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Weight change during chemotherapy changes the prognosis in non metastatic breast cancer for the worse

  • Emilie Thivat1, 2Email author,
  • Sophie Thérondel3,
  • Olivier Lapirot1,
  • Catherine Abrial1, 2,
  • Pierre Gimbergues4,
  • Emilie Gadéa1,
  • Eloïse Planchat1, 2,
  • Fabrice Kwiatkowski1,
  • Marie A Mouret-Reynier2, 3,
  • Philippe Chollet1, 2, 3 and
  • Xavier Durando2, 3
BMC Cancer201010:648

DOI: 10.1186/1471-2407-10-648

Received: 10 December 2009

Accepted: 25 November 2010

Published: 25 November 2010

Weight change during chemotherapy is reported to be associated with a worse prognosis in breast cancer patients, both with weight gain and weight loss. However, most studies were conducted prior to the common use of anthracycline-base chemotherapy and on North American populations with a mean BMI classified as overweight. Our study was aimed to evaluate the prognostic value of weight change during anthracycline-based chemotherapy on non metastatic breast cancer (European population) with a long term follow-up.

Patients included 111 women diagnosed with early stage breast cancer and locally advanced breast cancer who have been treated by anthracycline-based chemotherapy regimen between 1976 and 1989. The relative percent weight variation (WV) between baseline and postchemotherapy treatment was calculated and categorized into either weight change (WV > 5%) or stable (WV < 5%). The median follow-up was 20.4 years [19.4 - 27.6]. Cox proportional hazard models were used to evaluate any potential association of weight change and known prognostic factors with the time to recurrence and overall survival.

Baseline BMI was 24.4 kg/m2 [17.1 - 40.5]. During chemotherapy treatment, 31% of patients presented a notable weight variation which was greater than 5% of their initial weight.

In multivariate analyses, weight change (> 5%) was positively associated with an increased risk of both recurrence (RR 2.28; 95% CI: 1.29-4.03) and death (RR 2.11; 95% CI: 1.21-3.66).

Our results suggest that weight change during breast-cancer chemotherapy treatment may be related to poorer prognosis with higher reccurence and higher mortality in comparison to women who maintained their weight.

Age, tumour size, axillary node status, histological tumour type and standardized pathological grade are accepted as well-defined prognostic factors in breast cancer [1]. Various studies have also reported striking associations between overweight or obesity at breast cancer diagnosis and poorer prognosis with higher distant recurrence and mortality (for review [2]). As emphasized by Goodwin et al. [3], the risk of recurrence and death was respectively 1.78 (95% CI: 1.50-2.11) and 1.36 (95% CI: 1.19-1.55) times greater for obese patients over a 10 years follow-up period.

Moreover, numerous studies reported a weight gain after breast cancer development that might be attributable to the effects of some treatment regimens [4, 5]. Weight gain in breast cancer patients has been associated with anti-neoplastic chemotherapy in the majority of studies. Previous studies suggest that weight gain is more pronounced among premenopausal women and among those who were treated with a multiagent regimen [6, 7]. However, few reports have not observed increased weight gain during chemotherapy [5, 8, 9], in particular with anthracycline-containing regimens [10, 11] widely recognized as the gold standard treatment of women with breast cancer.

There is also substantial evidence that weight change during chemotherapy may be associated with a worse prognosis for the cancer patient, both with weight gain [12] and weight loss [13]. The findings reported by the few studies which have explored the prognostic value of weight gain after a diagnosis of breast cancer are mixed: four studies reported that weight gain was associated with a decreased overall survival and increased recurrence risk [8, 1416] whereas five others failed to report such associations [10, 9, 17]. Only one recent study reported some evidence that women with early breast cancer, who had a weight loss during treatment, were at higher risk of recurrence and death compared to women with no weight variation [18]. These discrepancies may be attributable to the heterogeneity of the methods implemented in the different studies, including the duration of post-diagnosis weight assessment, the definition of the prognostic outcomes with a short median of follow-up and treatment (chemotherapy and/or hormonotherapy...). The majority of the previous studies were conducted before anthracycline-base chemotherapy was commonly used. Additionally all the aforementioned studies focused on north American populations with a mean Body Mass Index (BMI) at breast cancer diagnosis classified as overweight [14, 19]. However no data is available on the prognostic impact of weight change during chemotherapy treatment in European breast cancer patients who presented a notably lower BMI [20].

Our study thus investigated the prognostic value (death and recurrence) of weight variation during anthracycline-based chemotherapy treatment of breast cancer in a French population with a long-term follow-up. We also verified the association of weight at breast cancer diagnosis with survival.

A retrospective chart review was performed using data from hospital medical records on all women with early stage breast cancer and locally advanced breast cancer who were treated at Jean Perrin Center (Clermont-Ferrand) between 1976 and 1989 in order to have at least 20 years of follow-up for the study. Among the 709 women treated with chemotherapy treatment for breast cancer, 111 women were included in the analysis. The study was approved by the Inter-regional Ethics Committee of the Rhône -Alpes-Auvergne Clinical Investigation Center (N°IRB5044). Subjects were selected if they had histologically confirmed stage I-III breast cancer, and received chemotherapy under the anthracycline-based chemotherapy regimen. Patients were excluded from this review if weight assessment did not include at least measurements at baseline and at the end of chemotherapy, if they had distant metastasis at diagnosis or a history of another malignancy.

Weight measurements

Weight was measured at the hospital by a nurse at the beginning of treatment and in the last chemotherapy cycle. The BMI was calculated by dividing weight (kg) by height (m) squared. The different subclasses of patients were categorized as followed: underweight (< 18.5), normal (18.5 - 25), overweight (25 - 29.9) or obese (≥ 30). The median BMI being of 24.4 kg/m2 rounded down to 24 kg/m2 was used to median-split the population, i.e. categorized women as having BMI less than 24, or more than 24 kg/m2.

A number of studies suggest that a 5% change in body weight is clinically meaningful [5]. Weight variations (WV) were calculated as the relative percent weight changes between weight measurement from baseline to post-chemotherapy treatment ((baseline weight - weight after chemotherapy)/baseline weight × 100). WV were categorized accordingly into weight change (WV > 5%) or stable (WV < 5%). The weight changing group (WV > 5%) combined women who lost weight (defined by a relative weight loss >5% between weight measurement from baseline to post-chemotherapy treatment) and women who gained weight (defined by a relative weight gain > 5%).

Information on the age of patients, menopausal status, hormonal receptors, tumour stage, nodal involvement, Scarff-Bloom-Richardson (SBR) grade, and on treatment received before and after chemotherapy were obtained from reviewing patients' medical records. We used the Tumour- Node-Metastasis (TNM) classification of stage of breast cancer at diagnosis as established by the American Joint Committee on Breast Cancer [21] which consists of 3 components: (i) tumour size (T); (ii) absence or presence and extent of regional lymph node metastasis (N); and (iii) absence or presence of distant metastasis (M).

Outcome assessment

Deaths and recurrences were last updated in June 2009. Recurrence included a local/regional cancer recurrence, distant recurrence/metastasis, or development of a contralateral primary breast cancer. Patients who died without recurrence of breast cancer beforehand have been censored for analysis of recurrence.

