Month of birth – an indicator for a variety of prenatal and early postnatal exposures – has been associated with life expectancy in adulthood. On the northern hemisphere, people born in the autumn live longer than those born during the spring. Only one study has followed a population longitudinally and no study has investigated the relation between month of birth and mortality risk below 50 years.In this nation-wide Swedish study, we included 6,194,745 subjects, using data from population-based health and administrative registries. The relation between month of birth (January – December) and mortality risk was assessed by fitting Cox proportional hazard regression models using attained age as the underlying time scale. Analyses were made for ages >30, >30 to 50, >50 to 80 and >80 years. Month of birth was a significant predictor of mortality in the age-spans >30, >50 to 80, and >80 years. In models adjusted for gender and education for ages >30 and >50 to 80 years, the lowest mortality was seen for people born in November and the highest mortality in those born in the spring/summer, peaking in May for mortality >30 years  and in April for mortality >50 to 80 years . In the ages >80 years the pattern was similar but the differences in mortality between birth months were smaller. For mortality within the age-span >30 to 50 years, results were inconclusive.Month of birth is associated to risk of mortality in ages above 50 years in Sweden. Further studies should aim at clarifying the mechanisms behind this association. A large body of epidemiological evidence suggests that risks of diseases and mortality in adult age is not only determined by genetics and lifestyle but also by factors acting in early life.Month of birth is frequently used as a proxy for a wide array of prenatal and early postnatal environmental exposures related to season. These include nutritional status, ambient temperature, sun exposure, exposure to infections and other environmental influences. Whether the effect of month of birth on mortality risk change during the life course is of interest to clarify, as risks of diseases and prevailing causes of deaths vary between age-spans. Most studies have only indirectly studied the influence of month of birth on mortality by cross-sectional reviews of death certificates from which average age at death has been calculated. In this nation-wide study, we included over 6 million subjects who lived in Sweden in 1991 and who were more than 30 years sometime during the follow-up time of 20 years. We assessed the relation between month of birth and mortality in the ages >30, >30 to 50, >50 to 80 and >80 years.st of January 1991. We included all subjects who were more than 30 years old before the end of follow up . From the Swedish Total Population Register, we also retrieved information about dates of emigration and death. Data on subjects’ educational level - categorized into primary, secondary and higher education (studies at university) - was obtained from The Education Register for the years 1990, 1995, 2000, 2005 and 2010. The highest educational level reached during the study time was defined as the subject’s education. Subjects were followed until death, emigration or end of follow-up (December 31st 2010). The study was approved by the regional ethics committee in Stockholm (Dnr 19803/2011). The committee waived the need for written consent from the participants.Data used in the study was obtained from population-based health and administrative registries in Sweden. Cross-linkage across these registries was possible using the person-unique Personal Identification Number (PIN), assigned to all Swedish residents The population was divided by month of birth into twelve groups. To adjust for cohort effects, all analyses were stratified on 10-year birth cohorts. The relation between quarter of birth , and education status was assessed with Chi-2 test. Quarter of birth year was used in order to decrease the number of comparisons in the test.st of January 1991. Subjects contributed with person-time only in the age-spans that they belonged to during the study time. For example, a subject who was 20 years at the start of the study entered the study at the age of 30, i.e. 10 years after start of study follow-up, and contributed with person-time during the 10 years of remaining study time, between the age of 30 and 40.We assessed the association between month of birth and mortality by fitting Cox proportional hazards regression models using attained age as the underlying time scale. The analyses only included ages above 30 years. Left truncation was adjusted for by calculating an individual’s age on the 1As previous studies have shown lowest mortality for subjects born in November, Six percent  of the subjects lacked education data. We therefore compared results from crude analyses of month of birth and mortality in the age-spans >30, >30 to 50, >50 to 80 and >80 years, when including and excluding subjects with missing information on education. As the influence of month of birth on mortality risks did not essentially differ between the models, subjects without education data were excluded in the analyses presented in the study  and the four months with the highest and lowest hazard ratios respectively. In complementary analyses, we also tested for differences in mortality between subjects born in December and January. Statistical analyses were conducted in SAS version 9.0 .A total of 6,194,745 subjects contributed with person-time to the study. During the time of follow-up, 1,287,927 (20.8%) of the subjects died. The proportion of the population that died during the study time increased with age-span, male sex and lower education. .Quarter of birth year was significantly associated to education status . Higher education was slightly more common among subjects born from January to June (monthly rates ranged from 27 to 28%) than among subjects born from July to December (25–26%). Subjects born from January to June were consequently less likely to have only secondary education  and primary education (31–32%) than subjects born from July to December .In the total population above 30 years of age, month of birth predicted mortality in the crude model, and adjusting for sex and education did not attenuate these associations. Compared to November, being born in January to September was associated with significantly increased mortality, and the excess hazard ratio was highest from April through July. When the analyses were stratified by sex (and adjusted for education), the patterns were similar, although there were slight sex differences with respect to month of birth and mortality risk. The lowest mortality risks were seen in October or November in all analyses .We found no interaction effects between sex and month of birth and mortality risk in a model where interaction variables were created with the four months of highest (April–July) and lowest (September–December) mortality risks in the crude analysis . In a similar model, there was a significant interaction between education and month of birth and mortality . The interaction was derived mainly from a lower month of birth-effect on mortality in the high risk months for the middle education group compared to the low and high education group (data not shown).Associations between month of birth and age-specific mortality are illustrated in In this large population-based study, we found that month of birth was a significant predictor of mortality in the age-spans >30, >50 to 80 and >80 years. The highest mortality was seen for people born in the spring/summer, peaking in April , May (>30 years) or August (>80 years), with a corresponding trough in the autumn. In the age-span >30 to 50 years, results were inconclusive.As for the ages above 50 years, the findings of this study are in broad agreement with previous reports from the northern hemisphere: autumn-born have a survival advantage compared to those born in the spring and summer. Seemingly, these effects were more pronounced in the age-span >50 to 80 years compared to >80 years. One possible explanation for this is selective mortality: irrespective of month of birth, the frailer will die first. With increasing age, the remaining population will constitute an increasingly homogenous group of robust individuals, and differences in mortality risk by month of birth will therefore gradually diminish. th century, food supply, especially fruit and vegetables, was much dependent on season. Mothers to children born in the autumn are likely to have had a better nutritional status during pregnancy - especially during the third trimester when the peak growth in fetal weight occurs Possible explanations for the association between month of birth and mortality after 50 years have been scrutinized by others. Recently, vitamin D-deficiency has been highlighted as an early life nutritional factor of potential importance to health later in life. It has been suggested that the prenatal period