Violence against women is a growing health problem, especially when perpetrated in intimate relationships. Despite increasing attention, there is little comparative evidence on the different types of violence involved and there is a paucity of research on sexual femicides. This study examines cases of violence against women in northern Italy, focusing on sexual and non-sexual femicides and comparing them with rape that does not result in femicides. The sample included 500 women who were victims of sexual and non-sexual femicides, and of rape. Results show sexual femicides mostly involved unknown victims or women who were prostitutes. Sexual femicidal offenders used improper weapons to kill their victims, acted in secluded locations, and fled the crime scene; their crime was more likely the result of predatory intentions, with antisociality and sexual deviance being the most significant factors related to this type of femicide. The criminal and violent pattern that characterized sexual femicides in this study shared significant similarities with the pattern of violence involved in rape. Rape victims were in fact mostly unknown, or involved in a brief relationship with their killer. When the victim was known it was more likely that the abuse occurred at home and in front of the woman’s children. Rapists were often under the effect of alcohol or drugs. Non-sexual femicides mainly involved known victims, and they were more often committed in the context of domestic disputes. It was not seldom that the long relationship between the victim and perpetrator was likely to be characterized by contentiousness, suggesting that the woman was often victim of an oppressive climate of emotional tension and domination. Morbid jealousy contributed to aggravating the tone of a controlling relationship. Non-sexual femicides bore more similarities to cases of rape within the pattern of intimate partner violence. Findings are discussed in terms of their implications for prevention and intervention. A major public health problem and human rights violation plaguing humanity is violence against women (hereafter abbreviated as VaW) . VaW takDespite a lack of reliable data making it difficult to estimate the true extent of VaW aggravating into femicide, the most consistent research finding across the world is that only a small proportion of femicide occurs within an anonymous setting . AccordiThe sexual component of violence against women  deserves special attention in so far it would lead to a more comprehensive understanding of those cases in which women are predominantly raped but not killed, those cases in which the rape occurs later and in a context of an oppressive, abusive and domineering relationship, and those cases in which women are also killed before, during or after being sexually abused. Notwithstanding that literature on femicide has grown in recent years, specialized studies on sexual femicide are still limited and within the general conceptual framework of sexual homicide .per se (rape ‘only’)  and of rape is essential for scientific and clinical reasons, but above all for preventive purposes, that is, also to prevent the pejorative transformation of violence into femicide. The multidimensional offensive pattern of rape includes sexual violence that is intertwined with intimate partner violence (rape within IPV)  or sexua ‘only’) .The following pages describe these forms of violence against women: sexual and non-sexual femicides, each compared with rape.At the first level of analysis, the primary focus brings together some of the worst and most extreme aspects of VaW by examining whether and to what extent sexual and non-sexual femicides are similar or different, and whether they share more similarities or differences with rape.At the second level of analysis, the focus is more specifically on sexual and non-sexual femicides compared with rape that occurs within IPV and rape ‘only.’self because it deprives the victim of any choices of when, how, and with whom to share her search for intimacy and for pleasure  added to the previous classification two new criteria: (1) a legally admissible admission by the offender regarding the sexual motive of the crime that intentionally or unintentionally results in homicide; (2) an indication of sexual elements of the crime from the offender’s personal effects .Non-sexual femicides is characterized by violence that escalates into a lethal epilog. Studies show that non-sexual femicide includes some or all of the following characteristics: threatened physical or sexual violence, economic and cultural violence, and emotional or psychological abuse perpetrated by a man  to the victim. In most cases, the perpetrator is a current or former intimate partner . Perhaps2 with non-sexual femicides in Scotland (United Kingdom). In their study, they examined data from a national police database and compared 89 male sexual femicidal offenders who had killed adult women with 306 male non-sexual femicidal offenders who had also killed adult women. The sexual femicides in their sample appeared to be more likely associated with instrumental aggression and sexual deviance, making sexual femicidal offenders more comparable to sex offenders than to other femicidal offenders.In a study by If sexual femicide differs from non-sexual femicide in terms of victims and perpetrators, this could have important implications not only for future research but also for policy, interventions, and rehabilitation programs for perpetrators.sexual proprietariness of a woman by a man  was conducted in Italy. While we are aware that violence against women is rarely a discrete event, especially when the victim knows the perpetrator and has a relationship with him, as shown in other studies , this stThis study addresses the following questions:(1)Are sexual and non-sexual femicides more similar or different in terms of victims, perpetrators, and characteristics of the exhibited violence?(2)Are all sexual femicides of the same kind?(3)What do sexual femicides have in common with rape?(4)What do non-sexual femicides have in common with rape?3.The data used in this study were collected from a multisite projectThe first phase of the study consisted in identifying the victims of femicide; information about age, profession, previous criminal records, prior involvement in violence, dynamics of the femicide; types of relationships between victims and perpetrators were also gathered.Information on further exploitation of victims’ bodies and mutilation, as stated in the pathology reports, was collected. Forensic files from the Court of Turin were also examined, along with files from the archive of the medical experts who assessed the cases. This multisite study excluded all cases of women’s natural death or suicide.In order to be able to explore differences between sexual femicides and rape that did not lead to the death of the victim, cases of sexual offending perpetrated by men against women, i.e., rape were examined.All information collected was classified according to the following dimensions: age, nationality and occupation of the victim and the perpetrator, location of the violence , medico-legal aspects of the murder , the perpetrator’s reaction after the crime , the types of sexual femicides . The type (known vs. unknown), intensity , duration of the relationship between victim and perpetrator (short- to medium-term vs. long-term), and the possible presence of children provided researchers with information to examine the context in which the crime occurred and to distinguish between sexual femicide, non-sexual femicide, and rape  the presence of violent incidents previously reported to the police, along with a pattern of frequent violent rows (contentiousness); (2) the dynamics of femicide and rape by examining the role of possessiveness, intrusive control, and obsessive desire for intimate exclusivity  in triggering the violence. In examining femicide, overkill  was also considered.Variables related to victims and perpetrators , locations of crime , and the medico-legal aspects of the offense  were directly extrapolated from the scientific and forensic material available. Variables related to the violence dynamics were coded by two independent judges. Coding protocols used to formulate each specific variable are extremely relevant for reconstructing the data into a coherent framework, to explain the meaning of each variable endorsed and to differentiate them. Behavioral variables were dichotomized  into either present or absent. This procedure identified 18 variables related to victim-perpetrator relationships, motive, and context of violence, as well as the variable “criminal career” that is here measured by criminal history recorded in the forensic file.Regarding the quality of the relationship, contentiousness implies the presence of negative, intense, erosive and enduring emotional strain between people in a relationship . AccordiBody exploitation implies interferences with the body of the victim, either when alive (as in the case of sex offending) or once dead (as in the case of femicide). In some cases, the perpetrator derives gratification  from inflicting severe suffering. In such cases, the exploitation of the body may begin on the living person and continue after death, or it may be practiced after death (see Body mutilation was based on the pathologist reports and was assessed as present (1) or absent (0). In line with the medical literature , mutilatRegarding motives for killing, two macro-categories were developed according to the typologies available in literature that distinguish an oppressive, domineering and multiproblematic relational condition (coded as 0) , and pregrievance femicides (coded as 0) and sexualized femicides (coded as 1). A femicide was coded as grievance when it was driven by high emotionality, resentment, and anger. A femicide was coded as sexualized when there was forensic evidence that it was functionally related to the sexual element of the offense , for the category ‘overkill’ , for the category of type of reaction by the perpetrator after the crime , for the category of type of body exploitation , for the category ‘motives of crime’ , for the category of sexual femicide  and for the categories of grievance and sexualized femicide , suggest a substantial inter-rater agreement coefficient for all of these variables  or absence (coded as 0) of assessed dimensions in each case. When a discrepancy emerged, the two independent raters discussed the case with the research group, and re-assessed it, until a better level of agreement was reached. The Cohen’s Kappa statistic  providesariables .Descriptive and multivariate analyses with Odds Ratios (ORs) were carried out to explore specifically the characteristics of the sample involved and to provide an outlet for comparing: (1) sexual femicide and non-sexual femicide with rape; and (2) sexual femicide and non-sexual femicide with the subcomponents of rape: rape within IPV and rape ‘only.’Specifically, 18 variables were examined and the OR was calculated to identify which of the 18 variables significantly and independently explained these types of violence. Also explored was whether the type of relationship between victim and perpetrator (known vs. unknown), and the intensity of the relationship  could affect the kind of violence the victim endured. The OR provides information about the existence, direction, and strength of an association between target and comparison groups regarding the likelihood of an event occurring . When ORA total of 500 cases of VaW were selected and included in the sample. Specifically, the sample included 365 cases of femicide and 135 cases of rape. n = 468) were on average 40.81 years old (SD = 15.65), mainly Italians  and employed at the time of the offense . Of them, more than a third had previous official criminal records . In only 6.1% of cases (n = 30) did the perpetrators suffer from a mental illness at the time of the crime, according to forensic psychiatric examinations.Perpetrators (n = 380) and professionally occupied . Victims were slightly older  than their perpetrators, but the difference was not significant, t(811) = 1.314, p = 0.09 (d = 0.09)4.In the present study, victims were also predominantly Italians  than perpetrators of non-sexual femicide , t(256) = 4.01, p < 0.001 (d = 0.65), while no difference emerged when compared with perpetrators of rape , t(179) = −0.70, p = 0.49 (d = 0.12). Perpetrators of rape were significantly younger than perpetrators of non-sexual femicide, t(345) = 5.59, p = 0.001 (d = 0.62).Perpetrators of sexual femicide were on average significantly younger  than victims of non-sexual femicide , t(360) = 5.98, p < 0.001 (d = 0.73). When comparing victims of sexual femicide with victims of rape , the former were slightly older than the latter, t(161) = 1.61, p = 0.06 (d = 0.25).Victims of sexual femicide were younger (t(347) = 7.24, p = 0.001 (d = 0.94).Victims of rape were also significantly younger than victims of non-sexual femicide, This study explored similarities and differences in looking first at sexual and non-sexual femicides. It also explored more specifically types of sexual femicides and explored whether certain factors were more likely involved in sexualized rather than in grievance femicide and vice versa. It finally compared cases in which rape was perpetrated without the escalation to femicide with cases in which it was, instead, a key part of the femicide dynamic.n = 365) or raped (n = 135) by 468 perpetrators. Overall, the great majority of the victims were known  to the perpetrator, while in 32.3% of cases (n = 160) the victims were unknown. In five cases it was not possible to establish whether the victims were known or unknown.Findings suggest that 500 victims were either killed (n = 239) they were in an intimate relationship, characterized by emotional, affective and sexual involvement. On average the intimate relationship was nearly 13 years long . In 53.5% of cases (n = 174) the relationship was characterized by contentiousness.When the victim and the perpetrator knew each other, in 71.3% of cases (n = 22) than in non-sexual femicide  . When the victim knew the perpetrator, the intensity of the relationship with him  did not make any difference in how the woman was killed, either by a sexual femicide  or by a non-sexual femicide .As shown in 5 . The victim and perpetrator were less likely to live together (cohabitation) in the case of sexual femicide than in non-sexual femicide , a finding that is consistent with the fact that sexual femicide was more likely to have occurred in public places rather than at home . Looking at the quality of the relationship, contentiousness  and morbid jealousy  were less likely to influence sexual femicide than non-sexual femicide. No difference was found regarding stalking .When looking at the dynamic of violence, in sexual femicides it was more likely that the motives behind the violence were predatory or antisocial in comparison with non-sexual femicide . It was more likely that sexual femicides occurred in a remote and secluded place in the outskirt of a city, in comparison with non-sexual femicides . Perpetrators of sexual femicides more likely used an improper weapon  to kill in comparison with non-sexual femicides , and also were eight times more likely to exploit the body of the victims even after the killing . Furthermore, body mutilation was ten times higher in sexual femicide than in non-sexual femicide . In sexual femicide perpetrators were more likely than in non-sexual femicide to escape after committing the crime , in an attempt to avoid punishment .No difference was found in their criminal careers  between sexual and non-sexual femicidal offenders  were at higher risk for overkill than the latter  .Contrary to expectations, overkill Of the 365 victims of femicides included in this study, 88 were women killed because of sexual femicide. For two cases of femicides it was not possible to establish whether the femicide was of a sexual nature or not.n = 22) of these victims were known to the perpetrator. As mentioned before, among these known victims, 72.7% (n = 16) had an intimate relationship with the perpetrator, although this relationship rarely lasted more than a decade  and only in a few cases  involved cohabitation between them (see Endnote 3). In 10.9% of cases (n = 5) the relationship was characterized by morbid jealousy and in 15.6% of cases (n = 10) by contentiousness. In only one case did sexual femicide occur in the presence of the victim’s children .Twenty-five percent (n = 68) in comparison with 10.5% of prostitutes (n = 29) in non-sexual femicides  and with 12.6% of prostitutes (n = 17) in rape . No significant differences emerge when comparing non-sexual femicides with rape.As shown in n = 67), while 23.9% (n = 21) were grievance femicides. Looking specifically at the types of sexual femicide, we found that the vast majority of cases were sexualized femicides  in comparison with grievance  femicides more likely involved unknown victims . When the victim and perpetrator knew each other, their relationship was characterized by a lower level of contentiousness in sexualized femicide  in comparison with grievance femicide  . Prostitutes were more likely the victims of sexualized  than grievance femicides  . Motives for sexualized femicides were mostly antisocial, characterized by sexual deviance, in comparison with grievance femicides in which the oppressive component of the relationship prevailed .When comparing sexualized with grievance femicides some significant differences emerged. Sexualized . In sexual femicide, the relationship between the victim and perpetrator was less likely characterized by contentiousness than in cases of rape . The victim and perpetrator were less likely to live together in cases of sexual femicide compared with rape . The intensity of the relationship between victim and perpetrator  was not significantly different in the cases of sexual femicides and rape. The duration of their relationship, jealousy and stalking were not significant in differentiating when comparing sexual femicides and rape.Typically, sexual femicide tended to occur more likely in secluded spaces  in comparison with rape, that occurred more likely in either the victim’s or the perpetrator’s home. Substance abuse by the perpetrator around the time of the violence was more likely in rape than in sexual femicide .The use of improper weapons was more likely in sexual femicide than rape . Body exploitation  and body mutilation  were more likely to occur in sexual femicide than in rape , and being in a long relationship with him  was more likely in non-sexual femicides rather than in rape (see note 3). When looking at the intensity of the relationship between victim and perpetrator , no differences were found when comparing non-sexual femicides with rape. The risk for children witnessing the violence was strongly increased in those cases in which their mother was raped rather than killed .It was more likely that an oppressive relationship was behind the motives for non-sexual femicide rather than rape . Non-sexual femicides were more likely committed at home in comparison with rape . As expected, substance use by the perpetrator was more frequent in rape than in non-sexual femicide .The exploitation of the victim’s body was almost four times more likely in the case of non-sexual femicide rather than in rape , while an active reaction of the perpetrator after the violence was more frequent in rape rather than in non-sexual femicide . No difference was found for body mutilation. All these findings are shown in Looking at these findings, and in line with previous studies  it is noThese findings suggest that while sexual and non-sexual femicides share the killing of the victims, some aspects could make them more criminogenically closer to rape.As reported in On the other hand, sexual femicide and rape ‘only’ involved mostly unknown victims. They were more likely to occur in public places and mainly for predatory and antisocial motives. In those cases of rape ‘only,’ it was more likely that the perpetrator was under the effect of alcohol or drugs. These offenders had also more previous criminal records in comparison with sexual femicidal offenders.Non-sexual femicides were similar to rape within IPV because both involved victims and perpetrators in a longer relationship, characterized by high level of contentiousness between them, and morbid jealousy. However, there were some differences when looking at the motives for violence which were more likely to be predatory and antisocial in non-sexual femicide than in rape within IPV. Perpetrators of rape within IPV were more likely to report previous criminal records. It was more likely that rape within IPV occurred at home and that abuse of substance was involved  , of motives , of characteristics of the crime dynamic (more likely to occur in secluded locations), and of perpetrators who were more likely to use improper weapons and to escape from the crime scene compared to non-sexual femicides. Sexual femicides also shared some similarities with rape, particularly regarding victim only known superficially or only involved in a short-term relationship with the perpetrator. Women who are victims of sexual femicide often experience a prolonged pattern of abuse and torture, are often left unclothed after being raped and mutilated. Whether or not they know their killer influences how and why they become the target of such extreme violence.Sexual femicide was seen in this study as an extreme form of control and domination in which sex was used to degrade the woman to the point of death. The manifestation of violence was characterized by a specific pattern. Victims of sexual femicide were indeed more likely to be unknown and to be killed in a remote public place, with an improper weapon. Results also show that victims who engaged in prostitution were at a higher risk than other women of being victim of sexual femicides. Prostitutes were more likely victims of a sexualized femicide.As emerged in other international studies , knowingHowever, most women in this study were killed by a man they knew, albeit with different levels of involvement with him and duration of the relationship see . It was It is In this research, rape emerged as part of a pattern of intimate partner violence (rape within IPV) or as a sexual crime (rape ‘only’). Known victims were more likely victims of intimate partner violence within which sexual violence clustered together abuse, control, domination, humiliation, making it a ‘composite’ form of violence  decided to leave her abusive and violent partner after countless episodes of physical, sexual, psychological, and economic violence in a relationship that lasted more than 10 years. For him, it was unacceptable that she tried to break up with him, and he demanded to get back the dentures he had paid for. The woman lost all her teeth in one of the abusive incidents she experienced in her relationship with him. She remained with him for many years after this incident.”Sexual femicides tend to be an extreme form of using the woman as a sexual commodity.Results show that in this study sexual femicides took different forms of sexualized and grievance, depending on the prevalent role of mostly sex or anger in the escalation into femicide, and on the relationship between perpetrator and victim.use, consume and dispose script is mostly featured in these types of sexual femicide because “what rape is to others is normal to prostitutes” . Sexualized femicides were likely to be driven by predatory schema in which the victim was dehumanized and her body made the target of further exploiting actions. This is in line with clinical studies that suggest that victims of sexualized femicides are used as a sexual commodity to satisfy sexual, sadistic, and pervert urges . Such exThe presence of body exploitation and body mutilation of victims was examined and then compared with both sexual and non-sexual femicide, and also compared with rape. In line with other studies , these fEven though not directly explored, stalking occurred in only about 10% of the sample. It is possible that many stalking behaviors went undetected or not reported by the victims . ResultsStalking has rarely been examined in studies of sexual and non-sexual femicide, and the extent of the association is not well understood . FurtherThese findings are not without limitations. All femicide data were retrospective, and it was not possible to gather first-hand information from family members about the quality of the relationship between victim and perpetrator, and from perpetrators about the motives behind the killing. The evidence gathered explains only part of the dynamics of the intimate partner violence that fostered the femicide. Furthermore, it was impossible, with these data, to reconstruct with preciseness the victimogenic factors that interacted with other factors to escalate into sexual and non-sexual femicides, and that distinguished them from those victimogenic factors involved in rape.Information on the victims were gathered through clinical and forensic reports so it was not possible to examine directly and in detail the conditions in which the victims were in before the crime . Specific information about victims could be helpful for organizing preventive interventions.We were unable to explore further the sexual nature of femicides because the information gathered were exclusively based on the forensic pathology reports, while access to the family members of the victims or to the offenders was not possible. Needless to say that getting in contact with the family members of the victims or perpetrators was beyond the scope of this study. Research findings  showed tDespite these limitations, this study contributes to understanding similarities and differences in these forms of violence against women. Non-sexual femicides and rape within IPV have in common aspects related to known victims with whom the perpetrators had an intimate relationship. The relationship between victim and perpetrator can make a difference in how, where and for how long the abuse is endorsed before transforming itself into the physical death of the woman.Sexual femicides and rape ‘only’ are likely to involve unknown victims. Sex was the common denominator of these types of violence against women.begin again in life, and not in death.While the ultimate goal of science and governments is to prevent all forms of violence against women, their intermediate goal should be to prevent violence from worsening to the point of killing. The fact that nearly a quarter of perpetrators had prior criminal records is an indication that violence against women in all its forms is a crime characterized by significant persistence over time, and it is possible that some violent incidents went unreported, contributing to the dark number that features in these different forms of violence against women. Governments should always rely on evidence-based research in order to make it possible for every woman to Given that ‘until death all is life,’ to paraphrase georgia.zara@unito.it.The dataset involves sensitive data  and only in particular circumstances could be made it available under the authorization of authorities. Requests to access the datasets should be directed to GZ, The studies involving human participants were reviewed and approved by in order to meet all the ethical standards, the researchers followed all possible procedures to ensure confidentiality, fair treatment of data and information, and to guarantee, at each stage of the research, that the material was treated with respect and discretion. The research protocol was organized according to The Italian Data Protection Authority Act nr. 9/2016 , to The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans (2013), and to the recent General Data Protection Regulation (GDPR) (2018). It was carried out in line with the Italian and the EU code of human research ethics and conduct in psychology, forensic pathology and legal medicine. Both research projects were approved by the Bioethics Committee of the University of Turin . The patients/participants provided their written informed consent to participate in this study.GZ, SG, SV, and FF conceived, planned, and organized the study, drafted the article, and interpreted the results. GZ and SV attained and coded the data and critically revised the article. GZ designed the study and analyzed the data. All authors contributed to the article and approved the submitted version.


