Kenya Master Health Facility List, 2020

This master list of health facilities was developed from a variety of government and non-government sources from 50 countries in sub-Saharan Africa. It uses multiple geocoding methods to provide a comprehensive spatial inventory of 98 745 public health facilities. Each data record represents a health facility and has 8 descriptive variables – Location identifiers including: country, first level administrative division, latitude, longitude and LL source (source of the coordinates). Coordinates are rounded off to four decimal places for uniformity, allowing an accuracy of 5–10 metres in decimal degrees coordinate format. This geocoded master facility list has been made publicly and freely available through both the figshare repository and through the World Health Organization’s Global Malaria Programme in Microsoft Excel format.

This dataset is a national master health facility list assembled from a variety of government and non-government sources from 50 countries and islands in sub Saharan Africa. This dataset was originally published in a spreadsheet (William Snow, Robert; Maina, Joseph; Ouma, Paul Onyango; Macharia, Peter Mwangi; Alegana, Victor Adagi; Mitto, Benard; et al. (2019): Public health facilities in sub Saharan Africa. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7725374.v1) with a data descriptor (Maina, J., Ouma, P.O., Macharia, P.M. et al. A spatial database of health facilities managed by the public health sector in sub Saharan Africa. Sci Data 6, 134 (2019). https://doi.org/10.1038/s41597-019-0142-2) and has now been republished by SAEON as a shapefile. The dataset compromises of 98,745 public health facilities generated utilising multiple geocoding methods to provide a comprehensive spatial inventory.

This data set shows the list of operating health facilities with geographical location

Electricity access in healthcare facilities in Sub-Saharan Africa was uneven in 2021. In the Central African Republic, ** percent of those facilities did not have access to electrical energy. In island countries, such as Mauritius and Madagascar, the share was even higher - 100 percent and ** percent of healthcare facilities lacked access to electricity in these nations, respectively. On the other hand, only ***** percent of health centers in Benin did not have electrical energy, the lowest share in Sub-Saharan Africa.

Abstract

Scaling-up of performance-based financing (PBF) has never been systematically evaluated in Central African Republic (CAR) on any meaningful scale. As such, this Impact Evalution's larger policy objectives are to: (a) identify the impact of PBF on maternal and child health (MCH) service coverage and quality, (b) identify key factors responsible for this impact, and (c) assess cost-effectiveness of PBF as a strategy to improve coverage and quality.

In doing so, the results from the impact evaluation will be useful to designing national PBF policy in CAR and will also contribute to the larger body of knowledge on PBF. The evaluation will rely on two main sources of data: 1. Household surveys: A household survey will be implemented at baseline (i.e., before implementation of PBF begins), and at endline (i.e., after PBF has been implemented for two years). 2. Facility-based surveys: A facility-based survey will be implemented at baseline and at endline.

The main targeted outcomes fall into two main groups: (a) Maternal and Child Health Service coverage indicators and (b) Quality of care indicators.

The study is a blocked-by-region cluster-randomized trial (CRT), having a pre-post with comparison design. The team relied primarily on experimental control to answer the main research questions for this study. The study will also include a qualitative component at endline to probe deeper for explanations or explore specific issues that are relevant to PBF.

Note: The Health Facility Baseline Survey is available online under Impact Evaluation Surveys Collection. The study is titled "Central African Republic Health Results-Based Financing Impact Evaluation 2012, Health Facility Baseline Survey."

Geographic coverage

Seven prefectures in the 2nd, 3rd, 4th and 6th regions of CAR covering a total population of approximately 2.5 million.

Analysis unit

The target population for the household baseline survey is households with at least one pregnant woman or a woman with a child who was born during the two years preceding the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

To select the households, a catchment area was established for each of the 97 Health Centers and 145 Health Posts. No more than 20 households were randomly selected for survey in the catchment area of each selected health facility.

Since the study was a cluster-randomized trial, the sample size estimation had to take into account design effects. In total, there were approximately 242 clusters defined by Health Centers and Health Posts (i.e., each facility constitutes a cluster) and at least 80 health facilities in each of the three study groups. This tally does not include the 9 prefecture hospitals which were not to be randomly assigned.

