The role of Seasonal Malaria Chemoprevention in the effect of Azithromycin on Child Mortality: A Secondary Analysis of the CHAT Cluster Randomized Clinical Trial

The role of Seasonal Malaria Chemoprevention in the effect of Azithromycin on Child Mortality: A Secondary Analysis of the CHAT Cluster Randomized Clinical Trial | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The role of Seasonal Malaria Chemoprevention in the effect of Azithromycin on Child Mortality: A Secondary Analysis of the CHAT Cluster Randomized Clinical Trial View ORCID Profile Elisabeth A. Gebreegziabher , Mamadou Ouattara , Mamadou Bountogo , Boubacar Coulibaly , Valentin Boudo , Thierry Ouedraogo , Elodie Lebas , View ORCID Profile Huiyu Hu , View ORCID Profile Kieran S. O’Brien , Michelle S. Hsiang , View ORCID Profile David V. Glidden , View ORCID Profile Benjamin F. Arnold , Thomas M. Lietman , Ali Sié , View ORCID Profile Catherine E. Oldenburg doi: https://doi.org/10.1101/2025.04.30.25326740 Elisabeth A. Gebreegziabher 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA 3 Department of Epidemiology and Biostatistics, University of California , San Francisco Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elisabeth A. Gebreegziabher For correspondence: amareelisabeth{at}gmail.com Mamadou Ouattara 2 Centre de Recherche en Sante de Nouna , Nouna, Burkina Faso Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mamadou Bountogo 2 Centre de Recherche en Sante de Nouna , Nouna, Burkina Faso Find this author on Google Scholar Find this author on PubMed Search for this author on this site Boubacar Coulibaly 2 Centre de Recherche en Sante de Nouna , Nouna, Burkina Faso Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valentin Boudo 2 Centre de Recherche en Sante de Nouna , Nouna, Burkina Faso Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thierry Ouedraogo 2 Centre de Recherche en Sante de Nouna , Nouna, Burkina Faso Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elodie Lebas 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Huiyu Hu 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Huiyu Hu Kieran S. O’Brien 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA 3 Department of Epidemiology and Biostatistics, University of California , San Francisco 6 Department of Ophthalmology, University of California , San Francisco, San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kieran S. O’Brien Michelle S. Hsiang 3 Department of Epidemiology and Biostatistics, University of California , San Francisco 4 Malaria Elimination Initiative, Institute for Global Health Sciences, University of California San Francisco , San Francisco, CA, USA 5 Division of Pediatric Infectious Diseases, Department of Pediatrics , San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David V. Glidden 3 Department of Epidemiology and Biostatistics, University of California , San Francisco Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David V. Glidden Benjamin F. Arnold 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA 6 Department of Ophthalmology, University of California , San Francisco, San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benjamin F. Arnold Thomas M. Lietman 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA 6 Department of Ophthalmology, University of California , San Francisco, San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ali Sié 2 Centre de Recherche en Sante de Nouna , Nouna, Burkina Faso Find this author on Google Scholar Find this author on PubMed Search for this author on this site Catherine E. Oldenburg 1 Francis I. Proctor Foundation, University of California San Francisco , San Francisco, CA, USA 3 Department of Epidemiology and Biostatistics, University of California , San Francisco 6 Department of Ophthalmology, University of California , San Francisco, San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine E. Oldenburg Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Objective Mass treatment with azithromycin (AZ) and administration of seasonal malaria chemoprevention (SMC) are both effective in reducing mortality among children under 5. However, it is not clear whether the benefit of AZ for mortality varies in the presence of routine SMC administration. The objective of this study was to examine whether the effect of mass AZ distribution on all-cause mortality among children less than 5 years of age varies with SMC administration season or SMC coverage. Methods This was a secondary analysis of the Community Health with Azithromycin Trial (CHAT), a cluster randomized placebo-controlled trial of 341 communities in the Nouna District of Burkina Faso. Communities randomized to intervention received treatment with twice yearly mass AZ while control communities receive placebo. All communities received SMC as standard-of-care. SMC administration and coverage data were provided from National Malaria Control Program. SMC administration season was defined as the period during and immediately