Self-reported continuity and coordination of antenatal care and its association with obstetric near miss in Uasin Gishu county, Kenya

African Journal of Primary Health Care and Family Medicine, Volume 15, No. 1, Year 2023

Interpretação

Background: Continuity and coordination of care are core principles of high-quality primary health care. Optimising continuity and coordination improves maternal satisfaction. However, their association with morbidity and mortality outcomes is unclear. The obstetric near-miss approach can be used to investigate whether continuity and coordination influences the occurrence of a severe maternal outcome. Aim: To compare self-reported continuity and coordination of care between obstetric nearmiss survivors and those without near miss during pregnancy, delivery and postpartum. Setting: Uasin Gishu county, Rift Valley region, Kenya. Methods: A cross-sectional survey targeting 340 postnatal mothers. Continuity of care index (COCI) and modified continuity of care index (MCCI) were used to estimate longitudinal continuity. The Likert scale was administered to measure perceived continuity and coordination of care. Mann–Whitney U test and binomial logistic regression were used for hypothesis testing. Results: COCI and MCCI were lower among near-miss survivors (COCI = 0.80, p = 0.0026), (MCCI = 0.62, p = 0.034). Near-miss survivors scored lower on items assessing coordination between a higher-level provider and usual antenatal clinic (mean = 3.6, p = 0.006) and general coordination of care during pregnancy (mean = 3.9, p = 0.019). Presence of a non-life-threatening morbidity in pregnancy was associated with occurrence of near miss (aOR = 4.34, p = 0.001). Conclusion: Near-miss survivors scored lower on longitudinal continuity and coordination of care across levels. Further research should focus on strengthening coordination, determining the optimal level of longitudinal continuity and improving systems for early identification and management of morbidities in pregnancy. Contribution: The results of this study show that while longitudinal and relational COC is important during the antenatal period, the presence of a non-life-threatening condition in pregnancy remains the most important predictor of the occurrence of a near miss This case–control study was part of a larger explanatory sequential mixed methods study22 aimed at evaluating continuity and coordination of care among obstetric near-miss cases at a tertiary hospital in the Rift Valley region of Kenya. The larger study was carried out in four phases: phase one examined determinants of obstetric near miss in the hospital under consideration. The second phase (the current study) compared continuity and coordination in obstetric near misses with normal deliveries using a cross-sectional survey. The third phase qualitatively assessed continuity and coordination among near-miss cases. The fourth phase involved the integration of findings from the quantitative and qualitative phases. Kenya has six levels of care, ranging from household-level services offered by community health volunteers to national referral hospitals. Facility-based antenatal services are available from Level 2 to Level 6. Therefore, pregnant women can choose to receive care from any facility, although geographic access, cost and personal preferences play a role. Level 2–Level 3 facilities offer basic ANC services such as pregnancy monitoring, blood pressure and urine monitoring, immunisations for pregnant women and human immunodeficiency virus (HIV) testing. High-risk pregnancies are referred to higher levels of care that have resources for laboratory and inpatient management. At all levels, midwives take the lead in offering antenatal services. The current study focused on the population of postnatal mothers from primary care facilities attending one of the two national referral hospitals (Level 6), anonymised here as Referral Hospital B (RH-B). This hospital has over 200 lower-level facilities in its catchment area. Referral Hospital B is a teaching referral hospital. The hospital provides services for up to 10 000 births annually.23 The target population was all postnatal mothers in RH-B within 42 days of delivery. Only mothers within the hospital at the time of the study were included. Mothers who were too sick to participate at the time of data collection were excluded from the study. Sample size was based on the methodology for ordinal outcomes in clinical research.24 Continuity and coordination were measured on a five-point ordinal scale from ‘strongly disagree’, ‘disagree’, ‘agree’ to ‘strongly agree’. We hypothesised that the odds of near-miss mothers being in the disagree categories would be twice that of women without near miss. Using an allocation ratio of 1:2, we determined that a sample of 89 participants in the near-miss group and 178 in those without near miss would achieve 80% power to detect an odds ratio of 2 when the significance level (alpha) was 0.05 using a two-sided Mann–Whitney U test. Although near miss is a relatively rare phenomenon, we considered the computed sample size achievable because near-miss survivors from the catchment population are referred to RH-B for postnatal care, thus increasing the available pool of participants. Postnatal women were consecutively sampled from the maternal and child health (MCH) clinic during May 2021. To increase the pool of available near-miss survivors, research assistants also visited postnatal inpatient wards where mothers who experienced a severe morbidity were receiving treatment. Mothers were then categorised into those with and without near misses during pregnancy and birth. For obstetric near-miss cases, the inclusion criteria were based on the World Health Organization categorisation.25 For the purpose of this study, we used disease-specific and management-specific criteria. Disease-specific criteria included: (1) eclampsia, (2) severe pre-eclampsia, (3) severe