Improving health information systems during an emergency: lessons and recommendations from an Ebola treatment centre in Sierra Leone.

BACKGROUND: The 2014-2016 West Africa Ebola epidemic highlighted the difficulty of collecting patient information during emergencies, especially in highly infectious environments. Health information systems (HISs) appropriate for such settings were lacking prior to this outbreak. Here we describe our development and implementation of paper and electronic HISs at the Sierra Leone Kerry Town Ebola treatment centre (ETC) from 2014 to 2015. We share our approach, experiences, and recommendations for future health emergencies. METHODS: We developed eight fact-finding questions about data-related needs, priorities, and restrictions at the ETC ("inputs") to inform eight structural decisions ("outputs") across six core HIS components. Semi-structured interviews about the "inputs" were then conducted with HIS stakeholders, chosen based on their teams' involvement in ETC HIS-related activities. Their responses were used to formulate the "output" results to guide the HIS design. We implemented the HIS using an Agile approach, monitored system usage, and developed a structured questionnaire on user experiences and opinions. RESULTS: Some key "input" responses were: 1) data needs for priorities (patient care, mandatory reporting); 2) challenges around infection control, limited equipment, and staff clinical/language proficiencies; 3) patient/clinical flows; and 4) weak points from staff turnover, infection control, and changing protocols. Key outputs included: 1) determining essential data, 2) data tool design decisions (e.g. large font sizes, checkboxes/buttons), 3) data communication methods (e.g. radio, "collective memory"), 4) error reduction methods (e.g. check digits, pre-written wristbands), and 5) data storage options (e.g. encrypted files, accessible folders). Implementation involved building data collection tools (e.g. 13 forms), preparing the systems (e.g. supplies), training staff, and maintenance (e.g. removing old forms). Most patients had basic (100%, n = 456/456), drug (96.9%, n = 442/456), and additional clinical/epidemiological (98.9%, n = 451/456) data stored. The questionnaire responses highlighted the importance of usability and simplicity in the HIS. CONCLUSIONS: HISs during emergencies are often ad-hoc and disjointed, but systematic design and implementation can lead to high-quality systems focused on efficiency and ease of use. Many of the processes used and lessons learned from our work are generalizable to other health emergencies. Improvements should be started now to have rapidly adaptable and deployable HISs ready for the next health emergency.

Development of a Pediatric Ebola Predictive Score, Sierra Leone1.

We compared children who were positive for Ebola virus disease (EVD) with those who were negative to derive a pediatric EVD predictor (PEP) score. We collected data on all children <13 years of age admitted to 11 Ebola holding units in Sierra Leone during August 2014-March 2015 and performed multivariable logistic regression. Among 1,054 children, 309 (29%) were EVD positive and 697 (66%) EVD negative, with 48 (5%) missing. Contact history, conjunctivitis, and age were the strongest positive predictors for EVD. The PEP score had an area under receiver operating characteristics curve of 0.80. A PEP score of 7/10 was 92% specific and 44% sensitive; 3/10 was 30% specific, 94% sensitive. The PEP score could correctly classify 79%-90% of children and could be used to facilitate triage into risk categories, depending on the sensitivity or specificity required.

Symptom- and Laboratory-Based Ebola Risk Scores to Differentiate Likely Ebola Infections.

Rapidly identifying likely Ebola patients is difficult because of a broad case definition, overlap of symptoms with common illnesses, and lack of rapid diagnostics. However, rapid identification is critical for care and containment of contagion. We analyzed retrospective data from 252 Ebola-positive and 172 Ebola-negative patients at a Sierra Leone Ebola treatment center to develop easy-to-use risk scores, based on symptoms and laboratory tests (if available), to stratify triaged patients by their likelihood of having Ebola infection. Headache, diarrhea, difficulty breathing, nausea/vomiting, loss of appetite, and conjunctivitis comprised the symptom-based score. The laboratory-based score also included creatinine, creatine kinase, alanine aminotransferase, and total bilirubin. This risk score correctly identified 92% of Ebola-positive patients as high risk for infection; both scores correctly classified >70% of Ebola-negative patients as low or medium risk. Clinicians can use these risk scores to gauge the likelihood of triaged patients having Ebola while awaiting laboratory confirmation.

Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the Sustainable Development Goals.