The survey was administered to women in sampled households who have delivered a baby within the two years preceding the survey. In addition, the survey teams weighed and measured the height of all children aged under 5 years, mothers of children less than 2 years, and pregnant women who were present in the household during the survey team's visit.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire is composed of two parts: the first is a household survey for all members of the household, while the second part is specifically for women between 15-49.

In addition to the questionnaires, the survey teams did the following: - Weigh and measure the height of all children aged under 5 years, mothers of children less than 2 years, and pregnant women who were present in the household during the survey team's visit.

Select areas that are within a particular distance of a healthcare facility.

Abstract

Scaling-up of performance-based financing (PBF) has never been systematically evaluated in Central African Republic (CAR) on any meaningful scale. As such, this Impact Evalution's larger policy objectives are to: (a) identify the impact of PBF on maternal and child health (MCH) service coverage and quality, (b) identify key factors responsible for this impact, and (c) assess cost-effectiveness of PBF as a strategy to improve coverage and quality.

In doing so, the results from the impact evaluation will be useful to designing national PBF policy in CAR and will also contribute to the larger body of knowledge on PBF. The evaluation will rely on two main sources of data: 1. Household surveys: A household survey will be implemented at baseline (i.e., before implementation of PBF begins), and at endline (i.e., after PBF has been implemented for two years). 2. Facility-based surveys: A facility-based survey will be implemented at baseline and at endline.

The main targeted outcomes fall into two main groups: (a) Maternal and Child Health Service coverage indicators and (b) Quality of care indicators.

The study is a blocked-by-region cluster-randomized trial (CRT), having a pre-post with comparison design. The team relied primarily on experimental control to answer the main research questions for this study. The study will also include a qualitative component at endline to probe deeper for explanations or explore specific issues that are relevant to PBF.

Note: The Baseline Household Survey is available online under Impact Evaluation Surveys Collection. The study is titled "Central African Republic Health Results-Based Financing Impact Evaluation 2012,
Baseline Household Survey."

Geographic coverage

PBF was implemented in public, Faith Based Organization (FBO) and not-for-profit non-governmental organization (NGO) facilities across 7 prefectures in the 2nd, 3rd, 4th and 6th regions of CAR covering a total population of approximately 2.5 million.

Analysis unit

Public and private health facilities (providing primary and/or secondary care).

Kind of data

Sample survey data [ssd]

Sampling procedure

The study is a blocked-by-region cluster-randomized trial (CRT), having a pre-post with comparison design. The research team relied primarily on experimental control to answer the main research questions for this study. Individual health facilities in each region were randomized to one of 3 study groups. Individual public and private not-for-profit Health Centers [Centres de Santé (CS)] and Health Posts [Poste de Santé (PS)] who met pre-established criteria in 7 prefectures from the 3 pilot regions were randomly assigned to each study group to create a factorial study design. This process of random allocation seeks to ensure that the two study groups are comparable in terms of observed and unobserved characteristics that could affect treatment outcomes so that average differences in outcome can be causally attributed.

The difference between a regular cluster-randomized trial (CRT) and a blocked CRT lies in the way in which the treatment units-the health facilities in this case-are randomly allocated into treatment and control conditions. In a regular CRT, health facilities would be randomly assigned into treatment and control conditions independent of the region (or prefecture) they belong to. In this blocked-by-region CRT, each region had its own randomization scheme. In other words, 3 random allocation processes, one for each region included in the evaluation (i.e., 3 blocks).

Mode of data collection

Face-to-face [f2f]

Research instrument

• Facility assessment module : The facility assessment module seeks to collect data on key aspects of facility functioning and structural aspects of quality of care. The respondent for this module is the individual in charge of the health facility at the time when the survey team visits the health facility. The full Facility Assessment module was conducted at all Health Centers and Hospitals. For Health Posts, a more simplified questionnaire that evaluates basic facility functioning was used.

