Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)

[EN] Objectives To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015. Design Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording. Setting 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES). Main outcomes The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45). Results Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines. Conclusions Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.VN is funded by a Public Health England PhD Studentship. RWA is supported by a Wellcome Trust Clinical Research Career Development Fellowship (206602/Z/17/Z). JMGG and CS contributions to this work were partially supported by the MTS4up Spanish project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R), the CrowdHealth H2020-SC1-2016-CNECT project (No. 727560) (JMGG) and the Inadvance H2020-SC1-BHC-2018-2020 project (No. 825750). PR and DA did not receive any direct funding for this project. Access to the Clinical Practice Research Datalink was supported by the UK Economic and Social Research Council (ES/P008321/1). Access to aggregated Hospital Episode Statistics was provided by Public Health England. This work was further supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust.Rockenschaub, P.; Nguyen, V.; Aldridge, RW.; Acosta, D.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2020). Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015). BMJ Open. 10(2):1-9. https://doi.org/10.1136/bmjopen-2019-034396S19102Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Burton, P. R., Murtagh, M. J., Boyd, A., Williams, J. B., Dove, E. S., Wallace, S. E., … Knoppers, B. M. (2015). Data Safe Havens in health research and healthcare. Bioinformatics, 31(20), 3241-3248. doi:10.1093/bioinformatics/btv279Cruz-Correia R , Rodrigues P , Freitas A . Chapter: 4, Data quality and integration issues in electronic health records. In: Information discovery on electronic health records. CRC Press, 2009: 55–95.Massoudi, B. L., Goodman, K. W., Gotham, I. J., Holmes, J. H., Lang, L., Miner, K., … Fu, P. C. (2012). An informatics agenda for public health: summarized recommendations from the 2011 AMIA PHI Conference. Journal of the American Medical Informatics Association, 19(5), 688-695. doi:10.1136/amiajnl-2011-000507Schlegel, D. R., & Ficheur, G. (2017). Secondary Use of Patient Data: Review of the Literature Published in 2016. Yearbook of Medical Informatics, 26(01), 68-71. doi:10.15265/iy-2017-032Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Herrett, E., Thomas, S. L., Schoonen, W. M., Smeeth, L., & Hall, A. J. (2010). Validation and validity of diagnoses in the General Practice Research Database: a systematic review. British Journal of Clinical Pharmacology, 69(1), 4-14. doi:10.1111/j.1365-2125.2009.03537.xSáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010Tate AR , Dungey S , Glew S , et al . Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? cross-sectional study using the CPRD database. BMJ Open 2017;7:e012905.doi:10.1136/bmjopen-2016-012905Calvert M , Shankar A , McManus RJ , et al . Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study. BMJ 2009;338:b1870.doi:10.1136/bmj.b1870Sáez, C., Robles, M., & García-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data Resource Profile: Clinical Practice Research Datalink (CPRD). International Journal of Epidemiology, 44(3), 827-836. doi:10.1093/ije/dyv098Herbert, A., Wijlaars, L., Zylbersztejn, A., Cromwell, D., & Hardelid, P. (2017). Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). International Journal of Epidemiology, 46(4), 1093-1093i. doi:10.1093/ije/dyx015Chisholm J . The read clinical classification. BMJ 1990;300:1092.doi:10.1136/bmj.300.6732.1092Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., … Hemingway, H. (2019). UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. Journal of the American Medical Informatics Association, 26(12), 1545-1559. doi:10.1093/jamia/ocz105Department for Communities and Local Government . The English Index of Multiple Deprivation (IMD) 2015 - Guidance. Available: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/464430/English_Index_of_Multiple_Deprivation_2015_-_Guidance.pdf [Accessed 8 Dec 2019].Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Borg, I., & Groenen, P. (2003). Modern Multidimensional Scaling: Theory and Applications. Journal of Educational Measurement, 40(3), 277-280. doi:10.1111/j.1745-3984.2003.tb01108.xSáez, C., & García-Gómez, J. M. (2018). Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics, 119, 109-124. doi:10.1016/j.ijmedinf.2018.09.015Conrad, N., Judge, A., Tran, J., Mohseni, H., Hedgecott, D., Crespillo, A. P., … Rahimi, K. (2018). Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. The Lancet, 391(10120), 572-580. doi:10.1016/s0140-6736(17)32520-5Herrett E , Shah AD , Boggon R , et al . Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013;346:f2350.doi:10.1136/bmj.f2350Pujades-Rodriguez M , Timmis A , Stogiannis D , et al . Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention. PLoS One 2014;9:e104671.doi:10.1371/journal.pone.0104671Lee S , Shafe ACE , Cowie MR . Uk stroke incidence, mortality and cardiovascular risk management 1999-2008: time-trend analysis from the general practice research database. BMJ Open 2011;1:e000269.doi:10.1136/bmjopen-2011-000269Bhatnagar, P., Wickramasinghe, K., Williams, J., Rayner, M., & Townsend, N. (2015). The epidemiology of cardiovascular disease in the UK 2014. Heart, 101(15), 1182-1189. doi:10.1136/heartjnl-2015-307516Taylor, C. J., Ordóñez-Mena, J. M., Roalfe, A. K., Lay-Flurrie, S., Jones, N. R., Marshall, T., & Hobbs, F. D. R. (2019). Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study. BMJ, l223. doi:10.1136/bmj.l223Gho JMIH , Schmidt AF , Pasea L , et al . An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open 2018;8:e018331.doi:10.1136/bmjopen-2017-018331Quint JK , Müllerova H , DiSantostefano RL , et al . Validation of chronic obstructive pulmonary disease recording in the clinical practice research Datalink (CPRD-GOLD). BMJ Open 2014;4:e005540.doi:10.1136/bmjopen-2014-005540Bhaskaran K , Forbes HJ , Douglas I , et al . Representativeness and optimal use of body mass index (BMI) in the UK clinical practice research Datalink (CPRD). BMJ Open 2013;3:e003389.doi:10.1136/bmjopen-2013-003389Booth, H. P., Prevost, A. T., & Gulliford, M. C. (2013). Validity of smoking prevalence estimates from primary care electronic health records compared with national population survey data for England, 2007 to 2011. Pharmacoepidemiology and Drug Safety, 22(12), 1357-1361. doi:10.1002/pds.3537Booth H , Dedman D , Wolf A . CPRD aurum frequently asked questions (FAQs). CPRD 2019.Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O. D. (2011). Systematic review of discharge coding accuracy. Journal of Public Health, 34(1), 138-148. doi:10.1093/pubmed/fdr054Marmot, M. G., Stansfeld, S., Patel, C., North, F., Head, J., White, I., … Smith, G. D. (1991). Health inequalities among British civil servants: the Whitehall II study. The Lancet, 337(8754), 1387-1393. doi:10.1016/0140-6736(91)93068-kKivimäki, M., Batty, G. D., Singh-Manoux, A., Britton, A., Brunner, E. J., & Shipley, M. J. (2017). Validity of Cardiovascular Disease Event Ascertainment Using Linkage to UK Hospital Records. Epidemiology, 28(5), 735-739. doi:10.1097/ede.0000000000000688Crosignani, P. G. (2003). Breast cancer and hormone-replacement therapy in the Million Women Study. Maturitas, 46(2), 91-92. doi:10.1016/j.maturitas.2003.09.002Wright FL , Green J , Canoy D , et al . Vascular disease in women: comparison of diagnoses in hospital episode statistics and general practice records in England. BMC Med Res Methodol 2012;12:161.doi:10.1186/1471-2288-12-161Herrett, E., Smeeth, L., Walker, L., & Weston, C. (2010). The Myocardial Ischaemia National Audit Project (MINAP). Heart, 96(16), 1264-1267. doi:10.1136/hrt.2009.192328Silver LE , Heneghan C , Mehta Z , et al . Substantial underestimation of incidence of acute myocardial infarction by hospital discharge diagnostic coding data: a prospective population-based study. Heart 2009;95.Health and Social Care Information Centre . Coding clinic guidance. 5th edn, 2010.Health & Social Care Information Centre . National Clinical Coding Standards - ICD-10. 4th edn, 2013. https://hscic.kahootz.com/connect.ti/t_c_home/view?objectId=31445829Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. doi:10.1080/07421222.1996.1151809