BACKGROUND: Despite remarkable progress in the improvement of child survival between 1990 and 2015, the Millennium Development Goal (MDG) 4 target of a two-thirds reduction of under-5 mortality rate (U5MR) was not achieved globally. In this paper, we updated our annual estimates of child mortality by cause to 2000-15 to reflect on progress toward the MDG 4 and consider implications for the Sustainable Development Goals (SDG) target for child survival. METHODS: We increased the estimation input data for causes of deaths by 43% among neonates and 23% among 1-59-month-olds, respectively. We used adequate vital registration (VR) data where available, and modelled cause-specific mortality fractions applying multinomial logistic regressions using adequate VR for low U5MR countries and verbal autopsy data for high U5MR countries. We updated the estimation to use Plasmodium falciparum parasite rate in place of malaria index in the modelling of malaria deaths; to use adjusted empirical estimates instead of modelled estimates for China; and to consider the effects of pneumococcal conjugate vaccine and rotavirus vaccine in the estimation. FINDINGS: In 2015, among the 5·9 million under-5 deaths, 2·7 million occurred in the neonatal period. The leading under-5 causes were preterm birth complications (1·055 million [95% uncertainty range (UR) 0·935-1·179]), pneumonia (0·921 million [0·812 -1·117]), and intrapartum-related events (0·691 million [0·598 -0·778]). In the two MDG regions with the most under-5 deaths, the leading cause was pneumonia in sub-Saharan Africa and preterm birth complications in southern Asia. Reductions in mortality rates for pneumonia, diarrhoea, neonatal intrapartum-related events, malaria, and measles were responsible for 61% of the total reduction of 35 per 1000 livebirths in U5MR in 2000-15. Stratified by U5MR, pneumonia was the leading cause in countries with very high U5MR. Preterm birth complications and pneumonia were both important in high, medium high, and medium child mortality countries; whereas congenital abnormalities was the most important cause in countries with low and very low U5MR. INTERPRETATION: In the SDG era, countries are advised to prioritise child survival policy and programmes based on their child cause-of-death composition. Continued and enhanced efforts to scale up proven life-saving interventions are needed to achieve the SDG child survival target. FUNDING: Bill & Melinda Gates Foundation, WHO.

Development and Deployment of the OpenMRS-Ebola Electronic Health Record System for an Ebola Treatment Center in Sierra Leone.

BACKGROUND: Stringent infection control requirements at Ebola treatment centers (ETCs), which are specialized facilities for isolating and treating Ebola patients, create substantial challenges for recording and reviewing patient information. During the 2014-2016 West African Ebola epidemic, paper-based data collection systems at ETCs compromised the quality, quantity, and confidentiality of patient data. Electronic health record (EHR) systems have the potential to address such problems, with benefits for patient care, surveillance, and research. However, no suitable software was available for deployment when large-scale ETCs opened as the epidemic escalated in 2014. OBJECTIVE: We present our work on rapidly developing and deploying OpenMRS-Ebola, an EHR system for the Kerry Town ETC in Sierra Leone. We describe our experience, lessons learned, and recommendations for future health emergencies. METHODS: We used the OpenMRS platform and Agile software development approaches to build OpenMRS-Ebola. Key features of our work included daily communications between the development team and ground-based operations team, iterative processes, and phased development and implementation. We made design decisions based on the restrictions of the ETC environment and regular user feedback. To evaluate the system, we conducted predeployment user questionnaires and compared the EHR records with duplicate paper records. RESULTS: We successfully built OpenMRS-Ebola, a modular stand-alone EHR system with a tablet-based application for infectious patient wards and a desktop-based application for noninfectious areas. OpenMRS-Ebola supports patient tracking (registration, bed allocation, and discharge); recording of vital signs and symptoms; medication and intravenous fluid ordering and monitoring; laboratory results; clinician notes; and data export. It displays relevant patient information to clinicians in infectious and noninfectious zones. We implemented phase 1 (patient tracking; drug ordering and monitoring) after 2.5 months of full-time development. OpenMRS-Ebola was used for 112 patient registrations, 569 prescription orders, and 971 medication administration recordings. We were unable to fully implement phases 2 and 3 as the ETC closed because of a decrease in new Ebola cases. The phase 1 evaluation suggested that OpenMRS-Ebola worked well in the context of the rollout, and the user feedback was positive. CONCLUSIONS: To our knowledge, OpenMRS-Ebola is the most comprehensive adaptable clinical EHR built for a low-resource setting health emergency. It is designed to address the main challenges of data collection in highly infectious environments that require robust infection prevention and control measures and it is interoperable with other electronic health systems. Although we built and deployed OpenMRS-Ebola more rapidly than typical software, our work highlights the challenges of having to develop an appropriate system during an emergency rather than being able to rapidly adapt an existing one. Lessons learned from this and previous emergencies should be used to ensure that a set of well-designed, easy-to-use, pretested health software is ready for quick deployment in future.