• Health worker interview module : A stratified random sample of clinical and lay health workers with maternal and child health service delivery responsibilities at sampled health facilities were interviewed as part of this module. The full Health Worker module was conducted at all Health Centers and Hospitals. For Health Posts, a more simplified questionnaire was used.

• Observations of patient-provider interaction module: While the health worker interview module collects information on what health workers know, the purpose of this module is to gather information on what health workers actually do with their patients.

• Patient exit interviews : A systematic random sample of 10 patients visiting the facility (5 patients aged under-five and 5 patients aged over 5) for curative care with a new complaint were interviewed to assess the patient's perception of quality of care and satisfaction at all Health Centers surveyed. If the patient is a child, the child's caregiver was interviewed. The 5 under-fives included in the patient exit sample were the same 5 children whose consultation with a provider was observed.

A survey conducted in 2020 revealed that the number of care beds in mental hospitals in Africa amounted to *** per 100,000 population. In 2017, the count was measured at two hospital beds per 100,000 population. Usually, nurses constitute the majority of health workers in mental hospitals in the continent.

Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis for African Countries

  Dataset Description

This dataset contains 'Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis' data for all 54 African countries, sourced from the World Health Organization (WHO). The data is structured with years as rows and countries as… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Proportion-Of-Health-Facilities-That-Have-A-Core-Set-for-African-Countries.

This data set shows the distribution of health facilities by type and ownership by Council and region Data and Resources Health Facilities by Council - 2014CSV Shows number of health facilities in the Council by type and Ownership Explore More information Go to resource Health Facilities by Region - 2014CSV Shows number of health facilities in Region by type and ownership Explore More information Go to resource Health Facilities by Council - 2012CSV This shows the number of health facilities by type and ownership Explore More information Go to resource Health Facilities by Council - 2013CSV This shows the number of health facilities by type and ownership Explore More information Go to resource

