (359.1) was the most common code for identifying eligible cases. The myotonic code (359.21) identified 83.4% of eligible myotonic dystrophy cases (786/943).

CONCLUSIONS

MD STARnet is the only multisite, population-based active surveillance system available for MD in the United States. Continuing our expanded surveillance will contribute important epidemiologic and health outcome information about several MDs.