Perinatal distress and depression in culturally and linguistically diverse (CALD) australian women: The role of psychosocial and obstetric factors

International Journal of Environmental Research and Public Health, Volume 16, No. 16, Year 2019

Interprétation

Perinatal distress and depression can have significant impacts on both the mother and baby. The present study investigated psychosocial and obstetric factors associated with perinatal distress and depressive symptoms among culturally and linguistically diverse (CALD) Australian women in Sydney, New South Wales. The study used retrospectively linked maternal and child health data from two Local Health Districts in Australia (N = 25,407). Perinatal distress was measured using the Edinburgh Postnatal Depression Scale (EPDS, scores of 10–12) and depressive symptoms, with EPDS scores of 13 or more. Multivariate multinomial logistic regression models were used to investigate the association between psychosocial and obstetric factors with perinatal distress and depressive symptoms. The prevalence of perinatal distress and depressive symptoms among CALD Australian women was 10.1% for antenatal distress; 7.3% for antenatal depressive symptoms; 6.2% for postnatal distress and 3.7% for postnatal depressive symptoms. Antenatal distress and depressive symptoms were associated with a lack of partner support, intimate partner violence, maternal history of childhood abuse and being known to child protection services. Antenatal distress and depressive symptoms were strongly associated with postnatal distress and depressive symptoms. Higher socioeconomic status had a protective effect on antenatal and postnatal depressive symptoms. Our study suggests that current perinatal mental health screening and referral for clinical assessment is essential, and also supports a re-examination of perinatal mental health policy to ensure access to culturally responsive mental health care that meets patients’ needs. The overall methodology used in this study has been described elsewhere [21,31,32]. For this study, linked retrospective maternal and child health data of all live births in public health facilities among the population of CALD women in Sydney Local Health District (SLHD) and South Western Sydney Local Health District (SWSLHD) between 2014 and 2016 (N = 25,407) were used. These data were routinely collected as part of standard care provided to women during pregnancy and the postnatal period. Antenatal information (e.g., demographic characteristics, antenatal depressive symptoms using the EPDS and history of intimate partner violence, IPV) were collected by qualified midwives at the first prenatal care visit. Additionally, during the first prenatal visit, women were asked to identify whether they belong to CALD, non-CALD, or Aboriginal or Torres Strait Islander subpopulations. CALD population was defined based on the Australian Bureau of Statistics description [16]. Postnatal data (e.g., information on post-birth depressive symptoms based on the EPDS) were also collected during postnatal visits by qualified child and family health nurses. These maternal and child health data were stored in the Local Health District’s Information Management and Technology Division database. The data were obtained from the Information Management and Technology Division and linked using individual identifiers. The 2016 Australian Bureau of Statistics, Census of Population and Housing indicated that almost half of Australians (45%) were either born overseas or had one or both parents who were born overseas (19%) [33]. In Sydney, the SLHD and SWSLHD cover 52% of the metropolitan area, with an estimated population of 1.6 million people of different cultural backgrounds [34,35]. In the Sydney metropolitan area, more than half of the population spoke English at home (58.4%). Other most common languages spoken at home included Mandarin (4.7%), Arabic (4.0%) and Cantonese (2.9%) [36]. SLHD is located in the centre and inner west of Sydney, while SWSLHD is located in the south-western region of the city. A number of maternal and child health services are provided to all communities across both districts, including those with socioeconomically disadvantaged populations [34,35]. The variables were broadly categorised into psychosocial and obstetric factors, and selected based on past studies [14,21,28] and data availability. The psychosocial factors included maternal age (categorised as <20, 20–34, or ≥35 years); socioeconomic status (categorised as low, middle or high); partner support (categorised as yes or no); maternal history of childhood abuse (categorised as yes or no); history of psychological IPV (categorised as yes or no); history of physical IPV (categorised as yes or no); being known to Family and Community Services (FACS or child protection services, yes or no); previous child in out-of-home care (OOHC, categorised as yes or no); and major nationality groups based on the country of birth (categorised as Oceania, North-West Europe, Southern-Eastern Europe, North Africa and the Middle East, South-East Asia, North-East Asia, Southern and Central Asia, Americas or Sub-Saharan Africa). Obstetric factors included the history of antenatal health problems (such as diabetes and/or hypertension, categorised as yes or no); alcohol use in pregnancy (categorised as yes or no), and type of delivery (categorised as normal vaginal, assisted vaginal or caesarean delivery). Information on the type of delivery was collected soon after birth, and data on other study factors were collected in the first postnatal visit. Socioeconomic status was calculated using the Socio-Economic Index for Areas (SEIFA). SEIFA is an indicator created by the Australian Bureau of Statistics using principal component analysis and is an area-based scale in Australia according to socio-economic advantage and disadvantage. The variables used in the estimation of SEIFA cover a number of areas, including household income, employment, education, occupation and housing, as well as other indicators of advantage and disadvantage [37]. In the present study, deciles of socioeconomic status were categorised into high (top 10% of the population), middle (middle 80%) and low (bottom 10%) groups, similar to previously published studies [6,21]. In accordance with NSW Health policy [38], IPV information was collected from mothers based on the following question: (i) “within the last year have you been hit, slapped or hurt in other ways by your partner or ex-partner?”