Cryptococcal meningitis is a leading cause of death in HIV-infected patients, with an estimated 223,000 cases in 2014 (Rajasingham et al., 2017). The vast majority of infections are due to C. neoformans, and occur in low-income tropical settings. Current international guidelines recommend initial induction treatment with amphotericin combined with flucytosine, followed by consolidation therapy with fluconazole (World Health Organization, 2018). This combination delivers the fastest rates of clearance of yeast from cerebrospinal fluid (CSF) and the best survival rates (Day et al., 2013; Molloy et al., 2018). However, even on this gold standard therapy, 30% of patients will die within 10 weeks of diagnosis (Day et al., 2013; Molloy et al., 2018). Adjunctive therapy with corticosteroids, which has proven beneficial in other forms of meningitis, results in worse outcomes (Beardsley et al., 2016).

Cryptococcal meningitis can also occur in HIV-uninfected patients, including immunocompetent people and those with other causes of immunosuppression. Survival rates are similar to those seen in HIV-infected patients. There are few data from randomized controlled trials to guide treatment in these circumstances. In Vietnam around 20% of cases of cryptococcal meningitis are in HIV-uninfected patients (Chau et al., 2010). Disease is predominantly due to the C. neoformans VNIa-5 lineage; C. gattii is responsible for around 25% of cases (Chau et al., 2010; Ashton et al., 2019; Day et al., 2011; Day et al., 2017).

There has been little progress in development of antifungal drugs for cryptococcal meningitis. Amphotericin and flucytosine are each more than 60 years old; the last novel drug class developed was the azoles, introduced 30 years ago. Access to flucytosine is severely restricted by availability and cost, meaning it is rarely used where disease burden is highest. Despite being off-patent, it has been subject to extraordinary price rises in recent years, with a 2-week course now costs around 30,000 USD in the USA (Merry and Boulware, 2016). Flucytosine is an unattractive prospect for generic manufacturers, because the location of the majority of patients and the few indications outside cryptococcal disease promise only limited financial returns. These same factors hamper the development of novel treatments for cryptococcal disease, and have driven interest in drug re-purposing (Butts et al., 2014; Dolan et al., 2009; Zhai et al., 2012). Re-purposing can be a solution for neglected diseases provided the new indication accounts for only a minority of total prescriptions, and the de facto indications are sufficiently prevalent to ensure availability, price stability, and affordability.

Tamoxifen, a selective estrogen receptor modulator used to treat breast cancer, has anti-cryptococcal activity, appearing to act synergistically when combined with other antifungals against the type strain in vitro, and to be fungicidal when combined with fluconazole in the mouse infection model (Butts et al., 2014; Dolan et al., 2009). We found it to act synergistically with amphotericin against two-thirds of clinical isolates of Cryptococcus neoformans and C. gattii from our archive and to have an additive interaction when combined with fluconazole in vitro (Hai et al., 2019).

Tamoxifen is concentrated in brain tissue (10- to 100-fold compared with plasma) and macrophage phagosomes (a site of growth for Cryptococcus spp.), is off-patent, cheap (~10US cents/tablet) and widely available (Lien et al., 1991a; Lien et al., 1991b). Therefore, it is a promising candidate for the treatment of cryptococcal meningitis. Pharmacokinetic data suggest that doses 5- to 10-fold that used in breast cancer (typically 30 mg/day) should deliver plasma concentrations of tamoxifen greater than the Minimum Inhibitory Concentration 90 (MIC90 16 µg/mL) of Vietnamese clinical isolates (Lien et al., 1991a). Such doses have been used, and well-tolerated, in small cell lung cancer, desmoid tumours, and prostate cancer. These illnesses have comparable or better 1-year survival rates than cryptococcal meningitis (Ngan et al., 2019). While generally well-tolerated, acute side effects that could be detrimental from short-course treatment include QT prolongation of the cardiac de/repolarization cycle, although the risk of life-threatening arrhythmias appears to be low (Grouthier et al., 2018).