Ebola Virus Disease in Children, Sierra Leone, 2014-2015.

Little is known about potentially modifiable factors in Ebola virus disease in children. We undertook a retrospective cohort study of children <13 years old admitted to 11 Ebola holding units in the Western Area, Sierra Leone, during 2014-2015 to identify factors affecting outcome. Primary outcome was death or discharge after transfer to Ebola treatment centers. All 309 Ebola virus-positive children 2 days-12 years old were included; outcomes were available for 282 (91%). Case-fatality was 57%, and 55% of deaths occurred in Ebola holding units. Blood test results showed hypoglycemia and hepatic/renal dysfunction. Death occurred swiftly (median 3 days after admission) and was associated with younger age and diarrhea. Despite triangulation of information from multiple sources, data availability was limited, and we identified no modifiable factors substantially affecting death. In future Ebola virus disease epidemics, robust, rapid data collection is vital to determine effectiveness of interventions for children.

Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis.

BACKGROUND: Trend data for causes of child death are crucial to inform priorities for improving child survival by and beyond 2015. We report child mortality by cause estimates in 2000-13, and cause-specific mortality scenarios to 2030 and 2035. METHODS: We estimated the distributions of causes of child mortality separately for neonates and children aged 1-59 months. To generate cause-specific mortality fractions, we included new vital registration and verbal autopsy data. We used vital registration data in countries with adequate registration systems. We applied vital registration-based multicause models for countries with low under-5 mortality but inadequate vital registration, and updated verbal autopsy-based multicause models for high mortality countries. We used updated numbers of child deaths to derive numbers of deaths by causes. We applied two scenarios to derive cause-specific mortality in 2030 and 2035. FINDINGS: Of the 6·3 million children who died before age 5 years in 2013, 51·8% (3·257 million) died of infectious causes and 44% (2·761 million) died in the neonatal period. The three leading causes are preterm birth complications (0·965 million [15·4%, uncertainty range (UR) 9·8-24·5]; UR 0·615-1·537 million), pneumonia (0·935 million [14·9%, 13·0-16·8]; 0·817-1·057 million), and intrapartum-related complications (0·662 million [10·5%, 6·7-16·8]; 0·421-1·054 million). Reductions in pneumonia, diarrhoea, and measles collectively were responsible for half of the 3·6 million fewer deaths recorded in 2013 versus 2000. Causes with the slowest progress were congenital, preterm, neonatal sepsis, injury, and other causes. If present trends continue, 4·4 million children younger than 5 years will still die in 2030. Furthermore, sub-Saharan Africa will have 33% of the births and 60% of the deaths in 2030, compared with 25% and 50% in 2013, respectively. INTERPRETATION: Our projection results provide concrete examples of how the distribution of child causes of deaths could look in 15-20 years to inform priority setting in the post-2015 era. More evidence is needed about shifts in timing, causes, and places of under-5 deaths to inform child survival agendas by and beyond 2015, to end preventable child deaths in a generation, and to count and account for every newborn and every child. FUNDING: Bill & Melinda Gates Foundation.

Every Newborn: progress, priorities, and potential beyond survival.

