| Literature DB >> 36011621 |
Tianyu Feng1, Zhou Zheng1, Jiaying Xu1, Peng Cao1, Shang Gao1, Xihe Yu1.
Abstract
Objective The aim of this study was to investigate the cost-effectiveness of Helicobacter pylori (H. pylori) screening and eradication treatment in an asymptomatic population in China and to explore the most cost-effective screening protocol for H. pylori. Method We used TreeAge 2019 to construct Markov models to assess the direct healthcare costs and quality-adjusted life years (QALYs) and the cost per year of life saved (YoLS) of three therapies, i.e., annual, triennial and five-yearly H. pylori screening. Excess probabilities were derived from published high quality studies and Meta-analyses, and costs and utilities were derived from the Chinese Yearbook of Health Care Statistics and published studies. Incremental cost-effectiveness ratios (ICERs) were used to describe the results. The willingness-to-pay threshold was set at China's Gross National Product per capita. Result In the asymptomatic population, the ICER per QALYs gained was US$1238.47 and US$1163.71 for every three and five years of screening compared to the annual screening group; the ICER per YoLS gained was US$3067.91 and US$1602.78, respectively. Conclusion Screening for H. pylori in asymptomatic populations in China and eradicating treatment for those who test positive is cost-effective. Increasing screening participation in asymptomatic populations is more effective than increasing the frequency of screening. From a national payer perspective, it is cost-effective to screen the general asymptomatic population in China for H. pylori and to eradicate those who test positive. Individuals need to choose a screening programme that they can afford according to their financial situation.Entities:
Keywords: Markov model; cost-effectiveness; gastric cancer; helicobacter pylori; screening
Mesh:
Year: 2022 PMID: 36011621 PMCID: PMC9408128 DOI: 10.3390/ijerph19169986
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Schematic representation of the Markov model. H. pylori uninfected (HP−); H. pylori infected (HP+); H. pylori successfully eradicated (HP Erad); gastric cancer (GC); gastric cancer cured (GC Cured); peptic ulcer disease (NUD). At the beginning, all individuals in the cohort entered the model from two healthy states, i.e., HP− and HP+. During each Markovian cycle (1 year), individuals moved between the different health states, the.
Parameters in the model. PUD, peptic ulcer disease; UBT, urea breath test; NA, not applicable; Local data were obtained from reports published by the National Health and Family Planning Commission of China or the State Food and Drug Administration of China.
| Variables | Baseline | Range | Distribution | References |
|---|---|---|---|---|
| Epidemiological parameters | ||||
| Annual | 2.2 | 0.8–4 | β | [ |
| UBT sensitivity (%) | 96 | 95–97 | β | [ |
| UBT specificity (%) | 94 | 92–95 | β | [ |
| Eradication rate of bismuth quadruple therapy(%) | 85.51 | 74.71–96.41 | β | [ |
| Five-year survival rate for gastric cancer (%) | 47 | 45.0–49.5 | β | [ |
| Annual | 2.82 | 2.6–2.9 | β | [ |
| Development of | 28 | 18–56 | β | [ |
| Risk of gastric cancer development (%) | 0.1186 | 0.099–0.14 | β | [ |
| Development of gastric cancer after | 0.17 | 0.07–0.36 | β | [ |
| Development of PUD without | 9 | 7.2–10.8 | β | [ |
| Development of PUD with | 14 | 13.3–14.7 | β | [ |
| PUD developing into gastric cancer (%) | 0.7 | 0.16–2 | β | [ |
| PUD mortality rate (%) | 2.53 | 2.44–2.63 | β | [ |
| Relapse after PUD cure with | 16.3 | 10.5–22.0 | β | [ |
| Relapse after PUD cure without | 11.899 | 7.665–16.06 | β | [ |
| 50 | 15.5–83.4 | β | [ | |
| Costs (US dollars) | ||||
| 20.87 | 12.04–40.14 | Gamma | Local data | |
| 28.27 | 9.78–59.23 | Gamma | Local data | |
| Average annual cost of PUD | 1288.3 | 257.66–6441.50 | Gamma | Local data |
| Average annual cost of Gastric cancer | 3182.33 | 636.47–15911.65 | Gamma | Local data |
| Health-state utility | ||||
| Health | 1 | NA | NA | Assumption |
| PUD | 0.886 | 0.841–0.922 | β | [ |
| Gastric cancer | 0.603 | 0.470–0.730 | β | [ |
| Cured gastric cancer | 0.951 | 0.928–0.969 | β | [ |
| Death | 0 | NA | NA | |
Distribution of gastric cancer and death states (each group had 10,000 persons).
| Cycles (Years) | Gastric Cancer | Death | ||||
|---|---|---|---|---|---|---|
| Annually | Every 3 Years | Every 5 Years | Annually | Every 3 Years | Every 5 Years | |
| 10 | 18 | 19 | 19 | 83 | 83 | 84 |
| 20 | 20 | 22 | 24 | 93 | 94 | 94 |
| 30 | 12 | 13 | 16 | 85 | 85 | 86 |
| 40 | 10 | 10 | 12 | 76 | 77 | 77 |
| 50 | 10 | 11 | 10 | 69 | 69 | 68 |
| 60 | 10 | 10 | 10 | 62 | 61 | 62 |
The result of base-case cost-effectiveness analysis. QALY, quality-adjusted life-year; EFF: effectiveness; ICER: incremental cost-effectiveness ration.
| Screening Programme | Cost | INCR Cost | Life Year | ICER YoLS | EFF | ICER |
|---|---|---|---|---|---|---|
| Annually | 2487.08 | 45.43 | 22.78 | |||
| Every 3 years | 2266.06 | −221.02 | 45.01 | 516.41 | 22.61 | 1317.48 |
| Every 5 years | 2241.19 | −245.88 | 44.96 | 522.05 | 22.59 | 1277.92 |
Figure 2Tornado diagram showing the deterministic sensitivity analysis of the Markov model simulation.
Figure 3Cost-Effectiveness acceptability curve.
Figure 4(A) CE plane showing the incremental costs and incremental QALYs of 1000 simulations for Annually group vs. Every 3 years group. (B) CE plane showing the incremental costs and incremental QALYs of 1000 simulations for Annually group vs. Every 3 years group.
The result of scenario sensitivity analyses. QALY, quality-adjusted life-year; EFF: effectiveness; ICER: incremental cost-effectiveness ration.
| Screening Age | Screening Programme | Cost | INCR Cost | EFF | INCR EFF | ICER |
|---|---|---|---|---|---|---|
| 20 | Annually | 2441.80 | 22.78 | |||
| Every 3 years | 2223.98 | −217.82 | 22.61 | −0.18 | 1238.48 | |
| Every 5 years | 2204.59 | −237.21 | 22.58 | −0.20 | 1163.71 | |
| 30 | Annually | 2406.57 | 21.65 | |||
| Every 3 years | 2184.48 | −222.09 | 21.5 | −0.15 | 1480.6 | |
| Every 5 years | 2159.73 | −246.84 | 21.48 | −0.17 | 1452 | |
| 40 | Annually | 2333.99 | 19.97 | |||
| Every 3 years | 2131.73 | −202.26 | 19.85 | −0.12 | 1685.5 | |
| Every 5 years | 2106.61 | −227.38 | 19.83 | −0.14 | 1624.14 | |
| 50 | Annually | 2102.4 | 17.46 | |||
| Every 3 years | 1916.41 | −185.99 | 17.37 | −0.09 | 2066.56 | |
| Every 5 years | 1895.36 | −207.04 | 17.36 | −0.1 | 2070.4 | |
| 60 | Annually | 1692.51 | 13.74 | |||
| Every 3 years | 1538.08 | −154.43 | 13.69 | −0.05 | 3088.6 | |
| Every 5 years | 1518.19 | −174.32 | 13.68 | −0.06 | 2905.33 | |
| 70 | Annually | 1084.71 | 8.21 | |||
| Every 3 years | 992.76 | −91.95 | 8.19 | −0.02 | 4597.5 | |
| Every 5 years | 970.41 | −114.3 | 8.18 | −0.03 | 3810 | |
| No screening | 3617.40 | 13.67 | ||||
| One screening only | 1591.63 | −2025.77 | 22.22 | 8.54 | −237.07 | |
Figure 5CE plane showing the incremental costs and incremental QALYs of 1000 simulations for One screening only group vs. No screening group.