following SMC (July-December) versus the months of no SMC (January-June). SMC coverage was assessed as proportion of the population covered and by whether it was below or above a threshold of 80%. We used Poisson regression models with person-time at risk used as an offset and robust standard error to analyze mortality rates by treatment group and SMC subgroups and assessed interaction on both the multiplicative and additive scales. Results Mortality was higher in SMC seasons for both arms, with a mortality rate of 10.3 per 1,000 person-years (95% CI: 9.0 to 11.6) in SMC seasons and 7.9 (95% CI: 6.9 to 9.0) in non-SMC seasons. Compared to placebo, the mortality rate in AZ clusters was 0.77 (95% CI: 0.60 to 0.98) during SMC season, while it was 0.89 (95% CI: 0.68 to 1.15) during the non-SMC seasons. The effect of AZ compared to placebo in clusters with <80% SMC coverage was 0.73 95%CI (0.56 to 0.96) and in clusters with ≥80% SMC coverage, it was 1.0 95%CI (0.59 to 1.69). The interaction between AZ and SMC season or coverage was not statistically significant on the additive or multiplicative scales. Conclusion While our findings did not reach statistical significance, they raise the question of whether prioritizing MDA AZ during high transmission periods or in regions with low SMC coverage could be beneficial. Further research is needed to determine if targeting these periods or areas could lead to greater reductions in child mortality. Introduction Many regions of sub-Saharan Africa have high child mortality rates.[ 1 ] Infectious diseases, in particular acute diarrheal and respiratory infections, and malaria, are the leading causes of illness and death in children in low- and middle-income countries.[ 2 ] Antimicrobial-based strategies such as mass drug administration of azithromycin (MDA AZ) and seasonal malaria chemoprevention (SMC) have been used to reduce child mortality in these settings.[ 3 , 4 ] Azithromycin (AZ) has been shown to reduce all-cause mortality as well as infectious morbidity due to diarrhea, pneumonia, and malaria.[ 3 , 5 , 6 ] The MORDOR study which randomized communities in Malawi, Niger, and Tanzania to four twice-yearly mass distributions of either oral AZ or placebo found that childhood mortality was lower in communities randomized to AZ, with the largest effect (a relative reduction in all-cause mortality of 18.1%) seen in Niger, where SMC was not being distributed.[ 3 ] The Community Health with Azithromycin Trial (CHAT) completed in 2023 and the AVENIR (Azithromycine pour la Vie des Enfants au Niger: Implementation et Recherche) trial in Niger also demonstrated that mass distribution of AZ remains effective at reducing child mortality in a setting receiving SMC.[ 7 , 8 ] The finding of these trials was consistent with MORDOR-Niger, showing a reduction in all-cause mortality overall in communities randomized to AZ compared to placebo.[ 7 ] SMC involves monthly administration of a 3-day regimen of sulfadoxine-pyrimethamine and amodiaquine (SP-AQ) during the malaria season in regions with highly seasonal transmission, where there is no evidence of resistance to these drugs.[ 9 ] SMC treats current infection and provides protection against future infections for up to 3-4 weeks.[ 10 ] As of 2020, following the WHO policy recommendation, 13 countries in Africa have adopted SMC with different scales of implementation.[ 11 , 12 ] Large-scale evaluations have found SMC effective at reducing malaria infection and all-cause mortality.[ 13 , 14 ] Despite the adoption of SMC, it is not clear how the antimalarial, antibacterial, anti-inflammatory and overall benefits of SP in SMC might affect the benefit of AZ for child mortality.[ 15 ] Since Sulfadoxine is a broad-spectrum, long acting antibiotic, it may influence infections that AZ is effective against. Previous evidence for the combined effect of AZ and SP in pregnant women showed that the combination of these drugs was superior in antimalarial activity compared to SP with chloroquinine[ 16 , 17 ] and SP alone.[ 18 ] A recent RCT found that children receiving the addition of AZ to SMC, versus placebo and SMC, had fewer episodes of gastrointestinal infections, upper respiratory tract infections, and nonmalarial febrile illnesses. However, there was no significant difference in incidence of death or hospitalization between these groups.[ 19 ] The lack of benefit of AZ for mortality observed in this trial raises questions about whether the mortality benefit of AZ may be lower when SMC is also administered. On a larger scale, these questions are relevant for the implementation of MDA AZ, particularly in relation to the timing of AZ administration (since SMC is administered seasonally) and for identifying communities that would benefit most based on SMC coverage. Communities with gaps in SMC coverage may also have other vulnerabilities and gaps in care, for which AZ might be particularly effective.[ 20 ] The potential heterogeneity in the effect of AZ calls for further studies before its widespread use can be recommended.