In this Series paper, we review trends since the 2005 Lancet Series on Neonatal Survival to inform acceleration of progress for newborn health post-2015. On the basis of multicountry analyses and multi-stakeholder consultations, we propose national targets for 2035 of no more than 10 stillbirths per 1000 total births, and no more than 10 neonatal deaths per 1000 livebirths, compatible with the under-5 mortality targets of no more than 20 per 1000 livebirths. We also give targets for 2030. Reduction of neonatal mortality has been slower than that for maternal and child (1-59 months) mortality, slowest in the highest burden countries, especially in Africa, and reduction is even slower for stillbirth rates. Birth is the time of highest risk, when more than 40% of maternal deaths (total about 290,000) and stillbirths or neonatal deaths (5·5 million) occur every year. These deaths happen rapidly, needing a rapid response by health-care workers. The 2·9 million annual neonatal deaths worldwide are attributable to three main causes: infections (0·6 million), intrapartum conditions (0·7 million), and preterm birth complications (1·0 million). Boys have a higher biological risk of neonatal death, but girls often have a higher social risk. Small size at birth--due to preterm birth or small-for-gestational-age (SGA), or both--is the biggest risk factor for more than 80% of neonatal deaths and increases risk of post-neonatal mortality, growth failure, and adult-onset non-communicable diseases. South Asia has the highest SGA rates and sub-Saharan Africa has the highest preterm birth rates. Babies who are term SGA low birthweight (10·4 million in these regions) are at risk of stunting and adult-onset metabolic conditions. 15 million preterm births, especially of those younger than 32 weeks' gestation, are at the highest risk of neonatal death, with ongoing post-neonatal mortality risk, and important risk of long-term neurodevelopmental impairment, stunting, and non-communicable conditions. 4 million neonates annually have other life-threatening or disabling conditions including intrapartum-related brain injury, severe bacterial infections, or pathological jaundice. Half of the world's newborn babies do not get a birth certificate, and most neonatal deaths and almost all stillbirths have no death certificate. To count deaths is crucial to change them. Failure to improve birth outcomes by 2035 will result in an estimated 116 million deaths, 99 million survivors with disability or lost development potential, and millions of adults at increased risk of non-communicable diseases after low birthweight. In the post-2015 era, improvements in child survival, development, and human capital depend on ensuring a healthy start for every newborn baby--the citizens and workforce of the future.

Neonatal cause-of-death estimates for the early and late neonatal periods for 194 countries: 2000-2013.

OBJECTIVE: To estimate cause-of-death distributions in the early (0-6 days of age) and late (7-27 days of age) neonatal periods, for 194 countries between 2000 and 2013. METHODS: For 65 countries with high-quality vital registration, we used each country's observed early and late neonatal proportional cause distributions. For the remaining 129 countries, we used multinomial logistic models to estimate these distributions. For countries with low child mortality we used vital registration data as inputs and for countries with high child mortality we used neonatal cause-of-death distribution data from studies in similar settings. We applied cause-specific proportions to neonatal death estimates from the United Nations Inter-agency Group for Child Mortality Estimation, by country and year, to estimate cause-specific risks and numbers of deaths. FINDINGS: Over time, neonatal deaths decreased for most causes. Of the 2.8 million neonatal deaths in 2013, 0.99 million deaths (uncertainty range: 0.70-1.31) were estimated to be caused by preterm birth complications, 0.64 million (uncertainty range: 0.46-0.84) by intrapartum complications and 0.43 million (uncertainty range: 0.22-0.66) by sepsis and other severe infections. Preterm birth (40.8%) and intrapartum complications (27.0%) accounted for most early neonatal deaths while infections caused nearly half of late neonatal deaths. Preterm birth complications were the leading cause of death in all regions of the world. CONCLUSION: The neonatal cause-of-death distribution differs between the early and late periods and varies with neonatal mortality rate level. To reduce neonatal deaths, effective interventions to address these causes must be incorporated into policy decisions.

Integrating The US Public Health And Medical Care Systems To Improve Health Crisis Response.

The operational cleavage between the US public health and medical care systems contributed to the country's difficulty in containing community spread of COVID-19 in the pandemic's first months. We provide an overview of the independent evolution of these two systems, drawing on case examples and publicly available outcome data, to demonstrate how three fundamental elements of epidemic response-case finding, mitigating transmission, and treatment-were undermined by the lack of coordination between public health and medical care and how these gaps contributed to health disparities. We propose policy initiatives to address these gaps and facilitate coordination across the two systems: build a case-finding diagnostic system to quickly identify and mitigate the emergence of health threats in communities, develop data systems that facilitate the transfer of critical health intelligence from medical institutions to public health departments, and establish referral pathways for public health practitioners to connect people with medical services. These policies are practicable because they build on existing efforts and those currently in development.