—physical IPV (ii) “are you frightened of your partner or ex-partner?”—psychological IPV. Maternal prenatal distress and depressive symptoms were also considered as potential factors associated with postnatal distress and depressive symptoms based on past studies [15,25]. The main outcome variables were antenatal distress and antenatal depressive symptoms, and postnatal distress and postnatal depressive symptoms, measured using the EPDS. The EPDS has been validated for use in the antenatal and postnatal periods to assess perinatal depressive symptoms in Australia and internationally [22,23,24]. In Australia, qualified midwives collect information on depressive symptoms at the first antenatal care visit using the EPDS. The total number of prenatal depressive symptoms are tallied to achieve a total score (out of 30). This score is entered into the Information Management and Technology Division database as a variable, which was categorised in the current study as ≤9, 10–12 or ≥13, with a score of 10–12 indicating distress and a score of 13 or more suggestive of maternal antenatal depressive symptoms [5,39,40]. Postnatal depressive symptoms were also collected during postnatal visits by child and family health nurses within the first 6-weeks of birth. Similar to antenatal depressive symptoms calculation, the overall number of postnatal depressive symptoms was calculated to obtain a score (out of 30), which was then categorised as ≤9, 10–12 or ≥13, with a score of 10–12 indicating distress and a score of 13 or more suggestive of postnatal depressive symptoms [10,39,40]. In the Local Health Districts, a woman whose responses indicate a higher EPDS score of ≥13 is referred to a clinician for formal assessment of depression and appropriate management, consistent with the NSW government guidelines on improving mental health outcomes for parents and infants [41]. However, women who score between 10 and 12 on the EPDS, signalling distress, are often underserved by current policy recommendations. Detailed information on the use of the EPDS in clinical practice and research in the Australian context has been published elsewhere [5,25,41]. In the current study, the EPDS cut-points used to indicate distress and depressive symptoms were based on previously published studies [6,15,25,28,42] and NSW government guidelines [41]. In the assessment of CALD women who could not communicate in English during the antenatal and postnatal periods, the English version of the EPDS was administered through qualified interpreters or via the use of non-English versions of the EPDS. The interpreters were certified by the National Accreditation Authority for Translators and Interpreters in Australia. The non-English versions of the EPDS were produced by the New South Wales Multicultural Health Communication Service. The EPDS has been translated and validated in a number of non-English speaking contexts [43], including studies of Iranian [44], Bangladeshi [45], Chinese [46], Serbian [47], and Greek women [48]. This population is part of the CALD community in the study cohort, where a high proportion of the women were from South East Asia (24.5%), North Africa and the Middle East (23.0%), and Southern and Central Asia (20.2%) [Table S1]. The initial analyses involved the calculation of frequencies of the study factors and outcomes (i.e., antenatal distress and depressive symptoms, and postnatal distress and depressive symptoms). Cross-tabulations of the study factors with the outcome variables were also conducted. This was followed by univariate regression models to investigate the relationship between each study factor and the outcome variables. Multivariate multinomial logistic regression models were used to investigate the association between each study factor and the outcome variables, with adjustment for potential cofounders and year of the data (2014–2016). The data were combined to increase the statistical power of the study. In the models, adjustments for the birthing facility and gender of the baby, as well as relevant psychosocial and obstetric factors were conducted, consistent with previous studies [21,31]. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated as the measure of association between the study factors and perinatal distress and perinatal depressive symptoms. We also examined the potential impact of missing data on the estimated measure of effect in sensitivity analyses that employed an imputed dataset. This was based on the original maternal and child health data which included complete data for perinatal distress and perinatal depressive symptoms. Multiple imputations by chained equations were employed, which assumes that data were missing at random [49]. This analytical approach also assumes that the known characteristics of study participants can be used to examine the characteristics of participants with missing data [50]. All study factors and outcome variables in the main analysis were included in the multiple imputation models. Revised odds ratios from the imputed data were estimated using the mim command, for comparison with the complete case analyses. Sensitivity analyses were conducted based on 25 multiple imputations [51], and all analyses were conducted in Stata (Stata Corp, version 15.0, College Station, TX, USA). The Sydney Local Health District and South Western Sydney Local Health District Human Research Ethics Committees approved the collection of the data from the Information Management and Technology Division database and subsequent analysis. Approval numbers HREC: LNR/11/LPOOL/463; SSA: LNRSSA/11/LPOOL/464 and Project No: 11/276 LNR; Protocol No X12-0164 and LNR/12/RPAH/266. The data were accessed in accordance with the ethics protocol that only allowed unit record information to be released to investigators included in the ethics submission.

