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Agriculture is widely recognized as a solution to food insecurity and poverty, especially in rural areas. However, 75% of the world’s poor live in rural areas, and agriculture is the primary source of their livelihood. One may wonder if the observed correlation between agriculture and poverty also suggests causation. If that is the case, then what such causal relationship might exist? Is agriculture a vehicle for poverty alleviation or a source of poverty trap? The role of climate change is rather undisputed: associated extreme weather phenomena cause severe negative impacts on agriculture, exacerbating rural poverty. However, climate-smart agriculture (CSA) is acclaimed to potentially reverse the situation by eliminating poverty and food insecurity. Against this backdrop, the paper investigates whether smallholder farmers who adopt CSA could achieve food security and better income. This aim was approached through three key research objectives (i) to examine the effects of climate change on smallholder farmers, (ii) to examine the extent to which smallholder farmers adopt CSA and the barriers to adoption, and (iii) to investigate empirically the effects of CSA practices in terms of food security and poverty alleviation. The Upper West and Upper East regions in Ghana were selected purposively for the case study, and the data collected were analyzed using inferential and descriptive techniques. The results revealed no statistically significant positive relationship between the adoption of CSA with food security and income. Poor socioeconomic and market conditions marred the expected positive effects of CSA, hence the need for the provision of agricultural infrastructures and inputs as well as the creation of market for commodities.

In the mainstream development debate during recent years, among others, there have been two influential camps opposing each other. The first one identifies the environmental context, the “natural” variable, as the ultimate cause that determines the socio-economic development trajectory of a society. The second influential approach argues that “the social” variable (i.e., political institutions) and not “the natural” variable is the sphere in which to look for the main explanatory variables for developmental outcomes. In this paper, we argue that although each approach has its own merits, they equally fail to address hot issues in the realm of economic development. They give an incomplete cause-and-effect picture of the ongoing, complex and vital-for-development processes, and they are equally incapable of identifying plausible explanatory variables regarding developmental successes and failures. Our aim is not to discredit the valuable body of work on either of these approaches, but instead to spark a discussion that would enable more creative, inter- and multidisciplinary research into the missing causal links between the propositions of key authors representing the aforementioned approaches and the fallacies that need to be addressed. Our position is that this is the only path that can lead to a meaningful synthesis capable of paving the way for a breakthrough in development economics and sustainable development studies.

Health analytics frequently involve tasks to predict outcomes of care. A foundational predictor of clinical outcomes is the medical diagnosis (Dx). The most used expression of medical Dx is the International Classification of Diseases (ICD-10-CM). Since ICD-10-CM includes >70,000 codes, it is computationally expensive and slow to train models with. Alternative lower-dimensionality alternatives include clinical classification software (CCS) and diagnosis-related groups (MS-DRGs). This study compared the predictive power of these alternatives against ICD-10-CM for two outcomes of hospital care: inpatient mortality and length of stay (LOS). Naïve Bayes (NB) and Random Forests models were created for each Dx system to examine their predictive performance for inpatient mortality, and Multiple Linear Regression models for the continuous LOS variable. The MS-DRGs performed highest for both outcomes, even outperforming ICD-10-CM. The admitting ICD-10-CM codes were, surprisingly, not underperformed by the primary ICD-10-CM Dxs. The CCS system, although having a much lower dimensionality than ICD-10-CM, has only slightly lower performance while the refined version of CCS only slightly outperformed the old CCS. Random Forests outperformed NB for MS-DRG, and ICD-10-CM, by a large margin. Results can provide insights to understand the compromise from using lower-dimensionality representations in clinical outcome studies.

Abstract Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the ‘Historical Decision’ bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.