[ 21 ] Studies are needed to refine key aspects of implementation, such as the optimal dose, frequency, timing,[ 22 ] and the populations to target or prioritize.[ 23 , 24 ] Additionally, understanding how malaria seasonality and the concurrent use of malaria interventions like SMC impact the effectiveness of MDA AZ programs can help in tailoring these interventions to enhance their benefits, use resources efficiently and develop integrated strategies. Using data from the CHAT cluster-randomized controlled trial in Burkina Faso[ 7 ] and the country’s National Malaria Control Program (NMCP) data on SMC administration and coverage, we examined whether the effect of mass AZ distribution on mortality among children aged 1-59 months varies with SMC administration season or SMC coverage. We hypothesized that the effect of AZ will vary with SMC season and coverage level. Methods Study Design, Setting, Population and Data Collection This was a post hoc secondary analysis of the CHAT trial (ClinicalTrials.gov, NCT03676764 ) and additionally included SMC data from NMCP. The methods and primary results have been reported in detail previously.[ 7 , 25 ] Complete methods for the trial are also shown in the trial protocol (Supplement 1). In brief, CHAT was a cluster-randomized placebo-controlled trial designed to evaluate the efficacy of mass AZ strategies.[ 25 ] The trial was conducted from August 2019 to February 2023 and enrolled children aged 1-59 months in Nouna District of Burkina Faso. It covered both the Nouna Health and Demographic Surveillance Site (one third of Nouna District),[ 26 ] as well as the non-HDSS area in Nouna.[ 25 ] A map of the study area is included in the primary analysis of the trial.[ 7 ] Communities were randomized in a 1:1 ratio to receive twice yearly distribution of a single oral dose of azithromycin or placebo. Children in these communities received their respective treatments for 3 years (from August 2019-February 2023). The overall coverage of MDA was approximately 90% for both AZ and placebo arms. Every 6 months, a census was conducted documenting births, deaths, pregnancies, and migration. CHAT had an open cohort design with children contributing different amounts of person time, and vital status recorded at each 6-month census (phases) for each child enrolled. Over the study period, 1,086 deaths over 119,139 person-years were observed ( Figure 1 ).[ 7 ] Six rounds of census and treatment occurred during the study corresponding to the following phases: 0-6 months, 6-12, 12-18, 18-24, 24-30 and 30-36 months ( Figure 2 ). Trial data were aggregated by cluster and these phases. All communities included in trial were included in these analyses. Download figure Open in new tab Figure 1. Cluster and Participant Flow Diagram Download figure Open in new tab Figure 2. Timeline of census and AZ treatment phases in CHAT trial and SMC seasons Four monthly cycles of SMC were administered per local standard-of-care to children under 5 during the malaria transmission season from July through October each year. SMC was administered to the study area over a 3-4 day period.[ 27 ] NMCP records were reviewed to obtain SMC coverage data, including number of children treated during each cycle and the timing of treatment at the health post (CSPS) level. Each community falls within a CSPS catchment area and was assigned the SMC coverage level of its corresponding CSPS. The CHAT trial which served as basis for these analyses was reviewed and approved by the Institutional Review Boards at the University of California, San Francisco (IRB Number: 17-24230) and the Comité National d’Ethique pour la Recherche (National Ethics Committee of Burkina Faso (IRB Number: 2018-8-111) in Ouagadougou, Burkina Faso. Statistical analysis The primary outcome for these analyses was all-cause mortality, assessed using a biannual census. The sample size was based on childhood mortality for the overall AZ versus placebo comparison in CHAT trial. Details of sample size calculation have been reported previously.[ 7 ] For the subgroup analysis, we calculated the minimum detectable effect (additive interaction effect per 1000 children) based on methods described by Hayes & Moulton for calculating power and sample size for examining interaction in cluster randomized trials, [ 28 ] This method accounts for clustering by using a randomization -unit level summary statistic.