Résumé de l’IA

Study Justification:

– Perinatal distress and depression can have significant impacts on both the mother and baby.
– Culturally and linguistically diverse (CALD) Australian women may face unique challenges in perinatal mental health.
– Understanding the psychosocial and obstetric factors associated with perinatal distress and depressive symptoms among CALD women is important for developing effective interventions and policies.

Study Highlights:

– The study used linked maternal and child health data from two Local Health Districts in Australia.
– The prevalence of perinatal distress and depressive symptoms among CALD Australian women was 10.1% for antenatal distress, 7.3% for antenatal depressive symptoms, 6.2% for postnatal distress, and 3.7% for postnatal depressive symptoms.
– Factors associated with perinatal distress and depressive symptoms included lack of partner support, intimate partner violence, maternal history of childhood abuse, and being known to child protection services.
– Antenatal distress and depressive symptoms were strongly associated with postnatal distress and depressive symptoms.
– Higher socioeconomic status had a protective effect on antenatal and postnatal depressive symptoms.

Study Recommendations:

– Current perinatal mental health screening and referral for clinical assessment is essential.
– Perinatal mental health policy should be re-examined to ensure access to culturally responsive mental health care that meets the needs of CALD women.

Key Role Players:

– Qualified midwives and child and family health nurses for screening and assessment.
– Clinicians for formal assessment of depression and appropriate management.
– Interpreters certified by the National Accreditation Authority for Translators and Interpreters.
– New South Wales Multicultural Health Communication Service for non-English versions of the Edinburgh Postnatal Depression Scale (EPDS).

Cost Items for Planning Recommendations:

– Training and resources for qualified midwives and child and family health nurses.
– Interpreter services for CALD women who cannot communicate in English.
– Development and distribution of culturally responsive mental health care materials and resources.
– Implementation and monitoring of revised perinatal mental health policy.
Please note that the provided cost items are general suggestions and may vary depending on the specific context and resources available.