The disease free survival (DFS) duration was defined as the time elapsed between the date of first diagnosis and the date of first relapse. The overall survival (OS) duration was the time elapsed between the date of initial diagnosis and the date of death or the last status report, whether the patient was alive or dead, whatever the cause.

Statistical analyses

Descriptive statistics were calculated for all variables used in this study and presented as median [range]. OS and DFS were estimated using Kaplan-Meier method [22]. A univariate analysis was performed using log rank methods. Parameters tested to be potentially correlated with OS or DFS were BMI and WV.

We realized multivariate analysis using Cox's proportional-hazard models [23] to evaluate the association of categories of baseline BMI and WV and well defined pronostic factors in breast cancer with the time to recurrence and mortality. Covariates considered as potential confounders in the above model included menopausal status, tumour stage, nodal involvement, and treatment after chemotherapy (hormonotherapy). A p value < 0.05 was considered to be statistically significant. Variance analysis (Chi2 or Kruskal-Wallis H tests) was used to test associations between initial BMI, WW and the covariates. Analyses were conducted using SEM software version 3.5 [24].

Characteristics of the population

Table 1 lists the main characteristics of patients. The median age at diagnosis was 54 years (32 - 55 years), and 55% of the women were post-menopausal at diagnosis. In all, 58 patients (52%) had a positive hormone receptor status and among them 28 were ER+/PR+ (25%). Seventeen percent of the women were diagnosed with stage I breast cancer while 48% had stage II and 35% had stage III respectively. With regards to tumour characteristics, 21 patients (19%) were T1, 49 T2 (44%), 17 T3 (15%) and 24 T4 (22%), respectively. Regarding clinical node involvement, 55 patients were N0 (50%), 49 N1 (44%), 6 N2 (5%) and 1 N3 (1%), respectively.
Table 1

Main characteristics of the population study

Characteristics, n = 111

stable weight

(n = 77)

changing weight

(n = 34)

Median age (years (range))

54 (32 - 74)

53 (37 - 70)

54 (32 - 74)

n (%)

Median BMI

24.4 (17.1 - 40.5)

24.6 (17.1 - 40.5)

24.4 (16.6 - 40.5)

9 (8)

7 (9)

2 (6)

56 (50)

36 (47)

20 (59)

31 (28)

21 (27)

10 (29)

15 (14)

13 (17)

2 (6)

Menopausal status

50 (45)

34 (44)

16 (47)

61 (55)

43 (56)

18 (53)

Oestrogene receptors

47 (42)

32 (42)

15 (44)

48 (44)

35 (45)

13 (38)

Progesteron receptors

39 (35)

29 (38)

10 (29)

52 (47)

37 (48)

15 (44)

Tumor stage

21 (19)

15 (19)

6 (18)

49 (44)

34 (44)

15 (44)

17 (15)

12 (16)

5 (18)

24 (22)

16 (21)

8 (24)

Clinical node involvement

55 (50)

34 (44)

21 (62)

49 (44)

37 (48)

12 (35)

6 (5)

5 (6)

1 (3)

1 (1)

1 (1)

SBR grade

9 (8)

8 (10)

1 (3)

61 (55)

40 (52)

21 (62)

22 (20)

8 (10)

4 (12)

Between 1976 and 1989, patients received a median number of 6 cycles [215] of polychemotherapy. The median lag time between diagnosis and the start of treatment was 1.7 months [0-9]. All patients were treated with an anthracycline-based regimen (AVCF 54%, AVCFM 44%, FAC 1%, FEC 1%). In all, 66 patients underwent a tumourectomy and 44 underwent a mastectomy. After chemotherapy, 97% received radiation and 44% a hormonal therapy (90% with tamoxifen).

BMI and weight variation

The initial median BMI was 24.4 kg/m2 [17.1 - 40.5 kg/m2]. The different subclasses of patients were distributed as followed: 9% were underweight, 56% normal, 31% overweight and 15% obese.

During chemotherapy, weight was stable with a median relative WV of 0 [-10.9 - 15.4%]. Using a threshold of 5%, 17% of patients lost weight, 69% were stable and 14% gained weight. Thus, 31% of patients presented a notable WV, higher than 5%.

Univariate analyses

The median of the follow-up was 20.4 years [19.4 - 27.6]. Among the 111 women, 57 died, 14 developed a local recurrent, and 56 a distant metastasis. Only few patients died from other causes than breast cancer. Among the 57 women who died, 47 developed breast cancer recurrence (83%). No patient was lost of follow-up. The median OS was 14.3 years [0.7 - 21.8] and median DFS 10 years [0.4 - 21]. The univariate analysis sh owed that OS (p = 0.002) and DFS (p = 0.0039) depended on tumour stage. We also found that nodal involvement influenced OS (p < 0.001) and DFS (p = 0.0024).

Concerning the initial BMI, patients with a baseline BMI of less than 24 kg/m2 had a better OS than those with an initial BMI of greater than 24 kg/m2 (p = 0.024; Figure 1A). DFS was also influenced by the BMI, as illustrated by a significant statistical difference between these two groups (p = 0.046; Figure 1B).
Figure 1

Kaplan-Meier overall survival (OS) (A) and desease-free survival (DFS) (B) of patients whom initial BMI was < 24 kg/m 2 (BMI < 24) and > 24 kg/m 2 (BMI > 24).

Moreover, OS and DFS were influenced by the WV. As the sample size is likely too small to detect significant effects of weight gain or loss as independent factors, we therefore chose to group women who gained weight with those who lost weight as a weight changing group compared to women with no weight variation. Indeed, a Kaplan-Meier analysis revealed a significant DFS difference between patients whose weight varied beyond 5% compared to patients who maintained their weight (p = 0.048; Figure 2B) while OS analysis was closed to significance (p = 0.061; Figure 2A).
Figure 2

Kaplan-Meier overall survival (OS) (A) and desease-free survival (DFS) (B) of patients whom weight variation was < 5% (stable) and > 5% (changing weight).

Variance analysis revealed a significant correlation between initial BMI and tumour stage (p = 0.0038), menopausal status (p = 0.00074) but not with clinical node involvement, hormonotherapy treatment, and WW. Results from the Chi2 test showed no significant correlation between weight variation and tumour stage, nodal involvement, menopausal status, initial BMI and administration of hormonotherapy after chemotherapy.

Multivariate analysis

The multivariate Cox model (Table 2) included WV, tumour stage, nodal involvement, initial BMI, menopausal status and treatment by hormonotherapy. As mostly patients received radiotherapy (97%), this factor was not included in multivariate analysis.
Table 2

Multivariate Cox model for overall survival (OS) and desease-free survival (DFS)

95% - CI

95% - CI

Weight variation

   < 5% (reference)

   > 5%

1.21 - 3.66

1.29 - 4.03

Clinical Node Involement

   N0 (reference)

0.99 - 2.61

1.09 - 2.91

0.98 - 6.80

1.19 - 8.49

0.99 - 16.40

1.29 - 24.76

Tumor stage

   T1 (reference)

0.96 - 1.81

0.85 - 1.60

0.93 - 3.27

0.73 - 2.57

0.90 - 5.90

0.60 - 4.13

Initial BMI

   < 24 kg/m2

   ≥ 24 kg/m2

0.81 - 2.74

0.86 - 2.93

Menopausal status

   Premenopausal (reference)

0.59 - 1.99

0.53 - 1.76

   no (reference)

0.53 - 1.65

0.44 - 1.14

* P value considered statistically significant (p < 0.05).

