As states have embraced additional flexibility to change coverage of and payment for Medicaid services, they have also faced heightened expectations for delivering high-value care. Efforts to meet these new expectations have increased the need for rigorous, evidence-based policy, but states may face challenges finding the resources, capacity, and expertise to meet this need. By describing state-university partnerships in more than 20 states, this commentary describes innovative solutions for states that want to leverage their own data, build their analytic capacity, and create evidence-based policy. From an integrated web-based system to improve long-term care to evaluating the impact of permanent supportive housing placements on Medicaid utilization and spending, these state partnerships provide significant support to their state Medicaid programs. In 2017, these partnerships came together to create a distributed research network that supports multi-state analyses. The Medicaid Outcomes Distributed Research Network (MODRN) uses a common data model to examine Medicaid data across states, thereby increasing the analytic rigor of policy evaluations in Medicaid, and contributing to the development of a fully functioning Medicaid innovation laboratory. Historically, states have leveraged flexibility in the administration of Medicaid benefits to address the needs of their populations and policy priorities. Through the implementation of Medicaid waivers and demonstrations, which allow states to deviate from federal law and test alternative approaches to pay for and deliver care, Medicaid agencies have accelerated their role as the country’s laboratories for questions around how to sustainably improve insurance coverage, increase access and enhance care quality 2. As staResearchers have long struggled with the ability to make scientific findings accessible and useful to policymakers in a timely, effective, and efficient manner that allows for practical application . A 2014 AcademyHealth, a non-profit professional association for health services and policy researchers, supports the production and use of evidence to inform policy and practice to improve health and health care. As the home for both the Medicaid Medical Director Network (MMDN) and State-University Partnership Learning Network (SUPLN), AcademyHealth has been directly engaged with state Medicaid clinical leaders and academic researchers on policy development and evaluation. This commentary describes innovative solutions for states that want to leverage their own data, build their analytic capacity, and create evidence-based policy.State Medicaid agencies, often facing budget constraints that limit their ability to invest in analytic infrastructure, may collaborate with state university researchers to provide analytic support related to Medicaid programs.Established in 2014, the SUPLN supports partnerships between state governmental entities, such as Medicaid agencies, and their state university research partners. The SUPLN aims to promote the use of evidence in state and agency policy and programmatic decision making, and focuses on transforming delivery of care to Medicaid enrollees. The SUPLN consists of 27 state-university partnerships across 23 states, with a few state agencies partnering with multiple universities in their states. With support from the Patient Centered Outcomes Research Institute (PCORI), AcademyHealth manages the SUPLN, facilitating dialogue and peer-to-peer learning to spread best practices. SUPLN is unique from other learning networks given its members’ role in state Medicaid policy making. More recently, AcademyHealth has focused on coordinating and promoting cross-state analyses of Medicaid data. Each member’s historical knowledge for their own state’s Medicaid program is powerful alone, but the trusted relationships between members have resulted in expanded insights into similarities and differences across Medicaid programs and policies.Individual state-university partnerships offer unique value because of the central role they serve in conducting Medicaid policy analysis. Through formal contractual relationships, university partners work routinely with their state’s Medicaid administrative data , some hosting Medicaid data on-site at their universities and others having direct access through the Medicaid agency’s portal. The benefit of this direct, on-going access is the availability of timely Medicaid data with potential for linkages to other state data sources . This partnership structure is mutually advantageous. State agencies have access to researchers with a broad range of specialized policy, health services research, and methodologic expertise that is not typically available through state employees. This expertise is also obtained in a cost-efficient manner because states, through their contracts with university partners, are eligible for federal financial match for qualified administrative expenditures related to research and evaluation. Through these partnerships, researchers obtain access to a rich source of data for studying vulnerable populations, funding to support policy-relevant research, and the opportunity to collaborate with state policymakers to conduct research that directly addresses their needs.A 2017 SUPLN survey found partnerships are largely involved in Medicaid policy analysis, with all partnerships engaging in Medicaid policy and program evaluation, and most focusing efforts on understanding the scope of issues facing Medicaid programs. .Partnerships conduct CMS quality improvement assessments, Medicaid benefit cost analyses, and analyses of specific beneficiary populations, such as pregnant women and those covered under Medicaid expansion. The following examples highlight the role SUPLN partnerships serve in generating research that informs Medicaid policy and practice demonstrating their collective capacity to provide Medicaid enrollee analysis within and across state Medicaid programs.LTSSMaryland—an integrated web-based information system that houses functional assessments, plans of care, and program enrollment information for Medicaid beneficiaries receiving long-term care. Hilltop links LTSSMaryland data to nursing home MDS data and Medicaid and Medicare administrative data to conduct analyses of the experience of individuals in the long-term care system.Since 1994, The Hilltop Institute at the University of Maryland, Baltimore County has maintained a collaborative and highly productive partnership with the Maryland Department of Health. In this capacity, Hilltop maintains an extensive data repository to support program development, research, policy analysis, and rate setting. For HealthChoice, Maryland’s Medicaid managed care program, Hilltop develops capitated payment rates for participating health plans, conducts the annual program evaluation for the Centers for Medicare & Medicaid Services (CMS), and performs policy research and analytics to support further program development. Hilltop developed and supports a decision support system for Maryland Medicaid with real-time data on eligibility, utilization, and expenditures. Hilltop managed development of The Medicaid Research Center in the Health Policy Institute at the University of Pittsburgh, conducted research on the quality of treatment for opioid use disorder in Pennsylvania’s Medicaid program for the Pennsylvania Department of Human Services 7. This rRutgers Center for State Health Policy, working with the New Jersey (NJ) Medicaid agency and Housing and Mortgage Finance Agency, linked six years (2011–2016) of statewide Homeless Management Information System (HMIS) data with comprehensive Medicaid eligibility, encounter and claims data to compare patterns of health services utilization among Medicaid-enrolled persons who have experienced homelessness. Under Medicaid expansion, New Jersey Medicaid covers many more persons who have used homeless services. Through joint funding from NJ Medicaid and The Nicholson Foundation, the Rutgers team is comparing potentially avoidable Medicaid spending among homeless persons to demographically and clinically similar beneficiaries who have not used homeless services, and evaluating the impact of permanent supportive housing placements on Medicaid utilization and spending during the study period.The Commonwealth of Virginia’s Department of Medical Assistance (DMAS) - Virginia Commonwealth University (VCU) partnership includes evaluations of the 2018 Medicaid Expansion, delivery and payment reforms for substance use under the Addiction and Recovery Treatment Services 1115 waiver, and Commonwealth Coordinated Care Plus programs. The DMAS-VCU partnership has adopted an embedded data analyst model to enable efficient data sharing between the two organizations. In this model, VCU employees serve as DMAS contractors and undergo data security training as well as trainings to learn to use the DMAS data warehouse to query the Medicaid data. Having the embedded VCU staff at DMAS enables real-time access to Medicaid files through VPN connections and secure DMAS-provisioned computers. This model ensures VCU analysts have the most current data DMAS has available to support critical Medicaid policy and program evaluations.Although university partners have become a critical asset for state Medicaid agencies, single state analyses have limited generalizability, due to variation across states in Medicaid benefit packages, eligibility criteria, payment systems, and other policies. Single state analyses are also limited in their ability to rigorously evaluate the effects of policies because they lack information on the counterfactual. Studying Medicaid one state at a time ultimately falls short of delivering on the promise of using state Medicaid programs as laboratories for testing health system innovations. At the same time, there are barriers to conducting multi-state analyses to inform Medicaid policy. States face numerous legal and regulatory restrictions to sharing data.To support multi-state analyses of Medicaid data, increase the analytic rigor of policy evaluations in Medicaid, and contribute to the development of a Medicaid innovation laboratory, we formed the Medicaid Outcomes Distributed Research Network (MODRN) in 2017. A distributed research network is composed of multiple organizations using a common data model to support centralized development of analytic programs with local execution of analyses 911121314.11121315MODRN executes 4 key functions of a distributed research network:Use of a common data model. An essential function of distributed research networks