Characteristics, treatment outcomes and experiences of COVID-19 patients under home-based care in Kapelebyong district in Uganda: a mixed-methods study

Tropical Medicine and Health, Volume 50, No. 1, Year 2022

Interprétation

Background: A rapid increase in community transmission of COVID-19 across the country overwhelmed Uganda’s health care system. In response, the Ministry of Health adopted the home-based care strategy for COVID-19 patients with mild-to-moderate disease. We determined the characteristics, treatment outcomes and experiences of COVID-19 patients under home-based care during the second wave in Kapelebyong district, in eastern Uganda. Methods: We conducted a sequential explanatory mixed-methods study. We first collected quantitative data using an interviewer-administered questionnaire to determine characteristics and treatment outcomes of COVID-19 patients under home-based care. Cured at home was coded as 1 (considered a good outcome) while being admitted to a health facility and/or dying were coded as 0 (considered poor outcomes). Thereafter, we conducted 11 in-depth interviews to explore the experiences of COVID-19 patients under home-based care. Multivariable logistic regression was used to assess factors associated with poor treatment outcomes using Stata v.15.0. Thematic content analysis was used to explore lived experiences of COVID-19 patients under home-based care using NVivo 12.0.0 Results: A total of 303 study participants were included. The mean age ± standard deviation of participants was 32.2 years ± 19.9. Majority of the participants [96.0% (289/303)] cured at home, 3.3% (10/303) were admitted to a health facility and 0.7% (2/303) died. Patients above 60 years of age had 17.4 times the odds of having poor treatment outcomes compared to those below 60 years of age (adjusted odds ratio (AOR): 17.4; 95% CI: 2.2–137.6). Patients who spent more than one month under home-based care had 15.3 times the odds of having poor treatment outcomes compared to those that spent less than one month (AOR: 15.3; 95% CI: 1.6–145.7). From the qualitative interviews, participants identified stigma, fear, anxiety, rejection, not being followed up by health workers and economic loss as negative experiences encountered during home-based care. Positive lived experiences included closeness to friends and family, more freedom, and easy access to food. Conclusion: Home-based care of COVID-19 was operational in eastern Uganda. Older age (> 60 years) and prolonged illness (> 1 months) were associated with poor treatment outcomes. Social support was an impetus for home-based care. We conducted a sequential explanatory mixed-methods study, where quantitative data were collected and analyzed first. This was later followed by qualitative data, which was collected to better understand the experiences of participants under COVID-19 home-based care. The study was conducted between November 2021 and February 2022 in Kapelebyong district in the eastern region of Uganda bordered by Napak district to the north, Katakwi district to the east, Amuria district to the south, Alebtong district to the west and Abim district to the north-west. Kapelebyong has a total population of 168,242 people. Of these, 94,578 (56.2%) are female while 73,664 (43.8%) are male. Kapelebyong district has one constituency of Kapelebyong, 11 sub-county level administrative units, 55 parish level administrative units and 327 villages. The district has the district task force and the sub-county task force for coordination of COVID-19 management. Kapelebyong has 14 health facilities; 1 health center (HC) 4, 3 HC3s (2 Government, 1 private not for profit), 9 HC2s and 1 Nursing home which is private for profit. Services offered include; out patients’ department, maternal and child health, laboratory services, HIV Services, family planning and theater services for only Kapelebyong HC4 with a bed capacity of 85. Health workers from HC3s and HC4 were trained and equipped with knowledge on diagnosis and management of COVID-19 patients under home-based care. Only patients that did not require admission at the time of diagnosis based on a clinician’s assessment were put under home-based care. All COVID-19 patients that needed admission were referred to Soroti regional referral hospital. All COVID-19 patients under home-based care in Kapelebyong district were eligible for this study. COVID-19 patients of all age groups, both male and female diagnosed using a PCR Test or a rapid diagnostic test, put under home-based care in Kapelebyong district and gave informed consent were included in this study. We excluded COVID-19 patients who were too sick to talk or those with severe mental disability. We sampled all COVID-19 patients under home-based care in Kapelebyong district that met the inclusion criteria and this gave us a sample size of 303. This sample size results in an absolute precision of 1.6% to 5.6%, i.e., the difference between the point estimate and the 95% confidence interval (CI) for prevalence values of poor outcomes ranging from 2 to 50%. We used the District Health Office database and health facility data base with locator information of all COVID-19 Patients under home-based care in Kapelebyong district. The district COVID-19 home-based care data are stored in the District Health Information Software 2 (DHIS2) tool. This data was cross-checked to ensure consistency with data at the health facilities stored at the facility COVID-19 home-based care registers. The facility COVID-19 home-based care registers capture identification and location information of all COVID-19 patients under home-based care within their catchment area such as; patients age, sex, date of COVID-19 