With the recent Ebola epidemic, the flaws in Liberia’s medical infrastructure have been made painfully obvious. Liberia, a country of four million people, has only 37 practicing doctors according to health officials. This is evidence of a serious lack in the availability of medical services to the majority of Liberians. An American gynecologist who visited the country in 2012 to provide services with a team from the Mt. Sinai Hospital observed families of hospital patients supplying their own food and bed linens due to the medical facility they were working in lacking funds for basic necessities. The root issue at the heart of many of Liberia’s woes stems from the long civil war. In addition to damaging the medical infrastructure, the country’s only medical school was forced to close for long periods of time, resulting in medical students taking an average eight years to graduate. There has been a serious push for reform and revitalization with medical facilities being rebuilt and medical students now on track to spend only three years in school. Liberia is facing a number of issues, and prior to the current epidemic has not prioritized health expenditures. The government spends an estimated 16.8 percent of their GDP, the lowest in the world, on healthcare. The average GDP spending on healthcare systems in sub-Saharan Africa is ~50 percent. Liberia’s healthcare system is highly dependent on international aid. Donors finance 50 percent of total health expenditures. Approximately 80 percent of all health services are provided by non-governmental organizations (NGOs) and will continue to be so for the foreseeable future. However, the Ministry of Health and Social Welfare has been working with NGOs such as Health Systems 20/20 to improve their existing infrastructure. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name NAME - Name of health facility TYPE1 - Primary classification in the geodatabase TYPE2 - Secondary classification in the geodatabase CITY - City location available SPA_ACC - Spatial accuracy of site location (1 – high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding themedical facility SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from OpenStreetMap, Wikimapia, GeoNames and other sources. OpenStreetMap is a free worldwide map, created by crowd-sourcing. Wikimapia is open-content mapping focused on gathering all geographical objects in the world. GeoNames is a geographical places database maintained and edited by the online community. Consistent naming conventions for geographic locations were attempted but name variants may exist, which can include historical or less widespread interpretations.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aizenman, Nurith and Beemsterboer, Nicole. “Why Patients Aren’t Coming to Liberia’s Redemption Hospital.” August 27, 2014. Accessed September 26, 2014. www.npr.org.“Liberia: ArcelorMittal Folds Partly – Terminates Expansion Contract.” All Africa. August 14, 2013. Accessed September 26, 2014. allafrica.com. Cohen, Elizabeth. “Ebola Patients Left to Lie on the Ground.” CNN. September 23, 2014. Accessed September 26, 2014. www.cnn.com.“Kingdom Care Medical Center Reaches Rural Communities with Health Care.” Daily Observer. January 28, 2014. Accessed September 26, 2014. www.liberianobserver.com. DigitalGlobe, "DigitalGlobe Imagery Archive." Accessed September 24, 2014.“Eternal Love Winning Africa: ELWA Hospital.” Eternal Love Winning Africa. January 2014. Accessed September 26, 2014. www.elwaministries.org.Freeman, Colin. “One Patient in a 200-bed Hospital: How Ebola has Devastated Liberia’s Health System.” The Telegraph. August 15, 2014. Accessed September 26, 2014. www.telegraph.co.uk.“Lewin Reaches Out to River Gee, Maryland.” Gale Global Issues. March 4, 2013. . Accessed September 26, 2014. find.galegroup.com. Gbelewala, Korboi. “Liberia: Health Offical – Ebola Death Toll Hits 11 in Lofa.” All Africa. June 24, 2014. Accessed September 26, 2014. allafrica.com. GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. www.geonames.org.Google, September 2014. Accessed September 2014. www.google.com.Kollie, Namotee P.M. “Liberia: C.B. Dunbar Hospital Receives Medical Supplies.” September 27, 2013. Accessed September 26, 2014. allafrica.com.“MSF Hands Over Last Hospitals to Ministry of Health after 20 Years of Emergency Aid in Liberia.” Medecins Sans Frontieres. June 25, 2010. Accessed September 26, 2014. www.msf.org. Nah, Vivian M. and Johnson, Obediah. “Liberia: Ebola Kills Woman at Duside Hospital in Firestone.” All Africa. April 4, 2014. Accessed September 26, 2014. allafrica.com. “Catholic Hospital Director Dies of Ebola in Liberia.” National Catholic Register. August 05, 2014. Accessed September 26, 2014. www.ncregister.com.OpenStreetMap, "Liberia." September 2014. Accessed September 18, 2014. www.openstreetmap.org.Senkpeni, Alpha Daffae. “No Ebola Gears for Clinic in Grand Bassa District #2.” Front Page Africa. August 12, 2014. Accessed September 26, 2014. www.frontpageafricaonline.com. “Seventh-day Adventist Cooper Hospital” Seventh-Day Adventist Church. November 18, 2004. Accessed September 26, 2014. www.adventistdirectory.org.“St. Benedict Menni Rehabilitation Centre, Liberia.” Sisters Hospitallers. January 2014. Accessed September 26, 2014. www.sistershospitallers.org. “Liberia – SOS Medical and Social Centres.” SOS Children’s Villages. January 2014. Accessed September 26, 2014. www.sos-medical-centres.org.“Liberia.” Sustainable Marketplace. January 2014. Accessed September 26, 2014. liberia.buildingmarkets.org. “Reconstruction of the Vinjama Hospital in Liberia.” Swiss Agency for Development and Cooperation (SDC). January 2014. Accessed September 26, 2014. www.sdc.admin.ch. Verdier, Lewis S. “Liberia: TB On the Rise in Pleebo.” All Africa. March 28, 2013. Accessed September 26, 2014. allafrica.com.Wikimapia, "Liberia." September 2014. Accessed September 22, 2014. wikimapia.org.“Snapper Hill Clinic.” Word Press. November 12, 2012. Accessed September 26, 2014. jbloodnc.wordpress.com.Sources (Metadata)Neporent, Liz. "Liberia's Medical Conditions Dire Even Before Ebola Outbreak." ABC News. August 4, 2014. Accessed October 3, 2014. abcnews.go.com."Liberia." Health Systems Strengthening: Where We Work:. January 1, 2014. Accessed October 3, 2014. www.healthsystems2020.org."Financing Liberia's Health Care." Health Systems Strengthening: News:. February 13, 2012. Accessed October 3, 2014. www.healthsystems2020.org.UNCLASSIFIED

Longitudinal datasets of demographic, social, medical and economic information from a rural demographic in northern KwaZulu-Natal, South Africa where HIV prevalence is extremely high. The data may be filtered by demographics, years, or by individuals questionnaires. The datasets may be used by other researchers but the Africa Centre requests notification that anyone contact them when downloading their data. The datasets are provided in three formats: Stata11 .dta; tables in a MS-Access .accdb database; and worksheets in a MS-Excel .xlsx workbook. Datasets are generated approximately every six months containing information spanning the whole period of surveillance from 1/1/2000 to present.

