Use of Interrupted Time Series Analysis in Understanding the Course of the Congenital Syphilis Epidemic in Brazil

Use of Interrupted Time Series Analysis in Understanding the Course of the Congenital Syphilis Epidemic in Brazil Rafael Pinto, a,b,c, *Ricardo Valentim, a,b Lyrene Fernandes da Silva, a Gustavo Fontoura de Souza, b,c Thaísa G ois Farias de Moura Santos Lima, b,d Carlos Alberto Pereira de Oliveira, b,e Marquiony Marques dos Santos, a,b Ang elica Espinosa Miranda, d,f Aliete Cunha-Oliveira, g,h Vivekanandan Kumar, i and Rifat Atun j a Federal University of Rio Grande do Norte, Natal, RN, Brazil b Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil c Federal Institute of Rio Grande do Norte, Natal, RN, Brazil d Ministry of Health, Brasília, DF, Brazil e State University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil f Federal University of Espírito Santo, ES, Brazil g Health Sciences Research Unit: Nursing (UICISA: E) and Nursing School of Coimbra (ESEnfC), Coimbra, Portugal h Center for Interdisciplinary Studies of the 20th Century (CEIS20), University of Coimbra, Coimbra, Portugal i Athabasca University, Athabasca, Canada j Harvard School of Public Health, Harvard University, Boston, MA, USA Summary Background To fight against the rising incidence of syphilis, the Brazilian Ministry of Health (MoH) launched the “Syphilis No!” Project (SNP), with specific resources funded by a parliamentary amendment. Then, in 2018, a national rapid response started to be implemented on the Brazilian Unified Health System (SUS, Sistema  Unico de Sa ude) in two strategic lines (1) to reinforce SUS's universal actions and (2) to implement specific ones to 100 municipalities chosen by the MoH as priorities for syphilis congenital response. In 2015, such localities represented 6895% of congenital syphilis cases in Brazil. In this context, SNP has implemented actions to strengthen epidemiological surveillance of acquired syphilis and congenital syphilis by instituting an integrated and collaborative response through health services networks and reinforcing interstate relations. Methods A quasi-experimental study using time series analysis was conducted to assess immediate impacts and changes to the trend in national congenital syphilis before and after the project, from September 2016 to December 2019. Data were assessed considering rates of congenital syphilis per 1,000 live births in all priority municipalities (n=100) covered by the project and in non-priority municipalities (n=5,470) from all five macro-regions of Brazil. Findings Priority municipalities showed a greater reduction (change in trend) in comparison to non-priority. The linear regression model revealed trend changes after the intervention, with both groups of municipalities showing a drop in the average monthly number of cases per 1,000 live births, with a reduction of -0¢21 (CI 95% -0¢33 to -0¢09; p=0¢0011) in priority municipalities and of -0¢10 (CI 95% -0.19 to -0.02; p=0¢0216) in non-priority municipalities. Interpretation The study using ITS provides important evidence on the direction, timing, and magnitude of the effects of interventions introduced as part of the SNP on congenital syphilis in Brazil. Our results suggest that the Syphilis No! Project influenced the trends of congenital syphilis in Brazil from 2018, with higher reductions achieved in the priority municipalities. Funding The research is funded by a grant to the Syphilis No! Project from Brazilian Ministry of Health (Project Number: 54/2017). The funders had no role in study design, analysis, decision to publish, or preparation of the manuscript. Copyright Ó2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: public health; policy; interrupted time series; segmented regression analysis; notifiable disease; syphilis *Corresponding author. E-mail address: [email protected] (R. Pinto). The Lancet Regional Health - Americas 2022;7: 100163 Published online 27 December 2021 https://doi.org/10.1016/j. lana.2021.100163 www.thelancet.com Vol 7 Month March, 2022 1 Articles Introduction Congenital syphilis (mother-to-child transmission or vertical transmission of syphilis of syphilis) causes damage to the fetus if the mother’s infection is not detected and properly treated during pregnancy. The World Health Organization (WHO) estimates that each year 930,000 pregnant women have probable active syphilis worldwide each year. It results in approximately 350,000 adverse birth outcomes, including 143,000 early fetal deaths/stillbirths, 62,000 neonatal deaths, 44,000 premature/low-birthweight babies, and 102,000 infected infants. 1,2 The Pan American Health Organization (PAHO) Member States, supported by WHO, in 2010 approved the Strategy and Plan of Action for the Elimination of Mother-to-Child Transmission of HIV and Congenital Syphilis. The strategy was set to reduce the incidence of congenital syphilis to ≤0¢5 cases per 1,000 live births by 2015. 3 Nonetheless, Brazil did not reach the congenital syphilis elimination goal, which led a resurgence of acquired syphilis, syphilis in pregnant women and congenital syphilis across the country. In 2010, 6,949 cases of congenital syphilis were reported to the Brazilian Ministry of Health (2¢4/1,000 live births), whereas, in 2015, this case count increased to 19,647 (6¢5/1,000 live births) accounting for a growth rate of 170¢83%. 4 To fight the syphilis epidemic in Brazil, in October of 2016, the Ministry of Health (MoH) issued the Strategic Actions Agenda for Reducing Syphilis in Brazil. 5 This agenda established a list of priorities aimed in collaboration with PAHO and other institutions such as universities, international agencies, and state and municipal representatives. Furthermore, such agenda prompted a parliamentary amendment with specific resources to implement a national rapid response in Brazil’s Unified Health System (SUS, Sistema  Unico de Sa ude). The Brazilian Ministry of Health invited state and municipal health managers to fight syphilis through the “Syphilis No!” Project—that included the “Applied Research for Intelligent Integration Aimed at Strengthening Healthcare Networks for Rapid Response to Syphilis”—to reduce acquired syphilis, syphilis in pregnant women, and congenital syphilis by expanding coverage of diagnosis (through rapid testing), and timely and proper treatment of pregnant women and sexual partners in prenatal care, childbirth, or abortion situations. Through the Project, the syphilis epidemic has been faced up through two strategic lines: (1) reinforcing universal actions of SUS and (2) implementing specific ones to 100 municipalities chosen by the MoH as priorities for syphilis congenital response, as in 2015 they represented 68¢95% of the number of congenital syphilis cases in Brazil. The universal line of intervention included acquiring and distributing testing and treatment supplies (crystalline and benzathine penicillin), enhancing the STI laboratories network and the situation rooms for epidemiological surveillance, educommunication strategies, 6 social interventions, and awareness campaigns performed to face syphilis in that period. At the same time, institutional support was granted to the priority municipalities, where Research and Intervention Supporters (RIS) carried out specific work with local health managers. Hence, this work provided technical cooperation to strengthen prevention actions aimed at reducing the vertical transmission of syphilis, such as implementing committees focused on investigating syphilis Research in Context Evidence before this study We searched PubMed for articles published up to Oct 1, 2021, using the terms "syphilis" AND ("temporal analysis" OR "time series" OR "regression methods") AND "public health", with no language restrictions. Our search identified six studies. Four of them report analysis of syphilis data in Brazil from a time series perspective, with different settings, showing the syphilis incidence rates increasing from 2007 to 2016 and the challenge of the Brazilian Ministry of Health to reduce the three forms of syphilis (in pregnancy, congenital and acquired). Apart from these studies, China reported that syphilis incidence rates have increased three-fold from 2005 to 2012 and time series analysis was an effective tool for modelling the historical and future incidence. Added value of this study We used interrupted time series to examine trends before and after the introduction of a public intervention project in Brazil in order to reduce acquired syphilis, syphilis in pregnant women, and congenital syphilis by expanding coverage of diagnosis (through rapid testing), and timely and appropriate treatment of