Gender-based distributional skewness of the United Republic of Tanzania’s health workforce cadres: A cross-sectional health facility survey

  • Human Resources for Health, Volume 11, No. 1, Year 2013

listen audio

Study Justification:
This study aims to assess the gender-based distribution of the health workforce cadres in the United Republic of Tanzania. The justification for this study is based on the need to understand the gender disparities within the health workforce and the potential implications of these disparities on healthcare access and quality. Existing evidence suggests that individuals may prefer healthcare providers of their own gender, and therefore, it is important to examine the gender distribution of health workers to ensure equitable access to healthcare services.
Highlights:
– The study found that the distribution of health workforce cadres in Tanzania is heavily skewed towards women. Overall, 75% of the health workers surveyed were women.
– The proportion of women among maternal and child health aides or medical attendants, nurses, and midwives was high, ranging from 86% to 91%.
– In contrast, the proportion of women among clinical officers and medical doctors was much lower, at 28% and 21% respectively.
– Multivariate analysis revealed significant gender disparities in the different cadres, with odds ratios indicating that women were more likely to be in certain cadres (MCHA/MA, nurse, midwife) and less likely to be in others (CO, MD).
Recommendations:
– The study recommends the need for more health workers to ensure effective delivery of quality health services in Tanzania.
– It emphasizes the importance of adequate representation of women in highly trained cadres, such as clinical officers and medical doctors, to address gender-specific roles and needs in healthcare.
– The study highlights the need for policies and interventions to address the gender-based distributional skewness in the health workforce, aiming for a more balanced representation of both men and women in different cadres.
Key Role Players:
– Ministry of Health: Responsible for policy development and implementation related to the health workforce.
– Health Professional Associations: Representing different cadres of health workers and advocating for their interests.
– Health Training Institutions: Providing education and training for health workers, including efforts to encourage more women to pursue careers in specialized professional jobs.
– Non-Governmental Organizations: Supporting initiatives to address gender disparities in the health workforce and promoting gender equality in healthcare.
Cost Items for Planning Recommendations:
– Recruitment and Training: Budget for recruiting and training more health workers, including efforts to encourage women to pursue careers in specialized cadres.
– Infrastructure and Equipment: Investment in healthcare facilities and equipment to accommodate the increased number of health workers.
– Salaries and Incentives: Provision of competitive salaries and incentives to attract and retain health workers, particularly in underserved areas.
– Monitoring and Evaluation: Budget for monitoring and evaluating the implementation and impact of policies and interventions aimed at addressing gender disparities in the health workforce.
Please note that the cost items provided are general categories and not specific cost estimates. Actual cost planning would require a detailed analysis and budgeting process.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is based on a secondary analysis of data collected in a cross-sectional health facility survey in the United Republic of Tanzania. The study provides specific details about the methodology used, including the sampling technique and statistical analysis. The results are presented clearly, showing the gender-based distribution of different health workforce cadres. The study also includes multivariate analysis to assess the relationship between gender and each cadre. However, the evidence is limited to data collected in 2008 and may not reflect the current situation. To improve the evidence, conducting a more recent survey would provide updated information on the gender-based distribution of the health workforce cadres in Tanzania.

Background: While severe shortages, inadequate skills and a geographical imbalance of health personnel have been consistently documented over the years as long term critical challenges in the health sector of the United Republic of Tanzania, there is limited evidence on the gender-based distribution of the health workforce and its likely implications. Extant evidence shows that some people may not seek healthcare unless they have access to a provider of their gender. This paper, therefore, assesses the gender-based distribution of the United Republic of Tanzania’s health workforce cadres.Methods: This is a secondary analysis of data collected in a cross-sectional health facility survey on health system strengthening in the United Republic of Tanzania in 2008. During the survey, 88 health facilities, selected randomly from 8 regions, yielded 815 health workers (HWs) eligible for the current analysis. While Chi-square was used for testing associations in the bivariate analysis, multivariate analysis was conducted using logistic regression to assess the relationship between gender and each of the cadres involved in the analysis.Results: The mean age of the HWs was 39.7, ranging from 15 to 63 years. Overall, 75% of the HWs were women. The proportion of women among maternal and child health aides or medical attendants (MCHA/MA), nurses and midwives was 86%, 86% and 91%, respectively, while their proportion among clinical officers (COs) and medical doctors (MDs) was 28% and 21%, respectively. Multivariate analysis revealed that the odds ratio (OR) and 95% confidence interval (CI) that a HW was a female (baseline category is ” male” ) for each cadre was: MCHA/MA, OR = 3.70, 95% CI 2.16-6.33; nurse, OR = 5.61, 95% CI 3.22-9.78; midwife, OR = 2.74, 95% CI 1.44-5.20; CO, OR = 0.08, 95% CI 0.04-0.17 and MD, OR = 0.04, 95% CI 0.02-0.09.Conclusion: The distribution of the United Republic of Tanzania’s health cadres is dramatically gender-skewed, a reflection of gender inequality in health career choices. MCHA/MA, nursing and midwifery cadres are large and female-dominant, whereas COs and MDs are fewer in absolute numbers and male-dominant. While a need for more staff is necessary for an effective delivery of quality health services, adequate representation of women in highly trained cadres is imperative to enhance responses to some gender-specific roles and needs. © 2013 Exavery et al.; licensee BioMed Central Ltd.

