Abstract
IPUMS Demographic and Health Surveys (IPUMS DHS), through its intuitive website (http://dhs.ipums.org/), eliminate barriers to overtime and cross-national analyses with the DHS. IPUMS DHS recently released simplified reproductive calendar data. These calendar data are harmonized across samples, distinguish “not in universe” cases from “no” responses, and do not require de-stringing. Variable names are hot links to important documentation, such as survey-question text and comparability concerns. Analysts can also select consistently coded variables relating to the woman, her household, and her social and environmental context without merging files.
INTRODUCTION
IPUMS Demographic and Health Surveys (IPUMS DHS) (Boyle, King, and Sobek 2022) eliminates barriers to overtime and cross-national analyses with the Demographic and Health Surveys (ICF 2021), the world’s longest running survey series on health and fertility in low- and middle-income countries. Through data discovery tools, thousands of harmonized variables, easy-to-access documentation, and social and environmental context variables linked to individual records, IPUMS DHS facilitates innovative research on family planning. We here introduce a new resource for family-planning scholars—IPUMS DHS reproductive calendar data.
The DHS calendar comprises a month-by-month history of reproductive events for a woman of childbearing age, typically for five years preceding the interview. Besides tracking pregnancies, births, and pregnancy terminations, most calendar datasets cover contraception use and reasons for its discontinuation. The calendar adds a longitudinal dimension to what is otherwise cross-sectional data (Kaneda, Smith, and Hagey 2014). Researchers can study the frequency of premature births and pregnancy terminations, choices regarding pregnancy timing, effectiveness and perceived disadvantages of contraceptive methods, levels and consequences of infecundity, how social and environmental contexts affect the timing of births, and more.
The complexity of calendar data in their original form results in their being underutilized, especially for comparative research. Searching for “family planning” on the DHS Program research repository returns 493 publications, versus 28 primarily single-sample publications based on the reproductive calendar. Our broader search in Google Scholar yielded just 76 DHS-calendar-based publications, with three-fourths of multicountry calendar studies carried out by a handful of research teams. As Finnegan, Sao, and Huchko (2019, 598) explain, “Although these data provide a more detailed picture of contraceptive behavior, in their raw form, they can be difficult to navigate without advanced data analysis skills.”
In 2022, IPUMS DHS (http://dhs.ipums.org/) released calendar data that are easier to use, as we explain below. Table 1 shows all currently available DHS samples that include calendar data. Countries in bold are already in IPUMS DHS; the remaining countries will be added over the next 18 months.
TABLE 1.
DHS samples with calendar data, by topics covered
| Birth, pregnancy, pregnancy termination | …and contraception | …and reason for stopping contraception | |
|---|---|---|---|
| Africa & Middle East | |||
| Angola | 2015 | ||
| Burkina Faso | 2003 | 2010 | |
| Burundi | 2010, 2016 | ||
| Benin | 2006 | 2011, 2017 | |
| Comoros | 2012 | ||
| Egypt | 1992, 1995, 2000, 2003, 2005, 2008, 2014 | ||
| Eswatini | 2006 | ||
| Ethiopia | 2011 | 2005, 2016 | |
| Gambia | 2013, 2019 | ||
| Ghana | 2003 | 2008 | 2014 |
| Guinea | 2005 | 2018 | |
| Kenya | 2008 | 1998, 2003, 2014 | |
| Lesotho | 2009 | 2014 | |
| Liberia | 2013, 2019a | ||
