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Objective To explore the relationship between homestead distance to hospital and access to care and to estimate the sensitivity of hospital-based surveillance in Kilifi district, Kenya. Methods In 2002-2006, clinical information was obtained from all children admitted to Kilifi District Hospital and linked to demographic surveillance data. Travel times to the hospital were calculated using geographic information systems and regression models were constructed to examine the relationships between travel time, cause-specific hospitalization rates and probability of death in hospital. Access to care ratios relating hospitalization rates to community mortality rates were computed and used to estimate surveillance sensitivity. Findings The analysis included 7200 admissions (64 per 1000 child-years). Median pedestrian and vehicular travel times to hospital were 237 and 61 minutes, respectively. Hospitalization rates decreased by 21% per hour of travel by foot and 28% per half hour of travel by vehicle. Distance decay was steeper for meningitis than for pneumonia, for females than for males, and for areas where mothers had less education on average. Distance was positively associated with the probability of dying in hospital. Overall access to care ratios, which represent the probability that a child in need of hospitalization will have access to care at the hospital, were 51-58% for pneumonia and 66-70% for meningitis. Conclusion In this setting, hospital utilization rates decreased and the severity of cases admitted to hospital increased as distance between homestead and hospital increased. Access to hospital care for children living in remote areas was low, particularly for those with less severe conditions. Distance decay was attenuated by increased levels of maternal education. Hospital-based surveillance underestimated pneumonia and meningitis incidence by more than 45% and 30%, respectively.
This analysis relied on data collected routinely by the Epidemiologic and Demographic Surveillance System (Epi-DSS) of the KEMRI–Wellcome Trust Research Programme in Kilifi district, Kenya. The Epi-DSS includes a demographic surveillance system covering an area measuring 900 km2 around Kilifi District Hospital (KDH) linked to hospital-based epidemiological surveillance. Kilifi district is a poor, primarily rural district on the Indian Ocean coast of Kenya that enjoys a tropical climate, with two rainy seasons and two dry seasons each year. Mortality in children less than 5 years of age has decreased in recent years but remains high at 65 deaths per 1000 live births.21 KDH serves as a primary care centre and first-level referral facility for the entire district. Inpatient care is available at three other hospitals in Kilifi district, at Malindi District Hospital in Malindi and at Coast Provincial General Hospital in Mombasa. For most residents of the study area, KDH is the nearest facility offering inpatient care. Demographic surveillance was initiated in 2000 to track births, deaths and migrations in a target population of 250 000 people. After the initial census, two to three enumeration rounds were conducted each year. Each resident received a unique personal identifier. From 16 April 2002 onwards, hospital and laboratory records including standard clinical data for all admitted children were linked to demographic records for Epi-DSS area residents based on personal identifier. This enabled us to determine the exact residency of each patient at the time of hospitalization. All data were entered into FileMaker 5.5 (FileMaker Inc., Santa Clara, United States of America), and cleaned in Stata 9.2 (StataCorp LP, College Station, USA). The Epi-DSS area was mapped using Magellan (Magellan Navigation Inc., Santa Clara, USA) and e-Trex (Garmin Ltd, Olathe, USA) geographic positioning systems (GPS) technology, which provided information on topography, footpaths and roads and on the human occupation of the area, including the coordinates of all homesteads. We mapped the seven matatu (local bus) routes in January 2007 and collected information on matatu speeds. All geographic data were imported via Datasend (Magellan Navigation Inc., Santa Clara, USA), Map Source (Garmin Ltd, Olathe, USA), or DNRGarmin (Minesotta Dept of Natural Resources, St Paul, USA) software into ArcGIS 9.2 (Esri, Redlands, USA) for mapping and analysis. Pedestrian and vehicular travel times to KDH were calculated using an ArcGIS cost-distance algorithm, which determines the shortest path from each homestead to the hospital assuming speeds of 5 km/h on roads and footpaths and of 2.5 km/h off-road in the pedestrian model, and matatu speeds on matatu routes and pedestrian speeds elsewhere in the vehicular model (i.e. individuals walk from home to the nearest matatu stage, then travel by matatu to hospital). Details of this method have been described previously.21 In stratified analyses, we used one-hour strata for pedestrian travel time and half-hour strata for vehicular travel time. Other variables of interest were ethnicity, maternal education, migration and time. Ethnicity data were collected routinely by the Epi-DSS. The majority ethnic groups in Kilifi district are of Mijikenda origin and include the Giriama and the Chonyi. Ethnic groups with less than 40 deaths during the study period were combined under the category “other.” Maternal education data were collected from all residents in 2004. We calculated the proportion of women 15–49 years old with any schooling in each administrative sublocation and used it to generate a sublocation-level categorical variable (proportion of mothers with any education < 0.5; 0.5 to < 0.6; 0.6 to < 0.7; and ≥ 0.7), which was then applied to individuals based on their residence. For migration, children whose mothers had migrated at least once from outside the Epi-DSS area between 2000 and 2006 were considered migrants. Finally, we analysed seasonal and annual trends in hospitalizations. Mean daily rainfall in Kilifi during the rainy season (April to June and October to November) was ≥ 5 mm between 2000 and 2006. We investigated the effects of travel time to KDH on all-cause hospitalization and on hospitalizations for pneumonia and suspected meningitis. Pneumonia was categorized as mild, severe or very severe. Mild pneumonia was defined as a history of acute cough or difficulty breathing plus an elevated respiratory rate (RR) for age (RR ≥ 50 breaths per min in children 0 to 11 months old and ≥ 40 breaths per min in those 12 to 59 months old), severe pneumonia as a history of cough or difficulty breathing plus lower chest wall indrawing, and very severe pneumonia as a history of cough or difficulty breathing plus hypoxia, lethargy, loss of consciousness, prostration or a history of convulsions.26 Suspected meningitis required one or more of the following signs: stiff neck, bulging fontanelle in children < 1 year of age, lethargy, loss of consciousness, prostration or history of convulsions (any seizure in children < 6 months of age; any partial seizure or at least two generalized seizures over the previous 24 hours in children 6–59 months of age). For each condition, we calculated admission rates per 1000 child–years (allowing multiple admissions per child) by pedestrian and vehicular travel time to hospital and by administrative location, as well as by sex, ethnic group, maternal education, migrant status, season and year for children < 5 years of age. We constructed log-linear regression models to identify predictors of the incidence of admission to KDH and logistic regression models to investigate risk factors for death in admitted children. To account for spatial clustering of disease events in these models, we used a spatial bootstrap method with 50 repetitions, randomly selecting 40 sublocations (with replacement) and estimating the regression model on all data from the selected sublocations in each repetition. The incidence analysis was restricted to 2004 through 2006, a period for which ascertainment of person–time by the Epi-DSS was complete. The case fatality ratio analysis used data from 2002 onwards, since it did not require population-based denominators. All analyses were conducted in Stata 9.2. For each pedestrian and vehicular travel time stratum we computed the ratios of both the pneumonia and the suspected meningitis hospitalization rate to the all-cause mortality rate measured in the community by the Epi-DSS (Rp = pneumonia admission rate/all-cause mortality rate and Rm = suspected meningitis admission rate/all-cause mortality rate). Assuming that children in the lowest stratum (stratum 0) had “perfect” access to care (e.g. that all pneumonia and meningitis cases requiring hospitalization presented to KDH and were admitted) and that the incidence of pneumonia and meningitis requiring hospitalization was directly proportional to the incidence of death, we calculated the stratum-specific probability that a child in need of hospitalization would access care at KDH (“access to care ratios”) as R[travel time stratum]/R[stratum 0] for pneumonia and suspected meningitis separately. We obtained 95% confidence intervals (CIs) by applying the delta method for variance calculation to successive log-transformations of these ratios, under the assumption that stratum-specific rates were independent. We compared the trends in pneumonia and meningitis ratios across travel time strata using the Cuzick extension to the Wilcoxon rank sum test.27 Access to care ratios for the entire Epi-DSS area were calculated as a weighted average of stratum-specific ratios, with person–years of observation as weights. The Kenya Medical Research Institute Ethical Review Committee and the Johns Hopkins Bloomberg School of Public Health Institutional Review Board approved this study.
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