test, date enrolled onto home-based care, place of residence, telephone number among others. The health facilities also had the list of all community health workers attached to each of these health facilities and their contacts. This information enabled us access all the COVID-19 patients under home-based care in Kapelebyong district. Additionally, the lead researcher in this study was the district focal person for home-based care services in Kapelebyong and played a key role in coordinating COVID-19 home-based care activities, so there was no challenge in accessing and enrolling participants into the study. The dependent variable was treatment outcomes of COVID-19 patients under home-based care. These were divided into good outcome used to define patients that cured at home and poor outcome used to define patients that were admitted to a health facility and/or died while under home care. The independent variables were; socio-demographic factors (age, marital status, tribe, educational level, income levels, occupation, and religion), presence of comorbidities, vaccination status, number of vaccination doses, monthly family income, duration in care and follow up by health workers. We calculated wealth tertiles from an asset based index using principal component analysis. The following assets were considered: radio, television, mobile phone bicycle, motorcycle, car, computer, permanent house and piped water. We used two trained research assistants to collect data electronically using an interviewer administered questionnaire designed in Kobo Toolbox (Cambridge, Massachusetts, USA). All participants were followed at their homes and face-to-face interviews were conducted in a secure environment that allowed free interaction between the participant and the interviewer after obtaining written informed consent from the participant. An abstraction tool prepared for the study was used to collect data about the deceased patients from their medical records. We summarized categorical variables as proportions and continuous variables as mean (standard deviation). We computed described analyses to determine the percentage of home-based care patients that cured, those that were eventually hospitalized, and those that were reported as dead at the time of interview. Dead patients were excluded from further analyses since they were not alive to be interviewed. We conducted multivariable logistic regression to determine the factors associated with poor treatment outcomes among COVID-19 patients under home-based care while controlling confounders. Factors with a p-value of less than 0.2 at bivariable analysis, and those known to affect treatment outcomes of COVID-19 patients from literature were included in the multivariable analysis. Adjusted odds ratios (AOR), 95% Confidence interval and p-values were calculated at a statistical significance at a p-value < 0.05. We used Stata V.15.0. (StataCorp LLC, College Station, Texas, United States of America) for analysis. We purposively selected 11 participants among those that were part of the quantitative interviews to explore their experiences while under home-based care. Participants were followed at home. We collected qualitative data on the experiences of patients under home-based care in Kapelebyong district using an in-depth interview guide. An interviewer that is experienced in conducting qualitative interviews conducted the face-to-face in-depth interviews in a secure environment that allowed free interaction between the interviewer and the participant. Probing questions were used to get rich information on the issues that arose during the discussions. The interviews would take between 20 and 30 min. With permission from the participants, the interviews were audio recorded, transcribed verbatim and translated into English for those conducted in Ateso. We used thematic content analysis to analyze the data. The analysis followed a five-step process. First, we read through the transcripts and became familiar with the data. Secondly, we organized data in a meaningful way and generated the initial codes. Once the data had been sufficiently coded and saturation reached, we identified themes. We then reviewed and modified themes and put together all data relevant to each theme. Data were managed in NVivo 12.0.0 (QRS International, Cambridge, MA). Examples of meanings units, codes, categories and themes from qualitative content of interviews about experiences of COVID-19 patients under home-based care are shown in Table Table11. Examples of meanings units, codes, categories and themes from qualitative content of interviews about experiences of COVID-19 patients under home-based care When my son's cough intensified, I called the doctor and they sent an ambulance to come and take him” [P11, 60 years female, IDI] When I got COVID-19, waking up became a problem because I had general body pains so it became a bit difficult. I would wake up at around 9-10 am and I wouldn't have energy to get out of bed” [P01, 35 years, female, IDI] After two weeks, I went back to hospital to do a test to confirm if I had recovered and I found the laboratory closed. I was then sent out by a health worker saying that they didn't know if I had recovered or not. I felt segregated, lonely and isolated” [P01, 35 years female, IDI] There's no way I could move anywhere because when people see you, they would think you're spreading the disease. Even the pupils I was teaching, they could see me and run. They even nicknamed, "corona". One day I tried to move out because they'd told her to be doing exercise, when the children saw them, they started shouting, "corona, corona" and we decided to come back home” … [P11, 60 years, female, IDI]

Résumé de l’IA

Study Justification:

– The study aimed to understand the characteristics, treatment outcomes, and experiences of COVID-19 patients under home-based care in Kapelebyong district, Uganda during the second wave of the pandemic.
– This information is important for policymakers and healthcare providers to assess the effectiveness of home-based care strategies and identify areas for improvement.
– The study provides insights into the factors associated with poor treatment outcomes and the experiences of patients, which can inform future interventions and support systems for COVID-19 patients.

Highlights:

– The study included 303 COVID-19 patients under home-based care in Kapelebyong district.
– Majority of the participants (96.0%) were cured at home, while 3.3% were admitted to a health facility and 0.7% died.
– Patients above 60 years of age had 17.4 times the odds of having poor treatment outcomes compared to those below 60 years of age.
– Patients who spent more than one month under home-based care had 15.3 times the odds of having poor treatment outcomes compared to those that spent less than one month.
– Qualitative interviews revealed negative experiences such as stigma, fear, anxiety, rejection, lack of follow-up by health workers, and economic loss. Positive experiences included closeness to friends and family, more freedom, and easy access to food.

Recommendations:

– Improve support and follow-up for COVID-19 patients under home-based care, especially for older patients and those with prolonged illness.
– Address stigma and discrimination faced by COVID-19 patients through community education and awareness campaigns.
– Strengthen coordination between health facilities and home-based care providers to ensure timely and appropriate care.
– Enhance economic support for COVID-19 patients and their families to mitigate the financial burden of the disease.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing policies related to COVID-19 home-based care.
– District Health Office: Coordinates COVID-19 management activities at the district level, including home-based care services.
– Health Facilities: Provide training and support to health workers involved in home-based care.
– Community Health Workers: Play a crucial role in identifying and monitoring COVID-19 patients under home-based care.
– Local Leaders and Community Organizations: Engage in community education and awareness campaigns to address stigma and discrimination.

Cost Items for Planning Recommendations:

– Training and capacity building for health workers involved in home-based care.
– Community education and awareness campaigns to address stigma and discrimination.
– Economic support programs for COVID-19 patients and their families.
– Coordination and monitoring systems to ensure timely and appropriate care.
– Data management and analysis for monitoring and evaluation purposes.

Force de la preuve

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 design, which includes both quantitative and qualitative data collection and analysis, provides a comprehensive understanding of the characteristics, treatment outcomes, and experiences of COVID-19 patients under home-based care in Kapelebyong district, Uganda. The sample size of 303 participants is adequate for the study objectives, and the data collection methods, such as interviewer-administered questionnaires and in-depth interviews, are appropriate for capturing both quantitative and qualitative data. The statistical analysis using multivariable logistic regression adds rigor to the study findings. However, there are a few actionable steps to improve the evidence. First, the abstract could provide more details on the sampling strategy and inclusion/exclusion criteria to enhance transparency and replicability. Second, the abstract could include information on the response rate and any potential biases in participant selection to assess the generalizability of the findings. Lastly, the abstract could summarize the key findings and implications of the study to provide a clear takeaway message for readers.

Abstrait

We conducted a sequential explanatory mixed-methods study, where quantitative data were collected and analyzed first. This was later followed by qualitative data, which was collected to better understand the experiences of participants under COVID-19 home-based care. The study was conducted between November 2021 and February 2022 in Kapelebyong district in the eastern region of Uganda bordered by Napak district to the north, Katakwi district to the east, Amuria district to the south, Alebtong district to the west and Abim district to the north-west. Kapelebyong has a total population of 168,242 people. Of these, 94,578 (56.2%) are female while 73,664 (43.8%) are male. Kapelebyong district has one constituency of Kapelebyong, 11 sub-county level administrative units, 55 parish level administrative units and 327 villages. The district has the district task force and the sub-county task force for coordination of COVID-19 management. Kapelebyong has 14 health facilities; 1 health center (HC) 4, 3 HC3s (2 Government, 1 private not for profit), 9 HC2s and 1 Nursing home which is private for profit. Services offered include; out patients’ department, maternal and child health, laboratory services, HIV Services, family planning and theater services for only Kapelebyong HC4 with a bed capacity of 85. Health workers from HC3s and HC4 were trained and equipped with knowledge on diagnosis and management of COVID-19 patients under home-based care. Only patients that did not require admission at the time of diagnosis based on a clinician’s assessment were put under home-based care. All COVID-19 patients that needed admission were referred to Soroti regional referral hospital. All COVID-19 patients under home-based care in Kapelebyong district were eligible for this study. COVID-19 patients of all age groups, both male and female diagnosed using a PCR Test or a rapid diagnostic test, put under home-based care in Kapelebyong district and gave informed consent were included in this study. We excluded COVID-19 patients who were too sick to talk or those with severe mental disability. We sampled all COVID-19 patients under home-based care in Kapelebyong district that met the inclusion criteria and this gave us a sample size of 303. This sample size results in an absolute precision of 1.6% to 5.6%, i.e., the difference between the point estimate and the 95% confidence interval (CI) for prevalence values of poor outcomes ranging from 2 to 50%. We used the District Health Office database and health facility data base with locator information of all COVID-19 Patients under home-based care in Kapelebyong district. The district COVID-19 home-based care data are stored in the District Health Information Software 2 (DHIS2) tool. This data was cross-checked to ensure consistency with data at the health facilities stored at the facility COVID-19 home-based care registers. The facility COVID-19 home-based care registers capture identification and location information of all COVID-19 patients under home-based care within their catchment area such as; patients age, sex, date of COVID-19 test, date enrolled onto home-based care, place of residence, telephone number among others. The health facilities also had the list of all community health workers attached to each of these health facilities and their contacts. This information enabled us access all the COVID-19 patients under home-based care in Kapelebyong district. Additionally, the lead researcher in this study was the district focal person for home-based care services in Kapelebyong and played a key role in coordinating COVID-19 home-based care activities, so there was no challenge in accessing and enrolling participants into the study. The dependent variable was treatment outcomes of COVID-19 patients under home-based care. These were divided into good outcome used to define patients that cured at home and poor outcome used to define patients that were admitted to a health facility and/or died while under home care. The independent variables were; socio-demographic factors (age, marital status, tribe, educational level, income levels, occupation, and religion), presence of comorbidities, vaccination status, number of vaccination doses, monthly family income, duration in care and follow up by health workers. We calculated wealth tertiles from an asset based index using principal component analysis. The following assets were considered: radio, television, mobile phone bicycle, motorcycle, car, computer, permanent house and piped water. We used two trained research assistants to collect data electronically using an interviewer administered questionnaire designed in Kobo Toolbox (Cambridge, Massachusetts, USA). All participants were followed at their homes and face-to-face interviews were conducted in a secure environment that allowed free interaction between the participant and the interviewer after obtaining written informed consent from the participant. An abstraction tool prepared for the study was used to collect data about the deceased patients from their medical records. We summarized categorical variables as proportions and continuous variables as mean (standard deviation). We computed described analyses to determine the percentage of home-based care patients that cured, those that were eventually hospitalized, and those that were reported as dead at the time of interview. Dead patients were excluded from further analyses since they were not alive to be interviewed. We conducted multivariable logistic regression to determine the factors associated with poor treatment outcomes among COVID-19 patients under home-based care while controlling confounders. Factors with a p-value of less than 0.2 at bivariable analysis, and those known to affect treatment outcomes of COVID-19 patients from literature were included in the multivariable analysis. Adjusted odds ratios (AOR), 95% Confidence interval and p-values were calculated at a statistical significance at a p-value < 0.05. We used Stata V.15.0. (StataCorp LLC, College Station, Texas, United States of America) for analysis. We purposively selected 11 participants among those that were part of the quantitative interviews to explore their experiences while under home-based care. Participants were followed at home. We collected qualitative data on the experiences of patients under home-based care in Kapelebyong district using an in-depth interview guide. An interviewer that is experienced in conducting qualitative interviews conducted the face-to-face in-depth interviews in a secure environment that allowed free interaction between the interviewer and the participant. Probing questions were used to get rich information on the issues that arose during the discussions. The interviews would take between 20 and 30 min. With permission from the participants, the interviews were audio recorded, transcribed verbatim and translated into English for those conducted in Ateso. We used thematic content analysis to analyze the data. The analysis followed a five-step process. First, we read through the transcripts and became familiar with the data. Secondly, we organized data in a meaningful way and generated the initial codes. Once the data had been sufficiently coded and saturation reached, we identified themes. We then reviewed and modified themes and put together all data relevant to each theme. Data were managed in NVivo 12.0.0 (QRS International, Cambridge, MA). Examples of meanings units, codes, categories and themes from qualitative content of interviews about experiences of COVID-19 patients under home-based care are shown in Table Table11. Examples of meanings units, codes, categories and themes from qualitative content of interviews about experiences of COVID-19 patients under home-based care When my son's cough intensified, I called the doctor and they sent an ambulance to come and take him” [P11, 60 years female, IDI] When I got COVID-19, waking up became a problem because I had general body pains so it became a bit difficult. I would wake up at around 9-10 am and I wouldn't have energy to get out of bed” [P01, 35 years, female, IDI] After two weeks, I went back to hospital to do a test to confirm if I had recovered and I found the laboratory closed. I was then sent out by a health worker saying that they didn't know if I had recovered or not. I felt segregated, lonely and isolated” [P01, 35 years female, IDI] There's no way I could move anywhere because when people see you, they would think you're spreading the disease. Even the pupils I was teaching, they could see me and run. They even nicknamed, "corona". One day I tried to move out because they'd told her to be doing exercise, when the children saw them, they started shouting, "corona, corona" and we decided to come back home” … [P11, 60 years, female, IDI]

Matériaux

N/A

Innovations

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

1. Telemedicine: Implementing telemedicine services can allow pregnant women to access healthcare remotely, reducing the need for in-person visits and increasing convenience, especially for those in remote areas.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information, reminders, and support for pregnant women can help them stay informed about their health, track their progress, and receive personalized recommendations.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal and postnatal care, education, and support to pregnant women in their communities can improve access to maternal health services, especially in underserved areas.

4. Transportation solutions: Developing transportation solutions, such as mobile clinics or transportation vouchers, can help overcome transportation barriers and ensure that pregnant women can easily access healthcare facilities for prenatal care, delivery, and postnatal care.

5. Maternal health hotlines: Establishing dedicated hotlines staffed by healthcare professionals who can provide guidance, answer questions, and address concerns related to maternal health can provide pregnant women with immediate access to information and support.

6. Maternal health awareness campaigns: Conducting targeted awareness campaigns to educate pregnant women and their families about the importance of prenatal care, safe delivery practices, and postnatal care can help improve maternal health outcomes and encourage early and regular healthcare seeking behavior.

7. Financial incentives: Implementing financial incentives, such as conditional cash transfers or subsidies, can help reduce financial barriers and encourage pregnant women to seek timely and appropriate maternal healthcare services.

8. Collaborative care models: Establishing collaborative care models that involve multiple healthcare providers, including obstetricians, midwives, nurses, and community health workers, can ensure comprehensive and coordinated care for pregnant women, improving access to a range of maternal health services.

9. Mobile clinics: Setting up mobile clinics that travel to remote or underserved areas can bring essential maternal health services, including prenatal care, screenings, and vaccinations, closer to pregnant women who may have limited access to healthcare facilities.

10. Maternal health information systems: Developing and implementing robust information systems that capture and analyze data related to maternal health can help identify gaps in access and quality of care, inform evidence-based decision-making, and improve overall maternal health outcomes.

It’s important to note that the specific context and needs of the community should be considered when implementing any innovation to improve access to maternal health.

AI Innovations Description

Based on the information provided, the study conducted in Kapelebyong district in Uganda focused on the characteristics, treatment outcomes, and experiences of COVID-19 patients under home-based care. While the study does not directly address maternal health, we can draw some recommendations that can be used to develop innovations to improve access to maternal health. Here are some potential recommendations:

1. Strengthen home-based care for maternal health: Based on the success of home-based care for COVID-19 patients, a similar approach can be adopted for maternal health. This could involve providing prenatal and postnatal care at home, ensuring that pregnant women receive necessary check-ups, monitoring, and support in the comfort of their own homes.

2. Utilize telemedicine and remote monitoring: The study highlighted the importance of follow-up by health workers during home-based care. Telemedicine and remote monitoring technologies can be utilized to provide virtual consultations, monitor vital signs, and offer guidance to pregnant women remotely. This can help improve access to maternal health services, especially in remote or underserved areas.

3. Address stigma and social support: The qualitative interviews revealed negative experiences such as stigma and rejection faced by COVID-19 patients under home-based care. Similar challenges may exist for pregnant women seeking maternal health services. It is important to address stigma and provide social support to pregnant women, ensuring they feel comfortable accessing care and have a supportive environment.

4. Improve access to healthcare facilities: The study mentioned that COVID-19 patients who required admission were referred to a regional referral hospital. Similarly, pregnant women who require specialized care should have access to appropriate healthcare facilities. Innovations can focus on improving transportation and infrastructure to ensure pregnant women can easily reach healthcare facilities when needed.

5. Enhance community health worker programs: The study mentioned the involvement of community health workers in the home-based care of COVID-19 patients. Similarly, community health worker programs can be expanded and strengthened to provide maternal health services at the community level. This can include prenatal education, assistance with birth preparedness, and postnatal support.

These recommendations can serve as a starting point for developing innovations to improve access to maternal health based on the findings and experiences highlighted in the study. It is important to further explore and adapt these recommendations to the specific context and needs of the target population.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening Community-Based Maternal Health Programs: Implementing community-based programs that focus on maternal health education, awareness, and support can help improve access to maternal health services. These programs can involve training community health workers to provide basic maternal health services, conducting regular community outreach programs, and promoting the importance of antenatal and postnatal care.

2. Telemedicine and Mobile Health Applications: Utilizing telemedicine and mobile health applications can help overcome geographical barriers and improve access to maternal health services. These technologies can enable pregnant women to consult with healthcare providers remotely, receive health information and reminders, and access prenatal and postnatal care services through mobile devices.

3. Transportation Support: Providing transportation support to pregnant women in remote or underserved areas can help overcome the challenge of accessing maternal health services. This can involve establishing transportation networks, subsidizing transportation costs, or implementing mobile clinics to reach women in remote areas.

4. Strengthening Health Infrastructure: Investing in the improvement and expansion of health facilities, particularly in rural and underserved areas, can enhance access to maternal health services. This includes ensuring the availability of skilled healthcare providers, essential medical equipment, and necessary medications for safe delivery and postnatal care.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the number of antenatal care visits, institutional delivery rates, postnatal care utilization, and maternal mortality rates.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population. This can involve conducting surveys, reviewing existing health records, and analyzing relevant data sources.

3. Define the intervention scenarios: Develop different scenarios based on the recommendations mentioned above. For each scenario, determine the expected changes in access to maternal health services, such as increased utilization rates or reduced travel distances.

4. Simulate the impact: Use modeling techniques, such as mathematical models or simulation software, to estimate the potential impact of each intervention scenario on the defined indicators. This can involve projecting changes in utilization rates, estimating the number of additional women accessing services, or predicting changes in maternal health outcomes.

5. Analyze and compare results: Analyze the simulated results for each intervention scenario and compare them to the baseline data. Assess the potential benefits, challenges, and trade-offs associated with each recommendation. This analysis can help prioritize interventions and inform decision-making processes.

6. Refine and iterate: Based on the analysis, refine the intervention scenarios and simulation models as needed. Repeat the simulation process to further explore the potential impact of different combinations or variations of the recommendations.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different innovations and interventions on improving access to maternal health services. This information can guide decision-making, resource allocation, and implementation strategies to effectively address the challenges in maternal healthcare access.

Auteur

Dima Health

Identifiers:

DOI: 10.1186/s41182-022-00486-5

ISSN: 13488945

Research Areas:

Community Interventions, COVID, Disability, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Uganda

Study Design:

Case-Control Study, Cross Sectional Study, Phenomenological Study

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Male & Female

Partagez ceci :