Research article Special Issues

Risk factors related to bacterial contamination by Enterobacteriaceae and fecal coliforms and the prevalence of Salmonella spp. in Algerian farms, slaughterhouses and butcheries: a two-year follow-up study

  • This study was conducted to investigate first the bacterial contamination by Enterobacteriaceae, fecal coliforms and the prevalence of Salmonella spp. and second to identify the main associated risk factors in Algerian farms, slaughterhouses and butcheries during a two-years period. Thus, a cross-sectional study was performed using a simple random sampling method to target 20 farms, 10 slaughterhouses and 5 butcheries. A structured questionnaire was further used to assess hygienic status of the farms and slaughterhouses. A total of 265 samples were collected from wall, floor, litter, food, water and animals' samples composed mainly of meat, neck skin and liver. Samples from walls and floors, from different sites were analyzed to evaluate the overall contamination and the hygiene of sites for Total viable bacteria, Enterobacteriaceae counts and Fecal coliforms counts. Furthermore, E.coli and salmonella spp. were identified in all samples. The overall contamination by sampling sites expressed as log10 CFU/g (mean ± SD) for Total Aerobic Microbial Count, Enterobacteriaceae count and fecal coliforms counts were around 4.71 ± 1.1, 4.73 ± 1.3 and 4.68 ± 1.2 respectively. The findings evidenced that the prevalence of E.coli and Salmonella spp. were 63.40% and 18.49% respectively. The highest rate of E.coli contamination was for poultry farms (70%), beef farms (64%) and butcheries (74.54%) followed by poultry meat slaughterhouses (60%) and sheep farms (48%) while beef slaughterhouses have the lowest rate of contamination (33.84%). For salmonella spp. the contamination was found to be mainly in poultry meat slaughterhouses (31.11%), butcheries (25.45%), followed by poultry farms (22%), beef farms (20%) and sheep farms (12%) while beef slaughterhouses have the lowest rate of contamination (4.61%). This study evidenced multifactor effects of microbial contamination in farms such as animal density, litter hygiene and scraping, manure storage, water and pest control, contact with other animals and decontamination process. Overall, this trial indicated a high rate of microbial contamination for which further studies are needed to determine all the potential risk factors in order to evaluate the corrective effects.

    Citation: Khireddine Ghougal, Amira Leila Dib, Nedjoua Lakhdara, Melisa Lamri, Sameh Baghezza, Abdennour Azizi, Rayane Merrad, Ahmed Zouikri, Daoud Cheraitia, Messaoud Trouni, Hichem Soualah, Elena Moreno, Elena Espigares, Mohammed Gagaoua. Risk factors related to bacterial contamination by Enterobacteriaceae and fecal coliforms and the prevalence of Salmonella spp. in Algerian farms, slaughterhouses and butcheries: a two-year follow-up study[J]. AIMS Agriculture and Food, 2021, 6(3): 768-785. doi: 10.3934/agrfood.2021046

    Related Papers:

    [1] Nguyen Thi Hong Tuyen, Truong Quang Dat, Huynh Thi Hong Nhung . Prevalence of depressive symptoms and its related factors among students at Tra Vinh University, Vietnam in 2018. AIMS Public Health, 2019, 6(3): 307-319. doi: 10.3934/publichealth.2019.3.307
    [2] Alfred M Levine, Donna B Gerstle . Female breast cancer mortality in relation to puberty on Staten Island, New York. AIMS Public Health, 2020, 7(2): 344-353. doi: 10.3934/publichealth.2020029
    [3] José Miguel Uribe-Restrepo, Alan Waich-Cohen, Laura Ospina-Pinillos, Arturo Marroquín Rivera, Sergio Castro-Díaz, Juan Agustín Patiño-Trejos, Martín Alonso Rondón Sepúlveda, Karen Ariza-Salazar, Luisa Fernanda Cardona-Porras, Carlos Gómez-Restrepo, Francisco Diez-Canseco . Mental health and psychosocial impact of the COVID-19 pandemic and social distancing measures among young adults in Bogotá, Colombia. AIMS Public Health, 2022, 9(4): 630-643. doi: 10.3934/publichealth.2022044
    [4] Soo-Foon Moey, Norfariha Che Mohamed, Bee-Chiu Lim . A path analytic model of health beliefs on the behavioral adoption of breast self-examination. AIMS Public Health, 2021, 8(1): 15-31. doi: 10.3934/publichealth.2021002
    [5] Soo-Foon Moey, Aaina Mardhiah Abdul Mutalib, Norfariha Che Mohamed, Nursyahirah Saidin . The relationship of socio-demographic characteristics and knowledge of breast cancer on stage of behavioral adoption of breast self-examination. AIMS Public Health, 2020, 7(3): 620-633. doi: 10.3934/publichealth.2020049
    [6] Erin Linnenbringer, Sarah Gehlert, Arline T. Geronimus . Black-White Disparities in Breast Cancer Subtype: The Intersection of Socially Patterned Stress and Genetic Expression. AIMS Public Health, 2017, 4(5): 526-556. doi: 10.3934/publichealth.2017.5.526
    [7] Karent Zorogastua, Pathu Sriphanlop, Alyssa Reich, Sarah Aly, Aminata Cisse, Lina Jandorf . Breast and Cervical Cancer Screening among US and non US Born African American Muslim Women in New York City. AIMS Public Health, 2017, 4(1): 78-93. doi: 10.3934/publichealth.2017.1.78
    [8] Carmen Giurgescu, Lara Fahmy, Jaime Slaughter-Acey, Alexandra Nowak, Cleopatra Caldwell, Dawn P Misra . Can support from the father of the baby buffer the adverse effects of depressive symptoms on risk of preterm birth in Black families?. AIMS Public Health, 2018, 5(1): 89-98. doi: 10.3934/publichealth.2018.1.89
    [9] Eleni L. Tolma, Kimberly Engelman, Julie A. Stoner, Cara Thomas, Stephanie Joseph, Ji Li, Cecily Blackwater, J. Neil Henderson, L. D. Carson, Norma Neely, Tewanna Edwards . The Design of a Multi-component Intervention to Promote Screening Mammography in an American Indian Community: The Native Women’s Health Project. AIMS Public Health, 2016, 3(4): 933-955. doi: 10.3934/publichealth.2016.4.933
    [10] Yan Lin, Xi Gong, Richard Mousseau . Barriers of Female Breast, Colorectal, and Cervical Cancer Screening Among American Indians—Where to Intervene?. AIMS Public Health, 2016, 3(4): 891-906. doi: 10.3934/publichealth.2016.4.891
  • This study was conducted to investigate first the bacterial contamination by Enterobacteriaceae, fecal coliforms and the prevalence of Salmonella spp. and second to identify the main associated risk factors in Algerian farms, slaughterhouses and butcheries during a two-years period. Thus, a cross-sectional study was performed using a simple random sampling method to target 20 farms, 10 slaughterhouses and 5 butcheries. A structured questionnaire was further used to assess hygienic status of the farms and slaughterhouses. A total of 265 samples were collected from wall, floor, litter, food, water and animals' samples composed mainly of meat, neck skin and liver. Samples from walls and floors, from different sites were analyzed to evaluate the overall contamination and the hygiene of sites for Total viable bacteria, Enterobacteriaceae counts and Fecal coliforms counts. Furthermore, E.coli and salmonella spp. were identified in all samples. The overall contamination by sampling sites expressed as log10 CFU/g (mean ± SD) for Total Aerobic Microbial Count, Enterobacteriaceae count and fecal coliforms counts were around 4.71 ± 1.1, 4.73 ± 1.3 and 4.68 ± 1.2 respectively. The findings evidenced that the prevalence of E.coli and Salmonella spp. were 63.40% and 18.49% respectively. The highest rate of E.coli contamination was for poultry farms (70%), beef farms (64%) and butcheries (74.54%) followed by poultry meat slaughterhouses (60%) and sheep farms (48%) while beef slaughterhouses have the lowest rate of contamination (33.84%). For salmonella spp. the contamination was found to be mainly in poultry meat slaughterhouses (31.11%), butcheries (25.45%), followed by poultry farms (22%), beef farms (20%) and sheep farms (12%) while beef slaughterhouses have the lowest rate of contamination (4.61%). This study evidenced multifactor effects of microbial contamination in farms such as animal density, litter hygiene and scraping, manure storage, water and pest control, contact with other animals and decontamination process. Overall, this trial indicated a high rate of microbial contamination for which further studies are needed to determine all the potential risk factors in order to evaluate the corrective effects.



