In 2022, the total number of nursing staff available at primary health centers (PHCs) across India was over nine thousand. The state of Maharashtra had the highest number of nursing staff at PHCs in the country. It was followed by Tamil Nadu and Andhra Pradesh.
This survey covering 252 primary health facilities and 30 local governments was carried out in the states of Kogi and Lagos in Nigeria in the latter part of 2002. Nigeria is one of the few countries in the developing world to systematically decentralize the delivery of basic health and education services to locally elected governments. Its health policy has also been guided by the Bamako Initiative to encourage and sustain community participation in primary health care services. The survey data provide systematic evidence on how these institutions of decentralization are functioning at the level local—governments and community based organizations—to deliver primary health service.
The evidence shows that locally elected governments indeed do assume responsibility for services provided in primary health care facilities. However, the service delivery environments between the two states are strikingly different. In largely urban Lagos, public delivery by local governments is influenced by the availability of private facilities and proximity to referral centers in the state. In largely rural Kogi, primary health services are predominantly provided in public facilities, but with extensive community participation in the maintenance of service delivery. The survey identified an issue which is highly relevant for decentralization policies—the non-payment of health staff salaries in Kogi—which is suggestive of problems with local accountability when local governments are heavily dependent on fiscal transfers from higher tiers of government.
Data were collected in 30 local governments, 252 health facilities, and from over 700 health workers, in Lagos and Kogi states.
Sample survey data [ssd]
A multi-stage sampling process was employed where first 15 local governments were randomly selected from each state; second, 100 facilities from Lagos and 152 facilities from Kogi were selected using a combination of random and purposive sampling from the list of all public primary health care facilities in the 30 selected LGAs that was provided by the state governments; third, the field data collectors were instructed to interview all staff present at the health facility at the time of the visit, if the total number of staff in a facility were less than or equal to 10. In cases where the total number of staff were greater than 10, the field staff were instructed to randomly select 10 staff, but making sure that one staff in each of the major ten categories of primary health care workers was included in the sample.
Health facilities were selected through a combination of random and purposive sampling. First, all facilities were randomly selected from the available list for 30 LGAs. This process resulted in no facility being selected from a few LGAs. Between 1-3 facilities were then randomly selected from these LGAs, and an equal number of facilities were randomly dropped from overrepresented LGAs, defined as those where the proportion of selected facility per LGA is higher than the average proportion of selected facilities for all sampled LGAs.
A list of replacement facilities was also randomly selected in the event of closure or non-functioning of any facility in the original sample. An inordinate amount of facilities were replaced in Kogi (27 in total), some due to inaccessibility given remote locations and hostile terrain, and some due to non-availability of any health staff. The local community volunteered in these cases that the reason there was no staff available was because of non-payment of salaries by the LGA. This characteristic of the functioning of health facilities in Kogi is a striking result that will be discussed in this report.
Face-to-face [f2f]
The approach adopted to addressing these issues revolves around extensive and rigorous survey work, at the level of the primary health care facilities and the local governments. Two basic survey instruments of primary data collection were agreed upon, based on collecting information from government officials and public service delivery facilities: 1. Survey of primary health care facilities—including interviews of facility managers and workers, as well as direct collection of data on inputs and outputs from facility records. 2. Survey of local governments (under whose jurisdiction the health facilities reside)—including interviewers of local government treasurers for information on budgeted resources and investment activity, and interviews of primary health care coordinators for roles, responsibilities, and outcomes at the local government level.
Survey instruments at the health facility level
The facility level survey instruments were designed to collect data along the following lines: 1. Basic characteristics of the health facility: who built it; when was it built; what other facilities exist in the neighborhood; access to the facility; hours of service etc. 2. Type of services provided: focusing on ante-natal care; deliveries; outpatient services, with special emphasis on malaria and routine immunization 3. Availability of essential equipment to provide the above services 4. Availability of essential drugs to provide the above services 5. Utilization of the above services, referral practices 6. Tracking and use of epidemiological and public health data 7. Characteristics of health facility staff: professional qualifications; training; salary structure, and whether payments are received in a timely fashion; informal payments received; fringe benefits received; do they have their own private practice; time allocation across different services; residence; place of origin 8. Sources of financing-who finances the building infrastructure and its maintenance; who finances the purchase of basic equipment; who finances the purchase of drugs; what is the user fee policy; revenues from user fees; retention rate of these revenues; financing available from the community 9. Management structure and institutions of accountability: activities of and interaction with the local government and with the community development committees
Survey instrument at the local government level
The local government survey instruments were designed to collect data along the following lines: 1. Basic characteristics: when was the local government created, population, proportion urban and rural, presence of an urban center, presence of NGOs and international donors 2. Number of primary health care facilities by type (types 1 and 2) and ownership (public-local government, state, and federal government; private-for-profit; private-not-for-profit) 3. Supervisory responsibilities over the general functioning of the primary health care centers 4. Health staff: number of staff by type of professional training and civil service cadre; salary; 5. Monitoring the performance of health staff: how is staff performance monitored and by whom; are staff rewarded for good performance or sanctioned for poor performance, and how; instances when local government has received complaints; what disciplinary action was taken 6. Budget and financing: data on actual LGA revenues and expenditure from available budget documents; 7. Management structures: functioning of the Primary Health Care Management Committee (PHCMC), the Primary Health Care Technical Committee (PHCTC), and the community based organizations-the Village Development Committee (VDC) and the District Development Committee (DDC) 8. Health services outputs at the local government level: records of immunization, and environmental health activities
The focus of the study is thus public service delivery outcomes as measured at the level of frontline delivery agencies—the public primary health care facilities. We also originally planned to include interviews of patients present at the health facilities, to get the user’s perspective on public service delivery, but found that difficult to follow-through given local capacity constraints in implementing a survey of this kind.
The survey instruments were developed through an iterative process of discussions between the World Bank team, NPHCDA, and local consultants at the University of Ibadan, over the months of March-May 2002. During May 2002, four questionnaires were finalized through repeated field-testing—1) Health Facility Questionnaire: to be administered to the health facility manager, and to collect recorded data on inputs and outputs at the facility level; 2) Staff Questionnaire: to be administered to individual health workers; 3) Local Government Treasurer Questionnaire: to collect local government budgetary information; and 4) Primary Health Care Coordinator Questionnaire: to collect information on local government activities and policies in primary health care service service delivery.
Random Data Checking Procedure
Following the dual data entry of all records by Nigerian consultants and the merging and cleaning of the data files(as outlined below) by World Bank staff, the hard copies of the questionnaires were randomly checked against the entries in the data files (*) for errors by World Bank staff. Five LGAs were selected at random in both the Kogi and Lagos states. In each of these ten LGAs, the hard copy of the PHC Coordinator Questionnaire, the hard copy of the LGA Treasurer Questionnaire, and up to five hard copies of both the Staff Questionnaires and the Health Facility Questionnaires were randomly selected and checked against the entries in the data files. While in several instances parts of the alphanumeric entries were abbreviated or omitted, no substantive differences between the hard copies of the
Nigeria is one of the few countries in the developing world that has systematically decentralized the delivery of basic services in health and education to locally elected governments and community-based organizations. Its health policy also has also been guided by the Bamako Initiative to encourage and sustain community participation in primary health care services.
This study uses an extensive survey of primary health facilities and local governments to analyze how local institutions - government and community-based - function in practice in delivering basic health services, and to draw lessons for improving public accountability.
The research was carried out in June-August 2002 in 30 local government areas of Lagos and Kogi states. Treasurers and primary health care coordinators in each of the 30 local governments, as well as managers and staff members in 252 health facilities were interviewed. Data from over 700 health workers was collected.
