Evaluation of health workforce competence in maternal and neonatal issues in public health sector of Pakistan: an Assessment of their training needs

<p>Abstract</p> <p>Background</p> <p>More than 450 newborns die every hour worldwide, before they reach the age of four weeks (neonatal period) and over 500,000 women die from complications related to childbirth. The major direct causes of neonatal death are infections (36%), Prematurity (28%) and Asphyxia (23%). Pakistan has one of the highest perinatal and neonatal mortality rates in the region and contributes significantly to global neonatal mortality. The high mortality rates are partially attributable to scarcity of trained skilled birth attendants and paucity of resources. Empowerment of health care providers with adequate knowledge and skills can serve as instrument of change.</p> <p>Methods</p> <p>We carried out training needs assessment analysis in the public health sector of Pakistan to recognize gaps in the processes and quality of MNCH care provided. An assessment of Knowledge, Attitude, and Practices of Health Care Providers on key aspects was evaluated through a standardized pragmatic approach. Meticulously designed tools were tested on three tiers of health care personnel providing MNCH in the community and across the public health care system. The Lady Health Workers (LHWs) form the first tier of trained cadre that provides MNCH at primary care level (BHU) and in the community. The Lady Health Visitor (LHVs), Nurses, midwives) cadre follow next and provide facility based MNCH care at secondary and tertiary level (RHCs, Taluka/Tehsil, and DHQ Hospitals). The physician/doctor is the specialized cadre that forms the third tier of health care providers positioned in secondary and tertiary care hospitals (Taluka/Tehsil and DHQ Hospitals). The evaluation tools were designed to provide quantitative estimates across various domains of knowledge and skills. A priori thresholds were established for performance rating.</p> <p>Results</p> <p>The performance of LHWs in knowledge of MNCH was good with 30% scoring more than 70%. The Medical officers (MOs), in comparison, performed poorly in their knowledge of MNCH with only 6% scoring more than 70%. All three cadres of health care providers performed poorly in the resuscitation skill and only 50% were able to demonstrate steps of immediate newborn care. The MOs performed far better in counselling skills compare to the LHWs. Only 50 per cent of LHWs could secure competency scale in this critical component of skills assessment.</p> <p>Conclusions</p> <p>All three cadres of health care providers performed well below competency levels for MNCH knowledge and skills. Standardized training and counselling modules, tailored to the needs and resources at district level need to be developed and implemented. This evaluation highlighted the need for periodic assessment of health worker training and skills to address gaps and develop targeted continuing education modules. To achieve MDG4 and 5 goals, it is imperative that such deficiencies are identified and addressed.</p

Maternal nutrition: how is Eastern and Southern Africa faring and what needs to be done?

Background: The progress in key maternal health indicators in the Eastern and Southern Africa Region (ESAR) over the past two decades has been slow.Objective: This paper analyzed available information on nutrition programs and nutrition-specific interventions targeting maternal nutrition in the ESAR and proposes steps to improve maternal nutrition in this region.Methods: Search was conducted in relevant databases. Meta-analysis was done where there was sufficient data, while data from the nutrition programs was abstracted for objectives, settings, beneficiaries, stakeholders, impact of interventions and barriers encountered during implementation.Results: Findings from our review suggest that multiple nutrition programs are in place in the ESAR; including programs that directly address nutrition indicators and those that integrate corresponding sectors like agriculture, health, education, and water and sanitation. However, their scale and depth differ considerably. These programs have been implemented by a diverse range of players including respective government ministries, international agencies, non government organisations and the private sector in the region. Most of these programs are clustered in a few countries like Kenya, Uganda and Ethiopia while others e.g. Comoros, Somalia and Swaziland have only had a limited number of initiatives.Conclusion: These programs have been associated with some improvements in overall maternal health and nutritional indicators; however these are insufficient to significantly contribute to the progress in the region. Efforts should be prioritized in countries with the greatest burden of maternal undernutrition and associated risk factors with a focus on existing promising interventions to improve maternal nutrition.Keywords: Maternal nutrition, Eastern and Southern Africa, undernutritio

Can Volunteer Community Health Workers Decrease Child Morbidity and Mortality in Southwestern Uganda? An Impact Evaluation

BACKGROUND: The potential for community health workers to improve child health in sub-Saharan Africa is not well understood. Healthy Child Uganda implemented a volunteer community health worker child health promotion model in rural Uganda. An impact evaluation was conducted to assess volunteer community health workers' effect on child morbidity, mortality and to calculate volunteer retention. METHODOLOGY/PRINCIPAL FINDINGS: Two volunteer community health workers were selected, trained and promoted child health in each of 116 villages (population ∼61,000) during 2006-2009. Evaluation included a household survey of mothers at baseline and post-intervention in intervention/control areas, retrospective reviews of community health worker birth/child death reports and post-intervention focus group discussions. Retention was calculated from administrative records. Main outcomes were prevalence of recent child illness/underweight status, community health worker reports of child deaths, focus group perception of effect, and community health worker retention. After 18-36 months, 86% of trained volunteers remained active. Post-intervention surveys in intervention households revealed absolute reductions of 10.2% [95%CI (-17.7%, -2.6%)] in diarrhea prevalence and 5.8% [95%CI (-11.5%, -0.003%)] in fever/malaria; comparative decreases in control households were not statistically significant. Underweight prevalence was reduced by 5.1% [95%CI (-10.7%, 0.4%)] in intervention households. Community health worker monthly reports revealed a relative decline of 53% in child deaths (<5 years old), during the first 18 months of intervention. Focus groups credited community health workers with decreasing child deaths, improved care-seeking practices, and new income-generating opportunities. CONCLUSIONS/SIGNIFICANCE: A low-cost child health promotion model using volunteer community health workers demonstrated decreased child morbidity, dramatic mortality trend declines and high volunteer retention. This sustainable model could be scaled-up to sub-Saharan African communities with limited resources and high child health needs

Harmonisation of variables names prior to conducting statistical analyses with multiple datasets: an automated approach

ABSTRACT: BACKGROUND: Data requirements by governments, donors and the international community to measure health and development achievements have increased in the last decade. Datasets produced in surveys conducted in several countries and years are often combined to analyse time trends and geographical patterns of demographic and health related indicators. However, since not all datasets have the same structure, variables definitions and codes, they have to be harmonised prior to submitting them to the statistical analyses. Manually searching, renaming and recoding variables are extremely tedious and prone to errors tasks, overall when the number of datasets and variables are large. This article presents an automated approach to harmonise variables names across several datasets, which optimises the search of variables, minimises manual inputs and reduces the risk of error. RESULTS: Three consecutive algorithms are applied iteratively to search for each variable of interest for the analyses in all datasets. The first search (A) captures particular cases that could not be solved in an automated way in the search iterations; the second search (B) is run if search A produced no hits and identifies variables the labels of which contain certain key terms defined by the user. If this search produces no hits, a third one (C) is run to retrieve variables which have been identified in other surveys, as an illustration. For each variable of interest, the outputs of these engines can be (O1) a single best matching variable is found, (O2) more than one matching variable is found or (O3) not matching variables are found. Output O2 is solved by user judgement. Examples using four variables are presented showing that the searches have a 100% sensitivity and specificity after a second iteration. CONCLUSION: Efficient and tested automated algorithms should be used to support the harmonisation process needed to analyse multiple datasets. This is especially relevant when the numbers of datasets or variables to be included are larg