    In recent years improvements in the diagnosis and treatment of cancer have increased survival rates. While breast cancer, the most common type of cancer [1], has seen a significantly increasing trend in age-standardized incidence rates in Chinese women [2], the 5-year relative survival rate has increased from 73.1% to 82.0% from 2003 to 2015 [3].Owing to its untreatable nature and the common long-term exposure of patients to the illness, breast cancer tends to evoke significant psychological stress and disorders in survivors, of which depression is particularly common [4]. Two studies have shown that the prevalence of depression in cancer patients is several-fold that of the general population [5],[6], while a recent meta-analysis found that the global prevalence of depression in breast cancer patients was 32.2% [7].

    Depression in female breast cancer survivors, even if it has not been properly diagnosed (e.g. the survivor has been experiencing specific depressive symptoms), may interfere with their ability to effectively cope with the disease, reduce treatment adherence, decrease quality of life, and increase the risk of recurrence and mortality [8][12]. Therefore, attention should be paid to female breast cancer survivors with depressive symptoms, regardless of a clinical diagnosis.

    Women in different age groups may face various challenges when coping with breast cancer, resulting in different depressive symptoms by age [13],[14]. For example, younger female breast cancer patients may experience psychological stress from life-stage-related needs that occur at a younger age (e.g. employment, childcare) [15] and from perceptions regarding the fact that diagnosis and treatment may cause the partial loss of their female identity, infertility, premature menopause, and sexual dysfunction [16][19]. Middle-aged breast cancer female survivors normally face psychological stress from having to take care of their parents—usually in later adulthood—and from considering the potential impact of breast cancer on their children [13]. Older female adults who incur normal ageing and comorbidities (e.g. chronic diseases, age-related diseases, and geriatric problems) tend to be affected by cancer symptoms, leading to the further decline of their bodily functions [20]. Therefore, various age-related sources of stress may lead to inconsistency and heterogeneity when comparing the depressive symptoms of female breast cancer patients by age.

    A few studies have analysed depressive symptoms in female breast cancer patients of different ages, but their sample had a limited age range (e.g. under 35 [21] or over 60 [22]), or their findings only compared the total scores for depressive symptoms between two age groups (e.g. ≤45 vs. 55–70 [23], ≤50 vs. >50 [24], or 18–39 vs. >39 [14]). A prior study corroborated this and concluded that, although research on the topic provided valuable evidence, researchers tend to only use the total or cut-off score of the depressive symptom scale, which conceals the heterogeneity of depressive symptoms in female cancer patients [25]. For example, even if two participants have the same total score on the depressive symptom scale, their scores may be based on different symptoms (e.g., suicidal ideation versus fatigue).

    Thus, we considered latent class analysis (LCA) appropriate to explore the subtypes of breast cancer related depression while considering the potential correlation between different depressive symptoms. The LCA is a type of person-centred analytical method that focuses on distinguishing a heterogeneous population that has co-occurring symptoms to reveal potential symptomatic heterogeneity [26]. It may be helpful when trying to objectively identify heterogeneous depressive subtypes in clinical practice; doing so may provide clinicians with more information on how to develop effective prevention and intervention programmes for female breast cancer patients with different depressive subtypes.

