Mark Jit

Chair and Professor of the Department of Global and Environmental Health

Professional overview

Mark Jit is the inaugural chair and a professor in the Department of Global and Environmental Health. He was formerly head of the Department of Infectious Disease Epidemiology & Dynamics and co-director of the Global Health Economics Centre (GHECO) at the London School of Hygiene & Tropical Medicine (LSHTM). He holds honorary appointments at LSHTM as well as the University of Hong Kong (HKU) and the National University of Singapore (NUS).

Dr. Jit’s research focuses on epidemiological and economic modeling of vaccines to support evidence-based public health decision making. He has published papers covering a range of vaccine-preventable or potentially vaccine-preventable diseases including COVID-19, measles, HPV, pneumococcus, rotavirus, influenza, Group B Streptococcus, dengue, EV71 and RSV as well as methodological papers advancing the ways vaccines are evaluated. This work has influenced many of the major changes to immunization policy in countries around the world. Dr. Jit has served on a number of expert advisory committees in the UK as well as for international organizations such as the World Health Organization. He also organises or contributes to academic and professional courses on vaccine modeling, economics and decision science around the world.

Dr. Jit received his BSc and PhD in Mathematics from University College London, specializing in mathematical biology, and a Master of Public Health degree from King’s College London.

Visit Dr. Jit's Google Scholar's page to learn more about his research portfolio.

Education

BSc, Mathematics, University College London

PhD, Mathematics, University College London

MPH, Public Health, King's College London

Honors and awards

Clarivate Highly Cited Researcher (20222023)

Fellow of the Academy of Medical Sciences (2023)

Training Fund Award, Health Protection Agency (2007)

Andrew Rosen Prize, University College London (1999)

Institute of Mathematics and its Applications Award (1998)

Departmental Research Studentship, University College London (1998)

Student Union Commendation, University College London (1997)

Fillon Prize, University College London (1996)

Pathfinder Award, University College London (1995)

Publications

Publications

Social contact patterns and their impact on the transmission of respiratory pathogens in rural China

A health technology assessment of COVID-19 vaccination for Nigerian decision-makers: Identifying stakeholders and pathways to support evidence uptake

A Scoping Review and Taxonomy of Epidemiological-Macroeconomic Models of COVID-19

An Application of an Initial Full Value of Vaccine Assessment Methodology to Measles-Rubella MAPs for Use in Low- and Middle-Income Countries

An overview of the perspectives used in health economic evaluations

Between now and later: a mixed methods study of HPV vaccination delay among Chinese caregivers in urban Chengdu, China

Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis

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Publication year

2024

Journal title

EClinicalMedicine

Volume

72

10.1016/j.eclinm.2024.102638

Abstract

Abstract

Background: Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. Methods: With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. Findings: We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5–179) and 100.5 in men (99.5–102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. Interpretation: In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).

Contribution of vaccination to improved survival and health: modelling 50 years of the Expanded Programme on Immunization

Cost-effectiveness of COVID rapid diagnostic tests for patients with severe/critical illness in low- and middle-income countries: A modeling study

COVID-19-related health utility values and changes in COVID-19 patients and the general population: a scoping review

Effectiveness and efficiency of immunisation strategies to prevent RSV among infants and older adults in Germany: a modelling study

Effects of sequential vs single pneumococcal vaccination on cardiovascular diseases among older adults: a population-based cohort study

Equity impact of HPV vaccination on lifetime projections of cervical cancer burden among cohorts in 84 countries by global, regional, and income levels, 2010–22: a modelling study

Estimating the health effects of COVID-19-related immunisation disruptions in 112 countries during 2020–30: a modelling study

Hartner, A. M., Li, X., Echeverria-Londono, S., Roth, J., Abbas, K., Auzenbergs, M., De Villiers, M. J., Ferrari, M. J., Fraser, K., Fu, H., Hallett, T., Hinsley, W., Jit, M., Karachaliou, A., Moore, S. M., Nayagam, S., Papadopoulos, T., Perkins, T. A., Portnoy, A., … Gaythorpe, K. A. (n.d.).

Publication year

2024

Journal title

The Lancet Global Health

Volume

12

Issue

4

Page(s)

e563-e571

10.1016/S2214-109X(23)00603-4

Abstract

Abstract

Background: There have been declines in global immunisation coverage due to the COVID-19 pandemic. Recovery has begun but is geographically variable. This disruption has led to under-immunised cohorts and interrupted progress in reducing vaccine-preventable disease burden. There have, so far, been few studies of the effects of coverage disruption on vaccine effects. We aimed to quantify the effects of vaccine-coverage disruption on routine and campaign immunisation services, identify cohorts and regions that could particularly benefit from catch-up activities, and establish if losses in effect could be recovered. Methods: For this modelling study, we used modelling groups from the Vaccine Impact Modelling Consortium from 112 low-income and middle-income countries to estimate vaccine effect for 14 pathogens. One set of modelling estimates used vaccine-coverage data from 1937 to 2021 for a subset of vaccine-preventable, outbreak-prone or priority diseases (ie, measles, rubella, hepatitis B, human papillomavirus [HPV], meningitis A, and yellow fever) to examine mitigation measures, hereafter referred to as recovery runs. The second set of estimates were conducted with vaccine-coverage data from 1937 to 2020, used to calculate effect ratios (ie, the burden averted per dose) for all 14 included vaccines and diseases, hereafter referred to as full runs. Both runs were modelled from Jan 1, 2000, to Dec 31, 2100. Countries were included if they were in the Gavi, the Vaccine Alliance portfolio; had notable burden; or had notable strategic vaccination activities. These countries represented the majority of global vaccine-preventable disease burden. Vaccine coverage was informed by historical estimates from WHO–UNICEF Estimates of National Immunization Coverage and the immunisation repository of WHO for data up to and including 2021. From 2022 onwards, we estimated coverage on the basis of guidance about campaign frequency, non-linear assumptions about the recovery of routine immunisation to pre-disruption magnitude, and 2030 endpoints informed by the WHO Immunization Agenda 2030 aims and expert consultation. We examined three main scenarios: no disruption, baseline recovery, and baseline recovery and catch-up. Findings: We estimated that disruption to measles, rubella, HPV, hepatitis B, meningitis A, and yellow fever vaccination could lead to 49 119 additional deaths (95% credible interval [CrI] 17 248–134 941) during calendar years 2020–30, largely due to measles. For years of vaccination 2020–30 for all 14 pathogens, disruption could lead to a 2·66% (95% CrI 2·52–2·81) reduction in long-term effect from 37 378 194 deaths averted (34 450 249–40 241 202) to 36 410 559 deaths averted (33 515 397–39 241 799). We estimated that catch-up activities could avert 78·9% (40·4–151·4) of excess deaths between calendar years 2023 and 2030 (ie, 18 900 [7037–60 223] of 25 356 [9859–75 073]). Interpretation: Our results highlight the importance of the timing of catch-up activities, considering estimated burden to improve vaccine coverage in affected cohorts. We estimated that mitigation measures for measles and yellow fever were particularly effective at reducing excess burden in the short term. Additionally, the high long-term effect of HPV vaccine as an important cervical-cancer prevention tool warrants continued immunisation efforts after disruption. Funding: The Vaccine Impact Modelling Consortium, funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation. Translations: For the Arabic, Chinese, French, Portguese and Spanish translations of the abstract see Supplementary Materials section.

Estimating the impact of vaccination: lessons learned in the first phase of the Vaccine Impact Modelling Consortium

Evaluating Scope and Bias of Population-Level Measles Serosurveys: A Systematized Review and Bias Assessment

Global vaccine coverage and childhood survival estimates: 1990–2019

Health impact and cost-effectiveness of vaccination using potential next-generation influenza vaccines in Thailand: a modelling study

Health-Related Quality of Life and Economic Burden Among Hospitalized Children with Hand, Foot, and Mouth Disease: A Multiregional Study in China

Healthcare utilisation in people with long COVID: an OpenSAFELY cohort study

How mathematical modelling can inform outbreak response vaccination

Immunogenicity and seroefficacy of pneumococcal conjugate vaccines: A systematic review and network meta-analysis

Feng, S., McLellan, J., Pidduck, N., Roberts, N., Higgins, J. P., Choi, Y., Izu, A., Jit, M., Madhi, S. A., Mulholland, K., Pollard, A. J., Procter, S., Temple, B., & Voysey, M. (n.d.).

Publication year

2024

Journal title

Health Technology Assessment

Volume

28

Issue

34

10.3310/YWHA3079

Abstract

Abstract

Background: Vaccination of infants with pneumococcal conjugate vaccines is recommended by the World Health Organization. Evidence is mixed regarding the differences in immunogenicity and efficacy of the different pneumococcal vaccines. Objectives: The primary objective was to compare the immunogenicity of pneumococcal conjugate vaccine-10 versus pneumococcal conjugate vaccine-13. The main secondary objective was to compare the seroefficacy of pneumococcal conjugate vaccine-10 versus pneumococcal conjugate vaccine-13. Methods: We searched the Cochrane Library, EMBASE, Global Health, MEDLINE, ClinicalTrials.gov and trialsearch.who.int up to July 2022. Studies were eligible if they directly compared either pneumococcal conjugate vaccine-7, pneumococcal conjugate vaccine-10 or pneumococcal conjugate vaccine-13 in randomised trials of children under 2 years of age, and provided immunogenicity data for at least one time point. Individual participant data were requested and aggregate data used otherwise. Outcomes included the geometric mean ratio of serotype-specific immunoglobulin G and the relative risk of seroinfection. Seroinfection was defined for each individual as a rise in antibody between the post-primary vaccination series time point and the booster dose, evidence of presumed subclinical infection. Each trial was analysed to obtain the log of the ratio of geometric means and its standard error. The relative risk of seroinfection (‘seroefficacy’) was estimated by comparing the proportion of participants with seroinfection between vaccine groups. The log-geometric mean ratios, log-relative risks and their standard errors constituted the input data for evidence synthesis. For serotypes contained in all three vaccines, evidence could be synthesised using a network meta-analysis. For other serotypes, meta-analysis was used. Results from seroefficacy analyses were incorporated into a mathematical model of pneumococcal transmission dynamics to compare the differential impact of pneumococcal conjugate vaccine-10 and pneumococcal conjugate vaccine-13 introduction on invasive pneumococcal disease cases. The model estimated the impact of vaccine introduction over a 25-year time period and an economic evaluation was conducted. Results: In total, 47 studies were eligible from 38 countries. Twenty-eight and 12 studies with data available were included in immunogenicity and seroefficacy analyses, respectively. Geometric mean ratios comparing pneumococcal conjugate vaccine-13 versus pneumococcal conjugate vaccine-10 favoured pneumococcal conjugate vaccine-13 for serotypes 4, 9V and 23F at 1 month after primary vaccination series, with 1.14- to 1.54-fold significantly higher immunoglobulin G responses with pneumococcal conjugate vaccine-13. Risk of seroinfection prior to the time of booster dose was lower for pneumococcal conjugate vaccine-13 for serotype 4, 6B, 9V, 18C and 23F than for pneumococcal conjugate vaccine-10. Significant heterogeneity and inconsistency were present for most serotypes and for both outcomes. Twofold higher antibody after primary vaccination was associated with a 54% decrease in risk of seroinfection (relative risk 0.46, 95% confidence interval 0.23 to 0.96). In modelled scenarios, pneumococcal conjugate vaccine-13 or pneumococcal conjugate vaccine-10 introduction in 2006 resulted in a reduction in cases that was less rapid for pneumococcal conjugate vaccine-10 than for pneumococcal conjugate vaccine-13. The pneumococcal conjugate vaccine-13 programme was predicted to avoid an additional 2808 (95% confidence interval 2690 to 2925) cases of invasive pneumococcal disease compared with pneumococcal conjugate vaccine-10 introduction between 2006 and 2030. Limitations: Analyses used data from infant vaccine studies with blood samples taken prior to a booster dose. The impact of extrapolating pre-booster efficacy to post-booster time points is unknown. Network meta-analysis models contained significant heterogeneity which may lead to bias. Conclusions: Serotype-specific differences were found in immunogenicity and seroefficacy between pneumococcal conjugate vaccine-13 and pneumococcal conjugate vaccine-10. Higher antibody response after vaccination was associated with a lower risk of subsequent infection. These methods can be used to compare the pneumococcal conjugate vaccines and optimise vaccination strategies. For future work, seroefficacy estimates can be determined for other pneumococcal vaccines, which could contribute to licensing or policy decisions for new pneumococcal vaccines.

Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study

Impact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT)

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Publication year

2024

Journal title

The Lancet Regional Health - Europe

Volume

40

10.1016/j.lanepe.2024.100908

Abstract

Abstract

Background: Long COVID is a major problem affecting patient health, the health service, and the workforce. To optimise the design of future interventions against COVID-19, and to better plan and allocate health resources, it is critical to quantify the health and economic burden of this novel condition. We aimed to evaluate and estimate the differences in health impacts of long COVID across sociodemographic categories and quantify this in Quality-Adjusted Life-Years (QALYs), widely used measures across health systems. Methods: With the approval of NHS England, we utilised OpenPROMPT, a UK cohort study measuring the impact of long COVID on health-related quality-of-life (HRQoL). OpenPROMPT invited responses to Patient Reported Outcome Measures (PROMs) using a smartphone application and recruited between November 2022 and October 2023. We used the validated EuroQol EQ-5D questionnaire with the UK Value Set to develop disutility scores (1-utility) for respondents with and without Long COVID using linear mixed models, and we calculated subsequent Quality-Adjusted Life-Months (QALMs) for long COVID. Findings: The total OpenPROMPT cohort consisted of 7575 individuals who consented to data collection, with which we used data from 6070 participants who completed a baseline research questionnaire where 24.6% self-reported long COVID. In multivariable regressions, long COVID had a consistent impact on HRQoL, showing a higher likelihood or odds of reporting loss in quality-of-life (Odds Ratio (OR): 4.7, 95% CI: 3.72–5.93) compared with people who did not report long COVID. Reporting a disability was the largest predictor of losses of HRQoL (OR: 17.7, 95% CI: 10.37–30.33) across survey responses. Self-reported long COVID was associated with an 0.37 QALM loss. Interpretation: We found substantial impacts on quality-of-life due to long COVID, representing a major burden on patients and the health service. We highlight the need for continued support and research for long COVID, as HRQoL scores compared unfavourably to patients with conditions such as multiple sclerosis, heart failure, and renal disease. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).

Inequitable Distribution of Global Economic Benefits from Pneumococcal Conjugate Vaccination