is the use of a common data model. MODRN’s Data Coordinating Center develops the common data model and guides university partners in extracting, transforming, and loading their respective states’ Medicaid data into an analytic data set following the rules of the common data model. Each university’s resulting analytic data set has the same content, structure, variable names and formats. MODRN’s common data model includes five files: enrollment, inpatient, outpatient, professional, and pharmacy. The data elements contained in this common data model will support a variety of Medicaid studies and can be modified over time to accommodate new data elements.Standardization of analytic code. Once the common data model is developed by participating universities, MODRN’s Data Coordinating Center distributes standardized programming syntax for the universities to use along with shell tables for aggregating results. Standardization of the analytic code facilitates efficient analyses of multi-state data and minimizes the likelihood of specification bias [ion bias .Local analyses of Medicaid data. SUPLN university partners conduct state Medicaid data analyses locally using the standardized analytic code. Each university obtains institutional review board (IRB) approval for research with their state’s Medicaid data, facilitates review of results by state Medicaid agency, and maintains control over data use and quality review.Data partners provide results, not data, to data coordinating center. Each university partner submits aggregate results (not row- or person-level data) to the data coordinating center for aggregation, which presents results organized in a common template.The first research project to use the analytic infrastructure of MODRN focused on the quality and outcomes of treatment of opioid use disorder (OUD) among Medicaid beneficiaries. Medicaid programs play a significant role in addressing the opioid crisis, covering nearly 40 percent of individuals with an opioid use disorder . MedicaiThe MODRN-OUD project aimed to provide a comprehensive assessment of OUD treatment quality and outcomes in Medicaid in order to inform policy decisions about coverage of OUD treatments in Medicaid. To achieve this objective, MODRN-OUD identified 20 access, quality and outcome measures and constructed these measures using the MODRN analytic approach outlined above. A number of sources were consulted to identify potential measures for inclusion: 1) a 2017 CMS guidance to State Medicaid Directors outlining indicators of access to high-quality OUD treatment , 2) a co2223242526While the benefits and potential of MODRN are exciting, we recognize the limitations within this model as well. Statistical analyses of distributed data require different approaches than those applied to pooled, individual-level data and we plan to build on approaches developed in other health DRNs 29. We alSUPLN offers myriad opportunities for rigorous individual and cross-state Medicaid policy analyses. From the use of federal matching to embedding researchers to establishing a sustainable distributed research network for timely cross-state collaborations, partnerships are striving to improve the quality of care for Medicaid beneficiaries.The common data model, infrastructure, and analytic tools developed for the MODRN – Opioid Use Disorder project can be adapted for use in future Medicaid research by SUPLN partnerships. The data elements contained in the common data model can identify other clinical populations relevant to Medicaid, and construct measures of health care utilization and outcomes for evaluating health system interventions. Future projects of policy relevance to Medicaid include those focused on behavioral and physical health integration, telehealth, use of newer more costly biopharmaceuticals, evaluating state Medicaid waiver demonstrations including those with work requirements, and many others. Many SUPLN partners are currently engaged in single state analyses that link Medicaid data to vital statistics, housing data, income maintenance programs , food and nutrition services , child welfare system records , and criminal justice records. The next frontier for MODRN will be to capitalize on these linkages to conduct multi-state analyses on the social determinants of health and evaluate opportunities to better integrate health and human services delivery.


Food and water contamination cause safety and health concerns to both animals and humans. Conventional methods for monitoring food and water contamination are often laborious and require highly skilled technicians to perform the measurements, making the quest for developing simpler and cost-effective techniques for rapid monitoring incessant. Since the pioneering works of Whitesides’ group from 2007, interest has been strong in the development and application of microfluidic paper-based analytical devices (μPADs) for food and water analysis, which allow easy, rapid and cost-effective point-of-need screening of the targets. This paper reviews recently reported μPADs that incorporate different detection methods such as colorimetric, electrochemical, fluorescence, chemiluminescence, and electrochemiluminescence techniques for food and water analysis. Science entitled “Advancing regulatory science” 2+ or RuPVP) polymer of the electrochemical device. The technique was specifically tested on grilled chicken, and the detection limit was ~150 nM.Finally, the only μPAD detection strategy based on electrochemiluminescence (ECL) detection for the specific analysis of food has been reported by Mani et al. . The worA review of microfluidic paper-based devices for food and water analysis has been presented.


Geographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.A systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.The search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.GIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base. Geographic Information Systems (GIS) are systems designed to store, manage and display spatial data and aid in analysis and interpretation of spatial data , 2. SpatNotably, GIS and spatial analysis can be powerful tools to understand, prevent, and help treat diseases. For example, as a visualization aid, GIS software can be used to map the geographic distribution of disease, associated risk factors, and services available for prevention and treatment. Furthermore, spatial analysis of this data can analyze risks for disease, epidemic trends over space and time, and disease hotspots , 11. TakHIV continues to be a major global health threat, particularly in Africa which accounts for 67% of the world’s infections, highlighting a need for additional approaches to understand and mitigate the epidemic . GIS andThis review included peer-reviewed articles that focused on HIV in Africa and involved GIS or spatial analysis. HIV-related topics included HIV prevalence or incidence, HIV-specific mortality, HIV risk factors, and implementation of HIV prevention or treatment services. Only articles explicitly based in a country or countries in Africa were included. Spatial analysis techniques and the GIS use were defined broadly for the purposes of this review in order to capture the diversity of emerging practices, i.e. we included articles that used any specialized GIS software or specifically incorporated any spatial analysis techniques. Articles were included regardless of participant characteristics, including age, race, sex, sexual orientation, HIV status or other factors. Articles were included regardless of sample size, scope or setting within Africa, type of program activities or interventions, or by study design.We excluded studies concerning non-opportunistic infection health outcomes in HIV-positive individuals or for which HIV infection was not the main focus but treated as a risk factor. We did not include studies that used geographic coordinates solely for study enrollment purposes, e.g to generate sampling frames, or basic visualization, e.g. simple display of points on a map. We excluded HIV phylogenetic studies, unless they involved other spatial analyses, on the basis that such research is complex and specialized enough to warrant its own review. Abstracts, posters, reviews and commentary pieces were also excluded.We developed a search strategy with terms relating to “HIV”, “geographic information systems”, and “spatial analysis” (see Appendix for full search strategy). This search was developed through an iterative process of incorporating new terms and refining those included based on results returned and identification of relevant citations. Reviewers conducted electronic searches of PubMed, EMBASE, and Web of Science on November 16, 2017, with no restriction on date or language of publication. Potentially eligible articles known to paper authors were also included in the initial search.Two reviewers independently screened article titles and abstracts of all initial search results. Articles deemed eligible for inclusion by either reviewer underwent full text review by three authors to determine final inclusion with adjudication done by majority vote when necessary. Reviewers extracted data to a master table, capturing details about location, objectives, study design, data source and study population, spatial analysis methodologies, software used, and results.Following data extraction, we categorized studies based on the primary content of the paper in an inductive, thematic analysis. Articles were permitted to sort into more than one category as relevant. We did not assess study quality due to study heterogeneity.As shown in Included studies spanned the continent in geographic focus. Eight papers were multi-country analyses, and 15 different countries were represented in single-country papers. Among single-country studies, 37 were based in Southern Africa, 16 in East Africa, and two each in West Africa and Central Africa. South Africa was the most common country of study with 17 papers. Dates of publication spanned from 2000 to 2017, with almost all (98%) published between 2006–2017 and over half published after 2014.Inductive thematic analysis found that GIS and spatial analysis were most commonly used to examine: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. We discuss methodological techniques in each theme and narratively and in tabular format highlight key findings below.Twenty-seven articles sorted into this thematic category which focused on the spatial distribution of HIV. The following subcategories were identified and used to further characterize studies: (i) spatial cluster