Introduction Tungiasis is a highly neglected tropical disease of the skin caused by an embedded female sand flea affecting the most resource-poor communities in sub-Saharan Africa, the Caribbean and South America. The global disease burden is unknown and systematic, fine-resolution spatial data on prevalence and environmental and ecological risk factors are rare.Methods We leveraged the Kilifi Health and Demographic Surveillance System of 90 257 households and asked whether they had a case of tungiasis in the household at interview during three survey rounds of routine surveys, undertaken between May 2021 and May 2022. Precise geospatial data to locate households were matched to macrolevel environmental, ecological and soil covariates, and multilevel logistic regression models were used to test for associations.Results A total of 1376 (1.5%) households reported a case in at least one survey during the year, while only 25 households did for all three surveys. The prevalence decreased over the three rounds from 1.1%, through 0.5–0.2%. The odds of having a tungiasis case in a household were higher in houses with earthen floors and walls, and in rural locations. The odds increased with increases in the number of children in a household and with population density (within 1 km radius), rainfall, Enhanced Vegetation Index, land surface temperature, aridity, altitude and organic carbon in the soil. However, the odds of having a tungiasis case in a household decreased with increasing aluminium content in the soil. These factors accounted for 23.9% of the variability in tungiasis distribution by household.Conclusion Tungiasis distribution was heterogenous and changed over time. Macro level environmental factors predicted the niche maps for tungiasis and could have applications in guiding local surveys and interventions.

Background: Civil registration systems in many low- and middle-income countries capture only a fraction of vital events, with deaths recorded less completely than births. Globally, female deaths are more often unregistered, yet evidence from Kenya is lacking. We examined gender bias in adult death registration across three Kenyan Health and Demographic Surveillance System (HDSS) sites. Methods: We conducted a cross-sectional household survey of 1,363 households reporting a recent death (2022–2023) across three HDSS sites in Nairobi, Kisumu, and Kilifi. Mixed-effects logistic regression identified determinants of registration, and Fairlie decomposition quantified the gender gap. Reported reasons for (non)registration were also analysed. Findings: Among 1,111 adult deaths, registration was 22·8% overall and significantly higher in males than females (26·1% vs 18·8%; p=0·006). Among females, registration odds were higher in those aged 45–64 than 65–84 years (AOR 2·32; p=0·019) and in Muslims (AOR 2·53; p=0·042), and lower in the unemployed (AOR 0·43; p=0·004). Among males, odds were lower for single individuals (AOR 0·30; p=0·012) and home deaths (AOR 0·64; p=0·048), and higher with employment or retirement (AOR 2·16; p=0·001) and greater wealth. Observable factors, principally economic activity and place of death, explained 50·2% of the 7·4 percentage-point gender gap. Inheritance needs and social benefits motivated registration; low awareness, perceived low benefit, and cost were key barriers. Interpretation: Gender bias in adult death registration persists in Kenya, driven by socioeconomic and structural barriers that disadvantage women, underscoring the need for gender-responsive civil registration and vital statistics reforms.

