| Literature DB >> 31906964 |
Patrick Brzoska1, Tuğba Aksakal2,3, Yüce Yilmaz-Aslan3.
Abstract
BACKGROUND: Studies from European and non-European countries have shown that migrants utilize cervical cancer screening less often than non-migrants. Findings from Germany are inconsistent. This can be explained by several limitations of existing investigations, comprising residual confounding and data which is restricted to only some regions of the country. Using data from a large-scale and nationwide population survey and applying the Andersen Model of Health Services Use as the theoretical framework, the aim of the present study was to examine the role that different predisposing, enabling and need factors have for the participation of migrant and non-migrant women in cervical cancer screening in Germany.Entities:
Keywords: Cervical cancer; Germany; Migrants; Screening; Survey; Utilization
Mesh:
Year: 2020 PMID: 31906964 PMCID: PMC6945536 DOI: 10.1186/s12889-019-8006-4
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Description of the study sample by migrant status (German Health Update 2014/2015, women age 20 years and above, n = 12,064)
| Population group | ||||
|---|---|---|---|---|
| Non-migrants | Migrants from EU countries | Migrants from non-EU countries | ||
| N | 11,081 | 490 | 493 | |
| Age | < 0.01 | |||
| 20–39 years | 3346 (30.2%) | 157 (32.0%) | 225 (45.6%) | |
| 40–59 years | 4501 (40.6%) | 198 (40.4%) | 180 (36.5%) | |
| 60 + years | 3234 (29.2%) | 135 (27.6%) | 88 (17.8%) | |
| Partnership status | < 0.01 | |||
| Partner | 6239 (56.3%) | 307 (62.7%) | 306 (62.1%) | |
| No partner | 4842 (43.7%) | 183 (37.3%) | 187 (37.9%) | |
| Socioeconomic status | < 0.01 | |||
| Low | 1579 (14.2%) | 74 (15.1%) | 105 (21.3%) | |
| Moderate | 6622 (59.8%) | 251 (51.2%) | 252 (51.1%) | |
| High | 2880 (26.0%) | 165 (33.7%) | 136 (27.6%) | |
| Social support | < 0.01 | |||
| Low | 1709 (15.4%) | 101 (20.6%) | 118 (23.9%) | |
| Moderate | 5858 (52.9%) | 256 (52.2%) | 269 (54.6%) | |
| High | 3514 (31.7%) | 133 (27.1%) | 106 (21.5%) | |
| Region | < 0.01 | |||
| Western Germany | 8118 (73.3%) | 436 (89.0%) | 439 (89.0%) | |
| Eastern Germany | 2963 (26.7%) | 54 (11.0%) | 54 (11.0%) | |
| Type of residential area | < 0.01 | |||
| Cities | 3340 (30.1%) | 205 (41.8%) | 225 (45.6%) | |
| Medium-sized towns | 3872 (34.9%) | 196 (40.0%) | 201 (40.8%) | |
| Small towns | 1768 (16.0%) | 54 (11.0%) | 39 (7.9%) | |
| Rural | 2101 (19.0%) | 35 (7.1%) | 28 (5.7%) | |
| Self-rated health status [1 “very good” to 5 “very poor”], mean (SD) | 2.2 (0.8) | 2.1 (0.8) | 2.2 (0.8) | 0.04 |
| Presence of chronic diseases | 0.07 | |||
| No | 5881 (53.1%) | 272 (55.5%) | 285 (57.8%) | |
| Yes | 5200 (46.9%) | 218 (44.5%) | 208 (42.2%) | |
| Utilization of cervical cancer screening | < 0.01 | |||
| Yes (at least once in life time) | 6342 (57.2%) | 258 (52.7%) | 247 (50.1%) | |
| No | 4739 (42.8%) | 232 (47.3%) | 246 (49.9%) | |
* p-value from chi-square test for categorical variables and analysis of variance for continuous variables
Results of the multivariable logistic regression model with utilization of cervical cancer screening as the dependent variable. Odds ratios (OR) and 95% confidence intervals (95%-CI) (German Health Update 2014/2015, women age 20 years and above, n = 12,064; Main effects model. No interaction effects included)
| Independent variable | Odds Ratio | 95%-CI | |
|---|---|---|---|
| Population group (Ref.: Non-migrants) | |||
| Migrants from EU countries | 0.80 | 0.66; 0.97 | 0.02 |
| Migrants from non-EU countries | 0.67 | 0.55; 0.81 | < 0.01 |
| Age | 0.86 | 0.85; 0.87 | < 0.01 |
| Partnership status (Ref.: No partner) | |||
| Partner | 1.59 | 1.47;1.73 | < 0.01 |
| Socioeconomic status (Ref.: Low) | |||
| Moderate | 1.49 | 1.33; 1.66 | < 0.01 |
| High | 1.83 | 1.61;2.09 | < 0.01 |
| Social support (Ref.: Low) | |||
| Moderate | 1.28 | 1.15; 1.42 | < 0.01 |
| High | 1.46 | 1.30; 1.65 | < 0.01 |
| Region (Ref.: Western Germany) | |||
| Easters Germany | 1.18 | 1.07; 1.30 | < 0.01 |
| Type of residential area (Ref.: Cities) | |||
| Medium-sized towns | 0.95 | 0.86; 1.04 | 0.25 |
| Small towns | 1.04 | 0.92; 1.17 | 0.56 |
| Rural | 0.50 | 0.85; 1.08 | 0.50 |
| Self-rated health status [1 “very good” to 5 “very poor”] | 0.85 | 0.80; 0.90 | < 0.01 |
| Presence of chronic diseases (Ref.: No) | |||
| Yes | 1.23 | 1.13; 1.35 | < 0.01 |
Fig. 1Probability of the utilization of cervical cancer screening by population group and age. Results of the multivariable logistic regression model with utilization of cervical cancer screening as the dependent variable and interaction effects between age and population group. (German Health Update 2014/2015, women age 20 years and above, n = 12,064; results from logistic regression model with interaction effects between age and migrant status)
Fig. 2Probability of the utilization of cervical cancer screening by population group and self-rated health status. Results of the multivariable logistic regression model with utilization of cervical cancer screening as the dependent variable and interaction effects between self-rated health and population group. (German Health Update 2014/2015, women age 20 years and above, n = 12,064; results from logistic regression model with interaction effects between self-rated health and migrant status)