| Madagascar | 2003 | 2008 | |
| Malawi | 2000 | 2010 | 2004, 2016 |
| Mali | 2001, 2006 | 2012, 2018 | |
| Mauritania | 2019 | ||
| Morocco | 1992, 2003 | ||
| Mozambique | 2003 | 2011 | |
| Namibia | 2006 | 2013 | |
| Niger | 2006 | 2012 | |
| Nigeria | 2008 | 2013, 2018 | |
| Rwanda | 2000, 2005 | 2010, 2014 | |
| Sao Tome and Principe | 2008 | ||
| Senegal | 2005 | 2010, 2012, 2014, 2015, 2016, 2017, 2018a, 2019a | |
| Sierra Leone | 2008 | 2013, 2019 | |
| South Africa | 2016 | ||
| Tanzania | 2010 | 2004, 2015 | |
| Uganda | 2001 | 2006 | 2011, 2016 |
| Zambia | 2007 | 2013, 2018 | |
| Zimbabwe | 1994,1999, 2005, 2010, 2015 | ||
| Europe | |||
| Albania | 2008 | 2017 | |
| Armenia | 2000, 2005, 2010, 2015 | ||
| Azerbaijan | 2006 | ||
| Kazakhstan | 1999 | ||
| Krygyz Republic | 2012 | ||
| Moldova | 2005 | ||
| Ukraine | 2007 | ||
| Latin America & Caribbean | |||
| Bolivia | 2003 | 2008 | 1994 |
| Brazil | 1991, 1996 | ||
| Colombia | 1990, 1995, 2000, 2005, 2010, 2015 | ||
| Dominican Republic | 1991, 1996, 1999, 2002 | ||
| Guatemala | 1995, 1998, 2015 | ||
| Guyana | 2009 | ||
| Honduras | 2005 | 2011 | |
| Nicaragua | 2001 | 1998 | |
| Paraguay | 1990 | ||
| Peru | 1991,1996, 2000, 2004–06, 2007–08, 2009, 2010, 2011, 2012 | ||
| Asia and Oceania | |||
| Afghanistan | 2015 | ||
| Bangladesh | 1994, 1997, 2000, 2004, 2007, 2011, 2014, 2018a | ||
| Cambodia | 2010, 2014 | ||
| India | 2005, 2015, 2019a | ||
| Indonesia | 1991, 1994, 1997, 2002, 2007, 2012, 2017 | ||
| Jordan | 1990, 1997, 2002, 2007, 2009, 2012, 2017 | ||
| Maldives | 2009, 2016 | ||
| Myanmar | 2015 | ||
| Nepal | 2006 | 2011, 2016 | |
| Pakistan | 2012, 2017 | ||
| Papua New Guinea | 2018 | ||
| Philippines | 1993, 1998, 2003 | ||
| Tajikistan | 2012, 2017 | ||
| Timor-Leste | 2009 | 2016 | |
| Turkey | 1993, 2003, 2008, 2013 | ||
| Vietnam | 1998 | 1997, 2002 | |
| Yemen | 2013 |
NOTE: Bold indicates countries currently included in IPUMS DHS
Indicates sample year not yet in IPUMS DHS
COLLECTION OF CALENDAR DATA
To collect the retrospective calendar data, the DHS interviewer fills in a standard form with boxes for individual months while asking a woman of childbearing age:
For each reported birth during the previous five years, what was the month of birth and duration of the pregnancy?
How many months have you been pregnant for a current pregnancy?
When did any terminated pregnancies occur?
Many calendar surveys also ask about contraceptive use (type and timing) and, for each period of contraceptive use, the reason for stopping use. The columns in Table 1 show the categories of questions included in each DHS calendar survey.
RESEARCH WITH THE CALENDAR DATA
The few publications based on calendar data demonstrate the material’s usefulness. Most commonly, the data are used to study contraceptive use, such as factors affecting contraceptive discontinuation (e.g., Stevens et al. 2022), method switching (e.g., Ali and Cleland 2010) and contraceptive failure (e.g., Bradley et al. 2019), and to illuminate the dynamics (Maxwell et al. 2018) and health consequences (Kannaujiya et al. 2020) of birth spacing. Pregnancy termination (e.g., Jain and Winfrey 2017) and patterns of infecundity (e.g., Mascarenhas et al. 2012) are also analyzed using calendar data. Using contextual variables integrated with IPUMS DHS (Boyle et al. 2020), researchers can also study the association between climate change and reproductive events or between man-made crises, such as political conflicts, and family planning. And multicountry, multiregion, and multiyear studies have been so rare that we have only a tantalizing glimpse into these topics in the published record.