pregnant women and sexual partners in prenatal care, childbirth, or abortion situations. Data were assessed considering rates of congenital syphilis per 1,000 live births in all priority municipalities (n=100) covered by the project and in non-priority municipalities (n=5,470) from all five macro-regions of Brazil. Our interrupted time series data indicate changes in trends towards reduction or stabilization in almost all regions (North, Northeast, South, and Southeast). This effect may be attributed to the project's universal actions, that is, general interventions that were designed and implemented in all Brazilian states and municipalities. Implication of all the available evidence In recent years (2010-2019) it is the first time we can see a change in the course of congenital syphilis in Brazil. The effects of the intervention at the national level reveals statistically significant trend changes in monthly rates of congenital syphilis in Brazil and shows rate variations by region. Articles 2 www.thelancet.com Vol 7 Month March, 2022 cases and reinforcing local planning of response on the project's axis, among others. The details of the project interventions and relevant timelines are presented in Appendix (Figures A1 to A5). The purpose of this study is to use interrupted time series to estimate intervention effects over time by comparing rates of congenital syphilis per 1,000 live births in the priority municipalities (n=100) covered by the Syphilis No! Project and in non-priority municipalities (n=5,470) in the five macro-regions of Brazil: North, Northeast, South, Southeast, and Midwest. Methods Considering that a randomized controlled trial (RCT) is infeasible to be performed in this study, we used a quasi-experimental research design with interrupted time series (ITS) analysis. Such design enables comparisons in population health outcomes before, during and after an intervention within a clearly defined span. 7 ITS is a good approach for evaluating longitudinal effects of health interventions. 8,9 Besides, it can be used as a statistical method for estimating intervention effects of a policy change on an outcome of interest. 10 The ITS design and the use of segmented regression analysis enables evaluation of immediate impact and slope associated with a policy intervention while controlling the overall trend in the rate of the outcome of interest. 10 This study was performed through a multidimensional, flexible, and adaptive framework aimed at discovery and temporal analysis of public health interventions. It was implemented using the Hermes system, 11,12 which manages a complete data life cycle 13 by 1) acquiring data from several external sources in different formats; 2) cleaning data for standardization and error removal; 3) transforming it into specific models; 4) publishing a dashboard; 5) preserving data as a structured database. Use of data sources The Hermes system was used to collect congenital syphilis data from the Notifiable Diseases Information System (SINAN, Sistema de Informa¸c~ ao de Agravos de Notifica¸c~ ao), provided by the Brazilian MoH. These data represent the total number of notified cases per municipality, given by month and year. The vital records of live births by municipality for each year, were retrieved from the Health Informatics Department of the Brazilian Ministry of Health (DATASUS) and used calculate monthly average live per municipality per year. Hermes derived the rates of congenital syphilis cases by dividing monthly cases per municipality by live births. Priority vs non-priority municipalities Brazil has 5,570 municipalities spread over five macro-regions—North, Northeast, South, Southeast and Midwest. Monthly rates per municipality were used to categorise municipalities into priority and non-priority. The MoH classified the project as a tool with two lines for inducing response on syphilis: universal and specific actions. Thus, priority municipalities (n=100) were defined on population and epidemiological criteria, namely: the 27 capitals, in addition to municipalities in metropolitan regions with more than 100,000 inhabitants that had, in 2015, the highest rates of both congenital syphilis incidence in children under one year of age and perinatal mortality. The priority municipalities represented 68¢95% of the total congenital syphilis cases