BackgroundShigellosis mainly affects children under 5 years of age living in low- and middle-income countries, who are the target population for vaccination. There are, however, limited data available to define the appropriate timing for vaccine administration in this age group. Information on antibody responses following natural infection, proxy for exposure, could help guide vaccination strategies.MethodsWe undertook a retrospective analysis of antibodies to five of the most prevalent Shigella serotypes among children aged <5 years in Kenya. Serum samples from a cross-sectional serosurvey in three Kenyan sites (Nairobi, Siaya, and Kilifi) were analyzed by standardized ELISA to measure IgG against Shigella sonnei and Shigella flexneri 1b, 2a, 3a, and 6. We identified factors associated with seropositivity to each Shigella serotype, including seropositivity to other Shigella serotypes.ResultsA total of 474 samples, one for each participant, were analyzed: Nairobi (n = 169), Siaya (n = 185), and Kilifi (n = 120). The median age of the participants was 13.4 months (IQR 7.0–35.6), and the male:female ratio was 1:1. Geometric mean concentrations (GMCs) for each serotype increased with age, mostly in the second year of life. The overall seroprevalence of IgG antibodies increased with age except for S. flexneri 6 which was high across all age subgroups. In the second year of life, there was a statistically significant increase of antibody GMCs against all five serotypes (p = 0.01–0.0001) and a significant increase of seroprevalence for S. flexneri 2a (p = 0.006), S. flexneri 3a (p = 0.006), and S. sonnei (p = 0.05) compared with the second part of the first year of life. Among all possible pairwise comparisons of antibody seropositivity, there was a significant association between S. flexneri 1b and 2a (OR = 6.75, 95% CI 3–14, p < 0.001) and between S. flexneri 1b and 3a (OR = 23.85, 95% CI 11–54, p < 0.001).ConclusionChildren living in low- and middle-income settings such as Kenya are exposed to Shigella infection starting from the first year of life and acquire serotype-specific antibodies against multiple serotypes. The data from this study suggest that Shigella vaccination should be targeted to infants, ideally at 6 or at least 9 months of age, to ensure children are protected in the second year of life when exposure significantly increases.

Background: The Kilifi Health and Demographic Surveillance System (KHDSS) was established in 2000 to define the incidence and prevalence of local diseases and evaluate the impact of community-based interventions. KHDSS morbidity data have been reported comprehensively but mortality has not been described. This analysis describes mortality in the KHDSS over 16 years. Methods: We calculated mortality rates from 2003–2018 in four intervals of equal duration and assessed differences in mortality across these intervals by age and sex. We calculated the period survival function and median survival using the Kaplan–Meier method and mean life expectancies using abridged life tables. We estimated trend and seasonality by decomposing a time series of monthly mortality rates. We used choropleth maps and random-effects Poisson regression to investigate geographical heterogeneity. Results: Mortality declined by 36% overall between 2003–2018 and by 59% in children aged <5 years. Most of the decline occurred between 2003 and 2006. Among adults, the greatest decline (49%) was observed in those aged 15–54 years. Life expectancy at birth increased by 12 years. Females outlived males by 6 years. Seasonality was only evident in the 1–4 year age group in the first four years. Geographical variation in mortality was ±10% of the median value and did not change over time. Conclusions: Between 2003 and 2018, mortality among children and young adults has improved substantially. The steep decline in 2003–2006 followed by a much slower reduction thereafter suggests improvements in health and wellbeing have plateaued in the last 12 years. However, there is substantial inequality in mortality experience by geographical location.

Background: The Kilifi Health and Demographic Surveillance System (KHDSS) was established in 2000 to define the incidence and prevalence of local diseases and evaluate the impact of community-based interventions. KHDSS morbidity data have been reported comprehensively but mortality has not been described. This analysis describes mortality in the KHDSS over 16 years. Methods: We calculated mortality rates from 2003–2018 in four intervals of equal duration and assessed differences in mortality across these intervals by age and sex. We calculated the period survival function and median survival using the Kaplan–Meier method and mean life expectancies using abridged life tables. We estimated trend and seasonality by decomposing a time series of monthly mortality rates. We used choropleth maps and random-effects Poisson regression to investigate geographical heterogeneity. Results: Mortality declined by 36% overall between 2003–2018 and by 59% in children aged <5 years. Most of the decline occurred between 2003 and 2006. Among adults, the greatest decline (49%) was observed in those aged 15–54 years. Life expectancy at birth increased by 12 years. Females outlived males by 6 years. Seasonality was only evident in the 1–4 year age group in the first four years. Geographical variation in mortality was ±10% of the median value and did not change over time. Conclusions: Between 2003 and 2018, mortality among children and young adults has improved substantially. The steep decline in 2003–2006 followed by a much slower reduction thereafter suggests improvements in health and wellbeing have plateaued in the last 12 years. However, there is substantial inequality in mortality experience by geographical location.

AIM:To investigate geographical change over time in the burden of neurological impairments in school-aged children in a demographic surveillance area.METHOD:We investigated changes in neurological impairment prevalence in five domains (epilepsy and cognitive, hearing, vision, and motor impairments) using similar two-phase surveys conducted in 2001 (n=10 218) and 2015 (n=11 223) and determined changes in location-level prevalence, geographical clustering, and significant risk factors for children aged 6 to 9 years (mean 7y 6mo, SD 1y) of whom 50.4% were males. Admission trends for preterm birth, low birthweight (LBW), and encephalopathy were determined using admission data to a local hospital.RESULTS:Overall prevalence for any neurological impairment decreased from 61 per 1000 (95% confidence interval [CI] 48.0-74.0) in 2001 to 44.7 per 1000 (95% CI 40.9-48.6) in 2015 (p<0.001). There was little evidence of geographical variation in the prevalence of neurological impairments in either survey. The association between neurological impairments and some risk factors changed significantly with year of survey; for example, the increased association of adverse perinatal events with hearing impairments (exponentiated coefficient for the interaction=5.94, p=0.03). Annual admission rates with preterm birth (rate ratio 1.08, range 1.07-1.09), LBW (rate ratio 1.08, range 1.06-1.10), and encephalopathy (rate ratio 1.08, range 1.06-1.09) significantly increased between 2005 and 2016 (p<0.001).INTERPRETATION:There was a significant decline in the prevalence of neurological impairments and differential changes in the associations of some risk factors with neurological impairments over the study period. Limited geographical variation suggests that similar interventions are appropriate across the defined area.

Background: In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS). This was part of a cross-sectional survey of health problems of young people, and we tested the feasibility of using the KHDSS platform for the monitoring of future interventions. Methods: Two facilities were used for this study. Clinical data from consenting participants aged 18-24 years were matched to KHDSS records. Data matching was achieved using national identity card numbers or otherwise using a matching algorithm based on names, sex, date of birth, location of residence and the names of other homestead members. A study form was administered to all matched patients to capture reasons for their visits and time taken to access the services. Distance to health facility from a participants’ homestead was also computed. Results: 628 participated in the study: 386 (61%) at Matsangoni Health Centre, and 242 (39%) at Pingilikani Dispensary. 610 (97%) records were matched to the KHDSS register. Most records (605; 96%) were matched within these health facilities, while 5 (1%) were matched during homestead follow-up visits. 463 (75.9%) of those matched were women. Antenatal care (25%), family planning (13%), respiratory infections (9%) and malaria (9%) were the main reasons for seeking care. Antenatal clinic visits (n=175) and malaria (n=27) were the commonest reasons among women and men, respectively. Participants took 1-1.5 hours to access the services; 490 (81.0%) participants lived within 5 kilometres of a facility. Conclusions: With a full-time research clerk at each health facility, linking health-facility attendance data to a longitudinal HDSS platform was feasible and could be used to monitor and evaluate the impact of health interventions on health care outcomes among young people.

Supplementary files for the submitted manuscript Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System Supplementary file 2 is the form used to collect demographic data from Unmatched participants Supplementary file 3 contains Unmatched Participants data. Variable description is as follows: Data clerk: Study staff who did the consenting, searched and linked study participant’s demographic data with the clinic visit data; Hmname: Homestead name. Name a by which a homestead is known and referred to; HmHead: Homestead head. The person who heads a home, and makes important decisions for the family; Location: Administrative unit in the government structure headed by a chief; Sublocation: A sub-unit of a location headed by an assistant chief; Ehtnicity: Ethnic group; Facility: Health facility name; Date: date information was collected.

Background: In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS). This was part of a cross-sectional survey of health problems of young people, and we tested the feasibility of using the KHDSS platform for the monitoring of future interventions. Methods: Two facilities were used for this study. Clinical data from consenting participants aged 18-24 years were matched to KHDSS records. Data matching was achieved using national identity card numbers or otherwise using a matching algorithm based on names, sex, date of birth, location of residence and the names of other homestead members. A study form was administered to all matched patients to capture reasons for their visits and time taken to access the services. Distance to health facility from a participants’ homestead was also computed. Results: 628 participated in the study: 386 (61%) at Matsangoni Health Centre, and 242 (39%) at Pingilikani Dispensary. 610 (97%) records were matched to the KHDSS register. Most records (605; 96%) were matched within these health facilities, while 5 (1%) were matched during homestead follow-up visits. 463 (75.9%) of those matched were women. Antenatal care (25%), family planning (13%), respiratory infections (9%) and malaria (9%) were the main reasons for seeking care. Antenatal clinic visits (n=175) and malaria (n=27) were the commonest reasons among women and men, respectively. Participants took 1-1.5 hours to access the services; 490 (81.0%) participants lived within 5 kilometres of a facility. Conclusions: With a full-time research clerk at each health facility, linking health-facility attendance data to a longitudinal HDSS platform was feasible and could be used to monitor and evaluate the impact of health interventions on health care outcomes among young people.

Background Adolescents tend to experience heightened vulnerability to risky and reckless behavior. Adolescents living in rural settings may often experience poverty and a host of risk factors which can increase their vulnerability to various forms of health risk behavior (HRB). Understanding HRB clustering and its underlying factors among adolescents is important for intervention planning and health promotion. This study examines the co-occurrence of injury and violence, substance use, hygiene, physical activity, and diet-related risk behaviors among adolescents in a rural setting on the Kenyan coast. Specifically, the study objectives were to identify clusters of HRB; based on five categories of health risk behavior, and to identify the factors associated with HRB clustering. Methods A cross-sectional survey was conducted of a random sample of 1060 adolescents aged 13-19 years living within the area covered by the Kilifi Health and Demographic Surveillance System. Participants completed a questionnaire on health behaviors which was administered via an Audio Computer-Assisted Self-Interview. Latent class analysis on 13 behavioral factors (injury and violence, hygiene, alcohol tobacco and drug use, physical activity, and dietary related behavior) was used to identify clustering and stepwise ordinal logistic regression with nonparametric bootstrapping identified the factors associated with clustering. The variables of age, sex, education level, school attendance, mental health, form of residence and level of parental monitoring were included in the initial stepwise regression model. Results We identified 3 behavioral clusters (Cluster 1: Low-risk takers (22.9%); Cluster 2: Moderate risk-takers (67.8%); Cluster 3: High risk-takers (9.3%)). Relative to the cluster 1, membership of higher risk clusters (i.e. moderate or high risk-takers) was strongly associated with older age (p<0.001), being male (p<0.001), depressive symptoms (p = 0.005), school non-attendance (p = 0.001) and a low level of parental monitoring (p<0.001). Conclusion There is clustering of health risk behaviors that underlies communicable and non-communicable diseases among adolescents in rural coastal Kenya. This suggests the urgent need for targeted multi-component health behavior interventions that simultaneously address all aspects of adolescent health and well-being, including the mental health needs of adolescents.

Background: Interventions to block malaria transmission from humans to mosquitoes are currently in development. To be successfully implemented, key populations need to be identified where the use of these transmission-blocking and/or reducing strategies will have greatest impact. Methods: We used data from a longitudinally monitored cohort of children from Kilifi county located along the Kenyan coast collected between 1998-2016 to describe the distribution and prevalence of gametocytaemia in relation to transmission intensity, time and age. Data from 2,223 children accounting for 9,134 person-years of follow-up assessed during cross-sectional surveys for asexual parasites and gametocytes were used in logistic regression models to identify factors predictive of gametocyte carriage in this cohort. Results: Our analysis showed that children 1-5 years of age were more likely to carry microscopically detectable gametocytes than their older counterparts. Carrying asexual parasites and recent episodes of clinical malaria were also strong predictors of gametocyte carriage. The prevalence of asexual parasites and of gametocyte carriage declined over time, and after 2006, when artemisinin combination therapy (ACT) was introduced, recent episodes of clinical malaria ceased to be a predictor of gametocyte carriage. Conclusions: Gametocyte carriage in children in Kilifi has fallen over time. Previous episodes of clinical malaria may contribute to the development of carriage, but this appears to be mitigated by the use of ACTs highlighting the impact that gametocidal antimalarials can have in reducing the overall prevalence of gametocytaemia when targeted on acute febrile illness.

Background:Interventions to block malaria transmission from humans to mosquitoes are currently in development. To be successfully implemented, key populations need to be identified where the use of these transmission-blocking and/or reducing strategies will have greatest impact.Methods:We used data from a longitudinally monitored cohort of children from Kilifi county located along the Kenyan coast collected between 1998-2016 to describe the distribution and prevalence of gametocytaemia in relation to transmission intensity, time and age. Data from 2,223 children accounting for 9,134 person-years of follow-up assessed during cross-sectional surveys for asexual parasites and gametocytes were used in logistic regression models to identify factors predictive of gametocyte carriage in this cohort.Results:Our analysis showed that children 1-5 years of age were more likely to carry microscopically detectable gametocytes than their older counterparts. Carrying asexual parasites and recent episodes of clinical malaria were also strong predictors of gametocyte carriage. The prevalence of asexual parasites and of gametocyte carriage declined over time, and after 2006, when artemisinin combination therapy (ACT) was introduced, recent episodes of clinical malaria ceased to be a predictor of gametocyte carriage. Conclusions:Gametocyte carriage in children in Kilifi has fallen over time. Previous episodes of clinical malaria may contribute to the development of carriage, but this appears to be mitigated by the use of ACTs highlighting the impact that gametocidal antimalarials can have in reducing the overall prevalence of gametocytaemia when targeted on acute febrile illness.

Prospective data on childhood tuberculosis (TB) incidence and case detection rates (CDRs) are scant, and the preventable burden of childhood TB has not been measured in prospective studies. We investigated 2,042 children (< 15 years of age) with suspected TB by using enhanced surveillance and linked hospital, demographic, notification, and verbal autopsy data to estimate the incidence, CDR, risk factors, and preventable burden of TB among children in Kenya. Estimated TB incidence was 53 cases/100,000 children/year locally and 95 cases/100,000 children/year nationally. The estimated CDR was 0.20-0.35. Among children < 5 years of age, 49% of cases were attributable to a known household contact with TB. This study provides much needed empiric data on TB CDRs in children to inform national and global incidence estimates. Moreover, our findings indicate that nearly half of TB cases in young children might be prevented by implementing existing guidelines for TB contact tracing and chemoprophylaxis.

In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS). This was part of a cross-sectional survey of health problems of young people, and we tested the feasibility of using the KHDSS platform for the monitoring of future interventions. Two facilities were used for this study. Clinical data from consenting participants aged 18-24 years were matched to KHDSS records. Data matching was achieved using national identity card numbers or otherwise using a matching algorithm based on names, sex, date of birth, location of residence and the names of other homestead members. A study form was administered to all matched patients to capture reasons for their visits and time taken to access the services. Distance to health facility from a participants’ homestead was also computed.

Background Insecticide resistance has emerged as one of the major challenges facing National Malaria Control Programmes in Africa. A well-coordinated national database on insecticide resistance ( IRBase ) can facilitate the development of effective strategies for managing insecticide resistance and sustaining the effectiveness of chemical-based vector control measures. The aim of this study was to assemble a database on the current status of insecticide resistance among malaria vectors in Kenya. Methods Data was obtained from published literature through PubMed, HINARI and Google Scholar searches and unpublished literature from government reports, research institutions reports and malaria control programme reports. Each data source was assigned a unique identification code and entered into Microsoft Excel 2010 datasheets. Base maps on the distribution of insecticide resistance and resistance mechanisms among malaria vectors in Kenya were generated using ArcGIS Desktop 10.1 (ESRI, Redlands, CA, USA). Results Insecticide resistance status among the major malaria vectors in Kenya was reported in all the four classes of insecticides including pyrethroids, carbamates, organochlorines and organophosphates. Resistance to pyrethroids has been detected in Anopheles gambiae ( s . s. ), An. arabiensis and An . funestus ( s . s. ) while resistance to carbamates was limited to An . gambiae ( s . s. ) and An . arabiensis. Resistance to the organochlorine was reported in An . gambiae ( s . s. ) and An . funestus ( s . s. ) while resistance to organophosphates was reported in An. gambiae ( s . l. ) only. The mechanisms of insecticide resistance among malaria vectors reported include the kdr mutations (L 1014S and L 1014F) and elevated activity in carboxylesterase, glutathione S-transferases (GST) and monooxygenases. The kdr mutations L 1014S and L 1014F were detected in An . gambiae ( s . s. ) and An. arabiensis populations. Elevated activity of monooxygenases has been detected in both An . arabiensis and An . gambiae ( s . s. ) populations while the elevated activity of carboxylesterase and GST has been detected only in An . arabiensis populations. Conclusions The geographical maps show the distribution of insecticide resistance and resistance mechanisms among malaria vectors in Kenya. The database generated will provide a guide to intervention policies and programmes in the fight against malaria.

Background: The lack of reliable, valid and adequately standardized measures of mental illnesses in sub-Saharan Africa is a key challenge for epidemiological studies on mental health. We evaluated the psychometric properties and feasibility of using a computerized version of the Major Depression Inventory (MDI) in an epidemiological study in rural Kenya. Methods: We surveyed 1496 participants aged 13-24 years in Kilifi County, on the Kenyan coast. The MDI was administered using a computer-assisted system, available in three languages. Internal consistency was evaluated using both Cronbach’s alpha and the Omega Coefficient. Confirmatory factor analysis was performed to evaluate the factorial structure of the MDI. Results: Internal consistency using both Cronbach’s Alpha (α= 0.83) and the Omega Coefficient (0.82; 95% confidence interval 0.81- 0.83) was above acceptable thresholds. Confirmatory factor analysis indicated a good fit of the data to a unidimensional model of MDI (χ2 (33, N = 1409) = 178.52 p < 0.001, TLI = 0.947, CFI = 0.961, and Root Mean Square Error of Approximation, RMSEA = .056), and this was confirmed using Item Response Models (Loevinger’s H coefficient 0.38) that proved the MDI was a unidimensional scale. Equivalence evaluation indicated invariance across sex and age groups. In our population, 3.6% of the youth presented with scores suggesting major depression using the ICD-10 scoring algorithm, and 8.7% presented with total scores indicating presence of depression (mild, moderate or severe). Females and older youth were at the highest risk of depression. Conclusions: The MDI has good psychometric properties. Given its brevity, relative ease of usage and ability to identify at-risk youth, it may be useful for epidemiological studies of depression in Africa. Studies to establish clinical thresholds for depression are recommended. The high prevalence of depressive symptoms suggests that depression may be an important public health problem in this population group.

Linking health facility data from young adults 18-24 years to longitudinal demographic data: experience from The Kilifi Health and Demographic Surveillance System. Christopher Nyundo, 1,4 Aoife M Doyle, 2 David Walumbe, 1,4 Mark Otiende, 1,4 Michael Kinuthia, 1 David Amadi, 1 Boniface Jibendi, 1 George Mochamah, 1,4 Norbert Kihuha, 1 Thomas N Williams, 1,3,4 David A Ross, 2, Evasius Bauni 1, 4 Affiliations: Corresponding author: Christopher Nyundo, KEMRI/Wellcome Trust Research Programme, PO Box 230, Kilifi, Kenya. Tel +254 070 400 23 18 e-mail: cnyundo@kemri-wellcome.org Abstract Background In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS) in coastal Kenya. This was part of a cross-sectional survey of health problems of young people and tested the feasibility of using this platform for the monitoring of future interventions. Methods Two facilities with youth friendly services, adequate space, a reliable power supply and staff who were willing to participate were used for this study. Clinical data from consenting participants aged 18-24 years were matched to KHDSS records by staff using laptop computers. Data matching was achieved using national identity card numbers or otherwise using a matching algorithm that was based on names, sex, date of birth, location of residence and the names of other homestead members. A questionnaire was administered to all matched patients that captured reasons for their visits and the time taken to access the services. Distance to health facility from residences of participants was computed using geographic information system. Results Six hundred and twenty-eight participated in the study: 386 (61%) at Matsangoni Health Centre, and 242 (39%) at Pingilikani Dispensary. Six hundred and ten (97%) records were matched to the KHDSS register. Most records (605; 96%) were matched within the health facilities while 5 (1%) were matched during homestead follow-up visits. Four hundred and sixty three (75.9%) of those matched were female. Antenatal care (25%), family planning (13%), respiratory infections (9%) and malaria (9%) were the main reasons for seeking care. Participants took a minimum of 10 minutes to a maximum of 480 with a median of 80 and a mean of 97 minutes to access the services. Four hundred and ninety (81.0%) participants lived within 5 kilometers of a health facility. Conclusions Antenatal clinic visits (n=177) and malaria (n=27) were the commonest reasons for clinic visits among females and males, respectively. With a full-time research clerk at each health facility, linking health-facility attendance data to longitudinal HDSS data was feasible and could provide information for monitoring the impact of health interventions on health care outcomes among young people in this setting. Key words: Record linkage, Demographic Surveillance, Adolescent Health, Kenya

Background. Malaria control strategies need to respond to geographical hotspots of transmission. Detection of hotspots depends on the sensitivity of the diagnostic tool used. Methods. We conducted cross-sectional surveys in 3 sites within Kilifi County, Kenya, that had variable transmission intensities. Rapid diagnostic test (RDT), microscopy, and polymerase chain reaction (PCR) were used to detect asymptomatic parasitemia, and hotspots were detected using the spatial scan statistic. Results. Eight thousand five hundred eighty-one study participants were surveyed in 3 sites. There were statistically significant malaria hotspots by RDT, microscopy, and PCR for all sites except by microscopy in 1 low transmission site. Pooled data analysis of hotspots by PCR overlapped with hotspots by microscopy at a moderate setting but not at 2 lower transmission settings. However, variations in degree of overlap were noted when data were analyzed by year. Hotspots by RDT were predictive of PCR/microscopy at the moderate setting, but not at the 2 low transmission settings. We observed long-term stability of hotspots by PCR and microscopy but not RDT. Conclusion. Malaria control programs may consider PCR testing to guide asymptomatic malaria hotspot detection once the prevalence of infection falls.

Experience from The Kilifi Health and Demographic Surveillance System: Raw data for linked participants and unmatched participantsDataset File 1: Linkage data. Age_group: age of participant on the day they visited the clinic, three missing values; facility: Matsangoni or Pingilikani; visit_date: date when participant visited clinic, three missing values; service_time_minutes: duration (in minutes) it took for participant to get served, 29 missing values; visit_reason: reason for visit, four missing values; exclusion: variable indicating the records that were included in the ‘clinic visit analysis’; visit_no: some participants made more than one visit during the study period - this variable indicates the number of visits made at any one instance; distance_to_facility: distance (in KM) from the homestead to health facility.Dataset File 2: Unmatched participant data. Data clerk: Study staff who did the consenting, searched and linked study participant’s demographic data with the clinic visit data; Hmname: Homestead name. Name a by which a homestead is known and referred to; HmHead: Homestead head. The person who heads a home, and makes important decisions for the family; Location: Administrative unit in the government structure headed by a chief; Sublocation: A sub-unit of a location headed by an assistant chief.