Decision makers are searching for reliable data and best practices to support the implementation and scale-up of the integrated community case management (iCCM) programs in underserved areas to reduce under-five mortality in low-income countries. This study assesses data quality and reporting systems of the World Health Organization supported Rapid Access Expansion program implementing iCCM in Abia and Niger States, Nigeria. This cross-sectional study used data from 16 primary health facilities in both states. Data were collected through review of registers and monthly summary reports of 140 community-oriented resource persons (CORPs), assessments of the five dimensions of the data reporting systems and 46 key informant interviews with stakeholders. Data quality was assessed by availability, completeness and consistency. Each component of the reporting system was assessed on a 3-point scale (weak, satisfactory and strong). Results show that both the structure, functions and capabilities, as well as data collection and reporting tools dimensions of the reporting system were strong, scoring (2.80, 2.73) for Abia and (2.88, 2.75) for Niger, respectively. Data management processes and links with national reporting system components scored low 2 s, indicating fair strength. Data availability, completeness and consistency were found to be good, an indication of adequate training and supervision of CORPs and community health extension workers. Indicator definitions and reporting guidelines were the weakest dimension of the system due to lack of data reporting guidelines in both states. In conclusion, the results indicate satisfactory data reporting systems and good quality data during early implementation of iCCM programs in the two states. Hence, countries planning to adopt and implement iCCM programs should first develop structures, establish national standardized tools for collecting and reporting data, provide for adequate training and supervision of community health workers and develop reporting guidelines for all reporting levels to ensure data quality.

Objective To understand the prevalence of comorbidities associated with traumatic brain injury (TBI) patients among active and reserve service members in the U.S. Military. Methods Active and reserve SMs diagnosed with an incident TBI from January 2017 to October 2019 were selected. Nineteen comorbidities associated with TBI as identified in the literature and by clinical subject matter experts were described in this article. Each patient’s medical encounters were evaluated from 6 months before to 2 years following the initial TBI diagnoses date in the Military Data Repository, if data were available. Time-to-event analyses were conducted to assess the cumulative prevalence over time of each comorbidity to the incident TBI diagnosis. Results We identified 47,299 TBI patients, of which most were mild (88.8%), followed by moderate (10.5%), severe (0.5%), and of penetrating (0.2%) TBI severity. Two years from the initial TBI diagnoses, the top five comorbidities within our cohort were cognitive disorders (51.9%), sleep disorders (45.0%), post-traumatic stress disorder (PTSD; 36.0%), emotional disorders (22.7%), and anxiety disorders (22.6%) across severity groups. Cognitive, sleep, PTSD, and emotional disorders were the top comorbidities seen within each TBI severity group. Comorbidities increased pre-TBI to post-TBI; the more severe the TBI, the greater the prevalence of associated comorbidities. Conclusion A large proportion of our TBI patients are afflicted with comorbidities, particularly post-TBI, indicating many have a complex profile. The military health system should continue tracking comorbidities associated with TBI within the U.S. Military and devise clinical practices that acknowledge the complexity of the TBI patient.

BackgroundMany US Military Service Members (SMs) newly diagnosed with mild Traumatic Brain Injury (mTBI) may exhibit a range of symptoms and comorbidities, making for a complex patient profile that challenges clinicians and healthcare administrators. This study used clustering techniques to determine if conditions co-occurred as clusters among those newly injured with mTBI and up to one year post-injury.MethodsWe measured the co-occurrence of 41 conditions among SMs diagnosed with mTBI within the acute phase, one or three months post-mTBI diagnosis, and chronic phase, one year post-mTBI diagnosis. Conditions were identified from the literature, clinical subject matter experts, and mTBI care guidelines. The presence of conditions were based on medical encounters recorded within the military health care data system. Through a two-step approach, we identified clusters. Principal component analysis (PCA) determined the optimal number of clusters, and hierarchical cluster analyses (HCA) identified the composition of clusters. Further, we explored how the composition of these clusters changed over time.ResultsOf the 42,018 SMs with mTBI, 23,478 (55.9%) had at least one condition of interest one-month post-injury, 26,831 (63.9%) three months post-injury, and 29,860 (71.1%) one year post injury. Across these three periods, six clusters were identified. One cluster included vision, cognitive, ear, and sleep disorders that occurred one month, three months, and one year post-injury. Another subgroup included psychological conditions such as anxiety, depression, PTSD, and other emotional symptoms that co-occurred in the acute and chronic phases post-injury. Nausea and vomiting symptoms clustered with cervicogenic symptoms one month post-injury, but later shifted to other clusters. Vestibular disorders clustered with sleep disorders and headache disorders one-month post-injury and included numbness and neuropathic pain one year post-injury. Substance abuse symptoms, alcohol disorders, and suicidal attempt clustered one year post-injury in a fifth cluster. Speech disorders co-occurred with headache disorders one month and one year post-injury to form a sixth cluster.ConclusionPCA and HCA identified six distinct subgroups among newly diagnosed mTBI patients during the acute and chronic phases post-injury. These subgroups may help clinicians better understand the complex profile of SMs newly diagnosed with mTBI.