In addition to its analytical objectives, the conduct of this study was specifically designed to promote evidence-based policy dialogue in Nigeria by engaging the active participation of the government agency responsible for monitoring and supervising outcomes in primary health care service delivery - National Primary Health Care Development Agency (NPHCDA). The agency was closely involved at every stage of the survey - from study design to its implementation and subsequent analysis.
Lagos and Kogi states
Primary health care facilities and local government authorities (LGA) in states of Lagos and Kogi.
Sample survey data [ssd]
A multi-stage sampling process was employed where first 15 local governments were randomly selected from each state; second, 100 facilities from Lagos and 152 facilities from Kogi were selected using a combination of random and purposive sampling from the list of all public primary health care facilities in the 30 selected local government authorities (LGAs) that was provided by the state governments; third, the field data collectors were instructed to interview all staff present at the health facility at the time of the visit, if the total number of staff in a facility were less than or equal to 10. In cases where the total number of staff were greater than 10, the field staff were instructed to randomly select 10 staff, but making sure that one staff in each of the major ten categories of primary health care workers was included in the sample.
Health facilities were selected through a combination of random and purposive sampling. First, all facilities were randomly selected from the available list for 30 LGAs. This process resulted in no facility being selected from a few LGAs. Between 1-3 facilities were then randomly selected from these LGAs, and an equal number of facilities were randomly dropped from overrepresented LGAs, defined as those where the proportion of selected facility per LGA is higher than the average proportion of selected facilities for all sampled LGAs. A list of replacement facilities was also randomly selected in the event of closure or non-functioning of any facility in the original sample. An inordinate amount of facilities were replaced in Kogi (27 in total), some due to inaccessibility given remote locations and hostile terrain, and some due to non-availability of any health staff. The local community volunteered in these cases that the reason there was no staff available was because of non-payment of salaries by the LGA.
Face-to-face [f2f]
The following survey instruments are available:
Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.
STATA cleaning do-files and data quality reports can also be found in external resources.
The Nigerian Government has prioritized improving the quality of healthcare delivery throughout its care facilities. There are multiple facets to implementing successful quality improvement processes, including providing a transparent system with quantifiable outcome measures and ensuring workforce engagement for healthcare providers.
The government project team contracted a healthcare management consulting firm to provide support to 80 primary healthcare facilities in six Nigerian states to meet international healthcare standards. The IE was designed to measure the effectiveness of two different levels of consulting services on healthcare quality outcomes:
Six Nigerian states: Anambra, Bauchi, Cross River, Ekiti, Kebbi and Niger.
The sampling frame for this impact evaluation consists of all 80 PHCs in the 6 states that are being covered by the project.
RANDOMIZATION Randomization of PHCs into Treatment A, Treatment B, and control followed these steps: 1. We assigned a random number to each of the 80 PHCs in our population. 2. These numbers were ranked in ascending order. 3. We ranked these numbers within each cluster (of 4 PHCs around a referral hospital). 4. The PHC with the highest random number in each was assigned to Treatment A, the second highest number was assigned to Treatment B, and the third highest number was assigned to the control group. This created groups of 20 for each treatment arm. 5. Lastly, the 20 PHCs with the fourth highest numbers were ranked again. Then, the 4 highest numbers were allocated to Treatment A, numbers 5-8 went to Treatment B, and the rest was assigned to the control group.
This resulted in the following group sizes: - Treatment A: 24 - Treatment B: 24 - Control group: 32
Face-to-face [f2f]
As data sources, the IE will use a combination of PHC administrative data, facility level survey data, the tools developed by the healthcare consulting firm, the SDI and SURE-P surveys, as well as additional instruments to assess the quality of care.
Out of 80 primary healthcare facilities, the response rates for: - round 1: 100% - round 2: 100% - round 3: 100% - round 4: 100% - round 5: 89% - round 6: 100% - round 7: 100%
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There is limited research soliciting the patient and staff perspectives on the overall effects of COVID-19 on the utilization and provision of primary care in Lebanon. The present study was part of a larger study on the overall effect of COVID-19 on both utilization and provision of essential health care services within the Lebanese primary health care network (PHCN). Here, we present the patient and staff perspectives on continuity of service provision, adherence to infection prevention and control measures, and the role of the PHCN in epidemic preparedness and response. We conducted a cross-sectional survey between June and July 2021 among patients who had received a health care service in 2019 or 2020 from registered primary healthcare centers (PHCs) in the network and among the respective PHC staff working during the same period. A total of 763 patients and 198 staff completed the surveys. Services were reported as interrupted by 15% of the total patients who used services either in 2020 only or in both 2019 and 2020. Access to chronic (67%) and acute medications (40%) were reported as the main interrupted services. Immunization also emerged as a foregone service in 2020. Among the staff, one third (33%) reported interruptions in the provision of services. Financial barriers rather than fear of COVID-19 were reported as main reasons for interruption. Both groups considered that the facilities implemented adequate infection prevention and control measures. They perceived that the PHCN maintained some essential healthcare services and that it should have played a bigger role in the response to the pandemic. There was a continuity in utilization and provision of services in the PHCN that was higher than expected, with non-communicable diseases and immunizations suffering more than other services.
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BackgroundHealth systems based on primary healthcare (PHC) have reduced costs and are effective for improved health outcomes. Kenya’s health system grapples with providing equitable access to essential health services, but there is increasing commitment by the government to strengthen primary healthcare. The aim of this paper is to provide a baseline assessment of the capacity and training needs of healthcare workers (HCWs) in Nakuru and Nyeri Counties and identify priorities for intervention.MethodsA cross-sectional study was carried out among 171 healthcare workers in Nyeri and Nakuru counties. Multistage sampling was employed to select sub-counties in the first stage and health facilities by level within each sub-county in the second level. Systematic random sampling was then employed to select HCWs from each level of facility. We targeted healthcare workers of all cadres within the health facilities, and included all who consented to participating. Structured self-administered pen-and-paper questionnaires were used for data collection, and a five-point Likert scale was used to measure the perceived capacity of the healthcare workers to provide primary healthcare. As for the training needs data, the participants selected any of the 12 components that they needed training in. Descriptive statistics was employed, and stacked bar charts were used to visualize the capacity and training needs for the components of PHC adopted in Kenya.ResultsIn total, we obtained questionnaires from 95 participants in Nakuru and 76 participants in Nyeri. Nakuru HCWs rated themselves higher than their Nyeri counterparts in maternal and newborn child healthcare, local endemic disease control, appropriate treatment of common diseases and injuries, provision of essential basic medication, dental health, HIV/AIDs & TB management, and primary eye care. In both counties, there were significant differences in capacity between the different levels of health facilities. We observed substantial capacity gaps for HIV/AIDs & TB management, mental health and dental health services in both counties.ConclusionThis study found a substantial capacity gap in several of the elements of PHC, especially in Nyeri County. Critical areas for intervention are HIV/AIDs & TB management and mental health training for both counties. Within the health system, there is need to strengthen the capacity of HCWs in lower-level health facilities to reduce the volume of referrals to secondary care facilities. We strongly recommend training programs in dental health, mental health, primary eye care, nutritional services and HIV/AIDs &TB management, that are carefully designed to emphasize skills and abilities.
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# IA2030 Action Plans and Impact Acceleration Reports: Survey responses from 931 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle 2022)
Note: This document includes markdown syntax to facilitate machine reading.
## Table of Contents
- Abstract
- Research Audience
- Credits
- Recommended Citation
- File List
- Related Data Sets
- Background
- Data Set Structure and Contents
- Data Cleaning, Data Privacy, and Anonymization
- Ethical Considerations
- Survey Implementation
- Survey Questionnaire Items
- Data Availability and Accessibility
- Copyright and License
- References
## Abstract
The Immunization Agenda 2030 (IA2030) has been endorsed at the World Health Assembly as the world’s strategy for immunization.
The Movement for IA2030 is a voluntary collective of immunization practitioners, principally from low- and middle-income countries, who have pledged to support each other to accelerate local action in support of this global immunization strategy.
This data set offers insights into how to understand national and sub-national challenges facing immunization programmes and what strategies show promise for addressing these challenges over time. It contains:
- raw, anonymized data in Excel CSV format. Copies of the three survey questionnaires are available for download in the Files section (below).
- responses to one, two, or three surveys from 931 national and subnational immunization practitioners participating in the Movement for Immunization Agenda (IA) 2030:
Survey 1: IA2030 Action Plan Survey: a 77-item questionnaire, fielded in May 2022, which asked respondents to identify a key challenge relevant to their immunization programme, root cause of the challenge, three corrective actions, and what they knew about vaccination coverage in their zone of intervention.
Survey 2: IA2030 Impact Acceleration Report Survey (2022): a 27-item questionnaire, fielded in June 2022, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators.
Survey 3: IA2030 Impact Acceleration Report Survey (2023): a 60-item questionnaire, fielded in June 2023, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators.
## Research Audience
Education and global health researchers with interest in human resources for health (HRH) and the characteristics, priority challenges, and experiences of national and sub-national immunization staff participating in the Movement for Immunization Agenda (IA) 2030.
## Credits
### Author
The Geneva Learning Foundation (TGLF)
18 Avenue Louis Casaï
CH1209 Geneva, Switzerland
research@learning.foundation
Principal Investigator and Corresponding Author
Reda Sadki, TGLF
reda@learning.foundation
Project Partners
- Biostat Global Consulting (BGC)
- Bridges to Development
- Centre for Change & Complexity in Learning (C3L)
Partner Roles and Responsibilities
- Design: TGLF
- Implementation (sample collection): TGLF
- Processing: TGLF, C3L
- Anonymization: BGC
- Submission: TGLF
- Maintenance of learning analytics database where data are stored: C3L
Funding
Wellcome Trust, Bill & Melinda Gates Foundation (BMGF)
## Recommended Citation
Action Plans and Impact Acceleration Reports: Responses from 931 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle). The Geneva Learning Foundation, 2023. (Version 1.0) [Data Set]. The Geneval Learning Foundation. https://doi.org/10.5281/zenodo.8298773
## File List
2023-09.IA2030_Action_Plans_and_Impact_Acceleration_Reports.README.md (this document)
2022-05.IA2030_Action_Plan_Survey.docx: List of items included in the Action Plan Survey
2022-06.IA2030_Impact_Acceleration_Report_Survey.docx: List of items included in the Impact Acceleration Report Survey questionnaire administered in June 2022.
2023-06.IA2030_Impact_Acceleration_Report_Survey.docx: List of additional items included in the Impact Acceleration Survey administered in June 2023 (the complete survey questionnaire also included all items included in the June 2022 Impact Acceleration Report Survey questionnaire).
2023-09.IA2030_Action_Plans_and_Impact_Acceleration_Reports_Survey_Dataset.csv: Anonymized Action Plans and Acceleration Reports Survey Dataset. Version 1: Geneva Learning Foundation (937 observations, 173 variables).
## Related Data Sets
The 2023-09.IA2030_Action_Plans_and_Impact Acceleration_Reports_Survey_Dataset.csv is a subset of data collected by The Geneva Learning Foundation (TGLF) during the first IA2030 Full Learning Cycle (FLC) during May 2022, June 2022, and June 2023. The complete IA 2030 Action Plans and Impact Acceleration Reports Survey data set is more comprehensive and includes information about respondent and programme characteristics as well as responses to open-text questions.
Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a data sharing agreement that would stipulate conditions of access (insights@learning.foundation).
The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. https://doi.org/10.5281/zenodo.7763922
The Geneva Learning Foundation, 2023. Full Learning Cycle (2022) Application for national and sub-national immunization staff to identify challenges and join the Movement for Immunization Agenda (IA 2030) (Version 1.0) [Data Set]. The Geneval Learning Foundation. https://doi.org/10.5281/zenodo.8199552
Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from TGLF’s Insights Unit (insights@learning.foundation).
## Background
The Immunization Agenda 2030 (IA2030), the global immunization strategy for 2021-2030, set ambitious targets for global immunization coverage and other key indicators (World Health Organization [WHO], 2020).
In response to the WHO Director-General’s call for a social movement to ensure immunization remains a priority for global and regional health agendas and promote broad societal support for immunization (WHO, 2020), TGLF, working with its global community of over 35,000 alumni, developed a learning programme intended to contribute to a “Movement for Immunization Agenda 2030.” As part of their participation in the programme, IA2030 participants developed a locally tailored action plan designed to address a key challenge relevant to their immunization programme. A project kick-off phase, the “Impact Accelerator Launch Pad”, supported participants during their initial implementation stages, to create momentum for sustained action. One month after initiation of their Action Plan(s), participants were invited to complete an Impact Acceleration survey questionnaire to assess progress towards their goals. One year later, programme participants were invited to complete a second Impact Acceleration questionnaire to assess continued progress towards their goal.
This data set includes participants’ responses to one or more questionnaires documenting their IA2030 experience:
- Survey 1: IA2030 Action Plan Survey: a 77-item questionnaire, fielded in May 2022, which asked respondents to identify a key challenge relevant to their immunization programme, root cause of the challenge, three corrective actions, and what they knew about vaccination coverage in their zone of intervention.
- Survey 2: IA2030 Impact Acceleration Report Survey (2022): a 27-item questionnaire, fielded in June 2022, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators.
- Survey 3: IA2030 Impact Acceleration Report Survey (2023): a 60-item questionnaire, fielded in June 2023, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators.
## Data Set Structure and Contents
The data set has 937 observations and 173 variables. Contents include the following:
- 937 responses (532 English; 405 French) to the Action Plan Survey. Six individuals submitted two distinct Action Plans.
- 538 responses (278 English; 260 French) to the 2022 Impact Acceleration Report Survey
- 236 responses (126 English; 110 French) to the 2023 Impact Acceleration Report Survey
- Column A describes the type of data included in each row of the data file
- The first two rows of the data set hold the question text in English and in French
- Column B contains a unique identifier for each respondent. The ID is a simple string starting with a number to indicate a unique person and then a dash, and then a 1 or a 2 to indicate whether it is their first or second entry for the 2022 Impact Acceleration Report Survey.
- Columns D-F indicate the language each respondent used for each of the three questionnaires (EN = English; FR = French)
- The columns whose names start with I1 were collected as part of the Action Plan Survey, I2 as part of the 2022 Impact Acceleration Report Survey, and I3 as part of the 2023 Impact Acceleration Report Survey. The columns appear in left-to-right order in which the original surveys were administered.
## Data Cleaning, Data Privacy, and Anonymization
Duplicate surveys
The policy objective of the Impact Evaluation (IE) is to build evidence on the impact and cost-effectiveness of the proposed Performance-Based-Financing (PBF) project in Tajikistan. More specifically, the IE would seek to ascertain: (i) the impact and cost-effectiveness of the PBF model implemented in Tajikistan; and (ii) whether PBF is more effective or cost-effective if implemented in conjunction with additional low cost interventions (Collaborative Quality Improvement, Citizen Report Cards). The results from the IE will help informing the MOH on whether PBF should be scaled-up to additional PHC level institutions in other regions.