[ 28 ] Using parameters from CHAT data at 24 months, with 139 clusters per arm, 27 person-years of observation per cluster per subgroup, cluster standard deviation of 0.0223 and alpha of 0.05 for 2-sided test, we estimated that we would have 80% power to detect a delta of 1.16 (additive interaction effect of ∼1 death per 1000 children). We conducted three analyses in which we assessed the effect of AZ (AZ vs placebo) in 1) the months of no SMC (January-June) versus months during and immediately following SMC (July-December), 2) by SMC coverage level (0-100%) on a continuous scale, and 3) by SMC coverage of below or above a threshold level of 80%, which was chosen a priori based on recommended WHO coverage levels. Descriptive analyses were used to report the mean number of children in each community, cluster-level mean percentages of children by age-group and sex, as well as SMC coverage levels. We used the interaction directed acyclic graph[ 29 ] shown in Figure 3 to visualize the relationship between SMC and effect of AZ on under 5 mortality examined in the interaction analyses. Download figure Open in new tab Figure 3. Interaction Directed Acyclic Graphs Illustrating the Interaction Between Mass AZ Treatment and SMC For the analysis by season, the six phases were categorized as non-SMC (January-June) versus the SMC (July-December) seasons of the year, based on the timing of treatment and census. A Poisson regression model was used to analyze data at the cluster phase level with person-years included as an offset and robust standard error clustered at the cluster level to account for repeating phases. This was used to estimate the mortality rate in AZ and placebo groups, their rate ratio by season, and their interaction on the multiplicative scale. The margins command in Stata was used to estimate incidence rate differences (IRDs) with 95% CIs using the delta method. Number needed to treat to avert one death was calculated using the rate differences. Relative excess risk due to interaction (RERI) with bootstrap 95% confidence limits and 1000 repetitions was used to assess interaction on the additive scale. For the analysis by coverage, we updated the SMC coverage for 2020 and 2021 originally reported by NMCP using 2006 denominators (estimates of total number of children), by the corresponding census denominators from CHAT to obtain more accurate coverage estimates for each CSPS. The SMC coverage for each cycle in each year for each CSPS was calculated as the number of children treated (obtained from NMCP) divided by the total number of children in the CSPS catchment area. SMC coverage percentages for each cluster and each year (2019, 2020, 2021) were linked to the trial data by cluster. Since SMC coverage estimates for 2019 were not available and given the high correlation (>90%) in CSPS level coverage between 2020 and 2021, coverage estimates for 2020 were used for both 2019 and 2020. For the coverage estimate for each year, we used the average coverage level across the 4 cycles of SMC distributed during that year. Clusters with coverage estimates above 100% (∼8%) were capped to 100%. SMC coverage was scaled for each unit increase to represent 10% increase in coverage. Given the short duration of protection of SMC,[ 30 ] for the analysis by coverage, we restricted the data to the second half of each year (July-December), the period during and immediately after SMC was administered. The analysis dataset included the treatment type (AZ vs placebo), number of deaths and person-time aggregated by cluster and phase year, along with the coverage estimate for each cluster and phase, in a longitudinal format. For the analysis by coverage level of 80% or above, we used the dataset for the coverage analysis described above and dichotomized coverage level as below or 80% and above and examined whether the effect of AZ varied in these subgroups. We considered this cutoff reasonable since it has been noted that 69% of children receiving all SMC cycles or 90% receiving at least one cycle, can be considered high coverage.[ 31 ] For both analyses by coverage level, we used Poisson regression models with robust standard errors and similar methods described above for examining interactions on additive and multiplicate scales. Due to the imbalance in SMC coverage between arms, we conducted sensitivity analysis adjusting for SMC coverage in the analysis by SMC season. Furthermore, we categorized the year into quarters, in a sensitivity analysis, to evaluate during which time of the year AZ distribution yielded the greatest benefit against mortality. Lastly, we examined the role of SMC coverage in the effect of AZ on child mortality, adjusting for age of children and distance to health facility, and their interactions with AZ,[ 29 ] as both may influence the level of SMC coverage and affect the benefit of AZ for mortality.