Analysis of Recurrent Times-to-Clinical Malaria Episodes and Plasmodium falciparum Parasitemia: A Joint Modeling Approach Applied to a Cohort Data

Christopher C Stanley; Mavuto Mukaka; Lawrence N Kazembe; Andrea G Buchwald; Don P Mathanga; Miriam K Laufer; Tobias F Chirwa

doi:10.3389/fepid.2022.924783

Analysis of Recurrent Times-to-Clinical Malaria Episodes and Plasmodium falciparum Parasitemia: A Joint Modeling Approach Applied to a Cohort Data

Front Epidemiol. 2022 Jul 8:2:924783. doi: 10.3389/fepid.2022.924783. eCollection 2022.

Authors

Christopher C Stanley^{1

2}, Mavuto Mukaka^{3

4}, Lawrence N Kazembe⁵, Andrea G Buchwald⁶, Don P Mathanga², Miriam K Laufer⁶, Tobias F Chirwa¹

Affiliations

¹ Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
² Malaria Alert Center, Kamuzu University of Health Sciences, Blantyre, Malawi.
³ Oxford Centre for Tropical Medicine and Global Health, Oxford, United Kingdom.
⁴ Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand.
⁵ Department of Statistics, University of Namibia, Windhoek, Namibia.
⁶ Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, United States.

Abstract

Background: Recurrent clinical malaria episodes due to Plasmodium falciparum parasite infection are common in endemic regions. With each infection, acquired immunity develops, making subsequent disease episodes less likely. To capture the effect of acquired immunity to malaria, it may be necessary to model recurrent clinical disease episodes jointly with P. falciparum parasitemia data. A joint model of longitudinal parasitemia and time-to-first clinical malaria episode (single-event joint model) may be inaccurate because acquired immunity is lost when subsequent episodes are excluded. This study's informativeness assessed whether joint modeling of recurrent clinical malaria episodes and parasitemia is more accurate than a single-event joint model where the subsequent episodes are ignored.

Methods: The single event joint model comprised Cox Proportional Hazards (PH) sub-model for time-to-first clinical malaria episode and Negative Binomial (NB) mixed-effects sub-model for the longitudinal parasitemia. The recurrent events joint model extends the survival sub-model to a Gamma shared frailty model to include all recurrent clinical episodes. The models were applied to cohort data from Malawi. Simulations were also conducted to assess the performance of the model under different conditions.

Results: The recurrent events joint model, which yielded higher hazard ratios of clinical malaria, was more precise and in most cases produced smaller standard errors than the single-event joint model; hazard ratio (HR) = 1.42, [95% confidence interval [CI]: 1.22, 2.03] vs. HR = 1.29, [95% CI:1.60, 2.45] among participants who reported not to use LLINs every night compared to those who used the nets every night; HR = 0.96, [ 95% CI: 0.94, 0.98] vs. HR = 0.81, [95% CI: 0.75, 0.88] for each 1-year increase in participants' age; and HR = 1.36, [95% CI: 1.05, 1.75] vs. HR = 1.10, [95% CI: 0.83, 4.11] for observations during the rainy season compared to the dry season.

Conclusion: The recurrent events joint model in this study provides a way of estimating the risk of recurrent clinical malaria in a cohort where the effect of immunity on malaria disease acquired due to P. falciparum parasitemia with aging is captured. The simulation study has shown that if correctly specified, the recurrent events joint model can give risk estimates with low bias.

Keywords: Plasmodium falciparum; cox proportional hazards model; joint modeling; longitudinal data; malaria parasitemia; mixed-effects model; recurrent clinical malaria; shared frailty model.