Esophageal cancer is an aggressive, often deadly disease globally that represents a significant health problem in Tanzania. The WHO reported 604,100 new esophageal cancer cases worldwide during 2020 and 544,076 deaths . In Eastern Africa, 16,137 cases and 15,188 deaths were related to this disease in 2020. Esophageal cancer is associated with various etiologic risk factors, and access to the disease treatment is a major barrier to survival. This study examined associations between the prevalence of four geographically stratified, population-level, etiologic risk factors , as well as two access-to-care predictors . Regional- and coarser-scale zonal incidence rates were calculated for 2006 through 2016 and evaluated for geographic differences in relation to risk factors and access to care predictors using Poisson regression. Differences in the geographic distribution of esophageal cancer were observed. Distance from the region of residence to the treatment center (Ocean Road Cancer Institute) was statistically associated with the geographic pattern of esophageal cancer incidence. Further research into etiologic risk factors, dietary practices, and nutrition is needed to better understand the associations with esophageal cancer in Tanzania and other parts of Eastern Africa. Esophageal cancer (EC) represents an important health problem because it often presents at an advanced stage at diagnosis, and limited effective treatment options are available –3. GlobaVarious etiologic factors and treatment access have been shown to influence the distribution and incidence of this disease –12. EC iEsophageal cancer in Tanzania was previously studied using registry data during 2006–2016 from the Ocean Road Cancer Institute (ORCI), the source of data for the present study . ORCI isTherefore, we conducted this study using data on esophageal cancer cases from the ORCI registry, the only national cancer hospital in Tanzania. Patients were referred from different parts of the country for chemo- and radiation therapy. We used the dataset of all esophageal cancer patients seen at ORCI from 2006 through 2016 to explore whether the geographic residence of patients could be linked to specific regional risk factors. We also investigated whether the distance from the patient's residence was related to delayed treatment of advanced-stage EC patients.ORCI patient registry data were combined with population-level data from the Tanzanian Demographic Health Survey and the Tanzanian Census, as they are essential to developing a comprehensive approach to understanding the epidemiology and management outcomes of esophageal cancer in Tanzania. We used data from the 2002 census as the population estimates were available for each region by year from 2006-2016. The more up-to-date 2012 census did not have population estimates by region per year. We decided that capturing the projected changes in the population by year when calculating the incidence rate was more important than using the more up-to-date census population from 2012 for all years between 2006 and 2016, as the relative change in population yearly would not bias the incidence rate as the population grew .It is known that esophageal cancer differs by Region in Tanzania, which could be explained by greater risk exposure or improved diagnosis, leading to more esophageal cancer being recorded. According to the African Esophageal Cancer Consortium, several risk factors for esophageal cancers are identified in East Africa . TherefoThis study was conducted at the Ocean Road Cancer Institute (ORCI). The study included all patients with EC seen at ORCI during the period of 2006 through 2016. The study utilized the hospital registry of the ORCI hospital, and all the EC medical records of the hospital were abstracted for this study. Esophageal cancer cases were identified from the ORCI logbook as having been referred to or directly coming to ORCI with EC. The logbook contained each patient's name, medical record number, age, sex, and district of residence. ORCI routinely collects information on the permanent place of residence of the patient, in addition to the place of residence at the time of treatment. The address that was used in this study was the permanent place of residence. The medical record number from the logbook was then used to retrieve the corresponding medical record of each patient. Data for the period of 2006-2013 was obtained from our previous study dataset . LogbookPossible risk factors for esophageal cancer were based on those from other settings similar to Tanzania , 18. ThoTanzania is administratively divided into 21 mainland Regions that have been grouped into 8 zones. Since Zanzibar has a separate government from the government of Tanzania and therefore is not part of the health care system of Tanzania or its governance, data from Zanzibar were not included in the analysis. Therefore, analyses were done for the residence of cases according to both regions and zones. In addition, regional annual incidence rates of EC from 2006 to 2016 were calculated for the 21 Tanzania mainland Regions using the ORCI data . In addiThe overall average and yearly incidence rates of esophageal cancer per 100,000 persons were calculated at the regional level, as this provided greater statistical power with more data points. The yearly incidence rates were also calculated at the zone levels that might be more representative of true geographic variation. The population for each zone was based on the 2002 census projected population numbers . AverageInformation about the number of hospitals in Tanzania was gathered for each of the 8 zones and 21 regions used in the analysis. ORCI collaborators and coauthors confirmed that esophageal cancer is clinically diagnosed at the regional hospital level in Tanzania. Poisson nonlinear regression was employed to examine the association between cases by zone compared to hospitals by zone and cases by region compared to hospitals by region. Poisson regression was chosen because the response and predictor variables were counted data from cancer cases at ORCI and hospital facilities across Tanzania. Results of this analysis were then plotted to examine the trend of the data with a coefficient of determination to approximate the model's goodness of fit. Results were further analyzed after removing Dar es Salaam because it is an extreme outlier. Finally, the Poisson regressions for cases by region compared to hospitals by region and cases by zone compared to hospitals were rerun without Dar es Salaam or Eastern Zone.The next step was to evaluate the associations between cancer incidence rates and each of the four risk factors by region and zone. To develop zonal prevalence rates for these risk factors, the regional prevalences were multiplied by the estimated population for each region to create the estimated number of people in each region affected by each etiologic factor. Next, the number of people affected by a particular etiologic factor from each region in the same zone was summed to obtain a zonal number of people affected by that etiologic factor. This numerator was divided by the zonal population to form a new prevalence value for that etiologic factor by zone. Poisson regression of the zonal and regional incidence rates compared to these four etiologic factors was also used to determine if there was a nonlinear trend due to cancer count data being count data and the predictor variable being continuous data. The natural log for these Poisson regressions was then taken to interpret the results more easily. No confounders were evaluated in our study.Data were analyzed using Microsoft Excel, SAS 9.4 Software, R Studio, and ArcMap 10.5.1. The study was approved by the Institutional Review Boards (IRBs) of the University of Michigan and ORCI in Tanzania.A total of 1,938 esophageal cancer patients were identified and included in this study . SummaryAverage incidence per 100,000 individuals, cases per hospital, etiologic risk factors, and distance from the patient's residence to ORCI varied by Zones and Regions . In geneDuring each of the 11 years from 2006-2016, there was a statistically significant difference among zones in the yearly incidence rates per 100,000 . The incP value < 0.01) for the Zonal Poisson analysis. For the regions, each additional hospital was associated with an increase in the incidence of EC by 1.60 times  for the Regional Poisson analysis.The association between Zonal and Regional cancer incidence per 100,000 and the number of hospitals was positively significant when all cases were analyzed . Among zP value < 0.01), given that the other covariates are held constant. Among the zones, a one percent increase in poverty prevalence was associated with a decrease in EC incidence by 0.57 times that of the previous incidence rate  for the Zonal Poisson analysis. However, in multivariate Poisson regression analysis, when distance and number of hospitals were added as predictors to the Region, the distance was the only statistically significant predictor and was inversely associated with regional esophageal cancer  in relation to local risk factors. The study revealed statistically significant differences in the incidence of EC among geographical zones. The study also illustrated that poverty was negatively associated with the incidence of EC at the levels of Region and Zone, which may reflect underdiagnoses, lack of ability of transportation to advanced medical centers, or decreased access to care. In addition, important regional risk factors were identified that could be difficult to access in case-control studies and should be considered in future studies. This analysis reaffirmed the previous findings by Gabel and colleagues , who alsOther studies have also investigated esophageal cancer in relation to lifestyle risk factors, access to diagnosis at hospitals, and distance to treatment. In Kenya, low socioeconomic status, smoking, snuff use, alcohol, tooth loss, cooking with charcoal and firewood, hot beverage use, and use of mursik were independently associated with EC . HoweverOur study has shown that access to care barriers such as distance to treatment may be more important than population-level risk factors in understanding geographic differences in EC in Tanzania. Therefore, early EC cases could be missed for various reasons. Diagnosis of EC is difficult as systems could be nonspecific Gastrointestinal reflux Disease- (GERD-) like symptoms . First, Furthermore, poverty, distance to the ORCI, and inadequate access to health care likely contributed to late diagnosis, misdiagnosis, and underrepresentation of EC in remote areas of Tanzania. In addition, improving the ability of patients in more remote regions to travel to seek care should help to reduce