Community-Based Outbreak Investigation And Response: Enhancing Preparedness, Public Health Capacity, And Equity.

Throughout the COVID-19 pandemic, the US has struggled with many aspects of the public health response, from determining where transmission is occurring to building trust with communities and implementing interventions. Three factors have contributed to these challenges: insufficient local public health capacity, siloed interventions, and underuse of a cluster-based approach to outbreak response. In this article we introduce Community-based Outbreak Investigation and Response (COIR), a local public health strategy developed during the COVID-19 pandemic that addresses these shortcomings. COIR can help local public health entities conduct disease surveillance more effectively, take a more proactive and efficient approach to mitigating transmission, coordinate response efforts, build community trust, and advance equity. We offer a practitioner's lens, informed through on-the-ground experience and engagement with policy makers, to highlight the financing, workforce, data system, and information-sharing policy changes needed to scale up COIR throughout the country. COIR can enable the US public health system to develop effective solutions to many of today's public health challenges and improve the nation's preparedness for public health crises in the years to come.

How many deaths are attributable to smoking in the United States? Comparison of methods for estimating smoking-attributable mortality when smoking prevalence changes.

BACKGROUND: The number of smoking-attributable deaths is commonly estimated using current and former smoking prevalences or lung cancer mortality as an indirect metric of cumulative population smoking. Neither method accounts for differences in the timing with which relative risks (RRs) for different diseases change following smoking initiation and cessation. We aimed to develop a method to account for time-dependent RRs. METHODS: We used birth cohort lung cancer mortality and its change over time to characterize time-varying cumulative smoking exposure. We analyzed data from the American Cancer Society Cancer Prevention Study II to estimate RRs for disease-specific mortality associated with current and former smoking, and change in RRs over time after cessation. RESULTS: When lung cancer was used to measure cumulative smoking exposure, 254,700 male and 227,000 female deaths were attributed to smoking in the US in 2005. A modified method in which RRs for different diseases decreased at different rates after cessation yielded similar but slightly lower estimates [251,900 (male) and 221,100 (female)]. The lowest estimates resulted from the method based on smoking prevalence [225,800 (male) and 163,700 (female)]. CONCLUSIONS: Although all methods estimated a large number of smoking attributable deaths, future efforts should account for temporal changes in smoking prevalence and in accumulation/reversibility of disease-specific risks.

The Clean pilot study: evaluation of an environmental hygiene intervention bundle in three Tanzanian hospitals.

BACKGROUND: Healthcare associated infections (HAI) are estimated to affect up to 15% of hospital inpatients in low-income countries (LICs). A critical but often neglected aspect of HAI prevention is basic environmental hygiene, particularly surface cleaning and linen management. TEACH CLEAN is an educational intervention aimed at improving environmental hygiene. We evaluated the effectiveness of this intervention in a pilot study in three high-volume maternity and newborn units in Dar es Salaam, Tanzania. METHODS: This study design prospectively evaluated the intervention as a whole, and offered a before-and-after comparison of the impact of the main training. We measured changes in microbiological cleanliness [Aerobic Colony Counts (ACC) and presence of Staphylococcus aureus] using dipslides, and physical cleaning action using gel dots. These were analysed with descriptive statistics and logistic regression models. We used qualitative (focus group discussions, in-depth interviews, and semi-structured observation) and quantitative (observation checklist) tools to measure why and how the intervention worked. We describe these findings across the themes of adaptation, fidelity, dose, reach and context. RESULTS: Microbiological cleanliness improved during the study period (ACC pre-training: 19%; post-training: 41%). The odds of cleanliness increased on average by 1.33 weekly during the pre-training period (CI = 1.11-1.60), and by 1.08 (CI = 1.03-1.13) during the post-training period. Cleaning action improved only in the pre-training period. Detection of S. aureus on hospital surfaces did not change substantially. The intervention was well received and considered feasible in this context. The major pitfalls in the implementation were the limited number of training sessions at the hospital level and the lack of supportive supervision. A systems barrier to implementation was lack of regular cleaning supplies. CONCLUSIONS: The evaluation suggests that improvements in microbiological cleanliness are possible using this intervention and can be sustained. Improved microbiological cleanliness is a key step on the pathway to infection prevention in hospitals. Future research should assess whether this bundle is cost-effective in reducing bacterial and viral transmission and infection using a rigorous study design.

