Appendix o
Ensu ing Epidemiological Consis ency in Risk-S a i ied Heal h Economic Models: A Case S udy
in B eas Cance Sc eening
Supplemen 1: Scoping Re iew Ma e ials
The scoping e iew discussed in he manusc ip was pe o med o unde line he gap o ou no el
app oach and o assess whe he ou app oach is, in ac , no el. We employed a b oad sea ch s ing ((
cance AND sc eening) AND (” Ma ko model” OR” Ma ko models” OR” Ma ko chain”) ) AND (” Risk
S a i ica ion” OR” isk s a i ied” OR” Risk-based”) aimed o mimic he sea ch s a egy a heal h
economic esea che migh employ o s a a isk-s a i ied cance sc eening e alua ion. We included
Ma ko models aimed a isk-s a i ied cance sc eening, excluding hose ha wo ked on p e-s a i ied
popula ions (meaning ha he model made no s a i ica ion i sel bu only modelled a single high- isk
popula ion).
The besides asce aining he ele an gap o ou p oposed me hod, we looked a h ee speci ic
cha ac e is ics in he a icles. Namely he sou ce o he (1) ini ial ansi ion p obabili ies (TP) (be o e
any isk-adjus men s), (2) he me hod o applying isk-s a i ica ion (RS) in he model, and (3) whe he
he epidemiological p edic ions whe e explici ly e i ied in he pape . Fo he i s ques ion, we ha e
h ee gene al ca ego ies o he TP sou ces: in e nal li e a u e (dedica ed obse a ional s udies, pilo
s udies, o epidemiological s udies di ec ly ocused on he same popula ion as he model), public
egis y da a ( aw p e alence and/o incidence da a om which ansi ion p obabili ies a e ex ac ed)
and ex e nal li e a u e (s udies ha published a model abou a di e en a ge popula ion). In some
cases he a icle discusses aking he model om an ex e nal sou ce wi hou explici ly men ioning he
TP one way o he o he , in which case we assume ha he ex e nal model is also he TP sou ce. We
assumed ha gene ally, in e nal li e a u e is a be e sou ce han na ional egis y da a, which i sel is
be e han using an ex e nal sou ce. Fo he second ques ion, we ound ha all models (100%) di ec ly
embedded he isk-adjus men di ec ly in o he TP, alida ing ou assessmen ha ou app oach was
a no el app oach. We u he looked a he mos used me hods o applying he isk-adjus men s (using
a mul iplie me hod such as a isk- a io, using p e-adjus ed p obabili ies om he ex e nal sou ce, o
making an in e nal es ima ion o he adjus ed TP). Las ly, we checked i he a icle explici ly alida es
he epidemiological esul s, di e en ia ing be ween s udies ha men ioned doing his and s udies ha
explici ly showed he esul s. While showing hese esul s is impo an o a numbe o easons (i
inc eases anspa ency, us , and helps o he esea che s selec he bes pe o ming models) we also
poin ou ha no showing hese esul s is no always he choice o he esea che .
Flow diag am o e iew wi h summa y esul s o included a icles.
Resul ables o included a icles.
Re
DOI
Coun y
Cance
Model Type
P ima y TP Sou ce
Epidemiological Valida ion
1A
10.1371/jou nal.pone.0202796
UK
Bladde
Decision T ee & Ma ko
Ex e nal Li e a u e
Yes, Discussed
2A
10.1093/jnci/djae019
USA
B eas
Ma ko Coho Model
Ex e nal Li e a u e
No
3A
10.1186/s12913-021-06396-2
Singapo e
B eas
Ma ko Coho Model
In e nal Li e a u e / Pilo s udy
No
4A
10.1038/s41598-023-29985-z
China
B eas
Ma ko Coho Model
Ex e nal Li e a u e
Yes, Shown
5A
10.1371/jou nal.pone.0217213
Ge many
B eas
Ma ko Mic oSim Model
Na ional Regis y Da a
No
6A
10.1016/j.j al.2017.12.022
Ge many
B eas
Ma ko Mic oSim Model
In e nal Li e a u e / Pilo s udy
Yes, Shown
7A
10.1186/s12913-024-11226-2
Malawi
Ce ical
Ma ko Coho Model
Ex e nal Li e a u e
No
8A
10.1111/jgh.15033
Japan
Colo ec al
Ma ko Coho Model
In e nal Li e a u e / Pilo s udy
Yes, Discussed
9A
10.1186/1471-2407-14-261
Aus alia
Colo ec al
Ma ko Mic oSim Model
In e nal Li e a u e / Pilo s udy
Yes, Discussed
10A
10.1007/s10198-017-0901-y
Japan
Gas ic
Ma ko Coho Model
Ex e nal Li e a u e
No
11A
10.1136/gu jnl-2021-325948
China
Gas ic
Ma ko Coho Model
Na ional Regis y Da a
Yes, Shown
12A
10.1016/j.j al.2021.04.1286
Aus alia
Li e
Ma ko Coho Model
Ex e nal Li e a u e
No
13A
10.1038/c g.2017.26
USA
Li e
Ma ko Coho Model
Ex e nal Li e a u e
Yes, Shown
14A
10.1186/s12916-024-03292-4
China
Lung
Ma ko Coho Model
Na ional Regis y Da a
Yes, Shown
15A
10.3389/ pubh.2024.1375533
China
Nasal
Ma ko Coho Model
In e nal Li e a u e / Pilo s udy
Yes, Discussed
16A
10.1016/j.c a c.2024.100791
I an
P os a e
Ma ko Coho Model
Ex e nal Li e a u e
No
17A
10.3390/genes10090641
Taiwan
P os a e
Ma ko Coho Model
Ex e nal Li e a u e
No
18A
10.1371/jou nal.pmed.1002998
USA
P os a e
Ma ko Coho Model
In e nal Li e a u e / Pilo s udy
No
19A
10.1002/p os.22964
Finland
P os a e
Ma ko Coho Model
In e nal Li e a u e / Pilo s udy
Yes, Discussed
20A
10.1001/jamane wo kopen.2020.37657
UK
P os a e
Ma ko Mic oSim Model
Ex e nal Li e a u e
No
21A
10.1016/j.gie.2021.08.008
China
S omach
Ma ko Coho Model
Ex e nal Li e a u e
Yes, Discussed
22A
10.1007/s40273-022-01160-8
China
S omach
Ma ko Mic oSim Model
Ex e nal Li e a u e
Yes, Discussed
23A
10.1001/jamane wo kopen.2020.37657
USA
P os a e
Ma ko Coho Model
Na ional Regis y Da a
Yes, Shown
Table 1: Gene al cha ac e is ics o Included a icles, sou ce o he p ima y T ansi ion P obabili ies (TP) and i he a icle discussed he epidemiological alida ion.
Re
DOI
Risk S a i ica ion in Model
Risk S a i ica ion Me hod
Embedding Me hod
1A
10.1371/jou nal.pone.0202796
Yes, Risk ac o s
Embedded in o TP
Risk Ra ios
2A
10.1093/jnci/djae019
Yes, Risk ac o s
Embedded in o TP
Embedded in Ex e nal Sou ces
3A
10.1186/s12913-021-06396-2
Yes, PRS s a i ied
Embedded in o TP
Risk Ra ios
4A
10.1038/s41598-023-29985-z
Yes, Risk ac o s (his o y, b eas issue)
Embedded in o TP
Embedded in Ex e nal Sou ces
5A
10.1371/jou nal.pone.0217213
Yes, Li e ime Risk
Embedded in o TP
Risk Ra ios
6A
10.1016/j.j al.2017.12.022
Yes, Risk ac o s (his o y, b eas issue)
Embedded in o TP
Risk Ra ios
7A
10.1186/s12913-024-11226-2
Yes, Risk ac o s (his o y, HIV)
Embedded in o TP
Embedded in Ex e nal Sou ces
8A
10.1111/jgh.15033
Yes, PRS s a i ied
Embedded in o TP
Risk Ra ios
9A
10.1186/1471-2407-14-261
Yes, His o y
Embedded in o TP
Risk Ra ios
10A
10.1007/s10198-017-0901-y
Yes, Risk ac o s
Embedded in o TP
Risk Ra ios
11A
10.1136/gu jnl-2021-325948
Yes, Risk ac o s (his o y, Smoking)
Embedded in o TP
Risk Ra ios
12A
10.1016/j.j al.2021.04.1286
Yes, His o y
Embedded in o TP
Embedded in Ex e nal Sou ces
13A
10.1038/c g.2017.26
Yes, Risk ac o s
Embedded in o TP
Risk Ra ios
14A
10.1186/s12916-024-03292-4
Yes, Risk ac o s (his o y, smoking issue)
Embedded in o TP
Risk Ra ios
15A
10.3389/ pubh.2024.1375533
Yes, PRS s a i ied
Embedded in o TP
Risk Ra ios
16A
10.1016/j.c a c.2024.100791
Yes, PSA
Embedded in o TP
Embedded in Ex e nal Sou ces
17A
10.3390/genes10090641
Yes, PRS s a i ied
Embedded in o TP
Risk Ra ios
18A
10.1371/jou nal.pmed.1002998
Yes, PRS s a i ied
Embedded in o TP
Risk Ra ios
19A
10.1002/p os.22964
Yes, PRS s a i ied
Embedded in o TP
Di ec Es ima ion o TP
20A
10.1001/jamane wo kopen.2020.37657
Yes, His o y
Embedded in o TP
Risk Ra ios
21A
10.1016/j.gie.2021.08.008
Yes, Li e ime Risk
Embedded in o TP
Embedded in Ex e nal Sou ces
22A
10.1007/s40273-022-01160-8
Yes, His o y
Embedded in o TP
Risk Ra ios
23A
10.1001/jamane wo kopen.2020.37657
Yes, PRS s a i ied
Embedded in o TP
Risk Ra ios
Table 2: Risk-s a i ica ion in included a icles, including he basis o he isk-s a i ica ion (gene al isk ac o s, Polygenic Risk Sco es, amily his o y, medical o social ac o s), he o e all me hod o
including a isk-adjus men o he model (in all cases i was di ec ly embedded in o he ansi ion p obabili ies) and i embedded, in wha way.
Re e ences o Included a icles
1A. Su on, A. J., Lamon , J. V., E ans, R. M., Williamson, K., O’Rou ke, D., Duggan, B., … Ruddock, M.
W. (2018). An ea ly analysis o he cos -e ec i eness o a diagnos ic classi ie o isk s a i ica ion o
haema u ia pa ien s (DCRSHP) compa ed o lexible cys oscopy in he diagnosis o bladde cance (K.
Tha o n, Ed.). Public Lib a y o Science (PLoS). h ps://doi.o g/10.1371/jou nal.pone.0202796
2A. Yanguela, J., Jackson, B. E., Reede -Hayes, K. E., Robe son, M. L., Rocque, G. B., Kuo, T.-M., …
Wheele , S. B. (2024). Simula ing he popula ion impac o in e en ions o educe acial gaps in
b eas cance ea men . Ox o d Uni e si y P ess (OUP). h ps://doi.o g/10.1093/jnci/djae019
3A. Wong, J. Z. Y., Chai, J. H., Yeoh, Y. S., Mohamed Riza, N. K., Liu, J., Teo, Y.-Y., … Ha man, M. (2021).
Cos e ec i eness analysis o a polygenic isk ailo ed b eas cance sc eening p og amme in
Singapo e. Sp inge Science and Business Media LLC. h ps://doi.o g/10.1186/s12913-021-06396-2
4A. Shi, J., Guan, Y., Liang, D., Li, D., He, Y., & Liu, Y. (2023). Cos -e ec i eness e alua ion o isk-
based b eas cance sc eening in U ban Hebei P o ince. Sp inge Science and Business Media LLC.
h ps://doi.o g/10.1038/s41598-023-29985-z
5A. A nold, M., P ei e , K., & Quan e, A. S. (2019). Is isk-s a i ied b eas cance sc eening
economically e icien in Ge many? (J. Bohlius, Ed.). Public Lib a y o Science (PLoS).
h ps://doi.o g/10.1371/jou nal.pone.0217213
6A. A nold, M., & Quan e, A. S. (2018). Pe sonalized Mammog aphy Sc eening and Sc eening
Adhe ence—A Simula ion and Economic E alua ion. Else ie BV.
h ps://doi.o g/10.1016/j.j al.2017.12.022
7A. Rasmussen, P. W., Ho man, R. M., Phi i, S., Makwaya, A., Kominski, G. F., Bas ani, R., …
Mouche aud, C. (2024). Cos -e ec i eness o app oaches o ce ical cance sc eening in Malawi:
compa ison o equencies, lesion ea men echniques, and isk-s a i ied app oaches. Sp inge
Science and Business Media LLC. h ps://doi.o g/10.1186/s12913-024-11226-2
8A. Sekiguchi, M., Iga ashi, A., Sakamo o, T., Sai o, Y., Esaki, M., & Ma suda, T. (2020). Cos ‐
e ec i eness analysis o colo ec al cance sc eening using colonoscopy, ecal immunochemical es ,
and isk sco e. Wiley. h ps://doi.o g/10.1111/jgh.15033
9A. Ouak im, D. A., Boussiou as, A., Locke , T., Hoppe , J. L., & Jenkins, M. A. (2014). Cos -
e ec i eness o amily his o y-based colo ec al cance sc eening in Aus alia. Sp inge Science and
Business Media LLC. h ps://doi.o g/10.1186/1471-2407-14-261
10A. Sai o, S., Azumi, M., Muneoka, Y., Nishino, K., Ishikawa, T., Sa o, Y., … Akazawa, K. (2017). Cos -
e ec i eness o combined se um an i-Helicobac e pylo i IgG an ibody and se um pepsinogen
concen a ions o sc eening o gas ic cance isk in Japan. Sp inge Science and Business Media
LLC. h ps://doi.o g/10.1007/s10198-017-0901-y
11A. Wang, Z., Han, W., Xue, F., Zhao, Y., Wu, P., Chen, Y., … Jiang, J. (2022). Na ionwide gas ic cance
p e en ion in China, 2021–2035: a decision analysis on e ec , a o dabili y and cos -e ec i eness
op imisa ion. BMJ. h ps://doi.o g/10.1136/gu jnl-2021-325948
12A. Ca e , H. E., Je ey, G. P., Ramm, G. A., & Go don, L. G. (2021). Cos -E ec i eness o a Se um
Bioma ke Tes o Risk-S a i ied Li e Ul asound Sc eening o Hepa ocellula Ca cinoma. Else ie
BV. h ps://doi.o g/10.1016/j.j al.2021.04.1286
13A. Goossens, N., Singal, A. G., King, L. Y., Ande sson, K. L., Fuchs, B. C., Besa, C., … Hoshida, Y.
(2017). Cos -E ec i eness o Risk Sco e–S a i ied Hepa ocellula Ca cinoma Sc eening in Pa ien s
wi h Ci hosis. O id Technologies (Wol e s Kluwe Heal h). h ps://doi.o g/10.1038/c g.2017.26
14A. Liu, Y., Xu, H., L , L., Wang, X., Kang, R., Guo, X., … Zhang, S. (2024). Risk-based lung cance
sc eening in hea y smoke s: a bene i –ha m and cos -e ec i eness modeling s udy. Sp inge Science
and Business Media LLC. h ps://doi.o g/10.1186/s12916-024-03292-4
15A. Yang, D.-W., Mille , J. A., Xue, W.-Q., Tang, M., Lei, L., Zheng, Y., … Jia, W.-H. (2024). Polygenic
isk-s a i ied sc eening o nasopha yngeal ca cinoma in high- isk endemic a eas o China: a cos -
e ec i eness s udy. F on ie s Media SA. h ps://doi.o g/10.3389/ pubh.2024.1375533
16A. Nah ijou, A., Hadian, M., & Mohamadkhani, N. (2024). Finding he PSA-based sc eening s opping
age using p os a e cance isk. Else ie BV. h ps://doi.o g/10.1016/j.c a c.2024.100791
17A. Yang, T.-K., Chuang, P.-C., Yen, A. M.-F., Chen, H.-H., & Chen, S. L.-S. (2019). Gene‒P os a e-
Speci ic-An igen-Guided Pe sonalized Sc eening o P os a e Cance . MDPI AG.
h ps://doi.o g/10.3390/genes10090641
18A. Callende , T., Embe on, M., Mo is, S., Eeles, R., Ko e-Ja ai, Z., Pha oah, P. D. P., & Pashayan, N.
(2019). Polygenic isk- ailo ed sc eening o p os a e cance : A bene i –ha m and cos -e ec i eness
modelling s udy (S. D. Shapi o, Ed.). Public Lib a y o Science (PLoS).
h ps://doi.o g/10.1371/jou nal.pmed.1002998
19A. Yen, A. M., Au inen, A., Schleu ke , J., Wu, Y., Fann, J. C., Tammela, T., … Chen, H. (2015).
P os a e cance sc eening using isk s a i ica ion based on a mul i‐s a e model o gene ic a ian s.
Wiley. h ps://doi.o g/10.1002/p os.22964
20A. Callende , T., Embe on, M., Mo is, S., Pha oah, P. D. P., & Pashayan, N. (2021). Bene i , Ha m,
and Cos -e ec i eness Associa ed Wi h Magne ic Resonance Imaging Be o e Biopsy in Age-based
and Risk-s a i ied Sc eening o P os a e Cance . Ame ican Medical Associa ion (AMA).
h ps://doi.o g/10.1001/jamane wo kopen.2020.37657
21A. Xia, R., Li, H., Shi, J., Liu, W., Cao, M., Sun, D., … Chen, W. (2022). Cos -e ec i eness o isk-
s a i ied endoscopic sc eening o esophageal cance in high- isk a eas o China: a modeling s udy.
Else ie BV. h ps://doi.o g/10.1016/j.gie.2021.08.008
22A. Qin, S., Wang, X., Li, S., Tan, C., Zeng, X., Luo, X., … Wan, X. (2022). Clinical Bene i and Cos
E ec i eness o Risk-S a i ied Gas ic Cance Sc eening S a egies in China: A Modeling S udy.
Sp inge Science and Business Media LLC. h ps://doi.o g/10.1007/s40273-022-01160-8
23A. Callende , T., Embe on, M., Mo is, S., Pha oah, P. D., & Pashayan, N. (2021). Bene i , ha m, and
cos -e ec i eness associa ed wi h magne ic esonance imaging be o e biopsy in age-based and isk-
s a i ied sc eening o p os a e cance . JAMA Ne wo k Open, 4(3), e2037657-e2037657.
Supplemen 2: Nume ical Example calcula ion o me hod
Age g oup and size
Incidence by s age,
cases WA p.y.
Incidence Ra e
Condi ional Su i al
P obabili y
Cumula i e Cance -
ee Su i al
S . 0
S . 1-4
S . 0
S . 1-4
S . 0
S . 1-4
S . 0
S . 1-4
j
Nj
C0j
C1-4j
I0j
I1-4j
q0j
q1-4j
S0j
S1-4j
0 - 9
340,191
0
0
0.00%
0.00%
100.00%
100.00%
98.10%
86.26%
Oc /19
372,442
0
0
0.00%
0.00%
100.00%
100.00%
98.10%
86.26%
20 - 29
382,265
1.9
22.6
0.00%
0.01%
100.00%
99.94%
98.10%
86.26%
30 - 39
429,841
28.5
203.9
0.01%
0.05%
99.93%
99.53%
98.10%
86.31%
40 - 49
429,095
130.5
796.3
0.03%
0.19%
99.70%
98.16%
98.17%
86.72%
50 - 59
461,210
246.4
1342.6
0.05%
0.29%
99.47%
97.13%
98.47%
88.35%
60 - 69
430,583
247.9
1553.3
0.06%
0.36%
99.43%
96.45%
98.99%
90.96%
70 - 79
325,830
101
1186.6
0.03%
0.36%
99.69%
96.42%
99.56%
94.31%
80-85
109,433
27.7
483.4
0.03%
0.44%
99.87%
97.81%
99.87%
97.81%
To . 3,280,890
783.8
5588.8
Table 3: Example calcula ion o he p e-s a i ica ion a - isk g oups, using na ional incidence da a. The i s s ep in ol es calcula ing he
incidence a e, hen he condi ional cance ee su i al p obabili y, which leads o he cumula i e cance - ee su i al. Taking he
emainde o hese cumula i e cance - ee su i al a es gi es he a - isk p opo ion o he speci ic age-g oup. The ele an o mula o
he calcula ion is shown in he bo om ow.
Age g oup and size
Incidence by s age,
p.y.
Dis ibu ion o
Incidence
Condi ional Su i al
Adjus men
Condi ional
T ansi ion Ra e
S . 0
S . 1
S . 0
S . 1
S . 0
S . 1
S . 0
S . 1
j
Nj
Ci
C1j
π0j
π1j
P0j
P1j
λ0j
λ1j
0 - 9
340,191
0
0
0.00%
0.00%
100.00%
100.00%
0.00%
0.00%
Oc /19
372,442
0
0
0.00%
0.00%
100.00%
100.00%
0.00%
0.00%
20 - 29
382,265
1.9
11.6
0.24%
0.42%
100.00%
100.00%
0.24%
0.42%
30 - 39
429,841
28.5
91.5
3.63%
3.34%
99.76%
99.58%
3.64%
3.35%
40 - 49
429,095
130.5
373.6
16.65%
13.63%
96.12%
96.24%
17.32%
14.17%
50 - 59
461,210
246.4
735.2
31.44%
26.83%
79.48%
82.61%
39.56%
32.48%
60 - 69
430,583
247.9
883.1
31.62%
32.23%
48.04%
55.77%
65.83%
57.79%
70 - 79
325,830
101
500.7
12.89%
18.27%
16.42%
23.54%
78.50%
77.61%
80-85
109,433
27.7
144.5
3.53%
5.27%
3.53%
5.27%
100.00%
100.00%
To . 3,280,890
783.8
2740.1
Table 4: Example calcula ion o he condi ional ansi ion p obabili y o he i s ansi ion om a - isk o cance . The i s s ep in ol es
calcula ing he age-dis ibu ion o he incidence. Then he condi ional cance - ee su i al adjus men is calcula ed. This can be hough
o as he p opo ion o he a - isk g oup ha has ye o de elop cance . The condi ional a e is hen calcula ed as he di ision o he age-
dis ibu ion by he su i al adjus men . In simple e ms, he po ion o he a - isk g oup ha is p e-p edic ed o ge cance a ha speci ic
age.
Supplemen 3: Ma ko Model Diag am
Figu e 1: Ma ko Model diag am o he comple e model wi h 4 dis inc isk-g oups. Each g oup has a sepa a e p opo ion o a - isk indi iduals (highe p opo ions in he ele a ed and
high isk-g oups) and indi idual le els o sc eening a es based on age and g oup.