Force de la preuve

N/A

Abstrait

The overall methodology used in this study has been described elsewhere [21,31,32]. For this study, linked retrospective maternal and child health data of all live births in public health facilities among the population of CALD women in Sydney Local Health District (SLHD) and South Western Sydney Local Health District (SWSLHD) between 2014 and 2016 (N = 25,407) were used. These data were routinely collected as part of standard care provided to women during pregnancy and the postnatal period. Antenatal information (e.g., demographic characteristics, antenatal depressive symptoms using the EPDS and history of intimate partner violence, IPV) were collected by qualified midwives at the first prenatal care visit. Additionally, during the first prenatal visit, women were asked to identify whether they belong to CALD, non-CALD, or Aboriginal or Torres Strait Islander subpopulations. CALD population was defined based on the Australian Bureau of Statistics description [16]. Postnatal data (e.g., information on post-birth depressive symptoms based on the EPDS) were also collected during postnatal visits by qualified child and family health nurses. These maternal and child health data were stored in the Local Health District’s Information Management and Technology Division database. The data were obtained from the Information Management and Technology Division and linked using individual identifiers. The 2016 Australian Bureau of Statistics, Census of Population and Housing indicated that almost half of Australians (45%) were either born overseas or had one or both parents who were born overseas (19%) [33]. In Sydney, the SLHD and SWSLHD cover 52% of the metropolitan area, with an estimated population of 1.6 million people of different cultural backgrounds [34,35]. In the Sydney metropolitan area, more than half of the population spoke English at home (58.4%). Other most common languages spoken at home included Mandarin (4.7%), Arabic (4.0%) and Cantonese (2.9%) [36]. SLHD is located in the centre and inner west of Sydney, while SWSLHD is located in the south-western region of the city. A number of maternal and child health services are provided to all communities across both districts, including those with socioeconomically disadvantaged populations [34,35]. The variables were broadly categorised into psychosocial and obstetric factors, and selected based on past studies [14,21,28] and data availability. The psychosocial factors included maternal age (categorised as <20, 20–34, or ≥35 years); socioeconomic status (categorised as low, middle or high); partner support (categorised as yes or no); maternal history of childhood abuse (categorised as yes or no); history of psychological IPV (categorised as yes or no); history of physical IPV (categorised as yes or no); being known to Family and Community Services (FACS or child protection services, yes or no); previous child in out-of-home care (OOHC, categorised as yes or no); and major nationality groups based on the country of birth (categorised as Oceania, North-West Europe, Southern-Eastern Europe, North Africa and the Middle East, South-East Asia, North-East Asia, Southern and Central Asia, Americas or Sub-Saharan Africa). Obstetric factors included the history of antenatal health problems (such as diabetes and/or hypertension, categorised as yes or no); alcohol use in pregnancy (categorised as yes or no), and type of delivery (categorised as normal vaginal, assisted vaginal or caesarean delivery). Information on the type of delivery was collected soon after birth, and data on other study factors were collected in the first postnatal visit. Socioeconomic status was calculated using the Socio-Economic Index for Areas (SEIFA). SEIFA is an indicator created by the Australian Bureau of Statistics using principal component analysis and is an area-based scale in Australia according to socio-economic advantage and disadvantage. The variables used in the estimation of SEIFA cover a number of areas, including household income, employment, education, occupation and housing, as well as other indicators of advantage and disadvantage [37]. In the present study, deciles of socioeconomic status were categorised into high (top 10% of the population), middle (middle 80%) and low (bottom 10%) groups, similar to previously published studies [6,21]. In accordance with NSW Health policy [38], IPV information was collected from mothers based on the following question: (i) “within the last year have you been hit, slapped or hurt in other ways by your partner or ex-partner?”—physical IPV (ii) “are you frightened of your partner or ex-partner?”—psychological IPV. Maternal prenatal distress and depressive symptoms were also considered as potential factors associated with postnatal distress and depressive symptoms based on past studies [15,25]. The main outcome variables were antenatal distress and antenatal depressive symptoms, and postnatal distress and postnatal depressive symptoms, measured using the EPDS. The EPDS has been validated for use in the antenatal and postnatal periods to assess perinatal depressive symptoms in Australia and internationally [22,23,24]. In Australia, qualified midwives collect information on depressive symptoms at the first antenatal care visit using the EPDS. The total number of prenatal depressive symptoms are tallied to achieve a total score (out of 30). This score is entered into the Information Management and Technology Division database as a variable, which was categorised in the current study as ≤9, 10–12 or ≥13, with a score of 10–12 indicating distress and a score of 13 or more suggestive of maternal antenatal depressive symptoms [5,39,40]. Postnatal depressive symptoms were also collected during postnatal visits by child and family health nurses within the first 6-weeks of birth. Similar to antenatal depressive symptoms calculation, the overall number of postnatal depressive symptoms was calculated to obtain a score (out of 30), which was then categorised as ≤9, 10–12 or ≥13, with a score of 10–12 indicating distress and a score of 13 or more suggestive of postnatal depressive symptoms [10,39,40]. In the Local Health Districts, a woman whose responses indicate a higher EPDS score of ≥13 is referred to a clinician for formal assessment of depression and appropriate management, consistent with the NSW government guidelines on improving mental health outcomes for parents and infants [41]. However, women who score between 10 and 12 on the EPDS, signalling distress, are often underserved by current policy recommendations. Detailed information on the use of the EPDS in clinical practice and research in the Australian context has been published elsewhere [5,25,41]. In the current study, the EPDS cut-points used to indicate distress and depressive symptoms were based on previously published studies [6,15,25,28,42] and NSW government guidelines [41]. In the assessment of CALD women who could not communicate in English during the antenatal and postnatal periods, the English version of the EPDS was administered through qualified interpreters or via the use of non-English versions of the EPDS. The interpreters were certified by the National Accreditation Authority for Translators and Interpreters in Australia. The non-English versions of the EPDS were produced by the New South Wales Multicultural Health Communication Service. The EPDS has been translated and validated in a number of non-English speaking contexts [43], including studies of Iranian [44], Bangladeshi [45], Chinese [46], Serbian [47], and Greek women [48]. This population is part of the CALD community in the study cohort, where a high proportion of the women were from South East Asia (24.5%), North Africa and the Middle East (23.0%), and Southern and Central Asia (20.2%) [Table S1]. The initial analyses involved the calculation of frequencies of the study factors and outcomes (i.e., antenatal distress and depressive symptoms, and postnatal distress and depressive symptoms). Cross-tabulations of the study factors with the outcome variables were also conducted. This was followed by univariate regression models to investigate the relationship between each study factor and the outcome variables. Multivariate multinomial logistic regression models were used to investigate the association between each study factor and the outcome variables, with adjustment for potential cofounders and year of the data (2014–2016). The data were combined to increase the statistical power of the study. In the models, adjustments for the birthing facility and gender of the baby, as well as relevant psychosocial and obstetric factors were conducted, consistent with previous studies [21,31]. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated as the measure of association between the study factors and perinatal distress and perinatal depressive symptoms. We also examined the potential impact of missing data on the estimated measure of effect in sensitivity analyses that employed an imputed dataset. This was based on the original maternal and child health data which included complete data for perinatal distress and perinatal depressive symptoms. Multiple imputations by chained equations were employed, which assumes that data were missing at random [49]. This analytical approach also assumes that the known characteristics of study participants can be used to examine the characteristics of participants with missing data [50]. All study factors and outcome variables in the main analysis were included in the multiple imputation models. Revised odds ratios from the imputed data were estimated using the mim command, for comparison with the complete case analyses. Sensitivity analyses were conducted based on 25 multiple imputations [51], and all analyses were conducted in Stata (Stata Corp, version 15.0, College Station, TX, USA). The Sydney Local Health District and South Western Sydney Local Health District Human Research Ethics Committees approved the collection of the data from the Information Management and Technology Division database and subsequent analysis. Approval numbers HREC: LNR/11/LPOOL/463; SSA: LNRSSA/11/LPOOL/464 and Project No: 11/276 LNR; Protocol No X12-0164 and LNR/12/RPAH/266. The data were accessed in accordance with the ethics protocol that only allowed unit record information to be released to investigators included in the ethics submission.

Matériaux

https://efashare.b-cdn.net/materials/65daf14d1c27018abfbf56f98177b27e29e5dfe3ce3ad5c696df6235329fef1a/ijerph-16-02945-s001.pdf

Innovations

Based on the provided description, here are some potential innovations that can be used to improve access to maternal health:

1. Culturally Responsive Mental Health Care: Develop and implement culturally responsive mental health care services that specifically address the needs of culturally and linguistically diverse (CALD) Australian women. This can include providing interpreters, translated materials, and culturally sensitive counseling and support.

2. Perinatal Mental Health Screening: Enhance perinatal mental health screening protocols to ensure that all pregnant women, especially CALD women, are screened for distress and depressive symptoms. This can involve training healthcare providers on culturally appropriate screening methods and using validated screening tools, such as the Edinburgh Postnatal Depression Scale (EPDS), in multiple languages.

3. Partner Support Programs: Establish programs that promote partner support during the perinatal period. This can involve providing education and resources for partners to better understand and support their pregnant partners, as well as offering counseling or support groups for couples.

4. Collaboration with Child Protection Services: Strengthen collaboration between maternal health services and child protection services to identify and support pregnant women who have a history of childhood abuse or are known to child protection services. This can involve sharing information and coordinating care to ensure the safety and well-being of both the mother and the baby.

5. Socioeconomic Support: Implement strategies to address socioeconomic disparities that may contribute to perinatal distress and depressive symptoms. This can include providing financial assistance, access to affordable housing, and employment support for pregnant women from low socioeconomic backgrounds.

6. Language Access: Improve language access for CALD women by ensuring the availability of qualified interpreters and translated materials in various languages. This can help overcome language barriers and ensure effective communication between healthcare providers and pregnant women.

7. Community Engagement: Engage with CALD communities to raise awareness about perinatal mental health and promote help-seeking behaviors. This can involve partnering with community organizations, religious institutions, and cultural groups to provide education, support, and resources.

8. Telehealth Services: Expand the use of telehealth services to increase access to maternal health care for CALD women, particularly those in remote or underserved areas. This can include virtual consultations, remote monitoring, and online support groups.

These innovations can help improve access to maternal health care and support for culturally and linguistically diverse Australian women, ultimately reducing perinatal distress and depressive symptoms and promoting better maternal and child health outcomes.

AI Innovations Description

The study described in the provided text focuses on perinatal distress and depression among culturally and linguistically diverse (CALD) Australian women. The researchers used linked retrospective maternal and child health data from two Local Health Districts in Australia to investigate the association between psychosocial and obstetric factors with perinatal distress and depressive symptoms.

The study found that antenatal distress and depressive symptoms were associated with a lack of partner support, intimate partner violence, maternal history of childhood abuse, and being known to child protection services. Antenatal distress and depressive symptoms were also strongly associated with postnatal distress and depressive symptoms. Higher socioeconomic status had a protective effect on antenatal and postnatal depressive symptoms.

Based on these findings, the study recommends several actions to improve access to maternal health for CALD women. Firstly, it suggests that current perinatal mental health screening and referral for clinical assessment is essential. This means that healthcare providers should routinely screen pregnant women for distress and depressive symptoms and refer them for further assessment if needed.

Additionally, the study supports a re-examination of perinatal mental health policy to ensure access to culturally responsive mental health care that meets the needs of CALD women. This includes providing interpreters or non-English versions of assessment tools, such as the Edinburgh Postnatal Depression Scale (EPDS), to CALD women who cannot communicate in English. It also involves considering the cultural and social factors that may contribute to perinatal distress and depression among CALD women and tailoring interventions accordingly.

Overall, the study highlights the importance of addressing psychosocial and obstetric factors in order to improve access to maternal health for CALD women. By implementing the recommendations from this study, healthcare systems can better support the mental health of CALD women during the perinatal period.

AI Innovations Methodology

Based on the provided description, the study aims to investigate psychosocial and obstetric factors associated with perinatal distress and depressive symptoms among culturally and linguistically diverse (CALD) Australian women in Sydney. The methodology used in the study involves the use of linked retrospective maternal and child health data from two Local Health Districts in Australia. The data includes information collected during pregnancy and the postnatal period, such as demographic characteristics, antenatal and postnatal depressive symptoms, partner support, history of childhood abuse, intimate partner violence, and other relevant factors. The data is stored in the Local Health District’s Information Management and Technology Division database.

To simulate the impact of recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Identify potential recommendations: Based on the study findings and existing literature, identify potential recommendations that could improve access to maternal health for CALD women. These recommendations could include interventions targeting psychosocial support, partner involvement, cultural responsiveness, and access to mental health care.

2. Define indicators: Define indicators that can measure the impact of the recommendations on improving access to maternal health. These indicators could include measures of antenatal and postnatal distress and depressive symptoms, utilization of maternal health services, satisfaction with care, and health outcomes for both mothers and babies.

3. Develop a simulation model: Develop a simulation model that incorporates the identified recommendations and indicators. The model should consider the complex interactions between various factors, such as psychosocial and obstetric factors, cultural factors, healthcare system factors, and individual characteristics of CALD women.

4. Input data: Input relevant data into the simulation model, including baseline data on the current state of access to maternal health for CALD women, as well as data on the potential impact of the recommendations. This data can be obtained from the study findings, existing literature, and relevant stakeholders.

5. Run simulations: Run simulations using the developed model to estimate the potential impact of the recommendations on improving access to maternal health. The simulations can be conducted for different scenarios, such as varying levels of implementation of the recommendations or different target populations.

6. Analyze results: Analyze the results of the simulations to assess the potential impact of the recommendations on improving access to maternal health. This analysis can include quantitative measures, such as changes in the indicators defined in step 2, as well as qualitative assessments of the feasibility and acceptability of the recommendations.

7. Refine and validate the model: Refine and validate the simulation model based on the analysis of the results. This may involve incorporating additional data, adjusting parameters, or modifying the model structure to improve its accuracy and reliability.

8. Communicate findings: Communicate the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community organizations. Present the potential impact of the recommendations on improving access to maternal health and highlight the importance of implementing these recommendations.

By following this methodology, researchers and policymakers can gain insights into the potential impact of recommendations on improving access to maternal health for CALD women. This information can inform decision-making and help prioritize interventions that can effectively address the needs of this population.

Auteur

Dima Health

Statistics:

Citations: 5

Identifiers:

DOI: 10.3390/ijerph16162945

ISSN: 16617827

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Technology and Innovations, Violence and Injury, Workforce

Study Countries:

Multi-Countries

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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