constitutes a critical window during which vitamin D-deficiency may predispose the fetus for adult-onset disease. In studies on rodents, fetal vitamin D-deficiency has led to retarded cardiac development th century than parents in high socioeconomic groups, education can also be seen as an indicator for the perinatal environment. The effects of month of birth on adult mortality would in this case interact with education. However, the interaction effect found in our study was in the form of a lower month of birth-effect on mortality risk for the group with secondary education compared to the low and high education group. This is not in line with any known hypothesis and could be a chance finding.Socioeconomic status - in this study indicated by educational level - is of importance to consider in month of birth studies. Socioeconomic status is a life course factor affecting disease and mortality risks. Preferences regarding the timing of having children may vary between couples of different socioeconomic groups. Month of birth-patterns in mortality may simply reflect such socioeconomic differences in seasonality of birth Cross-sectional studies have found that an effect of month of birth is present for a diverse range of causes of death, including cardiovascular disease and malignant neoplasms This study, comprising observations from over 6 million subjects, is the largest longitudinal population-based study assessing the relation between month of birth and mortality in adult age. It is also the first study in the field including longitudinal data from age-spans below 50 years. By excluding people born outside Sweden, we were able to create a homogenous study population with respect to seasonally dependent early life exposures as well as country-specific social factors. Further, we had access to information about subjects’ education  in our analyses.Generalizing the findings to other parts of the world may not be strictly possible, given the study setting being confined to Sweden, the Swedish climate and its specific seasonal exposures. Our findings regarding the older age-spans, however, are in line with other reports from the northern hemisphere.Due to the large number of subjects included in this study, we were able to detect very small risk differences between subjects born in different months that are hard to translate into biological or clinical relevant measures. The relevance of this study could thus be considered as providing clues for the identification of early life exposures of potential public health interest.In conclusion, this longitudinal population-based study assessed the association between month of birth and mortality in different age-spans in more than 6 million Swedes. Confirming previous findings from the northern hemisphere, spring/summer-born were shown to die sooner than autumn-born in ages 50 years and above. The associations remained after controlling for sex and education. Future studies on the topic should focus on elucidating the mechanisms underlying these observations.Figure S1Excess hazard ratio by month of birth. Including all Swedish-born subjects living in the country on the 1st of January 1991 and excluding subjects without education data (‰ compared to November).(TIF)Click here for additional data file.


Many studies have utilized diffusion tensor imaging (DTI) and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF), which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR) technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI) techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.Diffusion-weighted MRI (DW-MRI), the only non-invasive technique for probing human brain white matter structures  Recent advances in diffusion tensor imaging (DTI) techniques and white matter tractography have made it possible to visualize the fiber tracts comprised of coherently oriented axons and to map the anatomical connectivity patterns of healthy and diseased human brains Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only available tool for non-invasively probing human brain tissue microstructure and the microanatomical organization of human brain white matter To reduce the influence of CSF on the measurement of diffusivity and to increase the accuracy of the tractography, at least two kinds of solutions have been proposed to mitigate any CSF contamination. One is to improve the DTI sequence by suppressing the CSF signal using a radio-frequency pulse inversion recovery during DTI acquisition, such as the Fluid-Attenuated Inversion Recovery (FLAIR) technique Even though measures of diffusivity and tractography derived from the FLAIR-DTI technique should be more useful for analyzing the property of brain tissue and brain connectivity, no study to date has investigated the differences between human brain anatomical networks derived from FLAIR-DTI and those derived using conventional DTI (non-FLAIR-DTI) techniques. In this study, we acquired DTI datasets from twenty-two normal, healthy volunteers using both FLAIR-DTI and conventional DTI sequences, constructed anatomical networks using a deterministic tractography method, analyzed the topological properties of the networks, and compared the statistical differences between the anatomical networks constructed from the two types of DTI datasets. We hypothesized that the brain anatomical networks derived from the FLAIR-DTI datasets would have more fibers as edges because this method can capture the “real” anatomical connectivity patterns of the human brain due to the reduced CSF contamination. We expected that the brain anatomical networks constructed from the FLAIR-DTI datasets would exhibit more efficient small-world properties and that the topological properties of brain regions with high concentrations of CSF would show significant differences from those constructed using the conventional DTI dataset.SD = 20.05±2.73 yrs) participated in this study. None of the volunteers had a history of neurological or psychiatric disease or brain injury. The protocols were approved by the Review Board of the Institute of Cognitive Neuroscience and Learning at Beijing Normal University (BNU). Informed written consent was obtained from each participant prior to the MR scanning.Twenty-two right-handed healthy volunteers  = 256 mm×248 mm and data matrix = 128×124, 30 directions with b = 1000 s/mm2 and a b = 0 volume, and 68 transverse slices without gap covering the whole brain. When we acquired the conventional DTI and FLAIR-DTI datasets, we arranged the 30 diffusion-sensitive gradient directions in an icosahedral scheme. The parameters of the FLAIR-DTI sequence were same as those of the conventional DTI sequence except the inversion time (TI) = 2250 ms, and TR = 18000 ms.Each of the twenty-two subjects was scanned on a 3T Siemens Trio MR scanner using a conventional DTI sequence and a FLAIR-DTI sequence with a twelve-channel phased array head coil with the implementation of the parallel imaging scheme GRAPPA  and an acceleration factor of 2. Both the conventional DTI and the FLAIR-DTI scans were performed using a single-shot twice-refocused spin-echo diffusion-weighted EPI sequence 3, 176 sagittal slices).In addition, we also acquired 3D brain anatomical images using a T1-weighted MP-RAGE sequence for each subject  before further processing.Considering the fact that FLAIR-DTI yields lower signal-to-noise ratio (SNR) in the acquired raw images than those in the Conventional-DTI, we calculated the SNR values in each DTI dataset and adjusted its effect to explore the pure CSF effect on the properties of brain anatomical networks. SNR calculations were performed using the most commonly used “two region” method (SNRmean) described by Dietrich We followed the procedures described in previous studies The nodes were defined according to the automated anatomical labeling (AAL) template b = 0 image as the reference volume and using an affine registration from the FSL-FDT Toolbox (http://www.fmrib.ox.ac.uk/fsl). The corrected DTI datasets were then split and averaged to generate the averaged 31 volumes of DTI datasets that were subsequently used in fiber tracking. We reconstructed the fibers for the whole brain based on the Fiber Assignment by Continuous Tracking (FACT) algorithm, which was implemented in DtiStudio. Fiber tracking was terminated at voxels with a fractional anisotropy (FA)<0.20 and a tract turning-angle>45°.The white matter fibers of the whole brain for each subject were reconstructed using the DtiStudio software package (Version 3.0.3) The edges of the anatomical networks were defined as the anatomical connections between any pair of nodes. Given two ROIs Taking each ROI as a node and the weighted index between any pair of nodes as the edge, we obtained a weighted brain anatomical network for each subject for each type of DTI data.S), to characterize the global topological properties of the brain networks. Their definitions are provided in the We used seven global network parameters, the weighted