in Brazil. Other municipalities (n=5,470) were categorised as non-priority. Appendix (Figure A6) provides geographic, demographic and socio-economic characteristics of the five regions and presents the geographic location of 100 priority and 5,470 non-priority municipalities. The priority municipalities received support and technical cooperation from the project's network of Research and Intervention Supporters. Their work focused on the municipalities'needs within the axis of the project, as it was monitored through the “LUES” platform, 14 made available by the project. The supporters entered on the “LUES” platform all of their technical cooperation activities to health teams and local managers of priority municipalities on a monthly basis for supporting actions to prevent mother-to-child transmission (MTCT) of syphilis. Statistical analysis A quasi-experimental design was applied to analyze the time series data using a segmented linear regression model in the R software, adapted to exchange data with Hermes in order to assess the immediate impact and change in trend of national congenital syphilis rates pre and post the SNP intervention. What is more, May of 2018 was considered the intervention start time point for implementing the Syphilis No! Project. Appendix provides R code used in this analysis. Data must be collected regularly over time and at equally spaced intervals to perform a segmented regression analysis. 9,15 To avoid any bias related to the Covid19 pandemic, December of 2019 was defined as the end date for the data collection, totaling 20 months preintervention (Sep/2016 to Apr/2018) and 20 months post-intervention (May/2018 to Dec/2019). A linear relationship between time and the outcome within each segment was assumed, considering segmented regression models fit a least squares regression line for each segment of the independent variable (time), p<0¢05 was considered statistically significant. Thus, the next step in the analysis was to estimate the magnitude of the intervention and test the statistical significance of immediate impact and trend rates. Articles www.thelancet.com Vol 7 Month March, 2022 3 The regression model used to fit these data is straightforward. outcome t =b 0 +b 1 *t + b 2 * priority t +b 3 * priority t *i+b 4 * post-intervention t +b 5 *(t-T I ) * post-intervention t +b 6 * post-intervention t * priority t +b 7 * post-intervention t * t * priority t +e t Where b 0 is the baseline outcome for the non-priority group; b 1 is the pre-existing trend in the outcome of interest for the priority non-group; b 2 is the baseline difference between non-priority and priority groups; b 3 is the difference in trend between non-priority and priority group before intervention; b 4 is the immediate impact in the priority group; b 5 is the trend change in the priority group; b 6 is the difference in the immediate impact change between the non-priority and priority after intervention, and b 7 is the difference in trend change between the non-priority and priority group after intervention. Priority variable is coded 1 for priority and 0 for non-priority. The t variable is only an increment that counts up from 1 to 40 over the entire period. The postintervention variable was set to be 0 before the intervention and 1 after. T I is constant that represents the intervention moment, in this case is equal to 20. Figure 1 represents the model, including the immediate impact and trend change. Ordinary Least Squares (OLS) regression analysis assumes that error terms associated with each observation are uncorrelated. Correctly inspecting autocorrelation terms can avoid underestimated standard errors and overestimated significance of the effects of an intervention. 