All the variable in the table were mutually adjusted for each other.

Only WV still had a significant effect on OS. A change of weight of more than 5% was associated with an increased risk of death of 2.11; 95% CI: 1.21-3.66 (p = 0.0082).

Moreover, we found that WV was associated with a risk of recurrence of 2.28; 95% CI: 1.29-4.03 (p = 0.0046). Although DFS was significantly affected by clinical node involvement (p = 0.021). The tumour stage, initial BMI, menopausal status and treatment by hormonotherapy having a significant influence on DFS or on OS was not observed.

In this study, we demonstrate a relationship between weight variation during polychemotherapy treatment and both poorer disease-free survival and overall survival after diagnosis of breast cancer.

The present study is in agreement with previous studies which have found that overweight at the time of diagnosis increased both breast cancer recurrence and mortality. This result has been largely demonstrated in U.S. populations [2527]. The poorer prognosis of obesity at diagnosis could be explained by the fact that overweight women tend to be diagnosed with later stage cancer and therefore more adverse tumour characteristics than normal weight women [28]. For some authors, this association is restricted to women who detected their own cancer and not spread amongst cases detected by either screening mammography or clinical breast examination [29]. Additionally, lower screening rates may partly explain the higher breast cancer mortality in obese women [30]. In agreement with this, in our population who were diagnosed 20 years ago, when no screening mammography was systematically carried out, we observed a striking association between BMI and tumour size.

In our series however, with a median WV equal to zero during chemotherapy treatment of breast cancer patients, 31% of our population presented a significant weight variation (> 5%) whereas 68% had maintained their weight. Our findings of no significant median WV during chemotherapy treatment are in contrast with the vast majority of studies conducted in North America, which have generally reported an average weight gain ranging from 1.7 to 4.4 kg during the years that follow diagnosis in women treated by chemotherapy [5, 31, 32]. On the other hand, one study carried out in a Korean breast cancer population has already observed a lack of overall weight gain, with 10.4% of the population gaining more than 5% of baseline body weight at 1 year [31]. Regarding the baseline mean BMI, we can observe that populations who did not display a significant weight gain during chemotherapy treatment, including ours, are leaner (mean BMI of 24.4 kg/m2 in our study, 23.5 kg/m2 in Korean study) than the ones used in the U.S. studies [5] in which the mean BMI varied from to 26.3 to 27.4 kg/m2 [18, 32]. Moreover, one possible explanation is that the chemotherapy regimen administered to our patients incorporated anthracycline-based therapy as reported by Han and al. [33], whereas the majority of the earlier previous studies that observed a weight gain, involved non-anthracycline-based regimen. Other retrospective and prospective reports have not demonstrated increased weight gain with anthracycline-containing regimens compared with other regimens [10, 11]. Fisher et al. [34] noted that 14.4% of patients receiving treatment with the AC regimen gained ≥ 5% over pretreatment weight compared with 42.2% of patient receiving CMF. This result was comparable to our result (14% gained weight) indeed the fact that patient accrual took place prior to the widespread use of 5-HT3 receptor antagonists in the two studies (cancer-related treatment currently used to reduce the impact of nausea and emesis associated with anthracycline use).

The long-term follow-up of patients who received an anthracycline-based chemotherapy in this study demonstrates that weight variation may not only influence recurrence, but also patient outcome. In multivariate analysis, clinical node involvement was still significant, thus, weight change was the strongest parameter associated with OS and DFS in our series. Literature on prognostic value of weight variation is mixed and not easily comparable. These studies that generally evaluated post-diagnosis weight variation on different periods which varied from a few months to a few years after diagnosis, including different treatments (chemotherapy and or hormonotherapy, radiation only...), did not use the same prognostic outcomes, and sometimes with a short median of follow-up. Among the few studies which evaluated the prognostic value of weight change after breast cancer diagnosis, four studies have shown a poor prognosis [8, 1416] whereas five reported no relationship [9, 1719].

The majority of studies observed an impact of weight gain on patient outcome. The largest study to date included 5,204 Nurses' Health Study participants diagnosed with non-metastatic breast cancer between 1976 and 2000 treated with chemotherapy and/or hormonal therapy [14]. This study reported an increased risk of recurrence, breast cancer death and total mortality in patients who gained more than 2 kg/m2 by comparison to patients who maintained their weight. However this relationship was found only among women who never smoked and the definition of recurrence included reported lung, bone or brain cancer, but excluded any local recurrences in the ipsilateral breast or new primaries in the contralateral breast. Camoriano et al. reported weight gain having a significant effect on overall survival during treatment with cyclophosphamide, fluorouracile and prednisolone (CFD) or CFD plus tamoxifen, but not on recurrence and this only for premenopausal women [8]. Two other studies conducted prior the common use of anthracycline-based chemotherapy reported a correlation between weight gain and overall survival and/or disease-free survival (without defining which events were included) [15, 16].

Only one recent study reported some evidence that women with early stage breast cancer treated with chemotherapy and/or radiation and tamoxifen who had large weight loss (> 10%) were at higher risk of recurrence and death compared to women with no weight variation. This elevated risk was more pronounced among women who were obese before diagnosis or who had ER- or PR- tumours [19]. One obvious explanation for significant weight loss being related to an increased risk of death could be that the breast cancer disease process itself caused weight loss.

Literature on the prognostic value of weight variation reported some evidence that women who had gained or lost weight have a higher risk of recurrence and death compared to women with no weight variation. So, we chose to group women who gained weight with those who lost weight as a weight changing group. We hypothesized that weight change reflected a metabolic disorder by comparison to women who maintained their weight with an energy balance in equilibrium (Figure 3). Chemotherapy induced a decrease in energy expenditure (lowered basal metabolic rate, thermogenesis, and physical activity [4]) and different modifications in dietary intake (increase in appetite [14] or decreased ingestion of food due to chemotherapy related nausea and emesis) that can lead to weight gain or loss according to dietary behaviour of patient. Moreover, women with breast cancer receiving adjuvant chemotherapy underwent unfavourable changes in body composition with lean body loss due to a negative nitrogen balance [35] even in the absence of an overall weight change [4]. A lot of data has demonstrated that weight gain during chemotherapy was indicative of sarcopenic obesity [33]. Indeed, chemotherapy for breast cancer like taxane and anthracycline can increase inflammation [36, 37] which played a central role on different modifications induced by chemotherapy. Inflammatory cytokines interfered with the satiety centre [38] and catabolism of skeletal muscle protein responsive of sarcopenia but not independently of any of the considered obesity indexes [39].
Figure 3

Possible mechanisms to explain weight change during chemotherapy treatment of non metastatic breast cancer and its prognostic value.

Several mechanisms have been proposed to explain the adverse effect of weight gain on risk of recurrence and mortality. First, weight gain and all associated metabolic disorders may predispose women to diabetes or heart disease, thereby predisposing them to morbidity and mortality [6]. However, in our study, only a few patients died from causes other than breast cancer (14%). One possible mechanism was a greater aromatase activity in the excess adipose tissue [40] and an inhibition of synthesis of sex hormone-binding globuline associated with an increased in free estradiol level which stimulates neoplasic cells [41].