Abstract

Scaling-up of performance-based financing (PBF) schemes across sub-saharan Africa has developed rapidly over the past few years. Many studies have shown a positive association between PBF and health service coverage, and some with improvements in quality. However, a lack of controls and confounders in most studies that have been published on PBF initiatives means that the impact of PBF initiatives on service coverage, quality and health outcomes remains open to question. Moreover, few studies have examined the factors that influence the impact of PBF- an area of considerable operational significance since PBF often involves a package of constituent interventions: linking payment and results, independent verification of results, managerial autonomy to facilities and enhanced systematic supervision of facilities. As a result, the policy objectives of the following Impact Evaluation are to: (a) identify the impact of PBF on Maternal and Child Health (MCH) service coverage and quality; (b) identify key factors responsible for this impact; and (c) assess cost-effectiveness of PBF as a strategy to improve coverage and quality. The results from the impact evaluation will be useful to designing national PBF policy in Cameroon and will also contribute to the larger body of knowledge on Performance-based Financing (PBF).

The impact evaluation is a blocked-by-region cluster-randomized trial (CRT), having a pre-post with comparison design. The evaluation relies primarily on experimental control to answer the main research questions for this study. Individual health facilities in each region have been randomized to one of the 4 study groups. Individual public and private primary care health facilities in 14 districts from the 3 pilot regions have been randomly assigned to each study group to create a factorial study design.

The evaluation relied on two main sources of data: - Household surveys: A household survey was implemented at baseline (i.e., before implementation of PBF begins), and at endline (i.e., after PBF has been implemented for two years). - Facility-based surveys: A facility-based survey was implemented at baseline and at endline.

Note: The Household Baseline Survey is available online under Impact Evaluation Surveys Collection. The study is titled "Health Results-Based Financing Impact Evaluation 2012, Household Baseline Survey."

Geographic coverage

Littoral, North-West, South-West and East regions of Cameroon.

Analysis unit

Public and private health facilities (providing primary and/or secondary care).

Kind of data

Sample survey data [ssd]

Sampling procedure

The facility survey will be conducted at baseline and endline in all public CMAs, CSIs and District Hospitals in the 14 districts included in the impact evaluation and a sample of private facilities in these districts. Based on a health facility mapping exercise conducted prior to the baseline survey, there was a total of 242 primary care facilities and 20 secondary care facilities (district and private hospitals) in the 14 districts included in the impact evaluation. Primary care and secondary care facilities combined, this included 81 in the East, 91 in the North-West and 88 in the South-West for a total of 262. Out of these, 40 were private for profit facilities. As private for-profit facilities were added to the sample after the signature of the contract with IFORD (baseline survey firm), it was decided that a random sample of 20 primary care private for-profit facilities and all private hospitals would be taken, due to budget constraints. Thus the target number of facilities was 222 primary care facilities and 20 secondary care facilities (district hospitals and private hospitals). All facility team visits will be unannounced. The facility-based survey includes multiple components, described below.

Sampling deviation

The original expected sample - based on a minimum of 5 respondents for each module in each sampled facility- was in fact unrealistic given (i) the realities of the demand and supply of health services in the study districts and the (ii) data collection plan and budgeting. Due to budget constraints, each health facility was only visited for one day during unannounced visits. Thus the survey teams were limited to the number of patients and providers that were present on the day of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Components of the health facility baseline survey included the following surveys:

• Facility assessment module (F1): The facility assessment module seeks to collect data on key aspects of facility functioning and structural aspects of quality of care. The respondent for this module are individuals in charge of the health facility at the time when the survey team visits the health facility.