15 In the case of this study, a Durbin-Watson test was used to investigate the presence of autocorrelation, in addition to residual plots, Auto Correlation Function (ACF) plots, and partial-ACF plots. Autocorrelation was not detected in the statistical tests. R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination or the coefficient of multiple determination for multiple regression. It was used in this research’s model to determine how close the data are to the model and how it is presented in the results. R-squared is a value between 0 and 1, where 0 indicates that the model explains none of the variability of the response data around its mean, and 1 indicates that the model explains all the variability of the response data around its mean. Outliers are extreme values that do not seem to fit in the time series. If the outlier reflects an anticipatory or short-term history effect, the data point can be explicitly modeled. Alternatively, if the outlier is an unambiguous clear consequence of the intervention, it can be treated as a regular data point to evaluate its impact. A preliminary analysis was performed, and a single outlier was identified in the Midwest region after the intervention (July 2018). The Moving Average (MA) method was used to smooth the data for the purpose of explaining temporal trends. The MA used was of order three, which provides only a smoothing with the two months (before and after) the calculated observation. After this process, no outlier remained. The regression models obtained for each group (priority and non-priority municipalities), using data from the preand post-intervention periods (May 2018), were compared with the counterfactual for the post-intervention period considering the trend observed before the intervention. The counterfactual enables a comparison between the observed change and what would have happened had the intervention not taken place. The regression model parameters were obtained by point and interval estimation using 95% of confidence. Figure 1. Interrupted time-series parameterized as a segmented regression model, including the immediate impact and trend change. The solid lines represent the observed values, and dotted lines indicate the counterfactual for the post-intervention period. Articles 4 www.thelancet.com Vol 7 Month March, 2022 Role of the funding source The funders had no role in study design, data collection, data analysis, interpretation, decision to publish, or preparation of the manuscript. Results Results of the time series analysis comparing rates of congenital syphilis per 1,000 live births in the priority municipalities and non-priority municipalities are presented in Table 1 and Figures 2 to 7. The red dots represent the monthly rates of cases in priority municipalities and the blue dots in non-priority ones. The continuous lines indicate the regression models obtained for each group (priority and non-priority), using data from the preand post-intervention periods (May 2018). Moreover, the dotted lines indicate the counterfactual for the post-intervention period using the data trend observed before the intervention. Table 2 presents this comparison considering the last month of the analyzed period (20th month, December of 2019). These results describe the intervention effects in detail for each region, separately. That is because it captures the regionalized influence of the Unified Health System governance—which is interfederative, with clear attributions at local, state, and federal levels. In this sense, as Primary Health Care (PHC) is a responsibility of municipalities, evidence that the SNP effects can vary across regions can be captured. For the North region of Brazil, in Figure 2 and Table 1 (column 2), for both priority and non-priority municipalities, there was a reduction in the monthly rate of congenital syphilis cases in the post-intervention period. The monthly average reduction was -1¢77 cases per 1,000 live births in priority municipalities and -009 per 1,000 live births in non-priority municipalities, as shown in Table 1. In addition, the estimated linear regression models for each situation show changes in the monthly average trends, with a -0¢05 reduction in the priority municipalities and a -0¢07 reduction for the non-priority ones. The counterfactual presented in Table 2 shows that for the priority municipalities the model estimated 18¢08 cases per 1,000 live births at the end of the 20th month without intervention. In comparison, with intervention, 13¢73 cases were observed for the same time point, indicating a decline of 24¢09%. In the non-priority municipalities, the model estimated 5¢11 monthly cases per 1,000 live births in the 20th month. The observed value with the intervention was 3¢63 per 1,000 live births, which represents a fall of 28¢97%. Figure 3 and Table 1 (column 3) display the results for the Northeast region, where an increasing of 2¢23 cases per 1,000 live births in immediate