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Background: Developing countries with high maternal mortality need to invest in indicators that not only provide information about how many women are dying, but also where, and what can be done to prevent these deaths. The unmet Obstetric Needs (UONs) concept provides this information. This concept was applied at district level in Kenya to assess how many women had UONs and where the women with unmet needs were located. Methods: A facility based retrospective study was conducted in 2010 in Malindi District, Kenya. Data on pregnant women who underwent a major obstetric intervention (MOI) or died in facilities that provide comprehensive Emergency Obstetric Care (EmOC) services in 2008 and 2009 were collected. The difference between the number of women who experienced life threatening obstetric complications and those who received care was quantified. The main outcome measures in the study were the magnitude of UONs and their geographical distribution. Results: 566 women in 2008 and 724 in 2009 underwent MOI. Of these, 185 (32.7%) in 2008 and 204 (28.1%) in 2009 were for Absolute Maternal Indications (AMI). The most common MOI was caesarean section (90%), commonly indicated by Cephalopelvic Disproportion (CPD)-narrow pelvis (27.6% in 2008; 26.1% in 2009). Based on a reference rate of 1.4%, the overall MOI for AMI rate was 1.25% in 2008 and 1.3% in 2009. In absolute terms, 22 (11%) women in 2008 and 12 (6%) in 2009, who required a life saving intervention failed to get it. Deficits in terms of unmet needs were identified in rural areas only while urban areas had rates higher than the reference rate (0.8% vs. 2.2% in 2008; 0.8% vs. 2.1% in 2009). Conclusions: The findings, if used as a proxy to maternal mortality, suggest that rural women face higher risks of dying during pregnancy and childbirth. This indicates the need to improve priority setting towards ensuring equity in access to life saving interventions for pregnant women in underserved areas.
This was a facility based retrospective survey conducted in Malindi District, Kenya (currently Malindi and Magarini sub counties in Kilifi County) in the year 2010. The area is located in the Northern Coastal region, covering an area of 7, 792 square kilometres. Four divisions, namely, Malindi, Langobaya, Marafa and Magarini constitute the district. The total population in the district was 400,514 people in 2009, with a distribution of 140, 739 people in urban and 259, 775 in rural areas [43]. Malindi Division has a higher population density than the other three divisions as it has favourable topographic features and economic factors affecting human settlement. Malindi Town, the main urban centre in the district, is located in Malindi Division. The district had a total of 105 public and private health facilities [44]. Of these, 42 (40%) offer delivery services. One public and two private hospitals provide caesarean section services. The total fertility rate in the district was 4.8 children per woman of reproductive age and a crude birth rate of 38.1/1000 [45]. Identification and listing of all comprehensive EmOC (perform caesarean section and blood transfusion) facilities in the district were undertaken prior to data collection. All divisions, locations and sub locations were also identified and coded. The UON indicator restricts its scope to a standard list of Absolute Maternal Indications (AMI), that is, maternal life threatening conditions for which major obstetric surgery is performed to solve the problem [32]. The list of AMIs is based on the degree of severity of the indication, the relative stability of its incidence and relatively reproducible diagnosis [19,33]. The standard list of AMI adopted for this study included: The list of MOI included: Based on findings on existence and functionality of EmOC in the district [46], three comprehensive EmOC facilities met the inclusion criteria for the UON study. These were the Government District Hospital and two private hospitals. A nurse-midwife, qualified in assessing obstetric diagnosis, was trained in data collection. A form was filled for every woman who underwent a major surgical obstetric intervention or died in the health facilities in the target district. The possibility of women from the study district having received MOI in other districts was taken into account by reviewing records from the regional referral hospital to identify if the hospital had received any cases from the target district. The referral hospital was the Coast Province General Hospital, located 160 kilometers away from the study area, in Mombasa town. Data were collected on major obstetric interventions, the maternal indications, geographical origins of the women, and outcomes for mothers. The data were collected retrospectively for the periods 1st January 2008 to 31st December 2009. The principal data source was the operating theatre registers, where most MOI were recorded. Information about the indications for the interventions and other personal data on the women was obtained from patient delivery files, maternity ward registers and admission records for maternity or surgical wards. In filling the unmet obstetric need form, particular attention was paid to the way in which diagnoses were formulated in the registers, and recorded as closely as possible to the way they were usually expressed in clinical language. Where more than one indication for an intervention was performed, all were recorded. Validity of unmet obstetric need data was addressed in a number of ways. First, information on the surgical procedures performed for the women were obtained from delivery records, theatre registry and patients’ personal files to maximize comprehensiveness and consistency. Incomplete case records were cross matched with information from the three sources to provide a consistent determination of indications and outcomes. Calculation and analysis of the UONs were done for a given population (in a defined geographical area). The place of origin of the patient was therefore specified in the questionnaire. To get a more comprehensive picture and trends in unmet obstetric needs in the district, data for 2008 and 2009 were collected and analysed separately. The unmet obstetric need indicator was determined using the following formula: Unmet Obstetric Needs = (EB × RR) − (MOI for AMI) Where: EB = Expected births in the population: Obtained by multiplying the number of persons in a defined area, during a specified period by the crude birth rate for that region (expected births = population × crude birth rate). RR = Reference rate (1.4%): The low-end estimate of the proportion of deliveries that require a MOI to avoid a maternal death (95% CI, 1.27% -1.52%). This benchmark has been derived from previous UON studies [33]. The benchmark may be applied to data from more remote or dispersed populations in which women experiencing life-threatening indications die outside the formal health care system [33,38]. (EB × RR) = Estimated number of women experiencing absolute maternal indications in the population. (MOI for AMI) = Number of women in the population receiving major obstetric interventions (MOI) for absolute maternal indications (AMI) carried out in the same population during the same period. Thus, the expected births (EB) for Malindi District were obtained by multiplying the population in 2008 (385,460 persons) and 2009 (400, 514 persons), by the Crude Birth Rate (CBR) for the Coastal region (38.1/1000). The expected MOI for AMI were obtained by multiplying the Expected Birth (EB) by the Reference Rate (RR). The UON deficits were calculated according to the four divisions in the district and by rural and urban residence. For this study, a woman’s inclusion in an urban or rural area was based on the distance between her residence and the comprehensive care facility. Urban residents were defined as women residing within a radius of 10 kilometres from the comprehensive EmOC facility. Rural residents were defined as women residing more than 10 kilometres from the comprehensive EmOC facility. The UON rates among urban and rural women were compared using Chi-square test of association. The strength of the association was estimated using odds ratios (OR), with corresponding 95% confidence interval. The OR were calculated using the actual number of women who received intervention after developing a complication (actual MOI for AMI), while the denominator was the expected births for the respective year. Approval to conduct the study was obtained from the Kenya Medical Research Institute’s Ethical Review Committee (Scientific Steering Committee Number. 1808). Written permission was obtained from the Medical Officer of Health in the district prior to visiting the health facilities. All data have been maintained confidential and no individuals will be identified in dissemination of findings.
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