The Collaborative Quality Improvement intervention responds to policy concerns that performance incentives may not produce the desired improvements if providers lack the necessary competencies, data to inform decisions and knowledge. The Citizen Report Card attempts to improve the effectiveness of PBF by strengthening the 'short route of accountability', i.e., by increasing accountability of health facilities to their local constituents. Since PBF, collaborative quality improvement (CQI), and citizen report cards (CRC) have never been implemented in large scale in Tajikistan, it is to be expected that the results from the IE will be useful for designing national PHC policy in Tajikistan, and that they will also contribute to the larger body of knowledge on these interventions.
The IE employs both difference-in-difference and experimental approaches to identify the impact of the different combinations of interventions. Assignment to PBF was not random. Three districts in the Sughd region and 4 districts in the Khatlon region were selected to implement the program. All Rural Health Centers (RHCs) in these seven districts are covered by the program. Nine additional district (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.
Within the chosen 16 districts (treatment and control districts), clusters consisting of a RHC and its subsidiary Health Houses were randomly assigned to implement Collaborative Quality Improvement, Citizen Score Cards, or neither of these two interventions. The randomization was blocked by district. In sum, RHCs were assigned into six study arms.
The goal of the Facility-based survey is to measure multiple dimensions of quality of care and collect detailed information on key aspects of facility functioning. Household surveys are primarily used to measure health service coverage at the population level as well as select health outcome indicators measured through anthropometry or tests. The surveys also collect broader data on the health of the households, health seeking behaviors and barriers to use of health services. In addition, PBF and other administrative data would be used to track outcomes over time in the treatment groups 1-3 (the ones receiving performance-based payments). The baseline survey was implemented prior to the implementation of PBF in the 7 study treatment districts and a follow-up survey (endline) is planned to take place after three years of project implementation. The survey is largely based on the HRITF instruments that were modified to the Tajik and project context.
Three districts in the Sughd region and 4 districts in the Khatlon region were selected to implement the program. All Rural Health Centers in these seven districts are covered by the program. Nine additional district (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.
Health centers, Health providers
Sample survey data [ssd]
Table 1.4.1 of the survey report provided under the Related Materials tab presents the list of selected districts, their assignment into the PBF treatment and the number of RHCs in each district. As the set of RHCs in each study district were randomly assigned into three study arms, some RHCs were not included in the study when the number of RHCs in a district was not divisible by three. Excluded RHCs were randomly selected with all RHCs having identical probability of being selected. The sample size is of 216 Rural Health centers, 108 in PBF districts and 108 in control districts. Of the 216 RHCs, 66 are in Sughd and 150 are in Khatlon. The sample of facilities will be identical for the baseline and endline surveys.
An abridged Health Facility survey was implemented also in Health Houses. While some Rural Health Centers have one or more subsidiary Health Houses in their catchment areas, other do not have any. One Health House from each RHC with subsidiary HHs was to be included in the sample. Selection was random with each health house within a cluster having identical probability of being chosen. Non-selected health houses were ranked to serve as replacements in case the survey cannot be implemented in the selected HHs. Table 1.4.2 of the survey report presents the number of HHs selected for the sample for each district (that is, the number of RHC that have subsidiary health houses). Of the 216 RHC selected for the sample (after excluding some RHCs when the total number was not divisible by three), 150 have subsidiary HHs. Forty-three HHs were selected of the sample in Sughd and 107 in Khatlon.
Computer Assisted Personal Interview [capi]
A complete health facility survey was conducted in RHCs, whereas for health houses a shorter survey was implemented. A challenging and important goal of the facility-based survey is to collect different measures of quality of care in the health facilities. Form F1: Health Facility Assessment: 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 were the individuals in charge of the health facility at the time when the survey team visits the health facility. The main themes to be covered by the facility assessment include:
· Facility staffing, including the staffing complement of the facility, staff on duty at the time of the survey team's visit and staff present at the time of the survey team's visit · Facility infrastructure and equipment · Availability of drugs, consumables and supplies at the health facility · Supervision · Record keeping and reporting to the Health Management Information System · Service volumes
Form F2: Health Worker Questionnaire: A random sample of 4 health workers was to be taken at each of the Rural Health Centers and Health Houses included in the sample. Eligible health workers include doctors, nurses, midwife/auxiliary midwife, and any other health worker providing MCH or NCD care. In facilities with less than 4 health workers on their staff roster, all eligible health workers were to be interviewed. The main themes to be covered by this module include: · Role, responsibilities and characteristics of the interviewed health worker · Staff satisfaction and motivation · Technical knowledge on MCH and NCDs. Knowledge was assessed through the use of provider vignettes on MCH and NCD protocols and diagnosis.
Direct Observation of Patient-Provider Interactions: The goal of the direct observations is to assess adherence to protocols in terms of IMCI and hypertension management. At each Rural Health Center, up to 5 children under-five and up to 5 adults over 40 years who are potential candidates for hypertension identification/management services was to be selected. A member of the survey team observed consultations using a structured format to note whether key desired actions were carried out. In the case of patients under five, the instrument focuses on whether IMCI protocols are followed. For adults over 40 years, the instrument focuses on whether MoH and international protocols are followed. The direct observations were implemented only in RHCs.
Form F4: Patient Exit Interviews: The same set of patients who were selected for the direct observations of patient-provider interactions were also selected for exit interviews. If the patient is a child, the child's caregiver was interviewed. The exit interviews collected data on the patients' perceived quality of care and satisfaction with the care given. Additional information was collected on socio-economic background and the general health of the patient. Like the direct observations, the exit interviews were only administered in RHCs.
Criterion Based Audit: A target sample of 5 under-five and 5 adult (40+ years) medical records was selected using systematic random sampling methodology at each Rural Health Center to assess whether the content of clinical care delivered is complete and appropriate in light of clinical best practices. Each indicator in the criterion-based clinical care audit is scored through a review of patient records or other facility logs using a structured format. The criterion-based clinical audit focused on IMCI protocols (for under-fives) and hypertension screening and management (for adults over 40 years).
In Sughd, all health facilities
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BackgroundGlobally, reduction of patient waiting time has been identified as one of the major characteristics of a functional health system. In South Africa, 83% of the general population visiting primary healthcare (PHC) facilities must contend with long waiting times, overcrowding, staff shortages, poor quality of care, an ineffective appointment booking system, and a lack of medication. These experiences may, in turn, affect how patients view service quality.MethodsThis scoping review was guided by Arksey and O’Malley methodological framework. The primary literature search of peer-reviewed and review articles was achieved through PubMed/MEDLINE, Google Scholar, Science Direct, and World Health Organization (WHO) library databases, using waiting times, outpatient departments, factors, interventions, and primary healthcare facilities as keywords. Two independent reviewers screened abstracts and full articles, using the set inclusion and exclusion criteria. We used NVIVO® version 10 software to facilitate thematic analysis of the results from included studies.ResultsFrom the initial 250 records screened, nine studies were eligible for inclusion in this scoping review. Seven papers identified the factors contributing to waiting time, and five papers mentioned effective interventions implemented to reduce waiting times within PHC facilities. Our analysis produced three (patient factors, staff factors, and administrative systems) and two (manual-based waiting time reduction systems and electronic-based waiting time reduction systems) main themes pertaining to factors contributing to long waiting times and interventions to reduce waiting times, respectively.ConclusionOur results revealed that the patients, staff, and administrative systems all contribute to long waiting times within the PHC facilities. Patient waiting times recorded a wider and more evenly spread patient arrival pattern after the identified interventions in our study were implemented. There is a need to constantly strategize on measures such as implementing the use of an electronic appointment scheduling system and database, improving staff training on efficient patient flow management, and regularly assessing and optimizing administrative processes. By continuously monitoring and adapting these strategies, PHC facility managers can create a more efficient and patient-centered healthcare experience.