impact can be observed post-intervention in priority locations. There was also a 0¢34 reduction in cases for non-priority municipalities. A change in trend can be observed for both groups. Non-priority municipalities reversed the upward trend with a monthly slope of -0¢11 cases per 1,000 live births. Meanwhile, priority municipalities showed a slope of -0¢64 cases per thousand live births post intervention. Regarding the counterfactual (Table 2) for this region, at the end of the span analyzed in priority municipalities and without intervention, the model estimated 28¢11 cases per 1,000 live births, compared to 15¢75 cases observed after intervention (a drop of 43¢95% for this group). In non-priority municipalities, the model estimated 5¢33 cases in the last month of data collection, while the actual value with the intervention was 3¢63 per 1,000 live births, a decline of 31¢79%. As represented in Figure 4 and Table 1 (column 4), priority municipalities in the South region showed a sharp drop to -1¢76 monthly cases per 1,000 live births, and a trend change of -0¢28 monthly cases per 1,000 live births. Conversely, in the non-priority municipalities there was an increase of 0¢48 in monthly cases per 1,000 live births and a minor trend change of -0¢04 monthly cases per 1,000 live births. The model estimated 26¢13 cases per 1,000 live births if no intervention had taken place in the priority municipalities from the South region. The actual value with intervention, was 18¢42 cases per 1,000 live births, representing a decline of 29¢48%. A slight decrease of 4¢27% was observed for non-priority municipalities with an estimated 5¢82 counterfactual cases and 5¢57 actual cases per 1,000 live births. The non-priority municipalities in the Southeast region (Figure 5 and Table 1, column 5) went through an increase of 0¢13 monthly cases per 1,000 live births and a change in trend with a decrease of -0¢16 monthly cases. Whereas the priority municipalities experienced a -1¢11 reduction in monthly cases and slightly declining trend of -0¢02 monthly cases. The counterfactual for this region projected an estimated 20¢84 cases per 1,000 live births in priority municipalities (Table 2) in the absence of intervention. However, a reduction of 21¢21% to 16¢42 cases per 1,000 live births was observed. In the Southeast regions’s non-priority municipalities, the counterfactual was 8¢70 cases against 5¢85 actual cases per 1,000 live births, with a 32¢69% reductioninthepost-interventionperiod. In the Midwest region (Figure 6 and Table 1, column 6), there was a reduction of -0¢47 monthly cases in the priority municipalities following the interventions, but an increase of 0¢87 monthly cases in the non-priority municipalities. There was a reversal of the trend in the priority municipalities from 0¢09 monthly cases per 1,000 live births pre-intervention to -0¢07 post interventions. Conversely, in non-priority locations, there was a reversal of the trend in the opposite direction. In this manner, a downward trend of -0¢02 monthly cases before intervention changed to 0¢00 in the post-intervention period. The counterfactual for the post-intervention period in priority municipalities was 12¢52 cases per 1,000 live www.thelancet.com Vol 7 Month March, 2022 5 Articles North Northeast South Southeast Midwest Brazil value (CI 95%) p-value value (CI 95%) p-value value (CI 95%) p-value value (CI 95%) p-value value (CI 95%) p-value value (CI 95%) p-value Pre-Intervention Intercept 1¢45 (0¢71 to 2¢19) <0¢001 2¢45 (1¢51 to 3¢39) <0¢001 4¢65 (3¢49 to 5¢82) <0¢001 4¢45 (3¢61 to 5¢30) <0¢001 2¢54 (1¢71 to 3¢37) <0¢001 3¢44 (2¢71 to 4¢17) <0¢001 Differential between groups 11¢39 (10¢35 to 12¢44) <0¢001 15¢58 (14¢25 to 16¢91) <0¢001 15¢04 (13¢39 to 16¢68) <0¢001 8¢38 (7¢18 to 9¢58) <0¢001 6¢87 (5¢70 to 8¢04) <0¢001 10¢96 (9¢92 to 12¢00) <0¢001 Trend in priority group 0¢04 (-0¢05 to 0¢13) 0¢393 0¢18 (0¢07 to 0¢29) 0¢002 0¢13 (-0¢01 to 0¢27) 0¢062 0¢09 (-0¢01 to 0¢19) 0¢077 0¢09 (-0¢01 to 0¢19) 0¢063 0¢11 (0¢03 to 0¢20) 0¢011 Trend in non-priority group 0¢09 (0¢03 to 0¢16) 0¢004 0¢07 (0¢00 to 0¢15) 0¢062 0¢03 (-0¢07 to 0¢13) 0¢539 0¢11 (0¢04 to 0¢18) 0¢003 -0¢02 (-0¢08 to 0¢05) 0¢665 0¢08 (0¢01 to 0¢14) 0¢017 Post-Intervention Immediate impact in priority group -1¢77 (-3¢20 to -0¢34) 0¢016 2¢23 (0¢42 to 4¢05) 0¢016 -1¢76 (-4¢00 to 0¢49) 