Representation of Environmental Concepts Associated with Health Impacts in Computer Standardized Clinical Terminologies

Summary

Objective: To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies.

Methods: This study used a descriptive approach with methods informed by a procedural framework for standardized clinical terminology mapping. The United Nations Global Indicator Framework for the Sustainable Development Goals and Targets was used as the source document for concept extraction. The target terminologies were the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and the International Classification for Nursing Practice (ICNP). Manual and automated mapping methods were utilized. The lists of candidate matches were reviewed and iterated until a final mapping match list was achieved.

Results: A total of 119 concepts with 133 mapping matches were added to the final SNOMED CT list. Fifty-three (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. A total of 26 concepts with 27 matches were added to the final ICNP list. Eight (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches.

Conclusion: Following this evaluation, both strengths and gaps were identified. Gaps in terminology representation included concepts related to cost expenditures, affordability, community engagement, water, air and sanitation. The inclusion of these concepts is necessary to advance the clinical reporting of these environmental and sustainability indicators. As environmental concepts encoded in standardized terminologies expand, additional insights into data and health conditions, research, education, and policy-level decision-making will be identified.

Keywords

Nursing informatics - environmental health - standardized nursing terminology - systematized nomenclature of medicine - artificial intelligence

1 Introduction

Our health is interconnected with the air, water and land in which we live and work. However, environmental risks are not evenly distributed, as vulnerable populations may have a higher risk of exposure to air pollution, noise pollution and hazardous chemicals due to pre-existing health conditions, poor nutritional status and other environmental risk factors. In 2015, United Nations (UN) Member States formally adopted 17 Sustainable Development Goals (SDGs) to achieve by the year 2030. These goals provided a shared plan for the health, well-being, and prosperity of people and the planet. For example, SDG 3.9.1 considers the mortality rate attributed to ambient and household air pollution. Further SDG indicators consider the intersection of sustainability and human existence related to health, poverty, pollution, water, education, gender equality, energy, and economic growth [[1]]. To further these goals, various disciplines have come together to collaborate on the concept of One Health.

One Health is a collaborative, multisectoral approach which highlights the absolute interconnectedness between human health and the complex ecosystems in which we live and share [[2], [3]]. This transdisciplinary approach is an important consideration as human populations are expanding into new geographic areas which has led to potential opportunities for diseases to pass between animals and people. Each year, millions of people are impacted by antimicrobial-resistant germs, vector-borne diseases, and ill health related to the contamination of water. Weather changes impacting temperature and humidity are also associated with adverse outcomes related to respiratory, cardiovascular, and neurological conditions and infectious diseases [[4] [5] [6] [7]].

Environmental sustainability further informed this project. Morelli [[8]] describes environmental sustainability “…as a condition of balance, resilience, and interconnectedness that allows human society to satisfy its needs while neither exceeding the capacity of its supporting ecosystems to continue to regenerate the services necessary to meet those needs nor by our actions diminishing biological diversity.”

The question remains how well healthcare is advancing towards meeting environmental sustainability, One Health and the SDGs. One such area where healthcare clinician contributions can be leveraged in this evaluation is through secondary data reuse of clinical documentation. Clinicians assess, diagnose, provide interventions and evaluate the care of people and communities affected by environmental impacts. In many settings, this documentation is captured in electronic format with embedded standardized clinical terminologies. Standardized clinical terminologies are databases of clinical terms that have computer-readable codes, are arranged in a polyarchy with defined relationships, have conceptual uniqueness and permanence, and can expand through multiple granularities [[9]]. As a mechanism to support data reuse, standardized clinical terminologies can be used to facilitate the aggregation of clinical data across disparate settings and applied in larger-scale evaluations. Two healthcare terminology standards often used in this capacity, are the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), a multilingual clinical health terminology [[10]] and the International Classification for Nursing Practice (ICNP), a classification of nursing diagnosis, interventions, and outcomes specifically describing nursing practice [[11]]. These standardized terminologies are systematically organized and are computer-readable collections of medical terms that are defined, codified, and have synonyms associated with easier cross-mapping and comparison across systems and practice settings [[12]]. In 2021, the International Council of Nurses (ICN) and SNOMED International announced an agreement whereby ICNP would be managed and distributed by SNOMED International while content ownership would remain with ICN [[13]]. To support this work, reference sets and tables have been developed to describe the mapping equivalence between these two terminologies.

While much work has been advanced to represent healthcare concepts in SNOMED CT and ICNP, it is unknown if the suite of associated health concepts, represented in these SDGs, exists in these systems. Therefore, this study aimed to evaluate the representation of environmental concepts associated with health impacts in SNOMED CT and ICNP.

2 Objectives

The objective of this study was to evaluate the representation of environmental concepts associated with health impacts, in standardized clinical terminologies.

3 Methods

This study used a descriptive methodology informed by Block et al. [[14]] procedural framework for standardized clinical terminology mapping ([Figure 1]). As per the Canadian Tri-Council Policy Statement for Ethical Conduct for Research Involving Humans-Chapter 2 [[15]], this research study was excluded from requiring research ethics board approval, and as such, no formal ethics application was conducted.

Fig. 1 Procedural Framework for Standardized Clinical Terminology Mapping (inspired from [[14]]).

3.1 Source Concepts

The Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development document was chosen as the source reference material to perform concept extraction [[1]]. Developed by the Inter-Agency and Expert Group on SDG Indicators, it contains a descriptive list of targets and indicators. To define source concept inclusion, this research group focused on the Good Health and Well-Being SDG and those SDG indicators whose custodial agency was, or, in discussion to be, the World Health Organization [[16]]. The remaining indicators were excluded from this study.

3.2. Source Concept Extraction Protocol

A total of 41 SDG indicators were identified as health-related from the source document of the Global Indicator Framework document. These indicators were then divided among five nursing researchers (established & emerging) for concept extraction. For this paper, a concept targeted for extraction was defined as the identification of unique, explicit concepts which correspond to a singular meaning and term/phrase [[9]]. The researchers a) reviewed the assigned indicator; b) identified clinical concept(s); c) categorized the concepts into a domain type (as per SNOMED CT) [[17]]; and d) added possible synonyms or extensions as interpreted by their own clinical knowledge and expertise. These were then reviewed by the research team for verification of completeness and accuracy. Disagreement was discussed until consensus was achieved.

3.3 Conceptual Modeling

The researchers assigned domain type(s) to facilitate an understanding of the orientation and descriptive meaning of the extracted concepts (e.g., the extracted concept was “tuberculosis” and the domain type was “diagnosis”). The purpose of this step was to embed a dimension of meaning, awareness, and boundary to the conceptual phenomena the symbolized ‘word' was interpreted to portray [[18], [19]]. The domain types were informed by the conceptual domains used in SNOMED CT and ICNP to arrange health concepts into the terminologies of polyhierarchical ontology structures [[20]].

3.4 Target Terminology

The target terminologies were: 1) SNOMED CT International Edition (manual: 2022-09-30 version; automated 2022-07-31 version), and 2) ICNP 2019 Edition. All the extracted source concepts were prepared for SNOMED CT mapping. Given the scope of ICNP, only source concepts represented as diagnosis, interventions, and outcomes were included.

3.5 Mapping

The following mapping procedures were sequentially applied in this study:

• Mapping Coordination: Pre-coordination;

• Mapping Style: Manual and Automated (SNOMED CT); Manual (ICNP);

• Hierarchical Awareness: All extracted concepts had potential alignment to existing hierarchies in SNOMED CT and were therefore candidates for SNOMED CT mapping. All extracted concepts aligned to diagnosis, intervention and outcomes had potential alignment to existing hierarchies in ICNP and were therefore candidates for ICNP mapping;

• Systematic Search Strategy (see below);

• Cardinality (see below).

3.5.1 Systematic Search Strategy

Both manual and automated methods were used. These methods were chosen as a means to leverage the strengths of human and computer mapping selection, as well as, control for possible selection bias [[19], [21], [22]]. During the manual mapping phase, extracted concepts were uploaded into an excel spreadsheet and divided among four researchers where each concept was independently, manually mapped twice. [Table 1] outlines the systematic search strategy used in each terminology online browser.

Table 1 Systematic Search Strategy.

During the automated mapping phase, extracted concepts were uploaded into an excel spreadsheet. All punctuation was removed to facilitate CSV upload to the automated mapping tool Snap2SNOMED CT [[23]]. All concepts were tasked as an ‘automap', whereby the Snap2SNOMED CT software automatically assigned candidate matches.

3.5.2 Cardinality

The types of manual mapping matches were defined as:

• Direct match means that the concept matches exactly to the concept found in SNOMED CT or ICNP;

• Broader than match means that the concept found in SNOMED/ICNP was conceptually broader (more general) than the concept found in the source concept list;

• Narrower than match means that SNOMED/ICNP was conceptually narrower (more specific) than the concept found in the source concept list;

• No match means there were no matches in the target terminology (i.e., SNOMED CT, ICNP).

The cardinality of matches produced through the automated mapping tool did not include comments of match type, and was, therefore, not applicable.

3.6 Evaluation

The candidate lists, from manual mapping and automated mapping, were presented to two researchers who were not part of the manual or automated mapping phases. The researchers were tasked to review the extracted concept, and the candidate mapping matches, and then choose the ‘best' fit mapping and cardinality match(es). The two team members took note of any concepts or mapping matches which were ambiguous or required further review. The results of this work were then reviewed by several members of the researcher group over three meetings, where fit results, alignment, and ambiguity were discussed. This discussion iterated, refined and verified the final mapping list.

4 Results

A total of 169 concepts were initially extracted from the 41 source SDG indicators and used as candidates for SNOMED CT mapping. Of these 169 concepts, 31 were given a domain related to a diagnosis, intervention, or outcome and were therefore also mapped to ICNP. Through iterative review and group consensus, a total of 119 concepts with 133 mapping matches were added to the final SNOMED CT mapping list ([Table 2]). Similarly, a total of 26 concepts with 27 mapping matches were added to the final ICNP mapping list. Of these 133 SNOMED CT mapping matches, 53 (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. Of these 27 ICNP mapping matches, 8 (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches. Of note, some of these concepts were repeated through the extraction phase of the 41 SDG indicators (e.g., mortality rate) and for the purposes of reporting SNOMED CT mapping results, these repeats were counted. When no matches and repeated concepts were removed, a total of 107 unique concepts were represented in SNOMED CT.

Table 2 Final Mapping Results between UN Sustainability Concepts, SNOMED CT, and ICNP.

5 Discussion

Informed by the principle of One Health, whereby health is linked to the ecosystems with which we live, this study sought to explore the representation of environmental concepts associated with health impacts, in standardized clinical terminologies. We chose to extract concepts described in the UN Global Indicator Framework for the Sustainable Development Goals and Targets and mapped them using a structured process, to SNOMED CT and ICNP. The multiple methods used added strength to our approach (e.g., manual and automated mapping). As well, our international research team added a depth of meaningful interpretation through the lenses of varied clinical nursing, research, and lived experiences, aiding in the progression of concept extraction, synonym development, mapping selection, and iterative evaluation.

5.1 Mapping Results

Overall, our mapping processes found that the extracted concepts were moderately well represented in SNOMED CT and ICNP. Of the 133 SNOMED CT mapping matches found, 53 (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. Of those concepts which were direct matches, 14 (10.5%) were of the disorders (diagnosis) ontological axis. In contrast, only 1 (0.7%) broader than and 4 (3%) narrower than mapping matches were of the disorders axis. This strong coverage may be related to the generally well-developed diagnosis content of SNOMED CT [[21], [35], [36]]. Of the 27 ICNP mapping matches, 8 (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches. Of these, 4 (15%) of the no matches were related to specific medical diagnoses (e.g., tuberculosis, malaria, hepatitis B, and HIV) and would unlikely be candidates for specific ICNP inclusion (i.e., this was an anticipated ICNP mapping finding). One of the ICNP matches was equivalent to that found in SNOMED CT (i.e., substance abuse). This was expected given the work to cross-reference ICNP in SNOMED CT. However, there were several concepts chosen during the manual mapping process, which were different. For example, for the source concept “chronic respiratory disease”, the mapping match in SNOMED CT was “chronic disease of respiratory system (disorder)”, while the ICNP match was “Impaired Respiratory System Function”. This difference may relate to the specificity of using the nursing ICNP browser versus the larger cohort of concepts delivered through the SNOMED CT browser. This difference may also relate to specific nursing knowledge and interpretation of the concepts extracted from the source document (i.e., nursing diagnosis versus medical diagnosis). It also demonstrates the variance in manual mapping when two browsers, with related content, are used. Overall, we found value to explore both the browsers as a mechanism to crosswalk the extracted concepts between a large interdisciplinary terminology and a nursing-specific terminology. It also highlights an opportunity to further explore the UN Global Indicator Framework for the Sustainable Development Goals and Targets, for the inclusion, comprehensiveness and interpretation of nursing care delivery.

For concepts relating to cost expenditures, affordability, and community engagement, gaps were noted and SNOMED CT did not have extensive concept representation. For example, SDG 3.9.1 evaluates the proportion of the population with large household expenditures on health as a share of total household expenditure or income. The concepts of large household expenditure and health expenditure both had no matches in SNOMED CT. These concepts require further development in SNOMED CT as they relate to broader implications of sustainability as well as topics associated with social determinants of health. For example, researchers have reported on the relationship between large out-of-pocket health expenditures, poverty [[24]] and poor infant and maternal outcomes [[25] [26] [27]]. Further, researchers have demonstrated clinicians do document concepts related to expenditure and health costs, suggesting opportunities exist to use standardized terminology thoughtfully, as a way to report on and improve patient health and equity [[28]].

Finally, through the mapping process, we found many concepts had narrower than and broader than matches. Several of these concepts were related to water, air and sanitation. For example, the concepts of domestic wastewater and industrial wastewater were mapped to the broader than SNOMED CT concept: Waste water (substance); household air pollution and ambient air pollution concepts were mapped to the broader than SNOMED CT concept: Air pollution (event); sanitation services was mapped to the narrower than SNOMED CT concept: Access to toileting facilities (observable entity); and using safely managed sanitation services was mapped to the broader than SNOMED CT concept: Adequate sanitation (finding). The nuance and specificity of these source concepts are lost as these direct matches do not exist in SNOMED CT. Point-of-care clinicians, such as homecare and public healthcare nurses, interact with patients and observe community settings impacted by these environmental factors (e.g., household wood-burning stoves used for cooking and heating leading to the exasperation of upper respiratory conditions or having managed wastewater disposal in remote communities as a means to protect drinking water) [[29] [30] [31]]. Therefore, the evaluation, expansion, and inclusion of these concepts in SNOMED CT are necessary to advance the clinical reporting of these environmental and sustainability indicators.

5.2 Concept Representation for Artificial Intelligence and Data Reuse

Standardized clinical terminologies have long been used to provide data for population-based analytics, including trend analysis and public health surveillance, as well as clinical research [[32]]. In the context of capturing climate change impacts on health, capturing standardized environmentally-specific clinical data permits the contextualization of vast amounts of data to predict service demands and population needs during natural disasters and climate-related events, health promotion and disease prevention and benchmarking of any subsequent interventions. Researchers have also demonstrated the use of these terminologies in developing or training models based on methods broadly defined as artificial intelligence (AI) [[33], [34]]. SNOMED CT, for example, has been used in natural language processing tasks, utilizing machine learning methodologies to search and analyze free text clinical documentation entries [[35]]. Standardized clinical terminologies add to these approaches as a way of providing sharable and comparable data across settings; thus, helping to produce AI models that are transferable across settings. As environmental concepts encoded in standardized terminologies expand and ontological relationships associating these to health impacts develop, using AI methods could provide additional insights into trends in data and health conditions, as well as for predictive analytics to support better practice, research, education, and policy-level decision-making. While large-scale use of standardized clinical terminologies in advanced clinical AI applications has yet to be realized, its ability to formally represent healthcare language and its ability to be used in automatic encoding (e.g., natural language processing) suggests its use will be increasingly valuable as these technologies advance [[36]].

6 Limitations

This study has noted limitations. While the scope of this study phase modeled a component of a concept by defining a domain type, the explication of relationships amongst the set of concepts was not completed (e.g., using Unified Modeling Language to develop a conceptual model) [[12]]. Future researchers may wish to further develop these linkages. This study approached the extraction and mapping of environmental and sustainability health concepts from a descriptive, small-group, perspective. The intention was to begin exploring the representation of these types of concepts in two (SNOMED CT and ICNP) international standardized clinical terminologies. Ongoing work to expand, develop, and test these representations is recommended to strengthen, add rigour, and expand the conceptual nuances of this topic in these codified knowledge systems. As well, we limited our concept extraction to specific concepts associated to health and whose SDG indicator was linked to the World Health Organization. Work to expand and explore other SDG indicators for possible inclusion in clinical standardized terminologies is needed.

7 Conclusions

To track the impacts of climate change on human health and to develop climate-resilient systems able to respond to public health needs during climate-related events such as heatwaves, droughts, wildfires and floods, it is essential to have standardized data. By evaluating the representation of environmental concepts associated with health impacts, in standardized clinical terminologies, we provided a view into current content coverages as well as opportunities to expand. This study highlighted the availability, overlap, and gaps found for SDG terms in SNOMED CT and ICNP. However, future work is needed to more clearly define and model all possible environmental impacts from a One Health perspective. The availability of codified terminologies is important as it can enable clinicians, researchers and public health officials to manage vast amounts of clinical data in a way that can be easily retrieved, analyzed, related to the context in which the data was obtained, and embedded in AI applications. This permits policymakers and leaders to model demands on health services as a function of time and climate change pressures on the environment and prepares communities to adapt to these pressures whilst supporting the One Health approach.

No conflict of interest has been declared by the author(s).

Acknowledgements

We wish to thank Ms. Raluca Radu for her insights related to global, environmental, and health concepts, organizations and literature sources.

^* first co-author

• References

• 1 Inter-Agency and Expert Group on SDG Indicators. Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. New York: United Nations Statistics Division: Development Data and Outreach Branch, Department of Economic and Social Affairs (DESA); 2022a. [Available from: https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202020%20review_Eng.pdf]
• 2 Hardy E, Standley CJ. Identifying intersectional feminist principles in the One Health framework. One Health 2022;15:100404-. doi:10.1016/j.onehlt.2022.100404.
• 3 Mackenzie JS, Jeggo M. The one health approach-why is it so important? Trop Med Infect Dis 2019;4(2):88. doi:10.3390/tropicalmed4020088.
• 4 Executive Board. Health and the environment: climate and health: outcome of the WHO Conference on Health and Climate: report by the Secretariat: World Health Organization; 2015. [Available from: https://apps.who.int/iris/handle/10665/251707].
• 5 Patz JA, Frumkin H, Holloway T, Vimont DJ, Haines A. Climate Change: Challenges and Opportunities for Global Health. JAMA 2014;312(15):1565-80. doi:10.1001/jama.2014.13186.
• 6 Ramirez-Andreotta MD, Walls R, Youens-Clark K, Blumberg K, Isaacs KE, Kaufmann D, et al. Alleviating Environmental Health Disparities Through Community Science and Data Integration. Front Sustain Food Syst 2021;5. doi:10.3389/fsufs.2021.620470.
• 7 Yang X, Geng L. An Integrated Analysis of Social, Economic, and Environmental Indicators' Effects on Public Health and Health Inequality Globally: From the Perspective of Vulnerability. Soc Indic Res 2022;162(3):1261-79. doi:10.1007/s11205-022-02877-x.
• 8 Morelli J. Environmental Sustainability: A Definition for Environmental Professionals. J Environ Sci 2011;1(1). doi:10.14448/jes.01.0002.
• 9 Cimino JJ. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med 1998;37(4-5):394-403. [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3415631/]
• 10 International Health Terminology Standards Development Organisation: SNOMED International. (2022). Use SNOMED CT. [https://www.snomed.org/snomed-ct/Use-SNOMED-CT].
• 11 Coenen A. The International Classification for Nursing Practice (ICNP®) Programme: Advancing a Unifying Framework for Nursing. Online J Issues Nurs 2003;8(2). doi: 10.3912/OJIN.Vol8No02PPT01
• 12 Hardiker N. Harmonising ICNP and SNOMED CT: A Model for Effective Collaboration. Stud Health Technol Inform 2016;225:744-5. doi: 10.3233/978-1-61499-658-3-744.
• 13 International Health Terminology Standards Development Organisation: SNOMED International. New ICNP-SNOMED CT Nursing Practice Refset is first product to increase nursing visibility, safety and quality 2021. [Available from: https://www.snomed.org/news-and-events/articles/ICNP-SNOMEDCT-Nursing-Practice-Reference-Set].
• 14 Block LJ, Ronquillo C, Hardiker NR, Wong ST, Currie LM. Mapping of Wound Infection Concepts. 2020. In: Nurses and Midwives in the Digital Age [Internet]. Geneva: IOS Press EBooksStud Health Technol Inform; [431-5]. doi.org/10.3233/SHTI210764.
• 15 Government of Canada. TCPS 2 (2018) - Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans. 2018. [Available from: https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2018.html]
• 16 Inter-Agency and Expert Group on SDG Indicators. Tier Classification for Global SDG Indicators: as of 30 November 2022. New York: United Nations Statistics Division: Development Data and Outreach Branch, Department of Economic and Social Affairs (DESA); 2022b. [Available from: https://unstats.un.org/sdgs/files/Tier%20Classification%20of%20SDG%20Indicators_30%20Nov%202022_web.pdf].