The calendar data can enhance contraceptive counseling services in several ways. Analyses of the calendar data can reveal topics, such as intimate partner violence, that should be covered when health providers discuss contraception with women (MacQuarrie and Mallick 2021). These data can also be used to identify common patterns of contraceptive switching within communities so that health providers can share these patterns if a woman expresses dissatisfaction with her current contraceptive (Khalifa, Abdelaziz, and Sakr 2017). These and similar insights are valuable for local health providers and policymakers. They are also crucial for international organizations to improve their global, regional, and national operations.
IPUMS DHS REDUCES DATA-MANIPULATION ERRORS AND ENHANCES REPLICABILITY OF RESULTS
The IPUMS DHS version of calendar data offers five main advantages over the original DHS calendar data.
1. No need to destring data; inadvertent errors avoided
The original calendar data exist as 80-character-long string variables.1 IPUMS DHS staff destrung these data, created the variables listed in Table 2, provided meaningful variable names and labels, and cross-checked responses for consistency. A tricky task was distinguishing when a code of 0 meant “not in universe” (women not covered by a particular variable) versus a “no” response. For example, for the Kenya 2014 sample, women administered the “short form” questionnaire were not asked calendar questions, but an unwary researcher might mistakenly treat those short-form women’s responses as “No event” answers.
TABLE 2.
Topics covered by calendar variables in IPUMS DHS
| Technical variables | |
| CALCMC_MONTH | Century month date for each month of woman’s calendar |
| CALSEQ | Sequential number of woman-months reported for a woman, ending with interview month |
| CALSEQREV | Sequential number of woman-months reported for a woman, starting with interview month |
| CALSEQ_CMC | Unique woman-month identifier joining case ID number with CMC date |
| Did a specific event occur in a month? (yes/no) | |
| CALCONTR | Contraception used during month |
| CALPREG | Pregnancy during month |
| CALBIRTH | Birth during month |
| CALTERM | Pregnancy termination during month |
| CALABORT | Abortion during month |
| CALMISCAR | Miscarriage during month |
| CALSBIRTH | Stillbirth during month |
| What type of event occurred in the month? | |
| CALREPROD_ALLEVENTS | Pregnancy, termination, birth, or contraceptive use (by type) occurred |
| CALREPROD_PBT | Pregnancy, termination, or birth occurred |
| Details on contraceptive use | |
| CALCONTR_START | Woman began using a contraceptive method that month |
| CALCONTR_CHANGE | Woman began using a different contraceptive method that month |
| CALCONTR_STOP | Woman stopped using a contraceptive method that month |
| CALREASON | Reason woman stopped using a contraceptive method |
| Details on pregnancy | |
| CALPREG_LENGTH | Cumulative duration of non-truncated pregnancy in months |
| CALPREG_LONG | Cumulative duration of non-truncated pregnancy exceeds nine months |
| CALPREG_LC | Duration of reported pregnancy truncated by start of the calendar period |
| CALPREG_RC | Duration of reported pregnancy truncated by end of the calendar period |
2. Data harmonized across samples with consistent numeric codes
The original calendar data responses frequently have alphabetic and sample-specific codes. In IPUMS DHS, all variables have consistent numeric codes across samples, making it easy to conduct comparative analyses across countries and over time. IPUMS DHS handles variation in response options across samples through composite coding, which preserves all detail for single-sample analyses but also facilitates appropriate recoding when samples are pooled. Composite codes group related responses under a common first digit while preserving detail in later digits.
For example, in CALREASON, rationales for stopping contraception related to side effects share a common first digit of 1, while the second digit provides more detail (e.g., code 11 for gained weight, code 12 for lack of sexual satisfaction). Similarly, for CALREPROD_ALLEVENTS, related types of contraception (such as distinct types of periodic abstinence) are indicated by composite coding (code 041 for rhythm, code 042 for standard days method). IPUMS DHS allows researchers to study multiple countries and sample years or focus on details for individual samples.
