Modeling the Impact of Malaria Chemoprevention with sulphadoxine-pyrimethamine on the spread of Plasmodium falciparum dhps A581G mutation

Sulfadoxine-pyrimethamine (SP) is a key antimalarial used in chemoprevention strategies, including intermittent preventive treatment in pregnancy (IPTp); intermittent preventive treatment in infants (IPTi); seasonal malaria chemoprevention (SMC); and mass drug administration (MDA). Malaria chemoprevention using sulfadoxine-pyrimethamine (SP) has shown a positive impact in preventing millions of malaria cases in the setting were it has widely been implemented. However, the spread of SP resistance in <i>Plasmodium falciparum</i>, particularly the emergence of the <i>dhps</i> A581G mutation which confers fully resistance to SP, threatens its long-term effectiveness. This study uses a mathematical modeling approach to evaluate how different chemoprevention strategies could influence the spread of SP-resistant genotypes in settings where <i>dhps</i> K540E mutation is already near fixation (highly prevalent). We developed a deterministic age-structured compartmental model incorporating both human and mosquito populations. The study simulates malaria transmission dynamics and the competition between <i>dhps</i> K540E and resistant <i>dhps</i> A581G parasite strains under varying chemoprevention coverage levels and treatment cycles. Simulations were carried out considering the settings where <i>dhps</i> K540E is most prevalent and is already near fixation, while <i>dhps</i> A581G is rising in prevalence. Two intervention scenarios were examined: three monthly chemoprevention cycles and five monthly cycles per transmission season, with coverage levels varrying from 0% to 80%. Model simulations demonstrate that chemoprevention coverage and frequency significantly affect the spread of <i>dhps</i> A581G. At 45% chemoprevention coverage, <i>dhps</i> A581G increased from 11% to 20.6% (three cycles) to 27.8% (five cycles). ANOVA was performed, followed by a partial eta-squared <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow> <mml:mrow><mml:mi>p</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> </mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:math> to quantify the size effects of coverage levels and the number of treatment cycles while controlling for other variables in the model. Results revealed that both the chemoprevention coverage levels and the number of treatment cycles influence the spread of resistance, with 58% of the spread of resistance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow> <mml:mrow><mml:mi>P</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0.58</mml:mn> <mml:mo>,</mml:mo> <mml:mn>95</mml:mn> <mml:mi>%</mml:mi> <mml:mtext>CI:</mml:mtext> <mml:mn>0.57</mml:mn> <mml:mo>-</mml:mo> <mml:mn>1.00</mml:mn></mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:math> explained by the chemoprevention coverage level, making coverage level the most influential factor affecting the spread of resistance. The number of treatment cycles per year also matters, but its influence on the spread of resistance is relatively smaller, only 9.3% of the spread of resistance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow> <mml:mrow><mml:mi>P</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0.093</mml:mn> <mml:mo>,</mml:mo> <mml:mn>95</mml:mn> <mml:mi>%</mml:mi> <mml:mtext>CI:</mml:mtext> <mml:mn>0.09</mml:mn> <mml:mo>-</mml:mo> <mml:mn>1.00</mml:mn></mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:math> is explained by the number of treatment cycles. The interaction between chemoprevention coverage levels and the treatment cycles influences the spread of resistance by 9.0%, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow> <mml:mrow><mml:mi>P</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0.090</mml:mn> <mml:mo>,</mml:mo> <mml:mn>95</mml:mn> <mml:mi>%</mml:mi> <mml:mtext>CI:</mml:mtext> <mml:mn>0.08</mml:mn> <mml:mo>-</mml:mo> <mml:mn>1.00</mml:mn></mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:math> . Our findings highlight a trade-off between immediate public health gains from high chemoprevention coverage and the long-term risk of accelerating SP resistance. While chemoprevention using SP influences the spread of the <i>dhps</i> A581G mutation, careful policy planning, decisions, and ongoing molecular surveillance are essential to optimize the benefits of chemoprevention while minimizing the risk of the spread of the <i>dhps</i> A581G mutation.