    To the best of our knowledge, no prior study to date has detailed the characteristics of depressive symptoms by multiple age groups or used depressive subtypes to explore depressive symptom heterogeneity among female breast cancer patients. Thus, this study aimed to (1) describe the characteristics of depressive symptoms, (2) identify depressive subtypes, and (3) explore the relationship between depressive subtypes and age in Chinese female breast cancer patients.

    This study applied a cross-sectional design.

    Using the convenient sampling method, we recruited female breast cancer patients by contacting three tertiary comprehensive hospitals in Shandong Province, China from April 2013 through June 2019. The inclusion criteria were as follows: (1) at least 18 years of age; (2) has been diagnosed with breast cancer; and (3) able to understand and independently answer the questionnaire. The initial sample of 573 potential participants was reduced to 566 after excluding those who did not complete a Patient Health Questionnaire-9 (PHQ-9) or indicate age data.

    First, eligible patients were identified using medical records from cancer wards. Then, they were asked by the investigators, who were graduate students in the psychology department, whether or not they were willing to participate in the study. Participants who agreed gave their written informed consent before completing the questionnaire. After receiving approval from the cancer ward, we collected cancer stage data from patients' medical records. Other questionnaire items were answered through a self-report.

    All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and the Helsinki Declaration of 1975, as revised in 2000. The survey formed part of a research project on the mental health status of cancer patients, which was approved by the ethics committee of the School of Nursing at the Shandong University (Ethical Approval Number: 2016-R020).

    We measured depressive symptoms using the Chinese version of the 9-item PHQ-9, which the American Society of Clinical Oncology recommends for the evaluation of depressive symptoms in cancer patients [27]. The PHQ-9 assesses the symptoms of participants in the 2 weeks prior to the questionnaire application [28]. It is rated on a 4-point scale ranging from 0 (not at all) to 3 (nearly every day). The nine items assess nine depressive symptoms based on the diagnostic criteria for major depressive disorder described in the DSM-IV. Participants' total score in this measure represents depressive severity, categorised as minimal (0–4), mild (5–9), moderate (10–14), and moderately severe/severe depressive symptoms (≥15). The Cronbach's alpha in this study was 0.824.

    The covariate variables were age (≤35, 36–44, 45–59, and ≥60 years old), race (Han/other minorities), marital status (single/partnered), residential area (urban/rural), education level (high school or lower/above high school), occupation (retired/unemployed/employed), mode of payment of medical care (own expense/health insurance), religion (religious/not religious), and cancer stage (0/I/II/III/IV).

    All descriptive analyses were performed using SPSS (version 26.0). This study compared participants' age by sociodemographic, clinical (e.g. cancer stage), and depressive characteristics (i.e. participants' mean scores for each symptom and depressive severity category based on the total PHQ-9 score).

    We used a nonparametric analysis to examine the differences among the nine depressive symptoms and the depressive severity by age group. Univariate analyses, including ANOVA and Chi-square test, were used to determine differences in depressive subtypes by socio-demographic and clinical characteristics. Subsequently, analysis of variance and post-hoc tests were used to assess the differences in depressive subtypes by the nine depressive symptoms. We set the statistical significance for all analyses at P < 0.05.

    We used LCA to identify the differences in depressive subtypes among female breast cancer patients, namely, whether or not there were homogeneous clusters in heterogeneous groups. We recorded the nine symptoms measured in the PHQ-9 as binary variables (1 = have depressive symptoms vs. 0 = no depressive symptoms) and included them in the model.

    We tested goodness of fit for a series of models (i.e. 1–6 classes) using Mplus (version 7.4). By analysing a combination of statistical indicators—including Akaike information criterion, Bayesian information criterion, sample size adjustment BIC, Vuong-Lo-Mendell-Rubin likelihood ratio test, bootstrap likelihood ratio test, and clinical interpretability [29]—we deemed that the four-class model showed optimal fit to the data.

    The participants had an average age of 45.6 (SD = 11.5) years; most were married (93.3%); unemployed (42.4%); and had medical insurance (95.6%). Approximately half lived in urban areas (57.6%). A majority had stage II cancer (41.3%), followed by stage IV (20.8%), III (20.7%), I (13.8%), then 0 (3.4%). The four age groups differed in many other sociodemographic and clinical characteristics, which are described in detail in Table 1.

    Table 1.  Sociodemographic and clinical characteristics of the total sample and different age groups (N = 566).
    Variables Total (n = 566) ≤35 years old (n = 95) 36–44 years old (n = 223) 45–59 years old (n = 151) ≥60 years old (n = 97) P
    Age, x(SD)/n(%) 45.6 (11.5) 95 (16.8%) 266 (47.0%) 108 (19.1%) 97 (17.1%)
    Marriage 0.022
     Single 38 (6.7%) 10 (10.5%) 15 (6.7%) 3 (2.0%) 10 (10.3%)
     With partner 528 (93.3%) 85 (89.5%) 208 (93.3%) 148 (98.0%) 87 (89.7%)
    Residence 0.112
     Urban area 326 (57.6%) 55 (57.9%) 139 (61.7%) 75 (49.7%) 57 (58.8%)
     Rural area 240 (42.4%) 40 (42.1%) 84 (38.3%) 76 (50.3%) 40 (41.2%)
    Education <0.001
     ≤High school 321 (56.7%) 31 (32.6%) 113 (53.0%) 101 (66.9%) 76 (78.4%)
     >High school 245 (43.3%) 64 (67.4%) 110 (47.0%) 50 (33.1%) 21 (21.6%)
    Occupation <0.001
     Retirement 59 (10.4%) 1 (1.3%) 0 (0.0%) 13 (8.6%) 45 (47.9%)
     Unemployed 235 (41.5%) 28 (35.4%) 88 (39.5%) 75 (49.7%) 44 (46.8%)
     Employed 207 (36.6%) 50 (63.3%) 112 (50.2%) 40 (26.5%) 5 (5.3%)
    Payment 0.390
     Own expense 15 (2.7%) 2 (2.1%) 9 (4.0%) 3 (2.0%) 1 (1.0%)
     With health care 541 (95.6%) 93 (97.9%) 210 (94.2%) 144 (95.4%) 94 (99.0%)
     Religion 77 (13.6%) 14 (14.7%) 36 (16.1%) 16 (10.6%) 11 (11.3%) 0.405
     Race (Han) 557 (98.4%) 93 (97.9%) 265 (99.6%) 103 (95.4%) 96 (99.0%) 0.167
    Cancer stage <0.001
     Stage 0 19 (3.4%) 2 (2.1%) 8 (3.6%) 1 (0.7%) 8 (8.2%)
     Stage Ⅰ 78 (13.8%) 13 (13.7%) 35 (15.7%) 9 (6.0%) 21 (21.7%)
     Stage Ⅱ 234 (41.3%) 41 (43.2%) 111 (49.8%) 38 (25.2%) 44 (45.4%)
     Stage Ⅲ 117 (20.7%) 23 (24.2%) 45 (20.2%) 36 (23.8%) 13 (13.4%)
     Stage Ⅳ 118 (20.8%) 16 (16.8%) 24 (10.8%) 67 (44.4%) 11 (11.3%)