Insulin resistance may be a common mechanism to explain the poor prognosis of patients who experienced a weight loss or a weight gain (Figure 3). Indeed, insulin resistance has been shown in variety of cancer patients with body-weight loss [42], but was also seen in overweight women [43]. Fasting serum insulin concentration has been directly associated with an increase in both distant recurrence and death in women previously treated for breast cancer [42, 43]. There is a strong biological rational for an adverse prognostic effect of insulin. Insulin, a member of a family of growth factors that includes IGF-I and IGF-II, exerts a mitogenic effect on malignant breast cancer cells though IGF-I receptor. It is also hypothesized that visceral obesity increases both insulin-like growth factors (IGF-I, IGF-II) which stimulates the synthesis of sex steroid hormones [31] that are involved in the regulation of normal and malignant growth of epithelial breast cells. Several studies have reported a reduction in circulating concentration of IGF-I in malignant disease, which may also have been associated with nutritional decline and systemic inflammation [44]. Yoshikawa et al. hypothesized that inflammatory reactions might be involved in the development of insulin resistance [42]. Moreover, few studies reported an association between elevated inflammatory cytokines and a worse prognosis in breast cancer patients [45, 46].

Further research is needed to understand the biological mechanisms underlying the relationship between weight variation and breast cancer growth with exploration of insulin resistance in association with body composition, measurement of energy expenditures, calorie intake and inflammatory reaction.

The current study had several limitations including its study design (retrospective chart review) and a relatively small sample size to draw a conclusion on the independent effect of weight variation. Some interesting covariates like "normal " weight prior diagnosis, smoking status, physical activity or sociodemographic aspects (education) have not been explored because of the lack of this data in patients' medical records. Additionally, more detailed measurement of body shape and fat content are lacking. Currently a long-term measurement of weight during patient follow-up is ongoing and could offer the possibility to explore weight variation after treatment which could also affect prognostic outcomes.

Our results suggest that weight change during anthracycline-based treatment of early stage breast cancer is associated with increased risk of recurrence and poorer survival, though they may require additional confirmation. Furthermore, while we have speculated on potential biological targets, more research is needed to understand the biological mechanisms underlying the relationship between weight variation and breast cancer growth.

weight variation

Body Mass Index

disease free survival

overall survival

confidence Interval.

Acknowledgements and funding

The Authors thank Delphine Roux and Delphine Crouzet for acquisition of data, and David Belin for copyedit the paper.

Authors’ Affiliations

Division of Clinical Research, Centre Jean Perrin, Clermont-Ferrand, F-63011 France; University Clermont 1, UFR Médecine, Clermont-Ferrand, F-63001 France;, Centre d'Investigation Clinique
Oncology department, Centre Jean Perrin, Clermont-Ferrand, F-63011 France; University Clermont 1, UFR Médecine, Clermont-Ferrand, F-63001 France, Centre d'Investigation Clinique
Surgery department, Centre Jean Perrin, Clermont-Ferrand, F-63011 France; University Clermont 1, UFR Médecine, Clermont-Ferrand, F-63001 France, Centre d'Investigation Clinique

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  47. Pre-publication history

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The Primary Care Practitioner and the diagnosis of occupational diseases

BMC Public Health201010:405

DOI: 10.1186/1471-2458-10-405

Received: 5 April 2010

Accepted: 9 July 2010

Published: 9 July 2010

Rather than a clinical diagnosis, in occupational medicine the critical point is the etiological diagnosis. The first is useful for the therapy, the latter for preventive, epidemiological, regulatory, and insurance measures.

As with causality criteria which are employed in population studies, the answering of four easy questions allows a Primary Care Practitioner to establish a causal link between the work activities and a potential disease that a specific patient may present.

After determining the clinical diagnosis and the actual pathology of an occupational disease, the identity, duration, and intensity of the exposure have to be detected for establishing a close-causal effect. The judgment on the occupational origin of the disease requires an integrated approach using multiple sources of information, and goes beyond the clinical diagnosis. This may require consultation with a specialist in occupational medicine.

It is important that the Primary Care Practitioner takes an accurate medical history since this may be the only chance a patient has to have their occupational disease recognised and properly detected/identified. Proper identification of the causative nature of such diseases is important for establishing preventive measures in eliminating and controlling future cases against exposure, epidemiological reporting and studies (particularly in identifying the rates of disease), regulatory reporting requirements and insurance compensation.

A historical judgment by the Venice Appeal Court (Italy) in 2004 finally put an end to a controversial class-action law suit, which began over 10 years prior, by denouncing the deaths of 120 workers by various types of tumours. These workers had been employed since the 1950s in the chemical plants at Porto Marghera (Italy) and were involved in the production of vinyl chloride (VC) and polyvinyl chloride (PVC). VC and PVC were recognised cancer agents by the International Agency for Research on Cancer (IARC) and the European Economic Area (EEA) since the 1970s and repeated reports, notifications and denouncements about their possible role in the health problems of several workers in the 70s and 80s at Porto Marghera fell on deaf ears and were disregarded. Despite the occupational situation at these industrial plants being defined as "alarming" by some experts since 1977, governmental insurance companies were not willing to recognise the potential occupational nature of the health problems affecting the blue-collar workers, thus continuously denying them compensation. On 22 August 1994 the prosecuting attorney decided to intervene and began by investigating the real occupational mortality and disease rates of the workers involved in the management of VC and PCV, as data provided thus far by the industrial companies, underestimating the phenomenon, were not convincing [1]. On the assumption that the results of any consultations undertaken by occupational doctors employed by the industrial companies at this time were probably biased, the most reliable and effective health care resource available to these workers would have probably been their Primary Care Practitioner (PCP).

In occupational medicine the critical point is not clinical diagnosis (e.g. rhinitis), but instead the etiological diagnosis (e.g. rhinitis caused by anhydrides). The first diagnosis is useful for therapy while the latter is utilised for preventive, epidemiological, regulatory and insurance measures.

It has been reported that PCPs continue to do a poor job of obtaining an occupational history and it has been identified that additional medical education is needed to correct these inadequate practices [2, 3]. These issues are of particular importance since PCPs are often the first to see patients with occupational diseases [4]. This makes their findings of critical importance since they will often make the initial determination and sometimes undertake the only evaluation as to whether the patient's condition is a result of an occupational event.

Setting up an hypothesis of work

To detect an occupational disease it is necessary to investigate some aspects which are often neglected by solely taking the patient's medical history. The clinical history could be supplemented by employing some questions which rely on some of the criteria for cause-effect relationship normally used in occupational epidemiology population studies [5]. The answering of only four easy questions could be sufficient for a PCP to suspect a causal link between the work activities and the disease of a specific patient [3, 6].

The suspicion of an occupational disease is reasonable when the cause precedes the effect of an interval sufficient for the development of the pathological process (query 1) and one or more positive answers are obtained from the queries 2-4 of table 1[57].
Table 1

The four questions giving rise to suspicion of the occupational nature of a disease.

Criteria for causal relationship in epidemiology

Queries to be addressed to the patient

Temporal relationship

1. What is the time lag between the initial exposure and the start of the symptoms ?

Dose-effect relationship

2. Do the symptoms improve if the patient is not exposed any longer (e.g. if he/she changes work duties or is on holiday?

3. Do the symptoms worsen if the patient carries out specific duties or works in areas characterised by high levels of exposure?

Strength of the association

4. Are colleagues affected by the same symptoms related to the same exposure?

Expounding upon the information collected

It is necessary to define with clinical diagnosis the actual pathology of a disease along with the identity, duration and intensity of the exposure agent. This can be elucidated by identifying a specific chemical substance, physical, biological or psychological risk factor for that patient [810]. A simple inquiry about the work duties of the patient does not provide adequate information and in some cases this alone may be misinterpreted by the PCP [11].

Establishing the existence of an occupational disease often requires the examination of:

  1. 1

    Evidence from epidemiological studies where the disease in question is recognised as caused by the same exposure agent (substance);

  2. 2

    Chemical data sheets, reporting the chemical substances contained in each product; in particular information such as "risk sentences", describing the types of symptoms potentially caused by the substance;

  3. 3

    List of occupational diseases related to that specific industry or agricultural activity/practice;

  4. 4

    List of mandatory notifiable diseases.

In the case of occupational diseases the physician must [1214]:

  1. 1

    Notify the occurrence of this disease case to the legal/regulatory authorities, if there is any legal responsibility for such notification;

  2. 2

    Notify the occurrence of the occupational disease case to the public health authorities (if it is mandatorily notifiable);

  3. 3

    Certify the occupational disease so the patient can file/claim compensation from the government/insurance companies for this disease state.

Problems and solutions

Determining the duration of the exposure can be difficult and in some cases frustrating (particularly if there have been repeated periods of exposure and the patient has moved in and out of exposure scenarios), but should not cause great difficulty for the physician [6]. Simply identifying the type or types of exposure that the patient has from their employment can be accomplished through use of common occupational medicine/toxicological/industrial hygiene textbooks [1517]. By contrast, deriving the intensity of exposure from the patients' employment history can be far more complicated [6, 18]. However, the simple answer is that if the worker used/uses Personal Protection Equipment (PPE) (masks/respirators, gloves, etc.) this may be sufficient to provide a basic answer that exposure is likely to be occurring; although, may not provide sufficient information as to whether the PPE is sufficiently protective. In fact there are different forms of PPE and each has varying value in providing protection against harmful agents [6, 1921]. For instance, gloves protect against solvents and other toxicants only if chosen with the awareness of the type of materials suitable in each case[19].

To fully understand potential exposure, it is sometimes necessary to obtain the complete occupational history of the patient, occasionally going back several decades, as illustrated with the diseases mesothelioma and liver angiosarconoma. These diseases can occur up to 40 years since the exposure to asbestos [22] and vinyl chloride (VC) [23] respectively. This will assist the PCP in assessing the workplace exposure, preventing misunderstandings of practices and activities in the workplace and their over-reliance on descriptive information provided by the patient [11].

Finally, the judgment on the occupational origin of the disease can require an integrated approach using multiple sources of information. For instance, the presentation of cough and dyspnoea in a worker who smokes does not necessarily signify that the symptoms are solely attributable to smoking [24].

The guidelines hereby mentioned should be used as a preliminary tool. For further investigation on the potential occupational nature of a disease physicians should subsequently refer to the online database of the National Library of Medicine's Toxicology and Environmental Health Information Program (TEHIP). TEHIP is a relevant portal reporting extensive toxicological information worldwide, including several scientifically peer-reviewed databases residing within the web-based TOXNET system [25]. Haz-Map is one of these occupational toxicology databases available on the National Library of Medicine's TOXNET system. Haz-Map is designed primarily for health and safety professionals, but also for consumers seeking information about the health effects of exposure to chemicals and biological compounds at work. It links jobs and hazardous tasks with occupational diseases and their symptoms. The 1,595 chemicals and biological agents in the database are related to industrial processes and other activities such as hobbies. The linkage indicates the potential for exposure to the agents. The 224 occupational diseases and their symptoms are associated with hazardous job tasks. This association indicates an increased risk for significant exposure and subsequent disease [26]

As the clinical diagnosis of the disease may require appropriate tests (laboratory tests, diagnostic imaging) the etiological diagnosis may need consultation from a specialist in occupational medicine for adequate confirmation.

Lastly it is important for the PCP to be vigilant and aware that patients/workers might also be reluctant to disclose information on their potential occupational exposure because of the repercussions this may have on other people and their employer.

The taking of an accurate and complete history by the PCP may sometimes be the only chance a patient has to have the occupational nature of his/her disease detected [4, 27]. Failure in this respect can result in the patient not being listed in the appropriate registry for the relevant disease, the patient being denied insurance/governmental benefits and furthermore, the opportunity to take preventative measures in an attempt to eliminate future cases in other workers may be missed [28, 29].

Taken together, these factors will prevent the case from being included in epidemiological data, thus resulting in an underestimation or miscount of the true number of cases of a specific occupational disease [30].

A simple guide, as presented here, will assist PCPs overcome any of their inadequacies in occupational medicine, especially related to their understanding of the workplace environment [11], with the ultimate aim being to achieve a higher rate of preventable disease along with a mechanism for reducing occupational compensation for these diseases [22]. It will also allow physicians to gain a better understanding of occupational events and workplace illnesses through self-education involving case-by-case evaluation of occupational diseases. In addition we hope that by educating PCPs in this way their failure rate in detecting occupational disease will be reduced and thereby increase the surveillance of specific diseases by a resultant increase in the reporting by PCPs to regulatory agencies and, in part, to the epidemiological literature [30].

For these reasons it is crucial that the PCP be alert and aware of the complexities involved in this type of diagnosis and thoroughly investigates and pursues all avenues of inquiry into the disease(s) of their patients. Overall, PCPs are often the gatekeepers for detection, diagnosis and treatment of occupationally related diseases, which places them in a unique position to provide adequate care to these patients [4] along with obtaining occupational compensation for the injured worker [11]. Inadequate evaluation will only hinder the future prevention and proper identification and reporting of occupational diseases. Undertaking comprehensive evaluations into the patient's disease can have a dramatic impact on the patient's total health and well-being and result in improved quality of life [22].

Vinyl Chloride

Polyvinyl Chloride

Primary Care Practitioner

International Agency for Research on Cancer

European Economic Area

Personal Protection Equipment

Toxicology and Environmental Health Information Program.

We thank Ms. Daisy Alice Dunger for her advice in the drafting of the manuscript and Gianluca Marangi for his suggestions in the initial stages of the paper.

Authors’ Affiliations

Department of Environmental Medicine and Public Health, Padua University
Imperial College London, School of Public Health
Envirosafe Training and Consultants

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  31. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:

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Effectiveness of a web-based self-help smoking cessation intervention: protocol of a randomised controlled trial

  • Jeannet JAM Kramer1Email author,
  • Marc C Willemsen2,
  • Barbara Conijn1,
  • Andrée J van Emst2,
  • Suzanne Brunsting2 and
  • Heleen Riper1
BMC Public Health20099:32

DOI: 10.1186/1471-2458-9-32

Received: 08 December 2008

Accepted: 22 January 2009

Published: 22 January 2009

Cigarette smoking is a major risk factor for many chronic and fatal illnesses. Stopping smoking directly reduces those risks. The aim of this study is to investigate the effectiveness of a web-based interactive self-help programme for smoking cessation, known as the StopSite, by comparing it to an online self-help guide. Both interventions were based on cognitive-behavioural and self-control principles, but the former provided exercises, feedback and interactive features such as one-to-one chatrooms and a user forum, which facilitated mutual support and experience sharing.

