Trends in antenatal care visits and associated factors in Ghana from 2006 to 2018

BMC Pregnancy and Childbirth, Volume 22, No. 1, Year 2022

Interpretação

Introduction: Given that maternal mortality is a major global health concern, multiple measures including antenatal care visits have been promoted by the global community. However, most pregnant women in Ghana and other sub-Saharan African countries do not attain the recommended timelines, in addition to a slower progress towards meeting the required minimum of eight visits stipulated by the World Health Organization. Therefore, this study explored the trends in antenatal care visits and the associated factors in Ghana from 2006 to 2018 using the Multiple Indicator Cluster Surveys. Methods: The study used women datasets (N = 7795) aged 15 to 49 years from three waves (2006, 2011, and 2017-2018) of the Ghana Multiple Indicator Cluster Surveys (GMICS). STATA version 14 was used for data analyses. Univariable analyses, bivariable analyses with chi-square test of independence, and multivariable analyses with robust multinomial logistic regression models were fitted. Results: The study found a consistent increase in the proportion of women having adequate and optimal antenatal attendance from 2006 to 2018 across the women’s sociodemographic segments. For instance, the proportion of mothers achieving adequate antenatal care (4 to 7 antenatal care visits) increased from 49.3% in 2006 to 49.98% in 2011 to 58.61% in 2017-2018. In the multivariable model, women with upward attainment of formal education, health insurance coverage, increasing household wealth, and residing in the Upper East Region were consistently associated with a higher likelihood of adequate and/or optimal antenatal care attendance from 2006 to 2018. Conclusion: Women who are less likely to achieve optimal antenatal care visits should be targeted by policies towards reducing maternal mortalities and other birth complications. Poverty-reduction policies, promoting maternal and girl-child education, improving general livelihood in rural settings, expanding health insurance coverage and infrastructural access, harnessing community-level structures, and innovative measures such as telehealth and telemedicine are required to increase antenatal care utilization. Women datasets from three waves of the Ghana Multiple Indicator Cluster Survey (GMICS) conducted in 2006, 2011 and 2017-2018 were analyzed for this study. The GMICS is a cross-sectional survey conducted by the Ghana Statistical Service (GSS) in association with the Ghana Health Service (GHS), Ministry of Health (MOH), and the Ministry of Education [21]. Funding and technical support were provided by the United Nations International Children’s Emergency Fund (UNICEF) and other international donors [21]. The main aim of the MICS surveys is to collect data on key indicators that assist countries to produce evidence for use in national development plans, policies, and programmes as well as assess the advancements towards the Sustainable Development Goals (SDGs) and other internationally-signed agreements [21]. Trained research enumerators were engaged to collect the data on behalf of GSS and UNICEF using a multi-stage stratified cluster sampling approach. This approach nationally surveyed women in urban and rural areas from the previous ten administrative regions in Ghana: Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West. The initial stage of data collection involved identifying and selecting enumeration areas based on the 2010 Population and Housing Census of Ghana. These enumeration areas became the primary sampling units. Next, in the second stage, households were listed from each of the selected enumeration areas and a sample of households was selected using systematic random sampling. This stage enabled the recruitment of reproductive-aged women from selected households. Data of 7795 women aged 15 to 49 years from all the three waves who had delivered 2 years prior to the data collection periods were included in this study. Antenatal care attendance is the main outcome variable for this study. This variable was extracted from a single-item survey question asking women who had given birth 2 years prior to the data collection about the number of times they attended antenatal care. Women were specifically asked, “How many times did you receive antenatal care during this pregnancy?” Women responded by providing a single number or range of numbers. For those who responded by giving a range, the minimum number was recorded as their answer. Guided by the WHO’s recommendation, these numbers were categorized under 4-scale response format: “none = 1”, “1-3 visits = 2”, “4+ visits = 3” and 8+ visits = 4″. We decided to collapse the none and “1-3 visits” into one category as “less than 4 visits” because only one woman did not attend ANC in the 2006 data. This categorization makes it easy for us to compare the models for the three data waves. Therefore, the newly created