OBJECTIVES: World Health Organization issued Joint Statement on Data Protection and Privacy in the COVID-19 Response stating that collection of vast amounts of personal data may potentially lead to the infringement of fundamental human rights and freedoms. The Organization for Economic Cooperation and Development called on national governments to adhere to the international principles for data security and confidentiality. This paper describes the methods used to assist the Ministry of Health in bringing awareness of the data ownership, confidentiality and security principles to COVID-19 responders. METHODS: The Sierra Leone Epidemiological Data (SLED) Team data managers conducted training for groups of COVID-19 responders. Training included presentations on data confidentiality, information disclosure, physical and electronic data security, and cyber-security; and interactive discussion of real-life scenarios. A game of Jeopardy was created to test the participant's knowledge. RESULTS: This paper describes the methods used by the SLED Team to bring awareness of the DOCS principles to more than 2,500 COVID-19 responders. CONCLUSION: Similar efforts may benefit other countries where the knowledge, resources, and governing rules for protection of personal data are limited.

In 2021, the sepsis-related death rate among persons aged ≥65 years was 330.9 deaths per 100,000 population; the rate among men (371.7) was higher than that among women (297.4). Sepsis-related death rates among men were higher than those among women in each age group: 232.7 versus 173.0 (65–74 years), 477.3 versus 349.8 (75–84 years), and 1,037.8 versus 755.5 (≥85 years). Sepsis-related death rates increased with age from 201.1 among persons aged 65–74 years to 858.3 among those aged ≥85 years. Sepsis-related death rates increased with age among both men and women.

In 2015, the Sierra Leone Ministry of Health and Sanitation (MoHS) and the Centers for Disease Control and Prevention (CDC) agreed to consolidate data recorded by MoHS and international partners during the Ebola epidemic and create the Sierra Leone Ebola Database (SLED). The primary objectives were helping families to identify the location of graves of their loved ones who died from any cause at the time of the Ebola epidemic and creating a data source for epidemiological research. The Family Reunification Program fulfils the first SLED objective. The purpose of this paper is to describe the Family Reunification Program (Program) development, functioning and results. The MoHS, CDC, SLED Team, and Concern Worldwide developed, tested, and implemented methodology and tools to conduct the Program. Family liaisons were trained in protection of the personally identifiable information. The SLED Family Reunification Program allows families in Sierra Leone, who did not know the final resting place of their loved ones, to be reunited with their graves and to bring them relief and closure. Continuing family requests in search of the burial place of loved ones five years after the end of the epidemic shows that the emotional burden of losing a family member and not knowing the place of burial does not diminish with time. As of February 2021, the Program continues and is described to allow its replication for other emergency events including COVID-19 and new Ebola outbreaks.

BACKGROUND: During the 2014-2016 Ebola outbreak in West Africa, the Sierra Leone Ministry of Health and Sanitation (MoHS), the US Centers for Disease Control and Prevention, and responding partners under the coordination of the National Ebola Response Center (NERC) and the MoHS's Emergency Operation Center (EOC) systematically recorded information from the 117 Call Center system and district alert phone lines, case investigations, laboratory sample testing, clinical management, and safe and dignified burial records. Since 2017, CDC assisted MoHS in building and managing the Sierra Leone Ebola Database (SLED) to consolidate these major data sources. The primary objectives of the project were helping families to identify the location of graves of their loved ones who died at the time of the Ebola epidemic through the SLED Family Reunification Program and creating a data source for epidemiological research. The objective of this paper is to describe the process of consolidating epidemic records into a useful and accessible data collection and to summarize data characteristics, strength, and limitations of this unique information source for public health research. METHODS: Because of the unprecedented conditions during the epidemic, most of the records collected from responding organizations required extensive processing before they could be used as a data source for research or the humanitarian purpose of locating burial sites. This process required understanding how the data were collected and used during the outbreak. To manage the complexity of processing the data obtained from various sources, the Sierra Leone Ebola Database (SLED) Team used an organizational strategy that allowed tracking of the data provenance and lifecycle. RESULTS: The SLED project brought raw data into one consolidated data collection. It provides researchers with secure and ethical access to the SLED data and serves as a basis for the research capacity building in Sierra Leone. The SLED Family Reunification Program allowed Sierra Leonean families to identify location of the graves of loved ones who died during the Ebola epidemic. DISCUSSION: The SLED project consolidated and utilized epidemic data recorded during the Sierra Leone Ebola Virus Disease outbreak that were collected and contributed to SLED by national and international organizations. This project has provided a foundation for developing a method of ethical and secure SLED data access while preserving the host nation's data ownership. SLED serves as a data source for the SLED Family Reunification Program and for epidemiological research. It presents an opportunity for building research capacity in Sierra Leone and provides a foundation for developing a relational database. Large outbreak data systems such as SLED provide a unique opportunity for researchers to improve responses to epidemics and indicate the need to include data management preparedness in the plans for emergency response.

Purpose: Organizations responding to the 2014–2016 Ebola epidemic in Sierra Leone collected information from multiple sources and kept it in separate databases, including distinct data systems for Ebola hot line calls, patient information collected by field surveillance officers, laboratory testing results, clinical information from Ebola treatment and isolation facilities, and burial team records. Methods: After the conclusion of the epidemic, the Sierra Leone Ministry of Health and Sanitation and the U.S. Centers for Disease Control and Prevention partnered to collect these disparate records and consolidate them in the Sierra Leone Ebola Database. Results: The Sierra Leone Ebola Database data are providing a lasting resource for postepidemic data analysis and epidemiologic research, including identifying best strategies in outbreak response, and are used to help families locate the graves of family members who died during the epidemic. Conclusion: This report describes the Ministry of Health and Sanitation and Centers for Disease Control and Prevention processes to safeguard Ebola records while making the data available for public health research.

Developing a surveillance system during a public health emergency is always challenging but is especially so in countries with limited public health infrastructure. Surveillance for Ebola virus disease (Ebola) in the West African countries heavily affected by Ebola (Guinea, Liberia, and Sierra Leone) faced numerous impediments, including insufficient numbers of trained staff, community reticence to report cases and contacts, limited information technology resources, limited telephone and Internet service, and overwhelming numbers of infected persons. Through the work of CDC and numerous partners, including the countries' ministries of health, the World Health Organization, and other government and nongovernment organizations, functional Ebola surveillance was established and maintained in these countries. CDC staff were heavily involved in implementing case-based surveillance systems, sustaining case surveillance and contact tracing, and interpreting surveillance data. In addition to helping the ministries of health and other partners understand and manage the epidemic, CDC's activities strengthened epidemiologic and data management capacity to improve routine surveillance in the countries affected, even after the Ebola epidemic ended, and enhanced local capacity to respond quickly to future public health emergencies. However, the many obstacles overcome during development of these Ebola surveillance systems highlight the need to have strong public health, surveillance, and information technology infrastructure in place before a public health emergency occurs. Intense, long-term focus on strengthening public health surveillance systems in developing countries, as described in the Global Health Security Agenda, is needed.The activities summarized in this report would not have been possible without collaboration with many U.S and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Ebola virus disease (Ebola) was first detected in Sierra Leone in May 2014 and was likely introduced into the eastern part of the country from Guinea. The disease spread westward, eventually affecting Freetown, Sierra Leone's densely populated capital. By December 2014, Sierra Leone had more Ebola cases than Guinea and Liberia, the other two West African countries that have experienced widespread transmission. As the epidemic intensified through the summer and fall, an increasing number of infected persons were not being detected by the county's surveillance system until they had died. Instead of being found early in the disease course and quickly isolated, these persons remained in their communities throughout their illness, likely spreading the disease.

BACKGROUND: Although national cross-sectional estimates of the percentage of children enrolled in Medicaid are available, the percentage of children enrolled in Medicaid over longer periods of time is unknown. Also, the percentage and characteristics of children who rely on Medicaid throughout childhood, rather than transiently, are unknown. METHODS: We performed a longitudinal examination of Medicaid coverage among children across a 5-year period. Children 0 to 13 years of age in the 2004 National Health Interview Survey file were linked to Medicaid Analytic eXtract files from 2004 to 2008. The percentage of children enrolled in Medicaid at any time during the 5-year observation period and the number of years during which children were enrolled in Medicaid were calculated. Duration of Medicaid enrollment was compared across sociodemographic characteristics by using chi(2) tests. RESULTS: Forty-one percent of all US children were enrolled in Medicaid at least some time during the 5-year period, compared with a single-year estimate of 32.8% in 2004 alone. Of enrolled children, 51.5% were enrolled during all 5 years. Children with lower parental education, with lower household income, of minority race or ethnicity, and in suboptimal health were more likely to be enrolled in Medicaid during all 5 years. CONCLUSIONS: Longitudinal data reveal higher percentages of children with Medicaid insurance than shown by cross-sectional data. Half of children enrolled in Medicaid are enrolled during at least 5 consecutive years, and these children have higher risk sociodemographic profiles.

OBJECTIVE: To examine the strengths and limitations of the Center for Medicare and Medicaid Services' Chronic Condition Data Warehouse (CCW) algorithm for identifying chronic conditions in older persons from Medicare beneficiary data. DATA SOURCES: Records from participants of the NHANES I Epidemiologic Follow-up Study (NHEFS 1971-1992) linked to Medicare claims data from 1991 to 2000. STUDY DESIGN: We estimated the percent of preexisting cases of chronic conditions correctly identified by the CCW algorithm during its reference period and the number of years of claims data necessary to find a preexisting condition. PRINCIPAL FINDINGS: The CCW algorithm identified 69 percent of preexisting diabetes cases but only 17 percent of preexisting arthritis cases. Cases identified by the CCW are a mix of preexisting and newly diagnosed conditions. CONCLUSIONS: The prevalence of conditions needing less frequent health care utilization (e.g., arthritis) may be underestimated by the CCW algorithm. The CCW reference periods may not be sufficient for all analytic purposes.