This is a secondary analysis of data collected in 2008 in a cross-sectional health facility survey in the United Republic of Tanzania. The survey was conducted by the Ifakara Health Institute (IHI), United Republic of Tanzania, in collaboration with Columbia University, USA, as part of the implementation of the Health Systems Strengthening for Equity (HSSE) project. Based on the eight United Republic of Tanzanian zones, one region from each zone was selected randomly through a multi-stage sampling technique which brought up eight regions: Dodoma, Pwani, Mwanza, Tanga, Mbeya, Iringa, Tabora and Mtwara. From each district in these regions, two health facilities (one hospital and/or one health centre) providing emergency obstetric care (EmOC) were selected. This made a total of 88 health facilities from which 825 health workers (HWs) participated in the survey. Of these HWs, 815 (98.8%) with non-missing data on gender and cadre were extracted from the parent database for the current analysis. Selected HWs for the primary study responded to a self-administered provider questionnaire which comprised mostly closed-ended questions and a few open-ended ones. Broadly, the questions pertained to the HWs’ background and employment, pre- or in-service training programs attended, feelings of job satisfaction, and a discrete choice experiment which aimed at understanding factors that affect employment preferences. Following interviewer training, the tool was pre-tested in facilities similar to those actually surveyed to check for relevance and answerability of the survey questions. Original cadres were operationally regrouped by merging those that were closely related because the numbers of respondents for some cadres, such as specialists, were very small. Maternal and child health aide (MCHA), medical attendant (MA) and nursing assistant were joined to form a single category, “MCHA/MA”; registered public health nurse (PHN), enrolled public health nurse (EPHN), registered nurse (RN) and enrolled nurse (EN) were combined into a single category and referred to as “nurse”; registered midwife and enrolled midwife were grouped together as “midwife”; clinical officer (CO) remained unchanged; and assistant medical officer (AMO), medical officer (MO) and specialist were combined and referred to as “medical doctor (MD)”. Gender-specific proportions of the HW in various categories of socio-demographic characteristics were calculated. The degree of association between gender and sociodemographic characteristics was tested using Pearson’s Chi-square (χ2) and Student’s t-tests for categorical and continuous variables, respectively. Further analyses were performed using multivariate logistic regression to assess the relationship between gender and each of the cadres, controlling for potential confounders. Each of these cadres was assessed as a separate dependent variable with two (binary) categories that classified a HW as either an MD or not an MD, CO or not a CO and so on. As coding of the outcome variable in logistic regression requires, a code of ‘1’ was assigned if a HW belonged to a particular cadre and ‘0’ if not. Therefore, the probability that a HW was an MD for example was expressed in a multivariate logistic regression model as: where p^ is the expected probability that a HW is an MD; X1 through Xk are k distinct independent variables; and b0 through bk are the regression coefficients. The model was then re-written with the outcome expressed as the expected natural logarithm of the odds that a HW is an MD as: The main independent variable was gender (female = 1, male = 0). This variable was taken along with other several independent variables including age, educational attainment, region, health facility ownership, and health facility type. Educational attainment was included because of evidence from other countries showing that some women do not prefer courses that take a long time to graduate, resulting in fewer women in specialized professional jobs [26]. Facility type was included in the analysis as an indicator of facility location. In the United Republic of Tanzanian health system context, a hospital is the highest level of care that serves either a region (regional hospital) or a district (district hospital) and is usually located in the headquarters of regions or districts. Therefore, it may be appropriate to consider hospital locations in the United Republic of Tanzania as urban. Health centres on the other hand, which are the second highest level of care in the United Republic of Tanzania, exist mostly in rural and sometimes in urban settings. Therefore, this variable to a larger extent reflects the rural–urban distribution of HWs in the country. Data analysis was performed using STATA (Version 11) statistical software (Stata Corp, Texas, USA). Ethical approval to conduct the main survey from which this paper stems was granted by the Medical Research Coordinating Committee (MRCC) of the National Institute for Medical Research (NIMR) in the United Republic of Tanzania. Participation in the study was voluntary with all consenting individuals having to sign an informed consent form first. To ensure integrity and confidentiality, the database was anonymous with no information (e.g. names) that could identify the participant. Storage of completed questionnaires and consent forms was carefully managed, and access to the data was restricted to a few experts.

N/A

Based on the information provided, it appears that the study focused on analyzing the gender-based distribution of health workforce cadres in the United Republic of Tanzania. The study found that there is a significant gender imbalance in the distribution of health cadres, with female-dominated cadres including maternal and child health aides or medical attendants, nurses, and midwives, while male-dominated cadres include clinical officers and medical doctors.