analysis, and (ii) smoothed mapping.One of the most common spatial analytic techniques for evaluating the geographic distribution of HIV was local cluster detection , or the Thirteen studies used the Kulldorff spatial scan statistic  to identClusters of high and/or low HIV prevalence were detected, independent of administrative boundaries, in 20 of 22 countries where the Kulldorff spatial scan statistic was applied, though the geographic size and relative risk strength of the clusters varied widely. Studies that compared clusters over time found that they tended to persist with little change in location , 22, 25.Less commonly-used spatial statistics included Local Moran’s I and the τ statistic . Both ofSmoothed maps, or “heat maps” of HIV use spatial methods to create and display continuous gradations of HIV-related outcomes over space, using data gathered in a limited number of locations to predict values in unmeasured locations . EighteeTwo studies compared different mapping techniques, and the most comprehensive study found that a Bayesian geostatistical modeling approach was slightly more accurate than five other methods , 34. HowSmoothed mapping techniques generated maps which estimate HIV prevalence in areas where data is not available from surveys –34, 42. Detailed smoothed maps also enabled visual comparisons of the spatial patterns of HIV over time periods , 34, 35,Thirty-three studies sorted into this thematic subcategory which focused on the spatial epidemiology of HIV which is based on the concept that both HIV and the factors associated with infection vary spatially and have some degree of spatial dependence. Articles in this theme divided into three sub-topics based on underlying methods utilized: (i) spatial regression, (ii) joint spatial disease/risk factor modeling, and (iii) geography of risk factors.Spatial analysis contributed to the evaluation of risk factors for HIV by testing the assumption that data is independent of geography . Many stSeven studies used the results of spatial regression to map adjusted relative HIV risk over geographic areas, controlling for other factors like age, sex and employment. Maps of adjusted HIV risk visualized spatial patterns and heterogeneity, displaying relative risk by administrative area , 47–50 oFour studies examined how the relationship between HIV and a risk factor or other sexually transmitted infection (STI) changed over geographic space  53–56].–56.53–56These studies aimed to assist resource allocation decisions by identifying opportunities for geographic targeting of HIV prevention interventions , 56. TheThree studies compared spatial patterns of HIV and another STI, either HSV-2 , 55 or sGIS enabled the systematic measurement of spatial risk factors . For exaLocal cluster detection methods were used to identify clusters of HIV risk factors, for instance of condom non-use . These rSmoothed maps of risk factors showed geographic variation independent of administrative boundaries enabling new insights , 60–62. Risk factors were also examined with spatial regression methods, such as early sexual initiation  or HIV sTwenty-three articles contributed to understanding relationships between geography and implementation of HIV services. Two subcategories were the (i) effect of distance to HIV services on health outcomes, and (ii) planning and evaluating HIV service provision.GIS technology allowed for measurement of geographic distances and distribution of services and enabled analysis of the effect of distance on health outcomes and service uptake . For exaStudies identified in this review analyzed the effect of distance to care on nearly all stages of the HIV treatment cascade, including knowledge about HIV , follow-In some studies, access for HIV patients appeared to be mediated through the public transportation network more than through road access , 71, 75,Distance measures were extracted with GIS software like ArcGIS , 79, 81,GIS technology was used to examine the extent to which health care service expansion , 70, 82 Two studies used GIS tools to target HIV services and improve their efficiency , 82. OneThe review demonstrates the wide applications of GIS and spatial analysis to understanding HIV and supporting HIV care and prevention services in Africa. Most of the literature was published recently and rapid growth in the evidence-base was clear. We expect that GIS use and spatial analysis methodologies will continue to expand as more researchers and implementers develop knowledge and capacity in this growing field and increasingly recognize its usefulness.For example, in this review GIS tools were regularly used to display HIV prevalence, incidence or mortality data in order to communicate complex information about the epidemic in a clear and accessible way. The ability to identify spatial clusters and generate smoothed maps revealed variations within and across administrative regions, the boundaries of which often have little bearing on disease prevalence.In the context of HIV risk factor analysis, spatial analysis reflected the likelihood that people living close to each other share common exposures and disease outcomes and that location was critical in including in HIV risk factor analyses. The findings that spatial regressions of HIV which accounted for geographic correlation in the data had better fit than non-spatial regressions highlight the benefit of adding spatial data to certain analyses.Another recurring theme was the critical role of geography in informing access to HIV-related health services in low-income settings with poor existing health care infrastructure. GIS and spatial analysis may allow for more efficient allocation of resources and appropriate response targeting in many African settings.Of note, while this review was focused on more technologically and methodologically sophisticated applications, several articles not included in this review demonstrated the utility of simply visually displaying mapped data points, such as for plotting the locations of HIV services  and riskA number of gaps in the literature were also found by this review. West African countries were less represented, perhaps because they have lower HIV prevalence rates and less HIV research capacity. Central African and Horn countries were similarly less represented. Countries that did not include both HIV biomarkers and geographical coordinates in their DHS surveys also may be less likely to feature in spatial analysis studies because these were characteristics that determined inclusion in certain studies. Some important HIV related outcomes had minimal or no found spatial studies such as HIV medication shortages or stock-outs. Adherence and retention outcomes were also not substantially considered in relation to spatial factors. Finally, there was limited literature on HIV resistance and spatial analysis, potentially because these data are too sparse.Interesting directions and issues in spatial research were also found. The novel use of cell phone technology in some studies points to an important future area of spatial research. Data independent of national boundaries/national data collection practices to facilitate cross-country comparisons and multi-country analyses would be a helpful future direction given the mobility of persons and complexity of social networks. Privacy concerns continue to be an important area of consideration as GIS tools become easier to use and more broadly implemented.Our search strategy may have missed studies relating to GIS or spatial analysis and HIV, particularly as the vocabulary for GIS is dispersed and evolving. There is some risk of publication bias, potentially favoring articles that detect spatial heterogeneity over those that do not, or favoring particular spatial analysis methods over others. This review was not intended to assess the quality, methodological rigor or risk of bias within studies included for review, nor did we attempt to conduct a meta-analysis. In part, this decision reflects the wide diversity of contexts, approaches, and methodologies that emerged through the review, which were difficult to synthesize. There are not currently any best practice or guidelines for reporting of geospatial studies, and this may be an area for future development.This systematic review searched for and summarized evidence on the use of GIS and spatial analysis techniques for HIV in Africa. Our findings demonstrate the wide array of spatial approaches to HIV-related data. These applications include characterizing geographic distribution of HIV, evaluating HIV epidemiologic risk factors, and assessing and improving implementation of HIV services. The rapid growth and diversity of applications of GIS and spatial analysis to the field of HIV yields great potential for future insights and progress.S1 Table(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOC)Click here for additional data file.


Older people living in congregate environments are obvious beneficiaries of supportive services. The potential for prevention is clear, particularly among low-income elders living in subsidized housing; it is this group that is at high risk for significant healthcare and other costs, and it is this group that suffers considerably from a fragmented healthcare system. Policymakers have long seen the advantages of reaching this population, but most existing housing with services programs have focused more on social than health-related supports. The Right Care, Right Place, Right Time initiative (R3) was launched in July 2017 to demonstrate the value of supportive services to seniors living independently in affordable housing in the Greater Boston area, while reducing health care costs. The R3 program consists of two on-site wellness teams, including a wellness nurse and wellness coordinator. Each team is responsible for about 200 participants across two housing sites. The R3 evaluation included both quantitative and qualitative components. The quantitative component entails pre/post comparison as well as a control group analysis, focusing on various health and health utilization outcomes. The qualitative component includes key informant interviews examining program development and implementation and focus groups capturing the resident experience. The purpose of this symposium is for evaluation team members to report on the experiences of program participants, administrators/staff, housing managers/staff, and community partners with the R3 program, and to assess program impact. Edward Miller and Pamela Nadash will serve as chair and co-chair, respectively; Alisha Sanders as the discussant.