postpartum haemorrhage (blood loss of > 1000 mL), (4) severe sepsis and (5) ruptured uterus. Management-specific criteria included women who: (1) received blood transfusion (2) underwent an emergency caesarean section and/or (3) underwent a hysterectomy because of massive haemorrhage. The data collection instrument consisted of three sections. The first section was on sociodemographic and antenatal visitation characteristics. These included age, marital status, educational level, occupation and distance from usual antenatal clinic. Furthermore, the presence of any non-life-threatening morbidity in pregnancy was assessed to include previous caesarean section, infections, pregnancy-induced hypertension, diabetes mellitus, deep venous thrombosis, premature rupture of membranes, malaria and HIV. The second section was designed to assess longitudinal continuity by asking women about the number and sequence of antenatal visits, the type of providers seen and the name of the facility for each visit. Using this information, two indices that measure density and dispersion of antenatal visits26 were calculated as follows: (1) The continuity of care (COC) index measured the dispersion of visits by assigning a higher value to women who visit the same antenatal clinic. For example, a participant scored zero if all four antenatal visits were to a different facility. (2) The modified continuity index (MCI) was adjusted for utilisation by assigning a higher value to those with more frequent visits to the same providers. For all indices, a value of 1.0 was considered perfect longitudinal continuity, 0.75–0.99 was high, 0.50–0.74 was medium and below 0.50 was poor.11 The third part of the tool measured self-reported continuity and coordination of care using a five-point Likert scale adapted from the Nijmegen Continuity Questionnaire.27 This tool was developed in the context of both generalist and specialist medical practice in the Netherlands, among patients with chronic disease. It has since been used in more than 20 studies in chronic care settings, especially in Europe. One strength of this tool is that as items are generic, it can be adapted to various care settings.27 We modified the wording to reflect antenatal consultations and added items on sequential coordination of care. Content validation of the tool was performed by a group of community health educators in a local Kenyan University (KU). Each expert was asked to score the tool based on five criteria, namely: (1) measurement aim (discriminative vs. evaluative), (2) the target population, (3) the concept being studied and whether the subscale measured the concept of interest, (4) how items were selected and (5) clarity, brevity and interpretability. Cronbach’s alpha was used to assess internal consistency, whilst the intraclass correlation coefficient was used to assess reproducibility. Based on validation and pilot testing, items assessing social support during pregnancy, care navigation and community-based informal caregiving were removed. Cronbach’s alpha for internal consistency was then 0.775, and intraclass correlation coefficient for reproducibility was 0.776. A Cronbach’s alpha of 0.70–0.95 is considered good internal consistency. The final tool consisted of 16 items (Table 3), which we considered a unidimensional tool measuring ‘continuity and coordination of antenatal care’. Comparison of self-reported continuity and coordination of care between uncomplicated cases versus near-miss survivors. MWU, Mann–Whitney U test; ANC, antenatal care; s.d., standard deviation. Data were collected over a period of three weeks in May 2021. Five research assistants and the researcher (S.M.M) collected the data. The research assistants were registered nurse-midwives with undergraduate-level knowledge of midwifery and reproductive health. The researcher had a training session with the research assistants to ensure reliable data collection and that ethical considerations were followed. The research assistants obtained informed consent from the mothers and provided the questionnaires if they agreed to participate. The completion of the questionnaires took between 20 min and 30 min. The research assistants were available for clarifications and questions as the mothers completed the questionnaires. The tool was available in English and Kiswahili, and the mothers were free to choose the language that they were most comfortable in. The Statistical Package for Social Sciences (SPSS) version 26 was used for data analysis. Analysis was based on complete case analysis. Data entry and checking were conducted by the first author (S.M.). Data analysis was based on the complete case analysis. Distributional assumptions of scores were tested using the Kolmogorov–Smirnoff test and visualisation of QQ plots. Continuity of care and modified continuity of care (MCOC) indices were compared using independent samples Mann–Whitney U tests. The Likert scale was analysed as a unidimensional scale. Firstly, each item was summarised using means and standard deviations. A composite score was then computed for near-miss and normal delivery cases. The Mann–Whitney U test was then used to test the hypothesis of equality of means for the two groups. To test the effect of longitudinal continuity indices on occurrence of a near miss, continuity of care index (COCI) and MCOCI were entered into a binary logistic regression model adjusted for socio-demographic and antenatal characteristics. Ethical clearance to conduct this study was obtained from the Stellenbosch University Health Research Ethics Committee (ref. no. S20/02/039 [PhD]), the Moi Teaching and Referral Hospital Ethics Review Committee (ref. no. FAN 0003691) and the National Commission for Science, Technology and Innovation in Kenya (ref. no. NACOSTI/P/21/8398). Ethical consent from the various review committees was received in June and July 2021. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants involved in the study.

AI Digest

Study Justification:

– Continuity and coordination of care are important principles of high-quality primary health care.
– Optimizing continuity and coordination can improve maternal satisfaction.
– The association between continuity and coordination of care and morbidity and mortality outcomes is unclear.
– The obstetric near-miss approach can be used to investigate the influence of continuity and coordination on severe maternal outcomes.

Highlights:

– The study compared self-reported continuity and coordination of care between obstetric near-miss survivors and those without near miss during pregnancy, delivery, and postpartum.
– The study found that near-miss survivors had lower continuity and coordination scores compared to those without near miss.
– Near-miss survivors scored lower on items assessing coordination between higher-level providers and usual antenatal clinics, as well as general coordination of care during pregnancy.
– The presence of a non-life-threatening morbidity in pregnancy was associated with the occurrence of near miss.

Recommendations:

– Further research should focus on strengthening coordination of care, determining the optimal level of longitudinal continuity, and improving systems for early identification and management of morbidities in pregnancy.

Key Role Players:

– Community health volunteers
– Midwives
– Health educators
– Research assistants
– Policy makers

Cost Items for Planning Recommendations:

– Training sessions for research assistants
– Data collection materials (questionnaires)
– Data analysis software (SPSS)
– Ethical clearance fees
– Travel expenses for research assistants
– Publication fees

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a cross-sectional survey and includes statistical analysis. However, the study could be improved by using a larger sample size and conducting a longitudinal study to establish causality. Additionally, the abstract does not provide information on the representativeness of the sample or potential biases. To improve the evidence, future studies could consider using a more diverse sample and addressing potential confounding factors.

Resumo

This case–control study was part of a larger explanatory sequential mixed methods study22 aimed at evaluating continuity and coordination of care among obstetric near-miss cases at a tertiary hospital in the Rift Valley region of Kenya. The larger study was carried out in four phases: phase one examined determinants of obstetric near miss in the hospital under consideration. The second phase (the current study) compared continuity and coordination in obstetric near misses with normal deliveries using a cross-sectional survey. The third phase qualitatively assessed continuity and coordination among near-miss cases. The fourth phase involved the integration of findings from the quantitative and qualitative phases. Kenya has six levels of care, ranging from household-level services offered by community health volunteers to national referral hospitals. Facility-based antenatal services are available from Level 2 to Level 6. Therefore, pregnant women can choose to receive care from any facility, although geographic access, cost and personal preferences play a role. Level 2–Level 3 facilities offer basic ANC services such as pregnancy monitoring, blood pressure and urine monitoring, immunisations for pregnant women and human immunodeficiency virus (HIV) testing. High-risk pregnancies are referred to higher levels of care that have resources for laboratory and inpatient management. At all levels, midwives take the lead in offering antenatal services. The current study focused on the population of postnatal mothers from primary care facilities attending one of the two national referral hospitals (Level 6), anonymised here as Referral Hospital B (RH-B). This hospital has over 200 lower-level facilities in its catchment area. Referral Hospital B is a teaching referral hospital. The hospital provides services for up to 10 000 births annually.23 The target population was all postnatal mothers in RH-B within 42 days of delivery. Only mothers within the hospital at the time of the study were included. Mothers who were too sick to participate at the time of data collection were excluded from the study. Sample size was based on the methodology for ordinal outcomes in clinical research.24 Continuity and coordination were measured on a five-point ordinal scale from ‘strongly disagree’, ‘disagree’, ‘agree’ to ‘strongly agree’. We hypothesised that the odds of near-miss mothers being in the disagree categories would be twice that of women without near miss. Using an allocation ratio of 1:2, we determined that a sample of 89 participants in the near-miss group and 178 in those without near miss would achieve 80% power to detect an odds ratio of 2 when the significance level (alpha) was 0.05 using a two-sided Mann–Whitney U test. Although near miss is a relatively rare phenomenon, we considered the computed sample size achievable because near-miss survivors from the catchment population are referred to RH-B for postnatal care, thus increasing the available pool of participants. Postnatal women were consecutively sampled from the maternal and child health (MCH) clinic during May 2021. To increase the pool of available near-miss survivors, research assistants also visited postnatal inpatient wards where mothers who experienced a severe morbidity were receiving treatment. Mothers were then categorised into those with and without near misses during pregnancy and birth. For obstetric near-miss cases, the inclusion criteria were based on the World Health Organization categorisation.25 For the purpose of this study, we used disease-specific and management-specific criteria. Disease-specific criteria included: (1) eclampsia, (2) severe pre-eclampsia, (3) severe postpartum haemorrhage (blood loss of > 1000 mL), (4) severe sepsis and (5) ruptured uterus. Management-specific criteria included women who: (1) received blood transfusion (2) underwent an emergency caesarean section and/or (3) underwent a hysterectomy because of massive haemorrhage. The data collection instrument consisted of three sections. The first section was on sociodemographic and antenatal visitation characteristics. These included age, marital status, educational level, occupation and distance from usual antenatal clinic. Furthermore, the presence of any non-life-threatening morbidity in pregnancy was assessed to include previous caesarean section, infections, pregnancy-induced hypertension, diabetes mellitus, deep venous thrombosis, premature rupture of membranes, malaria and HIV. The second section was designed to assess longitudinal continuity by asking women about the number and sequence of antenatal visits, the type of providers seen and the name of the facility for each visit. Using this information, two indices that measure density and dispersion of antenatal visits26 were calculated as follows: (1) The continuity of care (COC) index measured the dispersion of visits by assigning a higher value to women who visit the same antenatal clinic. For example, a participant scored zero if all four antenatal visits were to a different facility. (2) The modified continuity index (MCI) was adjusted for utilisation by assigning a higher value to those with more frequent visits to the same providers. For all indices, a value of 1.0 was considered perfect longitudinal continuity, 0.75–0.99 was high, 0.50–0.74 was medium and below 0.50 was poor.11 The third part of the tool measured self-reported continuity and coordination of care using a five-point Likert scale adapted from the Nijmegen Continuity Questionnaire.27 This tool was developed in the context of both generalist and specialist medical practice in the Netherlands, among patients with chronic disease. It has since been used in more than 20 studies in chronic care settings, especially in Europe. One strength of this tool is that as items are generic, it can be adapted to various care settings.27 We modified the wording to reflect antenatal consultations and added items on sequential coordination of care. Content validation of the tool was performed by a group of community health educators in a local Kenyan University (KU). Each expert was asked to score the tool based on five criteria, namely: (1) measurement aim (discriminative vs. evaluative), (2) the target population, (3) the concept being studied and whether the subscale measured the concept of interest, (4) how items were selected and (5) clarity, brevity and interpretability. Cronbach’s alpha was used to assess internal consistency, whilst the intraclass correlation coefficient was used to assess reproducibility. Based on validation and pilot testing, items assessing social support during pregnancy, care navigation and community-based informal caregiving were removed. Cronbach’s alpha for internal consistency was then 0.775, and intraclass correlation coefficient for reproducibility was 0.776. A Cronbach’s alpha of 0.70–0.95 is considered good internal consistency. The final tool consisted of 16 items (Table 3), which we considered a unidimensional tool measuring ‘continuity and coordination of antenatal care’. Comparison of self-reported continuity and coordination of care between uncomplicated cases versus near-miss survivors. MWU, Mann–Whitney U test; ANC, antenatal care; s.d., standard deviation. Data were collected over a period of three weeks in May 2021. Five research assistants and the researcher (S.M.M) collected the data. The research assistants were registered nurse-midwives with undergraduate-level knowledge of midwifery and reproductive health. The researcher had a training session with the research assistants to ensure reliable data collection and that ethical considerations were followed. The research assistants obtained informed consent from the mothers and provided the questionnaires if they agreed to participate. The completion of the questionnaires took between 20 min and 30 min. The research assistants were available for clarifications and questions as the mothers completed the questionnaires. The tool was available in English and Kiswahili, and the mothers were free to choose the language that they were most comfortable in. The Statistical Package for Social Sciences (SPSS) version 26 was used for data analysis. Analysis was based on complete case analysis. Data entry and checking were conducted by the first author (S.M.). Data analysis was based on the complete case analysis. Distributional assumptions of scores were tested using the Kolmogorov–Smirnoff test and visualisation of QQ plots. Continuity of care and modified continuity of care (MCOC) indices were compared using independent samples Mann–Whitney U tests. The Likert scale was analysed as a unidimensional scale. Firstly, each item was summarised using means and standard deviations. A composite score was then computed for near-miss and normal delivery cases. The Mann–Whitney U test was then used to test the hypothesis of equality of means for the two groups. To test the effect of longitudinal continuity indices on occurrence of a near miss, continuity of care index (COCI) and MCOCI were entered into a binary logistic regression model adjusted for socio-demographic and antenatal characteristics. Ethical clearance to conduct this study was obtained from the Stellenbosch University Health Research Ethics Committee (ref. no. S20/02/039 [PhD]), the Moi Teaching and Referral Hospital Ethics Review Committee (ref. no. FAN 0003691) and the National Commission for Science, Technology and Innovation in Kenya (ref. no. NACOSTI/P/21/8398). Ethical consent from the various review committees was received in June and July 2021. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants involved in the study.

Materiais

N/A

Inovações

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

1. Telemedicine: Implementing telemedicine services can improve access to maternal health by allowing pregnant women to consult with healthcare providers remotely. This can be especially beneficial for women in remote or underserved areas who may have limited access to healthcare facilities.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health can empower pregnant women to take control of their own healthcare. These apps can provide educational content, appointment reminders, and access to telemedicine services.

3. Community health workers: Training and deploying community health workers can improve access to maternal health services, especially in rural areas. These workers can provide basic antenatal care, education, and referrals to healthcare facilities when necessary.

4. Transportation services: Lack of transportation can be a barrier to accessing maternal health services. Implementing transportation services, such as ambulances or community-based transportation programs, can ensure that pregnant women can reach healthcare facilities in a timely manner.

5. Mobile clinics: Setting up mobile clinics that travel to underserved areas can bring maternal health services directly to the communities that need them. These clinics can provide antenatal care, prenatal screenings, and basic healthcare services.

6. Financial incentives: Providing financial incentives, such as subsidies or cash transfers, to pregnant women who seek antenatal care can help overcome financial barriers and improve access to maternal health services.

7. Health education campaigns: Conducting targeted health education campaigns can raise awareness about the importance of antenatal care and encourage pregnant women to seek healthcare services. These campaigns can be conducted through various channels, such as radio, television, and community outreach programs.

8. Strengthening referral systems: Improving coordination and communication between different levels of healthcare facilities can ensure that pregnant women receive appropriate and timely care. Strengthening referral systems can help ensure that high-risk pregnancies are identified early and referred to higher levels of care.

It’s important to note that the specific innovations to be implemented should be based on the local context and needs of the community.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health is to strengthen coordination, determine the optimal level of longitudinal continuity, and improve systems for early identification and management of morbidities in pregnancy. This recommendation is based on the findings of the study, which showed that near-miss survivors scored lower on longitudinal continuity and coordination of care across levels. It was also found that the presence of a non-life-threatening morbidity in pregnancy was associated with the occurrence of a near miss. Therefore, by focusing on improving coordination, ensuring continuity of care, and early identification and management of morbidities, access to maternal health can be improved and the occurrence of severe maternal outcomes can be reduced.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations for improving access to maternal health:

1. Strengthen coordination: Enhance coordination between higher-level providers and antenatal clinics to ensure seamless care for pregnant women. This can involve establishing effective communication channels, referral systems, and information sharing mechanisms.

2. Improve longitudinal continuity: Focus on improving the continuity of care throughout the antenatal period. This can be achieved by encouraging pregnant women to consistently visit the same antenatal clinic and healthcare providers, which can lead to better coordination and personalized care.

3. Early identification and management of morbidities: Develop systems and protocols for early identification and management of non-life-threatening morbidities during pregnancy. This can involve regular screening, monitoring, and timely interventions to prevent complications and reduce the risk of severe maternal outcomes.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the number of antenatal visits, the proportion of women receiving care from the same provider throughout pregnancy, and the occurrence of severe maternal outcomes.

2. Collect baseline data: Gather baseline data on the selected indicators from the target population. This can be done through surveys, interviews, or existing health records.

3. Introduce the recommendations: Implement the recommended interventions, such as strengthening coordination and improving longitudinal continuity of care. Ensure that appropriate resources and support are provided for the implementation.

4. Monitor and evaluate: Continuously monitor the implementation of the recommendations and collect data on the selected indicators. This can involve regular data collection, analysis, and reporting.

5. Compare pre- and post-intervention data: Compare the data collected before and after the implementation of the recommendations. Analyze the changes in the selected indicators to assess the impact of the interventions on improving access to maternal health.

6. Adjust and refine: Based on the findings, make adjustments and refinements to the interventions as needed. This can involve identifying areas of improvement, addressing challenges, and scaling up successful strategies.

7. Continuous improvement: Establish a feedback loop and continuous improvement process to ensure ongoing monitoring, evaluation, and refinement of the interventions. This can involve regular reviews, stakeholder engagement, and learning from best practices.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make evidence-based decisions for further interventions and improvements.

Autor

Dima Health

Identifiers:

DOI: 10.4102/phcfm.v15i1.3452

ISSN: 20712928

Research Areas:

Community Interventions, Disparities, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations, Workforce

Study Countries:

Kenya

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Descriptive Study, Grounded Theory

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Female

Partilhar isto: