The relationship between attendance at birth and maternal mortality rates: an exploration of United Nations' data sets including the ratios of physicians and nurses to population, GNP per capita and female literacy.
The relationship between attendance at birth and maternal mortality rates: an exploration of United Nations' data sets including the ratios of physicians and nurses to population, GNP per capita and female literacy.


BACKGROUND
This is the third and final paper drawing on data taken from United Nations (UN) data sets. The first paper examined the global distribution of health professionals (as measured by ratios of physicians and nurses to population), and its relationship to gross national product per capita (GNP) (Wharrad & Robinson 1999). The second paper explored the relationships between the global distribution of physicians and nurses, GNP, female literacy and the health outcome indicators of infant and under five mortality rates (IMR and u5MR) (Robinson & Wharrad 2000). In the present paper, the global distribution of health professionals is explored in relation to maternal mortality rates (MMRs). The proportion of births attended by medical and nonmedical staff defined as "attendance at birth by trained personnel" (physicians, nurses, midwives or primary health care workers trained in midwifery skills), is included as an additional independent variable in the regression analyses, together with the ratio of physicians and nurses to population, female literacy and GNP.


OBJECTIVE
To extend our earlier analyses by considering the relationships between the global distribution of health professionals (ratios of physicians and nurses to population, and the proportion of births attended by trained health personnel), GNP, female literacy and MMR. <Design. Using a database on 155 countries, regression analyses were performed using numbers of physicians, and numbers of nurses, per 1000 population, the proportion of births attended by trained health personnel, GNP per capita and female literacy as independent variables and MMRs as the dependent variable.


RESULTS
Linear regression analyses show positive associations for MMRs and the ratios of physicians to population (73%, n=136), ratios of nurses to population (56%, n=137), and the proportion of births attended by trained health personnel (83%, n=118). Multiple regression analyses reveal a more complex picture, with nurses disappearing altogether when regressed with physicians, GNP, female literacy and MMR. The three variables, attendance at birth by trained personnel, GNP and physicians per 1000 population explained 87% of the variation in MMR (n=112) when included in the multiple regression analysis.


CONCLUSIONS
As in the previous papers, caution is required regarding the validity and reliability of the UN data sources used in these analyses. Maternal mortality rates are particularly susceptible to inaccuracies. Nevertheless, the strength of the positive correlations suggests that real relationships are identified between the independent variables and the dependent variable of MMR. The strength of the linear and multiple correlations between births attended by trained personnel and lower MMRs indicates that maternal deaths are substantially reduced when a high proportion of births are attended by health professionals, including primary health care workers trained in midwifery skills, with the maintenance of an aseptic environment, the identification of maternal and foetal complications, and the opportunity when necessary to transfer parturient mothers to centres with higher level skills and facilities.
Robinson
J J
JJ
Postgraduate Division of Nursing Studies, Medical School, University of Nottingham, Queen's Medical Centre, Nottingham, UK. jjarenginerow@enta.net
Wharrad
H
H
D015982:Bias; D036861:Delivery, Obstetric / Q000706:statistics & numerical data*; D004467:Economics; D004522:Educational Status; D005260:Female; D014943:Global Health; D006801:Humans; D016014:Linear Models; D008428:Maternal Mortality; D008503:Medical Staff / Q000600:supply & distribution*; D008507:Medically Underserved Area; D009035:Mothers / Q000193:education*; D009740:Nursing Staff / Q000600:supply & distribution*; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data*; D011156:Population Density; D011159:Population Surveillance; D011247:Pregnancy; D012044:Regression Analysis; D012307:Risk Factors; D014480:United Nations; D014481:United States