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This book analyzes the role of health apps to promote medicalization. It considers whether their use is an individual matter, rather than a political and social one, with some apps based on a medical framework positively promoting physical activity and meditation, or whether data-sharing can foster social discrimination.

To identify health systems-level barriers to treatment for women who screened positive for high-risk human papillomavirus (hrHPV) in a cervical cancer prevention program in Kenya. In a trial of implementation strategies for hrHPV-based cervical cancer screening in western Kenya in 2018-2019, women underwent hrHPV testing offered through community health campaigns, and women who tested positive were referred to government health facilities for cryotherapy. The current analysis draws on treatment data from this trial, as well as two observational studies that were conducted: 1) periodic assessments of the treatment sites to ascertain availability of resources for treatment and 2) surveys with treatment providers to elicit their views on barriers to care. Bivariate analyses were performed for the site assessment data, and the provider survey data were analyzed descriptively. Seventeen site assessments were performed across three treatment sites. All three sites reported instances of supply stockouts, two sites reported treatment delays due to lack of supplies, and two sites reported treatment delays due to provider factors. Of the 16 providers surveyed, ten (67%) perceived lack of knowledge of HPV and cervical cancer as the main barrier in women's decision to get treated, and seven (47%) perceived financial barriers for transportation and childcare as the main barrier to accessing treatment. Eight (50%) endorsed that providing treatment free of cost was the greatest facilitator of treatment. Patient education and financial support to reach treatment are potential areas for intervention to increase rates of hrHPV+ women presenting for treatment. It is also essential to eliminate barriers that prevent treatment of women who present, including ensuring adequate supplies and staff for treatment.

This report begins to fill this gap by investigating the role of within-facility accountability mechanisms in the education and health sectors of Jordan. To do this, an analysis of existing and original data from these sectors was conducted in which the association of within-facility monitoring and provider effort was quantified.

Background Whilst interest in efficient trial design has grown with the use of electronic health records (EHRs) to collect trial outcomes, practical challenges remain. Commonly raised concerns often revolve around data availability, data quality and issues with data validation. This study aimed to assess the agreement between data collected on clinical trial participants from different sources to provide empirical evidence on the utility of EHRs for follow-up in randomised controlled trials (RCTs). Methods This retrospective, participant-level data utility comparison study was undertaken using data collected as part of a UK primary care-based, randomised controlled trial (OPTiMISE). The primary outcome measure was the recording of all-cause hospitalisation or mortality within 3 years post-randomisation and was assessed across (1) Coded primary care data; (2) Coded-plus-free-text primary care data; and (3) Coded secondary care and mortality data. Agreement levels across data sources were assessed using Fleiss’ Kappa (K). Kappa statistics were interpreted using an established framework, categorising agreement strength as follows: <0 (poor), 0.00–0.20 (slight), 0.21–0.40 (fair), 0.41–0.60 (moderate), 0.61–0.80 (substantial), and 0.81–1.00 (almost perfect) agreement. The impact of using different data sources to determine trial outcomes was assessed by replicating the trial’s original analyses. Results Almost perfect agreement was observed for mortality outcome across the three data sources (K = 0.94, 95%CI 0.91–0.98). Fair agreement (weak consistency) was observed for hospitalisation outcomes, including all-cause hospitalisation or mortality (K = 0.35, 95%CI 0.28–0.42), emergency hospitalisation (K = 0.39, 95%CI 0.33–0.46), and hospitalisation or mortality due to cardiovascular disease (K = 0.32, 95%CI 0.19–0.45). The overall trial results remained consistent across data sources for the primary outcome, albeit with varying precision. Conclusion Significant discrepancies according to data sources were observed in recording of secondary care outcomes. Investigators should be cautious when choosing which data source(s) to use to measure outcomes in trials. Future work on linking participant-level data across healthcare settings should consider the variations in diagnostic coding practices. Standardised definitions for outcome measures when using healthcare systems data and using data from different data sources for cross-checking and verification should be encouraged.

Highlighting how optimized big data applications can be used for patient monitoring and clinical diagnosis, this book also explores the challenges, opportunities and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.

In order to reach the health-related Sustainable Development Goals (SDGs) by 2030, gains attained in access to primary healthcare must be matched by gains in the quality of services delivered. Despite the broad consensus around the need to address quality, studies on the impact of health system strengthening (HSS) have focused predominantly on measures of healthcare access. Here, we examine changes in the content of maternal and child care as a proxy for healthcare quality, to better evaluate the effectiveness of an HSS intervention in a rural district of Madagascar. The intervention aimed at improving system readiness at all levels of care (community health, primary health centers, district hospital) through facility renovations, staffing, equipment, and training, while removing logistical and financial barriers to medical care (e.g., ambulance network and user-fee exemptions). We carried out a district-representative open longitudinal cohort study, with surveys administered to 1,522 households in the Ifanadiana district of Madagascar at the start of the HSS intervention in 2014, and again to 1,514 households in 2016. We examined changes in healthcare seeking behavior and outputs for sick-child care among children <5 years old, as well as for antenatal care and perinatal care among women aged 15-49. We used a difference-in-differences (DiD) analysis to compare trends between the intervention group (i.e., people living inside the HSS catchment area) and the non-intervention comparison group (i.e., the rest of the district). In addition, we used health facility-based surveys, monitoring service availability and readiness, to assess changes in the operational capacities of facilities supported by the intervention. The cohort study included 657 and 411 children (mean age = 2 years) reported to be ill in the 2014 and 2016 surveys, respectively (27.8% and 23.8% in the intervention group for each survey), as well as 552 and 524 women (mean age = 28 years) reported to have a live birth within the previous two years in the 2014 and 2016 surveys, respectively (31.5% and 29.6% in the intervention group for each survey). Over the two-year study period, the proportion of people who reported seeking care at health facilities experienced a relative change of +51.2% (from 41.4% in 2014 to 62.5% in 2016) and -7.1% (from 30.0% to 27.9%) in the intervention and non-intervention groups, respectively, for sick-child care (DiD p-value = 0.01); +11.4% (from 78.3% to 87.2%), and +10.3% (from 67.3% to 74.2%) for antenatal care (p-value = 0.75); and +66.2% (from 23.1% to 38.3%) and +28.9% (from 13.9% to 17.9%) for perinatal care (p-value = 0.13). Most indicators of care content, including rates of medication prescription and diagnostic test administration, appeared to increase more in the intervention compared to in the non-intervention group for the three areas of care we assessed. The reported prescription rate for oral rehydration therapy among children with diarrhea changed by +68.5% (from 29.6% to 49.9%) and -23.2% (from 17.8% to 13.7%) in the intervention and non-intervention groups, respectively (p-value = 0.05). However, trends observed in the care content varied widely by indicator and did not always match the large apparent increases observed in care seeking behavior, particularly for antenatal care, reflecting important gaps in the provision of essential health services for individuals who sought care. The main limitation of this study is that the intervention catchment was not randomly allocated, and some demographic indicators were better for this group at baseline than for the rest of the district, which could have impacted the trends observed. Using a district-representative longitudinal cohort to assess the content of care delivered to the population, we found a substantial increase over the two-year study period in the prescription rate for ill children and in all World Health Organization (WHO)-recommended perinatal care outputs assessed in the intervention group, with more modest changes observed in the non-intervention group. Despite improvements associated with the HSS intervention, this study highlights the need for further quality improvement in certain areas of the district's healthcare system. We show how content of care, measured through standard population-based surveys, can be used as a component of HSS impact evaluations, enabling healthcare leaders to track progress as well as identify and address specific gaps in the provision of services that extend beyond care access.

Providing an overview of disease surveillance, this text frames a roadmap of how newer technologies may allow all countries of the world to reach compliance with the IHR (International Health Regulations) established by the World Health Organization as it pertains to disease detection.

Digitization shall improve the secondary use of health care data. The Government of the Kingdom of Saudi Arabia ordered a project to compile the National Master Plan for Health Data Analytics, while the Government of Estonia ordered a project to compile the Person-Centered Integrated Hospital Master Plan. This study aims to map these 2 distinct projects' problems, approaches, and outcomes to find the matching elements for reuse in similar cases. We assessed both health care systems' abilities for secondary use of health data by exploratory case studies with purposive sampling and data collection via semistructured interviews and documentation review. The collected content was analyzed qualitatively and coded according to a predefined framework. The analytical framework consisted of data purpose, flow, and sharing. The Estonian project used the Health Information Sharing Maturity Model from the Mitre Corporation as an additional analytical framework. The data collection and analysis in the Kingdom of Saudi Arabia took place in 2019 and covered health care facilities, public health institutions, and health care policy. The project in Estonia collected its inputs in 2020 and covered health care facilities, patient engagement, public health institutions, health care financing, health care policy, and health technology innovations. In both cases, the assessments resulted in a set of recommendations focusing on the governance of health care data. In the Kingdom of Saudi Arabia, the health care system consists of multiple isolated sectors, and there is a need for an overarching body coordinating data sets, indicators, and reports at the national level. The National Master Plan of Health Data Analytics proposed a set of organizational agreements for proper stewardship. Despite Estonia's national Digital Health Platform, the requirements remain uncoordinated between various data consumers. We recommended reconfiguring the stewardship of the national health data to include multipurpose data use into the scope of interoperability standardization. Proper data governance is the key to improving the secondary use of health data at the national level. The data flows from data providers to data consumers shall be coordinated by overarching stewardship structures and supported by interoperable data custodians.

To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries. Systematic review. PubMed and Embase databases were searched until 30 October 2017. Our final search was built on three themes: 'high-cost', 'patients', and 'cost' and 'cost analysis'. We included articles that reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Analyses were limited to studies that covered a broad range of services, across the continuum of care. Andersen's behavioural model was used to categorise characteristics and determinants into predisposing, enabling and need characteristics. The studies pointed to a high prevalence of multiple (chronic) conditions to explain high-cost patients' utilisation. Besides, we found a high prevalence of mental illness across all studies and a prevalence higher than 30% in US Medicaid and total population studies. Furthermore, we found that high costs were associated with increasing age but that still more than halve of high-cost patients were younger than 65 years. High costs were associated with higher incomes in the USA but with lower incomes elsewhere. Preventable spending was estimated at maximally 10% of spending. The top-10%, top-5% and top-1% high-cost patients accounted for respectively 68%, 55% and 24% of costs within a given year. Spending persistency varied between 24% and 48%. Finally, we found that no more than 30% of high-cost patients are in their last year of life. High-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.