This paper develops an empirical agent-based model to assess the impacts of Brexit on Scottish cattle farms. We first identify several trends and processes among Scottish cattle farms that were ongoing before Brexit: the lack of succession, the rise of leisure farming, the trend to diversify and industrialise, and, finally, the phenomenon of the “disappearing middle”, characterised by the decline of medium-sized farms and the polarization of farm sizes. We then study the potential impact of Brexit amid the local context and those ongoing social processes. We find that the impact of Brexit is indeed subject to pre-Brexit conditions. For example, whether industrialization is present locally can significantly alter the impact of Brexit. The impact of Brexit also varies by location: we find a clear divide between constituencies in the north (highland and islands), the middle  and the south. Finally, we argue that policy analysis of Brexit should consider the heterogeneous social context and the complex social processes under which Brexit occurs. Rather than fitting the world into simple system models and ignoring the evidence when it does not fit, we need to develop policy analysis frameworks that can incorporate real world complexities, so that we can assess the impacts of major events and policy changes in a more meaningful way. On 23 June 2016 the British people voted to exit the European Union . Many months before and after the vote, the media, the public and academics were engaged in heated debates and produced copious analyses to understand the possible impacts of such a major event. Most of the discussions, however, were general and at national level. Many treated Brexit as a shock to a society in equilibrium . DiffereResearchers have long been arguing for a complex system approach to policy analysis that takes into account local context and dynamics , 7. OstrEvidence shows that high-level national policy can have very different impacts under different local contexts. Working with organizations that promote sustainable living in the UK, Blake  has notiWhen a major event such as Brexit happens, it can easily become the sole focus of public attention. However, as research above has shown, the impact of a national event or policy can be highly sensitive to the local context –17. TherThis paper develops an empirical agent-based model to assess the impacts of Brexit on Scottish cattle farms. We identify a number of trends and processes among Scottish cattle farms that were on-going long before Brexit: the lack of succession, increases in lifestyle farming, diversification and industrialization, and the phenomenon of the “disappearing middle” characterised by the decline in medium sized farms , 19. UsiWe find that the impact of Brexit is indeed sensitive to pre-Brexit conditions. In particular, we find that the process of industrialization can significantly alter the Brexit’s impact on the local beef and dairy industry. The impact of Brexit also varies largely across regions: some will be more resilient than others; some will gain from Brexit while others will lose. As for the phenomenon of the disappearing middle, we conclude that Brexit has a very marginal impact on the long-term trend, mainly because the mechanisms behind the trends such as lack of succession are more fundamental and persistent than can be reversed by Brexit. Clearly, Brexit is not the only factor here.Over the past few decades, Scottish beef and dairy farms have been facing many challenges, despite the Protected Geographical Indication status of “Scotch beef”. The number of all cattle in Scotland has been in continuous decline since the 1970s: from 2.7 million in 1974 to about 1.8 million in 2016 , an averA smooth succession from one generation to the next is the key to the survival and growth of family-run farms –25. MoreThe demographic change in rural areas is a global process, and literature has been studying the endogenous and exogenous forces that shape the process. Brereton, Bullock  summarisResearch finds that farms who cannot identify an heir or whose owners are older and uncertain about succession tend to be smaller , less prAnother trend in European agriculture is the rise of leisure farming, especially in peri-urban areas. An increasing number of farmers no longer see farming as their main source of income, but a way to enjoy a rural lifestyle , 41. ForZasada  discusseAt the same time, some existing farms have diversified into tourism in response to the surging demand for the leisure and recreational activities in rural areas. For some farms, agri-tourism has become an important source of income , 48. ScoFarm tourism often displays a strong spatial clustering pattern . The reaTo sum up, having a farm in agricultural tourism often means more facilities in the nearby area, either by it bringing in potential customers and improving the infrastructure and publicity of the area, or because farms diversified into tourism are those that locate in attractive areas in the first place, which means their neighbours are also more likely to get into tourism in the future. Either way, evidence has shown that having neighbouring farms in tourism increases the probability that a farm will diversify into tourism as well.According to the EU Farm Structural Survey, which is conducted every 10 years on agricultural holdings in countries across the EU, 455 farms have gone into tourism between 2000 and 2010 out of 13,406 cattle farms in Scotland. Although the overall number of Scottish cattle farms in tourism increased only slightly from 682 to 709 during the same period, the data indicate that there are a lot of movements in the sector, including new entries and exits.As globalisation integrates food supply across the world, the agricultural sector in many countries have gone through tremendous restructuring and industrialization, characterised by a manufacturing approach to agricultural production and a high degree of specialization , 59. GarFarm location plays an important role in the level of scale efficiency and the tendency of farms to expand and industrialize. Dimara, Pantzios  found thOne indicator that a farm is industrialized or has the intention and potential to do so is whether the farm is run by the owner and their family members or by a professional manager. Having a professional manager indicates that the farm has a structure that more resembles a modern business, is less constrained by the limited labour supply of family members, and is thus more capable to expand. It is also likely that when a farm reaches a certain size, a family-run structure is no longer suitable, and a professional manager will be needed to run a larger enterprise. A modern management structure may in turn enable the farm to expand further. In the June Agricultural Census data, we find that farms that are run by a manager are on average 27% larger than those run by the family. Moreover, the very large farms with more than 800 cattle are almost exclusively run by professional managers. Researchers have observed a phenomenon termed the ‘disappearing middle’ among farms, which describes an increasing polarization in farm size and the decline of medium-sized farms. The numbers of both large and small farms have been increasing, while the number of medium-sized farms has been decreasing. Slee, Brown  found thk-means cluster analysis , which we interpret as being a single farm. Agricultural holdings make decisions regarding herd size in each time period. There are 13,406 cattle farms identified from the June Agricultural Census database between 2000 and 2012. The census data includes information on the farm owner, herd sizes of various types of cattle, areas of crops, grasses and so on. Among other things, in each time period the owner of a holding makes decisions about the size of the herd in every category, which is encapsulated in a ‘decision entity’. Spatial aggregation is done using Scottish Parliament constituency boundaries to capture the political implications of Brexit. They are the regions represented at the Scottish Parliament elections. Since the EU common agricultural policy is a devolved matter, Scottish Parliament constituencies are chosen over UK Parliament constituencies to better represent the interest of the area and its political position. There are 73 Scottish Parliament constituencies in Scotland. Each time step in the simulation represents a year, during which the holdings will make decisions regarding their decision to diversity, to industrialize, and the decision regarding the herd size of each type of cattle .A holding’s decision to diversify into tourism is influenced by its neighbours. As we discussed in section 2.3, agricultural tourism displays strong spatial correlation and externality. In this model, we will use a probabilistic function to determine whether a farm will diversify into tourism in the next period. The probability increases as the percentage of nearby farms already in tourism increases. We define ‘nearby’ farms as those located within a radius of 10 km, which is approximately 5–10 minutes’ drive.Industrialization is defined in this model as being run by a professional manager and expanding more quickly when conditions allow. The way we define and implement industrialization in the model is consistent with the description of industrialization in the literature, which entails the adoption of a manufacturing approach to agricultural production. A holding’s decision to industrialize depends on two factors. The first is size. We assume that a holding with fewer than 50 cattle will not consider industrialization, as it cannot sustain the wage of a professional manager. Once a farm reaches the minimum size, we assume that its decision to industrialize will depend on its neighbours. We use a radius of 15 km here because we believe information, land quality etc. can be shared on a larger spatial scale than tourism resources and facilities. Industrialized farms will expand more quickly when conditions are preferable.Data on farm succession and leisure farms are from June Agricultural Census. We assume that farms whose owner is over 65 are likely to be without a successor. Leisure farms are defined as those with no full-time workers on the farm. We assume that for leisure or diversified farms or farms without a successor, profit from farming is not a factor for their decision-making. We assume that farms without a successor will shrink (in herd size) rapidly since its owner cannot maintain it due to old age. We assume that leisure and diversified farms will maintain the same herd size throughout the model.For the profit-driven farms, we use a supply chain approach to model their decision making. The supply chain starts from service bull farmers, who supply to beef and dairy breeders, who supply to farmers who fatten beef and dairy calves, who supply to beef and dairy cattle finishers who supply the final product (beef and milk respectively) to the market. A holding may also play multiple roles in the supply chain. For example, it can own both beef calves and cattle. Decisions are made sequentially in reverse order of position in the supply chain. The most downstream suppliers–those who sell beef and dairy cattle–are the first to make a decision on herd size based on the price of beef and milk. The decisions made by them in turn determine the aggregate demand for beef and dairy calves, which in turn determines the demand for beef and dairy breeders. The process perpetuates through the chain until it reaches the most upstream suppliers. The main emergent results are the distribution of farm sizes in Scotland, in other words, the number of small, medium and large farm respectively. The results are emergent because they are based on decisions made by individual farms. The model does not impose any constraints or rules for certain distributional patterns to occur.The individual farmers adopt different behaviour and decision rules in response to changes in themselves (e.g. whether they have succession or off-farm income), and in the environment . The farmers are heterogenous. Based on the type and status of the farmers, only some (the profit seekers) will adapt to increase their chances of success.Individual farmers sense if their neighbours have diversified into tourism or decided to industrialize or intensify their farms. They also sense the global market price for their input and output.Farmers interact through observing their neighbours , and (with a probability) mimicking what their neighbours do.One source of stochasticity is whether a farmer will mimic their neighbours when their neighbours diversify into tourism or decide to industrialize or intensify the farm. Another source of stochasticity is whether the owner of the farm has a successor when he or she is more than 65 years old. To account for the variability caused by the stochasticity, for each parameter combination we run the model 40 times to 1) see the level of variability and check if there are cases of bifurcation and 2) if variability from stochasticity is small to moderate and there is no bifurcation, use the average of the runs for analysis and comparison.Individuals do not form collectives or aggregations in this model.The status and herd size of all farms are collected and then aggregated for analysis. We use all output data to imitate the empirical evidence from the census data.The data we use to initialize the model are from the June Agricultural Census (JAC) and the EU Farm Structure Survey (FSS). Both census and survey are compulsory and cover almost all farms in Scotland (see the supporting document for more information on the coverage of JAC and FSS). The data cover the time period between 2000 and 2012. The unique farm identifier also allows us to obtain the holding’s geographic location through the Integrated Administration and Control System, from which we derive each holding’s agricultural capacity from Macaulay Land Capability for Agriculture (LCA) data . The intThe successor variable is derived from the occupier’s age in JAC. If the age of the occupier is over 65 (the current pension age for men in the UK), we assume a high proportion of them will not have a successor. At the start of the model in 2010, 1,391 (10.4%) farms have no successor. The off-farm income variable is