The promise of prevention: the effects of four preventable risk factors on national life expectancy and life expectancy disparities by race and county in the United States.

BACKGROUND: There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the "Eight Americas") defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005. METHODS AND FINDINGS: We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age-sex-disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South--5-7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%-80%; the corresponding reduction for probabilities of dying from cancers would be 29%-50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities. CONCLUSIONS: Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US. Please see later in the article for the Editors' Summary.

Trends and cardiovascular mortality effects of state-level blood pressure and uncontrolled hypertension in the United States.

BACKGROUND: Blood pressure is an important risk factor for cardiovascular disease and mortality and has lifestyle and healthcare determinants that vary across states. Only self-reported hypertension status is measured at the state level in the United States. Our aim was to estimate levels and trends in state-level mean systolic blood pressure (SBP), the prevalence of uncontrolled systolic hypertension, and cardiovascular mortality attributable to all levels of higher-than-optimal SBP. METHODS AND RESULTS: We estimated the relationship between actual SBP/uncontrolled hypertension and self-reported hypertension, use of blood pressure medication, and a set of health system and sociodemographic variables in the nationally representative National Health and Nutrition Examination Survey. We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System to estimate state-specific mean SBP and uncontrolled hypertension. We used the comparative risk assessment methods to estimate cardiovascular mortality attributable to higher-than-optimal SBP. In 2001-2003, age-standardized uncontrolled hypertension prevalence was highest in the District of Columbia, Mississippi, Louisiana, Alabama, Texas, Georgia, and South Carolina (18% to 21% for men and 24% to 26% for women) and lowest in Vermont, Minnesota, Connecticut, New Hampshire, Iowa, and Colorado (15% to 16% for men and approximately 21% for women). Women had a higher prevalence of uncontrolled hypertension than men in every state by 4 (Arizona) to 7 (Kansas) percentage points. In the 1990s, uncontrolled hypertension in women increased the most in Idaho and Oregon (by 6 percentage points) and the least in the District of Columbia and Mississippi (by 3 percentage points). For men, the worst-performing states were New Mexico and Louisiana (decrease of 0.6 and 1.3 percentage points), and the best-performing states were Vermont and Indiana (decrease of 4 and 3 percentage points). Age-standardized cardiovascular mortality attributable to higher-than-optimal SBP ranged from 200 to 220 per 100,000 (Minnesota and Massachusetts) to 360 to 370 per 100,000 (District of Columbia and Mississippi) for women and from 210 per 100,000 (Colorado and Utah) to 370 per 100,000 (Mississippi) and 410 per 100,000 (District of Columbia) for men. CONCLUSIONS: Lifestyle and pharmacological interventions for lowering blood pressure are particularly needed in the South and Appalachia, and with emphasis on control among women. Self-reported data on hypertension diagnosis from the Behavioral Risk Factor Surveillance System can be used to obtain unbiased state-level estimates of blood pressure and uncontrolled hypertension as benchmarks for priority setting and for designing and evaluating intervention programs.

Are Americans feeling less healthy? The puzzle of trends in self-rated health.

Although self-rated health is proposed for use in public health monitoring, previous reports on US levels and trends in self-rated health have shown ambiguous results. This study presents a comprehensive comparative analysis of responses to a common self-rated health question in 4 national surveys from 1971 to 2007: the National Health and Nutrition Examination Survey, Behavioral Risk Factor Surveillance System, National Health Interview Survey, and Current Population Survey. In addition to variation in the levels of self-rated health across surveys, striking discrepancies in time trends were observed. Whereas data from the Behavioral Risk Factor Surveillance System demonstrate that Americans were increasingly likely to report "fair" or "poor" health over the last decade, those from the Current Population Survey indicate the opposite trend. Subgroup analyses revealed that the greatest inconsistencies were among young respondents, Hispanics, and those without a high school education. Trends in "fair" or "poor" ratings were more inconsistent than trends in "excellent" ratings. The observed discrepancies elude simple explanations but suggest that self-rated health may be unsuitable for monitoring changes in population health over time. Analyses of socioeconomic disparities that use self-rated health may be particularly vulnerable to comparability problems, as inconsistencies are most pronounced among the lowest education group. More work is urgently needed on robust and comparable approaches to tracking population health.