The attached models (MS Excel) evaluate the incremental cost and benefits associated with the implementation of a "next generation" primary healthcare community platform globally. Please contact Dr. Henry B. Perry for more information about the platform and the context of this modeling exercise. Model A estimates the incremental cost associated with the expansion of the existing program to match the new proposed program Model B estimates the economic benefit of the averted productivity loss associated with pediatric and maternal mortality Model C estimates the economic benefit of providing community health workers with a salary, assuming a proportion of this salary is spent in the community Model D estimates the economic benefit of pandemic preparedness and mitigation Model E estimates the economic benefit of the averted treatment costs and productivity loss associated with pediatric pneumonia and diarrhea cases averted Model F estimates the averted productivity loss associated with adult mortality due to HIV, tuberculosis, hypertension, and diabetes mellitus. The file "Compiling model" gathers the outputs from all models (copy-pasted; no direct link) to calculate the overall return on investment of the program over 20 years. Note that additional elements outside of the models presented here were included.
The 2018 endline survey of the impact evaluation (IE) for Health Performance-Based Financing (PBF) in Tajikistan sought to ascertain: (i) the impact and cost-effectiveness of the PBF model implemented in Tajikistan; and (ii) whether PBF is more effective or cost-effective if implemented in conjunction with additional low-cost interventions (Collaborative Quality Improvement, Citizen Report Cards). The results from the IE will help inform the Ministry of Health on whether PBF should be scaled-up to additional PHC level institutions in other regions.
The Collaborative Quality Improvement intervention responds to policy concerns that performance incentives may not produce the desired improvements if providers lack the necessary competencies to inform decisions and knowledge. The Citizen Report Card attempts to improve the effectiveness of PBF by strengthening the 'short route' of accountability (e.g., by increasing accountability of health facilities to their local constituents). Since PBF, collaborative quality improvement (CQI), and citizen report cards (CRC) have never been implemented on a large scale in Tajikistan, it is to be expected that the results from the IE will be useful for designing national PHC policy in Tajikistan, and that they will also contribute to the larger body of knowledge on these interventions.
The IE employs both difference-in-difference and experimental approaches to identify the impact of the different combinations of interventions. Assignment to PBF was not random. Three districts in the Sughd region and four districts in the Khatlon region were selected to implement the program. All Rural Health Centers (RHCs) in these seven districts are covered by the program. Nine additional districts (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.
Within the chosen 16 districts (treatment and control districts), clusters consisting of an RHC and its subsidiary Health Houses were randomly assigned to implement Collaborative Quality Improvement, Citizen Score Cards, or neither of these two interventions. The randomization was blocked by district. In sum, RHCs were assigned into six study arms.
The goal of the facility-based survey is to measure multiple dimensions of quality of care and collect detailed information on key aspects of facility functioning. Household surveys are primarily used to measure health service coverage at the population level as well as select health outcome indicators measured through anthropometry or tests. The surveys also collect broader data on the health of the households, health seeking behaviors and barriers to use of health services. In addition, PBF and other administrative data would be used to track outcomes over time in the treatment groups 1-3 (the ones receiving performance-based payments). The endline (follow-up) survey took place three years after project implementation. The survey is largely based on the HRITF instruments that were modified to the Tajik and project context.
Three districts in the Sughd region and four districts in the Khatlon region were selected to implement the program. All Rural Health Centers in these seven districts are covered by the program. Nine additional districts (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.
Health centers, Health workers, Patients (adults & children) Patient household
Sample survey data [ssd]
The major features of the sampling procedure include the following steps (they are discussed in more detail in a copy of the study's report located in "External Resources"):
Health Facilities: 1. Table 6-4 in the study's report presents the number of RHCs selected for the sample for each district. Of the 216 RHC selected for the sample (after randomly excluding some RHCs when the total was not divisible by three), 151 have subsidiary HHs. Forty-three HHs were selected of the sample in Sughd and 107 in Khatlon. 2. While some Rural Health Centers have one or more subsidiary Health Houses in their catchment areas, others do not have any. One Health House from each RHC with subsidiary HHs was to be included in the sample. The selection was random with each health house within a cluster having an identical probability of being chosen. Non-selected health houses were ranked to serve as replacements if the survey cannot be implemented in the selected HHs.
Households:
1. The evaluation relies on two samples of households. As the primary focus of the PBF intervention is on Maternal and Child Health (MCH) services, the main household sample is of households with women who experienced a recent pregnancy. This sample would not be appropriate to study the impact on the coverage of services related to Non-Communicable Diseases (NCD). Therefore, a second sample consists of households with individuals over the age of 40. The household samples are clustered according to the catchment area of each Rural Health Center (and its affiliated health houses).
2. The resulting targeted primary household sample size is of 4,320 households, with 20 in each of the 216 clusters in the six study arms. To be eligible to be included in the household survey sample, households must have had at least one woman aged 15-49 years who has had a child in the preceding three years. The same villages were covered for both the baseline and followed up survey and eligibility was determined at each round by a listing exercise.
3. The resulting targeted sample size for the secondary household sample is 1,584 households, with 22 in each of 72 clusters in two of the six study arms. Eligibility for this sample is determined by an individual over the age of 40 in the household. Eligibility for the two samples is determined by a common listing of households in selected villages. Households which satisfy both eligibility criteria can be randomly selected to count towards the sample size requirements for both.
4. A two-stage cluster sampling methodology was employed to identify random samples. First, villages were randomly selected out of a list of the villages served by each facility. The list was obtained from the MoH. RHCs have either single or multiple villages in their catchment areas while HHs typically serve a single village. If an RHC has at least one affiliated HH, then two villages were selected. One village was directly served by the RHC while the other included in the sub-catchment area of the HH. In each village, 100 households were listed. If the village had over 100 households, a random walk method was used to select the target number. A short questionnaire was conducted at each household to determine households' eligibility for the two samples. From all eligible households, the target sample for each catchment area was selected. In catchment areas in which two villages were included in the sample, half of the households were to be selected from each village.
Computer Assisted Personal Interview [capi]
The Tajikistan Health Results Based Financing Impact Evaluation 2018 - Health Facility Endline Survey includes the following 7 questionnaires.
Facility-Based Surveys: 1. Health facility assessment module 2. Health worker interview module 3. Observation of patient-provider interaction module 4. Patient exit interview modules
Household Survey: 5. Main household questionnaire 6. Women of reproductive age interview questionnaire 7. Adults over 40 years old questionnaire
Health Facilities: Of 216 RHCs selected for the impact evaluation, 210 were evaluated at both baseline and follow-up. Six RHCs evaluated at baseline were ineligible for selection at follow-up due to closure or re-registration (either upgraded to a district health center or downgraded to health house). These six RHCs and their respective health house and household enumeration areas were replaced before the start of the follow-up survey. A total of 151 health houses were assessed at baseline, and 150 at follow-up. Eleven health houses were close or re-registered as RHCs. Our analyses treat RHCs and health houses as panel data, where it is assumed the observed facility is measured at both time points. Therefore, both the original units which have been replaced and the replacement are excluded in the subsequent difference-in-difference and cross-sectional analyses.