To improve access to maternal health, here are some potential recommendations based on the findings of the study:

1. Promote gender equality in health career choices: Encourage and support initiatives that aim to increase the representation of women in highly trained cadres such as clinical officers and medical doctors. This can be done through targeted recruitment strategies, scholarships, mentorship programs, and awareness campaigns to challenge gender stereotypes and biases.

2. Strengthen training programs for female-dominated cadres: Invest in comprehensive and quality training programs for maternal and child health aides or medical attendants, nurses, and midwives. This includes providing opportunities for continuous professional development, specialized training in maternal health, and ensuring access to up-to-date medical knowledge and technologies.

3. Improve working conditions and incentives: Address the factors that may discourage women from pursuing careers in male-dominated cadres, such as clinical officers and medical doctors. This includes improving working conditions, providing competitive salaries and benefits, and creating a supportive and inclusive work environment that promotes work-life balance.

4. Increase the number of health workers: Address the overall shortage of health workers by increasing the recruitment and training of both male and female health professionals. This can be achieved through expanding medical and nursing schools, establishing training programs in underserved areas, and providing incentives for health workers to work in rural and remote areas where access to maternal health services may be limited.

5. Enhance community engagement and awareness: Promote community engagement and awareness programs that educate and empower individuals, especially women, about their rights to access maternal health services. This can include community health education sessions, outreach programs, and the involvement of community leaders and influencers in advocating for improved access to maternal health.

It is important to note that these recommendations are based on the information provided in the study and may need to be further tailored and contextualized to the specific needs and challenges of the United Republic of Tanzania’s health system.
AI Innovations Description
The recommendation that can be developed into an innovation to improve access to maternal health based on the described research is to address the gender-based distributional skewness of the health workforce in the United Republic of Tanzania. This can be achieved by implementing strategies to increase the representation of women in highly trained cadres such as clinical officers and medical doctors.

Some possible actions to achieve this could include:
1. Promoting gender equality in health career choices through awareness campaigns and educational programs that encourage women to pursue careers in medicine and related fields.
2. Providing scholarships and financial support specifically targeted towards women who are interested in pursuing careers as clinical officers and medical doctors.
3. Implementing policies and initiatives that create a supportive and inclusive work environment for women in the health sector, including mentorship programs and opportunities for career advancement.
4. Strengthening recruitment and retention strategies to attract and retain more women in highly trained cadres, such as offering competitive salaries and benefits packages.
5. Conducting further research and data collection to monitor progress and identify any barriers or challenges that may hinder the representation of women in these cadres.

By addressing the gender-based distributional skewness of the health workforce, the innovation can help ensure that women have access to healthcare providers of their gender, which may improve their willingness to seek maternal health services.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations to improve access to maternal health:

1. Increase the number of female health workers: Since some people may prefer to seek healthcare from providers of their own gender, increasing the number of female health workers can help improve access to maternal health services. This can be done through targeted recruitment and training programs.

2. Improve distribution of health workers: Addressing the geographical imbalance of health personnel is crucial for improving access to maternal health. This can be achieved by incentivizing health workers to work in rural and underserved areas, providing transportation and accommodation support, and establishing telemedicine services.

3. Enhance training and skills development: Investing in training and skills development programs for health workers, particularly in maternal and child health, can improve the quality of care provided. This can include specialized training in obstetrics, neonatal care, and emergency obstetric care.

4. Strengthen community engagement and awareness: Promoting community engagement and awareness about maternal health can help overcome cultural and social barriers that prevent women from seeking care. This can be done through community health education programs, involving community leaders, and utilizing local media channels.

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

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the number of antenatal care visits, skilled birth attendance, and postnatal care coverage.

2. Collect baseline data: Gather data on the current status of these indicators in the target population or region. This can be done through surveys, health facility records, and existing data sources.

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the recommended interventions and their potential impact on the selected indicators. This model should consider factors such as population demographics, health worker distribution, and healthcare utilization patterns.

4. Input intervention parameters: Define the specific parameters for each intervention, such as the number of additional female health workers to be recruited, the target areas for distribution, and the expected increase in training and skills development programs.

5. Run simulations: Use the simulation model to project the potential impact of the interventions on the selected indicators. This can be done by running multiple scenarios with different intervention parameters and comparing the outcomes.

6. Analyze results: Analyze the simulation results to assess the potential improvements in access to maternal health services. This can include quantifying the expected increase in antenatal care visits, skilled birth attendance rates, and postnatal care coverage.

7. Validate and refine the model: Validate the simulation results by comparing them with real-world data or expert opinions. Refine the model as needed to improve its accuracy and reliability.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on resource allocation and implementation strategies.

Statistics:
Citations: 12
Authors: 6
Identifiers:
DOI: 10.1186/1478-4491-11-28
Research Areas:
Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Workforce
Study Countries:
Tanzania
Study Design:
Case-Control Study, Cross Sectional Study
Study Approach:
Quantitative
Participants Gender:
Male & Female
Partagez ceci :
Facebook
Twitter
LinkedIn
WhatsApp
Email