categories are as follows: “less than 4 visits (undesirable)=0”, “4 to 7 visits (adequate)=1” and 8+ visits (optimal) = 2″. Age of woman, education, polygyny status, wanted last-child, parity, death of a previous child, health insurance, household wealth index, urban-rural residence, and region of residence were treated as explanatory variables as seen in Table 1. We selected the variables from the datasets based on their reported significance to the outcome variable in the literature [5, 22, 23]. All variables were available in all the three datasets except for health insurance which was only available in the 2011 and 2017-2018 datasets. The variables were measured with single-item self-report questions and simple categorical response options. For instance, age of woman was measured with the question, “How old are you?” and participants responded by indicating their age in numbers which was later categorized by UNICEF. Health insurance was measured with the question, “Are you covered by any health insurance?” with response format comprising “Yes = 1” and “No = 2”. Education was measured by asking participants to respond to the question, “What is the highest level and grade or year of school you have attended?” with responses ranging from, “early childhood education=0” to “higher = 6”. We used the Variance Inflation Factor (VIF) to check for the assumptions of multicollinearity among the independent variables, and we have not observed any violations. Cross-tabulation between ANC visits and study variables in Ghana from 2006 to 2017-2018 433 (29.7) 330 (22.7) 733 (25.5) 320 (22.0) 642 (22.3) 103 (7.1) 391 (13.6) 165 (11.3) 293 (10.2) 264 (18.1) 572 (39.3) 321 (22.0) 619 (21.5) 301 (20.7) 527 (18.3) 371 (25.5) 773 (26.9) 335 (23.0) 637 (22.2) 347 (23.9) 621 (21.6) 277 (19.0) 517 (18.0) 211 (14.5) 530 (18.5) 154 (10.6) 306 (10.7) 112 (7.7) 279 (9.7) 177 (12.2) 451 (15.7) 103 (7.1) 214 (7.5) 195 (13.4) 327 (11.4) 222 (15.2) 511 (17.8) 115 (7.9) 258 (9.0) 278 (19.1) 321 (11.2) 61 (4.2) 120 (4.2) 112 (3.3) 40 (2.7) 85 (3.0) 88 (2.6) Data analyses began by cleaning and recoding variables of interest in STATA version 14. The GMICS predefined survey weights for the differential probability selection of sample were accounted for with the Taylor linearization technique [24, 25]. This procedure adjusted for the clustering, stratification, and design effects within the datasets. Univariable analyses were initially performed on all three waves of datasets by calculating frequencies and percentages of all the variables (see Table Table11 – second, sixth, and tenth columns). Secondly, simple Poisson regression was used to determine whether there was a significant trend in ANC visits over the three data waves (2006, 2011, 2018) (Additional file 1). Furthermore, bivariable analyses were performed with a chi-square test of independence to estimate the relationship between the explanatory variables and the outcome variable as presented in Table Table1.1. Lastly, multivariable analyses with robust multinomial logistic regression models were conducted, treating the “less than 4 visits” category in the outcome variable (antenatal care attendance) as the base. All the explanatory variables were independently (Table 2) and simultaneously (see Table 3) regressed onto the outcome variable, regardless of the statistical significance value in the bivariable analyses. The same processes were repeated for all the three datasets used in this study, setting the significance alpha level at 0.05. The relative risk ratio and the adjusted relative risk ratio were reported. Unadjusted multinomial logit model showing correlates of ANC visits in Ghana from 2006 to 2017-2018 1.3 [0.9, 1.8] 1.9** [1.3, 2.8] 1.5* [1.1, 2.2] 2.1*** [1.4, 3.2] 1.2 [0.9, 1.6] 1.7** [1.2, 2.5] 1.2 [0.8, 1.7] 1.5 [0.9, 2.3] 1.2 [0.8, 1.7] 1.1 [0.7, 1.8] 1.2 [0.8, 1.7] 1.3 [0.9, 2.0] 1.0 [0.7, 1.5] 1.1 [0.7, 1.8] 1.2 [0.8, 1.8] 2.7*** [1.8, 4.2] 0.852 [0.6, 1.2] 1.298 [0.8, 2.1] 2.2*** [1.5, 3.2] 3.8*** [2.5, 5.9] 1.9** [1.3, 3.0] 5.8*** [3.7, 9.2] 1.6** [1.1, 2.1] 2.8*** [1.8, 4.2] 4.0** [1.5,11.01] 17.7*** [6.6, 47.4] 4.6*** [2.0, 10.6] 25.3*** [11.1, 57.5] 2.0** [1.3, 3.3] 7.9*** [4.5, 13.6] 1.4 [0.9, 2.2] 2.0* [1.1, 3.6] 1.7* [1.0, 2.8] 1.5 [0.9, 2.5] 1.6** [1.2, 2.2] 1.8** [1.2, 2.6] 1.5 [0.9, 2.4] 1.1 [0.6, 2.0] 0.8 [0.5, 1.4] 0.3*** [0.2, 0.6] 1.3 [0.9, 1.9] 0.7 [0.4, 1.3] 0.5*** [0.4, 0.6] 0.4*** [0.2, 0.5] 0.7* [0.6, 0.9] 0.6** [0.4, 0.8] 0.7* [0.5, 0.9] 0.5*** [0.4, 0.8] 1.0 [0.7, 1.5] 1.3 [0.9, 2.0] 2.0** [1.3, 3.0] 2.9*** [1.9, 4.5] 1.1 [0.8, 1.5] 1.7** [1.2, 2.4] 1.4 [1.0, 2.1] 1.9** [1.2, 3.0] 1.0 [0.6, 1.6] 1.3 [0.8, 2.0] 1.2 [0.9, 1.8] 1.6* [1.1, 2.4] 0.7* [0.5, 1.0] 0.5*** [0.4, 0.8] 0.7 [0.5, 1.0] 0.5*** [0.3, 0.7] 0.8 [0.6, 1.1] 0.5*** [0.3, 0.7] 0.4*** [0.3, 0.6] 0.3*** [0.2, 0.5] 0.6*** [0.5, 0.8] 0.5*** [0.4, 0.7] 1.4 [0.9, 2.0] 1.8* [1.1, 3.0] 1.0 [0.7, 1.6] 2.7*** [1.6, 4.3] 1.4 [1.0,2.0] 1.4 [0.9, 2.1] 1.3 [0.9, 2.0] 2.2* [1.2, 4.1] 2.9*** [1.5, 5.3] 8.3*** [4.1, 16.5] 1.4 [0.9, 2.1] 1.8* [1.1, 2.9] 2.3** [1.4, 3.8] 7.6*** [4.2, 13.6] 2.7*** [1.5, 4.8] 13.4*** [7.6, 23.9] 2.7*** [1.7, 4.3] 5.7*** [3.5, 9.3] 6.4*** [2.7, 14.8] 43.7*** [17.7, 108.0] 9.1*** [3.8, 21.7] 84.0*** [34.6, 203.8] 4.3*** [2.2, 8.4] 14.6*** [7.6, 27.7] 0.5*** [0.3, 0.7] 0.2*** [0.1, 0.3] 0.4*** [0.2, 0.6] 0.2*** [0.1, 0.3] 0.6** [0.4, 0.8] 0.3*** [0.2, 0.4] 0.7 [0.3, 1.5] 0.2** [0.1, 0.6] 0.6 [0.2, 2.2] 0.2** [0.1, 0.6] 0.7 [0.4, 1.5] 0.8 [0.4, 1.5] 0.6 [0.3, 1.3] 0.2** [0.1, 0.6] 1.3 [0.4, 4.8] 0.3 [0.1, 1.1] 0.8 [0.4, 1.6] 0.4* [0.2, 0.9] 0.6 [0.3, 1.2] 0.1*** [0.0, 0.2] 1.0 [0.3, 3.7] 0.2** [0.0, 0.7] 0.5 [0.2, 1.0] 0.1*** [0.1, 0.3] 0.5* [0.2, 1.0] 0.1*** [0.1, 0.3] 2.4 [0.5, 10.8] 0.7 [0.2, 2.7] 0.6 [0.3, 1.3] 0.3*** [0.1, 0.5] 1.3 [0.6, 2.9] 0.6 [0.3, 1.4] 1.4 [0.4, 5.6] 0.7 [0.2, 2.3] 1.1 [0.5, 2.2] 0.4** [0.2, 0.8] 1.1 [0.5, 2.5] 0.2** [0.1, 0.6] 1.1 [0.3, 4.4] 0.2** [0.0, 0.6] 0.9 [0.4, 1.8] 0.4* [0.182, 0.8] 0.8 [0.4, 1.7] 0.2** [0.1, 0.6] 0.7 [0.2, 2.3] 0.1*** [0.0, 0.3] 0.8 [0.4, 1.5] 0.2*** [0.1, 0.4] 1.7 [0.7, 3.9] 0.8 [0.3, 2.0] 1.8 [0.5, 7.0] 0.2** [0.1, 0.6] 3.0* [1.3, 6.9] 1.5 [0.7, 3.5] 1.3 [0.7, 2.7] 0.34** [0.1, 0.6] 2.2 [0.6, 8.0] 0.2** [0.1, 0.7] 1.0 [0.5, 1.9] 0.2*** [0.1,0.4] Adjusted multinomial logit model displaying correlates of ANC visits in Ghana from 2006 to 2017-2018 1.1 [0.7, 1.6] 1.3 [0.7, 2.3] 2.1** [1.3, 3.4] 2.7** [1.5,4.9] 1.0 [0.7, 1.5] 1.5 [0.9, 2.5] 1.3 [0.8, 2.1] 1.9 [1.0, 3.8] 2.1** [1.2, 3.6] 2.5** [1.3, 5.1] 1.1 [0.7, 1.9] 1.7 [0.9, 3.0] 1.5* [1.0,2.4] 1.5 [0.9, 2.6] 1.043 [0.7, 1.6] 1.6 [0.9, 2.6] 0.9 [0.7, 1.3] 1.2 [0.7, 2.1] 3.1*** [2.0, 4.7] 3.5*** [2.1, 6.0] 1.3 [0.8, 2.1] 2.0* [1.1, 3.4] 1.5 [1.0, 2.2] 1.8* [1.1, 3.1] 4.1** [1.4, 12.0] 8.0*** [2.6, 24.5] 1.3 [0.5, 3.5] 2.2 [0.8, 6.0] 1.2 [0.7, 2.2] 2.3* [1.2, 4.5] 1.2 [0.7, 2.1] 1.7 [0.9,3.4] 2.1* [1.1, 3.9] 1.3 [0.7, 2.6] 1.4 [1.0, 2.1] 1.3 [0.8, 2.1] 1.5 [0.8, 2.8] 1.3 [0.6, 3.0] 1.3 [0.6, 2.5] 0.6 [0.3, 1.2] 1.3 [0.8, 2.1] 0.9 [0.5, 1.6] 0.5*** [0.4, 0.6] 0.3*** [0.2, 0.5] 0.6*** [0.5, 0.8] 0.4*** [0.3, 0.6] 0.8 [0.6, 1.0] 0.646* [0.5, 0.9] 0.9 [0.5, 1.6] 1.2 [0.6, 2.4] 3.0*** [1.6, 5.7] 2.9** [1.4, 5.8] 1.1 [0.7, 1.8] 1.6 [0.9, 2.9] 1.1 [0.7, 1.8] 1.2 [0.7, 2.2] 1.2 [0.7, 2.0] 1.0 [0.6, 1.8] 1.1 [0.7, 1.7] 1.1 [0.7, 1.8] 0.8 [0.6, 1.2] 0.7 [0.5, 1.1] 0.9 [0.6, 1.4] 0.8 [0.5, 1.4] 0.8 [0.5, 1.2] 0.6* [0.4, 1.0] 0.6** [0.4, 0.9] 0.5*** [0.3, 0.7] 0.7* [0.5, 0.9] 0.7* [0.5, 0.9] 1.5 [1.0, 2.3] 2.3** [1.3, 4.2] 1.0 [0.6, 1.7] 1.9* [1.1, 3.3] 1.6* [1.1, 2.3] 1.3 [0.8, 2.2] 1.3 [0.8, 2.1] 2.4* [1.1, 5.0] 2.6** [1.3, 5.2] 4.8*** [2.3, 10.2] 1.5 [0.9, 2.5] 1.4 [0.8, 2.5] 1.9 [1.0, 3.6] 6.0*** [2.7, 13.4] 2.3* [1.1, 4.8] 5.9*** [2.5, 13.9] 2.799*** [1.566,5.002] 4.001*** [2.208,7.251] 3.335* [1.251,8.893] 15.64*** [4.868,50.25] 7.3** [2.1, 25.2] 24.3*** [6.7, 87.8] 3.8** [1.7, 8.5] 5.9*** [2.6, 13.1] 0.6* [0.4, 1.0] 0.7 [0.4, 1.2] 0.6 [0.3, 1.2] 0.6 [0.3, 1.1] 0.9 [0.6, 1.4] 0.6* [0.4, 1.0] 1.3 [0.6, 3.0] 0.8 [0.3, 1.9] 1.9 [0.4, 9.4] 0.9 [0.2, 3.7] 1.1 [0.5, 2.4] 2.0 [0.9, 4.4] 1.3 [0.6, 2.9] 0.9 [0.4, 2.5] 3.5 [0.8, 16.1] 1.3 [0.4, 4.9] 1.1 [0.6, 2.3] 1.0 [0.4, 2.1] 1.4 [0.6, 3.3] 0.5 [0.2, 1.4] 3.1 [0.7, 14.6] 1.1 [0.3, 4.3] 0.9 [0.4, 1.9] 0.6 [0.2, 1.3] 0.9 [0.4, 2.0] 0.5 [0.2, 1.0] 5.7* [1.1, 29.6] 2.2 [0.5, 9.0] 1.0 [0.5, 1.9] 0.6 [0.3, 1.3] 2.364 [1.0, 5.6] 1.9 [0.8, 4.3] 3.8 [0.8, 18.7] 2.7 [0.6, 11.2] 1.4 [0.6, 2.8] 0.6 [0.3, 1.3] 2.2 [0.9, 5.4] 0.9 [0.3, 2.8] 3.7 [0.8, 17.8] 0.9 [0.2, 3.8] 1.4 [0.7, 2.9] 1.0 [0.5, 2.3] 2.7* [1.2, 6.3] 1.8 [0.7, 4.8] 2.6 [0.6, 12.1] 1.0 [0.3, 3.9] 1.7 [0.8, 3.6] 1.1 [0.5, 2.6] 6.4*** [2.5, 16.4] 9.7*** [3.6, 26.5] 7.7* [1.6, 37.5] 2.4 [0.6, 9.6] 6.6*** [2.5, 17.1] 7.3*** [2.6, 20.5] 5.8*** [2.6, 12.6] 3.4* [1.2, 9.5] 8.3** [1.8, 39.1] 2.0 [0.5, 7.9] 2.1 [1.0, 4.5] 0.9 [0.4, 2.2] Exponentiated coefficients; 95% confidence intervals in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001. This study was performed following the Declaration of Helsinki and approved by the appropriate ethics committee. The original survey data utilized for this secondary data analysis study was collected by trained field enumerators on behalf of UNICEF and GSS. The MICS team of UNICEF-Ghana, The Ethical Review Board of the Ghana Health Service, and the Ghana Statistical Service approved the study that collected the original survey data. Therefore, ethics approval for this current study was not required since the data is secondary and is available in the public domain. Before the collection of the original survey data, informed consent was obtained from all the respondents. Adult verbal consents and child assents were obtained for the respondents younger than eighteen from their parents/guardians/adult household members to participate in the survey. Additionally, participants were assured of anonymity and confidentiality. More details regarding the data and ethical standards are available at: https://mics.unicef.org/surveys. All methods were performed in accordance with the relevant guidelines and regulations.

AI Digest

Study Justification:

– Maternal mortality is a major global health concern.
– Antenatal care visits are important for promoting maternal and child health.
– Most pregnant women in Ghana and other sub-Saharan African countries do not attain the recommended number of antenatal care visits.
– This study aims to explore the trends in antenatal care visits and associated factors in Ghana from 2006 to 2018.

Highlights:

– The study analyzed data from the Ghana Multiple Indicator Cluster Surveys conducted in 2006, 2011, and 2017-2018.
– The proportion of women achieving adequate antenatal care (4 to 7 visits) increased from 49.3% in 2006 to 58.61% in 2017-2018.
– Factors associated with higher likelihood of adequate and/or optimal antenatal care attendance include upward attainment of formal education, health insurance coverage, increasing household wealth, and residing in the Upper East Region.

Recommendations:

– Target women who are less likely to achieve optimal antenatal care visits to reduce maternal mortalities and birth complications.
– Implement poverty-reduction policies and promote maternal and girl-child education.
– Improve general livelihood in rural settings and expand health insurance coverage and infrastructural access.
– Harness community-level structures and consider innovative measures such as telehealth and telemedicine to increase antenatal care utilization.

Key Role Players:

– Ghana Statistical Service (GSS)
– Ghana Health Service (GHS)
– Ministry of Health (MOH)
– Ministry of Education
– United Nations International Children’s Emergency Fund (UNICEF)
– International donors

Cost Items for Planning Recommendations:

– Implementation of poverty-reduction policies
– Education programs for maternal and girl-child education
– Infrastructure development in rural areas
– Expansion of health insurance coverage
– Telehealth and telemedicine initiatives
– Training and capacity building for healthcare providers
– Monitoring and evaluation of antenatal care utilization programs

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study utilized a large sample size (N = 7795) and analyzed data from three waves of the Ghana Multiple Indicator Cluster Surveys. The study used appropriate statistical analyses, including univariable and multivariable regression models. The results showed a consistent increase in the proportion of women with adequate and optimal antenatal care attendance from 2006 to 2018. The study also identified several factors associated with higher likelihood of adequate and/or optimal antenatal care attendance. However, the abstract could be improved by providing more specific information about the statistical significance of the findings and the magnitude of the associations. Additionally, it would be helpful to include information about any limitations of the study and suggestions for future research.

Resumo

Women datasets from three waves of the Ghana Multiple Indicator Cluster Survey (GMICS) conducted in 2006, 2011 and 2017-2018 were analyzed for this study. The GMICS is a cross-sectional survey conducted by the Ghana Statistical Service (GSS) in association with the Ghana Health Service (GHS), Ministry of Health (MOH), and the Ministry of Education [21]. Funding and technical support were provided by the United Nations International Children’s Emergency Fund (UNICEF) and other international donors [21]. The main aim of the MICS surveys is to collect data on key indicators that assist countries to produce evidence for use in national development plans, policies, and programmes as well as assess the advancements towards the Sustainable Development Goals (SDGs) and other internationally-signed agreements [21]. Trained research enumerators were engaged to collect the data on behalf of GSS and UNICEF using a multi-stage stratified cluster sampling approach. This approach nationally surveyed women in urban and rural areas from the previous ten administrative regions in Ghana: Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West. The initial stage of data collection involved identifying and selecting enumeration areas based on the 2010 Population and Housing Census of Ghana. These enumeration areas became the primary sampling units. Next, in the second stage, households were listed from each of the selected enumeration areas and a sample of households was selected using systematic random sampling. This stage enabled the recruitment of reproductive-aged women from selected households. Data of 7795 women aged 15 to 49 years from all the three waves who had delivered 2 years prior to the data collection periods were included in this study. Antenatal care attendance is the main outcome variable for this study. This variable was extracted from a single-item survey question asking women who had given birth 2 years prior to the data collection about the number of times they attended antenatal care. Women were specifically asked, “How many times did you receive antenatal care during this pregnancy?” Women responded by providing a single number or range of numbers. For those who responded by giving a range, the minimum number was recorded as their answer. Guided by the WHO’s recommendation, these numbers were categorized under 4-scale response format: “none = 1”, “1-3 visits = 2”, “4+ visits = 3” and 8+ visits = 4″. We decided to collapse the none and “1-3 visits” into one category as “less than 4 visits” because only one woman did not attend ANC in the 2006 data. This categorization makes it easy for us to compare the models for the three data waves. Therefore, the newly created categories are as follows: “less than 4 visits (undesirable)=0”, “4 to 7 visits (adequate)=1” and 8+ visits (optimal) = 2″. Age of woman, education, polygyny status, wanted last-child, parity, death of a previous child, health insurance, household wealth index, urban-rural residence, and region of residence were treated as explanatory variables as seen in Table 1. We selected the variables from the datasets based on their reported significance to the outcome variable in the literature [5, 22, 23]. All variables were available in all the three datasets except for health insurance which was only available in the 2011 and 2017-2018 datasets. The variables were measured with single-item self-report questions and simple categorical response options. For instance, age of woman was measured with the question, “How old are you?” and participants responded by indicating their age in numbers which was later categorized by UNICEF. Health insurance was measured with the question, “Are you covered by any health insurance?” with response format comprising “Yes = 1” and “No = 2”. Education was measured by asking participants to respond to the question, “What is the highest level and grade or year of school you have attended?” with responses ranging from, “early childhood education=0” to “higher = 6”. We used the Variance Inflation Factor (VIF) to check for the assumptions of multicollinearity among the independent variables, and we have not observed any violations. Cross-tabulation between ANC visits and study variables in Ghana from 2006 to 2017-2018 433 (29.7) 330 (22.7) 733 (25.5) 320 (22.0) 642 (22.3) 103 (7.1) 391 (13.6) 165 (11.3) 293 (10.2) 264 (18.1) 572 (39.3) 321 (22.0) 619 (21.5) 301 (20.7) 527 (18.3) 371 (25.5) 773 (26.9) 335 (23.0) 637 (22.2) 347 (23.9) 621 (21.6) 277 (19.0) 517 (18.0) 211 (14.5) 530 (18.5) 154 (10.6) 306 (10.7) 112 (7.7) 279 (9.7) 177 (12.2) 451 (15.7) 103 (7.1) 214 (7.5) 195 (13.4) 327 (11.4) 222 (15.2) 511 (17.8) 115 (7.9) 258 (9.0) 278 (19.1) 321 (11.2) 61 (4.2) 120 (4.2) 112 (3.3) 40 (2.7) 85 (3.0) 88 (2.6) Data analyses began by cleaning and recoding variables of interest in STATA version 14. The GMICS predefined survey weights for the differential probability selection of sample were accounted for with the Taylor linearization technique [24, 25]. This procedure adjusted for the clustering, stratification, and design effects within the datasets. Univariable analyses were initially performed on all three waves of datasets by calculating frequencies and percentages of all the variables (see Table Table11 – second, sixth, and tenth columns). Secondly, simple Poisson regression was used to determine whether there was a significant trend in ANC visits over the three data waves (2006, 2011, 2018) (Additional file 1). Furthermore, bivariable analyses were performed with a chi-square test of independence to estimate the relationship between the explanatory variables and the outcome variable as presented in Table Table1.1. Lastly, multivariable analyses with robust multinomial logistic regression models were conducted, treating the “less than 4 visits” category in the outcome variable (antenatal care attendance) as the base. All the explanatory variables were independently (Table 2) and simultaneously (see Table 3) regressed onto the outcome variable, regardless of the statistical significance value in the bivariable analyses. The same processes were repeated for all the three datasets used in this study, setting the significance alpha level at 0.05. The relative risk ratio and the adjusted relative risk ratio were reported. Unadjusted multinomial logit model showing correlates of ANC visits in Ghana from 2006 to 2017-2018 1.3 [0.9, 1.8] 1.9** [1.3, 2.8] 1.5* [1.1, 2.2] 2.1*** [1.4, 3.2] 1.2 [0.9, 1.6] 1.7** [1.2, 2.5] 1.2 [0.8, 1.7] 1.5 [0.9, 2.3] 1.2 [0.8, 1.7] 1.1 [0.7, 1.8] 1.2 [0.8, 1.7] 1.3 [0.9, 2.0] 1.0 [0.7, 1.5] 1.1 [0.7, 1.8] 1.2 [0.8, 1.8] 2.7*** [1.8, 4.2] 0.852 [0.6, 1.2] 1.298 [0.8, 2.1] 2.2*** [1.5, 3.2] 3.8*** [2.5, 5.9] 1.9** [1.3, 3.0] 5.8*** [3.7, 9.2] 1.6** [1.1, 2.1] 2.8*** [1.8, 4.2] 4.0** [1.5,11.01] 17.7*** [6.6, 47.4] 4.6*** [2.0, 10.6] 25.3*** [11.1, 57.5] 2.0** [1.3, 3.3] 7.9*** [4.5, 13.6] 1.4 [0.9, 2.2] 2.0* [1.1, 3.6] 1.7* [1.0, 2.8] 1.5 [0.9, 2.5] 1.6** [1.2, 2.2] 1.8** [1.2, 2.6] 1.5 [0.9, 2.4] 1.1 [0.6, 2.0] 0.8 [0.5, 1.4] 0.3*** [0.2, 0.6] 1.3 [0.9, 1.9] 0.7 [0.4, 1.3] 0.5*** [0.4, 0.6] 0.4*** [0.2, 0.5] 0.7* [0.6, 0.9] 0.6** [0.4, 0.8] 0.7* [0.5, 0.9] 0.5*** [0.4, 0.8] 1.0 [0.7, 1.5] 1.3 [0.9, 2.0] 2.0** [1.3, 3.0] 2.9*** [1.9, 4.5] 1.1 [0.8, 1.5] 1.7** [1.2, 2.4] 1.4 [1.0, 2.1] 1.9** [1.2, 3.0] 1.0 [0.6, 1.6] 1.3 [0.8, 2.0] 1.2 [0.9, 1.8] 1.6* [1.1, 2.4] 0.7* [0.5, 1.0] 0.5*** [0.4, 0.8] 0.7 [0.5, 1.0] 0.5*** [0.3, 0.7] 0.8 [0.6, 1.1] 0.5*** [0.3, 0.7] 0.4*** [0.3, 0.6] 0.3*** [0.2, 0.5] 0.6*** [0.5, 0.8] 0.5*** [0.4, 0.7] 1.4 [0.9, 2.0] 1.8* [1.1, 3.0] 1.0 [0.7, 1.6] 2.7*** [1.6, 4.3] 1.4 [1.0,2.0] 1.4 [0.9, 2.1] 1.3 [0.9, 2.0] 2.2* [1.2, 4.1] 2.9*** [1.5, 5.3] 8.3*** [4.1, 16.5] 1.4 [0.9, 2.1] 1.8* [1.1, 2.9] 2.3** [1.4, 3.8] 7.6*** [4.2, 13.6] 2.7*** [1.5, 4.8] 13.4*** [7.6, 23.9] 2.7*** [1.7, 4.3] 5.7*** [3.5, 9.3] 6.4*** [2.7, 14.8] 43.7*** [17.7, 108.0] 9.1*** [3.8, 21.7] 84.0*** [34.6, 203.8] 4.3*** [2.2, 8.4] 14.6*** [7.6, 27.7] 0.5*** [0.3, 0.7] 0.2*** [0.1, 0.3] 0.4*** [0.2, 0.6] 0.2*** [0.1, 0.3] 0.6** [0.4, 0.8] 0.3*** [0.2, 0.4] 0.7 [0.3, 1.5] 0.2** [0.1, 0.6] 0.6 [0.2, 2.2] 0.2** [0.1, 0.6] 0.7 [0.4, 1.5] 0.8 [0.4, 1.5] 0.6 [0.3, 1.3] 0.2** [0.1, 0.6] 1.3 [0.4, 4.8] 0.3 [0.1, 1.1] 0.8 [0.4, 1.6] 0.4* [0.2, 0.9] 0.6 [0.3, 1.2] 0.1*** [0.0, 0.2] 1.0 [0.3, 3.7] 0.2** [0.0, 0.7] 0.5 [0.2, 1.0] 0.1*** [0.1, 0.3] 0.5* [0.2, 1.0] 0.1*** [0.1, 0.3] 2.4 [0.5, 10.8] 0.7 [0.2, 2.7] 0.6 [0.3, 1.3] 0.3*** [0.1, 0.5] 1.3 [0.6, 2.9] 0.6 [0.3, 1.4] 1.4 [0.4, 5.6] 0.7 [0.2, 2.3] 1.1 [0.5, 2.2] 0.4** [0.2, 0.8] 1.1 [0.5, 2.5] 0.2** [0.1, 0.6] 1.1 [0.3, 4.4] 0.2** [0.0, 0.6] 0.9 [0.4, 1.8] 0.4* [0.182, 0.8] 0.8 [0.4, 1.7] 0.2** [0.1, 0.6] 0.7 [0.2, 2.3] 0.1*** [0.0, 0.3] 0.8 [0.4, 1.5] 0.2*** [0.1, 0.4] 1.7 [0.7, 3.9] 0.8 [0.3, 2.0] 1.8 [0.5, 7.0] 0.2** [0.1, 0.6] 3.0* [1.3, 6.9] 1.5 [0.7, 3.5] 1.3 [0.7, 2.7] 0.34** [0.1, 0.6] 2.2 [0.6, 8.0] 0.2** [0.1, 0.7] 1.0 [0.5, 1.9] 0.2*** [0.1,0.4] Adjusted multinomial logit model displaying correlates of ANC visits in Ghana from 2006 to 2017-2018 1.1 [0.7, 1.6] 1.3 [0.7, 2.3] 2.1** [1.3, 3.4] 2.7** [1.5,4.9] 1.0 [0.7, 1.5] 1.5 [0.9, 2.5] 1.3 [0.8, 2.1] 1.9 [1.0, 3.8] 2.1** [1.2, 3.6] 2.5** [1.3, 5.1] 1.1 [0.7, 1.9] 1.7 [0.9, 3.0] 1.5* [1.0,2.4] 1.5 [0.9, 2.6] 1.043 [0.7, 1.6] 1.6 [0.9, 2.6] 0.9 [0.7, 1.3] 1.2 [0.7, 2.1] 3.1*** [2.0, 4.7] 3.5*** [2.1, 6.0] 1.3 [0.8, 2.1] 2.0* [1.1, 3.4] 1.5 [1.0, 2.2] 1.8* [1.1, 3.1] 4.1** [1.4, 12.0] 8.0*** [2.6, 24.5] 1.3 [0.5, 3.5] 2.2 [0.8, 6.0] 1.2 [0.7, 2.2] 2.3* [1.2, 4.5] 1.2 [0.7, 2.1] 1.7 [0.9,3.4] 2.1* [1.1, 3.9] 1.3 [0.7, 2.6] 1.4 [1.0, 2.1] 1.3 [0.8, 2.1] 1.5 [0.8, 2.8] 1.3 [0.6, 3.0] 1.3 [0.6, 2.5] 0.6 [0.3, 1.2] 1.3 [0.8, 2.1] 0.9 [0.5, 1.6] 0.5*** [0.4, 0.6] 0.3*** [0.2, 0.5] 0.6*** [0.5, 0.8] 0.4*** [0.3, 0.6] 0.8 [0.6, 1.0] 0.646* [0.5, 0.9] 0.9 [0.5, 1.6] 1.2 [0.6, 2.4] 3.0*** [1.6, 5.7] 2.9** [1.4, 5.8] 1.1 [0.7, 1.8] 1.6 [0.9, 2.9] 1.1 [0.7, 1.8] 1.2 [0.7, 2.2] 1.2 [0.7, 2.0] 1.0 [0.6, 1.8] 1.1 [0.7, 1.7] 1.1 [0.7, 1.8] 0.8 [0.6, 1.2] 0.7 [0.5, 1.1] 0.9 [0.6, 1.4] 0.8 [0.5, 1.4] 0.8 [0.5, 1.2] 0.6* [0.4, 1.0] 0.6** [0.4, 0.9] 0.5*** [0.3, 0.7] 0.7* [0.5, 0.9] 0.7* [0.5, 0.9] 1.5 [1.0, 2.3] 2.3** [1.3, 4.2] 1.0 [0.6, 1.7] 1.9* [1.1, 3.3] 1.6* [1.1, 2.3] 1.3 [0.8, 2.2] 1.3 [0.8, 2.1] 2.4* [1.1, 5.0] 2.6** [1.3, 5.2] 4.8*** [2.3, 10.2] 1.5 [0.9, 2.5] 1.4 [0.8, 2.5] 1.9 [1.0, 3.6] 6.0*** [2.7, 13.4] 2.3* [1.1, 4.8] 5.9*** [2.5, 13.9] 2.799*** [1.566,5.002] 4.001*** [2.208,7.251] 3.335* [1.251,8.893] 15.64*** [4.868,50.25] 7.3** [2.1, 25.2] 24.3*** [6.7, 87.8] 3.8** [1.7, 8.5] 5.9*** [2.6, 13.1] 0.6* [0.4, 1.0] 0.7 [0.4, 1.2] 0.6 [0.3, 1.2] 0.6 [0.3, 1.1] 0.9 [0.6, 1.4] 0.6* [0.4, 1.0] 1.3 [0.6, 3.0] 0.8 [0.3, 1.9] 1.9 [0.4, 9.4] 0.9 [0.2, 3.7] 1.1 [0.5, 2.4] 2.0 [0.9, 4.4] 1.3 [0.6, 2.9] 0.9 [0.4, 2.5] 3.5 [0.8, 16.1] 1.3 [0.4, 4.9] 1.1 [0.6, 2.3] 1.0 [0.4, 2.1] 1.4 [0.6, 3.3] 0.5 [0.2, 1.4] 3.1 [0.7, 14.6] 1.1 [0.3, 4.3] 0.9 [0.4, 1.9] 0.6 [0.2, 1.3] 0.9 [0.4, 2.0] 0.5 [0.2, 1.0] 5.7* [1.1, 29.6] 2.2 [0.5, 9.0] 1.0 [0.5, 1.9] 0.6 [0.3, 1.3] 2.364 [1.0, 5.6] 1.9 [0.8, 4.3] 3.8 [0.8, 18.7] 2.7 [0.6, 11.2] 1.4 [0.6, 2.8] 0.6 [0.3, 1.3] 2.2 [0.9, 5.4] 0.9 [0.3, 2.8] 3.7 [0.8, 17.8] 0.9 [0.2, 3.8] 1.4 [0.7, 2.9] 1.0 [0.5, 2.3] 2.7* [1.2, 6.3] 1.8 [0.7, 4.8] 2.6 [0.6, 12.1] 1.0 [0.3, 3.9] 1.7 [0.8, 3.6] 1.1 [0.5, 2.6] 6.4*** [2.5, 16.4] 9.7*** [3.6, 26.5] 7.7* [1.6, 37.5] 2.4 [0.6, 9.6] 6.6*** [2.5, 17.1] 7.3*** [2.6, 20.5] 5.8*** [2.6, 12.6] 3.4* [1.2, 9.5] 8.3** [1.8, 39.1] 2.0 [0.5, 7.9] 2.1 [1.0, 4.5] 0.9 [0.4, 2.2] Exponentiated coefficients; 95% confidence intervals in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001. This study was performed following the Declaration of Helsinki and approved by the appropriate ethics committee. The original survey data utilized for this secondary data analysis study was collected by trained field enumerators on behalf of UNICEF and GSS. The MICS team of UNICEF-Ghana, The Ethical Review Board of the Ghana Health Service, and the Ghana Statistical Service approved the study that collected the original survey data. Therefore, ethics approval for this current study was not required since the data is secondary and is available in the public domain. Before the collection of the original survey data, informed consent was obtained from all the respondents. Adult verbal consents and child assents were obtained for the respondents younger than eighteen from their parents/guardians/adult household members to participate in the survey. Additionally, participants were assured of anonymity and confidentiality. More details regarding the data and ethical standards are available at: https://mics.unicef.org/surveys. All methods were performed in accordance with the relevant guidelines and regulations.

Materiais

https://efashare.b-cdn.net/materials/24eeee647a79213ec0266c8fc0922a9fe606374f734a199320aaeda72f3a0d79/12884_2022_4404_MOESM1_ESM.docx

Inovações

The study titled “Trends in antenatal care visits and associated factors in Ghana from 2006 to 2018” provides valuable insights into the utilization of antenatal care (ANC) services in Ghana and the factors associated with it. The study found that there has been a consistent increase in the proportion of women receiving adequate and optimal antenatal care from 2006 to 2018. Factors such as upward attainment of formal education, health insurance coverage, increasing household wealth, and residing in the Upper East Region were consistently associated with a higher likelihood of adequate and/or optimal antenatal care attendance.

Based on the findings of the study, a recommendation can be developed into an innovation to improve access to maternal health in Ghana. One such recommendation is the implementation of telehealth and telemedicine initiatives. Telehealth involves the use of technology to provide healthcare services remotely, while telemedicine specifically refers to the use of telecommunication technology for medical consultations.

By leveraging telehealth and telemedicine, pregnant women in remote areas can receive virtual consultations with healthcare providers, access educational resources, and receive guidance on prenatal care. This approach can help overcome geographical barriers and improve access to maternal health services, especially in rural areas where healthcare facilities may be limited.

To implement telehealth and telemedicine initiatives, it is important to ensure that the necessary infrastructure, such as reliable internet connectivity and access to smartphones or other devices, is in place. Additionally, healthcare providers should be trained on how to effectively use these technologies to provide quality care and support to pregnant women.

By incorporating telehealth and telemedicine into the existing healthcare system, Ghana can enhance access to maternal health services, improve health outcomes for pregnant women, and contribute to the overall reduction of maternal mortality rates.

AI Innovations Description

The study titled “Trends in antenatal care visits and associated factors in Ghana from 2006 to 2018” provides valuable insights into the utilization of antenatal care (ANC) services in Ghana and the factors associated with it. Based on the findings of the study, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Telehealth and Telemedicine: The study suggests that innovative measures such as telehealth and telemedicine can be utilized to increase antenatal care utilization. Telehealth involves the use of technology to provide healthcare services remotely, while telemedicine specifically refers to the use of telecommunication technology for medical consultations. Implementing telehealth and telemedicine initiatives in Ghana can help overcome geographical barriers and improve access to maternal health services, especially in rural areas where healthcare facilities may be limited.

By leveraging telehealth and telemedicine, pregnant women in remote areas can receive virtual consultations with healthcare providers, access educational resources, and receive guidance on prenatal care. This approach can help address the challenges faced by pregnant women in accessing timely and adequate antenatal care, ultimately reducing maternal mortality and birth complications.

It is important to ensure that the necessary infrastructure, such as reliable internet connectivity and access to smartphones or other devices, is in place to support the implementation of telehealth and telemedicine initiatives. Additionally, healthcare providers should be trained on how to effectively use these technologies to provide quality care and support to pregnant women.

AI Innovations Methodology

To simulate the impact of the main recommendations from the study on improving access to maternal health, you can follow these steps:

1. Define the target population: Identify the specific population group that will be the focus of the intervention, such as pregnant women in rural areas of Ghana.

2. Determine the baseline data: Gather information on the current utilization of antenatal care services in the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop the intervention: Design and implement a telehealth and telemedicine program that aims to improve access to antenatal care services. This may involve setting up teleconsultation platforms, providing training to healthcare providers on virtual care delivery, and ensuring access to necessary technology and internet connectivity.

4. Implement the intervention: Roll out the telehealth and telemedicine program in the target population. Monitor the implementation process and address any challenges or barriers that arise.

5. Collect data: Gather data on the utilization of antenatal care services before and after the implementation of the telehealth and telemedicine program. This can be done through surveys, interviews, or by analyzing existing data sources.

6. Analyze the data: Compare the baseline data with the post-intervention data to assess the impact of the telehealth and telemedicine program on access to antenatal care services. Look for changes in the proportion of women receiving adequate and optimal antenatal care visits.

7. Evaluate the results: Assess the effectiveness of the telehealth and telemedicine program in improving access to antenatal care services. Consider factors such as the increase in the proportion of women receiving adequate and optimal care, the satisfaction of pregnant women with the virtual consultations, and any improvements in maternal health outcomes.

8. Refine and scale up the intervention: Based on the evaluation results, make any necessary adjustments to the telehealth and telemedicine program to further enhance its impact. If the intervention proves to be successful, consider scaling it up to reach a larger population and replicate it in other regions or countries.

By following these steps, you can simulate the impact of implementing telehealth and telemedicine initiatives on improving access to maternal health services in Ghana. This will help inform future policy and programmatic decisions to enhance antenatal care utilization and ultimately reduce maternal mortality rates.

Autor

Dima Health

Statistics:

Citations: 3

Identifiers:

DOI: 10.1186/s12884-022-04404-9

Research Areas:

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

Study Countries:

Ghana

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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