• 17 International Health Terminology Standards Development Organisation: SNOMED International. SNOMED CT: Starter Guide London: SNOMED International; 2017. [Available from: https://confluence.ihtsdotools.org/display/DOCSTART].
• 18 Chinn PL, Kramer MK. Knowledge development in nursing: theory and process. 10th ed. St Louis: Elsevier; 2018.
• 19 Polit DF, Beck CT. Nursing research: generating and assessing evidence for nursing practice. 11th ed. Baltimore: Wolters Kluwer Health; 2019.
• 20 Warren JJ, Coenen A. International Classification for Nursing Practice (ICNP): Most-frequently Asked Questions. J Am Med Inform Assoc 1998;5(4):335-6. doi:10.1136/jamia.1998.0050335.
• 21 Kim TY. Automating lexical cross-mapping of ICNP to SNOMED CT. Inform Health Soc Care 2016;41(1):64-77. doi:10.3109/17538157.2014.948173.
• 22 Kim TY, Hardiker N, Coenen A. Inter-terminology mapping of nursing problems. J Biomed Inform 2014;49:213-20. doi:10.1016/j.jbi.2014.03.001.
• 23 International Health Terminology Standards Development Organisation: SNOMED International. Snap2Snomed User Guide: SNOMED International; 2022. [Available from: https://confluence.ihtsdotools.org/display/S2SUG].
• 24 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJL. Household catastrophic health expenditure: a multicountry analysis. The Lancet (British edition). 2003;362(9378):111-7. doi:10.1016/S0140-6736(03)13861-5.
• 25 Barenberg AJ, Basu D, Soylu C. The Effect of Public Health Expenditure on Infant Mortality: Evidence from a Panel of Indian States, 1983-1984 to 2011-2012. J Dev Stud 2017;53(10):1765-84. doi:10.1080/00220388.2016.1241384.
• 26 Bein M, Coker-Farrell EY. The association between medical spending and health status: A study of selected African countries. Malawi Med J 2020;32(1):37-44. doi:10.4314/mmj.v32i1.8.
• 27 Kiross GT, Chojenta C, Barker D, Loxton D. The effects of health expenditure on infant mortality in sub-Saharan Africa: evidence from panel data analysis. Health Econ Rev 2020;10(1):5. doi:10.1186/s13561-020-00262-3.
• 28 Vale MD, Perkins DW. Discuss and remember: Clinician strategies for integrating social determinants of health in patient records and care. Soc Sci Med 2022;315:115548-. doi:10.1016/j.socscimed.2022.115548.
• 29 Huang Y, Chen H, Han B, Liu C, Chuang H, Lin L, et al. Personal exposure to household particulate matter, household activities and heart rate variability among housewives. PLOS One 2014;9(3):e89969. doi:10.1371/journal.pone.0089969.
• 30 Martin D, Bélanger D, Gosselin P, Brazeau J, Furgal C, Déry S. Drinking Water and Potential Threats to Human Health in Nunavik: Adaptation Strategies under Climate Change Conditions. Arctic 2007;60(2):195-202. doi:10.14430/arctic244.
• 31 Thompson LM. Household Air Pollution from Cooking Fires Is a Global Problem. Am J Nurs 2019;119(11):61-4. doi:10.1097/01.NAJ.0000605388.37442.ec.
• 32 International Health Terminology Standards Development Organisation: SNOMED International. Data Analytics with SNOMED CT 2021. [Available from: https://confluence.ihtsdotools.org/display/DOCANLYT/Data+Analytics+with+SNOMED+CT?preview=/123898500/123898612/doc_SNOMEDCTDataAnalytics_Current-en-US_INT_20210311.pdf].
• 33 Soriano IM, Peña JLC, Breis JTF, Román IS, Barriuso AA, Baraza DG, editors. Snomed2Vec: Representation of SNOMED CT Terms with Word2Vec. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS); 2019 5-7 June.
• 34 Topaz M, Lai K, Dowding D, Lei VJ, Zisberg A, Bowles KH, et al. Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application. Int J Nurs Stud 2016;64:25-31. doi:10.1016/j.ijnurstu.2016.09.0132019. doi:10.1109/CBMS.2019.00138.
• 35 Chang E, Mostafa J. The use of SNOMED CT, 2013-2020: A literature review. J Am Med Inform Assoc 2021;28(9):2017-26. doi:doi.org/10.1093/jamia/ocab084.
• 36 Gaudet-Blavignac C, Foufi V, Bjelogrlic M, Lovis C. Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review. JMIR 2021;23(1):e24594-e. doi:10.2196/24594.

Publication History

Article published online:
26 December 2023