Methods and design

We conducted a randomised controlled trial to compare the interactive intervention with the self-help guide. The primary outcome measure was prolonged abstinence from smoking. Secondary outcomes were point-prevalence abstinence, number of cigarettes smoked, and incidence of quit attempts reported at follow-up assessments. Follow-up assessments took place three and six months after a one-month grace period for starting the intervention after baseline. Analyses were based on intention-to-treat principles using a conservative imputation method for missing data, whereby non-responders were classified as smokers.

The trial should add to the body of knowledge on the effectiveness of web-based self-help smoking cessation interventions. Effective web-based programmes can potentially help large numbers of smokers to quit, thus having a major public health impact.

Trial registration

Smoking heightens the risks for many diseases, such as lung cancer [1], throat cancer [2], obstructive pulmonary disease (COPD) [3, 4]and cardiovascular diseases [5]. It strongly increases mortality as expressed in lost life-years, and it is the most prominent risk factor for such mortality [6, 7]. On average, smokers die about 10 years younger than non-smokers [7]. The World Health Organization (WHO) attributed 5.4 million deaths in 2005 to tobacco use, a figure expected to rise to 6.4 million a year by 2015 [8]. Stopping smoking is known to reduce the hazards of former smokers and increase their life expectancy. The earlier they quit, the more life-years they gain [7].

In view of the pernicious consequences for smokers and those around them and the positive effects of quitting, smoking cessation is an important topic on the political agenda. A current Dutch government policy aim is to reduce the percentage of smokers in the population from 28% in 2007 to 20% in 2010 [9]. Yet smokers are known to have a low likelihood of quitting: more than half of all current smokers want to stop, but less than 7% of quit attempts result in long-term abstinence. This may be partly due to the insufficient utilisation of effective help and support programmes [10].

STIVORO is a Dutch non-profit agency whose primary aim is to help people stop smoking. It offers a wide range of effective smoking cessation methods in a stepped-care approach. Smokers are advised to begin with less intensive, less costly methods (self-help guide, tailored advice) that do not involve intervention by a professional. If these do not work, more intensive forms of support are provided (coaching by telephone, group training, individual counselling). At present, however, a gap still exists between the less and the more intensive approaches to smoking cessation. To fill that void, STIVORO developed a web-based self-help intervention for adult smokers known as the StopSite in 2005. It enables participants to perform quitting activities anonymously, while interacting with others if desired. This conforms to the wishes expressed by most smokers to stop by themselves without professional help [11, 12].

A number of web-based self-help programmes for cigarette smoking have become available in the past decade. This is for good reason: the Internet makes it possible to reach vast numbers of smokers, and the programmes could therefore have a major positive impact on public health. Results from several randomised trials have suggested that web-based interventions can promote smoking cessation, especially if the information is appropriately tailored to the users [1315] and frequent automated contacts with the users are ensured [16]. All in all, the results for web-based self-help programmes are promising, but the number of effectiveness studies is still limited.

The primary objective of this study was to evaluate the effectiveness of the Dutch interactive online self-help intervention (the StopSite) by comparing it to a self-help guide provided on the Internet. A literature review has shown that self-help guides can have small positive effects on smoking cessation in comparison to no intervention [17]. We expected that the StopSite, by virtue of its interactive nature, would result in higher cessation rates than our online guide. Secondary study objectives were to explore possible dose-response effects (whether more visits to the StopSite would result in better outcomes) and whether particular subgroups would benefit more than others. This paper describes the trial protocol and discusses the design, methodology and some methodological issues involved.

Methods and design

Study design

The study is a randomised controlled trial. Participants were randomised into two groups: an experimental group performing the web-based interactive self-help intervention and a control group accessing the online self-help guide. The study protocol, interventions and informed consent procedure were approved by the Dutch medical ethics committee METiGG, Chamber North (registration number 5209).

Inclusion and exclusion criteria

All adults aged 18 and older who were currently smoking cigarettes or rolling tobacco, were willing to quit smoking within three months and had Internet access were eligible for the study. Candidates were excluded if they were already preparing to stop smoking with the support of a coach, a course or pharmacotherapy, or if they were already enrolled in another smoking cessation study.

Candidates were recruited over a one-year period using advertisements in daily and weekly national or regional newspapers or on the Internet. Enrolment took place via a website that provided information about the study and access to the informed consent form. After returning the signed form by post, candidates received an e-mail telling them how to access and complete an online baseline questionnaire. Those who failed to meet selection criteria were then informed by e-mail.

The randomisation procedure was automated and was carried out at the individual level after the baseline selection. Randomisation was stratified by gender and age (three age groups: 18 to 29, 30 to 45, and 46 and older). Block randomisation in blocks of two was performed to ensure equal distribution of participants over the two arms of the trial. Participants were informed by e-mail of their allocation to the StopSite or the self-help guide; each received a user name and password to log into the corresponding intervention.

The StopSite

The experimental group had access to the StopSite, which has three components. The main component is a self-control programme consisting of exercises based on cognitive-behavioural therapeutic principles. Included are exercises to monitor smoking behaviour, set goals, sustain motivation, analyse risk situations, consider alternative behaviours, and deal with social pressure. The self-control component also includes a device that counts down the days until the chosen quit date, and afterwards displays the number of cigarettes not smoked and the amount of money saved. In the second component, participants can engage in interaction with others in a users' forum or in one-to-one chatrooms with buddies; supportive interaction may motivate them to return to the site regularly. The third component links to a large body of state-of-the-art information about smoking cessation on the STIVORO website

The self-help guide

The comparison group received access to a Dutch online self-help guide developed by STIVORO for smokers who are considering stopping. It is entitled Quit Smoking: Why and How and includes advice on preparing to quit, information about withdrawal symptoms, tips on how to handle difficult moments, and general information about types of support materials. The information is not personalised and not interactive.

Both interventions were offered as stand-alone interventions without support from a professional coach. During the trial the participants were allowed to seek additional support if they wished.

Three assessments were made over time: a baseline assessment immediately preceding randomisation and intervention access, and two follow-up assessments at three and six months following a one-month post-baseline grace period. The grace period gave participants time to begin the intervention. At the follow-ups, they were invited by e-mail to complete the online assessment questionnaires, with e-mail reminders sent two weeks later if necessary. To stimulate response, we counted every completed questionnaire as a 'ticket' for a lottery to be held at the end of the study.

Outcomes were defined according to the guidelines provided by Mudde et al. [18, 19] and Hughes et al. [20] for evaluating smoking cessation interventions.

The primary outcome measure was prolonged abstinence [20] in the past three months. Non-abstinence was defined as having smoked on seven or more consecutive days or two weekends in a row (weekend smokers). Prolonged abstinence was calculated for both follow-up assessments separately and for the two follow-up periods combined. Participants were classified as non-smokers if they were prolonged abstinent at both follow-up assessments.

The secondary outcomes were

1) point-prevalence abstinence (no smoking, not even a puff, in the week preceding the questioning)

2) overall decrease in amounts of tobacco products smoked (including cigars) between baseline and the follow-up assessments.

