Altmetrics
Downloads
231
Views
343
Comments
0
A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
Submitted:
12 August 2024
Posted:
12 August 2024
You are already at the latest version
No. | Author, Year, Country | Analysis type | Intervention | Clinical outcome | Model | Time horizon | Discount rate | Currency | Perspective | Vaccine coverage | Funding | Health outcome | SA |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Lytle et al., 2023, Canada [46] | CEA | PCV20 vs. PCV13 PCV20 vs. PCV15 |
IPD Pneumonia AOM |
Markov | 10 years | 1.50% | 2022 CAD | Payer Society |
84% | Pfizer | QALY | DSA, PSA |
2 | Sevilla et al., 2022, Egypt [43] | CBA CUA |
PCV13 vs. no vaccination PCV10 vs. no vaccination PCV13 vs. PCV13 |
IPD Pneumonia AOM |
Markov | 100 years | 3% | 2016 USD | Society Payer |
100% | Pfizer | RoR QALY, ICER |
DSA, PSA |
3 | Dilokthornsakul et al., 2019, Thailand [47] | CEA | PCV10 vs. no vaccination PCV10 vs. no vaccination |
IPD ACP All cause-AOM |
Markov | Lifetime | 3% | 2018 TBH | Society | - | Pfizer | QALY, ICER | PSA |
4 | Krishnamoorthy et al., 2019, India [52] | CEA | PCV13 vs. no vaccination | IPD Pneumonia AOM |
UNIVAC decision support | 10 years | 3% | 2017 USD | Government | 88% | - | DALY, ICER | PSA |
5 | Shen et al., 2018, China [55] | CEA | PCV13 vs. no vaccination | IPD Pneumonia AOM |
Decision analytic model | 1 year | 3% | 2015 CNY | Payer | 85% | Pfizer | QALY | DSA |
6 | Dorji et al., 2018, Bhutan [44] | CUA | PCV13 vs. no vaccination PCV10 vs. no vaccination PCV13 vs. PCV10 |
IPD Pneumonia AOM |
Markov | 100 years | 3% | 2017 USD | Government | 97% | WHO | QALY, ICER | DSA, PSA |
7 | Huang et al., 2023, USA [48] | CEA | PCV15 vs. PCV13 | IPD Pneumonia AOM |
Markov | Lifetime | 3% | 2021 USD | Society | 91.9% | Merck | QALY, LY, ICER | PSA, DSA |
8 | Tajima et al., 2023, Japan [49] | CEA | PCV15 vs. PCV13 | IPD NBPP, Pneumococcal AOM |
Markov | 10 years | 2% | 2015 USD | Payer Society |
100% | Merck | QALY, ICER | PSA, DSA |
9 | Li et al., 2021, China [57] | CEA | PCV13 vs. no vaccination | IPD Pneumonia AOM |
Decision analytic | 1 year | 5% | 2019 CNY | Payer | 70% | - | QALY, ICER | DSA |
10 | Wilson et al., 2022, UK [58] | CEA | PCV15 vs. PCV13 PCV20 vs. PCV13 PCV20 vs. PCV15 |
IPD Pneumonia AOM |
Economic model | 5 years | 3.5% | 2021 GBP | Payer | 91% | Pfizer | QALY, LY, ICER | DSA |
11 | Chen et al., 2019, 180 countries* [56] | CEA | PCV13 vs. no vaccination | IPD Pneumonia AOM |
Decision tree | 30 years | 3% | 2015 International dollars | Healthcare | - | WHO, Gavi, Bill & Melinda Gates Foundation | DALY, ICER | DSA, PSA |
12 | Perdrizet et al., 2021, Philipine [53] | CEA | PCV13 vs. PCV10-GSK | IPD Pneumonia AOM |
Decision analytic model | 10 years | 7% | 2020 PHP | Society | 90% | Pfizer | LY, QALY, ICER | - |
13 | Warren et al., 2023, Greece [54] | CEA | PCV20 vs. PCV15 | IPD Pneumonia AOM |
Decision-analytic mode | 10 years | 3.5% | 2023 EUR | Payer | 84.5% | Pfizer | LY, QALY, ICER | PSA |
14 | Huang et al., 2023, South Africa [45] | CUA | PCV13 vs. PCV10-GSK PCV13 vs. PCV10-SII |
IPD Pneumonia AOM |
Decision-analytic forecasting models | 10 years | 5% | 2022 R | Payer | 90.7% | Pfizer | LY, QALY, ICER | - |
15 | Rozenbaum et al., 2024, USA [50] | CEA | PCV20 vs. PCV13 PCV20 vs.PCV15 |
IPD ACP OM |
Markov | 10 years | 3% | 2022 USD | Healthcare Society |
83.5% | Pfizer | QALY, LYs | DSA, PSA |
16 | Ta et al., 2024, Germany [51] | CEA | PCV20 vs. PCV13 PCV20 vs.PCV15 |
IPD ACP All-cause AOM |
Markov | 10 years | 3% | 2020 EUR | Society | 76.8% | Pfizer | LY, QALY, ICER | PSA, DSA |
No. | Item | Lytle et al., 2023 [46] | Sevilla et al., 2022 [43] | Dilokthornsaku et al., 2019 [47] | Krishnamoorthy et al., 2019 [52] | Shen et al., 2018 [55] | Dorji et al., 2018 [44] | Huang et al., 2023 [48] | Tajima et al., 2023 [49] | Li et al., 2021 [57] | Wilson et al., 2022 [58] | Chen et al., 2019 [56] | Perdrizet et al., 2021 [53] | Warren et al., 2023 [54] | Huang et al., 2023 [45] | Rozenbaum et al., 2024 [50] | Ta et al., 2024 [51] |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Title | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5 | 1 | 1 |
2 | Abstract | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
3 | Background and objective | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
4 | Health economic analysis plan | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
5 | Study population | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
6 | Setting and location | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5 | 1 | 0.5 | 1 | 1 | 1 | 1 |
7 | Comparators | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
8 | Perspective | 1 | 1 | 1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 1 | 1 | 1 |
9 | Time horizon | 1 | 1 | 1 | 0.5 | 1 | 0.5 | 0.5 | 1 | 1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 1 | 1 |
10 | Discount rate | 1 | 1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 1 | 0.5 | 0.5 | 1 | 1 |
11 | Selection of outcomes | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
12 | Measurement of outcomes | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
13 | Valuation of outcomes | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
14 | Measurement and valuation of resources and costs | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
15 | Currency, price date, and conversion | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
16 | Rationale and description of model | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
17 | Analytics and assumptions | 0.5 | 1 | 1 | 1 | 1 | 1 | 0.5 | 0.5 | 1 | 1 | 1 | 1 | 0.5 | 0.5 | 0.5 | 0.5 |
18 | Characterizing heterogeneity | 0.5 | 1 | 1 | 1 | 1 | 0.5 | 1 | 1 | 1 | 1 | 0.5 | 0.5 | 0 | 1 | 1 | 1 |
19 | Characterizing distributional effect | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |
20 | Characterizing uncertainty | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
21 | Approach to engagement with patients and others affected by the study | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
22 | Study parameters | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
23 | Summary of main results | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
24 | Effect of uncertainty | 1 | 1 | 1 | 1 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 |
25 | Effect of engagement with patients and others affected by the study | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
26 | Study findings, limitations, generalizability, and current knowledge | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
27 | Source of funding | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
28 | Conflicts of interest | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Total score | 24.5 | 26 | 25.5 | 24.5 | 24.5 | 24 | 24 | 24.5 | 24.5 | 23.5 | 23.5 | 23.5 | 22.5 | 21.5 | 24 | 25 | |
Conclusion | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good | Good |
Ref, country, currency | Schedule | Herd effect | Vaccination cost | Direct cost | Indirect cost | Total cost | LYs | Effectiveness | ICER | CE threshold | Cost-effective |
---|---|---|---|---|---|---|---|---|---|---|---|
PCV13 vs. No vaccination | |||||||||||
Sevilla et al., Egypt, 2016 USD [43] | 2+1 | Yes | 43.63 | -0.88 | - | 42.75 | - | 0.0462 QALY | 926 | GDP: 3,479 | Yes |
Dorji et al., Bhutan, 2017 USD [44] | 2+1 | Yes | - | - | - | 0.03 | - | 0.0007 QALY | 40 | GDP: 2708 | Yes |
Krishnamoorthy et al., India, 2017 USD [52] | 2+1 | No | 35,000,000 | -16,600,000 | - | -16,600,000 | - | 920,000 DALY | 467 | GDP: 1939.6 | Yes |
Dilokthornsakul et al., Thailand, 2018 TBH [47] | 2+1 3+1 |
No | - - |
- - |
- - |
2571 3693 |
0.03 0.03 |
0.0349 QALY 0.0380 QALY |
73,674 97,269 |
WTP: 160,000 | Yes |
Shen et al., China, 2015 CNY [55] | 3+1 | No Yes |
38,382,200,000 38,382,200,000 |
29,362,300,000 13,524,700,000 |
- - |
29,362,300,000 13,524,700,000 |
- - |
370,300 QALY 3,580,900 QALY |
79,304 3,777 |
GDP : 53,976 | Yes Yes |
Li et al., China, 2019 CNY [57] | 3+1 | Yes | -323,757,862 | -28,646,835 | - | -28,646,835 | - | 14,880 QALY | Dominant | GDP: 157,300 | Yes |
Chen et al., global, 2015 INTL dollar [56] | 2+1,3+1, 3+0 | Yes | 15,500,000,000 | 8,420,000,000 | -2,640,000,000 | 6,670,000,000 | - | 9,130,000 DALY | 724 | WTP: 1000 | Yes |
PCV10 vs. No vaccination | |||||||||||
Dilokthornsakul et al., Thailand, 2018 TBH [47] | 2+1 3+1 |
No | - - |
- - |
- - |
3881 5348 |
0.02 0.02 |
0.0228 QALY 0.0248 QALY |
170,437 215,948 |
WTP: 160,000 | No No |
Sevilla et al., Egypt, 2016 USD [43] | 2+1 | Yes | 38.43 | 38.05 | - | 38.05 | - | 0.0192 QALY | 1,984.414 | GDP: 3,479 | Yes |
Dorji et al.,Bhutan, 2017 USD [44] | 2+1 | Yes | - | - | - | 0.02 | - | 0.0006 QALY | 36 | GDP: 2708 | Yes |
PCV13 vs. PCV10 | |||||||||||
Sevilla et al., Egypt, 2016 USD [43] | 2+1 | Yes | 5.198 | 4.7 | - | 4.7 | - | 0.027 QALY | 173.98 | GDP: 3,479 | Yes |
Perdrizet et al., Philipine , 2020 PHP [53] | 3+1 | No | 3,159,192,812 | -1,399,247,136 | -10,875,530,146 | - 12,274,777,282 | 156,061 | 153,349 QALY | Cost-saving | - | Yes |
Huang et al., South Africa, 2022 R [45] | 2+1 | No | 587,690,427 | - 78,825,963 | - | - 78,825,963 | 4484 | 3191 QALY | Cost-saving | - | Yes |
Ref, country, currency | Schedule | Herd effect | Vaccine cost | Direct cost | Indirect cost | Total cost | LYs | Effectiveness | ICER | CE threshold | Cost-effective |
---|---|---|---|---|---|---|---|---|---|---|---|
PCV15 vs. PCV13 | |||||||||||
Huang et al., USA, 2021 USD [48] | 3+1 | Yes | 25,200 | -6,800,033,529 | -4,017,519,577 | -10,817,553,106 | 90,026 | 96,056 QALY | Dominant | - | Yes |
Tajima et al., Japan, 2022 JPY [49] | 3+1 | Yes | 3,091 | -235,135,797 | -130,475,159 | -365,610,955 | 7 | 24 QALY | Dominant | - | Yes |
Wilson et al., UK, 2021 GBP [58] | 1+1 vs. 1+1 2+1 vs. 1+1 |
No | 7,900,205 212,402,154 |
1,124,922 200,554,981 |
- | 1,124,922 200,554,981 |
262 475 |
361 QALY 640 QALY |
3112 313,229 |
WTP: 20,000 | Yes No |
PCV20 vs. PCV13 | |||||||||||
Lytle et al., Canada, 2022 CAD [46] | 2+1 | Yes | 82,002,815 | −3,226,480,346 | -656,062,710 | −3,882,543,056 | - | 47,056 QALY | Dominant | - | Yes |
Wilson et al., UK, 2021 GBP [58] | 1+1 vs. 1+1 2+1 vs. 1+1 |
No | 38,303,366 215,602,573 |
-459,192,688 -403,126,911 |
- | -459,192,688 -403,126,911 |
23,165 28,818 |
28,096 QALY 35,009 QALY |
Dominant Dominant |
WTP: 20,000 | Yes Yes |
Rozenbaum et al., USA, 2022 USD [50] | 3+1 | Yes | 2,338,463,867 | -19,189,701,809 | -3,726,859,511 | − 20,578,097,453 | 515,203 | 271,414 QALY | Dominant | - | Yes |
Ta et al., Germany, 2022 EUR [51] | 3+1 vs. 2+1 | Yes | 525,362,283 | -2,035,127,528 | -358,136,083 | -2,393,263,611 | 563,014 | 904,854 QALY | Dominant | - | Yes |
PCV20 vs. PCV15 | |||||||||||
Lytle et al., Canada, 2022 CAD [46] | 2+1 | Yes | 82,083,788 | −1,484,267,884 | --307,853,576 | -1,792,121,460 | - | 21,881 QALY | Dominant | - | Yes |
Wilson et al., UK, 2021 GBP [58] | 1+1 vs. 1+1 1+1 vs. 2+1 2+1 vs. 2+1 2+1 vs. 1+1 |
No | 30,403,161 -174,098,788 3,200,419 207,702,386 |
-460,317,610 -659,747,669 -603,681,892 -404,251,833 |
- | -460,317,610 -659,747,669 -603,681,892 -404,251,833 |
22,903 22,690 28,343 28,556 |
27,735 QALY 27,456 QALY 34,369 QALY 34,648 QALY |
Dominant Dominant Dominant Dominant |
WTP: 20,000 | Yes Yes Yes Yes |
Warren et al., Greece, 2023 EUR [54] | 3+1 | No | −4,566,825 | -58,138,419 | - | -58,138,419 | 551 | 486 QALY | 110,000 |
- | Yes |
Rozenbaum et al., USA, 2022 USD [50] | 3+1 | Yes | 2,437,771,654 | -8,003,928,578 | −1,898,767,496 | − 9,902,696,074 | 279,655 | 146,168 QALY | Dominant | - | Yes |
Ta et al., Germany, 2022 EUR [51] | 3+1 vs. 2+1 | Yes | 522,747,819 | -1,343,839,409 | -284,161,097 | -1,628,000,506 | 400,731 | 646,235 QALY | Dominant | - | Yes |
Ref | DSA | PSA | |
---|---|---|---|
The most impactful parameter on ICERs | Probability | Quadrant | |
PCV13 vs. no vaccination | |||
Sevilla et al. [43] | - Base-year incidence rates - Discount rate - PCV direct and indirect effects on inpatient pneumonia - Modeling horizon length |
- | - |
Dilokthornsakul et al. [47] | - | 100% | Northeast |
Krishnamoorthy et al. [52] | - | 100% | Northeast |
Shen et al. [55] | Incidence rates of inpatient pneumonia in ages 0–4 | - | - |
Dorji et al. [44] | - The variation in serotype coverage - Duration of vaccine protection - Excluding indirect vaccine effects (herd protection) - Discount rate |
- | - |
Li et al. [57] | - Incidence of inpatient pneumonia 0-2y, 2-4y, 18-34y - Total direct cost - Discount rate |
- | - |
Chen et al. [56] | - Disease incidence - Case fatality rate - Vaccine price |
100% | Northeast |
PCV10 vs. no vaccination | |||
Sevilla et al. [43] | - Base-year incidence rates - Discount rate - PCV direct and indirect effects on inpatient pneumonia - Modeling horizon length |
- | - |
Ref | DSA | PSA | ||
---|---|---|---|---|
Interest value | Most impactful parameter | Probability | Quadrant | |
PCV20 vs. PCV13 | ||||
Lytle et al. [46] | Cost | - Percentage of the indirect effect of PCV20 accrued - The steady-state indirect effects against hospitalized pneumonia - Age-specific serotype distribution of hospitalized pneumonia - The direct medical cost per hospitalized pneumonia episode |
100% | Southeast |
QALY | - Utility decrement of simple OM - Utility decrement of hospitalized pneumonia - Utility decrement of non-hospitalized pneumonia |
|||
Wilson et al. [58] | NMB | -Percentage PP cases that (≥65 years), - The hospitalized pneumonia incidence ( ≥65 years) - The direct costs for hospitalized pneumonia (≥65 years) |
- | - |
Rozenbaum et al. [50] | Cost | - Vaccine serotype coverage - Indirect effect accrual for PCV20 - PCV20 and PCV13 cost per dose |
100% | Southeast |
QALY | - Indirect effect accrual for PCV20 - Vaccine serotype coverage - Maximum indirect effect for all-cause hospitalized NBP |
|||
Ta et al. [51] | Cost | -Maximum indirect effect against hospitalized pneumonia (PCV20) - Serotype distribution by age - Incidence of hospitalized pneumonia - Cost per episode of hospitalized pneumonia |
100% | Southeast |
QALY | - Maximum indirect effects on hospitalized pneumonia (PCV20) - Serotype distribution by age - Baseline utilities - Hospitalized pneumonia incidence - CFR for hospitalized pneumonia |
|||
PCV15 vs. PCV13 | ||||
Huang et al. [48] | ICERs | - VEs against all-cause inpatient pneumonia - Vaccine coverage rate - Indirect effects - Incidence and fatality rates of bacteremic pneumonia in the elderly |
100% | Southeast |
Tajima et al. [49] | ICERs | - PCV15 and PCV13 serotype-specific VE in inpatient pneumonia (including serotype-specific VE for V114 and PCV13) - Direct and indirect cost per episode - Baseline incidence rate - Percentage attributable to S. pneumoniae - Serotype distribution - QALY decrement |
98.7% | Southeast |
PCV20 vs. PCV15 | ||||
Warren et al. [54] | - | - | 100% | Southeast |
Rozenbaum et al. [50] | Cost | - Indirect effect accrual for PCV20 - Cost per dose of PCV20 and PCV15 - Maximum indirect effect in hospitalized pneumonia for PCV20 - Vaccine serotype coverage |
100% | Southeast |
QALY | - Indirect effect accrual for PCV20 - Maximum indirect effect in hospitalized pneumonia for PCV20 - Indirect effect accrual for PCV15 - Vaccine serotype coverage |
|||
Ta et al. [51] | Cost | - Maximum indirect effect against hospitalized pneumonia (PCV20) - Serotype distribution by age - Incidence of hospitalized pneumonia - Cost per episode of hospitalized pneumonia |
98.4% | Southeast |
QALY | - Maximum indirect effects on hospitalized pneumonia (PCV20) - Serotype distribution by age - Baseline utilities - Hospitalized pneumonia incidence - Indirect effect accrual for PCV20 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 MDPI (Basel, Switzerland) unless otherwise stated