CSDE Affiliate Anne Conway (Social Work, University of Tennessee) and her co-author recently published their work in Chronobiology International, “Social jetlag longitudinally predicts internalizing and externalizing behavior for adolescent females, but not males“. Biological changes contribute to preferences for later bed and wake times during adolescence, yet the social constraints of school start times necessitate early wake times. This often results in social jetlag (i.e. misalignment between preferred sleep timing on weekends and school days). Authors examined whether social jetlag predicts adolescent internalizing and externalizing behavior over time and/or whether associations differ based on sex. They used data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development Study (n = 767) to test whether social jetlag at 6^th grade (ages 12–13 years) predicted internalizing and externalizing behavior at age 15 years and whether child sex moderated associations. Controlling for internalizing and externalizing behavior at 6^th grade (ages 12–13 years), results were that social jetlag at 6^th grade (ages 12–13 years) predicted more internalizing and externalizing behaviors at age 15 for females, but not males. These findings show that social jetlag during early adolescence confers risk for internalizing and externalizing behavior in females at mid-adolescence. Greater attention should be placed on identifying and addressing social jetlag in adolescent females.

CSDE Affiliate Bradley Wagenaar (Global Health, Epidemiology) and co-authors published their study “Development of novel composite data quality scores to evaluate facility-level data quality in electronic data in Kenya: a nationwide retrospective cohort study” in BMC Health Services Research. In this evaluation, authors aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. They leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. They provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya.

Authors evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), they extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). They assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data.

A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators.

Authors observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, they recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyond HIV clinics.

CSDE Affiliate Edmund Seto (Environmental and Occupational Health Sciences) and co-authors published their work in Environmental Science: Water, Research, & Technology, titled “Science-based pathogen treatment requirements for direct potable reuse“. Specifying appropriate pathogen treatment requirements is critical to ensure that direct potable reuse (DPR) systems provide consistent and reliable protection of public health. This study leverages several research efforts conducted on behalf of the California State Water Resources Control Board to provide guidance on selecting science-based pathogen treatment requirements for DPR. Advancements in pathogen detection methods have produced new robust, high-quality datasets that can be used to characterize the distribution of pathogen concentrations present in raw wastewater. Such probabilistic distributions should replace the deterministic point estimate approach previously used in regulatory development. Specifically, to calculate pathogen treatment requirements, pathogen distributions should be used in probabilistic quantitative microbial risk assessments that account for variability in concentrations. This approach was applied using the latest high-quality datasets to determine the log reduction targets necessary to achieve an annual risk goal of 1 in 10 000 infections per person as well as a more stringent daily risk goal of 2.7 × 10⁻⁷ infections per person. The probabilistic approach resulted in pathogen log reduction targets of 13-log₁₀ for enteric viruses, 10-log₁₀ for Giardia, and 10-log₁₀ for Cryptosporidium. An additional 4-log₁₀ level of redundancy provides protection against undetected failures while maintaining high degrees of compliance with the daily (99%) and annual risk goals (>99%). The limitations of the use of molecular pathogen data are also discussed. While the recommendations and findings are targeted for California, they are broadly applicable to the development of DPR regulations outside California and the U.S.

CSDE Affiliates Rachel Prusynski (Physical Therapy) and Tracy Mroz (Rehabilitation Medicine) published their research with co-authors in the International Journal of Environmental Research and Public Health, titled “Implementation of US Post-Acute Care Payment Reform and COVID-19 Policies: Examining Experiences of Health System Leaders, Staff, Patients, and Family Caregivers—A Protocol“. In fiscal year 2020, new national Medicare payment models were implemented in the two most common post-acute care settings (i.e., skilled nursing facilities (SNFs) and home health agencies (HHAs)), which were followed by the emergence of COVID-19. Given concerns about the unintended consequence of these events, this study protocol will examine how organizations responded to these policies and whether there were changes in SNF and HHA access, care delivery, and outcomes from the perspectives of leadership, staff, patients, and families. Authors conducted a two-phase multiple case study guided by the Institute of Medicine’s Model of Healthcare Systems. Phase I will include three cases for each setting and a maximum of fifty administrators per case. Phase II will include a subset of Phase I organizations, which are grouped into three setting-specific cases. Each Phase II case will include a maximum of four organizations. Semi-structured interviews will explore the perspectives of frontline staff, patients, and family caregivers (Phase II). Thematic analysis will be used to examine the impact of payment policy and COVID-19 on organizational operations, care delivery, and patient outcomes. The results of this study intend to develop evidence addressing concerns about the unintended consequences of the PAC payment policy during the COVID-19 pandemic.