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Introduction: Cameroon is classified by the World Health Organization (WHO) as having a critical shortage of health personnel. This is further complicated by the geographic distributional inequalities of the national health workforce. This shortfall impedes Cameroons’ progress of improving the human resources for health (HRH) to meet up with the Millennium Development Goals (MDGs) by 2015. However, it is unknown whether the health workforce of Cameroon is distributed equally across geographic regions. Additionally, indicators other than population levels have not been used to measure health care needs. This study aimed to assess the adequacy, evenness of distribution and challenges faced by the health workforce across the different regions of Cameroon. Methods: National health personnel availability and distribution were assessed by use of end-of-year census data for 2011 obtained from the MoPH data base. The inequalities and distribution of the workforce were estimated using Gini coefficient and Lorenz curve and linear regression was used to determine the relation between health personnel density and selected health outcomes. Alternative indicators to determine health care needs were illustrated using concentration curves. Results: Significant geographic inequalities in the availability of health workforce exist in Cameroon. Some regions have a higher number of physicians (per person) than others leading to poor health outcomes across the regions. 70 % of regions have a density of health personnel-to-population per 1,000 that is less than 1.5, implying acute shortage of health personnel. Poor working and living conditions, coupled with limited opportunities for career progress accounted for some documented 232 physicians and 205 nurses that migrated from the public sector. Significant distributional inequality was noticed when under-five infant mortality and malaria prevalence rate were used as indicators to measure health care needs. Conclusion: Our results show an absolute shortage of public health personnel in Cameroon that is further complicated by the geographic distributional inequalities across the regions of the nation. Cameroon aims to achieve universal health coverage by 2035; to realize this objective, policies targeting training, recruitment, retention and effective deployment of motivated and supported health workforce as well as the development and improvement of health infrastructures remain the major challenge.
We extracted indicators (age, cadres, sex, location, population and facilities) from HRH indicator compendium that have been pre-tested and used for research [23] as a guide to our study. We obtained data regarding public health HRH from the MoPH database on the last census performed in 2011, aimed at taking stock of all health personnel of the nation. This database contains staff demographic particulars as well as health facilities in line with our indicators [24]. Population data, under-five infant mortality, maternal mortality, measles immunization coverage and malaria prevalence rate were obtained from the National Institute of Statistics Cameroon [18], and the Central Bureau of Census and Population Studies Cameroon (BUCREP), [13]. We also use some published literature available at the MoPH archive that addressed the working and living conditions of health personnel as well as the suspension in the development of health infrastructures in Cameroon. We use this information to analyze some of the challenges facing the health workforce of the nation. This study did not involve with the collection of primary data, therefore ethical clearance was not needed for the study. However, permission was obtained from the Ministry of Public health, department of human resource for health to access the information used. Data analysis was done by the use of descriptive statistical method to create tables that grouped and classified the different cadres, ages, sex, and the geographical distribution of the health personnel and population across the regions of the country. The relation between selected health personnel densities and outcomes was assessed by linear regression of data on maternal mortality, under-five infant mortality and measles immunization coverage of the nation in 2011 [24] using IBM SPSS version 21.0 software package. The national health workforce distributional inequality was measured using Gini coefficient and Lorenz curve. Lorenz curves was used to characterize the distribution of health personnel and show the cumulative share of health workers against cumulative population share when the different locations are ranked from the lowest to the highest number of health personnel. The Gini index was used to measure the aggregate level of inequality with values ranging between 0 and 1, with higher values indicating higher levels of inequality. This was calculated as; where G is the Gini index, n is the number of observations and Xi the number of health personnel at ith location. Gini coefficient and Lorenz curve have been use in previous studies to measure health personnel inequalities [12, 14]. Also, we use concentration curves which have been used to typify socioeconomic inequalities in health [22, 25, 26], to describe the alternative ways of determining health care needs using indicators such as under-five infant mortality and malaria prevalence rate. Using the concentration curves, we graphically illustrate on the same diagram as the Lorenz curve, the importance of using alternative indicators of health care needs to show how the equitable distribution of health personnel could be determine using alternative measures of need. In order to plot our concentration curves, we calculated the concentration index which is done similarly as the Gini index. Figure 1, shows a graphical annotation of Gini index (A/ (A + B) and concentration index C/ (A + B). When the concentration curve lies above or below the diagonal line, the region “C” is given a negative or a positive value respectively. Illustration of Lorenz curve and concentration curve Congruent to the Gini index, the concentration index takes values from −1 and + 1, where index “0” means that the alternative measure of needs does not influence the overall level of inequality relative to when need is determine using the number of inhabitants. With a negative index value in which case the concentration curve lies above the diagonal, the demand for health care needs are higher in regions with proportional fewer health personnel. This implies that inequalities are larger when alternative measures of health care needs are used. Contrarily, inequalities will be smaller when the concentration curve lies below the diagonal with a positive concentration index implying health care needs are on an average proportionally distributed with the health workforce.
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