    Note: Indicates that the numbers/percentages may not add up to the total, due to missing data.

     | Show Table
    DownLoad: CSV

    Of all participants, 22.8% had moderate to severe depressive symptoms (i.e. PHQ-9 scores ≥10). The percentage for moderately severe/severe depressive symptoms (i.e. PHQ-9 scores ≥15) was 2.1% for participants aged ≥35, 9.4% for those aged 36–44, 5.3% for those aged 45–59, and 9.3% for those aged ≥60 (Figure 1).

    Figure 1.  The severity of depressive symptoms varies among breast cancer patients of different ages according to the PHQ-9 cutoff scores. Minimal depression (PHQ-9 score 0–4), mild (5–9), moderate (10–14) and moderately severe/severe (≥15). PHQ-9, Patient Health Questionnaire-9.
    Table 2.  Characteristics of depression in different age groups (N = 566).
    M (QL;QU)/n(%) Total (n = 566) ≤35 years 36–44 years 45–59 years ≥60 years P
    Anhedonia 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 0.095
    Sadness 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;2.0) 1.0 (0.0;1.0) 0.001
    Sleep disturbances 1.0 (1.0;1.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;2.0) 1.0 (0.0;2.0) 0.328
    Fatigue 1.0 (1.0;1.0) 1.0 (1.0;1.0) 1.0 (1.0;2.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 0.286
    Appetite disturbances 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 0.178
    Guilt or worthlessness 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 0.0 (0.0;1.0) 0.064
    Poor concentration 0.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.003
    Psychomotor agitation or retardation 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.003
    Suicidal ideation 0.0 (0.0;0.25) 0.0 (0.0;0.0) 0.0 (0.0;1.0) 0.0 (0.0;0.0) 0.0 (0.0;0.5) <0.001
    PHQ total 7.0 (4.0;9.0) 7.0 (4.0;9.0) 7.0 (4.0;10.0) 7.0 (4.0;9.0) 5.0 (2.0;9.0) 0.034
    PHQ average 0.8 (0.4;1.0) 0.8 (0.4;1.0) 0.8 (0.4;1.1) 0.8 (0.4;1.0) 0.6 (0.2;1.0) 0.034
    PHQ-9 (≥5) 384 (67.8%) 68 (71.6%) 153 (68.6%) 112 (74.2%) 51 (52.6%) 0.003
    PHQ (≥10) 129 (22.8%) 17 (17.9%) 63 (28.3%) 29 (19.2%) 20 (20.6%) 0.092
    PHQ degree 0.001
    Minimal 182 (32.2%) 27 (28.4%) 70 (31.4%) 39 (25.8%) 46 (47.4%)
    Mild 255 (45.1%) 51 (53.7%) 90 (40.4%) 83 (55.0%) 31 (32.0%)
    Moderate 89 (15.7%) 15 (15.8%) 42 (18.8%) 21 (13.9%) 11 (11.3%)
    Moderately severe/severe 40 (7.1%) 2 (2.1%) 21 (9.4%) 8 (5.3%) 9 (9.3%)

     | Show Table
    DownLoad: CSV

    There were significant differences were found for the following symptoms: sadness, poor concentration, psychomotor agitation/retardation, and suicidal ideation. In addition, participants of different age groups differed in the severity of depressive symptoms (see Table 2).

    Considering goodness of fit and clinical interpretability, we determined four depressive subtypes: Class 1, 2, 3, and 4; these accounted for 27%, 16%, 30%, and 27% of all participants, respectively (Table 3). Figure 2 and Table 4 show the probability and descriptive data of participants' scores for the nine depressive symptoms in the PHQ-9 by depressive subtype. Class 4 represented the highest probability (i.e. probability of experiencing a symptom) and score for all nine symptoms; thus, it was named the severe symptoms group; Class 3 represented a relatively high probability and score for all symptoms—except for psychomotor agitation/retardation and suicidal ideation, which showed lower levels—and thus, it was named the relatively severe symptoms group; Class 2 represented a medium probability and score for all nine symptoms—except for psychomotor agitation/retardation and suicidal ideation, which showed higher levels—and thus, it was named the moderate symptoms group; and Class 1 represented the lowest probability and score for all nine symptoms—except for psychomotor agitation/retardation, which showed higher levels—and therefore, it was named the mild symptoms group. There were significant differences between these four classes and the nine depressive symptoms assessed using the PHQ-9 (Table 4).