clustering coefficient  greater than the average normalized betweenness of the network, or SDThe hub regions of the brain anatomical networks were identified according to the normalized betweenness, Following the methodology used in previous studies t-test was performed on each network metric. The effect of SNR was adjusted for all of these analyses. Several previous studies p = 0.05. To solve the problem of multiple comparisons, a height statistical standard  p<0.01 (uncorrected) was adopted. To justify that the sample size in this study is enough to infer a statistically significant difference, we performed a power analysis according to a previous study To determine whether the CSF had significant effects on the topological properties of human brain anatomical networks formed using the FLAIR-DTI and conventional DTI datasets, a multiple linear regression analysis with a paired S) of the anatomical networks as well as results of the statistical comparisons. Compared with the anatomical network from the conventional DTI datasets, we found statistically significant higher values of S but a statistically significant lower value of We calculated the values of p<0.01, uncorrected). For the nodal global efficiency . From Statistical analyses revealed no significant differences in ence in  , Fig. 2.We first calculated each regional parameter for a subject and then averaged the regional parameter across all subjects. Thus, for the four regional parameters  to detect any statistically significant difference between the edges in the anatomical networks corresponding to the FLAIR-DTI and conventional DTI datasets, but no statistically significant difference was found.We compared the number of edges in the networks derived from the FLAIR-DTI and conventional DTI datasets. We found that the connectivity patterns of the anatomical networks derived from the two types of DTI datasets were similar to each other. However, we noticed more edges in the anatomical networks corresponding to the FLAIR-DTI datasets. In this study, we compared the topological properties of human brain anatomical networks constructed using graph theory and utilizing datasets collected by conventional DTI and FLAIR-DTI techniques. The anatomical network derived from the FLAIR-DTI datasets showed statistically significant high global efficiency , as well as shorter characteristic path lengths  were detected from the anatomical networks corresponding to both types of DTI datasets. The spatial pattern of the hubs detected from the anatomical networks corresponding to both the types of DTI datasets was consistent with the similarity of the betweenness nodes derived from both types of DTI datasets . The addIn order to compare the identified hubs in the present study with other studies, we also listed network hubs reported in previous studies . From TaOur results showed that the FLAIR-DTI technique is superior to the conventional DTI technique in global and local network efficiency metrics, especially in CSF-filled regions. However, several issues need to be addressed in the present study. First, the lower SNR and the longer scan time required for the FLAIR-DTI technique are drawbacks that need to be considered. The longer scan time for the FLAIR-DTI technique was not an important issue in this study because the subjects enrolled were healthy and cooperative during image acquisition. We controlled the SNR and found effects of CSF contamination on the topological properties of the anatomical networks. Therefore, the increased scan time was tolerable and the use of FLAIR is recommended for studying human brain anatomical networks, especially in normal healthy people. Actually, two kinds of approaches have been proposed to mitigate CSF contamination, one is the FLAIR-DTI technique and the other is the two tensor model. The two tensor model uses a data postprocessing approach to remove the CSF signal, while the FLAIR-DTI is a MRI technique to obtain the signals free from CSF contamination. Although the FLAIR-DTI technique may have suffered from disadvantages arising from a lower SNR and a longer scanning time, in this study we were able to find the effects of CSF on the properties of the anatomical network.q-space derived model to describe hindered and restricted (fast/slow) diffusion in brain tissue. GDTI uses higher order tensor model, such as 4th order tensor, to analyses the data. Although these DW-MRI strategies can deal with the crossing tracts problem in the low spatial resolution (2 mm isotropic) diffusion data in some degree, none of them has the capacity to distinguish the fibers crossing, twisting or kissing within a voxel, to determine exactly origins and terminations of fibers within the gray matter of a cortical area, and to distinguish efferent/afferent fibers or mixed projections. The single tensor model is a basic one to describe the DW-MRI signal behavior for low values of diffusion weighting . It does not appear to be consistently accurate in describing the signal behavior for higher values of diffusion-weighting .The single tensor model is still the basic one to process DTI data and is known to have problems with both CSF and crossing tracts At present, a growing number of DW-MRI strategies have been developed for measuring and interpreting complex diffusion properties of brain white matter. The methods vary in their acquisition sampling and analysis approaches Second, the tractography method also influences the accuracy of the calculated network properties. We only selected the FACT algorithm Third, no ground truth exists for which anatomical network is in fact “true”. No definitive answer exists about how to select the nodes in constructing human brain anatomical networks. A study has verified that selecting different brain templates to define network nodes influences the topological properties of the brain networks Using graph theoretical approaches, we explored the influence of CSF on human brain anatomical networks. Our study suggests that human brain anatomical networks derived from FLAIR-DTI datasets have higher network efficiency than those derived from conventional DTI datasets. CSF contamination influences not only the nodal properties of the brain regions close to the CSF-filled space but also the number and location of the identified hub regions in human brain anatomical networks. In addition, CSF contamination influences the backbone network derived from conventional DTI datasets. Because human brain anatomical networks have previously been constructed primarily from diffusion datasets acquired using the conventional DTI technique with the single tensor model, our findings may have implications for human brain anatomical networks and tractographic methods in studies of normal brain development and clinical applications. In order to pinpoint the differences in human brain anatomical networks between different study groups, we suggest that, if possible, selecting CSF suppression techniques such as the FLAIR-DTI sequence to eliminate the CSF signal will increase the accuracy of human brain anatomical networks.Figure S1Normal probability plot of the global parameters ((TIF)Click here for additional data file.Figure S2Normal probability plot of the global parameters (S).(TIF)Click here for additional data file.Table S1The signal-to-noise ratio (SNR) for the selected ROIs, fornix and splenium of the callosum (SCC), for each subject.(DOC)Click here for additional data file.Table S2Regions of the automated anatomical labeling (AAL) template and the corresponding abbreviations used in this study.(DOC)Click here for additional data file.Table S3Power analysis and normality test for different global parameters. Note: The bold digital number means that the value of the global parameters does not obey the normal distribution.(DOC)Click here for additional data file.Table S4Power analysis for nodal parameters in significant different regions.(DOC)Click here for additional data file.Table S5Normality test for the nodal parameters in the significant different regions. Note: The bold digital number means that the value of the nodal parameter ((DOC)Click here for additional data file.Table S6Global parameters of the brain anatomical networks derived from the conventional DTI and FLAIR-DTI datasets using the nonparametric permutation test.(DOC)Click here for additional data file.Table S7Statistically significant differences in the nodal parameters of the brain anatomical networks using the nonparametric permutation test. Note: Bold text indicates the brain regions showing significant differences in both nodal parameters, (DOC)Click here for additional data file.Text S1Definitions of nodal parameters and global parameters.(DOC)Click here for additional data file.Text S2Power analysis and normality test.(DOC)Click here for additional data file.Text S3Non-parametric permutation test.(DOC)Click here for additional data file.Text S4Results from the non-parametric permutation test.(DOC)Click here for additional data file.


Puma concolor) cubs. We examined cub body mass, survival, and age of natal dispersal in relation to specific timing of birth. We also investigated the role of maternal age relative to timing of births. We captured mountain lion cubs while in the natal den to determine birth date, which allowed for precise estimates of the population birth pulse and age of natal dispersal. A birth pulse occurred during June–August. Body mass of cubs was related to litter size and timing of birth; heaviest cubs occurred in litters of 2, and those born after 1 July. Cubs born within pulse months exhibited similar survival to those born out of the pulse. We found that cubs born April–June dispersed at younger ages than those born after 1 July. There was less variation in birth timing for 1st litters of females than older females. We hypothesize that cubs born after the peak in births of neonate prey are advantaged by the abundance of vulnerable prey and those cubs and mothers realize an evolutionary advantage.We investigated potential advantages in birth timing for mountain lion ( Puma concolor) is better characterized as a pulse over several months Pattern of births for mountain lions . Months with higher than expected births included the period June–August; thus, we defined our birth pulse as occurring from 1 June to 31 August. We further delineated the year as spring (April–May), early pulse (June), middle pulse (July), late pulse (August), autumn (September–October) and winter , so we censored those animals from analyses regarding cub weights. We found that timing of birth  and litter size  influenced body mass of cubs at 1 week of age, whereas sex  had no effect. Mean body mass for male (n = 34) and female (n = 40) cubs at 1 week of age was 1.162 kg±0.112 (SE) and 1.102 kg±0.095, respectively.We used data for 74  cubs originally captured in natal dens, at ages ranging from 1–4 weeks, to estimate influence of birth timing on weight at 1 week of age. We found few cubs born during winter .There was a declining trend in body mass as litter size increased. Mean body mass of cubs in 2-, 3-, and 4-cub litters were 1.114 kg±0.135, 1.053 kg±0.091, and 0.912 kg±0.089, respectively. Cubs born in litters of 2 were 22% larger at 1 week of age than cubs in litters of 4 ; however, a trend existed, with cubs born in spring lightest and body mass of cubs increasing through autumn . Cubs born during spring and early pulse averaged 230 g±72 (SE) lighter in mass than cubs born during the middle pulse through autumn .After accounting for sex and litter size, mean mass of cubs born within the birth pulse (June–August) was similar to cubs born outside the birth pulse . Cubs born before the ungulate birth peak (n = 10) weighed 869 g (mean)±96 (SE) whereas cubs born coincident with ungulate birth peaks and 30 days following (n = 20) weighed 934±109 g. Those cubs born 30–60 days after the ungulate birth peak (n = 31) weighed 1,174±100 g, whereas cubs born >60 days after the ungulate birth peak (n = 13) weighed 1172±102 g.When we reevaluated birth timing relative to ungulate birth periods, we found a strong relationship to timing of birth and weight at 1-week of age  = 0.55, SE = 0.08; Female, n = 36, S(i) = 0.58, SE = 0.08; 2-sample t76 = 0.264, 2-sided P>0.50); therefore, we pooled sexes in subsequent analyses. We found no support  for our hypothesis that survival was greater for cubs born within the birth pulse  = 0.55, 95% C.I. = 0.41–0.69) compared to those born outside the pulse  = 0.58, 95% C.I. = 0.41–0.74). However, we found an increasing trend in survival relative to prey abundance and vulnerability  = 0.50, SE = 0.09) had mean survival rates lower than those born 30–60 days after the ungulate birth peak, but before winter  = 0.53, SE = 0.09; Oct–Nov, n = 12, S(i) = 0.67, SE = 0.14).No difference was documented in sex-specific survival . There was evidence that cubs born during the birth pulse dispersed when 2.5 months younger (±1.3 SE) than those born outside the pulse . However, there was stronger evidence  that cubs born during spring, early-, and mid-pulse  dispersed 4±1.3 months younger than cubs born in the last month of the birth pulse and winter . When we excluded cubs born in July , there was strong evidence of a mean 4±1.5 month difference in dispersal ages .We obtained dispersal ages for 22 juvenile mountain lions . Male mountain lions dispersed at a mean age of 14.7 months±0.84 (SE) and females dispersed at 15.3±1.15 months. While accounting for variation in timing of birth, we found no sex-related differences in natal dispersal age (All cubs that were born in early or mid-pulse dispersed during the summer (June–August) or autumn (September–November) after attaining an age of 1-year. All cubs born late in the birth pulse dispersed during winter (December–February) or spring (March–May) after becoming 1-year of age.n = 11) and occurred mostly in July (n = 6), and few occurred in June or August  were dispersed throughout the year, while still exhibiting a pulse in June–August , mule deer (O. hemionus), and elk (Cervus elaphus) exhibit a scent-less hiding strategy for 2–4 weeks, presumably to avoid predators until they are mobile enough to flee More cubs were born during July and August  than any other 2-month period, which occurred >30 days after the births of ungulate neonates in our study area. Neonates of white-tailed deer  are presumably heavier and more experienced, than those dispersing at 13 months (born before the switch). Some of the advantages might influence survival through the subadult life-stage, which translates to an evolutionary advantage for both mother and offspring. Thus, births of mountain lions should trend toward a narrower as opposed to wider birthing period.st time mothers than for older mothers. These data are in accordance with the prediction that birth timing is related to prey availability Because of the stochastic nature of most cub mortalities However, breadth of the population birth pulse is maintained by variation in reproductive maturity, birth intervals, and the vagaries of mortality. Logan and Sweanor Ovis canadensis], guanaco [Llama guanacoe]), yet birth peaks are predictable , that first litters of mountain lions will be closely timed to the birth peak of those primary prey species. We predict that in environments where prey are not born in predictable peaks or the sum of births in multiple primary prey species provide abundant neonates throughout the year , that mountain lions will not exhibit a birth pulse or the pulse will be broader than 3–4 months, characteristic of populations studied in North America We offer a hypothesis and predictions regarding the birth pulse in mountain lions and recommend testing with data from other populations. We hypothesize that mountain lion births are adaptively timed closely to the availability of primary prey in environments, where primary prey exhibit predictable birth peaks, but that reproductive biology and random mortality events after the initial litter result in a broadened birth pulse that adheres less strongly to the timing of prey availability. We predict that in environments where the primary prey for mountain lions exhibit “hider" predator-avoidance strategies  and where timing of ungulate birth peaks is predictable  that first litters of mountain lions will be closely timed to follow the switch from “hider" to “fleer" by neonates. We also predict that in environments where primary prey species do not exhibit a “hiding" anti-predator strategy as neonates are capable of fleeing shortly after birth  forests, but also contains spruce (Picea glauca), aspen (Populus tremuloides), birch (Betula spp.), and oak (Quercus macrocarpa) trees Odocoileus virginianus), mule deer (O. hemionus), elk, American bison (Bison bison), mountain goat (Oreamnos americanus), and bighorn sheep as well as domestic livestock species. No other large carnivores were present in this system. Bobcat (Lynx rufus) and coyote (Canis latrans) are mesocarnivores that occur sympatrically with mountain lions.We studied mountain lions in the Black Hills ecoregion  of South Dakota, USA between 2006 and 2009. The Black Hills are a complex of forested ridges, valleys, and steep canyons Field Methods.