Dietary patterns were derived by exploratory factor analysis of food intake groups. Multiple linear regression models, adjusted by sociodemographic and geographical variables, were used to evaluate possible differences in dietary pattern scores between different CKD treatment groups. Results: Two food patterns were identified: an “Unhealthy” pattern  and a “Healthy” pattern . The Unhealthy pattern was inversely associated with nondialysis and dialysis treatment  and β: −0.80 , respectively) and the Healthy pattern was positively associated with renal transplant treatment ). Conclusions: Two dietary patterns were identified in Brazilian CKD individuals and these patterns were linked to CKD treatment modality.Background: We analyzed the dietary patterns of Brazilian individuals with a self-declared diagnosis of chronic kidney disease (CKD) and explored associations with treatment modality. Methods: Weekly consumption of 14 food intake markers was analyzed in 839 individuals from the 2013 Brazil National Health Survey with a self-declared diagnosis of CKD undergoing nondialysis ( Chronic kidney disease (CKD) is a common noncommunicable disease associated with high morbimortality and has an estimated worldwide prevalence of 11%–13% among adults . Early dThe crucial role of nutritional interventions in preventing or retarding the rate of progression of CKD and its complications is well established ,11. HoweDietary patterns can be derived by a priori and a posteriori analyses. The choice of method depends on the purpose of the study. In the a priori approach, indices are proposed, which allow the evaluation of the diet quality based on pre-existing conceptual criteria that form the basis for guidelines for healthy eating and specific nutritional recommendations ,18. The The aim of this study was to describe the dietary patterns of a population of Brazilian individuals from a national survey that included participants with a self-declared CKD diagnosis. As we have reported previously, the consumption of food items normally restricted to CKD patients differs from that of non-CKD individuals, and the treatment modality influences the type and food consumption frequency of CKD patients . Thus, iThis was a nationally representative cross-sectional study using data from the National Health Survey (Brazilian NHS) 2013 involving the Brazilian adult (≥18 years) population. The Brazilian NHS was conducted in partnership between the Ministry of Health and the Brazilian Institute of Geography and Statistics (IBGE) and its main objective was to produce nation-level data about the health status and lifestyles of the Brazilian population .n = 839 individuals).The Brazilian NHS sample was a subsample of the Master Sample of the Integrated Household Surveys System of the IBGE with stratified sampling and three clustering stages: census tracts, households, and individuals who were ≥18 years selected by a random sample of residents in each household. A total of 60,202 individuals were interviewed. The complete information regarding the process of sampling and weighting is available in a prior publication . The curThe Brazilian Ministry of Health’s National Commission of Ethics in Research approved the Brazilian NHS under protocol number 328,159 .Interviews were conducted from August 2013 to February 2014. The questionnaire was applied through face-to-face interviews and it was subdivided into three parts, related to: household, all the household residents, and individuals. The present study used data from the individual questionnaire and data related to all the household residents.The Brazilian NHS investigated the food consumption frequency of the adults through the weekly consumption of markers of food intake or behaviors related to intake ,28. The This study also considered the following sociodemographic variables—gender, age, education level , and race/skin color —and geographical variables—location of residence  and Brazilian geographical regions .For the current study, participants with a self-declared CKD diagnosis were grouped according to their treatment in the following categories: nondialysis-dependent group , dialysis-dependent group , untreated CKD group , and renal transplanted group . Self-reported anthropometric measurements (body weight and height) and self-reported presence of comorbidities  were used to characterize the population.p-value lower than 0.05 [For descriptive purposes, the values are presented as mean and 95% confidence intervals (95% CI) for continuous variables and as weighted percentages for categorical variables. Dietary patterns for 14 food intake markers were obtained by exploratory factor analysis . Kaiser–Meyer–Olkin (KMO) over 0.60  and Barthan 0.05  were useLinear regression models  were applied to assess whether there were differences in the dietary pattern scores between the CKD treatment groups as compared to the untreated CKD group (reference group). Negative score means indicated an inverse association and positive score means indicated a positive association. All analyses were performed using Stata software version 14.2, which considered a level of significance of 5% and the effects of complex sampling from the Brazilian NHS (survey module).n = 480) were not on dialysis , 6% (n = 48) were on dialysis , 2% (n = 17) underwent kidney transplantation , and the remaining 35% (n = 294) declared not to be under regular medical treatment . Among the 60,202 individuals interviewed in the Brazilian NHS, 839 individuals  self-reported a medical diagnosis of CKD and were included in this study. Out of 839 individuals, 57% (Based on the Cattel test graph (screen plot) , eigenvan = 454), three dietary patterns were found: snack pattern ; mixed pattern, consisting of healthy foods but with red meat ; and traditional pattern  [This study analyzed dietary patterns in a sample of the Brazilian NHS comprising Brazilian individuals who had a self-declared diagnosis of CKD and the association of CKD treatment modality with dietary patterns. Two dietary patterns, labeled Unhealthy and Healthy, were identified in the whole group. In a previous study comprising Brazilian nondialyzed patients with CKD stages 3 and 4 ( coffee) . The fin coffee)  also dem coffee) .Moreover, when exploring the influence of the CKD treatment, we observed that, compared to the untreated CKD group, the nondialysis-dependent and dialysis groups had an inverse association with the Unhealthy dietary pattern, while the renal transplanted group had a positive association with the Healthy dietary pattern. Of interest is the finding that the transplanted group adhered to a diet with better dietary quality. We speculate that this may suggest that renal transplanted patients, to a large extent, represent individuals under close medical follow-up who are highly motivated to follow medical prescriptions as well as prescriptions regarding nonpharmacological care, such as a healthier diet, since nonadherence to given prescriptions is associated with increased risk of graft loss and mortality, as previously shown ,36,37. IPrevious studies carried out in CKD individuals reported associations of dietary patterns with increased risk of CKD occurrence, loss of renal function, and death ,39,40,41Some limitations of our study should be considered when interpreting these results. Due to the cross-sectional design, we cannot establish a causal relationship but can provide evidence by associations. Food intake markers, as well as other methods of assessing food intake, are limited tools that may not include all foods consumed. Although we adjusted for sociodemographic and geographical factors, we cannot exclude residual confounding from other lifestyle factors that may be linked with dietary habits and that partly could influence these associations. In addition, the small sample size and thus low statistical power of some groups may reduce the possibility of observing significant differences. Lastly, the presence of CKD and treatment modality were self-reported and there were no available laboratorial measurements of kidney function that would verify the presence or severity of CKD. Despite these limitations, the novelty of this study is that it describes dietary patterns of individuals with CKD receiving different treatment modalities. The use of exploratory factor analysis to assess food consumption should be highlighted, as it simultaneously analyzes multiple variables (different food groups), thus facilitating the description of food items and allowing assessment of the combined effect of foods commonly ingested by the Brazilian CKD population.In conclusion, among Brazilian individuals with a self-reported diagnosis of CKD, two distinctly different dietary patterns associated with the modality of CKD treatment. The nondialysis-dependent and dialysis groups presented low adherence to the Unhealthy pattern, whereas individuals with a kidney transplant adhered to a better-quality diet. These findings may inform future recommendations about food and dietary patterns. While this new approach does not exclude the necessity of providing specific nutritional and energy recommendations, it adds a careful look at the quality of diets and addresses the principles and recommendations of healthy eating, in which the composition of the actual whole range of foods consumed comprises the principal dietary target.