derived from whether the occupier works full-time on the holding in JAC. If no-one  works full-time on the holding, we assume the family has off-farm income and are leisure farmers. At the start of the model in 2010, there are 5,459 (40.7%) leisure farmers by this definition. Both successor and leisure farm status are dynamic variables from annual data and are subject to change in the simulation.We categorize the farms into three size groups: small, medium and large by the total number of cattle. The cut-offs that we use to categorize the farms are derived from the clustering analysis using the 23 cattle-related variables in the JAC. The cut-off between small and medium-sized farm is 131 cattle, and the cut-off between medium-sized and large farm is 368 cattle. The values of the cut-off are derived from the cluster analysis on cattle farm size  and remain unchanged throughout the simulation. At the beginning of 2000, there are 1,030 (7.7%) large holdings, 4,424 (33%) medium-sized holdings and 7,952 (59.3%) small holdings.The input data include output and input price, average weight (beef) and milk yield (dairy) per cow, as well as the successor and off-farm income situation of each holding from 2000 to 2012. The data is input into the model in each time step . The price and yield data are obtained from the Economic Report on Scottish Agriculture (ERSA), which is published annually by the Scottish Government . The suck is the type of cattle, which can be beef or dairy, and i is the holding. Therefore, i’s profit from cattle type k, pk is the price for beef or dairy, yieldk is the yield per head in terms of beef and milk production, and i owns. The global variables feedPerHeadk are the amounts of feed needed per beef and dairy cattle. We assume it is higher for dairy cattle due to the lower turnover and the longer time they are kept on farm. For the same reason, we assume that the value for the global variable strawPerHeadk is higher for dairy cattle.For the profit-seeking farms that produce the final beef and dairy product, in each period they calculate profit from selling the final product in the last period as a guideline to make the production decision in the following period. The profit of the farm depends on the price of the final product, the price of input such as feed and bedding, the weight of the beef cattle and the yield of the dairy cattle, and the area of barley, oat and grass the holding has. The calculation of profit is shown in the following equation,i’s areas of grass, rough grazing, barley and oats respectively. The global variables rgrass, rrough, r_barley, and roats are the conversion rate from grass area to hay, rough grazing area to hay, barley area to straw and oats area to straw respectively. The second terms on the right hand side of both equations Some holdings have grass and rough grazing areas where the cattle can graze. Hence the amount of hay (cattle feed) the farmers need to purchase depends on the area of grass and rough grazing a holding has. Since grass is better for grazing than rough grazing, we assume a different conversion rate to dry feed for grass and rough grazing areas. Similarly, some holdings grow barley and oats, the straw of which can be used for bedding. Therefore, the amount of straw the farmers need to purchase depends on the area of oats and barley that grows on their own land. Farms can also be self-sufficient in feed or straw, in which case the purchase for feed or straw is zero. The calculation of the feed and straw produced on farm are shown in the following equations,We derive the Brexit scenarios from FAPRI -UK Brexit Report , which qDavis, Feng  describeIn this section we show results from the model as used to select the combinations of drivers that reproduce the pattern on the disappearing middle. We then show the results for the four selected such combinations of drivers using the above Brexit scenarios, with overall trends first, followed by spatial distribution of effects.In section 2 we discussed several patterns and mechanisms that could have caused the phenomenon of disappearing middle, including the impact of 1) succession, 2) leisure farming (leisure), 3) diversification and 4) industrialization. The four mechanisms are Boolean parameters that one can turn on and off. When turned on, the mechanism will play a role in the farmer’s decision making, and when turned off, it will not. For example, when succession is turned on, a farmer will consider the issue succession when making a decision and adopt a different behaviour rule based on whether he or she has a successor; when succession is turned off, on the other hand, farmers will not include the issue of succession in their decision making process. In other words, succession will not affect farmer decision making.Although all four mechanisms  can be present in Scottish cattle farming, it is not clear which ones are necessary to cause the observed pattern of disappearing middle. To find out, we allowed each contested mechanism to be switched on and off, and ran the model on each of the sixteen combinations of the four mechanisms for 40 replications. We then matched the range of the outcomes against the data, which is shown in We found out that, of the sixteen possible combinations of the four mechanisms, four have successfully reproduced the pattern of the disappearing middle (in red rectangle) in a qualitatively similar way. The four successful combinations are 1) succession-leisure-diversification-industrialization; 2) succession-leisure-diversification, but no industrialization; 3) succession-leisure-industrialization, but no diversification; and finally, 4) succession-leisure, but no industrialization or diversification. In other words, to reproduce the pattern of the disappearing middle, the issue of succession and leisure farming are necessary, but not industrialization or diversification, although they do not reverse the pattern. Models that do not include succession and leisure farming, on the other hand, have failed to reproduce the disappearing middle. They tend to overestimate the number of medium-sized and large farms and underestimate the number of small farms.Grimm, Revilla  first inIn the WTO scenario, because beef and dairy price will increase, the number of large cattle farms will increase significantly compared with the no Brexit and FTA  scenarios. The local preconditions determine to what extent the WTO arrangement will boost the number of large cattle farms. In the case of no industrialization, WTO Brexit will increase the number of large farms temporary, but does not alter the overall trend of decline of large farms. If, however, industrialization is present, the increasing in the number of large cattle farms will continue and the previous declining trend before Brexit will be reversed altogether.In the UTL scenario, because the beef and milk price are much lower after Brexit, the number of large farms will be much lower than in other scenarios. Yet whether industrialization exists in the local area before Brexit still determines the long-term trend of large cattle farms. If industrialization exists, the number of large cattle farms