Chilochibi Chiziba

Introduction: Although Zambia does not rank among the highest contributors to global malaria cases, in 2023, it had approximately eleven million cases. Furthermore, Zambia’s malaria incidence distribution is spatially heterogeneous at fine scales such as health facility catchment areas (HFCAs). HFCA-level malaria incidence stratification currently guides implementation and targeting approaches. We aim to enhance the current stratification approach by incorporating archetypical characteristics associated with malaria, including environmental, access, epidemiological, and socioeconomic factors. An archetype-based stratification approach may inform the tailoring of interventions to accelerate progress toward elimination. Methods: We used literature-informed data from 2017 to 2024 from the Zambia Health Information Management System and publicly available datasets. A zero-inflated negative binomial mixed-effects regression model was fitted to identify significant variables associated with malaria heterogeneity at the HFCA level. Significant variables were used to archetype HFCAs using Clustering Large Applications algorithm. Results: Significant variables included: walking travel time to nearest health facility; percentage of malaria attributed to pregnant women; percentage of malaria attributed to children under five (U5); Children U5’s bednet (Long-lasting insecticide-treated nets or Insecticide-treated nets) coverage; bednet use rate; elevation; enhanced vegetation index; precipitation; and housing quality. Four new archetypes were derived. Relative to malaria incidence-based stratification, the archetype-based stratification exhibited spatiotemporal stability over time. Across all four new archetypes, elevation, bednet use rate, and housing quality emerged as the top-contributing features. However, all four archetypes exhibited distinct features defining their identity. Conclusions: The study proposes embedding archetypical characteristics in the malaria incidence-based stratification approach to better inform intervention tailoring and prioritisation based on the distinct archetype features associated with malaria at the HFCAs level. Furthermore, the study provides a precursor for simulating potential intervention mixes based on malaria transmission archetypical characteristics, which can provide decision-makers with projected impact of varied investments in HFCAs with similar archetypical features.

Although Zambia does not rank among the highest contributors to global malaria cases, in 2023, it had approximately eleven million cases. Furthermore, Zambia’s malaria incidence distribution is spatially heterogeneous at fine scales such as health facility catchment areas (HFCAs). HFCA-level malaria incidence stratification currently guides implementation and targeting approaches. We aim to enhance the current stratification approach by incorporating archetypical characteristics that drive malaria, including environmental, access, epidemiological, and socioeconomic factors. An archetype-based stratification approach may inform the tailoring of interventions to accelerate progress toward elimination. We used literature-informed data from 2017 to 2024 from the Zambia Health Information Management System and publicly available datasets. A zero-inflated negative binomial mixed-effects regression model was fitted to identify significant variables influencing malaria heterogeneity at the HFCA level. The significant variables were used to archetype HFCAs using the Clustering Large Applications (CLARA) algorithm. Significant variables included: walking travel time to the nearest health facility; percentage of malaria attributed to pregnant women; percentage of malaria attributed to children under five (U5); Children U5’s bednet (Long-lasting insecticide-treated nets or Insecticide-treated nets) coverage; bednet use rate; elevation; enhanced vegetation index; precipitation; and housing quality. Four new archetypes were derived. Relative to malaria incidence-based stratification, the archetype-based stratification exhibited spatiotemporal stability over time. Across all four new archetypes, elevation, bednet use rate, and housing quality emerged as the most influential features. However, all four archetypes exhibited distinct features influencing their makeup. The study proposes embedding archetypical characteristics in the malaria incidence-based stratification approach to better inform intervention tailoring and prioritisation based on the distinct archetype features driving malaria at the HFCAs level. Furthermore, the study provides a precursor for simulating potential intervention mixes based on malaria transmission archetypical characteristics, which can provide decision-makers with projected impact of varied investments in HFCAs with similar archetypical features.

In Zambia-like settings, asymptomatic and clinical carriers not seeking treatment further complicate malaria elimination, making reactive case detection (RCD) essential for identifying undetected infections. However, RCD faces operational hurdles, including resource shortages, logistical challenges, limited community health workers (CHWs), and limitations in availability and sensitive rapid diagnostic tests (RDTs). Prioritizing specific improvement measures is critical to enhance intervention outcomes. A mathematical model of malaria transmission for low-transmission areas (fewer than 200 cases per 1,000 annually) was developed using published data to simulate RCD. This model assessed the impact of potential improvement measures designed to address the identified operational challenges affecting RCD. Improvement measures included increasing CHWs, adjusting response times, improving RDT sensitivity, and incorporating focal mass drug administration (fMDA). A shortage of CHWs and limited availability of RDTs have the most negative impact on RCD’s ability to reduce cases. In scenarios where CHWs or RDT availability for RCD were reduced by 50%, annual cases increased by approximately 22%. Only the incorporation of fMDA as an improvement measure succeeded countering the situation, resulting in a 43% reduction. Increasing CHWs to offset RCD inefficiencies caused by limited RDT sensitivity and difficulties finding individuals reduced cases by approximately 13 and 14%, respectively, reducing more cases than improving reaction time or increasing the screening radius. Although RCD is prone to challenges, the manipulation of improvement measures such as CHWs and fMDA provides promise for RCD to contribute towards malaria elimination. However, the participation of CHWs is voluntary and primarily motivated by informal incentives, often provided by donors. Finding sustainable means to ensure the sufficient availability of CHWs may guarantee continued RCD contributions toward maintaining stable malaria prevalence. More research is required to explore the application of RCD in archetypical transmission areas suitable for RCD as improvement measures to the identified challenges hindering RCD.

Introduction In Zambia, malaria prevalence varies spatially, posing challenges for intervention strategies. Asymptomatic and clinical carriers not accessing healthcare further complicate efforts, necessitating reactive case detection (RCD) to target undetected infections. However, operational hurdles, such as resource shortages and logistical complexities—including shortages of community health workers (CHWs), difficulties reaching residents, and limitations in malaria rapid diagnostic tests (RDTs)—hinder RCD’s effectiveness. Identifying effective improvement measures given circumstances that may lead to deficient intervention outcomes may improve the situation. Methods A mathematical model of malaria transmission conforming to Zambia’s low transmission areas defined as areas with an incidence of fewer than 200 malaria cases per 1,000 population per year was developed to simulate RCD using parameters and data from published articles. We explored the impact of literature-identified challenges on RCD performance in malaria detection and potential strategies to enhance detection rates. The examined factors and improvement measures included increasing CHWs, adjusting reaction time, RDT sensitivity, and implementing focal mass drug administration (fMDA). Results Simulation findings suggest that a shortage of CHWs and limited availability of RDTs have the highest negative impact on RCD compared to other challenges. In scenarios where CHWs or RDT availability for RCD were reduced by 50%, annual malaria cases were predicted to increase by approximately 17%. Only the incorporation of fMDA as an improvement measure succeeded in countering the situation. Increasing CHWs to offset RCD inefficiencies caused by limited RDT sensitivity or difficulties in finding individuals resulted in fewer cases than improving reaction time or increasing the screening radius. Conclusions Participation of CHWs is voluntary and primarily motivated by informal incentives, often provided by donors. Finding sustainable means to ensure the sufficient availability of CHWs may guarantee continued RCD contributions towards maintaining stable malaria prevalence and elimination. More research is required to explore the application of RCD in archetypical transmission areas suitable for RCD as improvement measures to the identified challenges hindering RCD. Furthermore, archetype-based targeting of interventions, including RCD, may also be explored to inform the optimisation of intervention resource allocation to overcome the widening gap in malaria funding.

According to the Demographic and Health Surveys (DHSs), Zambia has shown an increasing trend in the percentage of married women using contraceptives in the last three decades. As of 2018, this percentage increased from 34.2% in 2001 to 40.8% in 2007 and from 45% in 2013 to 48% in 2018. Despite the increasing trend in contraceptive use, the unmet needs remain relatively high. The low percentage of contraception use translates into 20% of women of reproductive age who are either married/partnered and want to stop or delay childbearing but are not using contraception. This study analyzed factors other than availability that influence women’s ability to make or influence the decision to use contraception using logistic regression using data from the Zambia 2013/2014 and 2018 DHSs. Furthermore, adjusted odds ratios and predicted probabilities were estimated using the fitted logistic regression. Data on 8,335 women were analyzed, and 13.7% (n = 1,145) had their husband as the sole decision maker for contraception use, while 86.3% (n = 7,189) made the decisions or participated in making the decision. Contrary to most literature, those with primary or secondary school education were less likely to decide than those without education. The data also associate women who contribute to daily household decisions to having a say in deciding to use contraception. Lastly, women using reversible contraception methods, other methods, hormonal methods, and fertility awareness were associated with less likelihood to decide on using contraceptives than those using barrier methods. Women with lower household decision-making powers are less likely to make or influence decisions to use contraception. Consequently, there is a need to prioritize such women in interventions aimed at increasing contraception use decision-making. Furthermore, more studies are required to investigate why uneducated women in Zambia are more likely to choose contraception. Also, the vast odds ratio difference between all other methods compared to barrier methods (condoms) indicates underlying factors that play a role, which warrants further studies.

Urban population growth in Nigeria may exceed the availability of affordable housing and basic services, resulting in living conditions conducive to vector breeding and heterogeneous malaria transmission. Understanding the link between community-level factors and urban malaria transmission informs targeted interventions. We analyzed Demographic and Health Survey Program cluster-level data, alongside geospatial covariates, to describe variations in malaria prevalence in children under 5 years of age. Univariate and multivariable models explored the relationship between malaria test positivity rates at the cluster level and community-level factors. Generally, malaria test positivity rates in urban areas are low and declining. The factors that best predicted malaria test positivity rates within a multivariable model were post-primary education, wealth quintiles, population density, access to improved housing, child fever treatment-seeking, precipitation, and enhanced vegetation index. Malaria transmission in urban areas will likely be reduced by addressing socioeconomic and environmental factors that promote exposure to disease vectors. Enhanced regional surveillance systems in Nigeria can provide detailed data to further refine our understanding of these factors in relation to malaria transmission.