David Mott

715 total citations
34 papers, 436 citations indexed

About

David Mott is a scholar working on Economics and Econometrics, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, David Mott has authored 34 papers receiving a total of 436 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Economics and Econometrics, 9 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Biomedical Engineering. Recurrent topics in David Mott's work include Health Systems, Economic Evaluations, Quality of Life (19 papers), Economic and Environmental Valuation (18 papers) and Medical Imaging Techniques and Applications (6 papers). David Mott is often cited by papers focused on Health Systems, Economic Evaluations, Quality of Life (19 papers), Economic and Environmental Valuation (18 papers) and Medical Imaging Techniques and Applications (6 papers). David Mott collaborates with scholars based in United Kingdom, Australia and Netherlands. David Mott's co-authors include Koonal Shah, Juan Manuel Ramos-Goñi, John Logue, Graham Read, Richard Cowan, Oliver Rivero‐Arias, Nancy Devlin, Mehdi Najafzadeh, Simone Kreimeier and Tommi Tervonen and has published in prestigious journals such as Blood, International Journal of Radiation Oncology*Biology*Physics and International Journal of Cancer.

In The Last Decade

David Mott

30 papers receiving 430 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Mott United Kingdom 12 210 96 81 79 70 34 436
T. Lizée France 7 62 0.3× 42 0.4× 48 0.6× 85 1.1× 154 2.2× 16 526
Alexis B. Guzman United States 8 112 0.5× 33 0.3× 26 0.3× 69 0.9× 33 0.5× 22 305
Richard Simcock United Kingdom 12 86 0.4× 161 1.7× 27 0.3× 34 0.4× 120 1.7× 43 623
Kang-Hsien Fan United States 5 78 0.4× 53 0.6× 45 0.6× 50 0.6× 597 8.5× 7 913
C. Louzado Canada 7 48 0.2× 29 0.3× 31 0.4× 50 0.6× 90 1.3× 11 442
Santino Butler United States 14 94 0.4× 31 0.3× 47 0.6× 51 0.6× 384 5.5× 33 626
Carol McPherson United States 9 35 0.2× 26 0.3× 33 0.4× 60 0.8× 191 2.7× 14 494
Corinne Tillier Netherlands 13 40 0.2× 56 0.6× 26 0.3× 70 0.9× 363 5.2× 31 506
Suzanne S. S. Mak Hong Kong 10 60 0.3× 72 0.8× 6 0.1× 50 0.6× 47 0.7× 16 414
Sheila D. Rustgi United States 12 113 0.5× 15 0.2× 12 0.1× 113 1.4× 93 1.3× 47 530

Countries citing papers authored by David Mott

Since Specialization
Citations

This map shows the geographic impact of David Mott's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Mott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Mott more than expected).

Fields of papers citing papers by David Mott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Mott. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Mott. The network helps show where David Mott may publish in the future.

Co-authorship network of co-authors of David Mott

This figure shows the co-authorship network connecting the top 25 collaborators of David Mott. A scholar is included among the top collaborators of David Mott based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Mott. David Mott is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Mott, David, Hareth Al‐Janabi, Becky Pennington, et al.. (2023). Modelling Spillover Effects on Informal Carers: The Carer QALY Trap. PharmacoEconomics. 41(12). 1557–1561. 7 indexed citations
3.
Mott, David, et al.. (2023). Resource allocation in public sector programmes: does the value of a life differ between governmental departments?. Cost Effectiveness and Resource Allocation. 21(1). 96–96. 2 indexed citations
4.
Skedgel, Chris, David Mott, Sofia Gameiro, et al.. (2023). Unmet Parenthood Goals, Health-Related Quality of Life and Apparent Irrationality: Understanding the Value of Treatments for Infertility. PharmacoEconomics - Open. 7(3). 337–344. 3 indexed citations
5.
Kreimeier, Simone, David Mott, Kristina Ludwig, et al.. (2022). EQ-5D-Y Value Set for Germany. PharmacoEconomics. 40(S2). 217–229. 31 indexed citations
6.
Vass, Caroline, Marco Boeri, Deborah A. Marshall, et al.. (2022). Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report. Value in Health. 25(5). 685–694. 41 indexed citations
7.
Mott, David, Laura Ternent, & Luke Vale. (2022). Do preferences differ based on respondent experience of a health issue and its treatment? A case study using a public health intervention. The European Journal of Health Economics. 24(3). 413–423. 6 indexed citations
8.
Skedgel, Chris, et al.. (2022). Considering Severity in Health Technology Assessment: Can We Do Better?. Value in Health. 25(8). 1399–1403. 20 indexed citations
9.
Mulhern, Brendan, Chris Sampson, Philip Haywood, et al.. (2022). Criteria for developing, assessing and selecting candidate EQ-5D bolt-ons. Quality of Life Research. 31(10). 3041–3048. 19 indexed citations
10.
Mott, David, Nancy Devlin, Simone Kreimeier, et al.. (2022). Analytical Considerations When Anchoring Discrete Choice Experiment Values Using Composite Time Trade-Off Data: The Case of EQ-5D-Y-3L. PharmacoEconomics. 40(S2). 129–137. 9 indexed citations
11.
Ramos-Goñi, Juan Manuel, Oliver Rivero‐Arias, Donna Rowen, et al.. (2022). Does Changing the Age of a Child to be Considered in 3-Level Version of EQ-5D-Y Discrete Choice Experiment–Based Valuation Studies Affect Health Preferences?. Value in Health. 25(7). 1196–1204. 19 indexed citations
12.
Mott, David, et al.. (2020). Reporting Quality of Marginal Rates of Substitution in Discrete Choice Experiments That Elicit Patient Preferences. Value in Health. 23(8). 979–984. 19 indexed citations
13.
Hampson, Grace, David Mott, Nancy Devlin, & Koonal Shah. (2019). Public Preferences for Health Gains and Cures: A Discrete Choice Experiment. RePEc: Research Papers in Economics. 3 indexed citations
14.
Mott, David, Grace Hampson, Martin Llewelyn, Jorge Mestre-Ferrándiz, & Michael M. Hopkins. (2019). A Multinational European Study of Patient Preferences for Novel Diagnostics to Manage Antimicrobial Resistance. Applied Health Economics and Health Policy. 18(1). 69–79. 7 indexed citations
15.
Shen, Jing, et al.. (2018). Conducting a Time Trade-Off Study Alongside a Clinical Trial: A Case Study and Recommendations. PharmacoEconomics - Open. 3(1). 5–20. 4 indexed citations
16.
17.
Mott, David & Mehdi Najafzadeh. (2015). Whose preferences should be elicited for use in health-care decision-making? A case study using anticoagulant therapy. Expert Review of Pharmacoeconomics & Outcomes Research. 16(1). 33–39. 15 indexed citations
18.
Logue, John, et al.. (1998). Clinical variability of target volume description in conformal radiotherapy planning. International Journal of Radiation Oncology*Biology*Physics. 41(4). 929–932. 103 indexed citations
19.
Adams, Elizabeth, D S Brettle, Anthony Jones, Alan R. Hounsell, & David Mott. (1997). Estimation of fetal and effective dose for CT examinations.. British Journal of Radiology. 70(831). 272–278. 14 indexed citations
20.
Logue, John, et al.. (1996). 1013 Clinical variability of target volume description and treatment plans in conformal radiotherapy in muscle invasive bladder cancer. International Journal of Radiation Oncology*Biology*Physics. 36(1). 250–250. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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