Diabetes prevalence and diagnosis in US states: analysis of health surveys.

BACKGROUND: Current US surveillance data provide estimates of diabetes using laboratory tests at the national level as well as self-reported data at the state level. Self-reported diabetes prevalence may be biased because respondents may not be aware of their risk status. Our objective was to estimate the prevalence of diagnosed and undiagnosed diabetes by state. METHODS: We estimated undiagnosed diabetes prevalence as a function of a set of health system and sociodemographic variables using a logistic regression in the National Health and Nutrition Examination Survey (2003-2006). We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System (2003-2007) to estimate state-level prevalence of undiagnosed diabetes by age group and sex. We assumed that those who report being diagnosed with diabetes in both surveys are truly diabetic. RESULTS: The prevalence of diabetes in the U.S. was 13.7% among men and 11.7% among women >/= 30 years. Age-standardized diabetes prevalence was highest in Mississippi, West Virginia, Louisiana, Texas, South Carolina, Alabama, and Georgia (15.8 to 16.6% for men and 12.4 to 14.8% for women). Vermont, Minnesota, Montana, and Colorado had the lowest prevalence (11.0 to 12.2% for men and 7.3 to 8.4% for women). Men in all states had higher diabetes prevalence than women. The absolute prevalence of undiagnosed diabetes, as a percent of total population, was highest in New Mexico, Texas, Florida, and California (3.5 to 3.7 percentage points) and lowest in Montana, Oklahoma, Oregon, Alaska, Vermont, Utah, Washington, and Hawaii (2.1 to 3 percentage points). Among those with no established diabetes diagnosis, being obese, being Hispanic, not having insurance and being >/= 60 years old were significantly associated with a higher risk of having undiagnosed diabetes. CONCLUSION: Diabetes prevalence is highest in the Southern and Appalachian states and lowest in the Midwest and the Northeast. Better diabetes diagnosis is needed in a number of states.

National, regional, and state-level all-cause and cause-specific under-5 mortality in India in 2000-15: a systematic analysis with implications for the Sustainable Development Goals.

BACKGROUND: India had the largest number of under-5 deaths of all countries in 2015, with substantial subnational disparities. We estimated national and subnational all-cause and cause-specific mortality among children younger than 5 years annually in 2000-15 in India to understand progress made and to consider implications for achieving the Sustainable Development Goal (SDG) child survival targets. METHODS: We used a multicause model to estimate cause-specific mortality proportions in neonates and children aged 1-59 months at the state level, with causes of death grouped into pneumonia, diarrhoea, meningitis, injury, measles, congenital abnormalities, preterm birth complications, intrapartum-related events, and other causes. AIDS and malaria were estimated separately. The model was based on verbal autopsy studies representing more than 100 000 neonatal deaths globally and 16 962 deaths among children aged 1-59 months at the subnational level in India. By applying these proportions to all-cause deaths by state, we estimated cause-specific numbers of deaths and mortality rates at the state, regional, and national levels. FINDINGS: In 2015, there were 25·121 million livebirths in India and 1·201 million under-5 deaths (under-5 mortality rate 47·81 per 1000 livebirths). 0·696 million (57·9%) of these deaths occurred in neonates. There were disparities in child mortality across states (from 9·7 deaths [Goa] to 73·1 deaths [Assam] per 1000 livebirths) and regions (from 29·7 deaths [the south] to 63·8 deaths [the northeast] per 1000 livebirths). Overall, the leading causes of under-5 deaths were preterm birth complications (0·330 million [95% uncertainty range 0·279-0·367]; 27·5% of under-5 deaths), pneumonia (0·191 million [0·168-0·219]; 15·9%), and intrapartum-related events (0·139 million [0·116-0·165]; 11·6%), with cause-of-death distributions varying across states and regions. In states with very high under-5 mortality, infectious-disease-related causes (pneumonia and diarrhoea) were among the three leading causes, whereas the three leading causes were all non-communicable in states with very low mortality. Most states had a slower decline in neonatal mortality than in mortality among children aged 1-59 months. Ten major states must accelerate progress to achieve the SDG under-5 mortality target, while 17 are not on track to meet the neonatal mortality target. INTERPRETATION: Efforts to reduce vaccine-preventable deaths and to reduce geographical disparities should continue to maintain progress achieved in 2000-15. Enhanced policies and programmes are needed to accelerate mortality reduction in high-burden states and among neonates to achieve the SDG child survival targets in India by 2030. FUNDING: Bill & Melinda Gates Foundation.

