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Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary \"infoveillance\" approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms \"H1N1\" versus \"swine flu\" over time; 2) conduct a content analysis of \"tweets\"; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool. Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords \"swine flu,\" \"swineflu,\" and/or \"H1N1.\" using Infovigil, an infoveillance system. Tweets using \"H1N1\" increased from 8.8% to 40.5% (R(2) = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data. This study illustrates the potential of using social media to conduct \"infodemiology\" studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.

The World Health Organization Disability Assessment Schedule (WHODAS 2.0) measures disability due to health conditions including diseases, illnesses, injuries, mental or emotional problems, and problems with alcohol or drugs. The 12 Item WHODAS 2.0 was used in the second Australian Survey of Mental Health and Well-being. We report the overall factor structure and the distribution of scores and normative data (means and SDs) for people with any physical disorder, any mental disorder and for people with neither. A single second order factor justifies the use of the scale as a measure of global disability. People with mental disorders had high scores (mean 6.3, SD 7.1), people with physical disorders had lower scores (mean 4.3, SD 6.1). People with no disorder covered by the survey had low scores (mean 1.4, SD 3.6). The provision of normative data from a population sample of adults will facilitate use of the WHODAS 2.0 12 item scale in clinical and epidemiological research.

In the second in a series of articles on the changing nature of global health institutions, Julio Frenk offers a framework to better understand national health systems and their role in global health.

PurposeTo compare the prevalence of diabetes-related complications and comorbidities, clinical characteristics, glycemic control, and treatment patterns in patients with type 2 diabetes (T2D) within a large integrated healthcare system in 2008 vs 2013.MethodsAn electronic health record system was used to create a cross-sectional summary of all patients with T2D as on 1 July 2008 and 1 July 2013. Differences between the two data sets were assessed after adjusting for age, gender, race, and household income.ResultsIn 2008 and 2013, 24 493 and 41 582 patients with T2D were identified, respectively, of which the majority were male (52.3% and 50.1%) and Caucasian (79% and 75.2%). The mean ages (years) were 64.8 and 64.3. The percentages of patients across the defined A1C categories were 64.3 and 66.7 for <7%, 21.1 and 18.8 for 7–7.9%, 7.8 and 7.5 for 8–8.9%, and 6.8 and 7.0 for ≥9% in 2008 and 2013, respectively. The most prevalent T2D-related comorbidities were hypertension (82.5% and 87.2%) and cardiovascular disease (26.9% and 22.3%) in 2008 and 2013, respectively. Thiazolidinedione and sulfonylurea use decreased, whereas metformin and dipeptidyl peptidase-4 inhibitor use increased in the 5-year period.ConclusionsPatients with T2D are characterized by a high number of comorbidities. Over 85% of the patients had an A1C<8% within our integrated health delivery system in 2008 and 2013. In 2008 and 2013, metformin therapy was the most commonly utilized antidiabetic agent, and sulfonylureas were the most commonly utilized oral antidiabetic agent in combination with metformin. As integrated health systems assume greater shared financial risk in newer payment models, achieving glycemic targets (A1C) and the management of comorbidities will become ever-more important, for preventing diabetes-related complications, as well as to ensure reimbursement for the medical care that is rendered to patients with diabetes.

The use of maternal health care is limited in India despite several programmatic efforts for its improvement since the late 1980's. The use of maternal health care is typically patterned on socioeconomic and cultural contours. However, there is no clear perspective about how socioeconomic differences over time have contributed towards the use of maternal health care in India. Using data from three rounds of National Family Health Survey (NFHS) conducted during 1992-2006, we analyse the trends and patterns in utilization of prenatal care (PNC) in first trimester with four or more antenatal care visits and skilled birth attendance (SBA) among poor and nonpoor mothers, disaggregated by area of residence in India and three contrasting provinces, namely, Uttar Pradesh, Maharashtra and Tamil Nadu. In addition, we investigate the relative contribution of public and private health facilities in meeting the demand for SBA, especially among poor mothers. We also examine the role of salient socioeconomic, demographic and cultural factors in influencing aforementioned outcomes. Bivariate analyses, concentration curve and concentration index, logistic regression and multinomial logistic regression models are used to understand the trends, patterns and predictors of the two outcome variables. Results indicate sluggish progress in utilization of PNC and SBA in India and selected provinces during 1992-2006. Enormous inequalities in utilization of PNC and SBA were observed largely to the disadvantage of the poor. Multivariate analysis suggests growing inequalities in utilization of the two outcomes across different economic groups. The use of PNC and SBA remains disproportionately lower among poor mothers in India irrespective of area of residence and province. Despite several governmental efforts to increase access and coverage of delivery services to poor, it is clear that the poor (a) do not use SBA and (b) even if they had SBA, they were more likely to use the private providers.