Health Workers: A total of 1,574 health workers were surveyed in the RHCs included in the analysis sample, 767 at baseline and 807 at follow-up. The average number of health workers fell slightly below the 4 per RHC target, as more remote RHCs did not have four staff members available. In health houses, the two staff per HH was achieved in the baseline sample but narrowly missed in the follow-up survey. Health workers who worked in both the rural health center and health house were treated as RHC employees.
Households: A total of 10,599 households were surveyed across 230 villages in 210 RHC catchment areas, 4910 at baseline and 5689 during follow-up covering 83,803
The Maternal and Child Health (MCH) project of the Subsidy Reinvestment and Empowerment Programme (SURE-P), was set up by the Federal Government of Nigeria to reduce maternal and newborn morbidity and mortality inthe country. MCH initiative is a follow-up program to Midwives Service Scheme, implemented by the Nigeria National Primary Health Care Development Agency, that provides demand and supply side incentives, community monitoring, and increased human resources to improve the rates and quality of antenatal care and skilled birth attendance in Nigeria.
On the supply-side, SURE-P aims to recruit, train and deploy 5,400 midwives and 14,100 community health extension workers, as well as to upgrade essential infrastructures and guarantee the adequate provision of supplies and equipment to primary health centres between the end of 2012 and 2015. In addition SURE-P will hire and train a total of 38,700 village health workers, who are expected to establish the connection between the primary healthcare centres (PHC) and pregnant women in each village.
On the demand-side, SURE-P introduces a CCT, whereby all pregnant women will be given a total cash payout of 5,000 Naira (about USD 32), conditional on attending antenatal care, skilled birth attendance and postnatal care. Also, an information campaign aims to target all women of reproductive age to encourage them to register with their nearest PHC.
The rigorous impact evaluation is being implemented to determine the causal impact of this programme. The IE comprises a quasi-experimental impact evaluation whose aim is to evaluate the SURE-P package, and four experimental evaluations which will evaluate the impact that distinct components have within the SURE-P package, such as: - the effect of alternative incentives regimes to midwives on their retention rates - the effect of conditional cash transfers on utilization of MCH services - the effect of community monitoring of essential commodities on incidence of stock-out of supplies at the PHC
The baseline data collection was carried out in September-November 2013. The first follow-up survey will be implemented in November 2014 - January 2015, after SURE-P Phase I implementation. The final data collection is planned one year later, from November 2015 to January 2016, after SURE-P Phase II implementation.
To gather baseline data four different groups of respondents were interviewed using different questionnaires. These respondents were: - Managers in all 500 SURE-P health facilities across the country - Midwives recruited by SURE-P - Women who gave birth three months preceding the survey, in sampled households in each SURE-P facility catchment area - Ward Development Committee (WDC) chairpersons or representatives in all SURE-P facilities.
Overall, 476 facility manager questionnaires, 1,291 midwife questionnaires, 2,378 household qustionnaires and 477 WDC questionnaires were administered.
The baseline data is documented here.
National
Sample survey data [ssd]
Primary Health Care facilities: The survey targeted all 500 SURE-P MCH Phase 1 PHC facilities.
Midwives: The survey targeted all midwives currently working for the SURE-P MCH (up to four per Primary Health Care facility). The list of all SURE-P midwives with their identification numbers was provided to the survey firm. Some midwives, whose names were not on the list, were found and interviewed during the survey.
Ward Development Committees: The survey targeted all 500 ward development committees operating in areas with SURE-P MCH Phase 1 Primary Health Care facilities.
Households sampling
The interviewers first visited the SURE-P facilities and asked for the names of the communities within its catchment area. The names of communities were written in a piece of paper, crumpled and placed in a bag. The papers were randomly drawn and two communities selected.
All structures in communities with 50 or less structures were listed. Communities with 50 to 100 structures were split into Enumeration Areas (EAs) of approximately 25 structures, out of those two EAs were randomly selected and fully listed. Communities with more than 100 structures were also split into EAs and three EAs randomly selected.
The listing was conducted using the World Bank designed listing form. All listed households with eligible women were entered into a generated sampling form. Households with the smallest numbers in the "sampling order" where chosen for sample.
A sketch of the community maps was also obtained.
Face-to-face [f2f]
Four questionnaires were used to collect data for SURE-P MCH IE baseline survey.
1) Primary Health Care Facilities Questionnaire includes the following sections: (i) general information; (ii) facility characteristics; (iii) administration and management; (iv) human resources; (v) organizational citizenship and behaviors; (vi) Maslach Burnout Inventory (MBI); (vii) patient records; (viii) community outreach; (ix) health services; (x) user fees; (xi) national protocols; (xii) equipment; (xiii) drug storage and availability;
2) Midwives Questionnaire includes the following sections: (i) general information; (ii) post-secondary education; (iii) exposure to rural settings; (iv) job attributes preferences; (v) Maslach Burnout Inventory (MBI); (vi) work conditions; (vii) family; (viii) altruism game; (ix) other sources of income; (x) household assets, land, and animals; (xi) non-experimental measure of intrinsic motivation; (xii) time preferences game; (xiii) community relations and support; (xiv) prosocial scales; (xv) midwifery courses preferences; (xvi) antenatal care (ANC); (xvii) opinions about work and family; (xviii) contact information; (ixi) risk preferences game; (xix) post-contract expectations*; (xix) social norms game.
The study tests the effectiveness of three different incentives regimes for midwives (monetary only, non-monetary only and monetary plus non-monetary) versus a control group. The midwives baseline survey was used to deliver the relevant contract to each midwife, with midwives in the control group receiving a generic letter. The post-contract expectations section of the midwives questionnaire asked a basic set of questions on midwives' expectations related to various aspects of their work immediately following receipt of their contract letter.
3) Households Questionnaire includes the following sections: (i) contact information; (ii) household roster; (iii) education; (iv) transfers and other income; (v) adverse events; (vi) household health services utilization and payment; (vii) community organizations; (viii) male adult expectations; (ix) reproductive health; (x) antenatal care service utilization; (xi) labor and delivery; (xii) Edinburg Postnatal Depression Scale; (xiii) postpartum care and breastfeeding; (xiv) female adult expectations; (xv) maternal knowledge; (xvi) delivery problems; (xvii) exposure to media and mobile phones; (xviii) village leader and ward development committee interaction; (xix) dwelling characteristics and household amenities; (xx) household assets; (xxi) food and non-food consumption.
4) Ward Development Committees Questionnaire includes the following sections: (i) general information; (ii) access to basic services and community characteristics; (iii) social capital and community empowerment; (iv) external shocks; (v) direct observation.
Data cleaning was carried out in phases at the end of the field exercise. Data cleaning commenced with the correction of wrongly captured midwives identification (ID) numbers in the midwives and post-contract surveys. Corrected midwives IDs were further matched using STATA to identify missing midwives IDs. At the end of this exercise, a number of missing IDs were discovered and addressed by conducting fresh interviews. Plateau and Taraba states recorded the highest cases of midwives with missing post-contract survey forms.
Household data was cleaned by identifying duplicate IDs within the facilities and by correcting household IDs which were not correctly recorded. The household listing and sampling order forms served as reference books for confirmation of the IDs where concerns were raised. Facility and WDC files were cleaned by identifying duplicated facility IDs within the states. Identified IDs were cleaned by calling the person in charge of the facilities and WDC chairs to clarify which facilities they fall under.
