A rapid assessment of the quality of neonatal healthcare in Kilimanjaro region, northeast Tanzania

BMC Pediatrics, Volume 12, Year 2012

Interpretation

Background: While child mortality is declining in Africa there has been no evidence of a comparable reduction in neonatal mortality. The quality of inpatient neonatal care is likely a contributing factor but data from resource limited settings are few. The objective of this study was to assess the quality of neonatal care in the district hospitals of the Kilimanjaro region of Tanzania.Methods: Clinical records were reviewed for ill or premature neonates admitted to 13 inpatient health facilities in the Kilimanjaro region; staffing and equipment levels were also assessed. Results: Among the 82 neonates reviewed, key health information was missing from a substantial proportion of records: on maternal antenatal cards, blood group was recorded for 52 (63.4%) mothers, Rhesus (Rh) factor for 39 (47.6%), VDRL for 59 (71.9%) and HIV status for 77 (93.1%). From neonatal clinical records, heart rate was recorded for3 (3.7%) neonates, respiratory rate in 14, (17.1%) and temperature in 33 (40.2%). None of 13 facilities had a functioning premature unit despite calculated gestational age <36 weeks in 45.6% of evaluated neonates. Intravenous fluids and oxygen were available in 9 out of 13 of facilities, while antibiotics and essential basic equipment were available in more than two thirds. Medication dosing errors were common; under-dosage for ampicillin, gentamicin and cloxacillin was found in 44.0%, 37.9% and 50% of cases, respectively, while over-dosage was found in 20.0%, 24.2% and 19.9%, respectively. Physician or assistant physician staffing levels by the WHO indicator levels (WISN) were generally low.Conclusion: Key aspects of neonatal care were found to be poorly documented or incorrectly implemented in this appraisal of neonatal care in Kilimanjaro. Efforts towards quality assurance and enhanced motivation of staff may improve outcomes for this vulnerable group. © 2012 Mbwele et al.; licensee BioMed Central Ltd. The study was conducted in Kilimanjaro region of north-eastern Tanzania where data were collected from the northern zonal referral hospital and 13 referring peripheral hospitals by a purposive sampling technique. Of the 13 peripheral facilities included, 1 was the Kilimanjaro regional hospital, 4 were government-supported district hospitals, 6 were missionary-supported hospitals designated as district hospitals, and 2 were health centres with inpatient facilities servingurban Moshi’s two largest wards. Each health care facility was visited unannounced and data were collected in a single day or at most over two days. In each facility, staffing numbers were recorded from nationally standardized system i.e. Health Management Information Systems (HMIS), used for collection of data regarding health care workers in Tanzania; these data were verified in discussion with senior administrative staff in each hospital. Vital registration records and antenatal cards, records care and referral records were reviewed at each facility as well as at the zonal referral hospital from 26th November, 2010 to 25th April, 2011. Maternity and paediatric wards were inspected for the presence of basic supplies and equipment using a pre-prepared standard check-list. In addition, health workers were requested, again using a standardized list, to estimate the availability of essential supplies in terms of the number of months per year a supply was typically available. In each hospital the senior nurse or clinician who was present in each of the relevant paediatric or maternity wards was asked to identify any neonate less than or equal to 30 days of age currently on the ward who had been admitted or retained post-delivery for a medical problem, including prematurity. In each case, the record of the last menstrual period (LMP) as recorded on the antenatal card was used to calculate gestational age. The case notes were inspected and standard informationwas extracted regarding the presence or absence of a list of 13 features judged to be essential to the record of a sick neonate, as shown in Figure Figure1.1. A record of Rh factorand blood group, syphilis and HIV screening, APGAR score and birth weight were extracted from all antenatal cards. Presence of a record of essential clinical information as documented in 82 case notes of neonates who were admitted or retained in the ward for medical reasons. Data were single- entered in MS Access (Microsoft Corp, Redmond, VA). Original forms were consulted in the case of missing results or values out of expected ranges, and data were corrected as needed by the principal investigator and the data manager. Data were analysed using Stata-10 (Stata Corp, TX, USA). Staffing levels were assessed usingworkload indicators of staffing need (WISN). The expected number of health workers was found by dividing a yearly total time required to attend all neonates by total time available per health care worker (standardised by World Health Organization manual) [24]. The time required to attend one neonate was derived from the facility based neonatal admissions [24,25]. Recordsof prescriptions for ampicillin, cloxacillin or gentamicin were extracted from case notes or prescription sheets and the dosages were assessed by reference to the WHO Emergency Triage Assessment and Treatment (ETAT) manual in the context of the neonate’s weight. The study was approved by the Kilimanjaro Christian Medical University Ethics Committee. Written approval for the study was also received from the Kilimanjaro Regional Medical Officer. Written consent was obtained from the senior medical officer of each health facility for health workerinterviews, case notes assessments, checklists of supplies, photographs, and for results to be published.In addition, written consent was obtained from the guardians (mothers) of all enrolled neonates.