3. Technical variables and indicators of data quality included
The original calendar data represent months as a long string of characters. As IPUMS DHS provides the data at the month level, researchers may wish to use the technical variables to organize and restrict their sample. For example, CALSEQ is an index number for each month in woman’s calendar timeline counting from the earlier month onward, and CALSEQREV does the opposite. This is helpful for limiting the data to a recent period, such as 12 months, 36 months, etc. Also available through IPUMS are indicators of data quality, such as pregnancies lasting more than nine months (in CALPREG_LONG) and pregnancies censored by the start and end of the calendar period (in CALPREG_LC and CALPREG_RC).
4. Complete documentation available online
IPUMS DHS’s user-friendly web interface allows researchers to immediately identify which samples include calendar data and what calendar variables are available. Online variable descriptions show, by sample, the codes and unweighted frequencies, the variable’s meaning and comparability issues, the wording of the survey question, and the universe of people included in a variable. For example, CALCONTR_CHANGE, which indicates whether the woman initiated using a different contraceptive method that month, has the following universe for the Afghanistan 2015 sample: “Woman months for ever-married women aged 15–49 who have calendar data on contraceptive use, who did not experience pregnancy, birth, or pregnancy termination in the preceding or current month, and who were not in the first month of the reproductive calendar.”
5. Customized multisample calendar data files can include characteristics of the woman and her household
For all analyses using IPUMS DHS data, researchers download a customized data file in their preferred format with the samples and variables relevant to their research project. Merging files is unnecessary, and irrelevant variables are excluded. When women months are the chosen unit of analysis, researchers can select not only calendar variables but also consistently coded and fully documented variables relating to the characteristics of the woman and her household at time of the interview, as well as variables relating to the social, environmental, and economic context.
METHOD
Any registered DHS user2 can use the IPUMS DHS version of calendar data. At the IPUMS DHS website, the user selects “Woman Months” as the unit of analysis and chooses one or more samples for their customized data file. Each record will be a month of reproductive calendar events (e.g., gave birth) for each woman in the chosen samples. In addition, when selecting calendar variables, a user can include variables from the women’s questionnaire (e.g., educational attainment), based on the household questionnaire (e.g., wealth quintile), and from external sources (e.g., precipitation). All selected variables will be added to each record.
By default, IPUMS DHS’s calendar data are delivered in a long format, with each row of data representing one month for a woman of childbearing age. These data can be easily converted into event format, with an event, such as pregnancy, birth, or spell of contraceptive use, on each row. Readers will find the Stata code for creating survival curves and conducting other analyses with the data, as well as for converting the data from long to the event format, in the User Notes on the IPUMS DHS website.3
EXAMPLE OF HOW TO USE THE IPUMS DHS CALENDAR DATA
To demonstrate how IPUMS DHS calendar data can be used for comparative analyses, we calculated the percentage of contraceptive method failure across 25 African countries. We focused on women who reported using the pill or an injection as a contraceptive method and analyzed data from the 60 months preceding each interview. Figure 1 shows, by country, the percentage of women who became pregnant while using each method. The percentage who became pregnant was consistently lower for women using injections (from 0 percent in Guinea up to 7.4 percent in South Africa), compared to women using the pill (from 2.2 percent in Guinea to 22.0 percent in Ghana). While injections appear more reliable, the data also reveal tremendous variation across countries in failure rates for both methods. The Stata code to produce these numbers is available in the User Notes on the IPUMS DHS website.
FIGURE 1.
