BackgroundPatients often express strong preferences for the forms of treatment available for their disease. Incorporating these preferences into the process of treatment decision-making might improve patients' adherence to treatment, contributing to better outcomes. We describe the methodology used in a study aiming to assess treatment outcomes when patients' preferences for treatment are closely matched to recommended treatments.MethodParticipants included patients with moderate and severe psoriasis attending outpatient dermatology clinics at the University Medical Centre Mannheim, University of Heidelberg, Germany. A self-administered online survey used conjoint analysis to measure participants' preferences for psoriasis treatment options at the initial study visit. Physicians' treatment recommendations were abstracted from each participant's medical records. The Preference Matching Index (PMI), a measure of concordance between the participant's preferences for treatment and the physician's recommended treatment, was determined for each participant at t1 (initial study visit). A clinical outcome measure, the Psoriasis Area and Severity Index, and two participant-derived outcomes assessing treatment satisfaction and health related quality of life were employed at t1, t2 (twelve weeks post-t1) and t3 (twelve weeks post-t2). Change in outcomes was assessed using repeated measures analysis of variance. The association between participants' PMI scores at t1 and outcomes at t2 and t3 was evaluated using multivariate regressions analysis.DiscussionWe describe methods for capturing concordance between patients' treatment preferences and recommended treatment and for assessing its association with specific treatment outcomes. The methods are intended to promote the incorporation of patients' preferences in treatment decision-making, enhance treatment satisfaction, and improve treatment effectiveness through greater adherence.

IntroductionRoutine health information systems are critical for monitoring service delivery. District Heath Information System, version 2 (DHIS2) is an open source software platform used in more than 60 countries, on which global initiatives increasingly rely for such monitoring. We used facility-reported data in DHIS2 for Gombe State, north-eastern Nigeria, to present a case study of data quality to monitor priority maternal and neonatal health indicators.MethodsFor all health facilities in DHIS2 offering antenatal and postnatal care services (n = 497) and labor and delivery services (n = 486), we assessed the quality of data for July 2016-June 2017 according to the World Health Organization data quality review guidance. Using data from DHIS2 as well as external facility-level and population-level household surveys, we reviewed three data quality dimensions—completeness and timeliness, internal consistency, and external consistency—and considered the opportunities for improvement.ResultsOf 14 priority maternal and neonatal health indicators that could be tracked through facility-based data, 12 were included in Gombe’s DHIS2. During July 2016-June 2017, facility-reported data in DHIS2 were incomplete at least 40% of the time, under-reported 10%-60% of the events documented in facility registers, and showed inconsistencies over time, between related indicators, and with an external data source. The best quality data elements were those that aligned with Gombe’s health program priorities, particularly older health programs, and those that reflected contact indicators rather than indicators related to the provision of commodities or content of care.ConclusionThis case study from Gombe State, Nigeria, demonstrates the high potential for effective monitoring of maternal and neonatal health using DHIS2. However, coordinated action at multiple levels of the health system is needed to maximize reporting of existing data; rationalize data flow; routinize data quality review, feedback, and supervision; and ensure ongoing maintenance of DHIS2.

BackgroundFamilies in high mortality settings need regular contact with high quality services, but existing population-based measurements of contacts do not reflect quality. To address this, in 2012, we designed linked household and frontline worker surveys for Gombe State, Nigeria, Ethiopia, and Uttar Pradesh, India. Using reported frequency and content of contacts, we present a method for estimating the population level coverage of high quality contacts.Methods and FindingsLinked cluster-based household and frontline health worker surveys were performed. Interviews were conducted in 40, 80 and 80 clusters in Gombe, Ethiopia, and Uttar Pradesh, respectively, including 348, 533, and 604 eligible women and 20, 76, and 55 skilled birth attendants. High quality contacts were defined as contacts during which recommended set of processes for routine health care were met. In Gombe, 61% (95% confidence interval 50-72) of women had at least one antenatal contact, 22% (14-29) delivered with a skilled birth attendant, 7% (4-9) had a post-partum check and 4% (2-8) of newborns had a post-natal check. Coverage of high quality contacts was reduced to 11% (6-16), 8% (5-11), 0%, and 0% respectively. In Ethiopia, 56% (49-63) had at least one antenatal contact, 15% (11-22) delivered with a skilled birth attendant, 3% (2-6) had a post-partum check and 4% (2-6) of newborns had a post-natal check. Coverage of high quality contacts was 4% (2-6), 4% (2-6), 0%, and 0%, respectively. In Uttar Pradesh 74% (69-79) had at least one antenatal contact, 76% (71-80) delivered with a skilled birth attendant, 54% (48-59) had a post-partum check and 19% (15-23) of newborns had a post-natal check. Coverage of high quality contacts was 6% (4-8), 4% (2-6), 0%, and 0% respectively.ConclusionsMeasuring content of care to reflect the quality of contacts can reveal missed opportunities to deliver best possible health care.

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached $8·8 trillion (95% uncertainty interval [UI] 8·7–8·8) or $1132 (1119–1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40·4 billion (0·5%, 95% UI 0·5–0·5) was development assistance for health provided to low-income and middle-income countries, which made up 24·6% (UI 24·0–25·1) of total spending in low-income countries. We estimate that $54·8 billion in development assistance for health was disbursed in 2020. Of this, $13·7 billion was targeted toward the COVID-19 health response. $12·3 billion was newly committed and $1·4 billion was repurposed from existing health projects. $3·1 billion (22·4%) of the funds focused on country-level coordination and $2·4 billion (17·9%) was for supply chain and logistics. Only $714·4 million (7·7%) of COVID-19 development assistance for health went to Latin America, despite this region reporting 34·3% of total recorded COVID-19 deaths in low-income or middle-income countries in 2020. Spending on health is expected to rise to $1519 (1448–1591) per person in 2050, although spending across countries is expected to remain varied. Interpretatio...