AI Digest

Study Justification:

– Neonatal mortality rates in Africa have not shown a significant reduction, unlike child mortality rates.
– The quality of inpatient neonatal care may be a contributing factor to high neonatal mortality rates.
– There is a lack of data on the quality of neonatal care in resource-limited settings, such as the Kilimanjaro region of Tanzania.

Study Highlights:

– Clinical records of ill or premature neonates admitted to 13 inpatient health facilities in the Kilimanjaro region were reviewed.
– Key health information was found to be missing from a significant proportion of records, including maternal antenatal cards and neonatal clinical records.
– None of the facilities had a functioning premature unit, despite a high percentage of evaluated neonates having a gestational age of less than 36 weeks.
– Medication dosing errors were common.
– Staffing levels were generally low.

Study Recommendations:

– Improve documentation of key health information in neonatal care records.
– Establish functioning premature units in all facilities to cater to the needs of premature neonates.
– Address medication dosing errors through training and monitoring.
– Increase staffing levels to meet the workload requirements for neonatal care.

Key Role Players:

– Health facility administrators and managers
– Medical officers and nurses
– Health information management staff
– Pharmaceutical staff
– Training and education providers

Cost Items for Planning Recommendations:

– Equipment and supplies for premature units
– Training programs for healthcare staff
– Monitoring and evaluation systems for medication dosing
– Recruitment and retention strategies for increasing staffing levels
– Health information management systems for improved documentation

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study provides detailed information on the quality of neonatal care in the Kilimanjaro region of Tanzania. The researchers reviewed clinical records, assessed staffing and equipment levels, and identified areas of improvement. However, the study was conducted in a specific region and may not be representative of neonatal care in other areas. To improve the evidence, future studies could include a larger sample size and a more diverse range of healthcare facilities.

Abstract

The study was conducted in Kilimanjaro region of north-eastern Tanzania where data were collected from the northern zonal referral hospital and 13 referring peripheral hospitals by a purposive sampling technique. Of the 13 peripheral facilities included, 1 was the Kilimanjaro regional hospital, 4 were government-supported district hospitals, 6 were missionary-supported hospitals designated as district hospitals, and 2 were health centres with inpatient facilities servingurban Moshi’s two largest wards. Each health care facility was visited unannounced and data were collected in a single day or at most over two days. In each facility, staffing numbers were recorded from nationally standardized system i.e. Health Management Information Systems (HMIS), used for collection of data regarding health care workers in Tanzania; these data were verified in discussion with senior administrative staff in each hospital. Vital registration records and antenatal cards, records care and referral records were reviewed at each facility as well as at the zonal referral hospital from 26th November, 2010 to 25th April, 2011. Maternity and paediatric wards were inspected for the presence of basic supplies and equipment using a pre-prepared standard check-list. In addition, health workers were requested, again using a standardized list, to estimate the availability of essential supplies in terms of the number of months per year a supply was typically available. In each hospital the senior nurse or clinician who was present in each of the relevant paediatric or maternity wards was asked to identify any neonate less than or equal to 30 days of age currently on the ward who had been admitted or retained post-delivery for a medical problem, including prematurity. In each case, the record of the last menstrual period (LMP) as recorded on the antenatal card was used to calculate gestational age. The case notes were inspected and standard informationwas extracted regarding the presence or absence of a list of 13 features judged to be essential to the record of a sick neonate, as shown in Figure Figure1.1. A record of Rh factorand blood group, syphilis and HIV screening, APGAR score and birth weight were extracted from all antenatal cards. Presence of a record of essential clinical information as documented in 82 case notes of neonates who were admitted or retained in the ward for medical reasons. Data were single- entered in MS Access (Microsoft Corp, Redmond, VA). Original forms were consulted in the case of missing results or values out of expected ranges, and data were corrected as needed by the principal investigator and the data manager. Data were analysed using Stata-10 (Stata Corp, TX, USA). Staffing levels were assessed usingworkload indicators of staffing need (WISN). The expected number of health workers was found by dividing a yearly total time required to attend all neonates by total time available per health care worker (standardised by World Health Organization manual) [24]. The time required to attend one neonate was derived from the facility based neonatal admissions [24,25]. Recordsof prescriptions for ampicillin, cloxacillin or gentamicin were extracted from case notes or prescription sheets and the dosages were assessed by reference to the WHO Emergency Triage Assessment and Treatment (ETAT) manual in the context of the neonate’s weight. The study was approved by the Kilimanjaro Christian Medical University Ethics Committee. Written approval for the study was also received from the Kilimanjaro Regional Medical Officer. Written consent was obtained from the senior medical officer of each health facility for health workerinterviews, case notes assessments, checklists of supplies, photographs, and for results to be published.In addition, written consent was obtained from the guardians (mothers) of all enrolled neonates.

Materials

N/A

Innovations

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

1. Digital Health Records: Implementing a digital health record system could help improve the documentation of key health information for both mothers and neonates. This would ensure that important data, such as blood group, Rh factor, VDRL, HIV status, heart rate, respiratory rate, and temperature, are accurately recorded and easily accessible.

2. Premature Units: Establishing functioning premature units in healthcare facilities would address the need for specialized care for premature neonates. This would help improve outcomes for premature babies and ensure that they receive the appropriate level of care.