The NRCM 2017 theme was Narrative review, outlining key challenges and recommendations for trainee collaborative research groups across medical and surgical disciplines based on the core themes from the NRCM 2017 meeting.Core themes of: (1) surgical oncology trials; (2) placebo-controlled surgical trials; (3) research funding; (4) medical student involvement in research; (5) emergency care; (6) patient and public involvement. Recommendations were made for planning future collaborative studies, based on these topic areas.The collaborative research model has demonstrated longevity and effectiveness in delivering high-quality, practice-changing research both within the NHS and internationally. Learning between groups and highlighting areas for interdisciplinary collaboration will drive a meaningful, patient-centred agenda for the future. The meeting was held at the Marriot Hotel in Birmingham on Friday 30th November 2017, and welcomed over 260 attendees and speakers from a range of medical and surgical specialities and diverse stages of training, discussing their views and experiences of collaborative research. Trainee-led regional and national collaborative networks adopt a novel approach to research and audit by ‘crowdsourcing’ data collection through frontline clinicians, allowing for a larger number of patients to be included in less time, reducing research waste, and permitting greater generalisability than single-centre studies The collaborative research model has grown exponentially over the last 10 years, both in scope, delivering complex studies including randomised trials, and breadth, growing networks both nationally and internationally NRCM 2017 brought individuals from across the UK and overseas to discuss their experience of overcoming these issues. Sharing barriers and solutions faced by other collaborative research groups can help achieve greater success for this research model in the future . This ed22.1Clinical trials are considered the gold standard in evaluating healthcare interventions. Within national and international settings, surgical trainees have been empowered to and increasingly involved in multi-centre, prospective cohort studies Within surgical oncology, there is a growing infrastructure to support trainee-led trials in cancer through the Clinical Studies Group (CGS) initiative from the National Cancer Research Institute (NCRI). The CGS collaborates between patients, researchers, funders and other relevant networks to provide a framework for developing cancer clinical trial ideas in the UK. The NCRI along with other national bodies such as the Royal College of Surgeons facilitate workshops to develop cancer trial research skills. These workshops work on global development of trial research as well as focusing on integrating trials into a busy department, trial design, research nurses and the need for a trial’s unit. The STAR-TREC trial  was given as an example of a trial where trainee involvement has been essential to set-up and early delivery.Learning point 1: Trainee research collaboratives should work with specialised clinical trials units and regional representatives from established supporting frameworks to lead and recruit to trials in surgical oncology.2.2Within the context of a traditional randomised drug trial, a novel intervention is compared against a placebo, whereby the latter is considered an ‘inert’ or non-active substance or component Learning point 2: Trainee collaboratives considering a placebo-controlled surgical trial should consult a Clinical Trial Unit with specialist experience of managing these studies, and consider future consensus recommendations.2.3Funding for clinical trials traditionally comes from three sources: (i) industry, (ii) research bodies such as the NIHR and, (iii) charities with special interests such as Cancer Research UK Trainees with experience of developing trials and securing funding emphasised the importance of proof of concept to support any funding applications by conducting a robust primary feasibility and or pilot randomised study Learning point 3: When seeking funding for a major interventional study, trainee research collaboratives should consider a pilot or feasibility study, contacting a Clinical Trials Unit early, engaging a multi-disciplinary team including PPI representatives and using data from observational collaborative studies to power and justify their study designs.3Traditionally, getting involved in research as a medical student can be difficult, in terms of finding the right project and a right supervisor or mentor to support students from study design to data collection, and eventually to publication. Historically, embedding medical students into research projects had been perceived to be challenging, with opponents quoting lack of understanding, poor data reliability, and lack of time for meaningful contributions. To overcome such barriers, the STARSurg collaborative was launched in 2013 and is the UK's only national student-driven collaborative with national representation across all medical schools in UK and Ireland. Since its inception, STARSurg has delivered four national cohort studies and has gained increasing participation amongst students, junior doctors and consultants Learning point 4: Students can deliver research with high levels of accuracy and case ascertainment, learning valuable skills for their future careers and bolstering postgraduate collaborative groups.4The busy emergency care setting could be perceived as an environment that is both hostile and challenging to undertake robust clinical research. Rapidly changing potential life-threatening clinical conditions, time-pressured decision-making with little time for significant deliberation during the consenting process, and unwell patients with decreased levels of consciousness are some of the many challenges faced by the researcher. This however is not an insurmountable barrier, evidenced by the success of the National Audit of Small Bowel Obstruction (NASBO), a trainee-led, national study with more than 460 collaborators from 131 centres Fluid Optimsation in Emergency Laparotomy (FLOELA) is a further example of high-quality emergency care research, a clinical trial investigating the effect of cardiac-output guided haemodynamic therapy during and shortly after emergency bowel surgery Learning point 5: Trainees are invaluable in recruitment to emergency care studies within their role of delivery of frontline care. Particular consideration of emergency care topics should be made by research collaboratives when planning future studies.4.1http://www.wmresearch.org.uk/. Collaborative authorship is now recognised at trainee-level for selection and progression at ARCP, and is acceptable as part of Certificate Completion of Training (CCT) criteria in a number of specialties.Over the past decade, it has been recognised that national and international collaborative research allows clinical questions to be addressed in a meaningful way by collecting high volume data from multiple centres. The contribution of many members of large complex teams across specialties, countries and disciplines requires authorship structures which credit different roles sufficiently and give clarity in terms of task involvement. The National Research Collaborative and UK Association of Surgeons in Training have created a draft framework for collaborative research authorship groups, which delineate roles including a writing group, steering group, statistical analysis, REDCap and data governance, national, regional and local leads, collaborators, validators and translators Learning point 5: Clarity in terms of contributions of large numbers of collaborators in collaborative research studies can be ensured by adherence to published frameworks. Transparency from the offset about the intended authorship model is crucial to maintain networks and avoid disincentivising collaboration.4.2Patient and public involvement (PPI) in research is recognised as best practice and is now an essential requirement to receive funding from many funders globally, including the UK, the Netherlands, Canada, Australia and the USA Patient involvement has been proven to improve key trial processes such as recruitment and retention, and make design and outcome assessment more patient relevant Learning point 6: Patient and public involvement is crucial for all study types and should be considered at all points in the study pathway. Good PPI within a study will make it more meaningful, impactful and fundable.5Within modern clinical research in the UK traditional ‘top-down’ research models have been replaced with ‘bottom-up’ trainee-led research collaborative projects. Proof-of-concept studies have demonstrated that trainees and students can play a key role in practice-changing research and are capable to leading high-quality cohort studies and randomised trials with support and guidance from senior members. Early engagement and mentoring of students and trainees alike will drive a new cadre of academic surgeons and NHS surgeons with strong academic interests in clinical research. The NRCM 2017 meeting reflects that successful research in the modern age, lies in collaboration of different groups to improve and deliver high-quality care to patients.1, Nepogodiev D1, Kamarajah S, Goh YL, Layton G, McKay SC, Singh J, Sinha Y, Wilkin R, Yeung DE, Bhangu A, Singh PGlasbey J1Meeting co-leads