    Table 3.  Model fit indices derived from latent class analysis on models with 1–6 classes.
    Model K Log (L) AIC BIC aBIC entropy LMR BLRT Class probability
    1 9 −3096.85 6211.70 6250.75 6222.18
    2 19 −2540.85 5119.70 5202.14 5141.82 0.86 0.00 0.00 368/198(0.65/0.35)
    3 29 −2441.40 4940.80 5066.62 4974.56 0.82 0.00 0.00 152/259/155(0.27/0.46/0.27)
    4 39 −2402.15 4882.30 5051.51 4927.70 0.78 0.02 0.00 155/91/170/150(0.27/0.16/0.30/0.27)
    5 49 −2368.00 4833.98 5046.57 4891.02 0.80 0.002 0.00 49/141/148/130/98(0.09/0.25/0.26/0.23/0.17)
    6 59 −2354.61 4827.22 5083.20 4895.90 0.81 0.46 0.09 107/38/146/52/77/146(0.19/0.07/0.26/0.09/0.14/0.26)

    Note: Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, sample size adjusted BIC; LMR, Vuong-Lo-Mendell-Rubin likelihood ratio test. BLRT, bootstrapped likelihood ratio test; Bold values indicates that a four-class model was determined as optimal one.

     | Show Table
    DownLoad: CSV
    Figure 2.  The five-class model and probability of nine depressive symptoms within each class (n = 566). Class 4: severe symptoms group; Class 3: relatively severe group (with lower concentration, psychomotor agitation/retardation and suicidal ideation); Class 2: moderate symptoms group (with higher poor concentration, psychomotor agitation/retardation and suicidal ideation); Class 1: mild symptoms group.
    Table 4.  The descriptive scores of nine depressive symptoms, M(SD).
    Items Class 4 (n = 150) Class 3 (n = 170) Class 2 (n = 91) Class 1 (n = 155) F Posthoc comparisons
    Anhedonia 1.40 (0.63) 1.19 (0.57) 0.86 (0.68) 0.26 (0.52) 109.39* c4>c3>c2>c1
    Sadness 1.41 (0.65) 1.42 (1.62) 0.75 (0.59) 0.19 (0.43) 53.55* c3≈c4>c2>c1
    Sleep disturbances 1.49 (0.70) 1.31 (1.03) 1.04 (0.87) 0.43 (0.62) 49.75* c4>c3>c2>c1
    Fatigue 1.59 (0.71) 1.20 (0.53) 1.13 (0.70) 0.49 (0.70) 73.87* c4>c3≈c2>c1
    Appetite disturbances 1.37 (0.68) 1.06 (0.81) 0.88 (0.73) 0.21 (0.43) 81.11* c4>c3>c2>c1
    Guilt or worthlessness 1.50 (0.67) 0.76 (0.67) 0.34 (0.67) 0.07 (0.26) 163.33* c4>c3>c2>c1
    Poor concentration 1.35 (0.64) 0.34 (0.61) 1.01 (0.71) 0.07 (0.26) 159.56* c4>c2>c3>c1
    Psychomotor agitation or retardation 1.30 (0.69) 0.00 (0.00) 0.81 (0.65) 0.04 (0.22) 288.45* c4>c2>c1≈c3
    Suicidal ideation 0.89 (0.73) 0.07 (0.30) 0.31 (0.61) 0.03 (0.16) 102.77* c4>c2>c3≈c1
    PHQ-9 total score 12.29 (3.85) 7.36 (2.81) 7.13 (2.29) 1.79 (1.47) 362.34* c4>c3≈c2>c1

    Note: *P < 0.01. Class 4: severe symptoms group; Class 3: relatively severe group (with lower concentration, psychomotor agitation/retardation and suicidal ideation); Class 2: moderate symptoms group (with higher poor concentration, psychomotor agitation/retardation and suicidal ideation); Class 1: mild symptoms group.

     | Show Table
    DownLoad: CSV

    Participants' depressive subtype distribution by age is presented in Figure 3. In the ≤35 age group, all four classes showed similar ratios (Class 1–4 were 28.4%, 24.2%, 24.2%, and 23.2%, respectively). In the 36–44 age group, 33.2% of the participants were categorized in Class 3, which was the highest proportion. In the 45–59 age groups, Class 4 showed the highest ratios (i.e. 50.3%). In the ≥60 age group, more than 50 percent of participants were categorized in the milder depressive subtypes, with Class 1 and 2 accounting for 12.4% percent and 42.3%, respectively.

    Figure 3.  The subtypes distribution in the four stage of age. Class 4: severe symptoms group; Class 3: relatively severe group (with lower concentration, psychomotor agitation/retardation and suicidal ideation); Class 2: moderate symptoms group (with higher poor concentration, psychomotor agitation/retardation and suicidal ideation); Class 1: mild symptoms group.

    This study aimed to analyse the depressive characteristics of breast cancer patients in different adult age groups. We were able to identify four latent depressive subtypes, and their distribution differed by age group. To the best of our knowledge, this is the first study to analyse and use potential depressive subtypes to explore the heterogeneity of depressive symptoms across various adult age groups in female breast cancer patients.

    The incidence of moderate to severe depressive symptoms in our sample was 22.8%; this number was similar to that reported in a previous study, which showed that such incidence in female breast cancer patients was 2–3 times higher than that in the general population [14]. Additionally, our analyses highlighted differences in single-symptom expression by age—specifically, such differences were observed for sadness, poor concentration, psychomotor agitation/retardation, and suicidal ideation. This result is supported by that of a previous study, which reported on individuals' symptomatology heterogeneity [25]. Another study showed that the age-related difference in depressive symptoms among female cancer patients is due to the impact of cancer and its treatment on specific areas of women's life (e.g. work, sex, and entertainment) [15]. Therefore, stakeholders involved in diminishing the risk of depression in breast cancer patients should focus on depressive symptom differences by age. Moreover, in-depth analyses on the causes of such age-related differences must be conducted and interventions aimed at groups characterised by specific depressive subtypes must be applied to reduce the risk of depression.