will stabilize after the initial shock of a UTL Brexit; otherwise the decline will continue. Studies have found that the industrialization or intensification of agricultural production can have a profound impact on the structure and the nature of farms by changing farms’ scale, efficiency, vulnerability, requirement of inputs and land, and the way they behave , 81, 82.Interestingly, Brexit does not seem to affect the decline of medium-sized cattle farms. Brexit or not, the existing trend of “the disappearing middle” seems to continue regardless. The influence of Brexit on small farms is small, too, and does not change the general increasing trend. However, the impact is distinguishable by the Brexit scenarios: the UTL arrangement increases the number of small cattle farms, whereas the WTO arrangement decreases it. The reason Brexit does not affect small or medium-sized farms as much is because other mechanisms such as succession, leisure farming and diversification dominate development of small farms, compared with which Brexit plays only a marginal role.More importantly, the impact of Brexit on the number of cattle is highly sensitive to the other mechanisms. As we previously discussed, the number of cattle in Scotland has been in a long-term trend of decline since the 1970s. Without industrialization, we find that even the most favourable (to the cattle sector) WTO scenario is insufficient to reverse the declining trend. Only in the second case where succession, leisure farming and industrialization are present but not diversification, is the general trend of decline reversed. In that case, a WTO arrangement will increase the number of cattle so much that it exceeds the initial level and starts to grow. Interestingly, even in the unfavourable scenario of UTL, the declining trend seems to be reversed and the number of cattle stabilized towards the end of the simulation. In all other three cases, on the other hand, although Brexit scenarios have produced temporary shocks to the number of cattle, they do not alter the general trend in the long term. The interactions between Brexit and the other mechanism are important: The model could predict completely opposite Brexit impacts (case 2 vs. the rest) based on which mechanisms are present simultaneously.This section shows the impact of Brexit on Scotland’s 73 constituencies. Throughout the rest of this section, we will use the following visualization: A shade of green means a percentage increase in the number of cattle between 2019 (Brexit) and 2030; whereas a shade of purple means a decrease. The darker the shade, the higher the percentage increase or decrease.Figs Figs The results have shown that local contexts, trends and mechanisms that exist before Brexit are important and can potentially change the direction of Brexit’s impact. For the 73 constituencies and the 13,406 cattle farms in Scotland, each will have a different Brexit story. A large, industrialized dairy farmer in southwest Scotland, for example, will probably still grow even in the least favourable Brexit scenario, whereas a small beef farmer in northern Scotland will struggle even in the most favourable Brexit scenario. For others with off-farm income or income diversification activities, Brexit may not even be a major factor. Therefore, depending on the type and size of the majority of cattle farms in each constituency, political representatives there will also face very different issues, needs and requests from cattle farmers after Brexit. Again, for each constituency and the politicians representing it, Brexit does not have the same impact.The results show the observed trend of polarization among Scottish cattle farms continuing until Brexit, and we were curious to find out if Brexit could potentially alter it. However, we found that the decline of medium-sized farms is persistent and more or less unaltered by Brexit. The reason is that the two main forces behind the trend: the lack of succession and the rise of leisure farming are more fundamental than can be moved by the market changes brought about by Brexit. Brexit may influence succession and leisure farming in some non-market ways, but it is unclear how.Policy frameworks devoid of local context and processes can easily lose meaning and insight. Ostrom  talks abThis study includes four combinations of mechanisms or processes that could possibly lead to the disappearing middle phenomenon. Given the data we used in this study, we could not rule them out using the POM approach . We willThe study has made several simplifications. The impact of Brexit is largely limited to market price ; whereas in real world, it may have a wider influence socially and culturally, such as the Brexit impact on agricultural tourism, immigration and income level. The farmers’ decision-making procedure  is highlIn this study, Industrialization is represented by the farm being run by a professional manager rather than the farm owner and the family, which is supported by literature  and ind. HoweverBecause we focus on the farmer’s decision to change existing herd size (not to decide on its level), we have not included certain geographical factors in the decision making algorithm, such as distance to market, soil quality and climate because they do not change (or change very little) over the course of the study, so will not influence the farmer’s decision to change the herd size. However, there might be interactions between these factors and Brexit. For example, Brexit may change the impact and importance of certain geographical factors; it may alter the development of agricultural tourism; and it may change the level of income. We did not consider these potential interacting effects because so far it is unclear if and how these effects will take place, and little evidence exists to support assumptions either way.The definition of a ‘cattle farm’ in this study (farms that has at least one head of cattle in the year 2000) means that we include more than 1500 (out of 13406 cattle farms) very small farms with very few (e.g. less than 10) cattle. Although some of those small farms with a few cattle may not be regarded as ‘proper’ cattle farms in a conventional definition, we believe it is important that we include them in this study, because they may reflect the emerging trends that drive the current phenomenon. The farms with few cattle may be the newly arrived leisure farmers from the city; or the retired farmers who have been gradually shrinking their herd due to lack of succession; or the farms diversified into tourism that keep a few cattle for their touristic values. Although small, these farms may reflect those ongoing processes, and thus it is important that the model and the analysis in the paper include them.From a modelling perspective, one of the most interesting aspects of our results is the fact that the same patterns of macro-level behaviour can be achieved with different ontological assumptions about the drivers thereof. Further, ontological assumptions generating matching patterns do not have consistent implications for the impact of Brexit. The work addresses emerging concerns in modelling pertaining to structural validation and uncertainty –88. To oWe develop an empirical agent-based model to investigate the impact of Brexit on Scottish cattle farms, while acknowledging that long before Brexit, changes have been ongoing among Scottish cattle farms that have led to the polarization of cattle farms and the decline of medium-sized farms. From empirical evidence, existing literature, and local expert knowledge, we identify four factors and drivers that could cause the disappearing middle phenomenon: the lack of succession, the rise of leisure farming, diversification and industrialization. We ask the following research questions: How does a major national event like Brexit interact and interfere with local context, trends and processes? To what extent does the impact of Brexit depend on the trends and conditions that exist before Brexit? And which types of farms will be mostly impact upon by Brexit?We find that pre-Brexit conditions could lead to very different conclusions regarding the impact of Brexit. In particular, whether industrialization is a dominant trend locally can significantly alter the size and direction of Brexit impact. We show the Brexit effect is more pronounced on large cattle farms, which are predominantly profit-seeking, than small and medium ones, many of which are diversified, living on off-farm income or closing down due to lack of succession. As for the phenomenon of the disappearing middle, we find that Brexit will not alter or reverse the long-term trend, mainly because the mechanisms behind it such as lack of succession are more fundamental and persistent than can be moderated by the market changes brought about by Brexit. Finally, the impact of Brexit varies across regions: some constituencies will be more resilient than others; the winners and losers will also depend on the Brexit scenario and the type of cattle.When talking about the impact of Brexit, we need to ask, “Brexit for whom?”, as Brexit would mean very different things to different people and groups. There is no one Brexit for all. The analysis of Brexit, therefore, should take into account the heterogeneity and the complex local context and social environment under which Brexit occurs. Although it might make policy analysis more challenging and the results less clear-cut, we should not shy away from the complexity that is an inevitable part of life. Instead of fitting the world into simple system frameworks and refuse to see the evidence when it does not fit, as Ostrom  correctlS1 Appendix(DOCX)Click here for additional data file.S1 Data(7Z)Click here for additional data file.


To measure the prevalence of smoking cessation during pregnancy and to identify factors associated with its occurrence.The present survey included all puerperal women living in the municipality of Rio Grande, RS, whose birth occurred between January 1 and December 31, 2013. A single standardized questionnaire was applied, in the hospital, within 48 hours of delivery. Multivariate analysis was performed using Poisson regression with robust variance.The prevalence of smoking cessation among the 598 parturients studied was 24.9% (95%CI 21.5-28.6). After adjusting for confounding factors, mothers aged 13 to 19 years , who had higher family income , higher educational level , higher number of prenatal appointments , and who did not smoke in the previous pregnancy  presented a higher prevalence ratio of smoking cessation.Although pregnancy is a window of opportunity for smoking cessation, the rate of cessation was low. The prevalence of cessation was higher among mothers with lower risk of complications, suggesting the need for interventions prioritizing pregnant women of lower socioeconomic levels. Smoking accounts for 5% to 8% of premature births, 13% to 19% of cases of low birth weight, and 5% to 7% of sudden infant death syndrome-Most of the existing studies on the subject have been conducted in developed countries and show that smoking cessation in the gestational period is more common among primiparous women, who live with their partners, have higher income and schooling, have planned their pregnancies, and used to smoke fewer cigarettes per dayMost studies address smoking cessation at defined periods, without considering pregnancy as a whole. However, women who continue to smoke during pregnancy may temporarily stop smoking, reduce the number of cigarettes, and have multiple relapses during the gestational period. Pickett et al.In addition to the lack of population-based studies on smoking cessation during pregnancy carried out in low- and middle-income countries, there is a lack of knowledge on cessation attempts, as well as on the identification of withdrawal periods during pregnancy.This study aimed to describe smoking cessation during pregnancy, to estimate its prevalence and to investigate associated factors.The data used in this analysis were collected as part of a larger study conducted every three years in Rio Grande, a municipality located in the so-called South Half of the state of Rio Grande do Sul. In 2013, the municipality had about 206 thousand inhabitants, according to an estimate by the Brazilian Institute of Geography and Statistics.The target population was comprised of women living in Rio Grande's urban and rural areas who had children in one of the two local maternities between January 1 and December 31, 2013. These maternities belong to Dr. Miguel Riet Corrêa Jr., from the Universidade Federal do Rio Grande and the Santa Casa de Misericórdia. Parturients whose newborns were weighted at less than 500 grams or born after less than 20 weeks’ pregnancy were excluded from the study. This criterion was adopted because the World Health Organization classifies these situations as fetal mortality, excluding them from perinatal statistics.All information presented in this article was obtained through the application by previously trained interviewers of a single standardized questionnaire, within 48 hours of delivery. The pilot study was conducted in 2012, in the same maternities, with the objective of testing the enunciation of questions and the logistics of data collection.The interviewers visited the delivery room of each of the maternities on a daily basis, checked the hospitalization registry book, and then confirmed the obtained information in each hospital's Medical and Statistical Service (SAME). Afterwards, they wrote down the mother's name and went to the infirmary. After confirming with the mother that she in fact resided in the municipality of Rio Grande, they explained the purpose of the study, inviting her to participate.The completed questionnaires were later coded by the interviewer and delivered to the project headquarters, where they were reviewed and typed into the Epidata 3.1 program,Information on smoking six and three months before becoming pregnant and also during each quarter of pregnancy was collected. Parturients who reported smoking six or three months prior or during pregnancy were included in the study. In addition, we investigated whether these women attempted to quit smoking, how many attempts were made, when they occurred and for how many days they went without smoking. Based on these data, the “cessation of smoking” outcome was designed and attributed to women who smoked six or three months prior to pregnancy, stopped smoking before the seventh month, and did not return to smoking until the end of pregnancy. Thus, only those who reported smoking six or three months before pregnancy were included in the crude and adjusted analyzes. The basis for determining the sixth month of pregnancy as the limit for cessation were studies which demonstrate a reduction in the risk of adverse effects when the cessation occurs within the firstThe following exposure variables, collected whenever possible in the continuous or discrete forms, were included in the analysis model: maternal age , cohabitation with partner , schooling , monthly family income (tertiles), work during pregnancy , parity , preterm birth , start of prenatal care (quarter), number of prenatal consultations performed , type of service in which the prenatal consultations were performed (public or private), support received from the child's father during pregnancy assessed as positive , husband/partner smoked for at least one quarter during pregnancy , number of cigarettes smoked per day before pregnancy , and smoking in the previous pregnancy .Descriptive analysis consisted in obtaining measures of prevalence for both the exposure and outcome variables. Pearson's chi-square test or Fisher's exact test were used for comparing proportions between independent variables and outcomeAdjusted analysis, carried out with the objective of controlling potential confounding factors, obeyed a previously established hierarchical modela priori that, in order to be kept in the model, the p-value of the association between the exposure variable and the outcome should be ≤ 0.20. The level of significance for the two-tailed tests was 5%.The model proposed for this analysis has three levels. The first level is comprised of variables related to the mother's demographic characteristics, as well as the family's socioeconomic characteristics ; the second level is comprised of variables related to reproductive history (history of preterm birth and parity); the third level is comprised of variables related to the current pregnancy and previous exposure to smoking . In this regression model, the variables are controlled using those at the same or previous levels. However, it was decided The measure of effect used was prevalence ratio (PR), obtained by means of Poisson regression with robust variance adjustmentThis research project was submitted and approved by the Research Ethics Committee of the Federal University of Rio Grande and by the Committee of Ethics in Health Research of Rio Grande's Santa Casa. The free and informed consent term was read by each participant, who signed two copies, one remaining in the participant's possession. In the case of adolescent parturients who agreed to participate in the study, the term was preferably signed by the spouse or by one of the parents. In addition, the right of non-participation in the research was protected, as well as the confidentiality of the information obtained.The 2013 Perinatal Study identified 2724 eligible parturients and interviewed 2653. Losses were 71 (2.6%) parturients in total. Among all interviewees, 749  had a history of current or previous smoking. One hundred and forty-eight were excluded, since they had stopped smoking prior to the six months preceding the current pregnancy. Three were excluded because they started smoking during pregnancy. Thus, the study's denominator was comprised of 598 puerperae.The According to data on attempted cessation, 56% of women attempted to quit smoking, 78% of whom went without smoking for at least seven consecutive days. The first attempt occurred more frequently (44%) in the first quarter of pregnancy. In addition, most women made only one attempt to stop smoking (77%). Overall prevalence of cessation was 24.9% (95%CI 21.5-28.6) . Table 3This study showed that in the period between six months prior to pregnancy and immediate postpartum 56% of women tried to quit smoking and 78% went without smoking for at least seven consecutive days. Only one in four quit smoking during the gestational period. Cessation was significantly associated with lower age and higher maternal schooling, higher family income, higher number of prenatal consultations, especially in the private sector, and absence of smoking in previous pregnancy.In the studied sample, 56% of the women reported having tried to quit smoking, a prevalence higher than the 38% observed by Pickett et al.The prevalence of cessation in this study was 25%, similar to the one observed by a study conducted in Porto Alegre, state of Rio Grande do Sul, which included only women who underwent prenatal care, and classified 25% of the participants as abstinent smokersIn a 2011 study conducted at two public maternity hospitals in the state of Rio de Janeiro, in one of the maternities the prevalence of cessation was greater from one month before pregnancy until the first quarter (18%), and from the first to the second quarter (13%) in the otherAlthough not significant in the crude analysis, in this study's adjusted analysis maternal age had an inverse association with cessation. Colman and Joyce,,Socioeconomic levels have been shown to be associated with several risk factors and different morbidities. This association also occurs for smoking during pregnancy, with a higher prevalence of cessation among pregnant women with better socioeconomic levels. Thus, as observed in this study, the majority of the researches demonstrate that women with higher income are more likely to stop smoking during pregnancyIn addition to the association with family income, the studied population had a high prevalence rate among parturients with at least nine years of schooling , which may indicate that this is an important feature in determining cessation during pregnancy. Besides higher socioeconomic levels, high maternal schooling may allow for greater access to information on the risks of smoking during pregnancy. The association with high schooling was also observed in three studies conducted in the United States, which gathered data from 1991-The type of service in which the pregnant woman performed prenatal consultations (public or private) is not in itself a risk factor, but may be an indicator of exposure to other factors such as low prenatal care quality, lower income, and lower schooling. In the crude analysis, the prevalence of cessation among users of the private service was 46% higher than among users of the public service. The direction of this association was expected to remain equal in the adjusted analysis, but after adjustment it was inverted, with a 40% lower prevalence of cessation among women who used the private service. This finding is difficult to explain, and may be due to variables not evaluated here. It could be that women with lower income are now having consultations in the private sector, possibly due to the financial support of a relative, especially the partner. These pregnant women began to have access to this type of service due to the generation of thousands of jobs in the municipality of Rio Grande, as a result of new oil rigs being constructed. However, this hypothesis would need to be further investigated. This variable was not evaluated in other studies, but in the United States there was a higher prevalence of smoking during pregnancy among women assisted by Medicaid,Prenatal consultations increase the possibility of interventions by health professionals. In addition, a low number of consultations may indicate late initiation of prenatal care, unwanted pregnancy, and worse socioeconomic status. In this study, the prevalence of cessation was higher among women who performed at least six prenatal consultations. This is in agreement with other research findings, in which number of prenatal consultationsHere, women who did not smoke in the previous pregnancy had a prevalence of cessation almost three times higher. Among women who smoked in the previous pregnancy, only 12% stopped smoking in the current pregnancy. A study conducted in Norway (1999-2008) showed that 31% of women who smoked in the first pregnancy did not do so on the secondWhen interpreting these data, one should keep in mind that it is comprised of information obtained from mothers’ reports, which may have led to an overestimation of smoking cessation prevalence. Kharrazi et al.In any case, the prevalence of smoking cessation observed in this study was low. If it has indeed been overestimated, one has to conclude that the need for research on this subject is even greater. It is widely known that the gestational period is highly appropriate to encourage smoking cessation, since in this period women are more willing to change in this respect. Whenever the pregnant woman is in the health service, the health team, especially physicians and nurses, need to work more intensely and repeatedly with the issue of smoking cessation. In this sense, this study may offer an expressive contribution: it was able to identify the characteristics of women in the puerperal period who abandoned smoking and the moment when this behavior has a greater change of occurring. This makes it possible to direct the actions of health professionals, allowing their interventions to achieve greater impact.Consideration should also be given to continuing actions for discouraging smoking after childbirth, in routine immunization visits, and for monitoring child development and growth through the fifth year of life. For many mothers, abstinence does not necessarily mean permanent cessation of smoking. To succeed, these interventions need to be repeated every time the mother is in the health services. Campaigns discouraging smoking could also be more intensively publicized by different types of media in order to reach these women at other times. Reducing the harmful impact of smoking on maternal and child health remains a major challenge to be overcome.


Cross-sectional survey.The objective of this study is to identify what characteristics of the family caregivers influenced the use of professional home care for persons with SCI in Switzerland.Community setting, nationwide in Switzerland.Questionnaires were filled out by the adult family caregivers of persons with SCI. Influence of characteristics of the caregivers was analyzed with regression models, adjusting for the characteristics of the person with SCI. Logistic regression was used for whether professional home care was used. Poisson regression was applied for the absolute and relative amount of professional home care.In total, 717 family caregivers participated in the study (31% response rate). Among the participants, 33% hired professional home care for 10 h per week on average. The level of dependency of the persons with SCI had a significant influence on the utilization of care. The availability and proximity of the primary family caregiver, namely being spouse and cohabiting, reduced the amount of services used, whereas caregivers who worked full time employed more services. Higher levels of education and income increased the use of professional home care. Compared with their reference groups, caregivers with older age and those with a migratory background used comparable or larger absolute amount of professional services, which, however, represented a smaller proportion of total hours of care.Adequate support requires consideration of the characteristics of both the caregiver and of the person with SCI. The needs of family caregivers should also be assessed systematically in the needs assessment. Persons with spinal cord injury (SCI), especially those with higher lesion levels, are dependent on care and assistance with daily living activities, with a majority provided by informal caregivers . CaregivIn previous SCI studies, the needs for caregiving of persons with SCI, indicated by the level of dependency, were found to be the major determinant of caregiving service , 7, 8. TIn Switzerland, as an example of needs-based care, professional home care mainly provides medical care and assistance in activities of daily living (ADLs). The care is provided upon prescription from a doctor, according to the caregiving needs, which are assessed using standardized procedures by qualified service providers  . MandatoThe objective of this study was to identify what characteristics of the family caregivers influenced the use of professional home care for persons with SCI in Switzerland, specifically on (a) whether professional home care was used and (b) the amount of professional home care used. Implications for the family caregivers and service provision will be discussed based on the findings.A questionnaire study was conducted in family caregivers of persons with SCI in Switzerland, from August 2016 to July 2017. Questionnaires were sent to persons with SCI, who forwarded it to their primary family caregivers. Eligible persons with SCI were contacted using information from the Swiss Spinal Cord Injury Cohort Study (SwiSCI) , cross-rThe contacted persons with SCI fulfilled the inclusion criteria of SwiSCI study : individIn the questionnaire, family caregivers provided information about their socio-demographic characteristics, household members, employment, and satisfaction about own health . The questions included when they started the caregiving, their weekly time investment, activities in which they assisted, and who else also provided assistance. They further provided information regarding the person with SCI, including age, type of SCI, cause of SCI, and wheelchair dependency, which were used as indicators of caregiving needs. Whether they utilized professional home care and hours requested per week were also reported, which were treated as outcomes of the study.First, a binary indicator was applied as whether professional home care was used. This was fitted with a multiple logistic regression model, adjusting for characteristics of the caregiver and the person with SCI. The results are reported in odds ratio (OR), where a number higher than 1 indicates higher odds of using professional home care compared with the reference group.Second, two Poisson regression models were used to analyze the amount requested for professional home care both in absolute and in relative terms. The relative term was defined as the proportion of professional home care with respect to the total hours of care , namely the weekly hours of professional home care divided by the total hours. It indicates the extent to which professional home care replaces the care undertaken by family caregivers. An increase in the absolute number of hours of professional home care may not always lead to an increase in the proportion. The results are reported as relative risk (RR), where a number higher than 1 indicates a higher quantity of professional home care compared with the reference group.All statistical analyses were conducted with Stata version 14 for Windows . For all regression analyses, the missing values of independent variables were imputed with multiple imputation, using chained equation . EstimatIn total, 717 questionnaires were returned, which resulted in a response rate of 31%. The family caregiver population was predominantly female (71%), with an average age of 57 years  and 56 years old, on average. Over half were paraplegic (60%) and the injury was caused by an accident in 71% of the cases. Most were wheelchair dependent (68%) and had lived with the injury for 18 years, on average.Table The use of professional home care is significantly associated with characteristics of the persons with SCI. Older persons with SCI  and persons with tetraplegia  were more likely to employ professional home care. Also, persons whose SCI was related to disease were more likely to use professional home care . Persons with SCI who were partially able to walk were less likely to use professional home care when compared with wheelchair-dependent persons .The number of hours requested for professional home care was associated with several characteristics of the family caregiver Table . More hoMore hours of services were requested for women with SCI , older care recipients , persons whose injury was caused by reasons other than an accident , and persons with tetraplegia . Fewer hours of professional home care were requested for persons with SCI, who were not wheelchair dependent  and for those who had lived with the injury for over 15 years .Out of the total care provision, the proportion of professional home care was greater among female caregivers , caregivers residing in the French-speaking part of Switzerland , caregivers with higher education levels , caregivers with a higher income level , non-spousal caregivers , and caregivers who were employed full time , and in cases where other informal caregivers were involved . The proportion was smaller for family caregivers living with the person with SCI , for caregivers who reported higher levels of satisfaction with their own health , and for those who indicated GP as their primary contact .Although the family caregiver’s age was unrelated to the hours of professional home care requested, the proportion of professional home care decreased with the age of the family caregiver . Caregivers with a migratory background, despite using more professional home-care hours, used a significantly smaller proportion of professional home care when compared with Swiss-born caregivers .A larger proportion of professional home care was requested for female care recipients , persons with SCI who were older , had tetraplegia , whose injury was caused by disease , and persons that were wheelchair dependent . Persons with SCI, who lived with the injury for over 15 years, requested a smaller proportion of professional home care .The results did not show severe multicollinearity (Supplementary Table Although the characteristics of the persons with SCI were decisive for the use of professional home care, the amount of the services used was linked to characteristics of the family caregivers. As for the persons with SCI, those with older age, tetraplegia, and SCI caused by a disease tend to develop more health conditions  and thusApart from the care demand in focus, greater availability and proximity of the primary family caregiver reduced the amount of professional home care requested. Married persons with SCI received substantial care from their spouses and, thus, reported less use of professional home care , 9. SpouContrary to a previous study , the invAdding to the existing research on SCI , this stContrary to our expectation, older family caregivers used comparable amount of professional home care as the younger caregivers and those with a migratory background hired more professional home care than Swiss-born caregivers; yet, in both cases the proportion of professional home care decreased. This finding suggests that these groups provided more caregiving, while also obtaining high levels of professional support. There are two potential reasons for this. First, the amount of professional home care does not sufficiently cover the actual need for care, requiring family caregivers to invest more time. Second, a majority of the caregiver’s assistance may not be related to care and may not be covered by insurance. For example, older caregivers may need more support for housekeeping tasks  and the This study was conducted with three limitations in mind. First, the questionnaires were forwarded to the primary family caregivers by the persons with SCI. This may partly explain the low response rate. Family members might not be considered as caregivers if they are responsible for usual household tasks. Therefore, the study may have presented family members who were more involved in assistance of ADL. Furthermore, time investment by other informal caregivers was not captured. Data analysis was based only on the time investment reported by the primary caregiver. This limits the accuracy of the calculation for the proportion of professional home care. The proportion of professional home care might have been smaller in cases with other informal caregivers.Second, the questionnaire was provided in the three official languages in Switzerland. Caregivers who did not understand any of these languages were excluded from participating. Almost 10% of the first-generation immigrants in Switzerland were estimated to lack proficiency of any of these three official languages . Given tThird, the study lacks the consideration of sociocultural factors on community level. The increased use of service in French-speaking region can be traced back to either the cultural diversity or organizational difference, which can influence utilization of health care , 26. ResUtilization of professional home care differed across characteristics of the family caregivers. Planning of adequate support requires consideration of the characteristics of both the caregiver and the individual with SCI, reflecting on how the care provision can account for them, such as the age and migratory background of the family caregiver. Otherwise, a dropout may occur due to escalating needs, which leads to unexpected deficits in caregiving. The needs of family caregivers should hence be assessed systematically in the regular needs assessment.The datasets generated and analyzed in the current study are available from the corresponding author on request.Supplementary Table 1