[ 3 , 20 , 32 ] We plotted the mortality rate and rate differences by SMC season, coverage level, and by a threshold coverage level of 80% to visualize changes in the effect of AZ, and in the mortality rates by arm. SAS 9.4 (SAS Institute, Cary, NC) was used for data cleaning and for generating the datasets for analyses. Stata version 14.2 (StataCorp, College Station, TX) was used for all other analyses. Results The CHAT trial involved 341 communities. Of these, 171 were randomized to receive Azithromycin, with 146 being analyzed, while 170 communities were randomized to receive placebo, with 139 being analyzed ( Figure 1 ). Characteristics of communities included in the trial are presented in Table 1 . Around 30% of the children in each arm were aged 12-23 months. The age and sex distributions were similar across arms. The SMC coverage estimates that were updated by recent denominators showed variability, with an overall average coverage of 79% (SD 0.59). The average SMC coverage across rounds was slightly higher in treatment versus placebo arm, 82% versus 73%, respectively. In both arms, SMC coverage level was slightly higher in the 4 th cycle compared to other cycles. In the CHAT trial, the mortality rate in the azithromycin group was 8.2 deaths per 1000 person-years while in the placebo group, it was 10.0 deaths per 1000 person-years.[ 7 ] View this table: View inline View popup Download powerpoint Table 1. Characteristics of communities/clusters in CHAT trial For both arms, mortality rate was higher in the SMC seasons: 7.9 per 1000 person-years, 95% CI (6.9 to 9.0) in the first half of the year, compared to 10.3 per 1000 person-years, 95%CI (9.0 to 11.6) in the second half of the year ( Figure 4a ) In both SMC and non-SMC seasons, mortality rate was higher in placebo arm ( Table 2 ). The interaction between AZ and SMC season was not statistically significant on either the multiplicative or additive scale. The effect of AZ compared to placebo was 0.77, 95%CI (0.6 to 0.98) in SMC season vs 0.89 (0.68 to 1.15) in non-SMC season ( Figure 4b ). Download figure Open in new tab Figure 4. a) Mortality rate by treatment and SMC season and b) Mortality rate difference (AZ vs placebo) by SMC season View this table: View inline View popup Download powerpoint Table 2. Effect of Azithromycin vs Placebo Distribution on Child Mortality by SMC season Table 3 shows the mortality rates by arm and the effect of AZ at different SMC coverage levels. The interaction between AZ and SMC coverage (both on continuous scale and dichotomous at 80% coverage threshold) was not statistically significant on either the multiplicative or additive scale (P>0.05)., However, the benefit of AZ appeared to slightly lower with increasing SMC coverage ( Figure 5a and 5b). The effect of AZ compared to placebo in clusters with <80% SMC coverage was 0.73 95%CI (0.56 to 0.96) and in clusters with ≥80% SMC coverage, it was 1.0 95%CI (0.59 to 1.69). Download figure Open in new tab Figure 5. a) Mortality rate by treatment arm at each level of SMC coverage and b) difference in mortality rate (AZ vs Placebo) by SMC coverage View this table: View inline View popup Download powerpoint Table 3. Effect of Azithromycin vs Placebo Distribution on Child Mortality by SMC coverage level Although the interaction was not statistically significant, the mortality rate was lower in the AZ arm compared to the placebo arm (IRR AZ vs placebo=0.73, 95%CI (0.56 to 0.96) in clusters with SMC coverage below 80%. No significant difference in the mortality rate was observed between the AZ and placebo arms in clusters with SMC coverage of 80% or above: IRR=1.0, 95%CI (0.59 to 1.69), Table 4 , Figure 6a and 6b). View this table: View inline View popup Download powerpoint Table 4. Effect of Azithromycin vs Placebo Distribution on Child Mortality by SMC coverage threshold of 80% Download figure Open in new tab Figure 6, a) Mortality rate by treatment and SMC coverage and b) Mortality rate difference (AZ vs placebo) by SMC coverage threshold of 80% In the sensitivity analysis, we found that adjusting for coverage in the analysis by season generated similar findings in which the effect of AZ compared to placebo was 0.77 95%CI (0.6 to 0.98) in SMC season vs 0.89 95%CI (0.68 to 1.15) in non-SMC season ( Supplementary Table 1 , Supplementary Figure S1 ). In the sensitivity analysis by quarters, the interaction between AZ and quarter did not reach statistical significance (P=0.203). However, the benefit of AZ appeared to be more pronounced when administered in the last 3 months of the year (October–December); IRD = -4.0 per 1,000 person-years (95% CI: -7.9 to -0.11) ( Supplementary Figure S2 ). In the analysis in which we examined the role of SMC coverage in the effect of AZ on child mortality, adjusting for age of children and distance to health facility and their interaction with AZ, we found that the results were similar in which we did not reach statistical significance but the benefit of AZ appeared to slightly lower with increasing SMC coverage ( Supplementary Figure S3 ). Discussion We evaluated how the effect of AZ changes by seasons of SMC administration and coverage. We found that the interaction between AZ and SMC season or coverage did not reach statistical significance on both the additive or multiplicative scales. Mortality rates appeared higher during SMC seasons for both AZ and placebo-treated clusters. Although not significant, the benefit of AZ may have been higher during these high-mortality SMC seasons. Mortality rates appeared to decrease with increasing SMC coverage in placebo clusters but not in AZ clusters. Compared to placebo, our results suggested that AZ may have had an increasing benefit as SMC coverage decreased. Its effect was also more pronounced in clusters with <80% versus ≥80% SMC coverage. The effect of AZ appeared higher during and after the months when SMC was administered, particularly towards the end of the year. This is likely due to the high malaria transmission and associated high child mortality during this period.[ 33 ] Since AZ may provide short-term infection prevention and treatment by clearing infections,[ 5 , 34 ] its impact may be higher when administered around peak morbidity and mortality periods. It is suggested that optimal administration of AZ requires targeting asynchronous peaks in morbidity and mortality from various causes, as AZ is not specific to a single pathogen.[ 34 ] Although other infectious diseases treated by AZ, such as pneumonia and diarrhea, may follow different seasonal patterns than malaria,[ 35 , 36 ] AZ appeared to have a larger effect during this period of elevated malaria transmission and mortality. Previous studies in this region have found that malaria rates remained high in November and December, extending beyond the final cycle of SMC in October.[ 37 ] Furthermore, the sensitivity analysis ( Supplementary Figure S2 ) suggests that the effect of AZ may be more pronounced during these late months of the year, compared to the third quarter when SMC is primarily administered. While the overall benefit of AZ appeared greater in the post-SMC period (July–December), its enhanced effect may have been concentrated in the final quarter, when malaria and potentially mortality rates remain high without further cycles of SMC. This likely higher benefit however may also be due to a higher number of deaths to be prevented or greater statistical power to detect an effect of AZ, given the higher mortality during this season. Given concerns about the frequency of AZ in relation to resistance,[ 21 , 22 ] as well as its short-term effects, future studies should examine whether fewer rounds of AZ, but administered during the malaria transmission and SMC season, particularly when mortality is elevated, could be beneficial. Although the interaction was not statistically significant, the slightly higher effect of AZ in clusters with lower SMC coverage may suggest that the benefit of AZ may be greater in areas with gaps in SMC coverage. A likely contributing factor could be that SMC coverage may be related to engagement with the healthcare system, with low-coverage areas likely facing obstacles to care.[ 32 ] AZ MDA has been found more effective for children who often have less access to preventive and curative care. [ 20 ] Therefore, the lower SMC coverage may reflect broader gaps in community and facility resources related to SMC administration. Previous studies have identified several barriers to SMC uptake, including local shortages, challenges reaching mobile populations and underserved areas, and issues related to community distributors’ working conditions, such as workload and incentives.[ 32 , 38 ] Therefore regions with lower SMC coverage may also have other gaps in routine child health interventions and care, where AZ may be particularly helpful. Although it did not reach statistical significance, the higher benefit of AZ in low SMC communities suggests that prioritizing AZ treatment in areas with inadequate SMC coverage might be beneficial., The slightly reduced benefit of AZ in areas with high SMC coverage may also partly be due to reduction in malaria disease burden following SMC administration.[ 27 ] In regions with lower SMC coverage, important gaps in malaria prevention remain,[ 31 ] making AZ MDA likely beneficial as it may have some efficacy against both malaria and bacterial infections. Although the effect of AZ on malaria infections has been inconsistent,[ 39 ] analysis of causes of death from the MORDOR trial found that malaria was one of the primary causes of mortality against which AZ was beneficial.[ 6 ] With effective malaria interventions like SMC, the impact of AZ may be less apparent.[ 40 ] This is also consistent with the lack of observed benefit of AZ for mortality in the household randomized trial, where all enrolled children received SMC.[ 19 ] This study has some limitations. First, it may have been underpowered to a detect significant interaction between AZ and SMC on the mortality outcome. For the analysis by coverage level, the data was further restricted to SMC seasons, which further reduced our sample size. However, we still observed patterns suggestive of a differential effect of AZ by season and SMC coverage. Considering the lack of statistical power, is it possible that the slight differences we observed may be due to chance. These findings should be confirmed in larger studies and examined in different settings. Second, the SMC data used in our analysis were programmatic data, not prospectively collected for research purposes and therefore had less specificity and details such as daily doses or whether treatment was directly observed. Additionally, the data were provided at the health post level, and any differences in coverage levels between clusters were not captured. The absence of data for 2019 is also another limitation in our analysis. Furthermore, although we adjusted for distance to facility and age group of children, there may be other unknown factors that can affect SMC coverage as well as the effect of AZ on child mortality. However, since the goal of this these analyses was to identify subgroups that potentially benefitted the most from AZ treatment, only confounding of the primary exposure-outcome (AZ-mortality) relationship needs to be taken into account.