0¢123 -1¢11 (-2¢75 to 0¢53) 0¢181 -0¢47 (-2¢07 to 1¢13) 0¢560 -0¢36 (-1¢77 to 1¢06) 0¢618 Immediate impact in non-priority group -0¢09 (-1¢10 to 0¢92) 0¢865 0¢34 (-0¢94 to 1¢62) 0¢598 0¢48 (-1¢11 to 2¢07) 0¢548 0¢13 (-1¢03 to 1¢29) 0¢822 0¢87 (-0¢26 to 2¢00) 0¢131 0¢28 (-0¢72 to 1¢28) 0¢583 Change in trend in priority group -0¢05 (-0¢18 to 0¢07) 0¢407 -0¢64 (-0¢80 to -0¢48) <0¢001 -0¢28 (-0¢48 to -0¢09) 0¢005 -0¢02 (-0¢16 to 0¢13) 0¢827 -0¢07 (-0¢21 to 0¢06) 0¢291 -0¢21 (-0¢33 to -0¢09) 0¢001 Change in trend in non-priority group -0¢07 (-0¢16 to 0¢01) 0¢099 -0¢11 (-0¢22 to 0¢00) 0¢058 -0¢04 (-0¢18 to 0¢10) 0¢579 -0¢16 (-0¢26 to -0¢06) 0¢003 0¢00 (-0¢09 to 0¢10) 0¢949 -0¢10 (-0¢19 to -0¢02) 0¢021 R 2 0¢9816 0¢9877 0¢9767 0¢9691 0¢9590 0¢9848 Table 1: Results of the time series analysis comparing rates of congenital syphilis per 1,000 live births between priority and non-priority municipalities. 6 www.thelancet.com Vol 7 Month March, 2022 Articles births at the end of the period (Table 2), compared to 16¢42 actual cases, which represents a fall of 21¢21%. By contrast, the comparison between the counterfactual (1¢94) and actual (2¢86) cases per 1,000 live births for the non-priority municipalities revealed a 47¢86% increase. The combined analysis of the national data reveals a decline in the immediate impact of congenital syphilis post-intervention, with an average monthly reduction of -0¢36 cases per 1,000 live births in the priority municipalities. Conversely, there was an increase of 0¢28 cases per 1,000 live births in non-priority municipalities. In the linear regression model, examination of the trends for both groups following the intervention, reveals a decrease in the growth of the average number of cases per 1,000 live births. Hence, there was a reduction of -0¢21 in priority municipalities and of -0¢10 in non-priority ones, as depicted in Figure 7 and Table 1 (column 7). Figure 2. Changes in congenital syphilis rates in the North region 20 months before and after the intervention. Figure 3. Changes in congenital syphilis rates in the Northeast region 20 months before and after the intervention. www.thelancet.com Vol 7 Month March, 2022 7 Articles For priority municipalities, the combined analysis also indicates a reduction of 28¢79% in the number of cases in the last month of the analyzed period (Table 2) when compared the counterfactual of 21¢93 with the actual number of 15¢61 cases per 1,000 live births. In the non-priority municipalities, there was a drop of 26¢20%. Accordingly, it went from 6¢34 counterfactual cases to 4¢68 actual cases per 1,000 live births. As to congenital syphilis in priority and non-priority municipalities, the most remarkable differences were found in the Northeast (15¢58 per 1,000 live births) and South (15¢04 per 1,000 live births) regions, followed by the North (11¢39 cases per 1,000 live births), Southeast (8¢38 cases per 1,000 live births) and Midwest (6¢87 cases per 1,000 live births). Figure 4. Changes in congenital syphilis rates in the South region 20 months before and after the intervention. Figure 5. Changes in congenital syphilis rates in the Southeast region 20 months before and after the intervention. 8 www.thelancet.com Vol 7 Month March, 2022 Articles Discussion The findings indicate a statistically significant reduction in the trend of congenital syphilis cases in Brazil in the 20 months following the Syphilis No! Project interventions. Additionally, the results reveal that priority municipalities experienced a more significant reduction in congenital syphilis cases when compared to other Brazilian municipalities with a greater reduction in the trend. For priority municipalities, the project’s impact on changing the course of congenital syphilis trends was more evident. Unlike the other four Brazilian regions, the most significant change in the growth trend was observed in the Northeast, which showed a sharp drop. Furthermore, decreasing post-intervention trends were observed in the Northeast and South regions. Meanwhile, there was stabilization of the trend in the North. Figure 6. Changes in congenital syphilis rates in the Midwest region 20 months before and after the intervention. Figure 7. Changes in congenital syphilis rates in Brazil 20 months before and after the intervention. www.thelancet.com Vol 7 Month March, 2022 9 Articles