Child Sexual Abuse (CSA) is a pressing human right issue and public health concern. We conducted a systematic review of quantitative and qualitative studies published in the past decade on CSA in India to examine the distribution of the prevalence estimates for both genders, to improve understanding of the determinants and consequences of CSA and identify gaps in the current state of research.For this systematic review, we searched electronic literature databases  for articles published in English on Child Sexual Abuse in India between January 1, 2006 and January 1, 2016 using 55 search terms. Data were extracted from published articles only.Fifty-one studies met inclusion criteria for the review. The review indicates that prevalence rates of CSA is high among both boys and girls in India. Due to heterogeneity of study designs and lack of standardised assessments, reported prevalence estimates varied greatly among both genders in different studies. There is a need to conduct representative studies using a validated instrument to obtain valid epidemiological estimates. Commercial sex workers, men who have sex with men, and women with psychiatric disorders were at higher risks for sexual abuse during childhood. In addition, the synthesis of qualitative data across studies included in the review suggests that exposure and perpetration of CSA is a multifaceted phenomenon grounded in the interplay between individual, family, community, and societal factors. The review indicates poor physical, behavioural, social, and mental health outcomes of CSA in India. We conclude with a research agenda calling for quantitative and qualitative studies to explore the determinants and perpetration of child sexual abuse in India from an ecological lens. This research agenda may be necessary to inform the development of a culturally tailored primary prevention and treatment strategy for CSA victims in India. The same search strategy was used across all three databases.The research questions, inclusion criteria, search strategy, search terms, search engines, and study protocol for the proposed study were developed in consultation with a panel of experts who have been working in the field of sexual violence in India (names included in Acknowledgments) and along the guidelines as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  S1 Chec. We searWe included studies that were published in English between January 1, 2006 and January 1, 2016, involved human subjects, and collected primary data on experience, perpetration, or response to CSA in India. Grey literature, reports, studies not collecting or analyzing original CSA data , commentaries, and editorials were not included in the study. The study period was chosen to report results from the recent literature on prevalence, correlates, and consequences of CSA in Indian context.e.g. cross-sectional studies, cohort studies, and case-control studies) that examined prevalence and incidence of CSA and/or associations of CSA with other independent or dependent variables. The qualitative studies that examined the experiences of respondents that had been victims of CSA were also included. Articles examining sexual abuse experience at ≤ 18 years were included. The review also included 1) articles in which CSA data were collected retrospectively from adults and 2) articles which use age bands which cross the 18-years cut-point . In the latter scenario, authors were contacted for disaggregate data on CSA for ages ≤ 18 years. CSA perpetration studies were also included if they measured perpetration of sexual violence against an individual ≤ 18 year of age (irrespective of the age of the perpetrator). Finally, interventions to prevent and/or treat CSA including randomised control trials were also included in this review.We included observational studies  designated as primary reviewers. In addition, the secondary reviewers (VC or AK) conducted relevancy check for 10% of the randomly selected articles at this step along with relevancy check for all the articles that were deemed as “cannot determine” by the primary reviewers. The primary and secondary reviewers conducted relevancy checks in teams that constituted of one primary and one secondary reviewer for the articles that were selected for full text screening (n = 965). The discrepancies on relevancy of the articles between primary and secondary reviewer were noted and discussed by the entire team in a monthly meeting.The eligible articles went through a standardized data extraction and quality assessment process. The data extraction form was refined during the extraction of the first few articles to ensure that the forms were comprehensive. The primary reviewers extracted the relevant data in MS Excel for each of the articles that were deemed suitable for inclusion. A few studies were based on same datasets, but we decided to include these studies as different if they assessed different forms of CSA or measured different outcomes associated with CSA. We extracted descriptive characteristics of the sample  from each quantitative Tables –8 and qunon-contact sexual abuse ; (b) contact sexual abuse without penetration ; and (c) forced intercourse , as previously classified in a WHO publication [Among quantitative studies we extracted three methodological moderators: (a) design of study ; (b) sampling strategy ; (c) measurement instrument . We also extracted the data on the time frame for which CSA was measured (childhood experience or past 12-months experience) for quantitative studies. Three categories of CSA were used: (a) lication . DependiA synthesis of the qualitative evidence, guided by the review questions, was conducted across the qualitative and mixed-methods studies that are included in the review. The synthesis was performed using an interpretive perspective in which themes were identified in the original papers, compared across studies, and then combined into a whole via a listing of descriptive themes in line with the determinants and perpetration of CSA informed by the socio-ecological framework , and theA separate quality assessment tool for quantitative and qualitative studies was developed for this review. The quality assessment tool for quantitative studies was adapted from Effective Public Health Practice Project (EPHPP) Quality Assessment Tool . The quaAmong the 51 studies included in the review, 35 studies were based purely on quantitative methods, 11 studies were based purely on qualitative methods while five studies utilized mixed-methods. Most quantitative studies were conducted among respondents from general population Tables –5 while A quarter (13/51) of studies that were included were based in educational institutions. A fifth of the studies (10/51) included community samples of young men and girls while a few other studies (5/51) included women or young adolescents attending gynecology, mental health facilities, or health camps organized by non-governmental organizations (NGOs). Approximately half of the other studies (24/51) were conducted in specific populations such as commercial sex workers, children in shelter homes, sex-trafficked young girls in observational homes, adolescent street boys, runaway children, men who have sex with men (MSM) and children in conflict with law. Most qualitative studies were conducted with specific populations as discussed above.Almost half the studies included in this review evaluated CSA through interviews with children below 18 years of age (25/51) while the rest of the studies evaluated retrospective recall of CSA experiences among adults. Twenty-one studies were exclusive with girls or women with 17 of these studies among female sex workers and trafficked girls. Nine studies were conducted exclusively with boys including runaway or adolescent boys living on the streets and MSMs. The rest of the studies (20/51) included both boys and girls either from the educational institutions, community samples, health camps, or children in conflict with law.Around 50% of the studies that utilized purely quantitative methodology, assessed all forms of CSA . Most of these studies were conducted among students in educational institutions or community populations. All the studies that utilized mixed-methods approach and seven studies among purely quantitative studies used a narrower definition (forced intercourse or rape) of CSA and majority of these studies were conducted among female sex workers. While most of these studies (36/40) evaluated childhood experience of CSA while four studies included the question on experience of CSA in the past year.Among the 37 studies using quantitative methods, only 16 studies reported use of standardized tools. The various tools that were used were 1. ISPCAN Child Abuse Screening Tool (ICSAT)–C and ICSAT- R, 2. Childhood Trauma Questionnaire, 4. Adapted Questionnaire from MoWCD study, 5. Finkelhor’s Sexual Abuse Scale (used in community studies), and 6. Sexual Abuse Screening Tool. Two studies were based on validation of ICSAT-C and ICSAT-R for measurement of CSA in six countries including India , 29.sexual abuse” in general [The prevalence of CSA ranged from 4%- 41% in studies conducted exclusively among young women below 18 years of age and who are current students while the studies reported a lifetime CSA prevalence of 3–39% among women above 18 years of age. There was a much wider range of CSA prevalence (4%- 57%) reported among boys in educational institutions. One third of the study sample of adolescent street boys reported forced intercourse , while a general . The predeterminants of CSA as one of the themes in the synthesis of qualitative data across studies included in this review as shown in The social-ecological model guided the emergence of Few studies (11/51) among the quantitative and mixed-methods studies included in the review reported about the characteristics of the CSA perpetrators , 52–61. The health outcomes of CSA can be grouped into mental health, physical health, behavioral and interpersonal . The stuParivartan (English: transformation), a primary prevention program for young boys that aims to prevent perpetration of gender-based violence through promotion of gender equitable norms [Our review found only five intervention studies , 66, 75.le norms . The prole norms , 66. An Of all included quantitative studies, approximately half the studies had high risks of selection bias with poor sampling strategy or sample sizes Tables –13. HalfAmong qualitative studies none of the studies included a description of the moderator or the interviewer´s characteristics while half the included studies did not report any theoretical framework in the study design . In addiOur systematic review summarizes what is known about the characteristics of CSA and the status of the research on CSA in India during the last decade. It adds to the scant knowledge of CSA and draws attention to the magnitude and severity of the ongoing epidemic in India. The reviewed literature estimates that 4–41% of the girls and 10–55% of the boys in school and college samples have experienced one form  of CSA in India. The prevalence figures are much higher among commercial sex workers, street adolescents and children, children working as domestic laborers, MSMs, and women with mental health problems. In addition to highlighting the high frequency of occurrence, the studies in this review begin to highlight the ecological determinants of CSA experience and perpetration along with adverse impact of CSA on social functioning, behavioral issues, mental health, and physical health.The review highlights the heterogeneity of the methodologies utilized between the included studies. It is difficult to generalize the estimates of CSA in India noted in this review because of the small sample sizes and non-random samples. Further, the different sampling strategies, varying operational definitions of CSA, different study populations , different study settings , and various instruments for measurement of CSA add to the practical, methodological, and statistical challenges of presenting pooled prevalence estimates, inter-study comparisons, and cross-population comparisons. Our review suggests that the studies that included standardized instruments and comprehensive definitions of CSA , reported higher prevalence of CSA. A currently available standardized instrument, like ICSAT- C and R, used globally and validated in Indian context could be a tool for methodologically robust measurement