Percentage of women becoming pregnant while using injectables (left) or the Pill (right) by country
LIMITATIONS
Limitations of IPUMS DHS Calendar Data
IPUMS DHS has not yet released integrated calendar data for all samples, initially focusing on Africa, the Middle East, and South Asia. The project is now “going global” and will release calendar data from other regions of the world within the next 18 months. Documentation is currently only in English, but we plan to release User Notes in French and Spanish. Finally, IPUMS DHS calendar data files are sometimes very large, making them difficult to download onto older computers. Download managers, such as DownloadThemAll!, which enable faster downloads and allow researchers to resume downloads after interruptions, may help alleviate this issue. Soon, IPUMS DHS will establish an API to reduce this concern as well. Questions about IPUMS DHS data and help troubleshooting difficulties in accessing these data can be directed to ipums@umn.edu.
Limitations of Calendar Data in General
Research has revealed some recall errors in DHS calendar data, particularly for women who are young, have repeatedly switched methods, or are in settings where short-acting methods (such as condoms) predominate (Bradley, Winfrey, and Croft 2015; MacQuarrie and Mallick 2021; Ontiri et al. 2020). Calendar data are nonetheless the primary source of information on contraceptive use in low- and middle-income countries.
CONCLUSION
The new IPUMS DHS calendar data break down barriers to research on reproduction and family planning. In IPUMS DHS, the calendar data are harmonized across samples, distinguish “not in universe” cases from “no” responses, and supply researchers with customized datasets that are ready to use without destringing or merging. In sum, IPUMS DHS greatly simplifies the use of a rich family-planning data source that has previously been underutilized.
ACKNOWLEDGMENTS
The authors appreciate the support from the Minnesota Population Center (P2C HD041023), funded through a grant from the Eunice Kennedy Shriver National Institute for Child Health and Human Development. IPUMS DHS is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development under grant number R01HD069471; and by USAID and ICF International. Work on this article was supported by The Leonard Davis Institute for International Relations Postdoctoral Fellowship to Nir Rotem.
Footnotes
CONFLICT OF INTEREST
The authors have no conflicts of interest to report.
For more information on the original DHS Calendar Data, see the DHS Program Calendar Data Tutorial at: https://www.dhsprogram.com/data/calendar-tutorial/.
Go to https://dhsprogram.com/data/Access-Instructions.cfm to register.
Exercises using the data in long format, including how to construct survival curves, are available at https://www.idhsdata.org/idhs/calendar_variables_practice_exercises.shtml. Instructions for converting the data from long to event format are available at https://www.idhsdata.org/idhs/calendar_variables_create_event_files.shtml.
Contributor Information
Elizabeth Heger Boyle, Sociology Department, University of Minnesota, Minneapolis, MN 55455, USA..
Nir Rotem, The Leonard Davis Institute for International Relations, The Hebrew University of Jerusalem, Jerusalem, 9190501, Israel..
Miriam L. King, Institute for Social Research and Data Innovation, University of Minnesota, Minneapolis, MN 55455, USA.
DATA AVAILABILITY STATEMENT
Data are available free online at http://dhs.ipums.org/.
REFERENCES
- Ali Mohamed M, and Cleland John. 2010. “Contraceptive Switching after Method-Related Discontinuation: Levels and Differentials.” Studies in Family Planning 41(2): 129–133. 10.1111/j.1728-4465.2010.00234.x. [DOI] [PubMed] [Google Scholar]
- Boyle Elizabeth Heger, King Miriam L., Garcia Sarah, Culver Corey, and Bourdeaux Jordan. 2020. “Contextual Data in IPUMS DHS: Physical and Social Environment Variables Linked to the Demographic and Health Surveys.” Population and Environment 41: 529–549. 10.1007/s11111-020-00348-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Boyle Elizabeth Heger, King Miriam L., and Sobek Matthew. 2022. “IPUMS DHS: Version 9.0.” IPUMS and ICF. 10.18128/D080.V9. [DOI] [Google Scholar]
- Bradley Sarah E. K., Winfrey William, and Croft Trevor N.. 2015. “Contraceptive Use and Perinatal Mortality in the DHS: An Assessment of the Quality and Consistency of Calendars and Histories.” DHS Methodological Reports No. 17. Rockville, MD: ICF. https://dhsprogram.com/publications/publication-mr17-methodological-reports.cfm. [Google Scholar]
- Bradley Sarah E.K., Polis Chelsea B., Bankole Akinrinola, and Croft Trevor. 2019. “Global Contraceptive Failure Rates: Who Is Most at Risk?” Studies in Family Planning 50(1): 3–24. 10.1111/sifp.12085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Finnegan Amy, Sao Saumya S., and Huchko Megan J.. 2019. “Using a Chord Diagram to Visualize Dynamics in Contraceptive Use: Bringing Data Into Practice.” Global Health: Science and Practice 7(4): 598–605. 10.9745/GHSP-D-19-00205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- ICF. 2021. Demographic and Health Surveys (Various) [Datasets]. Rockville, MD: ICF. [Google Scholar]
- Jain Anrudh K., and Winfrey William. 2017. “Contribution of Contraceptive Discontinuation to Unintended Births in 36 Developing Countries.” Studies in Family Planning 48(3): 269–278. 10.1111/sifp.12023. [DOI] [PubMed] [Google Scholar]
- Kaneda Toshiko, Smith Rhonda, and Hagey Jill. 2014. “Unmet Need for Family Planning: What Can We Learn From the DHS 5-Year Contraceptive Calendar Data?” PRB. June 30, 2014. https://www.prb.org/resources/unmet-need-for-family-planning-what-can-we-learn-from-the-dhs-5-year-contraceptive-calendar-data/. [Google Scholar]
- Kannaujiya Ajit Kumar, Kumar Kaushalendra, Upadhyay Ashish Kumar, McDougal Lotus, Raj Anita, and Singh Abhishek. 2020. “Short Interpregnancy Interval and Low Birth Weight Births in India: Evidence from National Family Health Survey 2015–16.” SSM - Population Health 12 (December): 100700. 10.1016/j.ssmph.2020.100700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Khalifa Mona, Abdelaziz Wafaa, and Sakr Ehab. 2017. “Changes in Contraceptive Use Dynamics in Egypt: Analysis of the 2008 and 2014 Demographic and Health Surveys.” DHS Working Papers No. 132. Rockville, Maryland: ICF. https://dhsprogram.com/publications/publication-wp132-working-papers.cfm. [Google Scholar]
- MacQuarrie Kerry LD, and Mallick Lindsay. 2021. “Intimate Partner Violence and Contraceptive Discontinuation in Kenya.” African Journal of Reproductive Health 25(2): 17–27. 10.29063/ajrh2021/v25i2.2. [DOI] [PubMed] [Google Scholar]
- Mascarenhas Maya N, Cheung Hoiwan, Mathers Colin D, and Stevens Gretchen A. 2012. “Measuring Infertility in Populations: Constructing a Standard Definition for Use with Demographic and Reproductive Health Surveys.” Population Health Metrics 10(1): 17. 10.1186/1478-7954-10-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Maxwell Lauren, Nandi Arijit, Benedetti Andrea, Devries Karen, Wagman Jennifer, and García-Moreno Claudia. 2018. “Intimate Partner Violence and Pregnancy Spacing: Results from a Meta-Analysis of Individual Participant Time-to-Event Data from 29 Low-and-Middle-Income Countries.” BMJ Global Health 3(1): e000304. 10.1136/bmjgh-2017-000304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ontiri Susan, Were Vincent, Kabue Mark, Biesma-Blanco Regien, and Stekelenburg Jelle. 2020. “Patterns and Determinants of Modern Contraceptive Discontinuation among Women of Reproductive Age: Analysis of Kenya Demographic Health Surveys, 2003–2014.” PLOS ONE 15(11): e0241605. 10.1371/journal.pone.0241605. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stevens Rose, Malbos Blandine, Gurmu Eshetu, Riou Jérémie, and Alvergne Alexandra. 2022. “Anemic Women Are More at Risk of Injectable Contraceptive Discontinuation due to Side Effects in Ethiopia.” Studies in Family Planning 53(1): 193–208. 10.1111/sifp.12186. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available free online at http://dhs.ipums.org/.