In developing countries, the private sector provides a substantial proportion of primary health care to low income groups for communicable and non-communicable diseases. These providers are therefore central to improving health outcomes. We need to know how their services compare to those of the public sector to inform policy options. We summarised reliable research comparing the quality of formal private versus public ambulatory health care in low and middle income countries. We selected studies against inclusion criteria following a comprehensive search, yielding 80 studies. We compared quality under standard categories, converted values to a linear 100% scale, calculated differences between providers within studies, and summarised median values of the differences across studies. As the results for for-profit and not-for-profit providers were similar, we combined them. Overall, median values indicated that many services, irrespective of whether public or private, scored low on infrastructure, clinical competence, and practice. Overall, the private sector performed better in relation to drug supply, responsiveness, and effort. No difference between provider groups was detected for patient satisfaction or competence. Synthesis of qualitative components indicates the private sector is more client centred. Although data are limited, quality in both provider groups seems poor, with the private sector performing better in drug availability and aspects of delivery of care, including responsiveness and effort, and possibly being more client orientated. Strategies seeking to influence quality in both groups are needed to improve care delivery and outcomes for the poor, including managing the increasing burden of non-communicable diseases.

  Few studies have looked at inequalities within urban areas, or quantified urban poverty adequately, although it is possible to do so using survey data.\\n For instance, the World Bank Health in Africa Initiative is developing a strategy to expand socially responsible private provision through a number of measures, including financial investment in appropriate health care companies serving low-income groups, supporting improved mechanisms for regulation, and working with governments to develop public-private partnerships [41]. [...]there is a critical challenge to universalize quality with corresponding benchmarking and regulation in both sectors.

Maternal and perinatal mortality could be reduced if all women delivered in settings where skilled attendants could provide emergency obstetric care (EmOC) if complications arise. Research on determinants of skilled attendance at delivery has focussed on household and individual factors, neglecting the influence of the health service environment, in part due to a lack of suitable data. The aim of this study was to quantify the effects of distance to care and level of care on women's use of health facilities for delivery in rural Zambia, and to compare their population impact to that of other important determinants. Using a geographic information system (GIS), we linked national household data from the Zambian Demographic and Health Survey 2007 with national facility data from the Zambian Health Facility Census 2005 and calculated straight-line distances. Health facilities were classified by whether they provided comprehensive EmOC (CEmOC), basic EmOC (BEmOC), or limited or substandard services. Multivariable multilevel logistic regression analyses were performed to investigate the influence of distance to care and level of care on place of delivery (facility or home) for 3,682 rural births, controlling for a wide range of confounders. Only a third of rural Zambian births occurred at a health facility, and half of all births were to mothers living more than 25 km from a facility of BEmOC standard or better. As distance to the closest health facility doubled, the odds of facility delivery decreased by 29% (95% CI, 14%-40%). Independently, each step increase in level of care led to 26% higher odds of facility delivery (95% CI, 7%-48%). The population impact of poor geographic access to EmOC was at least of similar magnitude as that of low maternal education, household poverty, or lack of female autonomy. Lack of geographic access to emergency obstetric care is a key factor explaining why most rural deliveries in Zambia still occur at home without skilled care. Addressing geographic and quality barriers is crucial to increase service use and to lower maternal and perinatal mortality. Linking datasets using GIS has great potential for future research and can help overcome the neglect of health system factors in research and policy. Please see later in the article for the Editors' Summary.

Diabetes costs represent a large burden to both patients and the health care system. However, few studies that examine the economic consequences of diabetes have distinguished between the two major forms, type 1 and type 2 diabetes, despite differences in underlying pathologies. Combining the two diseases implies that there is no difference between the costs of type 1 and type 2 diabetes to a patient. In this study, we examine the costs of type 1 diabetes, which is often overlooked due to the larger population of type 2 patients, and compare them to the estimated costs of diabetes reported in the literature. Using a nationally representative dataset, we estimate yearly and lifetime medical and indirect costs of type 1 diabetes by implementing a matching method to compare a patient with type 1 diabetes to a similar individual without the disease. We find that each year type 1 diabetes costs this country $14.4 billion (11.5-17.3) in medical costs and lost income. In terms of lost income, type 1 patients incur a disproportionate share of type 1 and type 2 costs. Further, if the disease were eliminated by therapeutic intervention, an estimated $10.6 billion (7.2-14.0) incurred by a new cohort and $422.9 billion (327.2-519.4) incurred by the existing number of type 1 diabetic patients over their lifetime would be avoided. We find that the costs attributed to type 1 diabetes are disproportionately higher than the number of type 1 patients compared with type 2 patients, suggesting that combining the two diseases when estimating costs is not appropriate. This study and another recent contribution provides a necessary first step in estimating the substantial costs of type 1 diabetes on the U.S.

  Abbreviations: DHS, Demographic and Health Surveys; GAVI, Global Alliance for Vaccines and Immunisation; ICD, International Classification of Diseases and Related Health Problems; IHP+, International Health Partnership; M&E, monitoring and evaluation; MDG, Millennium Development Goal; MICS, Multiple Indicator Cluster Survey; NHA, National Health Accounts; SDMX, Standard Data and Metadata eXchange; UNAIDS, Joint United Nations Programme on HIV/AIDS; UNFPA, United Nations Population Fund; UNICEF, United Nations Children's Fund; WHO, World Health Organization Margaret Chan is Director-General of the World Health Organization. A commonly used figure, by, for instance, the Global Fund to Fight AIDS, Tuberculosis and Malaria, is that 5% to 10% of program funds should be invested in data collection, monitoring, evaluation, and operational research; * Improving the efficiency of health information investments by closer collaboration between partners in support of one strong country M&E plan that covers all major disease and health programs and all data sources.\\n Increase Data Access and Use Better access to data and statistics in the public domain could generate important benefits at country and global levels by fostering collaboration and innovation in statistical and analytic methods, both for new data collection and for better use of existing data.