• Health worker interview module (F2): A stratified random sample of clinical health workers with maternal and child health service delivery responsibilities at sampled health facilities was interviewed as part of this module.

• Observations of patient-provider interaction module (F3 and F4): The purpose of this module is to gather information on what health workers actually do with their patients.

• Patient exit interviews (F5, F6 and F7): A systematic random sample of patients visiting the facility (an expected 5 patients aged under-five and 5 patients aged over 5) for curative care with a new complaint will be interviewed to assess the patient's perception of quality of care and satisfaction at all 245 primary care facilities surveyed. If the patient is a child, the child's caregiver will be interviewed. The 5 under-fives included in the patient exit sample will be the same 5 children whose consultation with a provider was observed. In addition to this, exit interviews will be conducted with all ANC clients whose consultation with a provider was observed.

Response rate

Overall, 93.8% of targeted facilities were surveyed. The remaining 6% were either inaccessible or not functional (closed down) at the time of the survey.

The problem of maternal mortality is a recurring problem in Sub-Saharan Africa and the poorest population and residing away from health centers is the most affected. AccessMod is a computer program designed to help these countries examine the geographical aspect of their health system. And so to map the physical accessibility in terms of travel time to the health center. The accessibility map helps to guide decision-makers by showing them areas of low access, ie places where the population must walk to reach health facilities. Increasing accessibility in these places will at the same time improve maternal health.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['healthcare'] IS NOT NULL OR tags['amenity'] IN ('doctors', 'dentist', 'clinic', 'hospital', 'pharmacy')

Features may have these attributes:

• name
• name:en
• amenity
• building
• healthcare
• healthcare:speciality
• operator:type
• capacity:persons
• addr:full
• addr:city
• source
• name:af
• name:nr
• name:st
• name:nso
• name:ssw
• name:ts
• name:tn
• name:ve
• name:xh
• name:zu

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Health Facilities across the Country as provided by KNBS in 2007. This dataset is perhaps derived from the Master Facility list and collected by the Ministry of Health.

Public Health Facilities in Sub Saharan Africa

This dataset shows the number of PHC, Health centers and head post by LGA in Edo state.

ObjectiveMaintaining provision and utilization of maternal healthcare services is susceptible to external influences. This study describes how maternity care was provided during the COVID-19 pandemic and assesses patterns of service utilization and perinatal health outcomes in 16 referral hospitals (four each) in Benin, Malawi, Tanzania and Uganda.MethodsWe used an embedded case-study design and two data sources. Responses to open-ended questions in a health-facility assessment survey were analyzed with content analysis. We described categories of adaptations and care provision modalities during the pandemic at the hospital and maternity ward levels. Aggregate monthly service statistics on antenatal care, delivery, caesarean section, maternal deaths, and stillbirths covering 24 months (2019 and 2020; pre-COVID-19 and COVID-19) were examined.ResultsDeclines in the number of antenatal care consultations were documented in Tanzania, Malawi, and Uganda in 2020 compared to 2019. Deliveries declined in 2020 compared to 2019 in Tanzania and Uganda. Caesarean section rates decreased in Benin and increased in Tanzania in 2020 compared to 2019. Increases in maternal mortality ratio and stillbirth rate were noted in some months of 2020 in Benin and Uganda, with variability noted between hospitals. At the hospital level, teams were assigned to respond to the COVID-19 pandemic, routine meetings were cancelled, and maternal death reviews and quality improvement initiatives were interrupted. In maternity wards, staff shortages were reported during lockdowns in Uganda. Clinical guidelines and protocols were not updated formally; the number of allowed companions and visitors was reduced.ConclusionVarying approaches within and between countries demonstrate the importance of a contextualized response to the COVID-19 pandemic. Maternal care utilization and the ability to provide quality care fluctuated with lockdowns and travel bans. Women's and maternal health workers' needs should be prioritized to avoid interruptions in the continuum of care and prevent the deterioration of perinatal health outcomes.