3. Staffing Optimization: Using workload indicators of staffing need (WISN) could help assess and optimize staffing levels in healthcare facilities. This would ensure that there are enough healthcare workers to attend to neonates and provide quality care.

4. Medication Dosing Guidelines: Developing and implementing medication dosing guidelines specific to neonates could help reduce medication dosing errors. This would ensure that neonates receive the correct dosage of medications, such as ampicillin, gentamicin, and cloxacillin.

5. Quality Assurance and Staff Motivation: Implementing quality assurance measures and enhancing staff motivation could improve the overall quality of neonatal care. This could include regular training and education programs, performance evaluations, and recognition programs to motivate healthcare workers and ensure they provide high-quality care.

These innovations have the potential to address the gaps identified in the study and improve access to maternal health in the Kilimanjaro region of Tanzania.

AI Innovations Description

Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Improve documentation and implementation of key aspects of neonatal care: The study found that key health information was missing from a substantial proportion of records, such as maternal antenatal cards and neonatal clinical records. Developing a digital health record system specifically for maternal and neonatal care can help improve documentation and ensure that essential information is recorded accurately. This innovation can include features such as automatic data entry, reminders for missing information, and real-time access to patient records for healthcare providers.

2. Enhance staffing levels and training: The study revealed that physician or assistant physician staffing levels were generally low. To address this issue, an innovation could focus on improving staffing levels by recruiting and training more healthcare workers, particularly those specialized in neonatal care. This can be done through partnerships with medical schools and training programs, as well as offering incentives to healthcare professionals to work in underserved areas.

3. Strengthen the availability of essential supplies and equipment: The study found that some facilities lacked functioning premature units and had medication dosing errors. An innovation to improve access to maternal health could involve establishing a centralized supply chain management system to ensure the availability of essential supplies and equipment in all healthcare facilities. This system can include regular monitoring of stock levels, efficient distribution mechanisms, and training for healthcare workers on proper medication dosing.

4. Implement quality assurance measures: The study identified several areas where the quality of neonatal care was lacking. To address this, an innovation could focus on implementing quality assurance measures, such as regular audits and performance evaluations, to ensure that healthcare providers adhere to best practices in maternal and neonatal care. This can also involve providing continuous education and training opportunities for healthcare workers to stay updated on the latest guidelines and protocols.

Overall, the development of these innovations can help improve access to maternal health by addressing the gaps and challenges identified in the study conducted in the Kilimanjaro region of Tanzania. By focusing on improving documentation, staffing levels, supplies and equipment, and implementing quality assurance measures, maternal and neonatal care can be enhanced, leading to better health outcomes for mothers and their babies.

AI Innovations Methodology

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

1. Improve documentation: Implement a standardized system for recording and documenting key health information for both mothers and neonates. This can include ensuring that blood group, Rh factor, VDRL, HIV status, heart rate, respiratory rate, and temperature are consistently recorded.

2. Enhance staffing levels: Increase the number of healthcare workers, particularly physicians or assistant physicians, to meet the workload demands and provide adequate care for neonates. This can be done by using workload indicators of staffing need (WISN) to determine the required number of health workers.

3. Strengthen equipment and supplies: Ensure that all health facilities have essential basic equipment, such as functioning premature units, intravenous fluids, oxygen, antibiotics, and accurate medication dosing guidelines. Regular assessments and checklists can help identify and address any gaps in supplies.

4. Quality assurance and staff motivation: Implement quality assurance measures to improve the overall quality of neonatal care. This can include regular training and education programs for healthcare workers, performance evaluations, and incentives to motivate staff to provide better care.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the percentage of complete and accurate documentation, staffing levels per patient, availability of essential equipment and supplies, and adherence to medication dosing guidelines.

2. Baseline data collection: Collect baseline data on the identified indicators from a representative sample of health facilities in the Kilimanjaro region. This can involve reviewing clinical records, conducting interviews with healthcare workers, and inspecting the facilities.

3. Introduce interventions: Implement the recommended interventions in a subset of health facilities while keeping a control group of facilities unchanged. This can involve training healthcare workers, improving documentation systems, providing necessary equipment and supplies, and monitoring adherence to medication dosing guidelines.

4. Data collection after intervention: Collect data on the same indicators from both the intervention and control group of health facilities after a specified period of time. This can involve repeating the same data collection methods used in the baseline assessment.

5. Data analysis: Compare the data collected before and after the interventions to assess the impact on access to maternal health. This can be done by calculating the percentage improvement in each indicator and conducting statistical analysis to determine the significance of the changes.

6. Evaluation and recommendations: Evaluate the results of the simulation and make recommendations based on the findings. This can include identifying the most effective interventions and strategies for improving access to maternal health in the Kilimanjaro region.

It is important to note that this methodology is a general framework and may need to be adapted based on the specific context and resources available in the Kilimanjaro region.

Author

Dima Health

Statistics:

Citations: 18

Identifiers:

DOI: 10.1186/1471-2431-12-182

Research Areas:

Health System and Policy, Infectious Diseases, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Tanzania

Study Approach:

Quantitative