underreporting of EC. Finally, there is a general shortage of equipment capable of diagnosis of EC as well as insufficient facilities in the region to tackle this problem. Thus, this study implicitly raises awareness of how improving access to health care at the ORCI cancer center should enhance accurate diagnosis and treatment success.Since ORCI is the main facility in Tanzania currently performing chemotherapy and radiotherapy, improving access to this facility by reducing the burden of travel on remote populations will be important in creating equitable esophageal cancer care for Tanzanians. Thus, the population of esophageal cancer cases at ORCI will likely underestimate the true extent of EC across Tanzania. Second, there may be considerable underreporting of cases in certain areas and among some populations if patients lack the financial resources or physical capacity to travel to ORCI for treatment. Third, a small number of EC cases were being treated with chemotherapy at Kilimanjaro Christian Medical Centre and Bugando Medical Centre from 2013-2016, possibly adding to the underreporting of cases near these hospitals.Our study has some limitations. First, the data from ORCI are from hospital-based registries, and there is no defined catchment area for cases. Therefore, some EC cases in this dataset were referred from other Regions. Second, the notion that esophageal cancer presents with nonspecific symptoms at first makes it harder to diagnose the disease at its early stages. Third, for the Poisson regressions, regional analysis had to be restricted to 21 mainland regions instead of the 25 current Regions due to the formation of 4 additional regions that were recently created but did not have stratified data before 2012. As a result, the geodesic distance underestimates the actual distance and may not reflect the relative time of travel that is related to vehicle availability and accessibility of roads. Also, as is done for most studies like ours, Euclidian or “crow fly” distance between the regional center and ORCI was used as a proxy measure. Thus, ORCI to Dodoma was measured as roughly 385 kilometers. This Euclidian distance represents a relative distance comparison among regions that represents how relatively difficult ORCI is to access. We recognize that there are limitations to this, as travel will be more or less difficult depending on the topography and infrastructure of each region. Finally, the multivariable Poisson regression was done as a regional analysis because there was no appropriate Zonal shapefile to analyze those finer-scale distances.Among the strengths of this study is the large number of carefully diagnosed, well-documented EC cases that were analyzed, spanning 11 years. These data provide a valuable resource to build upon, given that data were collected in a low-resource setting. Indeed, there is very limited information about esophageal cancer risk in Eastern Africa , 32. SmoFurther, cachexia in patients with EC contributes to the added risk of malnutrition and the need for nutrition support for patients with EC , 20. AltOur ability to estimate population-level data on the distance to and the number of hospital facilities using the Tanzanian Health Facility Registry could allow for further analysis of differences in access to treatment and diagnosis. The inclusion of distance from ORCI to the centroid of the regions as a predictor of access to care can help explain differential underreporting of EC. Efforts to improve early diagnosis of EC include gastric endoscopy and improving access to diagnostic and treatment facilities by reducing the travel burden to remote populations. Although this study analyzed certain population-level risk factors stratified by region from the 2015-2016 DHS and the 2014 Tanzania United Nations Development Programme Income Report, future studies should further address the prevalence of individual risk factors for the development of esophageal cancer in Tanzania. Additionally, a longitudinal study may answer several interesting questions that could not be addressed in this retrospective study; therefore, future longitudinal studies when resources are available would be valuable.


Power distribution across the global health landscape has undergone a fundamental shift over the past three decades. What was once a system comprised largely of bilateral and multilateral institutional arrangements between nation-states evolved into a varied landscape where these traditional actors were joined by a vast assemblage of private firms, philanthropies, non-governmental organizations and public–private partnerships. Financial resources are an explicit power source within global health that direct how, where and to whom health interventions are delivered, which health issues are (de)prioritized, how and by whom evidence to support policies and interventions is developed and how we account for progress. Financial resource allocations are not isolated decisions but rather outputs of negotiation processes and dynamics between actors who derive power from a multiplicity of sources. The aims of this paper are to examine the changes in the global health actor landscape and the shifts in power using data on disbursements of development assistance for health (DAH). A typology of actors was developed from previous literature and refined through an empirical analysis of DAH. The emergent network structure of DAH flows between global health actors and positionality of actors within the network were analysed between 1990 and 2015. The results reflect the dramatic shift in the numbers of actors, relationships between actors, and funding dispersal over this time period. Through a combination of the massive influx of new funding sources and a decrease in public spending, the majority control of financial resources in the DAH network receded from public entities to a vast array of civil society organizations and public–private partnerships. The most prominent of these was the Bill and Melinda Gates Foundation and the Global Fund for AIDS, TB and malaria, which rose to the third and fourth most central positions within the DAH network by 2015. Key messagesThe distribution of power across the global health landscape has undergone a fundamental shift over the past three decades.The previous system comprised primarily of bilateral and multilateral arrangements between nation-states has been transformed into a varied landscape of private firms, philanthropies, non-governmental organizations and public–private partnerships.The aims of this paper are to examine the changes in the global health actor landscape and the shifts in power using the emergent network structure of financial flows between global health actors and positionality of actors within the network since 1990.The results reflect the dramatic shift in the numbers of actors, relationships between actors and funding dispersal over this time period.international to global health has been described as a fundamental, system-wide shift in priorities and function , public–private partnerships (PPPs) and others to provide resources to roughly the same number of aid recipient countries. These non-traditional actors exert considerable influence on global health prioritization and agenda-setting, which was derived, at least in part, from the massive influx of funding ushered in by the United Nations (UN) Millennium Development Goals (MDGs) . Using a typology of actors developed from previous literature and refined through an empirical analysis of DAH, the characteristics of the global health landscape are described over the 25-year period leading up to and encompassing the MDG era (years 1990 through 2015). To examine aspects of power, the emergent network structure of DAH flows between global health actors and positionality of actors within the network were analysed over this same time period. To provide additional context to the empirical analysis, the following background sections describe what is meant by the global health landscape in the context of this study, concepts from previous works on power in global health, and the use of networks as analytical tools to examine power.The definition of ‘global health’ depends on the context and one’s aims. It is a contested term—in the first instance, for its lack of distinction from international health  or publiVarious typologies have been articulated to describe the disputed landscape of actors in global health. Those involved in global health governance  and finaPower asymmetries exist across all facets of society and directly impact health outcomes. The unequal distribution of power at the global level was reported as one of the main contributors to ‘the poor health of poor people, the social gradient in health within countries, and the substantial health inequities between countries’ by the World Health Organization’s (WHO) Commission of Social Determinants of Health . As desceconomic capital (i.e. funding). Direct financial support of these actors with technical expertise puts BMGF in a position to influence cultural capital (e.g. epistemic knowledge and recognized expertise) by deciding where to direct research funding—the outputs of which eventually become the evidence base (e.g. a database from IHME) for health policy and practice. Furthering this example, the dynamics of social capital  can be observed through the composition of WHO advisory boards and expert forums, where subject matter experts are convened alongside representatives from BMGF, the Global Fund for AIDS, TB, and Malaria (GFATM), the private sector and other interested parties to influence and develop WHO-backed policies , which are then taken up and disseminated by member states despite the organization’s lack of legal status. Importantly, the framework of capitals leads to a description of the system as a dynamic network of relationships and interactions between actors in global health, previously described by As exemplified by policies . These fThe Lancet remarked that all sources of power and the decisions that arise from power dynamics ‘should all be a much greater subject of scrutiny , the World Bank entities, the European Union and Regional Development Banks .Private entities consist of individuals, small and medium enterprises and corporations which contribute to development aid for health indirectly via tax contributions to government budgets. In addition, there are a variety of country-specific charitable giving mechanisms through which direct contributions can be made to CSOs, PPPs and philanthropic foundations focused on providing development aid for health , although what constitutes each of these does not differ from the broad definitions of PPPs in other sectors  within the source, channel and recipient country categories, then iteratively by the broader categories of global health actors, as outlined above, where relevant. Flows of DAH with unspecified sources, channels and recipients were not included in the analyses.directed, as in each edge indicates the direction of aid flows , and weighted, as in each edge has an attribute of the amount of aid funding that flowed between two given actors. The flows of DAH between actors constitute an emergent, unplanned network structure, which ‘evolved as a result of a myriad of individual aid allocations decisions driven by a variety of humanitarian, strategic, commercial, and political motives , then as a unimodal network including all actors. The bipartite graphs, networks with two disjointed sets of nodes with edges, were used to evaluate the network metrics within each of the functional roles, i.e. sources, channels and recipients. By the capturing metrics of actor nodes in their different functions  in the system, we are able to isolate particular characteristics related to being either a source, channel or recipient as some actors take on more than one of these roles. Out-degree measures the number of incident outgoing edges from a node. For the directed bipartite network of sources and channels, the out-degree metric for sources captures the number of channels directly funded by a given source. Conversely, in-degree measures the number of incident incoming edges to a node. For the bipartite network of channels and recipient countries, this captures the number of channels providing funds to implementing entities within a given country. Degree centrality, which counts all the incident edges connected to a given node, was utilized to provide an indication of the centrality of channels. These metrics were captured annually for years 1990–2015.d is the shortest distance between node i and j, w is the weighted adjacency matrix  and The DAH data were also projected as a unimodal network  to examine system-wide characteristics and actor positionality within the system. As described above, the most relevant metric related to power describes the centrality of a given actor. Previous work on knowledge networks has shown that because more central nodes ‘tend to have greater access to and control over valuable information flows, they have more power to influence others  was used for data cleaning and management, NetworkX package (version 2.5) in Python (version 3.7) was used for the network analyses, GEPHI (version 0.9.2) was used for network visualizations and R (version 4.0.2) was used for descriptive analyses and additional data visualizations.n = 1593) of CSO channels provided funding to countries for one single health area, of which two-thirds were dedicated solely to MDG target areas: HIV/AIDS, malaria, child and maternal health and nutrition.The representations of DAH as networks presents a striking visualization of the systemic changes between 1990 and 2015. Projected in a dual circle layout and ranked by out-degree, the inner circle consists of DAH recipient countries and the edge colour represents the source of funding . This stThe proportional distribution of DAH from sources and through channels has also shifted from a landscape largely dominated by public entities to more of a mixed picture, although most markedly across DAH channels . In 1990Aspects of power derived from the control of financial resources were examined through the network metrics in-degree, out-degree, degree centrality and closeness centrality. The actors were ranked from highest to lowest by metric within the source, channel and recipient country categories for each year  transferred between actors, describes how quickly a node can reach all other nodes in a network and therefore how well-connected or ‘important’ a node’s position is within the network. The average closeness centralities across all types of global health actors have increased since 1990 due to both the increased number of relationships as the number of actors in the network increased over time and the increased funding moving throughout the network . The traIn terms of individual actors, 42 of the 100 entities with the highest measures of closeness centrality are CSOs . This is not discernible in the aggregate categories in The landscape of global health actors shifted dramatically between 1990 and 2015 as underscored by those involved in disbursements of DAH. Throughout the MDG era, the system became denser, as the numbers of actors and relationships between actors increased substantially. During this same period, funding became more dispersed and less concentrated in flows from large bilateral and multilateral organizations.Amongst the public entities, the US government, through its bilateral aid agencies, remains a singular force in DAH, having maintained the most central position across all network metrics reported here across all years. However, through a combination of the massive influx of new funding sources and a decrease in public spending, the majority control of financial resources in the DAH network receded from public entities and gave way to a vast array of CSOs and PPPs. The most prominent of these were the BMGF and GFATM, which were found to have risen to the third and fourth most central, important positions within the DAH network by 2015. As a PPP, GFATM occupies the positions of both donor and funding recipient, the latter of which necessitates a degree of accountability to the organization’s largest donors to meet fundraising goals for its continued viability.Since the year 2000, thousands of NGOs and NPOs were created to facilitate cause-specific initiatives with the intention of contributing to progress towards the MDG targets. The substantial increase in CSO actors was in response to the space created by the perceived inefficiencies of more traditional donors, i.e. multilateral actors, to meet the disease-specific MDGs , compounet al. 2019. These countries have contributed more total DAH than some of the newest OECD-DAC countries, namely the Czech Republic and Poland. This illustrates the complex, dynamic nature of DAH underpinning the financial arrangements in global health, which does not comport with perceptions of the system as a flow of resources from richer to poorer countries nor the definitions of global health as partnerships without reference to power asymmetries.While many definitions of global health, including the one employed in this study, invoke a dynamic of transnational partnership and exchange, especially between high-income countries and low-/lower-middle-income countries, it is not necessarily representative of the DAH system as observed here. Present throughout the entire time period examined but visually most evident in 1990 due to the sheer volume of edges in later years were the contributions of the recipient countries to multilateral organizations  health was guided by the normative and formal functions of international and bilateral relationships, the contribution of greater resources to health-related programming gave private actors an increasingly important role in the system. Total funding for global health interventions has increased from approximately USD 7 billion in 1990 to over USD 36 billion in 2015, with an increase in proportionate contributions by corporations and private foundations from 8% in 1990 to an annual average of 22% since the year 2000 . While tHere underscores the importance of analysing power in global health and the application of Bourdieu’s capitals framework. Once actors who hold power derived from economic capital have been identified, we have a framework to explore the impacts of these asymmetries. For example, the charitable arms of pharmaceutical companies were found to be amongst the most powerful CSOs in terms of network centrality. Aside from specific cases, pharmaceutical solutions to global health issues have been shown to be problematic and unsustainable see e.g. . Yet, biIn terms of limitations, the results presented here rely on the quality and breadth of the data. Contributions by private individuals were not included in the analysis because those included in IHME’s DAH dataset were not presented as having been collected in a systematically robust way. Most public charities solicit funds from private individuals who in turn may influence arrangements in the DAH network. Importantly then, these results should be viewed as restricted to actor organizations, not inclusive of the power single individuals may exert. It is also important to re-emphasize that power derived from financial resources is not absolute or singular. While financial ties are explicit expressions of dynamic power arrangements, implicit forms of power, such as the development of health metrics for decision-making, may hold equal or greater weight in determining the direction and impacts of the global health system. Finally, it should be stressed that IHME, who produced the dataset used in these analyses, do themselves hold a significant position of power in the global health landscape. This exemplifies the limitations in our ability to interpret the results and underscores that the work presented here has contributed only a partial view. That is, that power is not singular or absolute in its source or presentation but embedded in the composition of, and relationships between, its origins.The establishment of the MDGs, with three of the eight goals explicitly targeting health, DAH became an important political tool and symbol. These goals were meant to serve as apolitical objectives around which everyone working in development could coalesce ’. To whaIt quickly becomes apparent how understanding power dynamics in global health is necessary to tackle health inequities (From here, this work can provide background on the utility of network analysis to observe power in global health. Adding the network structures of cultural, symbolic and social capitals to the one of economic capital presented here would provide a more complete view of the global health system. Further analyses linking the impact of power in DAH on funding decisions and the achievement of health targets are on-going, as is an examination of the dynamic roles of non-OECD-DAC countries in the DAH network. The extent to which these analyses can interrogate the meaning of ‘effectiveness’ in cost effectiveness analysis is similarly being explored. Power asymmetries impede our ability to fully realize health and wellness for all. They underscore the most important discussions happening today in global health, and elsewhere, related to economic and other inequities, climate, decolonization, racism and diversity—especially in light of the on-going COVID-19 pandemic as described by czac025_SuppClick here for additional data file.