In Vietnam induction treatment for cryptococcal meningitis consists of amphotericin combined with fluconazole, consistent with WHO recommendations where flucytosine is unavailable (World Health Organization, 2018). However, this combination is less effective than amphotericin with flucytosine, resulting in slower rates of fungal clearance and worse survival rates (Day et al., 2013; Molloy et al., 2018). The relationship between the rate of fungal clearance from CSF and survival is generally robust; improving the potency of antifungal therapy is likely to be an effective way to reduce deaths (Day et al., 2013; Molloy et al., 2018; Beardsley et al., 2016). The rate of clearance of yeast from CSF associated with an antifungal treatment (the early fungicidal activity, EFA) is a sensitive measure able to detect differences between treatment regimens likely to be associated with survival benefits with far fewer patients than studies powered to survival itself (Brouwer et al., 2004). Small studies powered to this endpoint can serve to filter treatment regimens that can be taken forward in larger trials (Brouwer et al., 2004; Bicanic et al., 2009). We performed an open-label randomised controlled trial to determine whether combining tamoxifen with amphotericin B and fluconazole results in enhanced EFA in HIV infected and uninfected patients with cryptococcal meningitis, and to generate safety data as a prelude to a larger trial powered to mortality (Ngan et al., 2019).

We wanted to determine whether tamoxifen could be repurposed as an affordable treatment for cryptococcal meningitis. Our study was powered to detect an increase in the rate of yeast clearance of at least −0.11 log10 CFU/ml/day when tamoxifen was added to standard of care therapy. Differences of this order of magnitude are associated with improved survival in patients in low-income settings (Day et al., 2013; Molloy et al., 2018; Beardsley et al., 2016). Despite having previously shown that tamoxifen had activity in vitro against historical clinical isolates of C. neoformans, we found its addition had no impact on EFA. Therefore we do not believe that proceeding to a larger trial, powered to survival, is justified.

It is not clear why tamoxifen did not provide benefit in our patients. The susceptibilities of the Cryptococcus isolates from this study to tamoxifen, fluconazole, and amphotericin, were similar to those of isolates from our previous clinical trials (Hai et al., 2019; O'Connor et al., 2020). However, in contrast with our previous findings we found evidence of synergy when tamoxifen was combined with amphotericin in only 12% (95CI 5%, 24%) of isolates from the trial. This compares with the rate in archived isolates of 67% (95CI 47%, 81%) (Hai et al., 2019). Synergy has been suggested as an explanation for the superiority of the amphotericin-flucytosine combination which has delivered improved yeast clearance and survival in a number of trials (Schwarz et al., 2003). In this study, we lack sufficient numbers of isolates where tamoxifen-amphotericin synergy is seen to be able to determine whether synergy per se influences EFA.

A second potential explanation is that we may have failed to attain sufficient concentrations of tamoxifen in our patients. We chose a dose of 300 mg/day, based upon the MIC90 of tamoxifen against our historical isolates (16 mg/L) and the expected plasma concentrations this would achieve. Given that tamoxifen is concentrated in the brain (10- to 100-fold), and in macrophage phagosomes, we consider it unlikely that we did not reach drug concentrations greater than the MIC90 at the disease site, although it is possible that absorption of orally administered drug was impaired in our patients.

The rates of adverse events in our study were similar between patients receiving tamoxifen and those in the control arm. Our study was powered to detect a difference in the rate of clearance of yeast from CSF and therefore may have lacked power to detect differences in rates of rarer adverse events. However, there was greater prolongation of the QTc interval in patients on tamoxifen. The mechanism through which tamoxifen causes QT interval prolongation in humans is unknown. In animals there is evidence that the block is multi-channel, due to both inhibition of the I_KR and I_Ca channels (Asp et al., 2013; He et al., 2003; Liu et al., 1998). Such multi-channel block is considered to confer a reduced risk of life-threatening arrhythmias compared with drugs that block single ion channels. While we did not have any cases of ventricular tachycardia in our study, there was an episode of cardiac arrest in the tamoxifen arm. There are multiple potential causes of cardiac arrest in patients with cryptococcal meningitis, including intracranial pathology and electrolyte disturbances. The cardiac arrest in our study occurred on day 21, 1 week after administration of tamoxifen had finished. However, given tamoxifen’s half-life of 5 to 7 days, and the doses used, it is possible that this event was related. Fluconazole is also a recognised cause of QT prolongation. Here, the mechanism is believed to be through modulation of the I_kr current of the cardiac depolarization cycle (Han et al., 2011). However, we found little evidence of significant QT prolongation in patients in the control arm of our study, and in fact the acute effect of administration of fluconazole was shortening of the QTc interval.

Our experience with tamoxifen is similar to that reported with the anti-depressant drug sertraline. Sertraline has in vitro fungicidal activity against Cryptococcus neoformans and a synergistic effect when combined with fluconazole. Results from a pilot dose-finding study of adjunctive sertraline for cryptococcal meningitis suggested it was a safe and potentially effective treatment, although no contemporaneous controls were enrolled in the trial (Rhein et al., 2016). Subsequently a large randomized controlled trial powered to mortality was stopped due to futility having enrolled 460 patients (Rhein et al., 2019). There was no difference in survival or EFA between the standard therapy or sertraline boosted treatment arms. Of note, a small randomized placebo controled trial from Mexico, published after the phase three trial had begun, found no difference in EFA when sertraline was added to amphotericin and fluconazole, although only 12 patients were enrolled and formal statistical testing was not performed (Villanueva-Lozano et al., 2018). However, it lends further support for the screening of antifungal treatments in small scale studies using this endpoint.

Other drugs suggested as repurposing candidates for cryptococcal meningitis include the calcium antagonists, such as nifedipine and its sister drugs, used to treat hypertension, and flubendazole, an antihelminthic (Truong et al., 2018). Flubendazole is perhaps the most promising of these, appearing to be more potent in vitro than fluconazole, and active against Cryptococcus isolates across a range of fluconazole susceptibilities. It crosses the blood brain barrier in mice, but data are lacking regarding humans (Fok and Kassai, 1998). While nifedipine crosses the blood brain barrier, it seems unlikley that normal doses and oral administration would reach the plasma levels needed to inhibit Cryptococcus growth. However, given our experiences with tamoxifen, and those of others with sertraline, we would caution that better laboratory screening methods than those currently in use are needed to identify potential new treatments for cryptococcal meningitis.

In the mean time, improving access to flucytosine remains a key goal. Progress has been made through efforst to increase generic manufacture through the the Unitaid- Clinton Health Access Initiative for Advanced HIV Disease Initiative’s partnership with the Global Fund and the President's Emergency Plan for AIDS Relief. This has resulted in price reductions allowing 2-week treatment courses to be procured for around $100 in some locations.

This appendix has been provided by the authors to give readers additional information about their work.

data{
//for longitudinal data
int<lower=1> N_long; // no. rows
int<lower=0,upper=1> y2_censInd[N_long]; // under detection limit index
int<lower=1> I; // no. patient
int<lower=1,upper=I> patid[N_long]; //patid index for random effect
vector [N_long]time_scale; // time_scale censoring data
real log10fc_obs[N_long];// log10fc observing data
real <upper=log10(5)>C;
int<lower=1> p_X;
int<lower=1> p_X_intercept;
int<lower=1> p_X_slope;
vector[p_X]mean_X;
vector<lower=0>[p_X]sd_X;
matrix[N_long,p_X] X_long_scale;//treatment on EFA

// for survival data
matrix[I,p_X_intercept] X_intercept_unscale;//treatment on EFA
matrix[I,p_X_slope] X_slope_unscale;//treatment on EFA;. ~trt.group only
int<lower=0> p_Z;
matrix<lower=0>[I,p_Z] Z; //treatment on survival
real<lower=0,upper=71>ttdeath[I];
int<lower=0> n_time_interval;
vector<lower=0,upper=71>[n_time_interval+1]time_spec;
vector<lower=0,upper=1>[I] death;
matrix<lower=0>[I,n_time_interval]zeros;
int<lower=1,upper=n_time_interval>index_interval[I];
}
parameters{
//for longitudinal data
vector[p_X] fix_eff_scale;//fixed effects for intercept
real<lower=0> sigma; // SD of error measurement
cholesky_factor_corr[2] L_Omega; // prior correlation
vector<lower=0,upper=pi()/2>[2] tau_unif;
//vector<lower=0>[2] tau; // prior scale
matrix[2,I] z;
// for survival data
vector[p_Z] beta;//fixed effects for survival
real<lower=0>lambda[n_time_interval]; // piecewise hazard rate
real eta;// trajectory parameter
real<lower=0> sigma_lambda;
}
transformed parameters {
// for longitudinal data
vector[p_X_slope] b_fix_unscale;//original fixed effects for slope
vector[p_X_intercept] a_fix_unscale;//original fixed effects for intercept
vector[N_long]fit_X_long_scale;
vector[I] a_unscale;// estimated intercept
vector[I] b_unscale;// estimated slope


matrix[I,2] U_raw; // patient random effects
matrix[I,2] U_raw_unscale; // patient random effects
// for survival data
vector[I] beta_hat;
real test;
vector<lower=0>[2] tau; // prior scale for random effects
for (k in 1:2) tau[k] = 2.5 * tan(tau_unif[k]);


test=sum((fix_eff_scale[2:p_X].*mean_X[2:p_X])./sd_X[2:p_X]);
a_fix_unscale[1]=fix_eff_scale[1]-sum((fix_eff_scale[2:p_X].*mean_X[2:p_X])./sd_X[2:p_X]);
a_fix_unscale[2:p_X_intercept]=fix_eff_scale[2:p_X_intercept]./sd_X[2:p_X_intercept];
b_fix_unscale=(fix_eff_scale[(p_X_intercept+1):p_X])./sd_X[(p_X_intercept+1):p_X];


U_raw = (diag_pre_multiply(tau,L_Omega) * z)';
for(i in 1:I){
U_raw_unscale[i,1]= U_raw[i,1]-(U_raw[i,2])*mean_X[(p_X_intercept+1)]/sd_X[(p_X_intercept+1)];
}
U_raw_unscale[,2] = U_raw[,2]/sd_X[(p_X_intercept+1)];
// compute individual intercept
fit_X_long_scale=X_long_scale*fix_eff_scale;
a_unscale=X_intercept_unscale*a_fix_unscale+U_raw_unscale[,1];// contains only random effects.
// // compute individual slope
for(i in 1:I){
b_unscale[i]=X_slope_unscale[i,]*b_fix_unscale+U_raw_unscale[i,2];
}
//for survival data
beta_hat=Z*beta;
}
model{
vector[N_long] mu;
vector[I] H;//cummulative hazard function;
vector[I] LL;//log density function
matrix[I,n_time_interval] integral_ht;// cummulative hazard function
// Likelihood for longitudinal component
// Set all priors for all parameters of longitudinal component
fix_eff_scale~normal(0,10);
to_vector(z) ~ normal(0,1);
tau_unif ~ uniform(0,pi()/2);
L_Omega ~ lkj_corr_cholesky(2);
for (k in 1:N_long){
mu[k]=fit_X_long_scale[k]+U_raw[patid[k],1]+(U_raw[patid[k],2])*time_scale[k];
if(y2_censInd[k]==0){
log10fc_obs[k] ~ normal(mu[k],sigma);
}else{
target +=normal_lcdf(C|mu[k],sigma);
}
}
//Likelihood for survival component
//Compute the cumulative hazard function from 0 to ttdeath.
integral_ht=zeros;
for(i in 1:I){
integral_ht[i,index_interval[i]] = lambda[index_interval[i]]*exp(eta*a_unscale[i]+beta_hat[i])*(exp((eta*b_unscale[i])*ttdeath[i])-exp((eta*b_unscale[i])*time_spec[index_interval[i]]))/(eta*b_unscale[i]);
for(j in 1:(index_interval[i]-1)){
integral_ht[i,j] = lambda[j]*exp(eta*a_unscale[i]+beta_hat[i])*(exp((eta*b_unscale[i])*time_spec[j+1])-exp((eta*b_unscale[i])*time_spec[j]))/(eta*b_unscale[i]);
}
//Integrated hazard for individual i from 0 to survival time t.surv[i]
H[i]=sum(integral_ht[i,]);// cummulative hazard function
//Survival function
LL[i]=(log(lambda[index_interval[i]])+eta*(a_unscale[i]+b_unscale[i]*ttdeath[i])+beta_hat[i])*death[i]-H[i];
}
target += sum(LL);
//Set all priors for all parameters of survival component
beta ~ normal(0,10);
if(n_time_interval>2){
lambda[1] ~ lognormal(0,5);
}else{
lambda[1] ~ normal(0,5);
}
for(i in 2:n_time_interval){
lambda[i]~lognormal(lambda[i-1],sigma_lambda);
}
sigma_lambda~cauchy(0,2.5);
eta ~ normal(0,10);
}
generated quantities {
vector[p_X_slope] b_fix_unscale_true;
b_fix_unscale_true[1]=b_fix_unscale[1];
for(i in 2:p_X_slope){
b_fix_unscale_true[i]=b_fix_unscale[1]+b_fix_unscale[i];

Section 3. The difference in QTc between two study arms over the first 2 weeks of study drug administration

Study day	Difference of QTC between study arms before drug using (95% CI)	Difference of QTC between study arms 2 hr after drug using (95% CI)
0	0.00 (0.00, 0.00)	0.00 (0.00, 0.00)
1	3.73 (-0.29, 7.74)	7.44 (3.45, 11.44)
2	7.63 (0.09, 15.18)	14.51 (6.99, 22.02)
3	11.91 (1.75, 22.06)	20.8 (10.69, 30.92)
4	16.73 (5.2, 28.25)	25.96 (14.47, 37.45)
5	22.13 (10.28, 33.97)	29.68 (17.85, 41.5)
6	27.55 (15.32, 39.78)	32.05 (19.82, 44.27)
7	32.29 (18.96, 45.62)	33.24 (19.92, 46.57)
8	35.63 (20.85, 50.42)	33.44 (18.67, 48.21)
9	37.07 (21.09, 53.04)	32.82 (16.86, 48.77)
10	36.82 (19.81, 53.83)	31.54 (14.55, 48.53)
11	35.32 (16.92, 53.72)	29.77 (11.39, 48.15)
12	32.97 (12.41, 53.54)	27.67 (7.13, 48.21)
13	30.21 (6.65, 53.77)	25.41 (1.89, 48.93)

Appendix 1—figure 1

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The clinical trial has been conducted in Vietnam under the Ministry of Health and local Ethical Committee approvals. Requests to share the clinical data underlying the trial have to be acknowledged by the local Ethical Committee (and therefore we cannot hand over the data repository or management to an external party). The original de-identified clinical data underlying the study are available by emailing the OUCRU Data Access Committee at DAC@oucru.org or ekestelyn@oucru.org (Head of the Clinical Trials Unit and Data Access Committee Chair). The review procedures (the data sharing policy and the data request form) are available on the OUCRU website at http://www.oucru.org/data-sharing/. The code for the study analysis is freely available at https://doi.org/10.5287/bodleian:XmeOzdR8z.

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Author details

1. Nguyen Thi Thuy Ngan

   1. Department of Tropical Medicine, Cho Ray Hospital, Ho Chi Minh City, Viet Nam
   2. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft, Project administration

   Competing interests

   No competing interests declared

2. Nhat Thanh Hoang Le

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

3. Nguyen Ngo Vi Vi

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Ninh Thi Thanh Van

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Validation, Investigation, Project administration

   Competing interests

   No competing interests declared

5. Nguyen Thi Hoang Mai

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Data curation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Duong Van Anh

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Phan Hai Trieu

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

8. Nguyen Phu Huong Lan

   The Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

9. Nguyen Hoan Phu

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

10. Nguyen Van Vinh Chau

    The Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam

    Contribution

    Resources, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

11. David G Lalloo

    Liverpool School of Tropical Medicine, Liverpool, United Kingdom

    Contribution

    Validation, Writing - review and editing

    Competing interests

    No competing interests declared

12. William Hope

    Centre of Excellence in Infectious Disease Research, Institute of Translational Medicine, Liverpool University, Liverpool, United Kingdom

    Contribution

    Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

13. Justin Beardsley

    1. The University of Sydney, Marie Bashir Institute, NSW, Camperdown, Australia
    2. Westmead Institute for Medical Research, Westmead, Australia

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

14. Nicholas J White

    1. Mahidol Oxford Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1897-1978

15. Ronald Geskus

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Formal analysis, Writing - review and editing

    Competing interests

    No competing interests declared

16. Guy E Thwaites

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2858-2087

17. Damian Krysan

    Department of Paediatrics and Microbiology/Immunology, Carver College of Medicine, University of Iowa, Iowa City, United States

    Contribution

    Conceptualization, Writing - review and editing

    Competing interests

    No competing interests declared

18. Luong Thi Hue Tai

    The Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

19. Evelyne Kestelyn

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Resources, Data curation, Supervision, Validation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5728-0918

20. Tran Quang Binh

    Department of Tropical Medicine, Cho Ray Hospital, Ho Chi Minh City, Viet Nam

    Contribution

    Supervision, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

21. Le Quoc Hung

    Department of Tropical Medicine, Cho Ray Hospital, Ho Chi Minh City, Viet Nam

    Contribution

    Supervision, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

22. Nguyen Le Nhu Tung

    The Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam

    Contribution

    Resources, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

23. Jeremy N Day

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    jday@oucru.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7843-6280

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