    We were able to identify four depressive subtypes: severe (Class 4), relatively severe (Class 3), moderate (Class 2), and mild depressive symptoms (Class 1). Differences in the severe, moderate, and mild groups were mainly because of depressive symptom severity, whereas the relatively severe group differed from the other three primarily owing to the presence of severe physio-somatic symptoms alongside lower psychomotor agitation/retardation and suicidal ideation. This suggests that, upon the application of LCA, both symptom characteristics and severity were important variables in determining depressive subtypes. Our results are somewhat consistent with those of previous studies showing symptomatic severity to be a crucial discriminating aspect of depressive subtypes [30],[31]. The relatively severe group with lower psychomotor agitation/retardation and suicidal ideation was discovered in this research, and it was also the most common subtype in our sample. A study that promoted a factorial analysis on the items of the PHQ-9 pointed out that the items on poor concentration and psychomotor agitation/retardation may neither belong to an affective-cognitive nor a somatic component [32]. This item contains two contradictory characteristics of psychological and cannot be clearly classified as one of the factors, which also confirms the heterogeneity of depressive symptoms. Previous studies have suggested that suicidal ideation in breast cancer patients is particularly linked to genetic characteristics (brain-derived neurotrophic factor methylation, BDNF met allele) [33]. Along with another study [34], these findings and our results suggest that depressive symptoms do not have a single structure but are comprised of different subtypes that seem to have different pathophysiological basis.

    The latent depressive subtypes we observed showed different distributions by age group. Specifically, patients aged 45–59 were more likely to have severe depressive subtype; those aged 36–44 were more likely to have relatively severe depressive subtype; those over 60 were more likely to have moderate symptoms group; and those under 35 were more likely to have mild depressive subtype. After a literature review, it is evident why female breast cancer patients aged 36 and over showed a greater tendency to experience relatively severe depressive symptoms. A study showed that perimenopausal women over 40 may suffer from poorer sleep quality and greater mood problems owing to fluctuating hormone levels, both of which are also depressive symptoms [35]. Another study revealed that breast cancer treatment may lead to menopause in women, while the stress caused by cancer and its treatment may influence depressive symptom severity [36]. When combined with the social responsibilities that female breast cancer survivors tend to have, such as caring for parents and children, the situation can lead them to experience the most severe depressive symptoms [13]. Therefore, stakeholders in the well-being of female breast cancer patients should place greater emphasis on their psychological status based on age. In clinical practice, the different depressive subtypes—and their relationships with specific age groups—described in this study may help stakeholders (e.g. physicians, psychologists, nurses) more accurately identify groups with similar symptoms across the cancer population. Greater accuracy could thereby facilitate the development and application of appropriate group interventions aimed at dealing with similar depressive symptoms.

    Despite the contributions highlighted above, this study had several limitations. First, the reliability and validity of individual symptom measurement tools—including those of the PHQ-9, which was utilised in this study—remain imprecise. Nonetheless, one advantage of the PHQ-9 is that all entries have the same response categories, which theoretically does not affect the comparability between different symptoms [33]. Future research should investigate scales for specific symptoms (e.g. suicidal ideation), such as the Suicide Severity Scale to assess suicidal behaviour [37]. Second, although the LCA assigns individuals to subtypes according to probability and evaluates the goodness of fit of different models based on statistical criteria, one study has shown that subjectivity in this procedure still exists [29]; therefore, we cannot exclude type I errors (i.e. false positives). Third, we applied convenient sampling and utilised a cross-sectional design, which are methodologies that limit the generalizability of our findings; thus, future research should consider larger populations, stratified random sampling, and a longitudinal design when analysing depressive symptoms in female breast cancer patients.

    This study described the characteristics of depressive symptoms in Chinese female breast cancer patients across different ages and identified four depressive subtypes. Our results support the heterogeneity of depressive symptoms; thus, we provide data on how to identify individual symptoms in different age groups and patients with similar symptoms characteristics. We hope that this study helps in identifying the potential mechanisms behind these relationships and develop targeted interventions for patients with a specific depressive subtype.