The COVID-19 pandemic made explicit the issues of communicating science in an information ecosystem dominated by social media platforms. One of the fundamental communication challenges of our time is to provide the public with reliable content and contrast misinformation. This paper investigates how social media can become an effective channel to promote engagement and (re)build trust. To measure the social response to quality communication, we conducted an experimental study to test a set of science communication recommendations on Facebook and Twitter. The experiment involved communication practitioners and social media managers from select countries in Europe, applying and testing such recommendations for five months. Here we analyse their feedback in terms of adoption and show that some differences emerge across platforms, topics, and recommendation categories. To evaluate these recommendations’ effect on users, we measure their response to quality content, finding that the median engagement is generally higher, especially on Twitter. The results indicate that quality communication strategies may elicit positive feedback on social media. A data-driven and co-designed approach in developing counter-strategies is thus promising in tackling misinformation. The COVID-19 pandemic has highlighted the challenge of conveying and communicating complexity and uncertainty . InformaFurthermore, individuals tend to process information, including scientific evidence, coherently with their system of beliefs , 4. OnliA crucial challenge is to improve the effectiveness and outreach of science communication , 16, espTrying to understand how the digital age has changed science communication, many research works investigated the implications of online communication for science. In 2013 Brossard provided an overview of the new media landscape, focusing in particular on the bias introduced by search engines, as well as the characteristics of the scientific content available online and its potential impact on the users , the anaTo address these questions, we set up an innovative experiment through a co-design approach which involved science communication practitioners in select European countries. Our results show a positive increase in public engagement with science content. Overall, our work highlights that quality communication elicits a positive response in social media and highlights the benefits of a data-driven, co-designed approach in developing tailored counter-strategies, making a further step towards communications able to (re)build trust promote engagement, and contrast misinformation.First, we designed a set of good practices (recommendations) for communicating science on social media, taking into account various aspects, from trustworthiness and scientific rigour, to presentation skills and societal impact, up to data-driven insights to develop tailored content. Then, we experimentally validated such recommendations with the direct involvement of science communication practitioners active on Facebook and Twitter. More specifically, a group of 22 science communication accounts and their social media managers received our set of recommendations and committed to test them in a period of five months.To identify good practices for science communication, we started with a preliminary exploration of the landscape of science communication in social media in select European countries, as reported in . To thisTo extend the analysis to controversial issues, along with general recommendations, we also included more specific suggestions for three topics which are often linked to strong beliefs and emotions, i.e., vaccines –33, climQuality criteria emerged by co-design activities  organised with relevant stakeholders across select countries in Europe  and incoTrustworthiness and scientific rigour. This first area includes elements of quality connected to trust, which is fundamental in science communication [nication . QualityPresentation and style. This second area includes a series of elements related to the quality of interaction with the public. These criteria focus on how scientific content is created and presented. The main challenge lies in being able to make science communication attractive without compromising its core values, i.e., trust, objectivity, transparency. In this direction, we provided the following recommendations: 1) using narrative and storytelling; 2) including calls to actions, e.g., asking questions, inviting to post or share content, organising flash mobs; 3) paying attention to the consistency and clarity of the message ; 4) ensuring that the length and complexity of sentences, the wording, and the assumptions are tailored for the target audience.Impact on society. This third area focuses on the societal mission of science communication and how it can meet the audience’s needs and concerns. In this sense, the quality of communication depends on the capacity of acting as the main intermediary between science and society. We included the following recommendations: 1) addressing real life issues, e.g., by stressing the potential impact of scientific discoveries on daily life; 2) promoting positive changes in users’ behaviours ; 3) following ethical standards and considering social responsibility.As for effectiveness, preliminary quantitative results  revealedTo set up the experiment, we considered the list of social media accounts produced in in . The lisBy social media managers we mean the person (or group of people) who is in charge of the social media account. The term is not used here in reference to the job, indeed some of the accounts taking part in the experiment are managed by individuals  for which social media is not their primary working activity. In the experimental period, the social media manager(s) followed our suggestions when producing (some of the) content for their accounts, so that we could monitor users’ response to it. Furthermore, we organised online sessions during and after the experiment to discuss the clarity of our recommendations, as well as their validity and possible refinements. These discussions also proved to be fruitful to identify good practices on how to convey the complexity and uncertainty of science on social media platforms, especially during a historical event such as the COVID-19 pandemic. We integrated the participants’ feedback in the final version of our recommendations. Our interdisciplinary approach allowed us to co-design the recommendations with the help of those who will apply them in their daily activity on social media, thus maximising the likelihood of their future adoption among professionals. Participants were either active on both Twitter and Facebook (10) or on only one platform . Each account is also associated with a topic label among General, Artificial Intelligence, Climate Change, and Vaccines according to its predominant subject. We assigned each account a code to track their social media activity and share with them a detailed document describing our recommendations. We also provided them with a checklist to facilitate their work and foster the adoption of our suggestions. Clearly, we did not expect that all items in the checklist were achieved simultaneously. Our advice was to follow the 3Ts rule, when possible, and to consider at least an element from the three aforementioned conceptual areas . Participants were strongly encouraged to get in touch with the research team in case of any questions or doubts.Self-reported data. To monitor the effectiveness of the recommendations, we asked the participants to fill out a Google form to report on all the posts where they applied any of our recommendations. More specifically, the author of the post was asked to indicate the topic of the post (if any), if the content was fact checked or not, if gender balance was considered, and so on. The participants did not have any incentive to cheat, on the contrary they were actively involved in the experiment in a co-design activity. Therefore, it is reasonable to assume that the information reported in the form is trustworthy. The final dataset contains the following information: the code of the participant; the platform in which the post was produced (either Facebook or Twitter); the URL of the post/tweet; the topic of the post (which may differ from the topic label associated to the account); the recommendations followed when creating the post within the categories Trustworthiness and Scientific Rigour, Presentation and Style, and Impact and Style.Platform-specific API. To compare the posts following the recommendations with another set of posts from the same participants, we downloaded the whole set of posts on the participants’ accounts during the period of observation that ranges from 27/02/2020 to 04/08/2020. The posts were downloaded using Twitter Standard APIs and the Facebook CrowdTangle service. In this case, we assume that the posts have the same topic label of the account that produced them. For example, an account labelled as Climate Change (thus posting mainly on the topic of climate change) could produce a post more focused on the use of artificial intelligence that will be however considered as a post about climate change. We downloaded 6,265 tweets from 19 Twitter accounts and 4,169 posts from 13 Facebook pages. The exact breakdown is provided in Tables So far, we have described the principles employed for crafting the recommendations and recruiting the participants to the experiment. In this section, we analyse their adoption rate in the tweets and posts of active participants for the four selected categories, namely: General, Artificial Intelligence, Climate Change, and Vaccines. A breakdown of the recommendations is reported in Supplementary Information.The number of tweets following the recommendations is 240, distributed across topics as follows: 130 posts on General, 14 on Artificial Intelligence, 62 on Climate Change, and 34 on Vaccines. A similar analysis was conducted for Facebook. In this case, the topic Vaccines is missing due to the limited number of posts (N = 5), which did not allow for a solid analysis. In this section, we analyse the joint adoption rate of the recommendations, i.e., the co-occurrence of two recommendations, intended as the number of times two suggestions are jointly adopted in the same post. Investigating the set of co-occurrences of recommendations could be useful to unveil preferential mechanisms of joint adoption.To evaluate the effectiveness of the proposed recommendations, we analyse the engagement of the posts that adopted them in comparison to the posts produced by the same accounts during the same time window, in which recommendations were not considered.A similar analysis was performed for Facebook. To sum up, our results show clues of a positive association between the adoption of the recommendations and engagement, particularly on Twitter. On Facebook, the topic of Vaccines was excluded from the analysis due to the small number of posts (N = 5), while for what concerns posts about Climate Change the results did not reach statistical significance. For General content, we had somewhat inconsistent results, with posts achieving more median likes, but fewer median comments when adopting the recommendations, while the difference in shares did not reach statistical significance. Despite these weaknesses, the results in terms of engagement are overall positive and encouraging. The recommendations proved to be particularly effective on Facebook for the topic of Artificial Intelligence, for which likes more than doubled, and shares increased more than five times.After having tested the overall usefulness of the proposed recommendations in terms of engagement, we aim at measuring their impact (either in groups or singularly) on posts. To perform this task, we rely on negative-binomial regression as this type of tool is well suited to handle count data with over dispersion . SimilarWe propose two alternative specifications. In the first specification, we regress our engagement measures on four variables (Trustworthiness and Scientific Rigour (R), Presentation and Style (S), Impact on Society (SI) and 3Ts (T)) obtained by computing the number of corresponding recommendations adopted in each post:Xi:This way, we are able to assess whether implementing recommendations from any of the conceptual areas presented in the section entitled Recommendations for Quality and Effectiveness can be associated with an improvement in engagement. The second specification takes into account the set of all recommendations in the form of dummy variables, Zi, we consider: page fixed effects , topic fixed effects , day-of-the-week fixed effects and content language . As a further robustness check we also implement log-linear OLS regression and quasipoisson regression. We do not observe substantial differences.To improve unconditional estimation  we also include a set of control variables in both of our specifications. For what concerns the set of control variables Summing up our results about quality recommendations, we find that Trustworthiness and Scientific Rigour, especially in the form of disclaim-conflict recommendations, does not seem to be repaid by users in terms of Twitter engagement. On Facebook, we find that Impact on Society recommendations pay back in terms of shares in aggregate terms. Also fact checking seems to pay back in terms of engagement, while the opposite is true for the link-sources recommendation.Coming to the purpose of testing the effectiveness of 3Ts rules, our results are mixed. On the one hand, the case of retweets corroborates our expectations, especially if we consider the coefficients associated with use-mentions and photos. On the other hand, we find that 3Ts rules seem to be counteractive for what concerns Facebook shares. In particular, the use of native videos and hashtags seem to be negatively associated with the number of shares.Although the recommendations are crafted taking into account the features of the social media platform in which they will be implemented, it is likely that something generating more engagement on one platform may fail on another due to the different segments of users involved and to the discrepancies in the design and recommendation algorithms implemented.Our results show an overall positive increase in engagement, which is statistically significant when compared to social media content for which the recommendations were not considered. The percentage change in the median engagement was particularly pronounced on Twitter, with an approximate improvement of at least two times in favourites and retweets. Moreover, we evaluated the effect of single recommendations in terms of engagement using a negative-binomial regression model. The results show that the recommendations have positive effects on Twitter, which mainly leverage on platform-specific aspects . On Facebook, this may be observed for recommendations that focus on societal issues and trustworthiness . To maximise the chances of adoption of the recommendations in the relevant communities, we applied a co-design approach and avoided any top-down solution. Here we discuss the results, contributions, and limitations of this study.Evaluating the helpfulness of the recommendations was limited by the nontrivial challenge of first defining and then measuring quality in quantitative terms. The recommendations proposed in this paper integrate 12 qualitative indicators that can be grouped into three main conceptual areas: trustworthiness and scientific rigour, presentation and style, and the societal impact of the communication . These ieffective usually depends on the goal that has been set for the communication. Effectiveness may be determined on different levels, from the ability to educate and raise awareness, to make the public interested in and supportive towards science, to even change individuals’ behaviour . In all these cases, science communication would have valuable effects. However, this is hardly measurable quantitatively, especially when analysing social media data, since one should trace a causality link between users’ exposure to online information and their offline behaviour. For these reasons, in this paper we focused on indicators of engagement that can be easily derived from the available data, such as the number of retweets, likes, comments, and shares. However, engagement can be considered as a good proxy of outreach and appreciation and, thus, of effectiveness.Similarly, measuring effectiveness is not straightforward, since being The analysis on Twitter shows that the posts following our recommendations achieved a significant higher median engagement than their counterpart produced in the same period. Only for tweets from accounts specialised in Artificial Intelligence this advantage did not reach a statistically significant level. This can be due to the limited number of posts created following the recommendations (N = 14). On the other hand, Artificial Intelligence was also the topic that achieved the best results on Facebook, a platform where the topic of Vaccines was scarcely represented in our sample, and the topic Climate Change did not reach statistical significance. Results for Facebook are similar and confirm a statistically significant advantage in terms of engagement derived by the adoption of the recommendations, except for climate change accounts.Trustworthiness and Scientific Rigour recommendations do not pay back in terms of engagement on Twitter, while Impact on Society recommendations seem to be effective to promote shares. Moreover, we found mixed evidence about the effectiveness of the 3Ts’ rule. In particular, we observed that the adoption of some recommendations  are positively associated with retweets and favourites on Twitter, while others such as the use of hashtags are negatively associated with shares on Facebook.To assess the relationship between recommendations adoption and engagement, we also considered the fact that not all recommendations were necessarily applied in each post. Therefore, we ran regressions to test the impact of recommendations while controlling for possible confounding effects. We noticed that Throughout the whole experiment, the role of the participants was key, not only in following our recommendations, but also in providing comments and suggestions for their further refinement and improvement. For most respondents the recommendations were easy-to-adopt and proved useful for communicating science in social media. Some recommendations were rarely applied  because of their requirement in terms of resources . In this regard, the pursuit of quality and effectiveness inevitably collide with the business model and affordability of science communication in social media, that is too often considered secondary to more traditional channels in terms of investment.The experiment may have been affected by the unfortunate outbreak of COVID-19. While the pandemic certainly contributed to highlight the key role of science communication, it has also called the public attention upon the new coronavirus, making the topics selected in this paper  less important. In addition, the dominance of the COVID-19 topic and the exceptional engagement that this generated online prevented us to compare posts adopting the recommendations with posts of the same accounts in the period before the experiment (and thus before the pandemic), as in our original plans. Comparing the two groups of posts (following/not following recommendations) in the same time window may be subject to biases that may hinder the effectiveness of such measurement. Unfortunately, we cannot claim that the sample of participants joining the experiment is representative of the entire Europe. Due to the high fragmentation of the science communication field, it is hard to obtain a representative sample of the entire community. The field is evolving very rapidly, and is constituted by a heterogeneous set of actors . HoweverIn conclusion, our work shows the goodness and potential of a data-driven approach in fostering quality and effective science communication on social media. Overall, the adoption of our recommendations results in positive outcomes in terms of engagement, showing that social media can be an effective channel to promote engagement with science (RQ1) and that quality content is able to involve users on social media (RQ2). Moreover, the experiment presented in this paper highlights the many benefits offered by the reciprocal collaboration between scholars and communication practitioners. These results may pave the way for further research developments. Future research will be devoted to replicate this study with a larger sample of participants in a more regular setting, and to investigate possible drivers of users’ response to quality content. In the context of the ever-evolving online environment, such work is not only desirable, but necessary to provide empirical evidence to tailoring effective science communication strategies.S1 TableRecommendations by thematic area.(PDF)Click here for additional data file.S2 TableSummary statistics for the distribution of Favourites and Retweets.(PDF)Click here for additional data file.S3 TableSummary statistics for the distribution of likes, comments and shares for General (G), Artificial Intelligence (AI), and Climate Change (CC) posts.(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.