Hospital-acquired pneumonia (HAP) bloodstream infections comprise a major cause of crude hospital mortality. This is a cross-sectional study that used claims data from the Centers for Medicare and Medicaid Services (N = 565,875). The study objective is to represent the progression of pneumonia-induced bloodstream infections using graph theory principles, where each path of the graph represents a different scenario of bloodstream-infection progression, and aims to further estimate the likelihood if hospital death for each path. To disseminate the results, the study makes available a prototype applet to navigate various paths of the graph interactively. Bayesian probabilities were calculated for each scenario, and multivariate logistic regression was conducted to estimate the adjusted OR for inpatient death after controlling for patient age, sex, and comorbidities. The mortality rate ranged from 4.99% for patients admitted with community pneumonia without bloodstream infection and reached 63.18% for cases admitted with bloodstream infection that progressed to hospital septicemia, sepsis, and septic shock. The prototype applet can be used to unfold bloodstream infection events and their associated risk for mortality and could be used in university curricula to assist educators in helping students understand the progression of pneumonia-induced bloodstream infections in a data-driven way.

Comorbidities can have a cumulative effect on hospital outcomes of care, such as the length of stay (LOS), and hospital mortality. This study examines patients hospitalized with congestive heart failure (CHF), a life-threatening condition, which, when it coexists with a burdened disease profile, the risk for negative hospital outcomes increases. Since coexisting conditions co-interact, with a variable effect on outcomes, clinicians should be able to recognize these joint effects. In order to study CHF comorbidities, we used medical claims data from the Centers for Medicare and Medicaid Services (CMS). After extracting the most frequent cluster of CHF comorbidities, we: (i) Calculated, step-by-step, the conditional probabilities for each disease combination inside this cluster; (ii) estimated the cumulative effect of each comorbidity combination on the LOS and hospital mortality; and (iii) constructed (a) Bayesian, scenario-based graphs, and (b) Bayes-networks to visualize results. Results show that, for CHF patients, different comorbidity constructs have a variable effect on the LOS and hospital mortality. Therefore, dynamic comorbidity risk assessment methods should be implemented for informed clinical decision making in an ongoing effort for quality of care improvements.

Hip replacement is the most common surgical procedure among Medicare patients in the US and worldwide. The hospital length of stay (LOS) for hip replacement admissions is therefore important to be controlled, contributing to savings for hospitals. This study combined medical claims and hospital structure and service data to examine LOS fluctuations and trends, and admission distribution patterns, during weekdays, for hip replacement cases. The study furthermore examined associations of these patterns with the LOS performance. Most hospitals were found to admit hip replacement cases at the start of the week (Monday through Wednesday). There is an upward LOS trend as we approach late weekday admissions. Multiple linear regression analysis showed that LOS weekday inconsistencies, a large proportion of hip replacement admissions on Thursday and Friday, the government ownership status, the bed size, and the critical access status are associated with an increased LOS. On the other hand, the rate of hip replacement admissions over total ones, and the hospital being accredited, are associated with a lower LOS. Findings stress out the need for hospitals to maintain an effective and balanced distribution of hip replacement admissions, evenly during the week, and the need for standardized case management, to avoid practice variability and, therefore, LOS fluctuations for their hip replacement cases.

Admission and discharge diagnoses of in-hospital patients are often in discord. Incorrect admission diagnoses are related to an increased cost of care and patient safety. Additionally, due to the seasonality of many conditions, this discord may vary across the year. In this paper, we used medical claims data to develop a methodological framework that examines these differences for Medicare beneficiaries. We provide examples for pneumonia, which is a condition with seasonal implications, and aneurysm, where early detection can be lifesaving. Following a Bayesian approach, our work quantifies and visualizes with time-series plots the degree that any clinical condition is correctly diagnosed upon admission. We examined differences in weekly intervals over a calendar year. Furthermore, the median length of stay and the mean hospital charges were compared between matching and non-matching {admission, discharge Dx} pairs, and 95% confidence intervals of the difference were estimated. We applied statistical process control methods, and then visualized the differences among the hospital charges and the length of stay, per week, with time-series plots. Our methodology and the visualizations underline the importance of a rigorous and non-delayed diagnostic process upon admission, since there are significant implications in terms of hospital outcomes and cost of care.

