The diagnosis of mental disorders: the problem of reification

A pressing need for interrater reliability in the diagnosis of mental disorders emerged during the mid-twentieth century, prompted in part by the development of diverse new treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition answered this need by introducing operationalized diagnostic criteria that were field-tested for interrater reliability. Unfortunately, the focus on reliability came at a time when the scientific understanding of mental disorders was embryonic and could not yield valid disease definitions. Based on accreting problems with the current DSM-fourth edition (DSM-IV) classification, it is apparent that validity will not be achieved simply by refining criteria for existing disorders or by the addition of new disorders. Yet DSM-IV diagnostic criteria dominate thinking about mental disorders in clinical practice, research, treatment development, and law. As a result, the modernDSMsystem, intended to create a shared language, also creates epistemic blinders that impede progress toward valid diagnoses. Insights that are beginning to emerge from psychology, neuroscience, and genetics suggest possible strategies for moving forward

The health workforce crisis in Bangladesh: shortage, inappropriate skill-mix and inequitable distribution

<p>Abstract</p> <p>Background</p> <p>Bangladesh is identified as one of the countries with severe health worker shortages. However, there is a lack of comprehensive data on human resources for health (HRH) in the formal and informal sectors in Bangladesh. This data is essential for developing an HRH policy and plan to meet the changing health needs of the population. This paper attempts to fill in this knowledge gap by using data from a nationally representative sample survey conducted in 2007.</p> <p>Methods</p> <p>The study population in this survey comprised all types of currently active health care providers (HCPs) in the formal and informal sectors. The survey used 60 unions/wards from both rural and urban areas (with a comparable average population of approximately 25 000) which were proportionally allocated based on a 'Probability Proportion to Size' sampling technique for the six divisions and distribution areas. A simple free listing was done to make an inventory of the practicing HCPs in each of the sampled areas and cross-checking with community was done for confirmation and to avoid duplication. This exercise yielded the required list of different HCPs by union/ward.</p> <p>Results</p> <p>HCP density was measured per 10 000 population. There were approximately five physicians and two nurses per 10 000, the ratio of nurse to physician being only 0.4. Substantial variation among different divisions was found, with gross imbalance in distribution favouring the urban areas. There were around 12 unqualified village doctors and 11 salespeople at drug retail outlets per 10 000, the latter being uniformly spread across the country. Also, there were twice as many community health workers (CHWs) from the non-governmental sector than the government sector and an overwhelming number of traditional birth attendants. The village doctors (predominantly males) and the CHWs (predominantly females) were mainly concentrated in the rural areas, while the paraprofessionals were concentrated in the urban areas. Other data revealed the number of faith/traditional healers, homeopaths (qualified and non-qualified) and basic care providers.</p> <p>Conclusions</p> <p>Bangladesh is suffering from a severe HRH crisis--in terms of a shortage of qualified providers, an inappropriate skills-mix and inequity in distribution--which requires immediate attention from policy makers.</p

Illness Mapping: A time and cost effective method to estimate healthcare data needed to establish community-based health insurance

Background: Most healthcare spending in developing countries is private out-of-pocket. One explanation for low penetration of health insurance is that poorer individuals doubt their ability to enforce insurance contracts. Community-based health insurance schemes (CBHI) are a solution, but launching CBHI requires obtaining accurate local data on morbidity, healthcare utilization and other details to inform package design and pricing. We developed the "Illness Mapping" method (IM) for data collection (faster and cheaper than household surveys). Methods. IM is a modification of two non-interactive consensus group methods (Delphi and Nominal Group Technique) to operate as interactive methods. We elicited estimates from "Experts" in the target community on morbidity and healthcare utilization. Interaction between facilitator and experts became essential to bridge literacy constraints and to reach consensus.The study was conducted in Gaya District, Bihar (India) during April-June 2010. The intervention included the IM and a household survey (HHS). IM included 18 women's and 17 men's groups. The HHS was conducted in 50 villages with1,000 randomly selected households (6,656 individuals). Results: We found good agreement between the two methods on overall prevalence of illness (IM: 25.9% ±3.6; HHS: 31.4%) and on prevalence of acute (IM: 76.9%; HHS: 69.2%) and chronic illnesses (IM: 20.1%; HHS: 16.6%). We also found good agreement on incidence of deliveries (IM: 3.9% ±0.4; HHS: 3.9%), and on hospital deliveries (IM: 61.0%. ± 5.4; HHS: 51.4%). For hospitalizations, we obtained a lower estimate from the IM (1.1%) than from the HHS (2.6%). The IM required less time and less person-power than a household survey, which translate into reduced costs. Conclusions: We have shown that our Illness Mapping method can be carried out at lower financial and human cost for sourcing essential local data, at acceptably accurate levels. In view of the good fit of results obtained, we assume that the method could work elsewhere as well

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Background: With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim: To evaluate the introduction of predictive risk stratification in primary care. Objectives: To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design: Randomised stepped-wedge trial with economic and qualitative components. Setting: Abertawe Bro Morgannwg University Health Board, south Wales. Participants: Patients registered with 32 participating general practices. Intervention: PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. Main outcome measures: Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources: Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results: Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations: In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions: Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research: (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration: Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding: The National Institute for Health Research Health Services Delivery and Research programme

Scaling-up interventions to improve infant and young child feeding in India: What will it take?

We assessed India's readiness to deliver infant and young child feeding (IYCF) interventions by examining elements related to policy, implementation, financing, and evidence. We based our analysis on review of (a) nutrition policy guidance and program platforms, (b) published literature on interventions to improve IYCF in India, and (c) IYCF program models implemented between 2007 and 2012. We find that Indian policies are well aligned with global technical guidance on counselling interventions. However, guidelines for complementary food supplements (CFS) need to be reexamined. Two national programs with the operational infrastructure to deliver IYCF interventions offer great potential for scale, but more operational guidance, capacity, and monitoring are needed to actively support delivery of IYCF counselling at scale by available frontline workers. Many IYCF implementation efforts to date have experimented with approaches to improve breastfeeding and initiation of complementary feeding but not with improving diet diversity or the quality of food supplements. Financing is currently inadequate to deliver CFS at scale, and governance issues affect the quality and reach of CFS. Available evidence from Indian studies supports the use of counselling strategies to improve breastfeeding practices and initiation of complementary feeding, but limited evidence exists on improving full spectrum of IYCF practices and the impact and operational aspects of CFS in India. We conclude that India is well positioned to support the full spectrum of IYCF using existing policies and delivery platforms, but capacity, financing, and evidence gaps on critical areas of programming can limit impact at scale