[ 41 ] We would not expect confounding to be an issue for the AZ-mortality effect due to the randomized nature of the intervention. There may also be measurement error in our coverage level estimates of SMC. To minimize this, we updated denominators using recent census estimates of the number of children and capped outliers. Lastly, since the CHAT trial included rural communities, the generalizability of these findings may be limited to such settings. Conclusion Although the interactions did not reach statistical significance, our findings raise the question of whether MDA AZ programs could be more effective if prioritized during the latter part of the year, when malaria transmission and mortality are elevated. Additionally, regions with lower SMC coverage may represent a useful target for AZ MDA interventions. Future studies should explore whether prioritizing AZ administration in these high-risk periods or areas with gaps in SMC coverage leads to greater reductions in child mortality. Ethics Approval The CHAT Randomized controlled trial was reviewed and approved by the Institutional Review Boards at the University of California, San Francisco and the Comité National d’Ethique pour la Recherche (National Ethics Committee of Burkina Faso) in Ouagadougou, Burkina Faso. Data Availability The datasets analyzed during the current study are not yet publicly available but will be made accessible through the Open Science Framework (OSF) repository upon finalization. The data will be shared in accordance with relevant open data sharing guidelines. A link to the repository will be provided once the data is available. Funding The CHAT trial was supported by the Gates Foundation (grant number OPP1187628). The conclusions and opinions expressed in this work are those of the authors alone and should not be attributed to the Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 License has already been assigned to the Author Accepted Manuscript version that may arise from this submission. Please note that works submitted as preprints have not undergone a peer review process. Research reported in this manuscript was also supported by the National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health & Human Development (NIH/NICHD) F31 Award (1F31HD114434-01A1: E.A.G.). Conflict of Interest None declared Data Availability The datasets analyzed during the current study are not yet publicly available but will be made accessible through the Open Science Framework (OSF) repository upon finalization. The data will be shared in accordance with relevant open data sharing guidelines. A link to the repository will be provided once the data is available. Supplementary Figures Download figure Open in new tab Supplementary Figure 1. a) Mortality rate by treatment and SMC season and b) Mortality rate difference (AZ vs Placebo) by season adjusting for SMC coverage level Download figure Open in new tab Supplementary Figure 2. Mortality rate difference (AZ vs placebo) by quarter of year Download figure Open in new tab Supplementary Figure 3. Mortality Rate by Treatment and SMC Coverage: a) Continuous Scale and c) Threshold Level; Mortality Rate Difference (AZ vs. Placebo) by SMC Coverage: b) Continuous Scale and d) Threshold Level, Adjusting for Distance to Facility, Age of Children, and Their Interaction. Supplementary Tables View this table: View inline View popup Download powerpoint Supplementary Table 1. 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OpenUrl PubMed 41. ↵ VanderWeele TJ , Knol MJ . A Tutorial on Interaction . Epidemiologic Methods . 2014 ; 3 ( 1 ): 33 – 72 . OpenUrl View the discussion thread. Back to top Previous Next Posted May 02, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The role of Seasonal Malaria Chemoprevention in the effect of Azithromycin on Child Mortality: A Secondary Analysis of the CHAT Cluster Randomized Clinical Trial Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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Gebreegziabher , Mamadou Ouattara , Mamadou Bountogo , Boubacar Coulibaly , Valentin Boudo , Thierry Ouedraogo , Elodie Lebas , Huiyu Hu , Kieran S. O’Brien , Michelle S. Hsiang , David V. Glidden , Benjamin F. Arnold , Thomas M. Lietman , Ali Sié , Catherine E. 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