across studies for national and international comparisons.As per the NCRB statistics for 2015, the legislative framework in India- The POCSO Act, 2012 has resulted in increased reporting of CSA . HoweverOur review also exposed gaps in the current understanding of CSA in some populations in India. The findings suggest that young boys in India have similar and sometimes higher prevalence of CSA as girls. This is in accordance with current understanding of CSA in India , 20, 80 The studies with at risk populations for CSA, like trafficked girls, also reported substantial variations with higher estimates from studies where the respondents were below 18 years of age and study included all forms of CSA. The few qualitative studies among MSMs and trafficked girls for commercial sex work included in the review suggest early childhood sexual abuse experiences that often reflect power differences between the child and the perpetrator are pathways that lead the victims into commercial sex work. In addition, our review points to increased risks of CSA among certain populations that include children of commercial sex workers, young girls with mental health issues, and adolescent boys and girls out of schools and in labor force (like domestic laborers etc.)Evidence suggests deviant sexual interests are a major risk factor for CSA . AccordiWhile our review yielded a combination of cross-sectional and qualitative studies that provides an insight into the linkages between a few psychological, physical, and behavioral health outcomes and CSA, it also reveals some knowledge gaps and potential research agenda. There is extensive research literature from high-income countries that links any Adverse Childhood Experiences (ACE) like CSA, abuse, neglect, parental violence etc. with poor psychological, social, and physiological outcomes across the lifespan . All ACEThe review also highlights the need for research aimed at designing and evaluating programs for primary prevention and treatment of CSA victims. The high prevalence of CSA in India calls for a multi-faceted ecological approach that also includes strategies for impacting policies, laws, and social and cultural norms of patriarchy and gender inequality that surround CSA , 17, 94.In addition to the limited causal inferences that can be drawn due to near exclusive cross-sectional study designs of most studies, this systematic review suffers some further limitations. Publication bias is a common and well-documented problem in systematic reviews. Despite comprehensive efforts to retrieve all the available data on CSA prevalence rates in India, we might still have failed to identify some non-referenced publications such as reports from civil society organizations that work in the field of CSA in India, other grey literature and literature such as journalistic articles, commentaries, and other reports available in local languages of India. Furthermore, it is likely that the results of this review are biased because not all unpublished data could be accessed. Furthermore, methodological weaknesses of studies limit the reliability and validity of the results. In addition, we included studies whose main aim was to evaluate the CSA experience among Indian children along with studies whose primary objectives may not have been CSA but included CSA as a covariate. However, our goal was not to critically evaluate each individual study, but to comprehensively review the information currently provided in the literature. Despite its limitations, this systematic review makes a significant contribution to research on CSA in India, since it systematically and comprehensively reviewed, structured, and summarized previous research on the prevalence of CSA, and in doing so, provides a future research agenda.CSA is a dark reality that is highly prevalent in India and adversely impacts health. Our literature review underscores the need for the development of a standardized definition of CSA and a validated tool for accurate measurement of CSA across India. Moreover, additional in-depth studies of CSA among the general and specific populations like commercial sex workers and MSMs are needed to develop effective ecological models for prevention and treatment of CSA that are sensitive to the diversity of vulnerabilities of children and adolescents in the Indian context. Furthermore, there is definitely a great need for more research concerning the perpetrators of child sexual abuse, including gathering more knowledge about paedophilia in India, in order to enhance primary preventive strategies.S1 Checklist(DOC)Click here for additional data file.S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


What are the sociodemographic characteristics, health conditions, health care utilization, and health care costs for different population segments of a multiethnic Asian population in Singapore divided according to a modified British Columbia Population Segmentation Framework?In this cross-sectional study, the study population of 1 181 024 individuals was divided into 8 segments with distinct and heterogeneous sociodemographic characteristics, health conditions, health care utilization, and health care cost patterns.This critical population-level health information can be used as baseline data to inform regional and national health priorities for health services research and policy. This cross-sectional study using data from Singapore national health records examines the sociodemographic characteristics, health conditions, health care utilization, and health care cost patterns for different segments of a multiethnic Asian population. Descriptive population-level health data are critical components of the evidence base for population health policy. Human populations often display marked heterogeneity in their health status among subgroups of the population. The recent widespread adoption of electronic health records provides opportunities to use routine real-world health care data to examine population health.To report population sociodemographic characteristics, health conditions, health care utilization, and health care costs for different population segments of a multiethnic Asian population divided according to a modified British Columbia Population Segmentation Framework.This population-based cross-sectional study used 2016 data from the Singapore Eastern Regional Health System, the largest Regional Health System in Singapore. Data were obtained from deidentified national-level electronic health records at the Ministry of Health Singapore. Participants included all residents in the Singapore Eastern Regional Health System catchment area in 2016. The descriptive analysis was conducted in August 2018.Socioeconomic profiles, disease prevalence, health care utilization, and cost patterns of population segments.The total size of the study population in 2016 was 1 181 024 residents . The population was divided into 8 segments: healthy with no outpatient utilization (493 483 residents), healthy with outpatient utilization (259 909 residents), healthy with inpatient admissions (49 588 residents), low complex (215 134 residents), medium complex (79 350 residents), high complex (44 445 residents), cancer (34 217 residents), and end of life (4898 residents). Overall, the 3 most prevalent conditions were chronic kidney disease (31.89%), hypertension (18.52%), and lipid disorders (18.33%). Distributions of chronic conditions differed across the segments. Different segments had varying health care utilization patterns: the high-complex segment had the highest number of primary care clinic visits , the cancer segment had the highest number of specialist outpatient clinic visits , and the end-of-life segment had the highest numbers of accident and emergency department visits  and inpatient admissions  during 2016. For health care costs, specialist outpatient clinic and inpatient care together made up more than 85% of the total cost of nearly 2 billion Singapore dollars. The end-of-life segment contributed approximately 50% of the health care cost per capita of 60 000 Singapore dollars.Different population segments displayed heterogeneity in sociodemographic characteristics, health conditions, health care utilization, and health care cost patterns. This critical health information can be used as baseline data to inform regional and national health priorities for health services research and policy. Interest in population health has been increasing in the medical literature, including many recent population-level studies.8 Population-based approaches to characterizing the complexity of people’s health, including the distribution, variations, and trends of diseases, will make a significant contribution to the field of population health.Population health is an emerging concept that is defined as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group.”10 For example, within King County, Washington, there is an approximately 18-year difference in male life expectancy among the US Census tracts, even though the overall health in King County is among the best in the country, ranking in the 95th percentile among county health nationwide.11 Population health science has long focused on aggregate health indicators; however, the study of variability and heterogeneity can yield a deeper understanding of population health characteristics and needs.12 Although such research is challenging, it is important to identify the different health care needs and health characteristics of subgroups within a particular population.13Human populations often display marked heterogeneity in their health and disease status, not only between populations but also among subgroups of the same population.14 Since its conception by Lynn et al,15 population segmentation has been widely applied in many health systems, including British Columbia, Canada,13 Northwest London, United Kingdom,16 and Lombardy, Italy.17 However, data on population segmentation in Asian settings are sparse.One promising approach to address the issue of population health heterogeneity is population segmentation. By dividing a heterogeneous population into somewhat homogeneous subgroups, population segmentation helps in understanding the distinctive needs of different parts of the population and developing care interventions and services that different subgroups of people might need.18 and National Population Health Survey by Statistics Canada.19 However, population-level surveys are labor intense and time-consuming and rely heavily on self-reported health measures. The interpretation and comparability of these measures are problematic when different persons understand and respond to a given question in different ways.20 The recent widespread adoption of electronic health records (EHRs) globally offers opportunities to use clinical data to examine population health because they provide real-world data available for analysis at any time. The use of EHRs not only