Influence of crop-water production functions on the expected performance of water conservation policies in irrigated agriculture

[EN] Agricultural economics Water Programming Models (WPM) has found that irrigators in water scarce areas have a rather inelastic response to water prices, making water pricing cost-ineffective towards water saving. We hypothesize that the predicted water saving performance of pricing is significantly underestimated by issues of model structure, due to the exclusion of deficit irrigation from the set of decision variables available to agents in conventional WPM. To test our hypothesis, we develop a model that integrates a continuous crop-water production function into a positive multi-attribute WPM, which allows us to assess agents¿ adaptive responses to pricing through deficit irrigation. The model is illustrated with an application to the El Salobral-Los Llanos irrigated area in Spain. Our results show that incorporating deficit irrigation as an adaptation option makes the water demand curve significantly more elastic as compared to an alternative model setting where deficit irrigation is precluded. We conclude that ignoring deficit irrigation can lead to a significant underestimation of the cost-effectiveness of water pricing towards water saving.The research leading to these results has received funding from the Program for the Attraction of Scientific Talent through the Project SWAN (Sustainable Watersheds: Emerging Economic Instruments for Water and Food Security), from the Biodiversity Foundation of the Spanish Ministry for Ecological Transition through the Project ATACC (Adaptacion ¿ Transformativa al Cambio Clim¿ atico en el Regadío) and from Programa Operativo FEDER Andalucía 2014-2020 through the project SEKECO (Evaluacion de estrategias de adaptacion a la sequía bajo el actual escenario de cambio climatico) Ref 1263831-R. This work was additionally supported by the ADAPTAMED (Design and evaluation of adaptation strategies to climate and global change in Mediterranean basins with intensive use of water for irrigation) national research project funded by the Spanish Ministry of Science and Innovation (RTI2018-101483-B-I00) with European FEDER funds.Sapino, F.; Pérez-Blanco, CD.; Gutiérrez-Martín, C.; Garcia-Prats, A.; Pulido-Velazquez, M. (2022). Influence of crop-water production functions on the expected performance of water conservation policies in irrigated agriculture. Agricultural Water Management. 259:1-15. https://doi.org/10.1016/j.agwat.2021.107248S11525

The sedimentary record of palaeoenvironments and sea-level change in the Gulf of Carpentaria, Australia, through the last glacial cycle

Environmental evolution of the Gulf of Carpentaria region, the world\u27s largest tropical epicontinental seaway, through the last glacial cycle has been determined from a series of six sediment cores. These cores form the focus of a multi-disciplinary study to elucidate sea level, climate and environmental change in the region. The sedimentary record reveals a series of facies including open shallow marine, marginal marine, estuarine, lacustrine and subaerial exposure, throughout the extent of the basin during this period. The partial or complete closure of the central basin from marine waters results from sea level falling below the height of one or both of the sills that border the Gulf—the Arafura Sill to the west (53 m below present sea level (bpsl)) and Torres Strait to the east (12 m bpsl). The extent and timing of these closures, and restriction of the shallow waterbody within, are intrinsic to local ocean circulation, available latent heat transport and the movement of people and animals between Australia and New Guinea. Whilst the occurrence of the palaeo-Lake Carpentaria has previously been identified, this study expands on the hydrological conditions of the lacustrine phases and extends the record through the Last Interglacial, detailing the previous sea-level highstand (MIS 5.5) and subsequent retreat. When sea levels were low during the MIS 6 glacial period, the Gulf was largely subaerially exposed and traversed by meandering rivers. The MIS 5 transgression (∼130 ka BP) led to marine then alternating marine/estuarine conditions through to MIS 4 (∼70 ka BP) when a protracted lacustrine phase, of varying salinity and depth/area, and including periods of near desiccation, persisted until about 12.2 cal ka BP. The lake expanded to near maximum size (∼190 000 km2) following the intensification/restoration of the Australian monsoon at 14 ka BP. This lake-full phase was short-lived, as by 12.2 cal ka BP, marine waters were entering the basin, coincident with the progressive sea-level rise. Fully marine conditions were restored by about 10.5 cal ka BP by westward connection to the Arafura Sea (Indian Ocean), whereas connections to the Pacific Ocean (Coral Sea) did not occur until about 8 cal ka BP

Evaluation of Polyciclic Aromatic Hydrocarbons in Water and Microplastics

Polycyclic Aromatic Hydrocarbons (PAHs) are a group of environmental contaminants, classified as potentially toxic, mutagenic and carcinogenic, being an important public health concern. In the present study, we assayed different samples of water (superficial water, groundwater and tap water) for five PAHs: pyrene (Pyr), 1-chloro-pyrene (1-ClPyr), 1-bromine-pyrene (1-BrPyr), benzo-a-anthracene (BaA) and 7-chloro-benzo-a-anthracene (7-ClBaA) by gas chromatography - mass spectrometry (GC-MS) after sample concentration by solid phase microextraction. The adsorption of most abundant PAHs (Pyr) by PET (polyethyleneterephthalate), HDPE (high density polyethylene), LDPE (low density PE), PP (polypropylene) and PS (polystyrene) particles with 4 mm diameter (microplastics) dispersed in freshwater was assessed by high pressure liquid chromatography (HPLC) after 3 and 30 days. Our data showed that, all types of plastic adsorbed Pyr without statiscally significant difference. Adsorption enhances Pyr stability contributing to its persistence /accumulation in the environment.Financial Support INSA (2018DSA1555)N/