Additional measures

At baseline, we assessed demographics and other potential confounding variables. The potential non-demographic confounding variables as defined by Mudde et al [18] were:

- current stage of change on a readiness-to-change scale. Possible stages for potential quitters are the immotive (no intention to quit), preparation and contemplation stages; the act of quitting is followed by the action stage (less than six months of abstinence) and maintenance stage (six or more months of abstinence)

- attitude towards quitting: expectations regarding effects of smoking cessation in terms of health improvement, sociability, lung cancer risk, relaxation problems, withdrawal symptoms, health of others, and boredom

- social influences on quitting: encouragement by others to quit (5 items) and presence of smokers in daily environment (5 items)

- self-efficacy expectations (abilities to refrain from smoking in difficult circumstances, 6 items).

Other potential confounders were:

- the level of nicotine dependence as measured by the number of cigarettes smoked per day in combination with time to first cigarette (Heaviness of Smoking Index) [21].

- expectations that a web-based self-help programme would be helpful to stop smoking (question constructed by the researchers).

In follow-up assessments, we inquired about the use of professional or other help or support materials (t1 and t2) and participants' evaluation of their assigned intervention (t1). A tracking tool automatically recorded logins to the interventions.

Sample size

The sample size was based on the expected contrast between the experimental group (StopSite) and the comparison group (self-help guide) in their rates of prolonged abstinence. We expected 5.5% of the comparison group [22] and 11% of the experimental group to report prolonged abstinence at 6-month follow-up. Based on a power of 80% using a two-tailed test with an alpha of .05, at least 353 participants were needed in each condition. Adjusting for an expected 40% dropout at 6 months, we needed a total of 1,104 participants. Sample size was calculated with Stata 7.0/SE.

Statistical analysis

We used t-tests, chi-square tests and logistic regression (p < .10) to assess whether the randomisation had resulted in two comparable groups at baseline and whether differential loss to follow-up had occurred. We then performed intention-to-treat (ITT) analyses including all randomised participants and classifying those not responding to follow-up questionnaires as smokers (the worst-case scenario); this is a conservative way of handling non-response. We also conducted analyses including only the participants that actually used the assigned intervention. The use of both interventions was monitored by the tracking tool that recorded participant logins; 'use of the intervention' was defined as visiting the StopSite or opening the self-help guide at least once.

Differences between groups at follow-up on dichotomous outcome measures (prolonged abstinence, point-prevalence abstinence and quit attempts) were analysed using chi-square tests or logistic regression, depending on the need to adjust for possible confounders. A linear risk model was used to obtain the risk difference (RD). The number needed to treat (NNT) was calculated as the inverse of the RD (number needed to treat stands for the number of people who must receive the intervention in order for one person to successfully stop smoking).

Between-group differences in tobacco use reduction from baseline to t1 and from baseline to t2 were analysed in linear regression models. Magnitudes of intervention effect were estimated using Cohen's d [23]. Effect sizes were first calculated for each condition separately by subtracting the mean posttest score from the mean pretest score and dividing the result by the standard deviation at pretest. The effect size of the comparison group was then subtracted from that of the experimental group. A difference in d of 0.5 would indicate that the experimental group mean was half a standard deviation greater than the control group mean. Values of d from 0.56 to 1.2 may be regarded as large, from 0.33 to 0.55 as moderate, and from 0 to 0.32 as small [24].

To identify subgroups that particularly benefited from the intervention, we examined the influence that interaction between the smokers' baseline characteristics and the intervention itself might have had on treatment response [25]. In a logistic regression model, the primary outcome (prolonged abstinence) was regressed on dummy-coded baseline characteristics (age and gender), the main intervention effect and the characteristic-by-intervention interaction term. It is the interaction term that is of interest here: it reflects any potential added benefit that the StopSite might have for participants with specific attributes.

To detect possible dose-response relationships, we used a logistic regression model to regress the primary outcome (prolonged abstinence) at t2 on the number of times logged in to the StopSite; only the respondents randomised to the StopSite (the experimental group) were included in the dose-response analyses. Tests were conducted at α = .05 (two-sided) with 95% confidence intervals. Analyses were performed with SPSS version 15.1.

This article describes the protocol of a randomised controlled trial that compared two self-help smoking cessation interventions delivered via the Internet. The primary aim was to assess the effectiveness of StopSite, a Dutch cognitive-behavioural self-help site with interactive elements but without involvement of a coach or therapist. The comparison intervention was a Dutch self-help guide entitled Quit Smoking: Why and How. Both interventions were developed by STIVORO, a Dutch organisation on smoking and health. We expected the StopSite to double the effect of the self-help guide in terms of tobacco abstinence. We did not include a no-treatment control group because of the greater potential for contamination by external interventions; no-treatment controls might be more likely to seek help elsewhere during the study.

Participants were recruited through advertisements in national and regional newspapers and through banners on the Internet. As a consequence, the study is likely to have included disproportionate numbers of smokers with higher motivations to quit; generalisability is therefore restricted to smokers who are already in the preparation or contemplation stage of change. This methodological weakness is also a potential strength, as it probably makes the trial sample a closer reflection of the population being targeted by the intervention: people who are already motivated to stop smoking, but who need some support in doing so and who prefer an intervention without interference of a coach or therapist.

The primary outcome measure was prolonged abstinence from smoking in the six months after the intervention, allowing for a one-month grace period after baseline for performing the intervention. This is a widely accepted, albeit minimum, time frame for smoking cessation trials [26]. The primary outcome, six-month abstinence, was estimated using abstinence measures at two assessment times (three and six months); it was therefore not an ideal measure of prolonged abstinence. We did not perform biochemical verification of self-reported abstinence. That is consistent with recommendations by the Subcommittee on Biochemical Verification of the Society for Research on Nicotine and Tobacco (SRNT), which suggest that biochemical validation may not be necessary or advisable in studies like ours with limited face-to-face contact and with postal, telephone or Internet data collection [27].

The entire trial was conducted via the Internet with the exception of the signed informed consent document that was returned by post. There was no face-to-face or telephone contact between participants and researchers during the trial. The screening and baseline questionnaires, randomisation outcomes, and invitations and reminders for online follow-up assessments were all sent by e-mail. Such an approach ensures a low threshold for study participation, but makes the study susceptible to higher attrition rates due to participant dropout or non-utilisation of the intervention [28].

The blinding of respondents to experimental conditions was not feasible because of the behavioural nature of both interventions. The participants were aware of the intervention to which they were receiving access, but unaware of whether it was the experimental or comparison intervention. No explicit descriptions of the interventions were given before randomisation, thus reducing the chance that participants would feel more disappointed or satisfied with their intervention in relation to the other.

We are grateful to Michael Dallas for the English language edit. The study was funded by a research grant from ZonMw, the Netherlands Organisation for Health Research and Development, grant no. 62000008.

Authors’ Affiliations

Innovation Centre of Mental Health and Technology (ICOM), Trimbos Institute

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  29. Pre-publication history

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© Kramer et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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Asa Simmons, family moved to Denton from North Louisiana.

Replies: 1

Re: Asa Simmons, family moved to Denton from North Louisiana.

Posted: 7 Jun 2014 9:51AM GMT
Classification: Query
Surnames: Simmons

J. A. Simmons, the immediate subject of this sketch was married June 7, 1865 to Miss Mary A. Malone who was born in Louisiana April 1846 . . .
Lela b. March 17, 1866
C. M. b. May 4, 1876
Willie I. b. Nov 11, 1870
S. B. b. Sept 7, 1872
Willis J. b. May 4, 1876
Allie V., b. June 1, 1888
Source: Biographical and Historical Memoirs of Louisiana, 1982, Volume 2, p. 881

1880 Lincoln Co. LA
J. A. Simmons, b. Georgia, 35, farmer
M. A. wife, b. LA, 32
Lila, b. LA, 13
C. son, b. LA, 12
Ida, b. LA, 9
Stanly, b. LA, 7
Willis, son, b. LA, 3
Baby, dau, b. LA, 1

1900 Fannin Co. Texas
J. A. Simmons, b. GA Sept 1844, widower, bottling works
Willie I., dau, b. LA Nov 1870
Wellis, son, b. LA May 1876, laborer bottling works
Allie, b. LA May 1879
Marietta, b. July 1881
James W., b. LA Dec 1883
Jessie R., son, b. LA June 1888
Ralph Walpole, boarder

James Wiley Simmons
Born: 6 Dec 1883
1918 Residence: Denton, Denton, TX
Occ: Physical Director National War Work Council, Chicago
Nearest Relative: Susan Gregg Simmons, Denton
Source: U. S. World War I Registration Cards 1917-18

James Wiley Simmons
Born: 6 Dec 1883, Rustin, LA
Father: Jesse Simmons, b. GA
Residence: Corpus Christi, Nueces, TX, 5 yrs
Informant: Susan Jane Simmons, Corpus Christi
Died: 7 Aug 1943, Fred Roberts Hospital, Corpus Christi, Nueces, TX
COD: Diabetis
Buried: Seaside Memorial park, Corpus Christi, Aug 9, 1943

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I am retired and over 70. I have roughly equal amounts of money in my Regular IRA, Roth IRA, and General (non-IRA) investment accounts. I want to withdraw money each month from my investment accounts for my general monthly expenses -- car payments, food, vacations, etc.

My tax bracket is near the top of the 15% level. If I withdraw from my Regular IRA I may move into the next bracket. If I sell stock in my regular account I may move into the next bracket.

Any money left in my accounts on my death will go to my spouse.

I am looking for suggestions (with rationale) as to which way will be best for withdrawing from my investment accounts.

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Before - Legends Under the Sea - Qui-Gon, Obi-Wan mission to assist another Jedi --- one-post

Discussion in 'Fan Fiction- Before, Saga, and Beyond' started by ardavenport, Jun 22, 2011.

Moderators: Briannakin, mavjade
  1. ardavenport Jedi Master

    Member Since:
    Dec 16, 2004
    star 4
    Title: Under The Sea
    Author: ardavenport
    Timeframe: pre-Episode I, pre-TPM
    Genre: drama
    Characters: OCs, Obi-Wan Kenobi, Qui-Gon Jinn
    Keywords: Obi-Wan Kenobi, Qui-Gon Jinn, Jedi, Jedi Code
    Summary: Another Jedi calls on Master Qui-Gon, a Jedi with a reputation, for help.
    Notes: Typo is my middle name, with missing words and errors that spell checkers do not catch being my speciality - if you see any, just post a reply or send a PM with the what and where and I will kill them with no mercy.
    Disclaimer: All characters and the Star Wars universe belong to George and Lucasfilm; I am just playing in their sandbox.

    Obi-Wan tilted his arms and shoulders to his right, guiding the droid-swimmer to the side to avoid the two Kalamni coming the other way through the narrow tunnel. Passing them, the nearly naked adult ignored the two offworlders, but the child stared with wide blue eyes at the two Jedi in breather face masks, flowing robes, clothes and boots. Ahead of him, his Master, Qui-Gon Jinn, went upward as soon as he emerged into the bright blue-green beyond the tunnel. Obi-Wan tightened his hold on a control grips on the swimmer to urge it faster and then upward in his Master's wake, the sound of water briefly loud in his ears with the sudden change in direction.

    They entered a large bright area of clear water, sunlight pouring down from the rippled surface above. On all sides, he saw a semi-regular pattern of thousands of round entryway holes in pale orange shell walls. Below them were the floating deep greens, purples and browns of a central garden. Kalmni younglings darted in and out of it with a few adults lazily drifting nearby.

    Qui-Gon's swimmer zoomed to their destination, his dark brown robe swirling and billowing around his legs. Holding on to the control grips with his hands, his feet dangling free after him, Obi-Wan stayed right behind him. He did not know where they were going, only that Jedi Knight Gu'usshuss had commed his Master for help. Qui-Gon did not say what was needed; he said he did not know and did not ask, though it seemed clear to Obi-Wan that he had an idea about what Gu'usshuss needed. But his Master refused to speculate about it and admonished Obi-Wan not to.

    Obi-Wan had objected to his Master keeping what he might know from him; he was nearly twenty years of age, an experienced Padawan. But Qui-Gon had only rebuked him about going into any situation with expectations that would prejudice his thoughts, tarnish his judgment.

    Qui-Gon slowed his ascent and Obi-Wan felt the water pressure change in his ears. His Master finally stopped at an entry hole about ten levels below the topmost one only a few body-lengths below the water surface. Their droid-swimmers turned themselves vertical, their blue side and tail fins retracting into their pale silvery bodies, allowing their passengers to alight in the entry hole. It was spacious enough for both Jedi to float free in it and the droids parked themselves in a side alcove.

    Qui-Gon reached for the triangular plate next to the door, but the pearly white barrier slid aside before he touched it. Jedi Knight Gu'usshuss floated just inside. He wore only minimal clothing over his smooth gray-green skin, beige tabbards covered his broad shoulders, crossed over and concealed his lower body, the long ends floating free. His lightsaber floated outward from the wide belt around his middle, a beige sash of fabric under that. The gill slits on his neck rippled when he saw both of them, but his large purple eyes remained expressionless. He moved to the side to let them in.

    An artificial image of a rippling, sunny blue surface covered most of the ceiling inside casing irregular shades of shadow and reflected blue light in the room. The oxygen in their masks, extracted from the water for them by the breather units, tasted saltier, more stale in the confined space.

    "Master Qui-Gon. I was expecting you to come alone." Gu'usshuss's low voice burbled in the water.

    Qui-Gon fo
  2. TraderWatto Jedi Youngling

    Member Since:
    Apr 24, 2011
    Great job! Qui-Gon looks very convincing here: he really was one who understood the Jedi Code both deeply and widely, so that he believed it cannot really state anything against basic life components, and he was brave and steady enough to explain this to the Council and help others.
  3. earlybird-obi-wan Jedi Grand Master

    Member Since:
    Aug 21, 2006
    star 6
    Love your surprising aliens and situations Obi and Qui always seem to encounter.

    Another of your great vignettes
  4. ardavenport Jedi Master

    Member Since:
    Dec 16, 2004
    star 4
    TraderWatto: Thanks for reading! Qui-Gon was sympathetic to the other Jedi, but he doesn't violate the code, though he does have a reputation for going his own way.

    earlybird-obi-wan: Thanks for reading! I've been wanting to do an underwater story for some time, since you saw them go underwater in TPM, and I finally got to write one.
Moderators: Briannakin, mavjade


text:
Boat Specifications

Make: Aloha Pontoon Boats
Model: Mahalo-24
Year: 2010
Length: 24 ft. 0 in.
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