    [1] MADR (2019) Ministère de l'agriculture et du développement rural: Statistiques agricoles et production animale.
    [2] Sraïri MT (2011) Le développement de l'élevage au Maroc: succès relatifs et dépendance alimentaire. Le Courrier de l'environnement de l'INRA 60: 91-101.
    [3] Heredia N, García S (2018) Animals as sources of food-borne pathogens: A review. Anim Nutr 4: 250-255. doi: 10.1016/j.aninu.2018.04.006
    [4] Hemalata V, Virupakshaiah D (2016) Isolation and identification of food borne pathogens from spoiled food samples. Int J Curr Microbiol Appl Sci 5: 1017-1025. doi: 10.20546/ijcmas.2016.506.108
    [5] Aklilu A, Kahase D, Dessalegn M, et al. (2015) Prevalence of intestinal parasites, salmonella and shigella among apparently health food handlers of Addis Ababa University student's cafeteria, Addis Ababa, Ethiopia. BMC Res Notes 8: 1-6. doi: 10.1186/s13104-014-0967-x
    [6] Hoffmann S, Devleesschauwer B, Aspinall W, et al. (2017) Attribution of global foodborne disease to specific foods: Findings from a World Health Organization structured expert elicitation. PLoS One 12: e0183641. doi: 10.1371/journal.pone.0183641
    [7] İnanç A, Mustafa AS (2018) Antibiotic Resistance of Escherichia coli O157: H7 Isolated from Chicken Meats. KSÜ Doğa Bilimleri Dergisi 21: 7-12.
    [8] Zhao X, Lin CW, Wang J, et al. (2014) Advances in rapid detection methods for foodborne pathogens. J Microbiol Biotechnol 24: 297-312. doi: 10.4014/jmb.1310.10013
    [9] Elmonir W, Abo-Remela E, Sobeih A (2018) Public health risks of Escherichia coli and Staphylococcus aureus in raw bovine milk sold in informal markets in Egypt. J Infect Dev Countries 12: 533-541. doi: 10.3855/jidc.9509
    [10] Allocati N, Masulli M, Alexeyev MF, et al. (2013) Escherichia coli in Europe: An overview. Int J Environ Res Public Health 10: 6235-6254. doi: 10.3390/ijerph10126235
    [11] Amézquita-López BA, Soto-Beltrán M, Lee BG, et al. (2018) Isolation, genotyping and antimicrobial resistance of Shiga toxin-producing Escherichia coli. J of Microbiol, Immunol Infect 51: 425-434. doi: 10.1016/j.jmii.2017.07.004
    [12] Djeffal S, Mamache B, Elgroud R, et al. (2018) Prevalence and risk factors for Salmonella spp. contamination in broiler chicken farms and slaughterhouses in the northeast of Algeria. Vet World 11: 1102.
    [13] Dib AL, Chahed A, Lakhdara N, et al. (2019) Preliminary investigation of the antimicrobial and mechanisms of resistance of Enterobacteria isolated from minced meat in the Northeast of Algeria: The case of butchers from Constantine. Integr Food Nutr Metab 6: 1-7.
    [14] OIE (2006) International Office of Epizootic: Guide to good farming practices for animal production food safety. Revue scientifique et technique (International Office of Epizootics) 25: 823-836.
    [15] Sobur MA, Sabuj AAM, Sarker R, et al. (2019) Antibiotic-resistant Escherichia coli and Salmonella spp. associated with dairy cattle and farm environment having public health significance. Vet World 12: 984.
    [16] Ibrahim RA, Cryer TL, Lafi SQ, et al. (2019) Identification of Escherichia coli from broiler chickens in Jordan, their antimicrobial resistance, gene characterization and the associated risk factors. BMC Vet Res 15: 1-16. doi: 10.1186/s12917-019-1901-1
    [17] Barlow RS, Mcmillan KE, Duffy LL, et al. (2015) Prevalence and antimicrobial resistance of Salmonella and Escherichia coli from Australian cattle populations at slaughter. J of food Prot 78: 912-920. doi: 10.4315/0362-028X.JFP-14-476
    [18] Rodriguez-Rivera LD, Cummings KJ, Loneragan GH, et al. (2016) Salmonella prevalence and antimicrobial susceptibility among dairy farm environmental samples collected in Texas. Foodborne Pathog Dis 13: 205-211. doi: 10.1089/fpd.2015.2037
    [19] Pangloli P, Dje Y, Oliver S, et al. (2003) Evaluation of methods for recovery of Salmonella from dairy cattle, poultry, and swine farms. J Food Prot 66: 1987-1995. doi: 10.4315/0362-028X-66.11.1987
    [20] Jaja IF, Bhembe NL, Green E, et al. (2019) Molecular characterisation of antibiotic-resistant Salmonella enterica isolates recovered from meat in South Africa. Acta Tropica 190: 129-136. doi: 10.1016/j.actatropica.2018.11.003
    [21] Hajian S, Rahimi E, Mommtaz H (2011) A 3-year study of Escherichia coli O157: H7 in cattle, camel, sheep, goat, chicken and beef minced meat, 2011 International Conference on Food Engineering and Biotechnology (IPCBEE), 163-165.
    [22] Nouichi S, Hamdi TM (2009) Superficial bacterial contamination of ovine and bovine carcasses at El-Harrach slaughterhouse (Algeria). Europ J Sci Res 38: 474-485.
    [23] Chong ES, Bidin Z, Bakar N, et al. (2017) Bacterial contamination on beef carcass at selected abattoirs located in Selangor, Malaysia. Malaysian Appl Biol 46: 37-43.
    [24] Duffy L, Small A, Fegan N (2010) Concentration and prevalence of Escherichia coli O157 and Salmonella serotypes in sheep during slaughter at two Australian abattoirs. Aust Vetj 88: 399-404. doi: 10.1111/j.1751-0813.2010.00623.x
    [25] Lee GY, Jang HI, Hwang IG, et al. (2009) Prevalence and classification of pathogenic Escherichia coli isolated from fresh beef, poultry, and pork in Korea. Int J Food Microbiol 134: 196-200. doi: 10.1016/j.ijfoodmicro.2009.06.013
    [26] Thorsteinsdottir T, Haraldsson G, Fridriksdottir V, et al. (2010) Prevalence and genetic relatedness of antimicrobial‐resistant escherichia coli isolated from animals, foods and humans in Iceland. Zoonoses Public Health 57: 189-196. doi: 10.1111/j.1863-2378.2009.01256.x
    [27] Ray B (2004) Microbial stress response in the food environment."Fund Food Microbiol". CRC press LLC., New York.
    [28] Phillips D, Sumner J, Alexander JF, et al. (2001) Microbiological quality of Australian beef. J Food Prot 64: 692-696. doi: 10.4315/0362-028X-64.5.692
    [29] Collobert JF, Dorey F, Dieuleveux V, et al. (2002) Qualité bactériologique de surface de carcasses de bovins. Sci Des Aliments 22: 327-334. doi: 10.3166/sda.22.327-334
    [30] Berends B, Van Knapen F, Snijders J, et al. (1997) Identification and quantification of risk factors regarding Salmonella spp. on pork carcasses. Int J Food Microbiol 36: 199-206.
    [31] Jarallah EM, Sahib SI, Yassen K (2014) Isolation and identification of some pathogenic bacterial species contaminated from meats in butchers shops and kebab restaurants in AL-Kut city. Euphrates J Agri Sci 4: 30-37.
    [32] Bantawa K, Rai K, Limbu DS, et al. (2018) Food-borne bacterial pathogens in marketed raw meat of Dharan, eastern Nepal. BMC Res Notes 11: 1-5. doi: 10.1186/s13104-018-3722-x
    [33] Salifou C, Boko K, Attakpa Y, et al. (2013) Evaluation de la qualité bactériologique de viande fraîche de bovins abattus aux abattoirs de Cotonou-Porto-Novo au cours de la chaîne de distribution. J Ani & Plant Sci 17: 2567-2579.
    [34] Adeyanju GT, Ishola O (2014) Salmonella and Escherichia coli contamination of poultry meat from a processing plant and retail markets in Ibadan, Oyo State, Nigeria. Springerplus 3: 1-9. doi: 10.1186/2193-1801-3-139
    [35] Makwana P, Nayak J, Brahmbhatt M, et al. (2015) Detection of Salmonella spp. from chevon, mutton and its environment in retail meat shops in Anand city (Gujarat), India. Vet World 8: 388.
    [36] Mansour AMA, Ishlak AMM, Haj-Saeed BA (2019) Evaluation of bacterial contamination on local and imported mutton in meat markets in Benghazi-Libya. Int J Agri Sci 4: 77-83.
    [37] Andrés S, Jiménez A, Sánchez J, et al. (2007) Evaluation of some etiological factors predisposing to diarrhoea in lambs in "La Serena"(Southwest Spain). Small Ruminant Res 70: 272-275. doi: 10.1016/j.smallrumres.2006.04.004
    [38] Kudva IT, Blanch K, Hovde CJ (1998) Analysis of Escherichia coli O157: H7 survival in ovine or bovine manure and manure slurry. Appl Environ Microbiol 64: 3166-3174.
    [39] Daniel D (2012) Le parasitisme printanier des agneaux à l'herbe. Réussir Pâ tre.
    [40] Elder RO, Keen JE, Siragusa GR, et al. (2000) Correlation of enterohemorrhagic Escherichia coli O157 prevalence in feces, hides, and carcasses of beef cattle during processing. Proc Natio Acad Sci 97: 2999-3003. doi: 10.1073/pnas.97.7.2999
    [41] Shere J, Bartlett K, Kaspar C (1998) Longitudinal study of Escherichia coli O157: H7 dissemination on four dairy farms in Wisconsin. Appl Environ Microbiol 64: 1390-1399.
    [42] Tablante NL, Myint MS, Johnson YJ, et al. (2002) A survey of biosecurity practices as risk factors affecting broiler performance on the Delmarva Peninsula. Avian Dis 46: 730-734. doi: 10.1637/0005-2086(2002)046[0730:ASOBPA]2.0.CO;2
    [43] Wilkins W, Rajić A, Waldner C, et al. (2010) Distribution of Salmonella serovars in breeding, nursery, and grow-to-finish pigs, and risk factors for shedding in ten farrow-to-finish swine farms in Alberta and Saskatchewan. Can J Vet Res 74: 81-90.
    [44] Gonzalez M, Lainez M, Vega S, et al. (2015) Sources for salmonella contamination during pig production in eastern Spain. J Anim Vet Sci 2: 37-42.
    [45] Bensid A (2018) Hygiène et inspection des viandes rouges. Algérie : Djelfainfo.
    [46] Eisel W, Linton R, Muriana P (1997) A survey of microbial levels for incoming raw beef, environmental sources, and ground beef in a red meat processing plant. Food Microbiol 14: 273-282. doi: 10.1006/fmic.1996.0094
    [47] Childers A, Keahey E, Kotula A (1977) Reduction of Salmonella and fecal contamination of pork during swine slaughter. J Am Vet Med Asso 171: 1161-1164.
    [48] Haileselassie M, Taddele H, Adhana K, et al. (2013) Food safety knowledge and practices of abattoir and butchery shops and the microbial profile of meat in Mekelle City, Ethiopia. Asian Pac J Trop Biom 3: 407-412. doi: 10.1016/S2221-1691(13)60085-4
    [49] Adebowale O, Alonge D, Agbede S, et al. (2010) Bacteriological assessment of quality of water used at the Bodija municipal abattoir, Ibadan, Nigeria. Sahel J Vet Sci 9: 63-67.
  • This article has been cited by:

    1. Yening Zhang, Yi He, Ying Pang, Zhongge Su, Yu Wang, Yuhe Zhou, Yongkui Lu, Yu Jiang, Xinkun Han, Lihua Song, Liping Wang, Zimeng Li, Xiaojun Lv, Yan Wang, Juntao Yao, Xiaohong Liu, Xiaoyi Zhou, Shuangzhi He, Lili Song, Jinjiang Li, Bingmei Wang, Lili Tang, Suicidal ideation in Chinese patients with advanced breast cancer: a multi-center mediation model study, 2024, 12, 2050-7283, 10.1186/s40359-024-01607-x
    2. Francisca Carvajal, José Manuel Lerma-Cabrera, Pía Herrera-Ponce de León, Sandra López-Arana, Depression symptoms are associated with demographic characteristics, nutritional status, and social support among young adults in Chile: a latent class analysis, 2024, 24, 1471-2458, 10.1186/s12889-024-20173-w
    3. Heeseung Park, Kyungwon Kim, Eunsoo Moon, Hyunju Lim, Hwagyu Suh, Taewoo Kang, Psychometric Properties of the Patient Health Questionnaire-9 in Patients With Breast Cancer, 2024, 21, 1738-3684, 521, 10.30773/pi.2023.0285
    4. Mareike Ernst, Tamara Schwinn, Judith Hirschmiller, Seonaid Cleare, Kathryn A. Robb, Elmar Brähler, Rüdiger Zwerenz, Jörg Wiltink, Rory C. O'Connor, Manfred E. Beutel, To what extent are psychological variables considered in the study of risk and protective factors for suicidal thoughts and behaviours in individuals with cancer? A systematic review of 70 years of research, 2024, 109, 02727358, 102413, 10.1016/j.cpr.2024.102413
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4763) PDF downloads(351) Cited by(4)

Figures and Tables

Figures(1)  /  Tables(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog