Health And Demographic Surveillance System Data Research Articles

Lessons Learned From Telephone-Based Data Collection for Health and Demographic Surveillance Systems During the COVID-19 Pandemic in Indonesia.

The Sleman Health and Demographic Surveillance System (HDSS) is a longitudinal survey held routinely since 2014 to collect demographic, social, and health changes in Sleman Regency, Special Region of Yogyakarta, Indonesia. During the COVID-19 pandemic in Indonesia, we needed to adjust our method of conducting data collection from in-person to telephone interviews. We describe the Sleman HDSS data collection strategy used and the opportunities it presented. First, the Sleman HDSS team completed a feasibility study and adjusted the standard operational procedures to conduct telephone interviews. Then, the Sleman HDSS team collected data via a telephone interview in September-October 2020. Ten interviewers were equipped with an e-HDSS data collection application installed on an Android-based tablet to collect data. The sample targeted was 5,064 households. The telephone-based data collection successfully interviewed 1,674 households (33% response rate) in 17 subdistricts. We changed the data collection strategy so that the Sleman HDSS could still be conducted and we could get the latest data from the population. Compared to in-person interviewing, data collection via telephone was sufficiently practical. The telephone interview was a safe and viable data collection method. To increase the response rate, telephone number activation could be checked, ways of building rapport could be improved, and engagement could be improved by using social capital.

Preterm birth and neonatal mortality in selected slums in and around Dhaka City of Bangladesh: A cohort study.

Although under-five mortality has declined appreciably in Bangladesh over the last few decades, neonatal mortality still remains high. The objective of the study is to assess the level and determinants of preterm birth and the contribution of preterm birth to neonatal mortality. Data for this study came from selected slums in and around Dhaka city, where; since 2015, icddr,b has been maintaining the Health and Demographic Surveillance System (HDSS). The HDSS data were collected by female Field Workers by visiting each household every three months; however, during the visit, data on the Last Menstrual Period (LMP) were also collected by asking each eligible woman to ascertain the date of conception. Gestational age was estimated in complete weeks by subtracting LMP from the date of the pregnancy outcome. In this study, 6,989 livebirths were recorded by HDSS during 2016-2018, and these births were followed for neonatal survival; both bivariate and multivariate analyses were performed. Out of total births, 21.7% were born preterm (before 37 weeks of gestation), and sub-categories were: 2.19% for very preterm (28 to 31 weeks), 3.81% for moderate preterm (32 to 33 weeks), and 15.71% for late preterm (34 to 36 weeks). The study revealed that preterm babies contributed to 39.6% of neonatal deaths; however, the probability of death was very high on the 1st day of birth (0.124 for very preterm, 0.048 for moderate preterm, 0.024 for late preterm, and 0.013 for term birth), and continued until the 3rd day. In the regression analysis, compared to the term neonates, the odds of neonatal mortality were 8.66 (CI: 5.63, 13.32, p<0.01), 4.13 (CI: 2.69, 6.34, p<0.01) and 1.48 (CI: 1.05, 2.08, p<0.05) respectively for very, moderate, and late preterm birth categories. The population attributable fraction for neonatal mortality was 23%, and sub-categories were 14% for very preterm, 10% for moderate preterm, and 6% for late preterm. Although urban slums are in proximity to many health facilities, a substantial proportion of preterm births contribute to neonatal deaths. So, pregnant women should be targeted, to ensure timely care during pregnancy, delivery, and post-partum periods to improve the survival of new-borns in general and preterm birth in particular.

Burden and causes of pregnancy-related mortality in The Gambia: evidence from census and health and demographic surveillance data

BackgroundThe Gambia lacks capacity to estimate its burden and causes of pregnancy-related mortality (PRM) to guide reproductive health programming. We used census and Health and Demographic Surveillance System (HDSS) data...

Evaluating pregnancy reporting in Siaya Health and Demographic Surveillance System through record linkage with ANC clinics.

Health and Demographic Surveillance Systems (HDSS) are important sources of population health data in sub-Saharan Africa, but the recording of pregnancies, pregnancy outcomes, and early mortality is often incomplete. This study assessed HDSS pregnancy reporting completeness and identified predictors of unreported pregnancies that likely ended in adverse outcomes. The analysis utilized individually-linked HDSS and antenatal care (ANC) data from Siaya, Kenya for pregnancies in 2018-2020. We cross-checked ANC records with HDSS pregnancy registrations and outcomes. Pregnancies observed in the ANC that were missing reports in the HDSS despite a data collection round following the expected delivery date were identified as likely adverse outcomes, and we investigated the characteristics of such individuals. Clinical data were used to investigate the timing of HDSS pregnancy registration relative to care seeking and gestational age, and examine misclassification of miscarriages and stillbirths. From an analytical sample of 2,475 pregnancies observed in the ANC registers, 46% had pregnancy registrations in the HDSS, and 89% had retrospectively reported pregnancy outcomes. 1% of registered pregnancies were missing outcomes, compared to 10% of those lacking registration. Registered pregnancies had higher rates of stillbirth and perinatal mortality than those lacking registration. In 77% of cases, women accessed ANC prior to registering the pregnancy in the HDSS. Half of reported miscarriages were misclassified stillbirths. We identified 141 unreported pregnancies that likely ended in adverse outcomes. Such cases were more common among those who visited ANC clinics during the first trimester, made fewer overall visits, were HIV-positive, and outside of formal union. Record linkage with ANC clinics revealed pregnancy underreporting in HDSS, resulting in biased measurement of perinatal mortality. Integrating records of ANC usage into routine data collection can augment HDSS pregnancy surveillance and improve monitoring of adverse pregnancy outcomes and early mortality.

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization.

BackgroundData anonymization and sharing have become popular topics for individuals, organizations, and countries worldwide. Open-access sharing of anonymized data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations.ObjectiveThis study aimed to highlight the requirements and possible solutions for sharing health surveillance event history data. The challenges lie in the anonymization of multiple event dates and time-varying variables.MethodsA sequential approach that adds noise to event dates is proposed. This approach maintains the event order and preserves the average time between events. In addition, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding the key variables that change over time, such as educational level or occupation, we make 2 proposals: one based on limiting the intermediate statuses of the individual and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information.ResultsAn anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created.ConclusionsThe proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker’s database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing.

Investigating clinic transfers among HIV patients considered lost to follow-up to improve understanding of the HIV care cascade: Findings from a cohort study in rural north-eastern South Africa.

Investigating clinical transfers of HIV patients is important for accurate estimates of retention and informing interventions to support patients. We investigate transfers for adults reported as lost to follow-up (LTFU) from eight HIV care facilities in the Agincourt health and demographic surveillance system (HDSS), South Africa. Using linked clinic and HDSS records, outcomes of adults more than 90 days late for their last scheduled clinic visit were determined through clinic and routine tracing record reviews, HDSS data, and supplementary tracing. Factors associated with transferring to another clinic were determined through Cox regression models. Transfers were graphically and geospatially visualised. Transfers were more common for women, patients living further from the clinic, and patients with higher baseline CD4 cell counts. Transfers to clinics within the HDSS were more likely to be undocumented and were significantly more likely for women pregnant at ART initiation. Transfers outside the HDSS clustered around economic hubs. Patients transferring to health facilities within the HDSS may be shopping for better care, whereas those who transfer out of the HDSS may be migrating for work. Treatment programmes should facilitate transfer processes for patients, ensure continuity of care among those migrating, and improve tracking of undocumented transfers.

Pregnancy Surveillance Methods within Health and Demographic Surveillance Systems

Background: Pregnancy identification and follow-up surveillance can enhance the reporting of pregnancy outcomes, including stillbirths and perinatal and early postnatal mortality. This paper reviews pregnancy surveillance methods used in Health and Demographic Surveillance Systems (HDSSs) in low- and middle-income countries. Methods: We searched articles containing information about pregnancy identification methods used in HDSSs published between January 2002 and October 2019 using PubMed and Google Scholar. A total of 37 articles were included through literature review and 22 additional articles were identified via manual search of references. We reviewed the gray literature, including websites, online reports, data collection instruments, and HDSS protocols from the Child Health and Mortality Prevention Study (CHAMPS) Network and the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH). In total, we reviewed information from 52 HDSSs described in 67 sources. Results: Substantial variability exists in pregnancy surveillance approaches across the 52 HDSSs, and surveillance methods are not always clearly documented. 42% of HDSSs applied restrictions based on residency duration to identify who should be included in surveillance. Most commonly, eligible individuals resided in the demographic surveillance area (DSA) for at least three months. 44% of the HDSSs restricted eligibility for pregnancy surveillance based on a woman’s age, with most only monitoring women 15-49 years. 10% had eligibility criteria based on marital status, while 11% explicitly included unmarried women in pregnancy surveillance. 38% allowed proxy respondents to answer questions about a woman’s pregnancy status in her absence. 20% of HDSSs supplemented pregnancy surveillance with investigations by community health workers or key informants and by linking HDSS data with data from antenatal clinics. Conclusions: Methodological guidelines for conducting pregnancy surveillance should be clearly documented and meticulously implemented, as they can have implications for data quality and accurately informing maternal and child health programs.

Pregnancy Surveillance Methods within Health and Demographic Surveillance Systems.

Background: Pregnancy identification and follow-up surveillance can enhance the reporting of pregnancy outcomes, including stillbirths and perinatal and early postnatal mortality. This paper reviews pregnancy surveillance methods used in Health and Demographic Surveillance Systems (HDSSs) in low- and middle-income countries. Methods: We searched articles containing information about pregnancy identification methods used in HDSSs published between January 2002 and October 2019 using PubMed and Google Scholar. A total of 37 articles were included through literature review and 22 additional articles were identified via manual search of references. We reviewed the gray literature, including websites, online reports, data collection instruments, and HDSS protocols from the Child Health and Mortality Prevention Study (CHAMPS) Network and the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH). In total, we reviewed information from 52 HDSSs described in 67 sources. Results: Substantial variability exists in pregnancy surveillance approaches across the 52 HDSSs, and surveillance methods are not always clearly documented. 42% of HDSSs applied restrictions based on residency duration to identify who should be included in surveillance. Most commonly, eligible individuals resided in the demographic surveillance area (DSA) for at least three months. 44% of the HDSSs restricted eligibility for pregnancy surveillance based on a woman's age, with most only monitoring women 15-49 years. 10% had eligibility criteria based on marital status, while 11% explicitly included unmarried women in pregnancy surveillance. 38% allowed proxy respondents to answer questions about a woman's pregnancy status in her absence. 20% of HDSSs supplemented pregnancy surveillance with investigations by community health workers or key informants and by linking HDSS data with data from antenatal clinics. Conclusions: Methodological guidelines for conducting pregnancy surveillance should be clearly documented and meticulously implemented, as they can have implications for data quality and accurately informing maternal and child health programs.

Patterns of child mortality in rural area of Burkina Faso: evidence from the Nanoro health and demographic surveillance system (HDSS)

BackgroundHalf of global child deaths occur in sub-Saharan Africa. Understanding child mortality patterns and risk factors will help inform interventions to reduce this heavy toll. The Nanoro Health and Demographic Surveillance System (HDSS), Burkina Faso was described previously, but patterns and potential drivers of heterogeneity in child mortality in the district had not been studied. Similar studies in other districts indicated proximity to health facilities as a risk factor, usually without distinction between facility types.MethodsUsing Nanoro HDSS data from 2009 to 2013, we estimated the association between under-5 mortality and proximity to inpatient and outpatient health facilities, seasonality of death, age group, and standard demographic risk factors.ResultsLiving in homes 40–60 min and > 60 min travel time from an inpatient facility was associated with 1.52 (95% CI: 1.13–2.06) and 1.74 (95% CI: 1.27–2.40) greater hazard of under-5 mortality, respectively, than living in homes < 20 min from an inpatient facility. No such association was found for outpatient facilities. The wet season (July–November) was associated with 1.28 (95% CI: 1.07, 1.53) higher under-5 mortality than the dry season (December–June), likely reflecting the malaria season.ConclusionsOur results emphasize the importance of geographical proximity to health care, distinguish between inpatient and outpatient facilities, and also show a seasonal effect, probably driven by malaria.

AUTOMATING THE IMPORT OF HEALTH AND DEMOGRAPHIC SURVEILLANCE SYSTEM DATA FROM ODK TO OPENHDS USING MIRTH CONNECT

Health and demographic surveillance systems (HDSS) are community-based platforms that collect longitudinal data on core demographic and socioeconomic indices as well as key health indicators at regular intervals within defined geographical populations. These systems were previously paper-based. However, the advent of electronic data collection and processing with heterogeneous technologies has posed a problem for data validation and integration. This paper describes the development of ETL channels for automated import of HDSS data from ODK to OpenHDS. The setting was the Cross River HDSS, University of Calabar, South-south Nigeria with a surveillance population of 37,808. Data collection by Fieldworkers was done with Android smartphones running the ODK Collect, while the Supervisors used Android tablets devices to review data prior to submission to an intermediate cloud-based ODK Aggregate server. The validated data was serviced by the OpenHDS and resided on MySQL server maintained locally at the Data Centre. Channels were developed in Mirth Connect to read field data from ODK, validate against the OpenHDS repository and insert onto MySQL database, rejecting inconsistent data when found. Twenty-six channels were developed, tested and deployed for import of data onto the OpenHDS. With contextual modification, this tool can be extended to other health and demographic surveillance systems (HDSSs) under the INDEPTH Network.

Neonatal and child mortality data in retrospective population-based surveys compared with prospective demographic surveillance: EN-INDEPTH study

BackgroundGlobal mortality estimates remain heavily dependent on household surveys in low- and middle-income countries, where most under-five deaths occur. Few studies have assessed the accuracy of mortality data or determinants of capturing births in surveys.MethodsThe Every Newborn-INDEPTH study (EN-INDEPTH) included a large, multi-country survey of women aged 15–49 interviewed about livebirths and their survival status in five Health and Demographic Surveillance Systems (HDSSs). The HDSSs undertake regular household visits to register births and deaths for a given population. We analysed EN-INDEPTH survey data to assess background factors associated with not recalling a complete date-of-birth. We calculated Kaplan-Meier survival estimates for both survey and HDSS data and describe age-at-death distributions during the past 5 years for children born to the same women. We assessed the proportion of HDSS-births that could be matched on month-of-birth to survey-births and used regression models to identify factors associated with matching.Results69,176 women interviewed in the survey reported 109,817 births and 3064 deaths in children under 5 years in the 5 years prior to the survey. In the HDSS data, the same women had 83,768 registered births and 2335 under-five deaths in the same period. A complete date-of-birth was not reported for 1–7% of survey-births. Birthdates were less likely to be complete for dead children and children born to women of higher parity or with little/no education. Distributions of reported age-at-death indicated heaping at full weeks (neonatal period) and at 12 months. Heaping was more pronounced in the survey data. Survey estimates of under-five mortality rates were similar to HDSS estimates of under-five mortality in two of five sites, higher in the survey in two sites (15%, 41%) and lower (24%) in one site. The proportion of HDSS-births matched to survey-births ranged from 51 to 89% across HDSSs and births of children who had died were less likely to be matched.ConclusionsMortality estimates in the survey and HDSS were not markedly different for most sites. However, neither source is a “gold standard” and both sources miss some events. Research is required to improve capture and accuracy to better track newborn and child survival targets.

Age patterns of under-5 mortality in sub-Saharan Africa during 1990-2018: A comparison of estimates from demographic surveillance with full birth histories and the historic record.

BACKGROUNDEstimates of under-5 mortality (U5M) for sub-Saharan African populations often rely heavily on full birth histories (FBHs) collected in surveys and model age patterns of mortality calibrated against vital statistics from other populations. Health and Demographic Surveillance Systems (HDSSs) are alternate sources of population-based data in much of sub-Saharan Africa, which are less formally utilized in estimation.OBJECTIVEIn this study we compare the age pattern of U5M in different African data sources (HDSSs, Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS)), and contrast these with the historical record as summarized in the Human Mortality Database and model age patterns.METHODSWe examined the relative levels of neonatal, postneonatal, infant, and child mortality across data sources. We directly compared estimates for DHS and MICS subnational regions with HDSS, and used linear regression to identify data and contextual attributes that correlated with the disparity between estimates.RESULTSHDSS and FBH data suggests that African populations have higher levels of child mortality and lower infant mortality than the historic record. This age pattern is most explicit for Western African populations, but also characterizes data for other subregions. The comparison between HDSS and FBH data suggests that FBH estimates of child mortality are biased downward. The comparison is less conclusive for neonatal and infant mortality.CONTRIBUTIONThis study questions the practice of using model age patterns derived from largely high-income settings for inferring or correcting U5M estimates for African populations. It also highlights the considerable uncertainty around the consistency of HDSS and FBH estimates of U5M.

The impact of HIV status on the distance traveled to health facilities and adherence to care. A record-linkage study from rural South Africa.

BackgroundFor people living with HIV (PLWH), the burden of travelling to a clinic outside of one’s home community in order to reduce the level of stigma experienced, may impact adherence to treatment and accelerate disease progression.MethodsThis study is set in the Agincourt Health and Demographic Surveillance System (HDSS) in South Africa. Probabilistic and interactive methods were used to individually link HDSS data with medical records. A regression analysis was used to assess whether travel distance was correlated with the condition for which individuals were seeking care (primarily HIV, diabetes or hypertension). For PLWH, a Cox proportional hazard regression model was used to test for an association between the distance travelled to the clinic and late attendance at follow-up visits.ResultsThe adjusted relative risk (RR) of travelling to a clinic more than 5 km from that nearest to their home for HIV patients compared to those being treated for other conditions was 2.78 (95% confidence interval (CI) = 2.23-3.48). The adjusted Cox regression model showed no evidence for an association between the distance travelled to a clinic and the rate of late visits. (RR = 1.00, 95% CI = 0.99-1.00).ConclusionsThe findings were consistent with the hypothesis that people living with HIV/AIDS would be willing to accept the burden of increased clinic travel distances in order to maintain anonymity and so limit their exposure to stigma from fellow community members. For those seeking HIV care the lack of an association between increased travel distances and late visit attendance suggests this may not impact treatment outcomes.

Biases in Survey Estimates of Neonatal Mortality: Results From a Validation Study in Urban Areas of Guinea-Bissau.

Neonatal deaths (occurring within 28 days of birth) account for close to one-half of all deaths among children under age 5 worldwide. In most low- and middle-income countries, data on neonatal deaths come primarily from household surveys. We conducted a validation study of survey data on neonatal mortality in Guinea-Bissau (West Africa). We used records from an urban health and demographic surveillance system (HDSS) that monitors child survival prospectively as our reference data set. We selected a stratified sample of 599 women aged 15-49 among residents of the HDSS and collected the birth histories of 422 participants. We cross-tabulated survey and HDSS data. We used a mathematical model to investigate biases in survey estimates of neonatal mortality. Reporting errors in survey data might lead to estimates of the neonatal mortality rate that are too high, which may limit our ability to track progress toward global health objectives.

Outcomes After Being Lost to Follow-up Differ for Pregnant and Postpartum Women When Compared With the General HIV Treatment Population in Rural South Africa.

Undetermined attrition prohibits full understanding of the coverage and effectiveness of HIV programs. Outcomes following loss to follow-up (LTFU) among antiretroviral therapy (ART) patients may differ according to their reasons for ART initiation. We compare the true outcomes of adult patients previously identified as LTFU by reason for ART initiation in 8 health facilities in north eastern South Africa. Adult HIV patient records were linked to health and demographic surveillance system (HDSS) data from 2014 to 2017. Outcomes of adults categorized as LTFU (>90 days late for the last scheduled clinic visit) were determined through clinic and routine tracing record reviews, consultation of HDSS data, and supplementary tracing. We calculated the proportion of patients per outcome category and performed competing risk survival analysis to estimate the cumulative incidence of death, transfer, migration, ART interruption, and re-engagement following LTFU. Of 895/1017 patients LTFU with an outcome ascertained, 120 (13.4%) had died, 225 (25.1%) re-engaged, 50 (5.6%) migrated out of the HDSS, 75 (8.4%) were alive and not on treatment, and 315 (35.1%) transferred their treatment. These outcomes varied by sex and pregnancy status at ART initiation. Mortality was less likely among pregnant women, patients with higher baseline CD4, and more likely among older patients. Patient survival and transfers to other facilities are considerably higher than those suggested in earlier studies. Outcomes differ for women who were pregnant or postpartum when initiating ART, with this population less likely to have died and more likely to have migrated.

Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data

Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data

Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data.

Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

Open-access for existing LMIC demographic surveillance data using DDI

Open-access for existing LMIC demographic surveillance data using DDI

Are health and demographic surveillance system estimates sufficiently generalisable?

ABSTRACT Sampling rules do not apply in a Health and Demographic Surveillance System (HDSS) that covers exhaustively a district-level population and is not meant to be representative of a national population. We highlight the advantages of HDSS data for causal analysis and identify in the literature the principles of conditional generalisation that best apply to HDSS. A probabilistic view on HDSS data is still justified by the need to model complex causal inference. Accounting for contextual knowledge, reducing omitted-variable bias, detailing order of events, and high statistical power brings credence to HDSS data. Generalisation of causal mechanisms identified in HDSS data is consolidated through systematic comparison and triangulation with national or international data.