The paper discusses the potential of action research to meet the challenges entailed in institutional design for urban water management. Our overall aim is to briefly present action research and discuss its methodological merits with regard to the challenges posed by the different conceptual bases for extrapolating the effects of institutional design on institutional change. Thus, our aim is to explore how Action Research meets the challenge of scoping the field in an open fashion for determining the appropriate mechanisms of institutional change and supporting the emerging of new water institutions. To accomplish this aim, we select the Water Framework Directive (WFD) as an illustrative driving force requiring changes in water management practices and implying the need for the emergence of new institutions. We employ a case of urban water management in the Volos Metropolitan Area, part of the Thessaly region in Greece, where a Pilot River Basin Plan was implemented. By applying action research and being involved in a long process of interaction between stakeholders, we examine the emergence of new institutions dealing with urban water management under the general principles of the major driving force for change: the WFD.

The paper investigates actions and decisions of agricultural resource users and explores their implications for institutional change and natural resource management in the post-socialist context of Central Asia. More specifically, the authors propose a novel methodological approach for the aforementioned context to support policy-relevant research that explicitly addresses behavioral responses of pastoralists in Kyrgyzstan. The paper builds on distributive and economic theories of institutional change and combines findings from lab and field framed economic experiments with complementary qualitative methods (questionnaires, group discussions and semi-structured interviews). By these means the authors test the impact of a specific variable on institutional change in pasture use: the role of power and specifically the difference in the ability of players to “survive” in a bargaining game without an agreement. The impact of power asymmetries and its implications for cooperation and the efficiency of bargaining outcomes are discussed and analyzed. Experimental results largely confirm findings reported in the literature: as players learn about the game and the behavior of others, they adjust their decisions accordingly; the subjects also exhibit other-regarding preferences, resulting to the prevalence of relatively equally distributed gains as an outcome. Furthermore, the findings of the study suggest that under the condition of incomplete information about the preferences of other players, the experimental subjects internalize the game as a group. The authors propose that an explanatory variable for such situations might be that actual shared beliefs of pasture users assist players to economize on information processing and coordinate the bargaining in an effective way. From this perspective, the paper raises a series of questions regarding the proposition that power asymmetry leads to inefficient bargaining outcomes, and provides some first insights for further investigation.

Patients evaluate the quality of home health agencies (HHAs) using the Health Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. This paper describes a prototype community health information system to help patients select appropriate and quality HHAs, according to the location, proprietary status, type of service, and year of HHA establishment. Five HCAHPS indicators were selected: “summary rating”, “quality of care”, “professional care”, “communication”, and “recommend agency”. Independent t-test analysis showed that agencies offering Speech Pathology, Medical-Social, or Home Health Aide services, receive significantly worse HCAHPS ratings, while mean ratings vary significantly across different US states. Multiple comparisons with post hoc ANOVA revealed differences between and within HHAs of different proprietary status (p < 0.001): governmental HHAs receiving higher ratings than private HHAs. Finally, there was observed a relationship between all five quality rating variables and the HHA year of establishment (Pearson, p < 0.001). The older the agency is, the better the HCAPS summary ratings. Findings provided the knowledge to design of a consumer health information system, to provide rankings filtered according to user criteria, comparing the quality rankings of eligible HHAs. Users can also see how a specific agency is ranked against eligible HHAs. Ultimately, the system aims to support the patient community with contextually realistic comparisons in an effort to choose optimal HH service.

We explore the links between Cyprus's colonial past, divided present, and current water scarcity. With reference to the concept of fit, we tackle the question of whether we can observe fit in settings where institutions for collective action work differently than we would expect. We perform a secondary analysis of interview materials on Cyprus's water conflicts, extracting arguments for and against different solutions to water scarcity. Two perspectives on fit emerge: "island fit", which supports island-wide institutions; and "patronage fit", which embodies institutions that link Cypriots to their respective patrons Turkey and Greece. The analysis reveals a preference for island-wide institutional arrangements. However, rather than resting on biophysical considerations, such preference is linked to the feeling of unity of the two communities inhabiting Cyprus. We therefore observe institutions that face a trade-off between fitting to social groupings and fitting to biophysical circumstances.