facilitates clinical decision-making but also reduces the potential for inaccuracy and incompleteness of data entry and storage.21 In this population-level health study, we report population sociodemographic characteristics, health conditions, health care utilization, and health care costs of different population segments of a multiethnic Asian population divided according to a modified British Columbia Population Segmentation Framework.13Traditionally, population health studies rely heavily on population-level surveys, such as the National Health Interview Survey by the US Census BureauInternational Classification of Diseases, Tenth Revision), and health care utilization nationwide, including polyclinic visits, general practitioner visits, specialist outpatient clinic visits, accident and emergency department visits, and inpatient admissions. Polyclinics are large public primary care clinics located throughout the country that provide subsidized primary care, including medical treatment, preventive health care, and health education.22 Charlson Comorbidity Index23 and Elixhauser Comorbidity Index24 scores were also calculated for overall population and each population segment. The data sets captured comprehensive data and covered health facility visits to all health care professionals and to public health care institutions, all hospitalizations and outpatient operations in private hospitals, and outpatient visits that are covered under MediSave  and MediShield . Data from general practitioners were included if they participate in Community Health Assist Scheme, a government program that enables Singapore citizens from low- and middle-income households and those born on or before December 31, 1949, to receive subsidies for medical and dental care.This population-based cross-sectional descriptive study used routinely collected retrospective data from the Ministry of Health Singapore Division of Policy Research and Evaluation. We extracted the following data for each resident in the study population and presented population-level data in this study: demographic characteristics , disease diagnosis (as defined by diagnosis codes from the STROBE) reporting guideline for cross-sectional studies.25This study was exempted from formal review by the SingHealth Centralized Institutional Review Board. The need for informed consent was waived because the data were deidentified. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  in 2016. The Eastern RHS is the largest RHS established by the Ministry of Health Singapore to integrate health service across health care systems in the Eastern region across care settings—primary care, intermediate care, long-term care, community-based care, and tertiary care—to deliver comprehensive care to the regional population in eastern Singapore.13 The framework was chosen and modified by a panel of local experts in Singapore who are senior clinicians and health policy leaders in the Eastern RHS and Ministry of Health Singapore. They come from a variety of specialties with extensive experience in health policy and clinical care. This ensures that policy implementation is relevant to the specific health care system setting within Singapore. The definitions and criteria for the modified British Columbia Population Segmentation Framework are finalized via focused group discussions among the Ministry of Health Singapore and Eastern RHS experts  was used for statistical analysis, and Tableau statistical software version 2018.2.2 (Tableau Software) was used for data visualization.13 The largest segment was healthy with no outpatient utilization (493 483 residents) and the smallest was end of life (4898 residents).The total size of the study population in 2016 was 1 181 024 residents. As shown in The median (interquartile range) age of the study population was 40 (22-57) years, and 576 663 participants (48.83%) were male. The Overall, the 5 most prevalent diseases were chronic kidney disease (31.89%), hypertension (18.52%), lipid disorders (18.33%), diabetes (8.70%), and osteoarthritis (5.67%). In 13 For example, cancer without metastasis was defined under the cancer category in the modified British Columbia Population Segmentation Framework, as shown in The distribution of chronic conditions across the different segments is shown in As shown in The distribution of health care cost in 2016 across different segments is shown in The distribution of health care cost per capita in 2016 across different segments is illustrated in eTable 1 in the The distribution of health care cost per capita in 2016 in different age groups across segments is shown in eTable 2 in the 28 our data show that the prevalence of diabetes among residents in the Singapore Eastern RHS was 8.70% in 2016. The lower diabetes prevalence might be multifactorial, including recent national-level concerted health promotion efforts by the Ministry of Health Singapore and different data-collection methods. The data presented here may serve as a starting point for further population health monitoring, care intervention, and policy evaluation. Future research opportunities include tracking the longitudinal health care utilization and cost of these population segments, examining the predictive ability of segment membership on health care utilization, and analyzing how individuals move between the population segments in the long term.In this article, we describe the heterogeneous health profiles of population segments in the Singapore Eastern RHS based on real-world data from routine EHRs. We found that different segments within a population have different sociodemographic characteristics, health conditions, health care utilization, and health care costs. This article provides a critical overview of and baseline information on health status at a population level to inform population health management. For example, compared with the Singapore National Health Survey, which reported a crude prevalence of diabetes of 11.3% among Singapore residents aged 18 to 69 years in 2010,29 Real-world data provide useful information that may yield new insights and stimulate new research questions and aid in the design of other interventional studies, such as a clinical trial, and facilitate policy making and practice.30 For example, our study found that the healthy with inpatient admission segment unexpectedly accounted for the largest proportion of total cost (21.56%), even though its population size was less than 5% of the total population. This finding warrants further research efforts in understanding the mechanism behind this phenomenon. One explanation for the participants’ health behavior is their private health insurance plan status, which might lead to higher inpatient service rates even though they do not have complex chronic conditions.The present study adds to the emerging field of using real-world health data by demonstrating that routine clinical care data can be used for population health profiling and monitoring. Recently, there has been substantial enthusiasm for using routine health care data to provide real-world evidence; however, the widespread use of EHRs, administrative databases, and smart devices has heretofore not led to wide use of these data for clinical insights at the population health level, despite the obvious advantages of using routine care data, which is that the use of such data is both pragmatic and generalizable.31 Functional, psychosocial, quality-of-life, and behavioral variables have been proposed and used as important population health indicators.32 The US Institute of Medicine has also given serious attention to population health measurement and encouraged summary measures that include health-related quality-of-life data.33 Furthermore, a population health perspective also requires attention to resource utilization and allocation.1 Some summary measures have been developed to include a broad spectrum of health indicators in different policy and research contexts, such as EQ-5D34 and the Health Utilities Index.35 Although these data are often made available via population-based health surveys, they may not be routinely collected and stored in many health systems, including our data sets. To further complicate this matter, there is heterogeneity in data definition, collection, and reporting in the current medical literature of population health policy. One of the more widely accepted models is the Organisation for Economic Co-operation and Development health indicators framework,36 which reports health-related data in the following 5 categories: health status, risk factors for health, access to care, quality and outcome of care, and health care resources. However, controversy remains over what the universally important, relevant, and practical health indicators and performance measures are that researchers and policy makers should use, both conceptually and at the implementation level. This is likely dependent on each health care system’s policy priority within their economic and political context, objectives of the funding policy, and data availability.The optimal framework for and measures of population health status have long been debated. It is recognized that traditional health indicators alone, such as life expectancy, may not be sufficient for measuring and monitoring population health.This study has limitations. First, it is retrospective and relies on existing data sources. Second, our study may not be generalizable to other health care system settings because they may not be collecting the exact same type of data that we presented and, thus, may not find our study readily replicable; however, they may find our segmentation framework useful as a reference for their policy needs. Third, our database does not include patients who are exclusively managed in the private health care sector, which was small because MediSave is Singapore’s mandatory national medical savings account program and MediShield is Singapore’s national health insurance program that automatically covers all Singapore residents.Population health measures need to be developed and validated in close consultation with stakeholders, taking local policy context and priorities into considerations. It remains a critical task for the field of population health research to determine what to measure and how to measure it, whether by population surveys or routine clinical databases. Future research efforts are ongoing to link our routine health care data with collaborators’ databases that are rich in social and behavioral data to further refine population segmentation frameworks.There are wide differences in the health characteristics of the Eastern RHS population in Singapore. Different population segments displayed heterogeneity in sociodemographic characteristics, health conditions, health care facility utilization, and cost. These data can be used to inform regional and national health priorities for health services research and policy.