Primary Health Care Facilities Questionnaire Target: 500; interviewed: 476; response rate: 95%
Midwives Questionnaire Target: 1,215; interviewed: 1,285; response rate: 106%
Ward Development Committees Questionnaire Target: 500; interviewed: 473; response rate: 95%
Household Questionnaire Target: 2,500; interviewed: 2,384; response rate: 95%
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Abstract The objective of this article was to systematize scientific evidence on the work of community health workers during the pandemic period between 2020 and 2022 in countries in Africa, Asia, Europe and North America. The methodology used was literature review and evidence synthesis in the PubMed and Web of Science databases referring to international scientific productions on the community health workers’ actions. A total of 23 studies were selected for analysis in the categories: characterization of studies; scope of routine actions; and changes in the work process during the pandemic. The scope of the program of these professionals was presented in different ways in the countries studied. Even with interruptions or adaptations, some routine actions were maintained. However, its attributions suffered considerable changes due to the need for social distancing, especially the use of technologies. Strong evidence of its relevance was identified in countries that had or did not have the community health workers program previously. Thus, in view of the comprehensive scope of action, the community health workers should not constitute a temporary solution and not integrated with the formal health organization, but as a fundamental part of a health system to achieve the improvement of the population quality of life.
https://www.ed.ac.uk/usher/respire/abouthttps://www.ed.ac.uk/usher/respire/about
The ever evolving role of ASHA demands an up-to-date comprehensive assessment of the workload, incentives and understanding of the work profile from the perspectives of the health system, community and ASHA herself in order to guide successful future implementation as well as sustainability of the programme. This study had a broad interest in both the full range of tasks and the different situations in which ASHA work and the changing context in which their role is interpreted. This study therefore used a mixed-methods approach (MMA) to assess and explore ASHAs’ perspectives of their workload alongside that of local healthcare colleagues in both rural and village contexts.
Background: Globally, Community Health Workers (CHWs) are integral contributors to many health systems. In India, Accredited Social Health Activists (ASHAs) have been deployed since 2005. Engaged in multiple health care activities, they are a key link between the health system and population. ASHAs are expected to participate in new health programmes, prompting interest in their current workload from the perspective of the health system, community and their family.
Methods: This MMA design was conducted in rural and tribal Primary Health Centers (PHCs), in Pune district, Western Maharashtra, India. All ASHAs affiliated with these PHCs were invited to participate in the quantitative study, those agreeing to contribute in-depth interviews (IDI) were enrolled in an additional qualitative study. Key informants’ interviews were conducted with the Auxiliary Nurse Midwife, Block Facilitators and Medical Officers of the same PHCs. Quantitative data were analysed using descriptive statistics. Qualitative data were analysed thematically.
Results: We recruited 67 ASHAs from the two PHCs. ASHAs worked up to 20 hours/week in their village of residence, serving populations of approximately 800-1200, embracing an increasing range of activities, despite a workload that contributed to feelings of being rushed and constant tiredness. They juggled household work, other paid jobs and their ASHA activities. Practical problems with travel added to time involved, especially in tribal areas where transport is lacking. Their sense of benefiting the community and respect and recognition in village brought happiness and job satisfaction. They were willing to take on new tasks. ASHAs perceived themselves as voluntary community health workers rather than as "health activists."
Conclusions: ASHAs were struggling to balance their significant ASHA workload, and domestic tasks. They were proud of their role as CHWs and willing to take on new activities. Strategies to recruit, train, enhance skills, incentivise, and retain ASHAs, need to be prioritised.
for more information, please see : https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/asha-workload
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BackgroundThe performance of primary health care providers regarding DM management is poor in rural China, and effective training methods for providers are urgently needed. This study aimed to evaluate the effect of web-based training for improving knowledge about DM management among primary health care providers in rural China and to further compare the effects of the training effect between primary health care providers with different backgrounds.MethodsA pre-post intervention study was conducted from April to August 2014. In this study, a total of 901 primary health care workers were recruited from six counties in Hubei province. To evaluate the effect of the web-based training, the knowledge achievement of participants was measured with multiple choice questions (MCQ) at baseline, at the end of two weeks of training and at three months after training. A mixed linear model (MLM) was used to measure group differences in the mean scores at baseline and follow-up.ResultsAfter the web-based training, the knowledge scores of the village doctors increased from 73.58 at baseline to 89.98 at posttest and to 84.57 three months after the training. For township health workers, we observed an upward trend in scores from 78.87 at the pre-test to 91.72 at the second test, and at the three months after the training, the scores increased to 94.91. For village doctors, greater knowledge achievement was observed between the scores at baseline and after two weeks of training(adjusted difference: 3.55, P = 0.03) compared to that observed for the township health workers, while decreased their knowledge achievement between baseline and the third-test compared with that of township health workers (adjusted difference: 5.05, P = 0.01).ConclusionsThis study suggested that web-based training was an effective method for improving the knowledge of primary health care providers about management of DM in remote areas. Compared with the effect of the training on village doctors, the training had a poor short-term effect on township health workers but a better long-term effect.
The objectives of the Kiribati Census changed over time shifting from earlier years where they were essentially household registrations and counts, to now where a national population census stands supreme as the most valuable single source of statistical data for Kiribati.
Census data is now widely used to evaluate: - The availability of basic household needs in key sectors, to identify disadvantaged areas and help set priorities for action plans; - Benefits of development programmes in particular areas, such as literacy, employment and family planning; In addition, census data is useful to asses manpower resources, identify areas of social concern and for the improvement in the social and economic status of women by giving more information and formulating housing policies and programmes and investment of development funds.
The census objective is to make a quick and sweeping count to avoid double counting—or under-counting for that matter. This is the main reason why the questions are often restricted to a manageable size—i.e. not to wantonly list any question one thinks of. The whole purpose of the questionnaire design is to ensure the most needed questions are asked, in addition to the count, structure and distribution of the population.
National coverage.
Households and Individuals.
The universe of the 2015 Kiribati Census is all occupied HHs in Kiribati. HHs are defined as a group of people (related or not) who pool their money, cook and eat together. It is not the physical structure (dwelling) in which people live.
Census/enumeration data [cen]
There is no sampling for the population census as it is a full coverage.
Face-to-face [f2f]
The questionnaire for this Census is composed of 7 sections and was published in English.
The questionnaires used are SPC questionnaires and were coded and entered in computers on the 6th July 2015 -and went on for three weeks.
The English questionnaire is provided as an external resource.
When the questionnaires started arriving back from the islands (field) the census staff first task was to make sure all the questionnaire books have been accounted for and that they have been properly filled in. When the checks have been made the next immediate task was to enter some of the information into the ACCESS database.
Data entry is the next step after coding and it was completed in early June 2016. The program used is CSPRO.
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The Maternal and Child Health (MCH) project of the Subsidy Reinvestment and Empowerment Programme (SURE-P), was set up by the Federal Government of Nigeria to reduce maternal and newborn morbidity and mortality inthe country. MCH initiative is a follow-up program to Midwives Service Scheme, implemented by the Nigeria National Primary Health Care Development Agency, that provides demand and supply side incentives, community monitoring, and increased human resources to improve the rates and quality of antenatal care and skilled birth attendance in Nigeria. On the supply-side, SURE-P aims to recruit, train and deploy 5,400 midwives and 14,100 community health extension workers, as well as to upgrade essential infrastructures and guarantee the adequate provision of supplies and equipment to primary health centres between the end of 2012 and 2015. In addition SURE-P will hire and train a total of 38,700 village health workers, who are expected to establish the connection between the primary healthcare centres (PHC) and pregnant women in each village. On the demand-side, SURE-P introduces a CCT, whereby all pregnant women will be given a total cash payout of 5,000 Naira (about USD 32), conditional on attending antenatal care, skilled birth attendance and postnatal care. Also, an information campaign aims to target all women of reproductive age to encourage them to register with their nearest PHC. The rigorous impact evaluation is being implemented to determine the causal impact of this programme. The IE comprises a quasi-experimental impact evaluation whose aim is to evaluate the SURE-P package, and four experimental evaluations which will evaluate the impact that distinct components have within the SURE-P package, such as: the effect of alternative incentives regimes to midwives on their retention rates the effect of conditional cash transfers on utilization of MCH services the effect of community monitoring of essential commodities on incidence of stock-out of supplies at the PHC The baseline data collection was carried out in September-November 2013. The first follow-up survey will be implemented in November 2014 January 2015, after SURE-P Phase I implementation. The final data collection is planned one year later, from November 2015 to January 2016, after SURE-P Phase II implementation. To gather baseline data four different groups of respondents were interviewed using different questionnaires. These respondents were: Managers in all 500 SURE-P health facilities across the country Midwives recruited by SURE-P Women who gave birth three months preceding the survey, in sampled households in each SURE-P facility catchment area Ward Development Committee (WDC) chairpersons or representatives in all SURE-P facilities. Overall, 476 facility manager questionnaires, 1,291 midwife questionnaires, 2,378 household qustionnaires and 477 WDC questionnaires were administered. The baseline data is documented here.
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Abstract This article aims to map the literature on the applications and perceptions regarding the use of digital technologies in the practices of community health workers. This is a scoping review conducted on PubMed, Bireme, SciELO, Web of Science, Embase, and Scopus. A total of 63 articles reporting the use of digital technologies by these workers in 24 countries were included. As a result, it was identified that support for maternal and child health is the most prevalent condition in these practices. The identified benefits involve increased access, improved work management, qualification, diversification, expanded training, and increased legitimacy of the profession. The challenges are reflected in limitations regarding community engagement, continuity of care, internet access, electricity, and digital literacy. In conclusion, it supports analyses regarding the irreversibility of the use of information and communication technologies in the world of work, emphasizing the need for their rational use while ensuring comprehensive, universal, and equitable access.
The 2017 Ghana Maternal Health Survey (2017 GMHS) was designed to produce representative estimates for maternal mortality indicators for the country as a whole, and for each of the three geographical zones, namely Coastal (Western, Central, Greater Accra and Volta), Middle (Eastern, Ashanti and Brong Ahafo) and Northern (Northern, Upper East and Upper West). For other indicators such as maternal care, fertility and child mortality, the survey was designed to produce representative results for the country as whole, for the urban and rural areas, and for each of the country’s 10 administrative regions.
The primary objectives of the 2017 GMHS were as follows: • To collect data at the national level that will allow an assessment of the level of maternal mortality in Ghana for the country as a whole and for three zones: Coastal (Western, Central, Greater Accra, and Volta regions), Middle (Eastern, Ashanti, and Brong Ahafo regions), and Northern (Northern, Upper East, and Upper West regions) • To identify specific causes of maternal and non-maternal deaths, in particular deaths due to abortionrelated causes, among adult women • To collect data on women’s perceptions of and experiences with antenatal, maternity, and emergency obstetrical care, especially with regard to care received before, during, and following the termination or abortion of a pregnancy • To measure indicators of the utilisation of maternal health services, especially post-abortion care services • To allow follow-on studies and surveys that will be used to observe possible reductions in maternal mortality as well as abortion-related mortality
The information collected through the 2017 GMHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.
National coverage
Sample survey data [ssd]
The sample for the 2017 GMHS was designed to provide estimates of key reproductive health indicators for the country as a whole, for urban and rural areas separately, for three zonal levels (Coastal, Middle, and Northern), and for each of the 10 administrative regions in Ghana (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West).
The sampling frame used for the 2017 GMHS is the frame of the 2010 Population and Housing Census (PHC) conducted in Ghana. The 2010 PHC frame is maintained by GSS and updated periodically as new information is received from various surveys. The frame is a complete list of all census enumeration areas (EAs) created for the PHC.
The 2017 GMHS sample was stratified and selected from the sampling frame in two stages. Each region was separated into urban and rural areas; this yielded 20 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before the sample selection, according to administrative units at different levels, and by using a probability proportional to size selection at the first stage of sampling.
In the first stage, 900 EAs (466 EAs in urban areas and 434 EAs in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was implemented from 25 January to 9 April 2017 in all of the selected EAs. The resulting lists of households then served as a sampling frame for the selection of households in the second stage. The household listing operation included inquiring of each household if there had been any deaths in that household since January 2012 and, if so, the name, sex, and age at time of death of the deceased person(s).
Some of the selected EAs were very large. To minimise the task of household listing, each large EA selected for the 2017 GMHS was segmented. Only one segment was selected for the survey with probability proportional to segment size. Household listing was conducted only in the selected segment. Thus, in the GMHS, a cluster is either an EA or a segment of an EA. As part of the listing, the field teams updated the necessary maps and recorded the geographic coordinates of each cluster. The listing was conducted by 20 teams that included a supervisor, three listers/mappers, and a driver.
For further details on sample design, see Appendix A of the final report.
Face-to-face [f2f]
Three questionnaires were used in the 2017 GMHS: the Household Questionnaire, the Woman’s Questionnaire, and the Verbal Autopsy Questionnaire.
All electronic data files for the 2017 GMHS were transferred via the IFSS to the GSS central office in Accra, where they were stored on a password-protected computer. The data processing operation included registering and checking for any inconsistencies and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of openended questions. The data were processed by five GSS staff members. Data editing was accomplished using CSPro software. Secondary editing and data processing were initiated in June and completed in November 2017.
A total of 27,001 households were selected for the sample, of which 26,500 were occupied at the time of fieldwork. Of the occupied households, 26,324 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,304 eligible women were identified for individual interviews; interviews were completed with 25,062 women, yielding a response rate of 99%.
The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017 Ghana Maternal Health Survey (2017 GMHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 GMHS is only one of many samples that could have been selected from the same population, using the same design and sample size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall in. For example, for any given statistic calculated from a sample survey, the true value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.
If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 GMHS sample is the result of a multi-stage stratified sampling, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed by SAS programs developed by ICF International. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends
See details of the data quality tables in Appendix C of the survey final report.
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ObjectiveTo assess the psychometric properties of the short form Community Attitudes toward Mentally Illness (SF-CAMI) scale among medical students and primary healthcare workers in China.MethodsOriginal English version CAMI was translated following a standard procedure. and then short-form CAMI developed through the multistage procedure. The psychometric properties were tested among two separate samples which contained 1,092 primary healthcare workers and 1,228 medical students. Reliability was assessed by internal consistency reliability and test–retest reliability. Exploratory factor and confirmatory factor analyses were performed to determine the structure and to assess the validity of the scale.ResultsThe Chinese version of SF-CAMI consists of 20 items and with three subscales: Benevolence, Fear and Exclusion, and Support and Tolerance. The confirmatory factor analysis indicated good fitting models for medical students and primary healthcare workers. The Cronbach α of total scale for both samples was good (0.82 for medical students and 0.85 for primary healthcare workers), and acceptable test–retest reliability was found (intraclass correlation coefficient is 0.62 for medical students and 0.60 for primary healthcare workers).ConclusionThe Chinese version of SF-CAMI performed good reliability and validity among both primary healthcare workers and medical students, provide more feasible and available tools for assessing the effect of mental health service programs in China.
In 2022, the total number of nursing staff available at primary health centers (PHCs) across India was over nine thousand. The state of Maharashtra had the highest number of nursing staff at PHCs in the country. It was followed by Tamil Nadu and Andhra Pradesh.