—We used foot-snares When cubs were approximately 9 months old, we recaptured them by using the same techniques as those used for adult mountain lions and refitted cubs with larger radiocollars  or global positioning system (GPS) collars . We used fixed-wing aircraft or satellite-telemetry to relocate subadults and mothers. When a juvenile mountain lion left the natal area, we recorded the mid-date between the last location in the natal area and the first location outside the natal area. In some cases, dispersing mountain lions made exploratory movements and returned to the natal area and subsequently left again or established a home area at least partly overlapping the natal area . We recorded dispersal age as the mid-date the juvenile made the initial exploratory movement, regardless of subsequent return to the natal area. If an animal was not located outside the natal area until the animal became 3 years old, we censored the animal from analyses. These animals were censored because exploratory movements can be of short duration st one-third), middle , late , and outside pulse. In addition, we delineated cub births as before ungulate birth peak (before 1 June), before neonate ungulates switch predator avoidance strategies (hiding vs. fleeing unpublished data). The birth peak for mule deer was 7–14 June unpublished data).We plotted all birth dates and defined the birth pulse as the period where ≥40% of births occurred and number of births were higher than expected F-tests within a MANOVA framework to test for differences in birth timing, while accounting for differences in sex, litter size, and age at capture. We used a known-fates model in Program MARK t-test to determine if differences in survival occurred between male and female cubs, despite timing of birth. We ran Program MARK analyses with and without sex as a covariate to determine if sex was related to survival. We analyzed cub survival in 2 ways. First, we placed cubs in categories; within or outside of the birth pulse to replicate the analysis conducted by Laundré and Hernández We used JMP 8.0  statistical software to perform statistical analyses. We used chi-squared test for independence to determine if the monthly distribution of litters was uniform through the year st litter, >1st litter). We visually compared birth month frequencies by mother's age and litter sequence.To investigate the role of mother's age on birth timing, we calculated the monthly frequency distribution of litters born by mother's age . We also calculated the monthly frequency distribution of litters born by litter sequence (i.e., 1


Non-parasitic hepatic cysts with biliary communication are rare. The clinical symptoms involved are not specific to this condition, thereby making diagnosis difficult and treatment controversial. Here, we report a case of 70-year-old woman complaining of abdominal satiety, combined with non-specific pain in the right upper quadrant. The abdominal contrast-enhanced MRI-scan revealed a large and thick-walled septus cystic lesion in the liver. During operation, the biliary fistula was confirmed in the cyst cavity. A silica gel tube was inserted via the cystic duct for cholangiography, which demonstrated communication between the cyst and biliary tract. We performed wide-scale cyst wall resection; the biliary fistula was completely repaired by the closure of communicated bile ducts. The postoperative course was uneventful, and the patient was discharged with no sign of cholangitis or any other symptoms. The novel surgical management via wide resection of the cyst wall and closure of biliary communication proved to be an adequate and effective procedure for treating nonparasitic hepatic cysts with biliary communication. Nonparasitic hepatic cyst occurs in approximately 5% of the population, with an increasing rise in incidence with ageA 70-year-old woman was admitted to the Hepatobiliary Department of Tianjin Medical University Cancer Institute and Hospital, with complaints of abdominal satiety combined with non-specific pain in the right upper quadrant, without nausea, vomit, dyspepsia, and weight loss. The patient was diagnosed to have a liver cyst for approximately 40 years, without a history of congenital cysts. Thirteen years ago, the patient was treated by percutaneous transhepatic drainage, which was guided by ultrasonography. Two years after drainage, the hepatic cyst was found to recur during the follow-up. At this time, the cyst was approximately 3 cm in size, as shown by ultrasonography. However, no treatment was administered because the cyst was asymptomatic. During the subsequent interviews, the patient recalled the obscure onset of intermittent seizures that lasted for approximately five months.Upon physical examination, a mass was palpated in the patient’s right upper abdomen. Laboratory examination showed that the parameters of routine hematological and biochemical examinations, including tests of liver and renal function, were within normal limits. Similar results were obtained for the expression levels of tumor markers, including the blood carcinogenic embryonic antigen (CEA) and the alpha-fetoprotein (AFP). The hydatid serology test was negative. Abdominal ultrasonography identified a cyst located in the liver measuring 30 cm in diameter. The abdominal contrast-enhanced MRI-scan likewise revealed a large, thick-walled septus and cystic lesion in the liver, measuring 27.9 cm × 17.9 cm × 14 cm, which occupied almost the entire liver. A slight enhancement of the septum was present after the injection of the intravenous contrast agent. The diagnosis of cystadenocarcinoma could not be completely excluded , we identified the bile ducts that communicated with the cyst. The results showed two extravasations of the contrast medium from the hepatic bile duct branches (We then performed cystic wall resection and closed the communicated bile ducts by careful suturing. The contrast medium was subsequently reinjected, but leakage from biliary tract was no longer detected. Finally, we used omental tissue to cover the wound surface and close the abdominal cavity.Pathological examination confirmed the diagnosis of a hepatic cyst (et al.Nonparasitic hepatic cysts are normally asymptomatic. These cysts are always incidental findings during laparotomy and have been recently diagnosed more frequently before operation because of the increasing use of abdominal imaging and ultrasonography. For symptomatic cases, the cysts often exhibit pressure effects, with abdominal pain as the predominant symptomet al.et al.Several techniques such as ERCP, cholangiography, and cytography may help clinicians make diagnosis and avoid postoperative complications. Takeshi For hepatic cysts with biliary communication, various therapeutic options have been described, including percutaneous aspiration, deroofing, cystojejunostomy, and even partial liver resection. However, uniform management has not yet to be clearly defined. The choice for therapeutic options remains debatable. The simplest method of treatment is percutaneous aspiration, which may be effective for the immediate palliation of symptoms but may invariably result in high recurrence of a cyst, particularly when the cyst exceeds 10 cm in diameterSeveral authors recommended cystojejunostomy as an effective surgical method for cysts with biliary communicationet al.et al.The laparoscopic approach seems preferable because it has been associated with reduced morbidity and shorter hospital stay, as compared with open surgeryThe main reason for the recurrence of the cyst is the reconstitution of the cystic wall after the operation, with the diaphragm forming a part of this wallIn conclusion, non-parasitic hepatic cysts with biliary communication are rare, and their preoperative diagnosis is difficult. The careful detection and imaging techniques, such as cholangiography and ERCP, are useful intraoperative diagnostic techniques. Although the treatment is controversial, we advocate surgical management by wide resection of the cyst wall combined with closure of the communicated bile ducts as an effective treatment procedure. We believe that this procedure would significantly decrease the risk of infection and additional surgery and maximally avoid recurrence after the operation.


The influence of environmental conditions early in life – including temperature and season – on health later in life has so far not attracted much attention.Using data from the Helsinki Birth Cohort Study of 13,345 men and women, the influence of temperature and season at month of conception on birth weight, and on cardiovascular diseases and obesity-related traits in later life was studied.Linear regressions were fitted to examine the relationship between birth weight/obesity-related variables/hypertension and alternatively month of conception and average temperature of month of conception. The incidence of both coronary heart disease and cerebrovascular disease was assumed to follow a Weibull hazard model, and was modelled accordingly using survival analysis techniques.In women, unusually cold temperatures at month of conception predicted lower body mass index (BMI) and fat percentage, and protected from obesity. Warmer temperatures at month of conception were associated with higher risk for hypertension. In men, warmer temperatures around conception predicted lower BMI. No seasonal influences were detected on obesity-related variables, nor were there seasonal or temperature mediated influences on birth weight, coronary heart disease or cerebrovascular disease observed.We suggest that ambient temperature has an influence on obesity-related outcomes and hypertension. This merits further study, also with regard to other health outcomes and from a global perspective. The Developmental Origins of Health and Disease (DOHaD) hypothesis proposes that conditions present early in life have long-term consequences on later health. Traditionally nutritional status, growth, living conditions and maternal stress have been focused upon –4. EnvirHowever, season and temperature might be an important factor for health, potentially influencing hormonal and other adaptive responses. Already in 1941, C. A. Mills  reportedLow birth weight has been associated with seasonality and lower outdoor temperatures in many studies, as well as with warmer temperatures during pregnancy –9. Howev2)  . Other h2) .Finland is one of the northernmost countries, with both maritime and continental influences. Due to the continental influence and the Gulfstream, average temperatures are slightly higher than in other countries of the same latitude. In the capital city of Helsinki – one of the southernmost cities of the country, located on the 60th latitude – the average winter temperature is around −5°C and the summer average about 16°C. The yearly temperature range of Helsinki is very broad, ranging between −34°C and +34°C. Due to large seasonal variation, the climate of Helsinki provides a good basis for assessing the influence of season and temperature on human health outcomes.In this study, we examine the influence of temperature and season on birth weight, cardiovascular disease and obesity in adult life in term births, using month of conception as a reference.The Helsinki Birth Cohort Study includes 13,345 men and women, of whom 4,585 were born at the Helsinki City Maternity Hospital and 8,760 at the University Hospital in Helsinki between 1934 and 1944. They attended child welfare clinics in the city and were living in Finland in 1971 when a unique personal identification number had been allocated to each resident of the country . The coh2.From the register cohort consisting of 13,345 people, 2,003 individuals were randomly selected and invited for a clinical examination in the years 2001–2004, as previously described , 22. HeiData on average monthly temperature for the years 1923–1944 were obtained from the Finnish meteorological institute weather station Kaisaniemi, which is in the centre of Helsinki  The overall temperature quartile variable was formed according to whether the average temperature of a month was below the first quartile of the observations during the years 1923–1944, above the third quartile, or between the first and the third quartile. It indicates, whether the average temperature of a specific month was cold, warm or in the middle compared to all monthly average measurements of the time series available from 1923 to 1944. Therefore, the month of January would generally fall in the coldest group, that is, below the first quartile. (ii) The month-specific temperature quartile variable was formed according to whether the average temperature of a specific month was below the first quartile of the observations of all the other observations of the same calendar month in the years 1923–1944, above the third quartile, or between the first and the third quartile. It indicates, if, for example, November of a specific year was unusually cold, compared to all other Novembers of the time series available from 1923 to 1944.The model fit and statistical significance was assessed using ANOVA.All of the analyses were performed using R-software base and survival package , 25.In the register-based sample , we excluded 2,108 subjects due to incomplete data, and/or gestational ages below 37 complete weeks, the commonly used limit of preterm birth, or above 44 weeks, in which case the records were deemed unlikely to be correct. A total of 11,237 people with adequate data were included in the analysis on birth weight, coronary heart disease, hypertension and cerebrovascular disease. Fifty-two percent of the participants were males. In the clinical study sample , we excluded 325 subjects, due to incomplete data, gestational ages below 37 completed weeks or above 44 weeks, and one because of an improbable high BMI >50. Eventually, the sample size was 1,678 with 46% males . The monWarm temperatures around conception were found to be associated with a significantly higher probability of hypertension in women, that is, those who were conceived during the warmest temperatures of the time series from 1923 to 1944 (overall quartiles, register sample, Cold temperatures around conception showed to have influences on obesity-related variables in women (clinical sample, Season did not show any influences on the outcomes studied .We studied the influences of season and temperature at month of conception on birth weight and later health outcomes in term-born subjects. In women, we observed protective influences on several obesity-related variables – BMI, obesity and fat percentage – among those conceived during unusually cold months. Furthermore, the risk of developing hypertension was enhanced with increasing temperatures at the time of conception. In men, warmer temperatures at month of conception were related to lower BMI. Our results suggest that temperatures at conception may have long-term influences on health outcomes.We observed that women who were conceived during unusually cold months had lower values/risks for all obesity-related outcomes studied, that is, BMI, obesity and fat percentage. Unusual temperatures always require some form of adaptation, including a change of clothing, and depending on the season also a change in heating. Such adaptation usually requires time. Extremely cold temperatures, for example in February, would also lead to an increased need for heating and potential drops in indoor temperature are possible. This time of adjustment implies freezing, and therefore a potential lowering of the core temperature, which could have influences on the conceived embryo or on the selection of the sperm that fuses with the ovum. However, men who were conceived in unusually warm months had lower BMI. This association would need further investigation in which seasons these influences take place, as unusually warm winters differ essentially from unusually hot summers. The underlying mechanisms and explanations for these findings are unknown. Among people living in a region not used to colder temperatures it would mean that the bodies of the people are not acclimated to heat, and would therefore be easily overheated/stressed in unusually hot summers – also due to the fact that the inside temperature of the houses would rise unusually and not even cool down adequately at night due to the very well-insulated houses. A study in reptiles showed that temperature at conception can have significant impact on the offspring, by changing the sex ratio . A changA study from Hertfordshire, UK, reported seasonality of BMI in men by birth month, with higher rates in January–June compared to the second half of the year . FurtherOur study focused on term-born subjects. Potential impacts of prematurity and very low birth weight on life-long health have been discussed elsewhere , 28–30. We did not find an influence of temperature extremes nor of seasonality on birth weight, despite the fact that Finland is a country with large temperature differences, and the winters of 1939/40–1941/42 were extremely cold. Temperatures in those years reached monthly average temperatures between −13°C and −16°C. Several studies on birth weight showed seasonal patterns indicating an influence of outdoor temperatures on the birth weight , 9. The The study years of 1939–1944 were war years, involving severe stress and nutritional restrictions. In Helsinki, there was food rationing for many comestibles and the quality and quantity of the food was occasionally inadequate . HoweverIn this study, we used monthly average temperature measurements, as we did not have access to daily temperature measurements for all years. The analysis could be refined including daily temperatures, as the temperature extremes get smoothed using averages. However, we would not expect the results to change by including daily temperatures in the analysis. To our knowledge, there is no evidence to suggest that a few days of extreme temperatures would have an influence in utero on later health. Heat waves or cold spells which last for a week would certainly noticeably increase the monthly average, unless the same month contained an unusually cold week, which is a relatively improbable expectation. Furthermore, daily temperatures would add further challenges to the analysis, one is the high variation of the daily temperatures, and another is the necessity to include other parameters, for example, time lags.In a pilot study, we have considered conditions and timing of pregnancy rather than the month of conception. However, we could not find any correlation with either season or temperature. Additionally, we used also month of birth as a reference point. However, in favour of better comprehensibility we decided to only use month of conception.Our study examined the influences of season and temperature at month of conception on birth weight, coronary heart disease, cerebrovascular disease, hypertension, BMI, obesity and fat percentage in term births. The effect of temperature and season on the unborn in relation to adult health has not been focused upon in great detail previously. One reason might be the publication bias because of negative findings. We did observe temperature influences on all obesity-related variables as well as on hypertension in women and on BMI in men. There were no findings in relation to coronary heart disease and cerebrovascular disease nor were there any seasonal influences detected. It would be important to study this area in more detail – also on other health outcomes and in other regions of the world – as they are potentially important underlying factors. Furthermore, climate change with more extreme cold and extreme warm temperatures may exacerbate the influences of temperature on disease.The Ethical committees at Helsinki University Central Hospital and at the National Institute for Health and Welfare have approved the Helsinki Birth Cohort Study.


In recent years, with socioeconomic changes in the society, the presence of women in the workplace is inevitable. The differences in working condition, especially for pregnant women, has adverse consequences like low birth weight.This study was conducted with the aim to model the relationship between working conditions, socioeconomic factors, and birth weight.This study was conducted in case-control design. The control group consisted of 500 women with normal weight babies, and the case group, 250 women with low weight babies from selected hospitals in Tehran. Data were collected using a researcher-made questionnaire to determine mothers’ lifestyle during pregnancy with low birth weight with health-affecting social determinants approach. This questionnaire investigated women’s occupational lifestyle in terms of working conditions, activities, and job satisfaction. Data were analyzed with SPSS-16 and Lisrel-8.8 software using statistical path analysis.The final path model fitted well  and showed that among direct paths, working condition (β=-0.032), among indirect paths, household income (β=-0.42), and in the overall effect, unemployed spouse (β=-0.1828) had the most effects on the low birth weight. Negative coefficients indicate decreasing effect on birth weight.Based on the path analysis model, working condition and socioeconomic status directly and indirectly influence birth weight. Thus, as well as attention to treatment and health care , special attention must also be paid to mothers’ socioeconomic factors. In recent years, with socioeconomic changes in the society, women’s role has also significantly changed, and their share of the labor market has increased dramatically, comprising 42% of the working population, they are now an essential part of the national economy . In IranMost women’s desire to work out of home often means delaying marriage and pregnancy until older years, which affects the person’s health and ultimately the society, as well . In addiThe effect of working conditions on adverse pregnancy outcomes is a controversial subject. Most studies on the impact of working conditions on pregnancy outcomes have not identified equal working conditions as a risk factor, and also, a number of studies have not found any relationship between working conditions and adverse pregnancy outcomes, including low birth weight. On the other hand, several reviews and meta-analyses have reported the impact of working duration, work shift, and job stresses on these outcomes . AdverseBut, despite achievements in health in recent years, the continued high incidence of low birth weight is indicative of the importance of the need to attend to its influencing factors. The socioeconomic changes in the society and increased number of working women, and also the conflicting results of studies conducted in relation to the effect of employment on adverse pregnancy out comes, also given the fact that research into occupational hazards has become a priority in determining preventative interventions while identifying mechanisms and causal factors , it was The present study is part of a larger study titled ‘Design of assessment tool and communication model of mother’s lifestyle during pregnancy with low birth weight’, which has been conducted in two stages.This case-control study was conducted in Tehran in 2012. The data were collected through a researcher-made questionnaire, designed to measure lifestyle with the approach of social determinants of health. Regarding the psychometrics of the questionnaire, face and content validities, criterion validity  and consIn this study, the results related to occupational lifestyle have been presented with 18 items of 5-option Likert style including work environment, duty, working shift, job satisfaction, and employer’s perceived empathy.In this study, the city of Tehran was first divided into five geographical zones; north, south, east, west and central. Subsequently, from the hospitals in each zone that had a maternity ward, clusters which included one or two government or social security hospitals, were selected according to their delivery rates. Following a review of the literature, the required sample size was determined by considering a 10% prevalence rate for low birth weight, and calculating research variables. The number of items in the measurement tool and key concepts were determined as 3 to 10 samples for each variable .Accordingly, 250 infants with a weight of 2 500 g or less were selected to the case group and 500 infants weighing more than 2 500 g were placed in the control group. The inclusion criteria included:1- Iranian women 15-45 years at a gestational age of 37-42 weeks based on the first day of their last menstruation period (LMP) or sonography, who went to the selected hospitals for their delivery.2- Lack of problems such as; multiple pregnancy, cardiovascular diseases, diabetes, renal diseases, thyroid disorders, pulmonary diseases, autoimmune disorders, pre-eclampsia, placental abruption, premature rupture of membranes, hepatitis, AIDS and other problems. Mothers who had not used any drugs which could affect the birth weight during pregnancy.3- Willingness to participate in the research.Infants weighing up to 4 500 g, with no known medical problems such as; congenital abnormalities, cardiac or pulmonary diseases, etc.After obtaining permission from university and hospital authorities, we presented the required information to the study population and invited them to participate. The questionnaire was then filled out by a team of trained people. First, the researcher selected mothers with inclusion criteria in the delivery room and monitored them until delivery. At the time of delivery, a researcher went to the delivery room, and immediately after delivery, if the infant had no medical problems; congenital disorders, cardiac-pulmonary diseases, etc, and the infant’s weight was 2 500 g or lower using the scale in the delivery room, it was placed in the case group. If it weighed between 2 500 g and 4 500 g, it was placed in the control group . MeasureIn this section, the socioeconomic information  were also collected and included in the study results.In this study, the good fit of a conceptual model of path analysis  was studEthical considerations; the study was conducted following consent given by the chancellors of the University of Tehran, Shahid Beheshti University and the General Department of Social Security for their affiliated hospitals. Moreover, prior to the study, the pregnant women signed an informed consent form after they had been informed of the objectives of the study and were assured that their information would remain confidential and that they could withdraw from the study at any time and also we consider the Patient Privacy.The study was approved by the Welfare and Rehabilitation Sciences University and the Ethics Committee of the Research Center for the Social Determinants of Health.In this study after considering the establishment of the normality assumption in numerical variable by checking with Kolmogorov Smirnov test, it was found that there was no significant difference in terms of; mean age, BMI, pregnancy age and pregnancy intervals found between the case and control groups. However, mothers’ mean weight increase during pregnancy was significantly different in the two groups (P=0.002). In evaluating educational levels, the chance of delivering a low birth weight infant by illiterate mothers was three times higher than in the educated mothers group . Husbands’ occupation and mothers’ employment were among the other factors which were related to low infant birth weight. If the husband was unemployed, the probability of this outcome was 4.5 times higher , in addition, mothers' employment increased the chance of delivering infants with a low birth weight by 5.4 times . To perform path analysis, first, using bivariate analyses, correlations between variables were found. It can be seen that birth weight is directly correlated with mother’s education level, and indirectly (inversely) with working conditions. Also, education and household income are directly and significantly correlated with working conditions. Education is also directly and significantly correlated with income and family size . In path analysis, first the effects of socioeconomic variables  on birth weight were examined. The results of a preliminary model did not show a good fit (df = 0), and only the parameter of income had a zero path coefficient (β = 0). Thus, this parameter was excluded from the model in direct path on pregnancy outcome, and it was only considered indirectly through its effect on working conditions . After tAccording to the path diagram, in direct paths, working conditions with β=-0.32 and in indirect paths, income with β = -0.042 had the most effects on birth weight. Unlike other variables, these two variables affected birth weight only through one path (direct or indirect), and the model shows that, due to the negative effects of these variables, mothers whose position is not favorable in terms of these variables, will deliver low birth weight babies.Unemployment of spouse directly (β = -0.09) and indirectly through affecting working conditions (β = -0.0928) and with overall effect (β = -0.01828) has a negative effect on birth weight. Thus, mothers with unemployed husbands are more likely to have low weight infants. Not having the support and help of the spouse with household chores, also adversely affects birth weight with overall effect (β = -0.1164), and for this reason, these mothers are likely to have low weight babies.According to the path model, mother’s education level with overall effect (β = 0.1104) positively affects birth weight, and birth weight will be higher with higher level of mother’s education. In this model, 15% of low weight parameter variance is explained by the parameters affecting it. In this study, through path analysis, we attempted to find a strong relationship between theoretical and practical issues in research. According to the results, working conditions had the highest direct and negative effect on birth weight, which shows that with more unfavorable working conditions, low birth weight is more likely to occur. Based on the epidemiologic evidence, 5 most common occupational factors including; long working hours, working shifts, lifting loads, standing position, and heavy physical work are involved in the incidence of several pregnancy outcomes such as; preterm birth and LBW, and also as determinants in prenatal and neonatal mortality . They alMost of the socioeconomic status parameters  directly or indirectly affected birth weight. But income only indirectly affected birth weight, and through its direct and positive effect on working conditions, followed by negative effect on birth weight, it shows that in poor family income conditions, mother is forced to take jobs with unfavorable conditions, which can negatively affect birth weight. Thus, family income plays an important role in birth weight. This factor is related to mothers’ job and number of prenatal care visits. This finding is in agreement with the results of Zarbakhash Bhari et al. that found low birth weight families were in lower levels of socioeconomic status compared to normal birth weight families . InequalAnother socioeconomic parameter is unemployment of the spouse. This parameter confirms previous findings by its direct and positive effect on mother’s working condition and direct and negative effect on birth weight and the overall negative effect on birth weight. Unemployment of the spouse is accompanied by lower family income, which leads to repeated adverse conditions for the mother and subsequently for her fetus and the newborn. Hawamdeh et al. also believe that material deprivation and economic inequalities caused by poor working conditions such as; poor nutrition, poverty, residential conditions, insufficient income through psycho-social factors of lifestyle and physio-pathological changes have important effects on the incidence of chronic diseases and mental health of people . GeneralMother’s education, with direct and positive effect on working conditions and birth weight shows that with higher mother’s education level, the birth weight also increases. This finding is in line with the results of many studies. Educated mothers can have better jobs and better conditions and incomes. They also receive prenatal cares and appropriate nutrition, and do not smoke. All these conditions help having a child with normal weight and favorable conditions .Another parameter investigated was lack of spouse’s support and help around home. This parameter, with direct and negative effect on working conditions and birth weight shows that mothers that lack help and support of their spouses at home deliver low birth weight babies. It seems family and social support protects the person against stressful life events through a buffering mechanism, and leads to the well-being of the mother . Lack ofSince birth weight increases risk of mortality, morbidity, and disability in childhood and in adulthood, it is important to identify its influencing factors. According to the path analysis model, working conditions and socioeconomic status directly and indirectly affect birth weight. Thus, as well as attention to treatment and health care , special attention must also be paid to the mothers’ socioeconomic factors. This is largely possible through attention to socioeconomic problems of mothers and with supportive laws based on their needs.Positive points in this study were equality between study groups in terms of the intervening factors such as gestational age Residual per capita etc.In this study, mothers were interviewed shortly after their delivery; thus, there is the possibility that frustrations resulting from their delivery or pregnancy may have had an influence on their responses. Furthermore, the researchers only studied variables that were amenable to investigation via interviews with the mothers. There may have been other elements as well which affect birth weights that were not taken into consideration in this study. Additional studies should therefore be conducted in this field taking into consideration the previously mentioned factors.