Effects of water re-allocation in the Ebro river basin: A multiregional input-output and geographical analysis

The quality and availability of water are affected by numerous variables, through which the evaluation of water uses from different perspectives, and policy proposals to save water have now become essential. This paper aims to study water use and the water footprint from a river basin perspective, taking into account regions, sectors, and municipalities, while considering the physical frontier along with the administrative sectors. To this end, we have constructed a multi-regional input-output table for the Ebro river basin, disaggregating the primary sector into 18 different crops and 6 livestock groups. We pay special attention to crop production because it is the most water-consuming industry. The construction of the multi-regional input-output model represents an important contribution to the literature, in itself, since, to the best of our knowledge, it is the first to be built for this large basin. We extend this multi-regional input-output model to assess the water footprint by sectors and regions within the basin. We use these data to propose two scenarios: reallocating final demand to reduce the blue water footprint (scenario 1), and increasing value added (scenario 2). These scenarios outline the opportunity costs of saving water in socioeconomic terms in the basin. In another application, we downscale the multi-regional input-output model results at the municipal level and depict them using a geographical information system, identifying the hotspots and the areas that would pay for the socioeconomic opportunity costs of saving water. Our results suggest that saving 1 hm 3 of blue water could cost around €41, 500 of value added if we consider the entire basin. However, this water re-allocation implies losses and gains at the municipal level: some municipalities would reduce value added by more than €30, 000, while others would gain more than €85, 000 of value added. These tools and results can be useful for policy makers when considering re-allocating water. The contribution and the novelty of this paper is the construction of the multiregional input-output model for the Ebro river basin, and its link with geographical systems analysis at the municipal level

Monitoring the prevalence of chronic conditions: which data should we use?

<p>Abstract</p> <p>Background</p> <p>Chronic diseases are an increasing threat to people’s health and to the sustainability of health organisations. Despite the need for routine monitoring systems to assess the impact of chronicity in the population and its evolution over time, currently no single source of information has been identified as suitable for this purpose. Our objective was to describe the prevalence of various chronic conditions estimated using routine data recorded by health professionals: diagnoses on hospital discharge abstracts, and primary care prescriptions and diagnoses.</p> <p>Methods</p> <p>The ICD-9-CM codes for diagnoses and Anatomical Therapeutic Chemical (ATC) codes for prescriptions were collected for all patients in the Basque Country over 14 years of age (n=1,964,337) for a 12-month period. We employed a range of different inputs: hospital diagnoses, primary care diagnoses, primary care prescriptions and combinations thereof. Data were collapsed into the morbidity groups specified by the Johns Hopkins Adjusted Clinical Groups (ACGs) Case-Mix System. We estimated the prevalence of 12 chronic conditions, comparing the results obtained using the different data sources with each other and also with those of the Basque Health Interview Survey (ESCAV). Using the different combinations of inputs, Standardized Morbidity Ratios (SMRs) for the considered diseases were calculated for the list of patients of each general practitioner. The variances of the SMRs were used as a measure of the dispersion of the data and were compared using the Brown-Forsythe test.</p> <p>Results</p> <p>The prevalences calculated using prescription data were higher than those obtained from diagnoses and those from the ESCAV, with two exceptions: malignant neoplasm and migraine. The variances of the SMRs obtained from the combination of all the data sources (hospital diagnoses, and primary care prescriptions and diagnoses) were significantly lower than those using only diagnoses.</p> <p>Conclusions</p> <p>The estimated prevalence of chronic diseases varies considerably depending of the source(s) of information used. Given that administrative databases compile data registered for other purposes, the estimations obtained must be considered with caution. In a context of increasingly widespread computerisation of patient medical records, the complementary use of a range of sources may be a feasible option for the routine monitoring of the prevalence of chronic diseases.</p