Surviving Ebola: A historical cohort study of Ebola mortality and survival in Sierra Leone 2014-2015.

BACKGROUND: While a number of predictors for Ebola mortality have been identified, less is known about post-viral symptoms. The identification of acute-illness predictors for post-viral symptoms could allow the selection of patients for more active follow up in the future, and those in whom early interventions may be beneficial in the long term. Studying predictors of both mortality and post-viral symptoms within a single cohort of patients could also further our understanding of the pathophysiology of survivor sequelae. METHODS/PRINCIPAL FINDINGS: We performed a historical cohort study using data collected as part of routine clinical care from an Ebola Treatment Centre (ETC) in Kerry Town, Sierra Leone, in order to identify predictors of mortality and of post-viral symptoms. Variables included as potential predictors were sex, age, date of admission, first recorded viral load at the ETC and symptoms (recorded upon presentation at the ETC). Multivariable logistic regression was used to identify predictors. Of 263 Ebola-confirmed patients admitted between November 2014 and March 2015, 151 (57%) survived to ETC discharge. Viral load was the strongest predictor of mortality (adjusted OR comparing high with low viral load: 84.97, 95% CI 30.87-345.94). We did not find evidence that a high viral load predicted post-viral symptoms (ocular: 1.17, 95% CI 0.35-3.97; musculoskeletal: 1.07, 95% CI 0.28-4.08). Ocular post-viral symptoms were more common in females (2.31, 95% CI 0.98-5.43) and in those who had experienced hiccups during the acute phase (4.73, 95% CI 0.90-24.73). CONCLUSIONS/SIGNIFICANCE: These findings may add epidemiological support to the hypothesis that post-viral symptoms have an immune-mediated aspect and may not only be a consequence of high viral load and disease severity.

Improving child survival through environmental and nutritional interventions: the importance of targeting interventions toward the poor.

CONTEXT: The United Nations Millennium Development Goals (MDGs) set targets related to important global poverty, health, and sustainability issues. A critical but underinvestigated question for planning and allocating resources toward the MDGs is how interventions related to one MDG might affect progress toward other goals. OBJECTIVES: To estimate the reduction in child mortality as a result of interventions related to the environmental and nutritional MDGs (improving child nutrition and providing clean water, sanitation, and fuels) and to estimate how the magnitude and distribution of the effects of interventions vary based on the economic status of intervention recipients. DESIGN, SETTING, AND POPULATION: Population-level comparative risk assessment modeling the mortality effects of interventions on child nutrition and environmental risk factors, stratified by economic status. Data on economic status, child underweight, water and sanitation, and household fuels were from the nationally representative Demographic and Health Surveys for 42 countries in Latin America and the Caribbean, South Asia, and sub-Saharan Africa. Data on disease-specific child mortality were from the World Health Organization. Data on the hazardous effects of each MDG-related risk factor were from systematic reviews and meta-analyses of epidemiological studies. MAIN OUTCOME MEASURE: Child mortality, stratified by comparable international quintiles of economic status. RESULTS: Implementing interventions that improve child nutrition and provide clean water and sanitation and clean household fuels to all children younger than 5 years would result in an estimated annual reduction in child deaths of 49,700 (14%) in Latin America and the Caribbean, 0.80 million (24%) in South Asia, and 1.47 million (31%) in sub-Saharan Africa. These benefits are equivalent to 30% to 48% of the current regional gaps toward the MDG target on reducing child mortality. Fifty percent coverage of the same environmental and nutritional interventions, as envisioned by the MDGs, would reduce child mortality by 26,900, 0.51 million, and 1.02 million in the 3 regions, respectively, if the interventions are implemented among the poor first. These reductions are 30% to 75% larger than those expected if the same 50% coverage first reached the wealthier households, who nonetheless are in need of similar interventions. CONCLUSIONS: Interventions related to nutritional and environmental MDGs can also provide substantial gains toward the MDG of reducing child mortality. To maximize the reduction in childhood mortality, such integrated management of interventions should prioritize the poor.